Advanced methods and algorithm for high precision astronomical imaging
International Nuclear Information System (INIS)
Ngole-Mboula, Fred-Maurice
2016-01-01
One of the biggest challenges of modern cosmology is to gain a more precise knowledge of the dark energy and the dark matter nature. Fortunately, the dark matter can be traced directly through its gravitational effect on galaxies shapes. The European Spatial Agency Euclid mission will precisely provide data for such a purpose. A critical step is analyzing these data will be to accurately model the instrument Point Spread Function (PSF), which the focus of this thesis.We developed non parametric methods to reliably estimate the PSFs across an instrument field-of-view, based on unresolved stars images and accounting for noise, under sampling and PSFs spatial variability. At the core of these contributions, modern mathematical tools and concepts such as sparsity. An important extension of this work will be to account for the PSFs wavelength dependency. (author) [fr
Energy Technology Data Exchange (ETDEWEB)
Carey, G.F.; Young, D.M.
1993-12-31
The program outlined here is directed to research on methods, algorithms, and software for distributed parallel supercomputers. Of particular interest are finite element methods and finite difference methods together with sparse iterative solution schemes for scientific and engineering computations of very large-scale systems. Both linear and nonlinear problems will be investigated. In the nonlinear case, applications with bifurcation to multiple solutions will be considered using continuation strategies. The parallelizable numerical methods of particular interest are a family of partitioning schemes embracing domain decomposition, element-by-element strategies, and multi-level techniques. The methods will be further developed incorporating parallel iterative solution algorithms with associated preconditioners in parallel computer software. The schemes will be implemented on distributed memory parallel architectures such as the CRAY MPP, Intel Paragon, the NCUBE3, and the Connection Machine. We will also consider other new architectures such as the Kendall-Square (KSQ) and proposed machines such as the TERA. The applications will focus on large-scale three-dimensional nonlinear flow and reservoir problems with strong convective transport contributions. These are legitimate grand challenge class computational fluid dynamics (CFD) problems of significant practical interest to DOE. The methods developed and algorithms will, however, be of wider interest.
Pisano, Aurora; Weichert, Dieter
2015-01-01
Articles in this book examine various materials and how to determine directly the limit state of a structure, in the sense of limit analysis and shakedown analysis. Apart from classical applications in mechanical and civil engineering contexts, the book reports on the emerging field of material design beyond the elastic limit, which has further industrial design and technological applications. Readers will discover that “Direct Methods” and the techniques presented here can in fact be used to numerically estimate the strength of structured materials such as composites or nano-materials, which represent fruitful fields of future applications. Leading researchers outline the latest computational tools and optimization techniques and explore the possibility of obtaining information on the limit state of a structure whose post-elastic loading path and constitutive behavior are not well defined or well known. Readers will discover how Direct Methods allow rapid and direct access to requested information in...
Robust Algebraic Multilevel Methods and Algorithms
Kraus, Johannes
2009-01-01
This book deals with algorithms for the solution of linear systems of algebraic equations with large-scale sparse matrices, with a focus on problems that are obtained after discretization of partial differential equations using finite element methods. Provides a systematic presentation of the recent advances in robust algebraic multilevel methods. Can be used for advanced courses on the topic.
Advanced incomplete factorization algorithms for Stiltijes matrices
Energy Technology Data Exchange (ETDEWEB)
Il`in, V.P. [Siberian Division RAS, Novosibirsk (Russian Federation)
1996-12-31
The modern numerical methods for solving the linear algebraic systems Au = f with high order sparse matrices A, which arise in grid approximations of multidimensional boundary value problems, are based mainly on accelerated iterative processes with easily invertible preconditioning matrices presented in the form of approximate (incomplete) factorization of the original matrix A. We consider some recent algorithmic approaches, theoretical foundations, experimental data and open questions for incomplete factorization of Stiltijes matrices which are {open_quotes}the best{close_quotes} ones in the sense that they have the most advanced results. Special attention is given to solving the elliptic differential equations with strongly variable coefficients, singular perturbated diffusion-convection and parabolic equations.
Advanced algorithms for information science
Energy Technology Data Exchange (ETDEWEB)
Argo, P.; Brislawn, C.; Fitzgerald, T.J.; Kelley, B.; Kim, W.H.; Mazieres, B.; Roeder, H.; Strottman, D.
1998-12-31
This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). In a modern information-controlled society the importance of fast computational algorithms facilitating data compression and image analysis cannot be overemphasized. Feature extraction and pattern recognition are key to many LANL projects and the same types of dimensionality reduction and compression used in source coding are also applicable to image understanding. The authors have begun developing wavelet coding which decomposes data into different length-scale and frequency bands. New transform-based source-coding techniques offer potential for achieving better, combined source-channel coding performance by using joint-optimization techniques. They initiated work on a system that compresses the video stream in real time, and which also takes the additional step of analyzing the video stream concurrently. By using object-based compression schemes (where an object is an identifiable feature of the video signal, repeatable in time or space), they believe that the analysis is directly related to the efficiency of the compression.
Advanced algorithms for information science
International Nuclear Information System (INIS)
Argo, P.; Brislawn, C.; Fitzgerald, T.J.; Kelley, B.; Kim, W.H.; Mazieres, B.; Roeder, H.; Strottman, D.
1998-01-01
This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). In a modern information-controlled society the importance of fast computational algorithms facilitating data compression and image analysis cannot be overemphasized. Feature extraction and pattern recognition are key to many LANL projects and the same types of dimensionality reduction and compression used in source coding are also applicable to image understanding. The authors have begun developing wavelet coding which decomposes data into different length-scale and frequency bands. New transform-based source-coding techniques offer potential for achieving better, combined source-channel coding performance by using joint-optimization techniques. They initiated work on a system that compresses the video stream in real time, and which also takes the additional step of analyzing the video stream concurrently. By using object-based compression schemes (where an object is an identifiable feature of the video signal, repeatable in time or space), they believe that the analysis is directly related to the efficiency of the compression
Mastorakis, Nikos E
2009-01-01
Features contributions that are focused on significant aspects of current numerical methods and computational mathematics. This book carries chapters that advanced methods and various variations on known techniques that can solve difficult scientific problems efficiently.
Grouping genetic algorithms advances and applications
Mutingi, Michael
2017-01-01
This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to spe...
Advances in multi-sensor data fusion: algorithms and applications.
Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying
2009-01-01
With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.
Advanced differential quadrature methods
Zong, Zhi
2009-01-01
Modern Tools to Perform Numerical DifferentiationThe original direct differential quadrature (DQ) method has been known to fail for problems with strong nonlinearity and material discontinuity as well as for problems involving singularity, irregularity, and multiple scales. But now researchers in applied mathematics, computational mechanics, and engineering have developed a range of innovative DQ-based methods to overcome these shortcomings. Advanced Differential Quadrature Methods explores new DQ methods and uses these methods to solve problems beyond the capabilities of the direct DQ method.After a basic introduction to the direct DQ method, the book presents a number of DQ methods, including complex DQ, triangular DQ, multi-scale DQ, variable order DQ, multi-domain DQ, and localized DQ. It also provides a mathematical compendium that summarizes Gauss elimination, the Runge-Kutta method, complex analysis, and more. The final chapter contains three codes written in the FORTRAN language, enabling readers to q...
Star identification methods, techniques and algorithms
Zhang, Guangjun
2017-01-01
This book summarizes the research advances in star identification that the author’s team has made over the past 10 years, systematically introducing the principles of star identification, general methods, key techniques and practicable algorithms. It also offers examples of hardware implementation and performance evaluation for the star identification algorithms. Star identification is the key step for celestial navigation and greatly improves the performance of star sensors, and as such the book include the fundamentals of star sensors and celestial navigation, the processing of the star catalog and star images, star identification using modified triangle algorithms, star identification using star patterns and using neural networks, rapid star tracking using star matching between adjacent frames, as well as implementation hardware and using performance tests for star identification. It is not only valuable as a reference book for star sensor designers and researchers working in pattern recognition and othe...
Advanced metaheuristic algorithms for laser optimization
International Nuclear Information System (INIS)
Tomizawa, H.
2010-01-01
A laser is one of the most important experimental tools. In synchrotron radiation field, lasers are widely used for experiments with Pump-Probe techniques. Especially for Xray-FELs, a laser has important roles as a seed light source or photo-cathode-illuminating light source to generate a high brightness electron bunch. The controls of laser pulse characteristics are required for many kinds of experiments. However, the laser should be tuned and customized for each requirement by laser experts. The automatic tuning of laser is required to realize with some sophisticated algorithms. The metaheuristic algorithm is one of the useful candidates to find one of the best solutions as acceptable as possible. The metaheuristic laser tuning system is expected to save our human resources and time for the laser preparations. I have shown successful results on a metaheuristic algorithm based on a genetic algorithm to optimize spatial (transverse) laser profiles and a hill climbing method extended with a fuzzy set theory to choose one of the best laser alignments automatically for each experimental requirement. (author)
Accuracy verification methods theory and algorithms
Mali, Olli; Repin, Sergey
2014-01-01
The importance of accuracy verification methods was understood at the very beginning of the development of numerical analysis. Recent decades have seen a rapid growth of results related to adaptive numerical methods and a posteriori estimates. However, in this important area there often exists a noticeable gap between mathematicians creating the theory and researchers developing applied algorithms that could be used in engineering and scientific computations for guaranteed and efficient error control. The goals of the book are to (1) give a transparent explanation of the underlying mathematical theory in a style accessible not only to advanced numerical analysts but also to engineers and students; (2) present detailed step-by-step algorithms that follow from a theory; (3) discuss their advantages and drawbacks, areas of applicability, give recommendations and examples.
International Nuclear Information System (INIS)
Beauwens, B.; Arkuszewski, J.; Boryszewicz, M.
1981-01-01
Results obtained in the field of linear iterative methods within the Coordinated Research Program on Transport Theory and Advanced Reactor Calculations are summarized. The general convergence theory of linear iterative methods is essentially based on the properties of nonnegative operators on ordered normed spaces. The following aspects of this theory have been improved: new comparison theorems for regular splittings, generalization of the notions of M- and H-matrices, new interpretations of classical convergence theorems for positive-definite operators. The estimation of asymptotic convergence rates was developed with two purposes: the analysis of model problems and the optimization of relaxation parameters. In the framework of factorization iterative methods, model problem analysis is needed to investigate whether the increased computational complexity of higher-order methods does not offset their increased asymptotic convergence rates, as well as to appreciate the effect of standard relaxation techniques (polynomial relaxation). On the other hand, the optimal use of factorization iterative methods requires the development of adequate relaxation techniques and their optimization. The relative performances of a few possibilities have been explored for model problems. Presently, the best results have been obtained with optimal diagonal-Chebyshev relaxation
Recent Advancements in Lightning Jump Algorithm Work
Schultz, Christopher J.; Petersen, Walter A.; Carey, Lawrence D.
2010-01-01
In the past year, the primary objectives were to show the usefulness of total lightning as compared to traditional cloud-to-ground (CG) networks, test the lightning jump algorithm configurations in other regions of the country, increase the number of thunderstorms within our thunderstorm database, and to pinpoint environments that could prove difficult for any lightning jump configuration. A total of 561 thunderstorms have been examined in the past year (409 non-severe, 152 severe) from four regions of the country (North Alabama, Washington D.C., High Plains of CO/KS, and Oklahoma). Results continue to indicate that the 2 lightning jump algorithm configuration holds the most promise in terms of prospective operational lightning jump algorithms, with a probability of detection (POD) at 81%, a false alarm rate (FAR) of 45%, a critical success index (CSI) of 49% and a Heidke Skill Score (HSS) of 0.66. The second best performing algorithm configuration was the Threshold 4 algorithm, which had a POD of 72%, FAR of 51%, a CSI of 41% and an HSS of 0.58. Because a more complex algorithm configuration shows the most promise in terms of prospective operational lightning jump algorithms, accurate thunderstorm cell tracking work must be undertaken to track lightning trends on an individual thunderstorm basis over time. While these numbers for the 2 configuration are impressive, the algorithm does have its weaknesses. Specifically, low-topped and tropical cyclone thunderstorm environments are present issues for the 2 lightning jump algorithm, because of the suppressed vertical depth impact on overall flash counts (i.e., a relative dearth in lightning). For example, in a sample of 120 thunderstorms from northern Alabama that contained 72 missed events by the 2 algorithm 36% of the misses were associated with these two environments (17 storms).
Time-advance algorithms based on Hamilton's principle
International Nuclear Information System (INIS)
Lewis, H.R.; Kostelec, P.J.
1993-01-01
Time-advance algorithms based on Hamilton's variational principle are being developed for application to problems in plasma physics and other areas. Hamilton's principle was applied previously to derive a system of ordinary differential equations in time whose solution provides an approximation to the evolution of a plasma described by the Vlasov-Maxwell equations. However, the variational principle was not used to obtain an algorithm for solving the ordinary differential equations numerically. The present research addresses the numerical solution of systems of ordinary differential equations via Hamilton's principle. The basic idea is first to choose a class of functions for approximating the solution of the ordinary differential equations over a specific time interval. Then the parameters in the approximating function are determined by applying Hamilton's principle exactly within the class of approximating functions. For example, if an approximate solution is desired between time t and time t + Δ t, the class of approximating functions could be polynomials in time up to some degree. The issue of how to choose time-advance algorithms is very important for achieving efficient, physically meaningful computer simulations. The objective is to reliably simulate those characteristics of an evolving system that are scientifically most relevant. Preliminary numerical results are presented, including comparisons with other computational methods
Advances of evolutionary computation methods and operators
Cuevas, Erik; Oliva Navarro, Diego Alberto
2016-01-01
The goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be eﬀective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method.
Advanced life support for cardiac arrest beyond the algorithm
DEFF Research Database (Denmark)
Rudolph, Søren Steemann; Isbye, Dan Lou; Pfeiffer, Peter
2018-01-01
In an advanced emergency medical service all parts of the advanced life support (ALS) algorithm can be provided. This evidence-based algorithm outlines resuscitative efforts for the first 10-15 minutes after cardiac arrest, whereafter the algorithm repeats itself. Restoration of spontaneous...... circulation fails in most cases, but in some circumstances the patient may benefit from additional interventional approaches, in which case transport to hospital with ongoing cardiopulmonary resuscitation is indicated. This paper has summarized treatments outside the ALS algorithm, which may be beneficial...
Directory of Open Access Journals (Sweden)
Alireza Khosravi
2015-12-01
Full Text Available BACKGROUND: The aim of this study is to present an objective method based on support vector machines (SVMs and gravitational search algorithm (GSA which is initially utilized for recognition the pattern among risk factors and hypertension (HTN to stratify and analysis HTN’s risk factors in an Iranian urban population. METHODS: This community-based and cross-sectional research has been designed based on the probabilistic sample of residents of Isfahan, Iran, aged 19 years or over from 2001 to 2007. One of the household members was randomly selected from different age groups. Selected individuals were invited to a predefined health center to be educated on how to collect 24-hour urine sample as well as learning about topographic parameters and blood pressure measurement. The data from both the estimated and measured blood pressure [for both systolic blood pressure (SBP and diastolic blood pressure (DBP] demonstrated that optimized SVMs have a highest estimation potential. RESULTS: This result was particularly more evident when SVMs performance is evaluated with regression and generalized linear modeling (GLM as common methods. Blood pressure risk factors impact analysis shows that age has the highest impact level on SBP while it falls second on the impact level ranking on DBP. The results also showed that body mass index (BMI falls first on the impact level ranking on DBP while have a lower impact on SBP. CONCLUSION: Our analysis suggests that salt intake could efficiently influence both DBP and SBP with greater impact level on SBP. Therefore, controlling salt intake may lead to not only control of HTN but also its prevention.
Advances in Biosensing Methods
Directory of Open Access Journals (Sweden)
Reema Taneja
2007-02-01
Full Text Available A fractal analysis is presented for the binding and dissociation (if applicable kinetics of analyte-receptor reactions occurring on biosensor surfaces. The applications of the biosensors have appeared in the recent literature. The examples provided together provide the reader with a perspective of the advances in biosensors that are being used to detect analytes of interest. This should also stimulate interest in applying biosensors to other areas of application. The fractal analysis limits the evaluation of the rate constants for binding and dissociation (if applicable for the analyte-receptor reactions occurring in biosensor surfaces. The fractal dimension provides a quantitative measure of the degree of heterogeneity on the biosensor surface. Predictive relations are presented that relate the binding co-efficient with the degree of heterogeneity or the fractal dimension on the biosensor surface
Daylighting simulation: methods, algorithms, and resources
Energy Technology Data Exchange (ETDEWEB)
Carroll, William L.
1999-12-01
This document presents work conducted as part of Subtask C, ''Daylighting Design Tools'', Subgroup C2, ''New Daylight Algorithms'', of the IEA SHC Task 21 and the ECBCS Program Annex 29 ''Daylight in Buildings''. The search for and collection of daylighting analysis methods and algorithms led to two important observations. First, there is a wide range of needs for different types of methods to produce a complete analysis tool. These include: Geometry; Light modeling; Characterization of the natural illumination resource; Materials and components properties, representations; and Usability issues (interfaces, interoperability, representation of analysis results, etc). Second, very advantageously, there have been rapid advances in many basic methods in these areas, due to other forces. They are in part driven by: The commercial computer graphics community (commerce, entertainment); The lighting industry; Architectural rendering and visualization for projects; and Academia: Course materials, research. This has led to a very rich set of information resources that have direct applicability to the small daylighting analysis community. Furthermore, much of this information is in fact available online. Because much of the information about methods and algorithms is now online, an innovative reporting strategy was used: the core formats are electronic, and used to produce a printed form only secondarily. The electronic forms include both online WWW pages and a downloadable .PDF file with the same appearance and content. Both electronic forms include live primary and indirect links to actual information sources on the WWW. In most cases, little additional commentary is provided regarding the information links or citations that are provided. This in turn allows the report to be very concise. The links are expected speak for themselves. The report consists of only about 10+ pages, with about 100+ primary links, but
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2017-01-01
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2014-01-01
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
Advances in algorithms, languages, and complexity
Ko, Ker-I
1997-01-01
This book contains a collection of survey papers in the areas of algorithms, languages and complexity, the three areas in which Professor Ronald V. Book has made significant contributions. As a fonner student and a co-author who have been influenced by him directly, we would like to dedicate this book to Professor Ronald V. Book to honor and celebrate his sixtieth birthday. Professor Book initiated his brilliant academic career in 1958, graduating from Grinnell College with a Bachelor of Arts degree. He obtained a Master of Arts in Teaching degree in 1960 and a Master of Arts degree in 1964 both from Wesleyan University, and a Doctor of Philosophy degree from Harvard University in 1969, under the guidance of Professor Sheila A. Greibach. Professor Book's research in discrete mathematics and theoretical com puter science is reflected in more than 150 scientific publications. These works have made a strong impact on the development of several areas of theoretical computer science. A more detailed summary of h...
B ampersand W PWR advanced control system algorithm development
International Nuclear Information System (INIS)
Winks, R.W.; Wilson, T.L.; Amick, M.
1992-01-01
This paper discusses algorithm development of an Advanced Control System for the B ampersand W Pressurized Water Reactor (PWR) nuclear power plant. The paper summarizes the history of the project, describes the operation of the algorithm, and presents transient results from a simulation of the plant and control system. The history discusses the steps in the development process and the roles played by the utility owners, B ampersand W Nuclear Service Company (BWNS), Oak Ridge National Laboratory (ORNL), and the Foxboro Company. The algorithm description is a brief overview of the features of the control system. The transient results show that operation of the algorithm in a normal power maneuvering mode and in a moderately large upset following a feedwater pump trip
Advanced Markov chain Monte Carlo methods learning from past samples
Liang, Faming; Carrol, Raymond J
2010-01-01
This book provides comprehensive coverage of simulation of complex systems using Monte Carlo methods. Developing algorithms that are immune to the local trap problem has long been considered as the most important topic in MCMC research. Various advanced MCMC algorithms which address this problem have been developed include, the modified Gibbs sampler, the methods based on auxiliary variables and the methods making use of past samples. The focus of this book is on the algorithms that make use of past samples. This book includes the multicanonical algorithm, dynamic weighting, dynamically weight
Feng, T.; Timmermans, H.J.P.
2016-01-01
Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods
Algorithmic and experimental methods in algebra, geometry, and number theory
Decker, Wolfram; Malle, Gunter
2017-01-01
This book presents state-of-the-art research and survey articles that highlight work done within the Priority Program SPP 1489 “Algorithmic and Experimental Methods in Algebra, Geometry and Number Theory”, which was established and generously supported by the German Research Foundation (DFG) from 2010 to 2016. The goal of the program was to substantially advance algorithmic and experimental methods in the aforementioned disciplines, to combine the different methods where necessary, and to apply them to central questions in theory and practice. Of particular concern was the further development of freely available open source computer algebra systems and their interaction in order to create powerful new computational tools that transcend the boundaries of the individual disciplines involved. The book covers a broad range of topics addressing the design and theoretical foundations, implementation and the successful application of algebraic algorithms in order to solve mathematical research problems. It off...
Immune Algorithm Complex Method for Transducer Calibration
Directory of Open Access Journals (Sweden)
YU Jiangming
2014-08-01
Full Text Available As a key link in engineering test tasks, the transducer calibration has significant influence on accuracy and reliability of test results. Because of unknown and complex nonlinear characteristics, conventional method can’t achieve satisfactory accuracy. An Immune algorithm complex modeling approach is proposed, and the simulated studies on the calibration of third multiple output transducers is made respectively by use of the developed complex modeling. The simulated and experimental results show that the Immune algorithm complex modeling approach can improve significantly calibration precision comparison with traditional calibration methods.
Advanced defect detection algorithm using clustering in ultrasonic NDE
Gongzhang, Rui; Gachagan, Anthony
2016-02-01
A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed A-scans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as `legitimate reflector' or `artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone.
Advanced Multilevel Monte Carlo Methods
Jasra, Ajay; Law, Kody; Suciu, Carina
2017-01-01
This article reviews the application of advanced Monte Carlo techniques in the context of Multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations which can be biased in some sense, for instance, by using the discretization of a associated probability law. The MLMC approach works with a hierarchy of biased approximations which become progressively more accurate and more expensive. Using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider Markov chain Monte Carlo and sequential Monte Carlo methods which have been introduced in the literature and we describe different strategies which facilitate the application of MLMC within these methods.
Advanced Multilevel Monte Carlo Methods
Jasra, Ajay
2017-04-24
This article reviews the application of advanced Monte Carlo techniques in the context of Multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations which can be biased in some sense, for instance, by using the discretization of a associated probability law. The MLMC approach works with a hierarchy of biased approximations which become progressively more accurate and more expensive. Using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider Markov chain Monte Carlo and sequential Monte Carlo methods which have been introduced in the literature and we describe different strategies which facilitate the application of MLMC within these methods.
VIRTEX-5 Fpga Implementation of Advanced Encryption Standard Algorithm
Rais, Muhammad H.; Qasim, Syed M.
2010-06-01
In this paper, we present an implementation of Advanced Encryption Standard (AES) cryptographic algorithm using state-of-the-art Virtex-5 Field Programmable Gate Array (FPGA). The design is coded in Very High Speed Integrated Circuit Hardware Description Language (VHDL). Timing simulation is performed to verify the functionality of the designed circuit. Performance evaluation is also done in terms of throughput and area. The design implemented on Virtex-5 (XC5VLX50FFG676-3) FPGA achieves a maximum throughput of 4.34 Gbps utilizing a total of 399 slices.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Energy Technology Data Exchange (ETDEWEB)
Xiu, Dongbin [Univ. of Utah, Salt Lake City, UT (United States)
2017-03-03
The focus of the project is the development of mathematical methods and high-performance computational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly efficient and scalable numerical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
A Tomographic method based on genetic algorithms
International Nuclear Information System (INIS)
Turcanu, C.; Alecu, L.; Craciunescu, T.; Niculae, C.
1997-01-01
Computerized tomography being a non-destructive and non-evasive technique is frequently used in medical application to generate three dimensional images of objects. Genetic algorithms are efficient, domain independent for a large variety of problems. The proposed method produces good quality reconstructions even in case of very small number of projection angles. It requests no a priori knowledge about the solution and takes into account the statistical uncertainties. The main drawback of the method is the amount of computer memory and time needed. (author)
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
Energy Technology Data Exchange (ETDEWEB)
Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)
2014-08-15
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka
2014-01-01
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology
Experimental Methods for the Analysis of Optimization Algorithms
DEFF Research Database (Denmark)
of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists......, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on different...
Advanced methods of fatigue assessment
Radaj, Dieter
2013-01-01
The book in hand presents advanced methods of brittle fracture and fatigue assessment. The Neuber concept of fictitious notch rounding is enhanced with regard to theory and application. The stress intensity factor concept for cracks is extended to pointed and rounded corner notches as well as to locally elastic-plastic material behaviour. The averaged strain energy density within a circular sector volume around the notch tip is shown to be suitable for strength-assessments. Finally, the various implications of cyclic plasticity on fatigue crack growth are explained with emphasis being laid on the DJ-integral approach. This book continues the expositions of the authors’ well known reference work in German language ‘Ermüdungsfestigkeit – Grundlagen für Ingenieure’ (Fatigue strength – fundamentals for engineers).
Advanced construction methods in ACR
International Nuclear Information System (INIS)
Elgohary, M.; Choy, E.; Yu, S.K.W.
2002-01-01
The ACR - Advanced CANDU Reactor, developed by Atomic Energy of Canada Limited (AECL), is designed with constructability considerations as a major requirement during all project phases from the concept design stage to the detail design stage. This necessitated a much more comprehensive approach in including constructability considerations in the design to ensure that the construction duration is met. For the ACR-700, a project schedule of 48 months has been developed for the nth replicated unit with a 36 month construction period duration from First Concrete to Fuel Load. An overall construction strategy that builds on the success of the construction methods that are proven in the construction of the Qinshan CANDU 6 project has been developed for the ACR. The overall construction strategy comprises the 'Open Top' construction technique using a Very Heavy Lift crane, parallel construction activities, with extensive modularization and prefabrication. In addition, significant applications of up to date construction technology will be implemented, e.g. large volume concrete pours, prefabricated rebar, use of climbing forms, composite structures, prefabricated permanent formwork, automatic welding, and utilization of the latest electronic technology tools such as 3D CADDs modelling yields a very high quality, clash free product to allow construction to be completed 'right the first time' and eliminates rework. Integration of 3D CADDs models and scheduling tools such as Primavera has allowed development of actual construction sequences and an iterative approach to schedule verification and improvement. Modularization and prefabrication are major features of the ACR design in order to achieve the project schedule. For the reactor building approximately 80% of the volume will be installed as modules or prefabricated assembles. This ensures critical path activities are achieved. This paper examines the advanced construction methods implemented in the design in order to
Magnet sorting algorithms for insertion devices for the Advanced Light Source
International Nuclear Information System (INIS)
Humphries, D.; Hoyer, E.; Kincaid, B.; Marks, S.; Schlueter, R.
1994-01-01
Insertion devices for the Advanced Light Source (ALS) incorporate up to 3,000 magnet blocks each for pole energization. In order to minimize field errors, these magnets must be measured, sorted and assigned appropriate locations and orientation in the magnetic structures. Sorting must address multiple objectives, including pole excitation and minimization of integrated multipole fields from minor field components in the magnets. This is equivalent to a combinatorial minimization problem with a large configuration space. Multi-stage sorting algorithms use ordering and pairing schemes in conjunction with other combinatorial methods to solve the minimization problem. This paper discusses objective functions, solution algorithms and results of application to magnet block measurement data
Advances and applications of optimised algorithms in image processing
Oliva, Diego
2017-01-01
This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing. The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing co.
GTV-based prescription in SBRT for lung lesions using advanced dose calculation algorithms
International Nuclear Information System (INIS)
Lacornerie, Thomas; Lisbona, Albert; Mirabel, Xavier; Lartigau, Eric; Reynaert, Nick
2014-01-01
The aim of current study was to investigate the way dose is prescribed to lung lesions during SBRT using advanced dose calculation algorithms that take into account electron transport (type B algorithms). As type A algorithms do not take into account secondary electron transport, they overestimate the dose to lung lesions. Type B algorithms are more accurate but still no consensus is reached regarding dose prescription. The positive clinical results obtained using type A algorithms should be used as a starting point. In current work a dose-calculation experiment is performed, presenting different prescription methods. Three cases with three different sizes of peripheral lung lesions were planned using three different treatment platforms. For each individual case 60 Gy to the PTV was prescribed using a type A algorithm and the dose distribution was recalculated using a type B algorithm in order to evaluate the impact of the secondary electron transport. Secondly, for each case a type B algorithm was used to prescribe 48 Gy to the PTV, and the resulting doses to the GTV were analyzed. Finally, prescriptions based on specific GTV dose volumes were evaluated. When using a type A algorithm to prescribe the same dose to the PTV, the differences regarding median GTV doses among platforms and cases were always less than 10% of the prescription dose. The prescription to the PTV based on type B algorithms, leads to a more important variability of the median GTV dose among cases and among platforms, (respectively 24%, and 28%). However, when 54 Gy was prescribed as median GTV dose, using a type B algorithm, the variability observed was minimal. Normalizing the prescription dose to the median GTV dose for lung lesions avoids variability among different cases and treatment platforms of SBRT when type B algorithms are used to calculate the dose. The combination of using a type A algorithm to optimize a homogeneous dose in the PTV and using a type B algorithm to prescribe the
Advanced illumination control algorithm for medical endoscopy applications
Sousa, Ricardo M.; Wäny, Martin; Santos, Pedro; Morgado-Dias, F.
2015-05-01
CMOS image sensor manufacturer, AWAIBA, is providing the world's smallest digital camera modules to the world market for minimally invasive surgery and one time use endoscopic equipment. Based on the world's smallest digital camera head and the evaluation board provided to it, the aim of this paper is to demonstrate an advanced fast response dynamic control algorithm of the illumination LED source coupled to the camera head, over the LED drivers embedded on the evaluation board. Cost efficient and small size endoscopic camera modules nowadays embed minimal size image sensors capable of not only adjusting gain and exposure time but also LED illumination with adjustable illumination power. The LED illumination power has to be dynamically adjusted while navigating the endoscope over changing illumination conditions of several orders of magnitude within fractions of the second to guarantee a smooth viewing experience. The algorithm is centered on the pixel analysis of selected ROIs enabling it to dynamically adjust the illumination intensity based on the measured pixel saturation level. The control core was developed in VHDL and tested in a laboratory environment over changing light conditions. The obtained results show that it is capable of achieving correction speeds under 1 s while maintaining a static error below 3% relative to the total number of pixels on the image. The result of this work will allow the integration of millimeter sized high brightness LED sources on minimal form factor cameras enabling its use in endoscopic surgical robotic or micro invasive surgery.
Advanced entry guidance algorithm with landing footprint computation
Leavitt, James Aaron
The design and performance evaluation of an entry guidance algorithm for future space transportation vehicles is presented. The algorithm performs two functions: on-board trajectory planning and trajectory tracking. The planned longitudinal path is followed by tracking drag acceleration, as is done by the Space Shuttle entry guidance. Unlike the Shuttle entry guidance, lateral path curvature is also planned and followed. A new trajectory planning function for the guidance algorithm is developed that is suitable for suborbital entry and that significantly enhances the overall performance of the algorithm for both orbital and suborbital entry. In comparison with the previous trajectory planner, the new planner produces trajectories that are easier to track, especially near the upper and lower drag boundaries and for suborbital entry. The new planner accomplishes this by matching the vehicle's initial flight path angle and bank angle, and by enforcing the full three-degree-of-freedom equations of motion with control derivative limits. Insights gained from trajectory optimization results contribute to the design of the new planner, giving it near-optimal downrange and crossrange capabilities. Planned trajectories and guidance simulation results are presented that demonstrate the improved performance. Based on the new planner, a method is developed for approximating the landing footprint for entry vehicles in near real-time, as would be needed for an on-board flight management system. The boundary of the footprint is constructed from the endpoints of extreme downrange and crossrange trajectories generated by the new trajectory planner. The footprint algorithm inherently possesses many of the qualities of the new planner, including quick execution, the ability to accurately approximate the vehicle's glide capabilities, and applicability to a wide range of entry conditions. Footprints can be generated for orbital and suborbital entry conditions using a pre
An assembly sequence planning method based on composite algorithm
Directory of Open Access Journals (Sweden)
Enfu LIU
2016-02-01
Full Text Available To solve the combination explosion problem and the blind searching problem in assembly sequence planning of complex products, an assembly sequence planning method based on composite algorithm is proposed. In the composite algorithm, a sufficient number of feasible assembly sequences are generated using formalization reasoning algorithm as the initial population of genetic algorithm. Then fuzzy knowledge of assembly is integrated into the planning process of genetic algorithm and ant algorithm to get the accurate solution. At last, an example is conducted to verify the feasibility of composite algorithm.
Advanced Source Deconvolution Methods for Compton Telescopes
Zoglauer, Andreas
The next generation of space telescopes utilizing Compton scattering for astrophysical observations is destined to one day unravel the mysteries behind Galactic nucleosynthesis, to determine the origin of the positron annihilation excess near the Galactic center, and to uncover the hidden emission mechanisms behind gamma-ray bursts. Besides astrophysics, Compton telescopes are establishing themselves in heliophysics, planetary sciences, medical imaging, accelerator physics, and environmental monitoring. Since the COMPTEL days, great advances in the achievable energy and position resolution were possible, creating an extremely vast, but also extremely sparsely sampled data space. Unfortunately, the optimum way to analyze the data from the next generation of Compton telescopes has not yet been found, which can retrieve all source parameters (location, spectrum, polarization, flux) and achieves the best possible resolution and sensitivity at the same time. This is especially important for all sciences objectives looking at the inner Galaxy: the large amount of expected sources, the high background (internal and Galactic diffuse emission), and the limited angular resolution, make it the most taxing case for data analysis. In general, two key challenges exist: First, what are the best data space representations to answer the specific science questions? Second, what is the best way to deconvolve the data to fully retrieve the source parameters? For modern Compton telescopes, the existing data space representations can either correctly reconstruct the absolute flux (binned mode) or achieve the best possible resolution (list-mode), both together were not possible up to now. Here we propose to develop a two-stage hybrid reconstruction method which combines the best aspects of both. Using a proof-of-concept implementation we can for the first time show that it is possible to alternate during each deconvolution step between a binned-mode approach to get the flux right and a
Research on Palmprint Identification Method Based on Quantum Algorithms
Directory of Open Access Journals (Sweden)
Hui Li
2014-01-01
Full Text Available Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%.
Advancements to the planogram frequency–distance rebinning algorithm
International Nuclear Information System (INIS)
Champley, Kyle M; Kinahan, Paul E; Raylman, Raymond R
2010-01-01
In this paper we consider the task of image reconstruction in positron emission tomography (PET) with the planogram frequency–distance rebinning (PFDR) algorithm. The PFDR algorithm is a rebinning algorithm for PET systems with panel detectors. The algorithm is derived in the planogram coordinate system which is a native data format for PET systems with panel detectors. A rebinning algorithm averages over the redundant four-dimensional set of PET data to produce a three-dimensional set of data. Images can be reconstructed from this rebinned three-dimensional set of data. This process enables one to reconstruct PET images more quickly than reconstructing directly from the four-dimensional PET data. The PFDR algorithm is an approximate rebinning algorithm. We show that implementing the PFDR algorithm followed by the (ramp) filtered backprojection (FBP) algorithm in linogram coordinates from multiple views reconstructs a filtered version of our image. We develop an explicit formula for this filter which can be used to achieve exact reconstruction by means of a modified FBP algorithm applied to the stack of rebinned linograms and can also be used to quantify the errors introduced by the PFDR algorithm. This filter is similar to the filter in the planogram filtered backprojection algorithm derived by Brasse et al. The planogram filtered backprojection and exact reconstruction with the PFDR algorithm require complete projections which can be completed with a reprojection algorithm. The PFDR algorithm is similar to the rebinning algorithm developed by Kao et al. By expressing the PFDR algorithm in detector coordinates, we provide a comparative analysis between the two algorithms. Numerical experiments using both simulated data and measured data from a positron emission mammography/tomography (PEM/PET) system are performed. Images are reconstructed by PFDR+FBP (PFDR followed by 2D FBP reconstruction), PFDRX (PFDR followed by the modified FBP algorithm for exact
Advancing analytical algorithms and pipelines for billions of microbial sequences.
Gonzalez, Antonio; Knight, Rob
2012-02-01
The vast number of microbial sequences resulting from sequencing efforts using new technologies require us to re-assess currently available analysis methodologies and tools. Here we describe trends in the development and distribution of software for analyzing microbial sequence data. We then focus on one widely used set of methods, dimensionality reduction techniques, which allow users to summarize and compare these vast datasets. We conclude by emphasizing the utility of formal software engineering methods for the development of computational biology tools, and the need for new algorithms for comparing microbial communities. Such large-scale comparisons will allow us to fulfill the dream of rapid integration and comparison of microbial sequence data sets, in a replicable analytical environment, in order to describe the microbial world we inhabit. Copyright © 2011 Elsevier Ltd. All rights reserved.
Blind source separation advances in theory, algorithms and applications
Wang, Wenwu
2014-01-01
Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms, and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.
Selection of parameters for advanced machining processes using firefly algorithm
Directory of Open Access Journals (Sweden)
Rajkamal Shukla
2017-02-01
Full Text Available Advanced machining processes (AMPs are widely utilized in industries for machining complex geometries and intricate profiles. In this paper, two significant processes such as electric discharge machining (EDM and abrasive water jet machining (AWJM are considered to get the optimum values of responses for the given range of process parameters. The firefly algorithm (FA is attempted to the considered processes to obtain optimized parameters and the results obtained are compared with the results given by previous researchers. The variation of process parameters with respect to the responses are plotted to confirm the optimum results obtained using FA. In EDM process, the performance parameter “MRR” is increased from 159.70 gm/min to 181.6723 gm/min, while “Ra” and “REWR” are decreased from 6.21 μm to 3.6767 μm and 6.21% to 6.324 × 10−5% respectively. In AWJM process, the value of the “kerf” and “Ra” are decreased from 0.858 mm to 0.3704 mm and 5.41 mm to 4.443 mm respectively. In both the processes, the obtained results show a significant improvement in the responses.
Directory of Open Access Journals (Sweden)
Jianning Wu
2015-01-01
Full Text Available The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.
Wu, Jianning; Wu, Bin
2015-01-01
The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.
Advanced algorithms for radiographic material discrimination and inspection system design
Energy Technology Data Exchange (ETDEWEB)
Gilbert, Andrew J. [Pacific Northwest National Laboratory, Richland, WA 99354 (United States); McDonald, Benjamin S., E-mail: benjamin.mcdonald@pnnl.gov [Pacific Northwest National Laboratory, Richland, WA 99354 (United States); Deinert, Mark R., E-mail: mdeinert@mines.edu [Colorado School of Mines, Golden, CO 80401 (United States)
2016-10-15
X-ray and neutron radiography are powerful tools for non-invasively inspecting the interior of objects. However, current methods are limited in their ability to differentiate materials when multiple materials are present, especially within large and complex objects. Past work has demonstrated that the spectral shift that X-ray beams undergo in traversing an object can be used to detect and quantify nuclear materials. The technique uses a spectrally sensitive detector and an inverse algorithm that varies the composition of the object until the X-ray spectrum predicted by X-ray transport matches the one measured. Here we show that this approach can be adapted to multi-mode radiography, with energy integrating detectors, and that the Cramér–Rao lower bound can be used to choose an optimal set of inspection modes a priori. We consider multi-endpoint X-ray radiography alone, or in combination with neutron radiography using deuterium–deuterium (DD) or deuterium–tritium (DT) sources. We show that for an optimal mode choice, the algorithm can improve discrimination between high-Z materials, specifically between tungsten and plutonium, and estimate plutonium mass within a simulated nuclear material storage system to within 1%.
Comparison of genetic algorithms with conjugate gradient methods
Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.
1972-01-01
Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.
The linogram algorithm and direct fourier method with linograms
International Nuclear Information System (INIS)
Edholm, P.R.
1990-01-01
This text is an attempt to describe the linogram algorithm based on a somewhat simplified mathematical description of the algorithm which is also more similar to the actual digital implementation. Another algorithm with linograms, which may be called a direct fourier method is also presented. (K.A.E.)
Advanced Fuel Cycle Economic Tools, Algorithms, and Methodologies
Energy Technology Data Exchange (ETDEWEB)
David E. Shropshire
2009-05-01
The Advanced Fuel Cycle Initiative (AFCI) Systems Analysis supports engineering economic analyses and trade-studies, and requires a requisite reference cost basis to support adequate analysis rigor. In this regard, the AFCI program has created a reference set of economic documentation. The documentation consists of the “Advanced Fuel Cycle (AFC) Cost Basis” report (Shropshire, et al. 2007), “AFCI Economic Analysis” report, and the “AFCI Economic Tools, Algorithms, and Methodologies Report.” Together, these documents provide the reference cost basis, cost modeling basis, and methodologies needed to support AFCI economic analysis. The application of the reference cost data in the cost and econometric systems analysis models will be supported by this report. These methodologies include: the energy/environment/economic evaluation of nuclear technology penetration in the energy market—domestic and internationally—and impacts on AFCI facility deployment, uranium resource modeling to inform the front-end fuel cycle costs, facility first-of-a-kind to nth-of-a-kind learning with application to deployment of AFCI facilities, cost tradeoffs to meet nuclear non-proliferation requirements, and international nuclear facility supply/demand analysis. The economic analysis will be performed using two cost models. VISION.ECON will be used to evaluate and compare costs under dynamic conditions, consistent with the cases and analysis performed by the AFCI Systems Analysis team. Generation IV Excel Calculations of Nuclear Systems (G4-ECONS) will provide static (snapshot-in-time) cost analysis and will provide a check on the dynamic results. In future analysis, additional AFCI measures may be developed to show the value of AFCI in closing the fuel cycle. Comparisons can show AFCI in terms of reduced global proliferation (e.g., reduction in enrichment), greater sustainability through preservation of a natural resource (e.g., reduction in uranium ore depletion), value from
Advanced optimization of permanent magnet wigglers using a genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Hajima, Ryoichi [Univ. of Tokyo (Japan)
1995-12-31
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.
Advanced optimization of permanent magnet wigglers using a genetic algorithm
International Nuclear Information System (INIS)
Hajima, Ryoichi
1995-01-01
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms
Advanced accelerator methods: The cyclotrino
International Nuclear Information System (INIS)
Welch, J.J.; Bertsche, K.J.; Friedman, P.G.; Morris, D.E.; Muller, R.A.
1987-04-01
Several new and unusual, advanced techniques in the small cyclotron are described. The cyclotron is run at low energy, using negative ions and at high harmonics. Electrostatic focusing is used exclusively. The ion source and injection system is in the center, which unfortunately does not provide enough current, but the new system design should solve this problem. An electrostatic extractor that runs at low voltage, under 5 kV, and a microchannel plate detector which is able to discriminate low energy ions from the 14 C are used. The resolution is sufficient for 14 C dating and a higher intensity source should allow dating of a milligram size sample of 30,000 year old material with less than 10% uncertainty
Advanced soft computing diagnosis method for tumour grading.
Papageorgiou, E I; Spyridonos, P P; Stylios, C D; Ravazoula, P; Groumpos, P P; Nikiforidis, G N
2006-01-01
To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied. One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM. The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively. An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.
Information theoretic methods for image processing algorithm optimization
Prokushkin, Sergey F.; Galil, Erez
2015-01-01
Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics ("adaptivity criterion") using noise reduction algorithms as an example. The method allows finding an "orthogonal decomposition" of the filter parameter space into the "filter adaptivity" and "filter strength" directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical "information restoration" rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).
Indian Academy of Sciences (India)
ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...
Advanced Fine Particulate Characterization Methods
Energy Technology Data Exchange (ETDEWEB)
Steven Benson; Lingbu Kong; Alexander Azenkeng; Jason Laumb; Robert Jensen; Edwin Olson; Jill MacKenzie; A.M. Rokanuzzaman
2007-01-31
The characterization and control of emissions from combustion sources are of significant importance in improving local and regional air quality. Such emissions include fine particulate matter, organic carbon compounds, and NO{sub x} and SO{sub 2} gases, along with mercury and other toxic metals. This project involved four activities including Further Development of Analytical Techniques for PM{sub 10} and PM{sub 2.5} Characterization and Source Apportionment and Management, Organic Carbonaceous Particulate and Metal Speciation for Source Apportionment Studies, Quantum Modeling, and High-Potassium Carbon Production with Biomass-Coal Blending. The key accomplishments included the development of improved automated methods to characterize the inorganic and organic components particulate matter. The methods involved the use of scanning electron microscopy and x-ray microanalysis for the inorganic fraction and a combination of extractive methods combined with near-edge x-ray absorption fine structure to characterize the organic fraction. These methods have direction application for source apportionment studies of PM because they provide detailed inorganic analysis along with total organic and elemental carbon (OC/EC) quantification. Quantum modeling using density functional theory (DFT) calculations was used to further elucidate a recently developed mechanistic model for mercury speciation in coal combustion systems and interactions on activated carbon. Reaction energies, enthalpies, free energies and binding energies of Hg species to the prototype molecules were derived from the data obtained in these calculations. Bimolecular rate constants for the various elementary steps in the mechanism have been estimated using the hard-sphere collision theory approximation, and the results seem to indicate that extremely fast kinetics could be involved in these surface reactions. Activated carbon was produced from a blend of lignite coal from the Center Mine in North Dakota and
Advanced reconstruction algorithms for electron tomography: From comparison to combination
Energy Technology Data Exchange (ETDEWEB)
Goris, B. [EMAT, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium); Roelandts, T. [Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Batenburg, K.J. [Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Centrum Wiskunde and Informatica, Science Park 123, NL-1098XG Amsterdam (Netherlands); Heidari Mezerji, H. [EMAT, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium); Bals, S., E-mail: sara.bals@ua.ac.be [EMAT, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium)
2013-04-15
In this work, the simultaneous iterative reconstruction technique (SIRT), the total variation minimization (TVM) reconstruction technique and the discrete algebraic reconstruction technique (DART) for electron tomography are compared and the advantages and disadvantages are discussed. Furthermore, we describe how the result of a three dimensional (3D) reconstruction based on TVM can provide objective information that is needed as the input for a DART reconstruction. This approach results in a tomographic reconstruction of which the segmentation is carried out in an objective manner. - Highlights: ► A comparative study between different reconstruction algorithms for tomography is performed. ► Reconstruction algorithms that uses prior knowledge about the specimen have a superior result. ► One reconstruction algorithm can provide the prior knowledge for a second algorithm.
developed algorithm for the application of british method of concret
African Journals Online (AJOL)
t-iyke
Most of the methods of concrete mix design developed over the years were geared towards manual approach. ... Key words: Concrete mix design; British method; Manual Approach; Algorithm. ..... Statistics for Science and Engineering.
Improved artificial bee colony algorithm based gravity matching navigation method.
Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang
2014-07-18
Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.
Advanced Methods of Biomedical Signal Processing
Cerutti, Sergio
2011-01-01
This book grew out of the IEEE-EMBS Summer Schools on Biomedical Signal Processing, which have been held annually since 2002 to provide the participants state-of-the-art knowledge on emerging areas in biomedical engineering. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications. The book provides an overview of a compelling group of advanced biomedical signal processing techniques, such as mult
Scalable force directed graph layout algorithms using fast multipole methods
Yunis, Enas Abdulrahman; Yokota, Rio; Ahmadia, Aron
2012-01-01
We present an extension to ExaFMM, a Fast Multipole Method library, as a generalized approach for fast and scalable execution of the Force-Directed Graph Layout algorithm. The Force-Directed Graph Layout algorithm is a physics-based approach
A method for evaluating discoverability and navigability of recommendation algorithms.
Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis
2017-01-01
Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the discoverability and navigability of recommendation algorithms. The proposed method tackles this by means of first evaluating the discoverability of recommendation algorithms by investigating structural properties of the resulting recommender systems in terms of bow tie structure, and path lengths. Second, the method evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three data sets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis, and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms.
Advanced computational electromagnetic methods and applications
Li, Wenxing; Elsherbeni, Atef; Rahmat-Samii, Yahya
2015-01-01
This new resource covers the latest developments in computational electromagnetic methods, with emphasis on cutting-edge applications. This book is designed to extend existing literature to the latest development in computational electromagnetic methods, which are of interest to readers in both academic and industrial areas. The topics include advanced techniques in MoM, FEM and FDTD, spectral domain method, GPU and Phi hardware acceleration, metamaterials, frequency and time domain integral equations, and statistics methods in bio-electromagnetics.
Development of advanced MCR task analysis methods
International Nuclear Information System (INIS)
Na, J. C.; Park, J. H.; Lee, S. K.; Kim, J. K.; Kim, E. S.; Cho, S. B.; Kang, J. S.
2008-07-01
This report describes task analysis methodology for advanced HSI designs. Task analyses was performed by using procedure-based hierarchical task analysis and task decomposition methods. The results from the task analysis were recorded in a database. Using the TA results, we developed static prototype of advanced HSI and human factors engineering verification and validation methods for an evaluation of the prototype. In addition to the procedure-based task analysis methods, workload estimation based on the analysis of task performance time and analyses for the design of information structure and interaction structures will be necessary
Selection method of terrain matching area for TERCOM algorithm
Zhang, Qieqie; Zhao, Long
2017-10-01
The performance of terrain aided navigation is closely related to the selection of terrain matching area. The different matching algorithms have different adaptability to terrain. This paper mainly studies the adaptability to terrain of TERCOM algorithm, analyze the relation between terrain feature and terrain characteristic parameters by qualitative and quantitative methods, and then research the relation between matching probability and terrain characteristic parameters by the Monte Carlo method. After that, we propose a selection method of terrain matching area for TERCOM algorithm, and verify the method correctness with real terrain data by simulation experiment. Experimental results show that the matching area obtained by the method in this paper has the good navigation performance and the matching probability of TERCOM algorithm is great than 90%
Advanced Computational Methods for Monte Carlo Calculations
Energy Technology Data Exchange (ETDEWEB)
Brown, Forrest B. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2018-01-12
This course is intended for graduate students who already have a basic understanding of Monte Carlo methods. It focuses on advanced topics that may be needed for thesis research, for developing new state-of-the-art methods, or for working with modern production Monte Carlo codes.
Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes
Spechler, Joshua A.; Hoppe, Daniel J.; Sigrist, Norbert; Shi, Fang; Seo, Byoung-Joon; Bikkannavar, Siddarayappa A.
2013-01-01
Segment mirror phasing, a critical step of segment mirror alignment, requires the ability to sense and correct the relative pistons between segments from up to a few hundred microns to a fraction of wavelength in order to bring the mirror system to its full diffraction capability. When sampling the aperture of a telescope, using auto-collimating flats (ACFs) is more economical. The performance of a telescope with a segmented primary mirror strongly depends on how well those primary mirror segments can be phased. One such process to phase primary mirror segments in the axial piston direction is dispersed fringe sensing (DFS). DFS technology can be used to co-phase the ACFs. DFS is essentially a signal fitting and processing operation. It is an elegant method of coarse phasing segmented mirrors. DFS performance accuracy is dependent upon careful calibration of the system as well as other factors such as internal optical alignment, system wavefront errors, and detector quality. Novel improvements to the algorithm have led to substantial enhancements in DFS performance. The Advanced Dispersed Fringe Sensing (ADFS) Algorithm is designed to reduce the sensitivity to calibration errors by determining the optimal fringe extraction line. Applying an angular extraction line dithering procedure and combining this dithering process with an error function while minimizing the phase term of the fitted signal, defines in essence the ADFS algorithm.
Wang, Xu; Shi, Fang; Sigrist, Norbert; Seo, Byoung-Joon; Tang, Hong; Bikkannavar, Siddarayappa; Basinger, Scott; Lay, Oliver
2012-01-01
Large aperture telescope commonly features segment mirrors and a coarse phasing step is needed to bring these individual segments into the fine phasing capture range. Dispersed Fringe Sensing (DFS) is a powerful coarse phasing technique and its alteration is currently being used for JWST.An Advanced Dispersed Fringe Sensing (ADFS) algorithm is recently developed to improve the performance and robustness of previous DFS algorithms with better accuracy and unique solution. The first part of the paper introduces the basic ideas and the essential features of the ADFS algorithm and presents the some algorithm sensitivity study results. The second part of the paper describes the full details of algorithm validation process through the advanced wavefront sensing and correction testbed (AWCT): first, the optimization of the DFS hardware of AWCT to ensure the data accuracy and reliability is illustrated. Then, a few carefully designed algorithm validation experiments are implemented, and the corresponding data analysis results are shown. Finally the fiducial calibration using Range-Gate-Metrology technique is carried out and a <10nm or <1% algorithm accuracy is demonstrated.
A Method for Improving the Progressive Image Coding Algorithms
Directory of Open Access Journals (Sweden)
Ovidiu COSMA
2014-12-01
Full Text Available This article presents a method for increasing the performance of the progressive coding algorithms for the subbands of images, by representing the coefficients with a code that reduces the truncation error.
Quantitative Methods in Supply Chain Management Models and Algorithms
Christou, Ioannis T
2012-01-01
Quantitative Methods in Supply Chain Management presents some of the most important methods and tools available for modeling and solving problems arising in the context of supply chain management. In the context of this book, “solving problems” usually means designing efficient algorithms for obtaining high-quality solutions. The first chapter is an extensive optimization review covering continuous unconstrained and constrained linear and nonlinear optimization algorithms, as well as dynamic programming and discrete optimization exact methods and heuristics. The second chapter presents time-series forecasting methods together with prediction market techniques for demand forecasting of new products and services. The third chapter details models and algorithms for planning and scheduling with an emphasis on production planning and personnel scheduling. The fourth chapter presents deterministic and stochastic models for inventory control with a detailed analysis on periodic review systems and algorithmic dev...
Recent advances in radial basis function collocation methods
Chen, Wen; Chen, C S
2014-01-01
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...
Algorithms for monitoring warfarin use: Results from Delphi Method.
Kano, Eunice Kazue; Borges, Jessica Bassani; Scomparini, Erika Burim; Curi, Ana Paula; Ribeiro, Eliane
2017-10-01
Warfarin stands as the most prescribed oral anticoagulant. New oral anticoagulants have been approved recently; however, their use is limited and the reversibility techniques of the anticoagulation effect are little known. Thus, our study's purpose was to develop algorithms for therapeutic monitoring of patients taking warfarin based on the opinion of physicians who prescribe this medicine in their clinical practice. The development of the algorithm was performed in two stages, namely: (i) literature review and (ii) algorithm evaluation by physicians using a Delphi Method. Based on the articles analyzed, two algorithms were developed: "Recommendations for the use of warfarin in anticoagulation therapy" and "Recommendations for the use of warfarin in anticoagulation therapy: dose adjustment and bleeding control." Later, these algorithms were analyzed by 19 medical doctors that responded to the invitation and agreed to participate in the study. Of these, 16 responded to the first round, 11 to the second and eight to the third round. A 70% consensus or higher was reached for most issues and at least 50% for six questions. We were able to develop algorithms to monitor the use of warfarin by physicians using a Delphi Method. The proposed method is inexpensive and involves the participation of specialists, and it has proved adequate for the intended purpose. Further studies are needed to validate these algorithms, enabling them to be used in clinical practice.
Method and algorithm for image processing
He, George G.; Moon, Brain D.
2003-12-16
The present invention is a modified Radon transform. It is similar to the traditional Radon transform for the extraction of line parameters and similar to traditional slant stack for the intensity summation of pixels away from a given pixel, for example ray paths that spans 360 degree at a given grid in the time and offset domain. However, the present invention differs from these methods in that the intensity and direction of a composite intensity for each pixel are maintained separately instead of combined after the transformation. An advantage of this approach is elimination of the work required to extract the line parameters in the transformed domain. The advantage of the modified Radon Transform method is amplified when many lines are present in the imagery or when the lines are just short segments which both occur in actual imagery.
Stochastic Recursive Algorithms for Optimization Simultaneous Perturbation Methods
Bhatnagar, S; Prashanth, L A
2013-01-01
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from sim...
Advanced analysis methods in particle physics
Energy Technology Data Exchange (ETDEWEB)
Bhat, Pushpalatha C.; /Fermilab
2010-10-01
Each generation of high energy physics experiments is grander in scale than the previous - more powerful, more complex and more demanding in terms of data handling and analysis. The spectacular performance of the Tevatron and the beginning of operations of the Large Hadron Collider, have placed us at the threshold of a new era in particle physics. The discovery of the Higgs boson or another agent of electroweak symmetry breaking and evidence of new physics may be just around the corner. The greatest challenge in these pursuits is to extract the extremely rare signals, if any, from huge backgrounds arising from known physics processes. The use of advanced analysis techniques is crucial in achieving this goal. In this review, I discuss the concepts of optimal analysis, some important advanced analysis methods and a few examples. The judicious use of these advanced methods should enable new discoveries and produce results with better precision, robustness and clarity.
A Flexible Reservation Algorithm for Advance Network Provisioning
Energy Technology Data Exchange (ETDEWEB)
Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex
2010-04-12
Many scientific applications need support from a communication infrastructure that provides predictable performance, which requires effective algorithms for bandwidth reservations. Network reservation systems such as ESnet's OSCARS, establish guaranteed bandwidth of secure virtual circuits for a certain bandwidth and length of time. However, users currently cannot inquire about bandwidth availability, nor have alternative suggestions when reservation requests fail. In general, the number of reservation options is exponential with the number of nodes n, and current reservation commitments. We present a novel approach for path finding in time-dependent networks taking advantage of user-provided parameters of total volume and time constraints, which produces options for earliest completion and shortest duration. The theoretical complexity is only O(n2r2) in the worst-case, where r is the number of reservations in the desired time interval. We have implemented our algorithm and developed efficient methodologies for incorporation into network reservation frameworks. Performance measurements confirm the theoretical predictions.
NATO Advanced Study Institute on Methods in Computational Molecular Physics
Diercksen, Geerd
1992-01-01
This volume records the lectures given at a NATO Advanced Study Institute on Methods in Computational Molecular Physics held in Bad Windsheim, Germany, from 22nd July until 2nd. August, 1991. This NATO Advanced Study Institute sought to bridge the quite considerable gap which exist between the presentation of molecular electronic structure theory found in contemporary monographs such as, for example, McWeeny's Methods 0/ Molecular Quantum Mechanics (Academic Press, London, 1989) or Wilson's Electron correlation in moleeules (Clarendon Press, Oxford, 1984) and the realization of the sophisticated computational algorithms required for their practical application. It sought to underline the relation between the electronic structure problem and the study of nuc1ear motion. Software for performing molecular electronic structure calculations is now being applied in an increasingly wide range of fields in both the academic and the commercial sectors. Numerous applications are reported in areas as diverse as catalysi...
Advanced symbolic analysis for VLSI systems methods and applications
Shi, Guoyong; Tlelo Cuautle, Esteban
2014-01-01
This book provides comprehensive coverage of the recent advances in symbolic analysis techniques for design automation of nanometer VLSI systems. The presentation is organized in parts of fundamentals, basic implementation methods and applications for VLSI design. Topics emphasized include statistical timing and crosstalk analysis, statistical and parallel analysis, performance bound analysis and behavioral modeling for analog integrated circuits . Among the recent advances, the Binary Decision Diagram (BDD) based approaches are studied in depth. The BDD-based hierarchical symbolic analysis approaches, have essentially broken the analog circuit size barrier. In particular, this book • Provides an overview of classical symbolic analysis methods and a comprehensive presentation on the modern BDD-based symbolic analysis techniques; • Describes detailed implementation strategies for BDD-based algorithms, including the principles of zero-suppression, variable ordering and canonical reduction; • Int...
Koichi, Shungo; Iwata, Satoru; Uno, Takeaki; Koshino, Hiroyuki; Satoh, Hiroko
2007-01-01
We describe a rigorous and fast algorithm for advanced canonical coding of planar chemical structures based on the algorithm of Faulon et al. (J. Chem. Inf. Comput. Sci. 2004, 44, 427-436). Our algorithm works well even for highly symmetric structures; moreover, an advantage of our algorithm includes providing a rigorous canonical numbering of atoms with a consideration of stereochemistry and recognizing symmetric moieties. The planar structural line notation with the canonical numbering is also fit for use with stereochemical line notation. These capabilities are usable for general purposes in chemical structural coding and are particularly essential for detecting equivalent atoms in NMR studies. This algorithm was implemented on a 13C NMR chemical shift prediction system CAST/CNMR. Applications of the algorithm to several organic compounds demonstrate the practical efficiency of the rigorous coding.
Advanced computer algebra algorithms for the expansion of Feynman integrals
International Nuclear Information System (INIS)
Ablinger, Jakob; Round, Mark; Schneider, Carsten
2012-10-01
Two-point Feynman parameter integrals, with at most one mass and containing local operator insertions in 4+ε-dimensional Minkowski space, can be transformed to multi-integrals or multi-sums over hyperexponential and/or hypergeometric functions depending on a discrete parameter n. Given such a specific representation, we utilize an enhanced version of the multivariate Almkvist-Zeilberger algorithm (for multi-integrals) and a common summation framework of the holonomic and difference field approach (for multi-sums) to calculate recurrence relations in n. Finally, solving the recurrence we can decide efficiently if the first coefficients of the Laurent series expansion of a given Feynman integral can be expressed in terms of indefinite nested sums and products; if yes, the all n solution is returned in compact representations, i.e., no algebraic relations exist among the occurring sums and products.
Advanced computer algebra algorithms for the expansion of Feynman integrals
Energy Technology Data Exchange (ETDEWEB)
Ablinger, Jakob; Round, Mark; Schneider, Carsten [Johannes Kepler Univ., Linz (Austria). Research Inst. for Symbolic Computation; Bluemlein, Johannes [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)
2012-10-15
Two-point Feynman parameter integrals, with at most one mass and containing local operator insertions in 4+{epsilon}-dimensional Minkowski space, can be transformed to multi-integrals or multi-sums over hyperexponential and/or hypergeometric functions depending on a discrete parameter n. Given such a specific representation, we utilize an enhanced version of the multivariate Almkvist-Zeilberger algorithm (for multi-integrals) and a common summation framework of the holonomic and difference field approach (for multi-sums) to calculate recurrence relations in n. Finally, solving the recurrence we can decide efficiently if the first coefficients of the Laurent series expansion of a given Feynman integral can be expressed in terms of indefinite nested sums and products; if yes, the all n solution is returned in compact representations, i.e., no algebraic relations exist among the occurring sums and products.
Advanced methods in diagnosis and therapy
International Nuclear Information System (INIS)
1987-01-01
This important meeting covers the following topics: use and optimization of monoclonal antibobies in oncology: - Tumor markers: Clinical follow-up of patients through tumor marker serum determinations. - Cancer and medical imaging: The use of monoclonal antibodies in immunoscintigraphy. - Immunoradiotherapy: Monoclonal antibodies as therapeutic vectors. Advanced methods in diagnosis: - Contribution of monoclonal antibodies in modern immunochemistry (RIA, EIA). - Interest of monoclonal antibody in immunohistochemical pathology diagnosis. - In vitro diagnosis future prospects: with receptors and oncogenes. - Immunofluoroassay: a new sensitive immunoanalytical procedure with broad applications. Recent advances in brachitherapy: - Interest of computer processing. Blood products irradiation: - Interest in transfusion and bone marrow transplantations [fr
Mathematics for natural scientists II advanced methods
Kantorovich, Lev
2016-01-01
This book covers the advanced mathematical techniques useful for physics and engineering students, presented in a form accessible to physics students, avoiding precise mathematical jargon and laborious proofs. Instead, all proofs are given in a simplified form that is clear and convincing for a physicist. Examples, where appropriate, are given from physics contexts. Both solved and unsolved problems are provided in each chapter. Mathematics for Natural Scientists II: Advanced Methods is the second of two volumes. It follows the first volume on Fundamentals and Basics.
Advanced signal separation and recovery algorithms for digital x-ray spectroscopy
International Nuclear Information System (INIS)
Mahmoud, Imbaby I.; El-Tokhy, Mohamed S.
2015-01-01
X-ray spectroscopy is widely used for in-situ applications for samples analysis. Therefore, spectrum drawing and assessment of x-ray spectroscopy with high accuracy is the main scope of this paper. A Silicon Lithium Si(Li) detector that cooled with a nitrogen is used for signal extraction. The resolution of the ADC is 12 bits. Also, the sampling rate of ADC is 5 MHz. Hence, different algorithms are implemented. These algorithms were run on a personal computer with Intel core TM i5-3470 CPU and 3.20 GHz. These algorithms are signal preprocessing, signal separation and recovery algorithms, and spectrum drawing algorithm. Moreover, statistical measurements are used for evaluation of these algorithms. Signal preprocessing based on DC-offset correction and signal de-noising is performed. DC-offset correction was done by using minimum value of radiation signal. However, signal de-noising was implemented using fourth order finite impulse response (FIR) filter, linear phase least-square FIR filter, complex wavelet transforms (CWT) and Kalman filter methods. We noticed that Kalman filter achieves large peak signal to noise ratio (PSNR) and lower error than other methods. However, CWT takes much longer execution time. Moreover, three different algorithms that allow correction of x-ray signal overlapping are presented. These algorithms are 1D non-derivative peak search algorithm, second derivative peak search algorithm and extrema algorithm. Additionally, the effect of signal separation and recovery algorithms on spectrum drawing is measured. Comparison between these algorithms is introduced. The obtained results confirm that second derivative peak search algorithm as well as extrema algorithm have very small error in comparison with 1D non-derivative peak search algorithm. However, the second derivative peak search algorithm takes much longer execution time. Therefore, extrema algorithm introduces better results over other algorithms. It has the advantage of recovering and
Pressure algorithm for elliptic flow calculations with the PDF method
Anand, M. S.; Pope, S. B.; Mongia, H. C.
1991-01-01
An algorithm to determine the mean pressure field for elliptic flow calculations with the probability density function (PDF) method is developed and applied. The PDF method is a most promising approach for the computation of turbulent reacting flows. Previous computations of elliptic flows with the method were in conjunction with conventional finite volume based calculations that provided the mean pressure field. The algorithm developed and described here permits the mean pressure field to be determined within the PDF calculations. The PDF method incorporating the pressure algorithm is applied to the flow past a backward-facing step. The results are in good agreement with data for the reattachment length, mean velocities, and turbulence quantities including triple correlations.
Optimum design for rotor-bearing system using advanced generic algorithm
International Nuclear Information System (INIS)
Kim, Young Chan; Choi, Seong Pil; Yang, Bo Suk
2001-01-01
This paper describes a combinational method to compute the global and local solutions of optimization problems. The present hybrid algorithm uses both a generic algorithm and a local concentrate search algorithm (e.g simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The present algorithm can be supplied to minimize the resonance response (Q factor) and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables
An advanced analysis method of initial orbit determination with too short arc data
Li, Binzhe; Fang, Li
2018-02-01
This paper studies the initial orbit determination (IOD) based on space-based angle measurement. Commonly, these space-based observations have short durations. As a result, classical initial orbit determination algorithms give poor results, such as Laplace methods and Gauss methods. In this paper, an advanced analysis method of initial orbit determination is developed for space-based observations. The admissible region and triangulation are introduced in the method. Genetic algorithm is also used for adding some constraints of parameters. Simulation results show that the algorithm can successfully complete the initial orbit determination.
Advanced statistical methods in data science
Chen, Jiahua; Lu, Xuewen; Yi, Grace; Yu, Hao
2016-01-01
This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a fu...
An advanced algorithm for deformation estimation in non-urban areas
Goel, Kanika; Adam, Nico
2012-09-01
This paper presents an advanced differential SAR interferometry stacking algorithm for high resolution deformation monitoring in non-urban areas with a focus on distributed scatterers (DSs). Techniques such as the Small Baseline Subset Algorithm (SBAS) have been proposed for processing DSs. SBAS makes use of small baseline differential interferogram subsets. Singular value decomposition (SVD), i.e. L2 norm minimization is applied to link independent subsets separated by large baselines. However, the interferograms used in SBAS are multilooked using a rectangular window to reduce phase noise caused for instance by temporal decorrelation, resulting in a loss of resolution and the superposition of topography and deformation signals from different objects. Moreover, these have to be individually phase unwrapped and this can be especially difficult in natural terrains. An improved deformation estimation technique is presented here which exploits high resolution SAR data and is suitable for rural areas. The implemented method makes use of small baseline differential interferograms and incorporates an object adaptive spatial phase filtering and residual topography removal for an accurate phase and coherence estimation, while preserving the high resolution provided by modern satellites. This is followed by retrieval of deformation via the SBAS approach, wherein, the phase inversion is performed using an L1 norm minimization which is more robust to the typical phase unwrapping errors encountered in non-urban areas. Meter resolution TerraSAR-X data of an underground gas storage reservoir in Germany is used for demonstrating the effectiveness of this newly developed technique in rural areas.
Directory of Open Access Journals (Sweden)
Peigang Ning
Full Text Available OBJECTIVE: This work aims to explore the effects of adaptive statistical iterative reconstruction (ASiR and model-based iterative reconstruction (MBIR algorithms in reducing computed tomography (CT radiation dosages in abdominal imaging. METHODS: CT scans on a standard male phantom were performed at different tube currents. Images at the different tube currents were reconstructed with the filtered back-projection (FBP, 50% ASiR and MBIR algorithms and compared. The CT value, image noise and contrast-to-noise ratios (CNRs of the reconstructed abdominal images were measured. Volumetric CT dose indexes (CTDIvol were recorded. RESULTS: At different tube currents, 50% ASiR and MBIR significantly reduced image noise and increased the CNR when compared with FBP. The minimal tube current values required by FBP, 50% ASiR, and MBIR to achieve acceptable image quality using this phantom were 200, 140, and 80 mA, respectively. At the identical image quality, 50% ASiR and MBIR reduced the radiation dose by 35.9% and 59.9% respectively when compared with FBP. CONCLUSIONS: Advanced iterative reconstruction techniques are able to reduce image noise and increase image CNRs. Compared with FBP, 50% ASiR and MBIR reduced radiation doses by 35.9% and 59.9%, respectively.
Editorial: Latest methods and advances in biotechnology.
Lee, Sang Yup; Jungbauer, Alois
2014-01-01
The latest "Biotech Methods and Advances" special issue of Biotechnology Journal continues the BTJ tradition of featuring the latest breakthroughs in biotechnology. The special issue is edited by our Editors-in-Chief, Prof. Sang Yup Lee and Prof. Alois Jungbauer and covers a wide array of topics in biotechnology, including the perennial favorite workhorses of the biotech industry, Chinese hamster ovary (CHO) cell and Escherichia coli. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
A SAR IMAGE REGISTRATION METHOD BASED ON SIFT ALGORITHM
Directory of Open Access Journals (Sweden)
W. Lu
2017-09-01
Full Text Available In order to improve the stability and rapidity of synthetic aperture radar (SAR images matching, an effective method was presented. Firstly, the adaptive smoothing filtering was employed for image denoising in image processing based on Wallis filtering to avoid the follow-up noise is amplified. Secondly, feature points were extracted by a simplified SIFT algorithm. Finally, the exact matching of the images was achieved with these points. Compared with the existing methods, it not only maintains the richness of features, but a-lso reduces the noise of the image. The simulation results show that the proposed algorithm can achieve better matching effect.
Benchmarking Advanced Control Algorithms for a Laser Scanner System
DEFF Research Database (Denmark)
Stoustrup, Jakob; Ordys, A.W.; Smillie, I.
1996-01-01
The paper describes tests performed on the laser scanner system toassess feasibility of modern control techniques in achieving a requiredperformance in the trajectory following problem. The two methods tested areQTR H-infinity and Predictive Control. The results are ilustated ona simulation example....
A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization
International Nuclear Information System (INIS)
Mahmoudi, S.M.; Aghaie, M.; Bahonar, M.; Poursalehi, N.
2016-01-01
Highlights: • The Gravitational Search Algorithm (GSA) is introduced. • The advantage of GSA is verified in Shekel’s Foxholes. • Reload optimizing in WWER-1000 and WWER-440 cases are performed. • Maximizing K eff , minimizing PPFs and flattening power density is considered. - Abstract: In-core fuel management optimization (ICFMO) is one of the most challenging concepts of nuclear engineering. In recent decades several meta-heuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. This paper presents a new method of using Gravitational Search Algorithm (GSA) for in-core fuel management optimization. The GSA is constructed based on the law of gravity and the notion of mass interactions. It uses the theory of Newtonian physics and searcher agents are the collection of masses. In this work, at the first step, GSA method is compared with other meta-heuristic algorithms on Shekel’s Foxholes problem. In the second step for finding the best core, the GSA algorithm has been performed for three PWR test cases including WWER-1000 and WWER-440 reactors. In these cases, Multi objective optimizations with the following goals are considered, increment of multiplication factor (K eff ), decrement of power peaking factor (PPF) and power density flattening. It is notable that for neutronic calculation, PARCS (Purdue Advanced Reactor Core Simulator) code is used. The results demonstrate that GSA algorithm have promising performance and could be proposed for other optimization problems of nuclear engineering field.
Advanced algorithm for MPPT control of photovoltaic systems
Energy Technology Data Exchange (ETDEWEB)
Liu, C.; Wu, B.; Cheung, R. [Ryerson Polytechnic Univ., Toronto, ON (Canada). Dept. of Electrical and Computer Engineering
2006-07-01
Although photovoltaic (PV) energy is a renewable, environmentally sound source of electricity, it is relatively costly. The maximum power point tracking (MPPT) of the PV output for all sunshine conditions is key to keeping the output power per unit cost low for successful PV applications. The MPPT control is challenging, because the sunshine condition that determines the amount of sun energy into the PV array may change at any time, and the voltage/current characteristic of PV arrays is highly nonlinear. The 5 components of a PV system for the grid-connected applications are a PV array that converts solar energy to electric energy; a dc-dc converter that converts low dc voltages produced by the PV arrays to a high dc voltage; an inverter that converts the high dc voltage to a single- or three-phase ac voltage; a digital controller that controls the converter operation with MPPT capability; and, an ac filter that absorbs voltage/current harmonics generated by the inverter. The technical requirements in developing a practical PV system include an optimal control that can extract the maximum output power from the PV arrays under all operating and weather conditions, and a high performance-to-cost ratio to help commercialize developed PV technologies. This paper proposed a new method for the MPPT control of PV systems. The new method uses one estimate process for every two perturb processes in search of the maximum PV output for all sunshine conditions. In this estimate-perturb-perturb (EPP) method, the perturb process conducts the search over a highly nonlinear PV characteristic, and the estimate process compensates the perturb process for irradiance-changing conditions. The EPP method improves the tracking accuracy and speed of the MPPT control compared to other methods. This paper demonstrated that the EPP method can provide accurate and reliable MPPT even under rapidly changing irradiance conditions. A grid-connected PV system using three MPPT controls was
A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
Directory of Open Access Journals (Sweden)
Akpona Okujeni
2014-07-01
Full Text Available Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR, kernel ridge regression (KRR, artificial neural networks (NN, random forest regression (RFR and partial least squares regression (PLSR. Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN or limited (RFR and PLSR performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.
Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano
2016-07-07
Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.
Chen, Ming-Chih; Hsiao, Shen-Fu
In this paper, we propose an area-efficient design of Advanced Encryption Standard (AES) processor by applying a new common-expression-elimination (CSE) method to the sub-functions of various transformations required in AES. The proposed method reduces the area cost of realizing the sub-functions by extracting the common factors in the bit-level XOR/AND-based sum-of-product expressions of these sub-functions using a new CSE algorithm. Cell-based implementation results show that the AES processor with our proposed CSE method has significant area improvement compared with previous designs.
Influence of crossover methods used by genetic algorithm-based ...
Indian Academy of Sciences (India)
numerical methods like Newton–Raphson, sequential homotopy calculation, Walsh ... But the paper does not touch upon the elements of crossover operators. ... if SHE problems are solved with optimization tools like GA (Schutten ..... Goldberg D E 1989 Genetic algorithms in search, optimization and machine learning.
A block matching-based registration algorithm for localization of locally advanced lung tumors
Energy Technology Data Exchange (ETDEWEB)
Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D., E-mail: gdhugo@vcu.edu [Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, 23298 (United States)
2014-04-15
Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm{sup 3}), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0
A block matching-based registration algorithm for localization of locally advanced lung tumors
International Nuclear Information System (INIS)
Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D.
2014-01-01
Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm 3 ), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0.001). Left
The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography
Energy Technology Data Exchange (ETDEWEB)
Aarle, Wim van, E-mail: wim.vanaarle@uantwerpen.be [iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Palenstijn, Willem Jan, E-mail: willemjan.palenstijn@uantwerpen.be [iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Centrum Wiskunde & Informatica, Science Park 123, NL-1098 XG Amsterdam (Netherlands); De Beenhouwer, Jan, E-mail: jan.debeenhouwer@uantwerpen.be [iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Altantzis, Thomas, E-mail: thomas.altantzis@uantwerpen.be [Electron Microscopy for Materials Science, University of Antwerp, Groenenborgerlaan 171, B-2020 Wilrijk (Belgium); Bals, Sara, E-mail: sara.bals@uantwerpen.be [Electron Microscopy for Materials Science, University of Antwerp, Groenenborgerlaan 171, B-2020 Wilrijk (Belgium); Batenburg, K. Joost, E-mail: joost.batenburg@cwi.nl [iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Centrum Wiskunde & Informatica, Science Park 123, NL-1098 XG Amsterdam (Netherlands); Mathematical Institute, Leiden University, P.O. Box 9512, NL-2300 RA Leiden (Netherlands); Sijbers, Jan, E-mail: jan.sijbers@uantwerpen.be [iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium)
2015-10-15
We present the ASTRA Toolbox as an open platform for 3D image reconstruction in tomography. Most of the software tools that are currently used in electron tomography offer limited flexibility with respect to the geometrical parameters of the acquisition model and the algorithms used for reconstruction. The ASTRA Toolbox provides an extensive set of fast and flexible building blocks that can be used to develop advanced reconstruction algorithms, effectively removing these limitations. We demonstrate this flexibility, the resulting reconstruction quality, and the computational efficiency of this toolbox by a series of experiments, based on experimental dual-axis tilt series. - Highlights: • The ASTRA Toolbox is an open platform for 3D image reconstruction in tomography. • Advanced reconstruction algorithms can be prototyped using the fast and flexible building blocks. • This flexibility is demonstrated on a common use case: dual-axis tilt series reconstruction with prior knowledge. • The computational efficiency is validated on an experimentally measured tilt series.
The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography
International Nuclear Information System (INIS)
Aarle, Wim van; Palenstijn, Willem Jan; De Beenhouwer, Jan; Altantzis, Thomas; Bals, Sara; Batenburg, K. Joost; Sijbers, Jan
2015-01-01
We present the ASTRA Toolbox as an open platform for 3D image reconstruction in tomography. Most of the software tools that are currently used in electron tomography offer limited flexibility with respect to the geometrical parameters of the acquisition model and the algorithms used for reconstruction. The ASTRA Toolbox provides an extensive set of fast and flexible building blocks that can be used to develop advanced reconstruction algorithms, effectively removing these limitations. We demonstrate this flexibility, the resulting reconstruction quality, and the computational efficiency of this toolbox by a series of experiments, based on experimental dual-axis tilt series. - Highlights: • The ASTRA Toolbox is an open platform for 3D image reconstruction in tomography. • Advanced reconstruction algorithms can be prototyped using the fast and flexible building blocks. • This flexibility is demonstrated on a common use case: dual-axis tilt series reconstruction with prior knowledge. • The computational efficiency is validated on an experimentally measured tilt series
Toward optimal feature selection using ranking methods and classification algorithms
Directory of Open Access Journals (Sweden)
Novaković Jasmina
2011-01-01
Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.
Advanced Analysis Methods in High Energy Physics
Energy Technology Data Exchange (ETDEWEB)
Pushpalatha C. Bhat
2001-10-03
During the coming decade, high energy physics experiments at the Fermilab Tevatron and around the globe will use very sophisticated equipment to record unprecedented amounts of data in the hope of making major discoveries that may unravel some of Nature's deepest mysteries. The discovery of the Higgs boson and signals of new physics may be around the corner. The use of advanced analysis techniques will be crucial in achieving these goals. The author discusses some of the novel methods of analysis that could prove to be particularly valuable for finding evidence of any new physics, for improving precision measurements and for exploring parameter spaces of theoretical models.
Indian Academy of Sciences (India)
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
Iterative algorithm for the volume integral method for magnetostatics problems
International Nuclear Information System (INIS)
Pasciak, J.E.
1980-11-01
Volume integral methods for solving nonlinear magnetostatics problems are considered in this paper. The integral method is discretized by a Galerkin technique. Estimates are given which show that the linearized problems are well conditioned and hence easily solved using iterative techniques. Comparisons of iterative algorithms with the elimination method of GFUN3D shows that the iterative method gives an order of magnitude improvement in computational time as well as memory requirements for large problems. Computational experiments for a test problem as well as a double layer dipole magnet are given. Error estimates for the linearized problem are also derived
Advances in Packaging Methods, Processes and Systems
Directory of Open Access Journals (Sweden)
Nitaigour Premchand Mahalik
2014-10-01
Full Text Available The food processing and packaging industry is becoming a multi-trillion dollar global business. The reason is that the recent increase in incomes in traditionally less economically developed countries has led to a rise in standards of living that includes a significantly higher consumption of packaged foods. As a result, food safety guidelines have been more stringent than ever. At the same time, the number of research and educational institutions—that is, the number of potential researchers and stakeholders—has increased in the recent past. This paper reviews recent developments in food processing and packaging (FPP, keeping in view the aforementioned advancements and bearing in mind that FPP is an interdisciplinary area in that materials, safety, systems, regulation, and supply chains play vital roles. In particular, the review covers processing and packaging principles, standards, interfaces, techniques, methods, and state-of-the-art technologies that are currently in use or in development. Recent advances such as smart packaging, non-destructive inspection methods, printing techniques, application of robotics and machineries, automation architecture, software systems and interfaces are reviewed.
Scalable force directed graph layout algorithms using fast multipole methods
Yunis, Enas Abdulrahman
2012-06-01
We present an extension to ExaFMM, a Fast Multipole Method library, as a generalized approach for fast and scalable execution of the Force-Directed Graph Layout algorithm. The Force-Directed Graph Layout algorithm is a physics-based approach to graph layout that treats the vertices V as repelling charged particles with the edges E connecting them acting as springs. Traditionally, the amount of work required in applying the Force-Directed Graph Layout algorithm is O(|V|2 + |E|) using direct calculations and O(|V| log |V| + |E|) using truncation, filtering, and/or multi-level techniques. Correct application of the Fast Multipole Method allows us to maintain a lower complexity of O(|V| + |E|) while regaining most of the precision lost in other techniques. Solving layout problems for truly large graphs with millions of vertices still requires a scalable algorithm and implementation. We have been able to leverage the scalability and architectural adaptability of the ExaFMM library to create a Force-Directed Graph Layout implementation that runs efficiently on distributed multicore and multi-GPU architectures. © 2012 IEEE.
Improvement in PWR automatic optimization reloading methods using genetic algorithm
International Nuclear Information System (INIS)
Levine, S.H.; Ivanov, K.; Feltus, M.
1996-01-01
The objective of using automatic optimized reloading methods is to provide the Nuclear Engineer with an efficient method for reloading a nuclear reactor which results in superior core configurations that minimize fuel costs. Previous methods developed by Levine et al required a large effort to develop the initial core loading using a priority loading scheme. Subsequent modifications to this core configuration were made using expert rules to produce the final core design. Improvements in this technique have been made by using a genetic algorithm to produce improved core reload designs for PWRs more efficiently (authors)
Improvement in PWR automatic optimization reloading methods using genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Levine, S H; Ivanov, K; Feltus, M [Pennsylvania State Univ., University Park, PA (United States)
1996-12-01
The objective of using automatic optimized reloading methods is to provide the Nuclear Engineer with an efficient method for reloading a nuclear reactor which results in superior core configurations that minimize fuel costs. Previous methods developed by Levine et al required a large effort to develop the initial core loading using a priority loading scheme. Subsequent modifications to this core configuration were made using expert rules to produce the final core design. Improvements in this technique have been made by using a genetic algorithm to produce improved core reload designs for PWRs more efficiently (authors).
A Novel Assembly Line Balancing Method Based on PSO Algorithm
Directory of Open Access Journals (Sweden)
Xiaomei Hu
2014-01-01
Full Text Available Assembly line is widely used in manufacturing system. Assembly line balancing problem is a crucial question during design and management of assembly lines since it directly affects the productivity of the whole manufacturing system. The model of assembly line balancing problem is put forward and a general optimization method is proposed. The key data on assembly line balancing problem is confirmed, and the precedence relations diagram is described. A double objective optimization model based on takt time and smoothness index is built, and balance optimization scheme based on PSO algorithm is proposed. Through the simulation experiments of examples, the feasibility and validity of the assembly line balancing method based on PSO algorithm is proved.
Advances on geometric flux optical design method
García-Botella, Ángel; Fernández-Balbuena, Antonio Álvarez; Vázquez, Daniel
2017-09-01
Nonimaging optics is focused on the study of methods to design concentrators or illuminators systems. It can be included in the area of photometry and radiometry and it is governed by the laws of geometrical optics. The field vector method, which starts with the definition of the irradiance vector E, is one of the techniques used in nonimaging optics. Called "Geometrical flux vector" it has provide ideal designs. The main property of this model is, its ability to estimate how radiant energy is transferred by the optical system, from the concepts of field line, flux tube and pseudopotential surface, overcoming traditional raytrace methods. Nevertheless this model has been developed only at an academic level, where characteristic optical parameters are ideal not real and the studied geometries are simple. The main objective of the present paper is the application of the vector field method to the analysis and design of real concentration and illumination systems. We propose the development of a calculation tool for optical simulations by vector field, using algorithms based on Fermat`s principle, as an alternative to traditional tools for optical simulations by raytrace, based on reflection and refraction law. This new tool provides, first, traditional simulations results: efficiency, illuminance/irradiance calculations, angular distribution of light- with lower computation time, photometrical information needs about a few tens of field lines, in comparison with million rays needed nowadays. On the other hand the tool will provides new information as vector field maps produced by the system, composed by field lines and quasipotential surfaces. We show our first results with the vector field simulation tool.
Experimental methods for the analysis of optimization algorithms
Bartz-Beielstein, Thomas; Paquete, Luis; Preuss, Mike
2010-01-01
In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on diffe
Classical Methods and Calculation Algorithms for Determining Lime Requirements
Directory of Open Access Journals (Sweden)
André Guarçoni
Full Text Available ABSTRACT The methods developed for determination of lime requirements (LR are based on widely accepted principles. However, the formulas used for calculation have evolved little over recent decades, and in some cases there are indications of their inadequacy. The aim of this study was to compare the lime requirements calculated by three classic formulas and three algorithms, defining those most appropriate for supplying Ca and Mg to coffee plants and the smaller possibility of causing overliming. The database used contained 600 soil samples, which were collected in coffee plantings. The LR was estimated by the methods of base saturation, neutralization of Al3+, and elevation of Ca2+ and Mg2+ contents (two formulas and by the three calculation algorithms. Averages of the lime requirements were compared, determining the frequency distribution of the 600 lime requirements (LR estimated through each calculation method. In soils with low cation exchange capacity at pH 7, the base saturation method may fail to adequately supply the plants with Ca and Mg in many situations, while the method of Al3+ neutralization and elevation of Ca2+ and Mg2+ contents can result in the calculation of application rates that will increase the pH above the suitable range. Among the methods studied for calculating lime requirements, the algorithm that predicts reaching a defined base saturation, with adequate Ca and Mg supply and the maximum application rate limited to the H+Al value, proved to be the most efficient calculation method, and it can be recommended for use in numerous crops conditions.
Parallel algorithms for nuclear reactor analysis via domain decomposition method
International Nuclear Information System (INIS)
Kim, Yong Hee
1995-02-01
In this thesis, the neutron diffusion equation in reactor physics is discretized by the finite difference method and is solved on a parallel computer network which is composed of T-800 transputers. T-800 transputer is a message-passing type MIMD (multiple instruction streams and multiple data streams) architecture. A parallel variant of Schwarz alternating procedure for overlapping subdomains is developed with domain decomposition. The thesis provides convergence analysis and improvement of the convergence of the algorithm. The convergence of the parallel Schwarz algorithms with DN(or ND), DD, NN, and mixed pseudo-boundary conditions(a weighted combination of Dirichlet and Neumann conditions) is analyzed for both continuous and discrete models in two-subdomain case and various underlying features are explored. The analysis shows that the convergence rate of the algorithm highly depends on the pseudo-boundary conditions and the theoretically best one is the mixed boundary conditions(MM conditions). Also it is shown that there may exist a significant discrepancy between continuous model analysis and discrete model analysis. In order to accelerate the convergence of the parallel Schwarz algorithm, relaxation in pseudo-boundary conditions is introduced and the convergence analysis of the algorithm for two-subdomain case is carried out. The analysis shows that under-relaxation of the pseudo-boundary conditions accelerates the convergence of the parallel Schwarz algorithm if the convergence rate without relaxation is negative, and any relaxation(under or over) decelerates convergence if the convergence rate without relaxation is positive. Numerical implementation of the parallel Schwarz algorithm on an MIMD system requires multi-level iterations: two levels for fixed source problems, three levels for eigenvalue problems. Performance of the algorithm turns out to be very sensitive to the iteration strategy. In general, multi-level iterations provide good performance when
Cabaret, S; Coppier, H; Rachid, A; Barillère, R; CERN. Geneva. IT Department
2007-01-01
The GCS (Gas Control System) project team at CERN uses a Model Driven Approach with a Framework - UNICOS (UNified Industrial COntrol System) - based on PLC (Programming Language Controller) and SCADA (Supervisory Control And Data Acquisition) technologies. The first' UNICOS versions were able to provide a PID (Proportional Integrative Derivative) controller whereas the Gas Systems required more advanced control strategies. The MultiController is a new UNICOS object which provides the following advanced control algorithms: Smith Predictor, PFC (Predictive Function Control), RST* and GPC (Global Predictive Control). Its design is based on a monolithic entity with a global structure definition which is able to capture the desired set of parameters of any specific control algorithm supported by the object. The SCADA system -- PVSS - supervises the MultiController operation. The PVSS interface provides users with supervision faceplate, in particular it links any MultiController with recipes: the GCS experts are ab...
Advanced Suspension and Control Algorithm for U.S. Army Ground Vehicles
2013-04-01
magnetorheological fluid damper . This report provides a record of the research findings from this research project on advanced suspension and control...nonlinear control algorithm that can effectively work with semi-active dampers , such as the magnetorheological (MR) fluid damper . This research...rheological fluid effects). This is because the viscous damping force for high shaft speed becomes excessive and will transmit the terrain-induced
Genetic Algorithms: A New Method for Neutron Beam Spectral Characterization
International Nuclear Information System (INIS)
David W. Freeman
2000-01-01
A revolutionary new concept for solving the neutron spectrum unfolding problem using genetic algorithms (GAs) has recently been introduced. GAs are part of a new field of evolutionary solution techniques that mimic living systems with computer-simulated chromosome solutions that mate, mutate, and evolve to create improved solutions. The original motivation for the research was to improve spectral characterization of neutron beams associated with boron neutron capture therapy (BNCT). The GA unfolding technique has been successfully applied to problems with moderate energy resolution (up to 47 energy groups). Initial research indicates that the GA unfolding technique may well be superior to popular unfolding methods in common use. Research now under way at Kansas State University is focused on optimizing the unfolding algorithm and expanding its energy resolution to unfold detailed beam spectra based on multiple foil measurements. Indications are that the final code will significantly outperform current, state-of-the-art codes in use by the scientific community
Exergetic optimization of turbofan engine with genetic algorithm method
Energy Technology Data Exchange (ETDEWEB)
Turan, Onder [Anadolu University, School of Civil Aviation (Turkey)], e-mail: onderturan@anadolu.edu.tr
2011-07-01
With the growth of passenger numbers, emissions from the aeronautics sector are increasing and the industry is now working on improving engine efficiency to reduce fuel consumption. The aim of this study is to present the use of genetic algorithms, an optimization method based on biological principles, to optimize the exergetic performance of turbofan engines. The optimization was carried out using exergy efficiency, overall efficiency and specific thrust of the engine as evaluation criteria and playing on pressure and bypass ratio, turbine inlet temperature and flight altitude. Results showed exergy efficiency can be maximized with higher altitudes, fan pressure ratio and turbine inlet temperature; the turbine inlet temperature is the most important parameter for increased exergy efficiency. This study demonstrated that genetic algorithms are effective in optimizing complex systems in a short time.
Methods and Algorithms for Economic MPC in Power Production Planning
DEFF Research Database (Denmark)
Sokoler, Leo Emil
in real-time. A generator can represent a producer of electricity, a consumer of electricity, or possibly both. Examples of generators are heat pumps, electric vehicles, wind turbines, virtual power plants, solar cells, and conventional fuel-fired thermal power plants. Although this thesis is mainly...... concerned with EMPC for minutes-ahead production planning, we show that the proposed EMPC scheme can be extended to days-ahead planning (including unit commitment) as well. The power generation from renewable energy sources such as wind and solar power is inherently uncertain and variable. A portfolio...... design an algorithm based on the alternating direction method of multipliers (ADMM) to solve input-constrained OCPs with convex objective functions. The OCPs that occur in EMPC of dynamically decoupled subsystems, e.g. power generators, have a block-angular structure. Subsystem decomposition algorithms...
Experimental Methods for the Analysis of Optimization Algorithms
DEFF Research Database (Denmark)
, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on different...... in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies. This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment......In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However...
Frequency-Dependent FDTD Algorithm Using Newmark’s Method
Directory of Open Access Journals (Sweden)
Bing Wei
2014-01-01
Full Text Available According to the characteristics of the polarizability in frequency domain of three common models of dispersive media, the relation between the polarization vector and electric field intensity is converted into a time domain differential equation of second order with the polarization vector by using the conversion from frequency to time domain. Newmark βγ difference method is employed to solve this equation. The electric field intensity to polarizability recursion is derived, and the electric flux to electric field intensity recursion is obtained by constitutive relation. Then FDTD iterative computation in time domain of electric and magnetic field components in dispersive medium is completed. By analyzing the solution stability of the above differential equation using central difference method, it is proved that this method has more advantages in the selection of time step. Theoretical analyses and numerical results demonstrate that this method is a general algorithm and it has advantages of higher accuracy and stability over the algorithms based on central difference method.
Energy Technology Data Exchange (ETDEWEB)
Santi, Peter Angelo [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Cutler, Theresa Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Favalli, Andrea [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Koehler, Katrina Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Henzl, Vladimir [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Henzlova, Daniela [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Parker, Robert Francis [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Croft, Stephen [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-12-01
In order to improve the accuracy and capabilities of neutron multiplicity counting, additional quantifiable information is needed in order to address the assumptions that are present in the point model. Extracting and utilizing higher order moments (Quads and Pents) from the neutron pulse train represents the most direct way of extracting additional information from the measurement data to allow for an improved determination of the physical properties of the item of interest. The extraction of higher order moments from a neutron pulse train required the development of advanced dead time correction algorithms which could correct for dead time effects in all of the measurement moments in a self-consistent manner. In addition, advanced analysis algorithms have been developed to address specific assumptions that are made within the current analysis model, namely that all neutrons are created at a single point within the item of interest, and that all neutrons that are produced within an item are created with the same energy distribution. This report will discuss the current status of implementation and initial testing of the advanced dead time correction and analysis algorithms that have been developed in an attempt to utilize higher order moments to improve the capabilities of correlated neutron measurement techniques.
Advanced Control Method for Hypersonic Vehicles
National Aeronautics and Space Administration — This research effort aims to develop software control algorithms that will correct for roll reversal before it happens. Roll reversal occurs when an aircraft is...
Advanced Control Method for Hypersonic Vehicles
National Aeronautics and Space Administration — This research effort aims to develop software control algorithms that will correct for roll reversal before it happens. Roll reversal occurs when an aircraft is...
Indian Academy of Sciences (India)
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
An Advanced Coupled Genetic Algorithm for Identifying Unknown Moving Loads on Bridge Decks
Directory of Open Access Journals (Sweden)
Sang-Youl Lee
2014-01-01
Full Text Available This study deals with an inverse method to identify moving loads on bridge decks using the finite element method (FEM and a coupled genetic algorithm (c-GA. We developed the inverse technique using a coupled genetic algorithm that can make global solution searches possible as opposed to classical gradient-based optimization techniques. The technique described in this paper allows us to not only detect the weight of moving vehicles but also find their moving velocities. To demonstrate the feasibility of the method, the algorithm is applied to a bridge deck model with beam elements. In addition, 1D and 3D finite element models are simulated to study the influence of measurement errors and model uncertainty between numerical and real structures. The results demonstrate the excellence of the method from the standpoints of computation efficiency and avoidance of premature convergence.
Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods
Directory of Open Access Journals (Sweden)
Saadia Zahid
2015-01-01
Full Text Available Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs with artificial neural networks (ANNs. Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.
MACHINE LEARNING METHODS IN DIGITAL AGRICULTURE: ALGORITHMS AND CASES
Directory of Open Access Journals (Sweden)
Aleksandr Vasilyevich Koshkarov
2018-05-01
Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.
International Nuclear Information System (INIS)
Powell, Jade; Heng, Ik Siong; Torres-Forné, Alejandro; Font, José A; Lynch, Ryan; Trifirò, Daniele; Cuoco, Elena; Cavaglià, Marco
2017-01-01
The data taken by the advanced LIGO and Virgo gravitational-wave detectors contains short duration noise transients that limit the significance of astrophysical detections and reduce the duty cycle of the instruments. As the advanced detectors are reaching sensitivity levels that allow for multiple detections of astrophysical gravitational-wave sources it is crucial to achieve a fast and accurate characterization of non-astrophysical transient noise shortly after it occurs in the detectors. Previously we presented three methods for the classification of transient noise sources. They are Principal Component Analysis for Transients (PCAT), Principal Component LALInference Burst (PC-LIB) and Wavelet Detection Filter with Machine Learning (WDF-ML). In this study we carry out the first performance tests of these algorithms on gravitational-wave data from the Advanced LIGO detectors. We use the data taken between the 3rd of June 2015 and the 14th of June 2015 during the 7th engineering run (ER7), and outline the improvements made to increase the performance and lower the latency of the algorithms on real data. This work provides an important test for understanding the performance of these methods on real, non stationary data in preparation for the second advanced gravitational-wave detector observation run, planned for later this year. We show that all methods can classify transients in non stationary data with a high level of accuracy and show the benefits of using multiple classifiers. (paper)
Theoretical and algorithmic advances in multi-parametric programming and control
Pistikopoulos, Efstratios N.; Dominguez, Luis; Panos, Christos; Kouramas, Konstantinos; Chinchuluun, Altannar
2012-01-01
This paper presents an overview of recent theoretical and algorithmic advances, and applications in the areas of multi-parametric programming and explicit/multi-parametric model predictive control (mp-MPC). In multi-parametric programming, advances include areas such as nonlinear multi-parametric programming (mp-NLP), bi-level programming, dynamic programming and global optimization for multi-parametric mixed-integer linear programming problems (mp-MILPs). In multi-parametric/explicit MPC (mp-MPC), advances include areas such as robust multi-parametric control, multi-parametric nonlinear MPC (mp-NMPC) and model reduction in mp-MPC. A comprehensive framework for multi-parametric programming and control is also presented. Recent applications include a hydrogen storage device, a fuel cell power generation system, an unmanned autonomous vehicle (UAV) and a hybrid pressure swing adsorption (PSA) system. © 2012 Springer-Verlag.
Theoretical and algorithmic advances in multi-parametric programming and control
Pistikopoulos, Efstratios N.
2012-04-21
This paper presents an overview of recent theoretical and algorithmic advances, and applications in the areas of multi-parametric programming and explicit/multi-parametric model predictive control (mp-MPC). In multi-parametric programming, advances include areas such as nonlinear multi-parametric programming (mp-NLP), bi-level programming, dynamic programming and global optimization for multi-parametric mixed-integer linear programming problems (mp-MILPs). In multi-parametric/explicit MPC (mp-MPC), advances include areas such as robust multi-parametric control, multi-parametric nonlinear MPC (mp-NMPC) and model reduction in mp-MPC. A comprehensive framework for multi-parametric programming and control is also presented. Recent applications include a hydrogen storage device, a fuel cell power generation system, an unmanned autonomous vehicle (UAV) and a hybrid pressure swing adsorption (PSA) system. © 2012 Springer-Verlag.
Advanced continuous cultivation methods for systems microbiology.
Adamberg, Kaarel; Valgepea, Kaspar; Vilu, Raivo
2015-09-01
Increasing the throughput of systems biology-based experimental characterization of in silico-designed strains has great potential for accelerating the development of cell factories. For this, analysis of metabolism in the steady state is essential as only this enables the unequivocal definition of the physiological state of cells, which is needed for the complete description and in silico reconstruction of their phenotypes. In this review, we show that for a systems microbiology approach, high-resolution characterization of metabolism in the steady state--growth space analysis (GSA)--can be achieved by using advanced continuous cultivation methods termed changestats. In changestats, an environmental parameter is continuously changed at a constant rate within one experiment whilst maintaining cells in the physiological steady state similar to chemostats. This increases the resolution and throughput of GSA compared with chemostats, and, moreover, enables following of the dynamics of metabolism and detection of metabolic switch-points and optimal growth conditions. We also describe the concept, challenge and necessary criteria of the systematic analysis of steady-state metabolism. Finally, we propose that such systematic characterization of the steady-state growth space of cells using changestats has value not only for fundamental studies of metabolism, but also for systems biology-based metabolic engineering of cell factories.
Numerical methods design, analysis, and computer implementation of algorithms
Greenbaum, Anne
2012-01-01
Numerical Methods provides a clear and concise exploration of standard numerical analysis topics, as well as nontraditional ones, including mathematical modeling, Monte Carlo methods, Markov chains, and fractals. Filled with appealing examples that will motivate students, the textbook considers modern application areas, such as information retrieval and animation, and classical topics from physics and engineering. Exercises use MATLAB and promote understanding of computational results. The book gives instructors the flexibility to emphasize different aspects--design, analysis, or computer implementation--of numerical algorithms, depending on the background and interests of students. Designed for upper-division undergraduates in mathematics or computer science classes, the textbook assumes that students have prior knowledge of linear algebra and calculus, although these topics are reviewed in the text. Short discussions of the history of numerical methods are interspersed throughout the chapters. The book a...
Images Encryption Method using Steganographic LSB Method, AES and RSA algorithm
Moumen, Abdelkader; Sissaoui, Hocine
2017-03-01
Vulnerability of communication of digital images is an extremely important issue nowadays, particularly when the images are communicated through insecure channels. To improve communication security, many cryptosystems have been presented in the image encryption literature. This paper proposes a novel image encryption technique based on an algorithm that is faster than current methods. The proposed algorithm eliminates the step in which the secrete key is shared during the encryption process. It is formulated based on the symmetric encryption, asymmetric encryption and steganography theories. The image is encrypted using a symmetric algorithm, then, the secret key is encrypted by means of an asymmetrical algorithm and it is hidden in the ciphered image using a least significant bits steganographic scheme. The analysis results show that while enjoying the faster computation, our method performs close to optimal in terms of accuracy.
Integrated Graphics Operations and Analysis Lab Development of Advanced Computer Graphics Algorithms
Wheaton, Ira M.
2011-01-01
The focus of this project is to aid the IGOAL in researching and implementing algorithms for advanced computer graphics. First, this project focused on porting the current International Space Station (ISS) Xbox experience to the web. Previously, the ISS interior fly-around education and outreach experience only ran on an Xbox 360. One of the desires was to take this experience and make it into something that can be put on NASA s educational site for anyone to be able to access. The current code works in the Unity game engine which does have cross platform capability but is not 100% compatible. The tasks for an intern to complete this portion consisted of gaining familiarity with Unity and the current ISS Xbox code, porting the Xbox code to the web as is, and modifying the code to work well as a web application. In addition, a procedurally generated cloud algorithm will be developed. Currently, the clouds used in AGEA animations and the Xbox experiences are a texture map. The desire is to create a procedurally generated cloud algorithm to provide dynamically generated clouds for both AGEA animations and the Xbox experiences. This task consists of gaining familiarity with AGEA and the plug-in interface, developing the algorithm, creating an AGEA plug-in to implement the algorithm inside AGEA, and creating a Unity script to implement the algorithm for the Xbox. This portion of the project was unable to be completed in the time frame of the internship; however, the IGOAL will continue to work on it in the future.
Advancing UAS methods for monitoring coastal environments
Ridge, J.; Seymour, A.; Rodriguez, A. B.; Dale, J.; Newton, E.; Johnston, D. W.
2017-12-01
Utilizing fixed-wing Unmanned Aircraft Systems (UAS), we are working to improve coastal monitoring by increasing the accuracy, precision, temporal resolution, and spatial coverage of habitat distribution maps. Generally, multirotor aircraft are preferred for precision imaging, but recent advances in fixed-wing technology have greatly increased their capabilities and application for fine-scale (decimeter-centimeter) measurements. Present mapping methods employed by North Carolina coastal managers involve expensive, time consuming and localized observation of coastal environments, which often lack the necessary frequency to make timely management decisions. For example, it has taken several decades to fully map oyster reefs along the NC coast, making it nearly impossible to track trends in oyster reef populations responding to harvesting pressure and water quality degradation. It is difficult for the state to employ manned flights for collecting aerial imagery to monitor intertidal oyster reefs, because flights are usually conducted after seasonal increases in turbidity. In addition, post-storm monitoring of coastal erosion from manned platforms is often conducted days after the event and collects oblique aerial photographs which are difficult to use for accurately measuring change. Here, we describe how fixed wing UAS and standard RGB sensors can be used to rapidly quantify and assess critical coastal habitats (e.g., barrier islands, oyster reefs, etc.), providing for increased temporal frequency to isolate long-term and event-driven (storms, harvesting) impacts. Furthermore, drone-based approaches can accurately image intertidal habitats as well as resolve information such as vegetation density and bathymetry from shallow submerged areas. We obtain UAS imagery of a barrier island and oyster reefs under ideal conditions (low tide, turbidity, and sun angle) to create high resolution (cm scale) maps and digital elevation models to assess habitat condition
Dongarra, Jack; Ltaief, Hatem; Luszczek, Piotr R.; Weaver, Vincent M.
2012-01-01
We propose to study the impact on the energy footprint of two advanced algorithmic strategies in the context of high performance dense linear algebra libraries: (1) mixed precision algorithms with iterative refinement allow to run at the peak performance of single precision floating-point arithmetic while achieving double precision accuracy and (2) tree reduction technique exposes more parallelism when factorizing tall and skinny matrices for solving over determined systems of linear equations or calculating the singular value decomposition. Integrated within the PLASMA library using tile algorithms, which will eventually supersede the block algorithms from LAPACK, both strategies further excel in performance in the presence of a dynamic task scheduler while targeting multicore architecture. Energy consumption measurements are reported along with parallel performance numbers on a dual-socket quad-core Intel Xeon as well as a quad-socket quad-core Intel Sandy Bridge chip, both providing component-based energy monitoring at all levels of the system, through the Power Pack framework and the Running Average Power Limit model, respectively. © 2012 IEEE.
Dongarra, Jack
2012-11-01
We propose to study the impact on the energy footprint of two advanced algorithmic strategies in the context of high performance dense linear algebra libraries: (1) mixed precision algorithms with iterative refinement allow to run at the peak performance of single precision floating-point arithmetic while achieving double precision accuracy and (2) tree reduction technique exposes more parallelism when factorizing tall and skinny matrices for solving over determined systems of linear equations or calculating the singular value decomposition. Integrated within the PLASMA library using tile algorithms, which will eventually supersede the block algorithms from LAPACK, both strategies further excel in performance in the presence of a dynamic task scheduler while targeting multicore architecture. Energy consumption measurements are reported along with parallel performance numbers on a dual-socket quad-core Intel Xeon as well as a quad-socket quad-core Intel Sandy Bridge chip, both providing component-based energy monitoring at all levels of the system, through the Power Pack framework and the Running Average Power Limit model, respectively. © 2012 IEEE.
Navigation Algorithm Using Fuzzy Control Method in Mobile Robotics
Directory of Open Access Journals (Sweden)
Cviklovič Vladimír
2016-03-01
Full Text Available The issue of navigation methods is being continuously developed globally. The aim of this article is to test the fuzzy control algorithm for track finding in mobile robotics. The concept of an autonomous mobile robot EN20 has been designed to test its behaviour. The odometry navigation method was used. The benefits of fuzzy control are in the evidence of mobile robot’s behaviour. These benefits are obtained when more physical variables on the base of more input variables are controlled at the same time. In our case, there are two input variables - heading angle and distance, and two output variables - the angular velocity of the left and right wheel. The autonomous mobile robot is moving with human logic.
Fibonacci’s Computation Methods vs Modern Algorithms
Directory of Open Access Journals (Sweden)
Ernesto Burattini
2013-12-01
Full Text Available In this paper we discuss some computational procedures given by Leonardo Pisano Fibonacci in his famous Liber Abaci book, and we propose their translation into a modern language for computers (C ++. Among the other we describe the method of “cross” multiplication, we evaluate its computational complexity in algorithmic terms and we show the output of a C ++ code that describes the development of the method applied to the product of two integers. In a similar way we show the operations performed on fractions introduced by Fibonacci. Thanks to the possibility to reproduce on a computer, the Fibonacci’s different computational procedures, it was possible to identify some calculation errors present in the different versions of the original text.
National Aeronautics and Space Administration — SSCI proposes to develop and test a framework referred to as the ADVANCE (Algorithm Design and Validation for Adaptive Nonlinear Control Enhancement), within which...
Determination of Selection Method in Genetic Algorithm for Land Suitability
Directory of Open Access Journals (Sweden)
Irfianti Asti Dwi
2016-01-01
Full Text Available Genetic Algoirthm is one alternative solution in the field of modeling optimization, automatic programming and machine learning. The purpose of the study was to compare some type of selection methods in Genetic Algorithm for land suitability. Contribution of this research applies the best method to develop region based horticultural commodities. This testing is done by comparing the three methods on the method of selection, the Roulette Wheel, Tournament Selection and Stochastic Universal Sampling. Parameters of the locations used in the test scenarios include Temperature = 27°C, Rainfall = 1200 mm, hummidity = 30%, Cluster fruit = 4, Crossover Probabiitiy (Pc = 0.6, Mutation Probabilty (Pm = 0.2 and Epoch = 10. The second test epoch incluides location parameters consist of Temperature = 30°C, Rainfall = 2000 mm, Humidity = 35%, Cluster fruit = 5, Crossover Probability (Pc = 0.7, Mutation Probability (Pm = 0.3 and Epoch 10. The conclusion of this study shows that the Roulette Wheel is the best method because it produces more stable and fitness value than the other two methods.
New approach for measuring 3D space by using Advanced SURF Algorithm
Energy Technology Data Exchange (ETDEWEB)
Youm, Minkyo; Min, Byungil; Suh, Kyungsuk [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Lee, Backgeun [Sungkyunkwan Univ., Suwon (Korea, Republic of)
2013-05-15
The nuclear disasters compared to natural disaster create a more extreme condition for analyzing and evaluating. In this paper, measuring 3D space and modeling was studied by simple pictures in case of small sand dune. The suggested method can be used for the acquisition of spatial information by robot at the disaster area. As a result, these data are helpful for identify the damaged part, degree of damage and determination of recovery sequences. In this study we are improving computer vision algorithm for 3-D geo spatial information measurement. And confirm by test. First, we can get noticeable improvement of 3-D geo spatial information result by SURF algorithm and photogrammetry surveying. Second, we can confirm not only decrease algorithm running time, but also increase matching points through epi polar line filtering. From the study, we are extracting 3-D model by open source algorithm and delete miss match point by filtering method. However on characteristic of SURF algorithm, it can't find match point if structure don't have strong feature. So we will need more study about find feature point if structure don't have strong feature.
Assessing semantic similarity of texts - Methods and algorithms
Rozeva, Anna; Zerkova, Silvia
2017-12-01
Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.
Damped time advance methods for particles and EM fields
International Nuclear Information System (INIS)
Friedman, A.; Ambrosiano, J.J.; Boyd, J.K.; Brandon, S.T.; Nielsen, D.E. Jr.; Rambo, P.W.
1990-01-01
Recent developments in the application of damped time advance methods to plasma simulations include the synthesis of implicit and explicit ''adjustably damped'' second order accurate methods for particle motion and electromagnetic field propagation. This paper discusses this method
Radev, Dimitar; Lokshina, Izabella
2010-11-01
The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.
Advanced Aqueous Phase Catalyst Development using Combinatorial Methods, Phase II
National Aeronautics and Space Administration — Combinatorial methods are proposed to develop advanced Aqueous Oxidation Catalysts (AOCs) with the capability to mineralize organic contaminants present in effluents...
Computing homography with RANSAC algorithm: a novel method of registration
Li, Xiaowei; Liu, Yue; Wang, Yongtian; Yan, Dayuan
2005-02-01
An AR (Augmented Reality) system can integrate computer-generated objects with the image sequences of real world scenes in either an off-line or a real-time way. Registration, or camera pose estimation, is one of the key techniques to determine its performance. The registration methods can be classified as model-based and move-matching. The former approach can accomplish relatively accurate registration results, but it requires the precise model of the scene, which is hard to be obtained. The latter approach carries out registration by computing the ego-motion of the camera. Because it does not require the prior-knowledge of the scene, its registration results sometimes turn out to be less accurate. When the model defined is as simple as a plane, a mixed method is introduced to take advantages of the virtues of the two methods mentioned above. Although unexpected objects often occlude this plane in an AR system, one can still try to detect corresponding points with a contract-expand method, while this will import erroneous correspondences. Computing homography with RANSAC algorithm is used to overcome such shortcomings. Using the robustly estimated homography resulted from RANSAC, the camera projective matrix can be recovered and thus registration is accomplished even when the markers are lost in the scene.
International Nuclear Information System (INIS)
Frassinetti, L.; Olofsson, K.E.J.; Brunsell, P.R.; Drake, J.R.
2011-01-01
The EXTRAP T2R feedback system (active coils, sensor coils and controller) is used to study and develop new tools for advanced control of the MHD instabilities in fusion plasmas. New feedback algorithms developed in EXTRAP T2R reversed-field pinch allow flexible and independent control of each magnetic harmonic. Methods developed in control theory and applied to EXTRAP T2R allow a closed-loop identification of the machine plant and of the resistive wall modes growth rates. The plant identification is the starting point for the development of output-tracking algorithms which enable the generation of external magnetic perturbations. These algorithms will then be used to study the effect of a resonant magnetic perturbation (RMP) on the tearing mode (TM) dynamics. It will be shown that the stationary RMP can induce oscillations in the amplitude and jumps in the phase of the rotating TM. It will be shown that the RMP strongly affects the magnetic island position.
Frassinetti, L.; Olofsson, K. E. J.; Brunsell, P. R.; Drake, J. R.
2011-06-01
The EXTRAP T2R feedback system (active coils, sensor coils and controller) is used to study and develop new tools for advanced control of the MHD instabilities in fusion plasmas. New feedback algorithms developed in EXTRAP T2R reversed-field pinch allow flexible and independent control of each magnetic harmonic. Methods developed in control theory and applied to EXTRAP T2R allow a closed-loop identification of the machine plant and of the resistive wall modes growth rates. The plant identification is the starting point for the development of output-tracking algorithms which enable the generation of external magnetic perturbations. These algorithms will then be used to study the effect of a resonant magnetic perturbation (RMP) on the tearing mode (TM) dynamics. It will be shown that the stationary RMP can induce oscillations in the amplitude and jumps in the phase of the rotating TM. It will be shown that the RMP strongly affects the magnetic island position.
Improved algorithms and advanced features of the CAD to MC conversion tool McCad
International Nuclear Information System (INIS)
Lu, L.; Fischer, U.; Pereslavtsev, P.
2014-01-01
Highlights: •The latest improvements of the McCad conversion approach including decomposition and void filling algorithms is presented. •An advanced interface for the materials editing and assignment has been developed and added to the McCAD GUI. •These improvements have been tested and successfully applied to DEMO and ITER NBI (Neutral Beam Injector) applications. •The performance of the CAD model conversion process is shown to be significantly improved. -- Abstract: McCad is a geometry conversion tool developed at KIT to enable the automatic bi-directional conversions of CAD models into the Monte Carlo (MC) geometries utilized for neutronics calculations (CAD to MC) and, reversed (MC to CAD), for visualization purposes. The paper presents the latest improvements of the conversion algorithms including improved decomposition, void filling and an advanced interface for the materials editing and assignment. The new implementations and features were tested on fusion neutronics applications to the DEMO and ITER NBI (Neutral Beam Injector) models. The results demonstrate greater stability and enhanced efficiency of McCad conversion process
An Online Scheduling Algorithm with Advance Reservation for Large-Scale Data Transfers
Energy Technology Data Exchange (ETDEWEB)
Balman, Mehmet; Kosar, Tevfik
2010-05-20
Scientific applications and experimental facilities generate massive data sets that need to be transferred to remote collaborating sites for sharing, processing, and long term storage. In order to support increasingly data-intensive science, next generation research networks have been deployed to provide high-speed on-demand data access between collaborating institutions. In this paper, we present a practical model for online data scheduling in which data movement operations are scheduled in advance for end-to-end high performance transfers. In our model, data scheduler interacts with reservation managers and data transfer nodes in order to reserve available bandwidth to guarantee completion of jobs that are accepted and confirmed to satisfy preferred time constraint given by the user. Our methodology improves current systems by allowing researchers and higher level meta-schedulers to use data placement as a service where theycan plan ahead and reserve the scheduler time in advance for their data movement operations. We have implemented our algorithm and examined possible techniques for incorporation into current reservation frameworks. Performance measurements confirm that the proposed algorithm is efficient and scalable.
Directory of Open Access Journals (Sweden)
Ronghui Zhang
2017-05-01
Full Text Available Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.
Multicycle Optimization of Advanced Gas-Cooled Reactor Loading Patterns Using Genetic Algorithms
International Nuclear Information System (INIS)
Ziver, A. Kemal; Carter, Jonathan N.; Pain, Christopher C.; Oliveira, Cassiano R.E. de; Goddard, Antony J. H.; Overton, Richard S.
2003-01-01
A genetic algorithm (GA)-based optimizer (GAOPT) has been developed for in-core fuel management of advanced gas-cooled reactors (AGRs) at HINKLEY B and HARTLEPOOL, which employ on-load and off-load refueling, respectively. The optimizer has been linked to the reactor analysis code PANTHER for the automated evaluation of loading patterns in a two-dimensional geometry, which is collapsed from the three-dimensional reactor model. GAOPT uses a directed stochastic (Monte Carlo) algorithm to generate initial population members, within predetermined constraints, for use in GAs, which apply the standard genetic operators: selection by tournament, crossover, and mutation. The GAOPT is able to generate and optimize loading patterns for successive reactor cycles (multicycle) within acceptable CPU times even on single-processor systems. The algorithm allows radial shuffling of fuel assemblies in a multicycle refueling optimization, which is constructed to aid long-term core management planning decisions. This paper presents the application of the GA-based optimization to two AGR stations, which apply different in-core management operational rules. Results obtained from the testing of GAOPT are discussed
Energy Technology Data Exchange (ETDEWEB)
Ziver, A.K. E-mail: a.k.ziver@imperial.ac.uk; Pain, C.C; Carter, J.N.; Oliveira, C.R.E. de; Goddard, A.J.H.; Overton, R.S
2004-03-01
A non-generational genetic algorithm (GA) has been developed for fuel management optimisation of Advanced Gas-Cooled Reactors, which are operated by British Energy and produce around 20% of the UK's electricity requirements. An evolutionary search is coded using the genetic operators; namely selection by tournament, two-point crossover, mutation and random assessment of population for multi-cycle loading pattern (LP) optimisation. A detailed description of the chromosomes in the genetic algorithm coded is presented. Artificial Neural Networks (ANNs) have been constructed and trained to accelerate the GA-based search during the optimisation process. The whole package, called GAOPT, is linked to the reactor analysis code PANTHER, which performs fresh fuel loading, burn-up and power shaping calculations for each reactor cycle by imposing station-specific safety and operational constraints. GAOPT has been verified by performing a number of tests, which are applied to the Hinkley Point B and Hartlepool reactors. The test results giving loading pattern (LP) scenarios obtained from single and multi-cycle optimisation calculations applied to realistic reactor states of the Hartlepool and Hinkley Point B reactors are discussed. The results have shown that the GA/ANN algorithms developed can help the fuel engineer to optimise loading patterns in an efficient and more profitable way than currently available for multi-cycle refuelling of AGRs. Research leading to parallel GAs applied to LP optimisation are outlined, which can be adapted to present day LWR fuel management problems.
An efficient non-dominated sorting method for evolutionary algorithms.
Fang, Hongbing; Wang, Qian; Tu, Yi-Cheng; Horstemeyer, Mark F
2008-01-01
We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.
2D automatic body-fitted structured mesh generation using advancing extraction method
Zhang, Yaoxin; Jia, Yafei
2018-01-01
This paper presents an automatic mesh generation algorithm for body-fitted structured meshes in Computational Fluids Dynamics (CFD) analysis using the Advancing Extraction Method (AEM). The method is applicable to two-dimensional domains with complex geometries, which have the hierarchical tree-like topography with extrusion-like structures (i.e., branches or tributaries) and intrusion-like structures (i.e., peninsula or dikes). With the AEM, the hierarchical levels of sub-domains can be identified, and the block boundary of each sub-domain in convex polygon shape in each level can be extracted in an advancing scheme. In this paper, several examples were used to illustrate the effectiveness and applicability of the proposed algorithm for automatic structured mesh generation, and the implementation of the method.
International Nuclear Information System (INIS)
Santi, P.; Favalli, A.; Hauck, D.; Henzl, V.; Henzlova, D.; Ianakiev, K.; Iliev, M.; Swinhoe, M.; Croft, S.; Worrall, L.
2015-01-01
One of the most distinctive and informative signatures of special nuclear materials is the emission of correlated neutrons from either spontaneous or induced fission. Because the emission of correlated neutrons is a unique and unmistakable signature of nuclear materials, the ability to effectively detect, process, and analyze these emissions will continue to play a vital role in the non-proliferation, safeguards, and security missions. While currently deployed neutron measurement techniques based on 3He proportional counter technology, such as neutron coincidence and multiplicity counters currently used by the International Atomic Energy Agency, have proven to be effective over the past several decades for a wide range of measurement needs, a number of technical and practical limitations exist in continuing to apply this technique to future measurement needs. In many cases, those limitations exist within the algorithms that are used to process and analyze the detected signals from these counters that were initially developed approximately 20 years ago based on the technology and computing power that was available at that time. Over the past three years, an effort has been undertaken to address the general shortcomings in these algorithms by developing new algorithms that are based on fundamental physics principles that should lead to the development of more sensitive neutron non-destructive assay instrumentation. Through this effort, a number of advancements have been made in correcting incoming data for electronic dead time, connecting the two main types of analysis techniques used to quantify the data (Shift register analysis and Feynman variance to mean analysis), and in the underlying physical model, known as the point model, that is used to interpret the data in terms of the characteristic properties of the item being measured. The current status of the testing and evaluation of these advancements in correlated neutron analysis techniques will be discussed
A genetic algorithm based method for neutron spectrum unfolding
International Nuclear Information System (INIS)
Suman, Vitisha; Sarkar, P.K.
2013-03-01
An approach to neutron spectrum unfolding based on a stochastic evolutionary search mechanism - Genetic Algorithm (GA) is presented. It is tested to unfold a set of simulated spectra, the unfolded spectra is compared to the output of a standard code FERDOR. The method was then applied to a set of measured pulse height spectrum of neutrons from the AmBe source as well as of emitted neutrons from Li(p,n) and Ag(C,n) nuclear reactions carried out in the accelerator environment. The unfolded spectra compared to the output of FERDOR show good agreement in the case of AmBe spectra and Li(p,n) spectra. In the case of Ag(C,n) spectra GA method results in some fluctuations. Necessity of carrying out smoothening of the obtained solution is also studied, which leads to approximation of the solution yielding an appropriate solution finally. Few smoothing techniques like second difference smoothing, Monte Carlo averaging, combination of both and gaussian based smoothing methods are also studied. Unfolded results obtained after inclusion of the smoothening criteria are in close agreement with the output obtained from the FERDOR code. The present method is also tested on a set of underdetermined problems, the outputs of which is compared to the unfolded spectra obtained from the FERDOR applied to a completely determined problem, shows a good match. The distribution of the unfolded spectra is also studied. Uncertainty propagation in the unfolded spectra due to the errors present in the measurement as well as the response function is also carried out. The method appears to be promising for unfolding the completely determined as well as underdetermined problems. It also has provisions to carry out the uncertainty analysis. (author)
Advanced Method of the Elastomagnetic Sensors Calibration
Directory of Open Access Journals (Sweden)
Mikulas Prascak
2004-01-01
Full Text Available Elastomagnetic method (EM method is a highly sensitive non-contact evaluation method for measuring tensile and compressive stress in steel. The latest development of measuring devices and EM sensors has shown that the thermomagnetic phenomenon has a stron influence on th accuracy during the EM sensor calibration. To eliminate the influence of this effect a two dimensional regression method is presented.
Recent advances in boundary element methods
Manolis, GD
2009-01-01
Addresses the needs of the computational mechanics research community in terms of information on boundary integral equation-based methods and techniques applied to a variety of fields. This book collects both original and review articles on contemporary Boundary Element Methods (BEM) as well as on the Mesh Reduction Methods (MRM).
Data Clustering on Breast Cancer Data Using Firefly Algorithm with Golden Ratio Method
Directory of Open Access Journals (Sweden)
DEMIR, M.
2015-05-01
Full Text Available Heuristic methods are problem solving methods. In general, they obtain near-optimal solutions, and they do not take the care of provability of this case. The heuristic methods do not guarantee to obtain the optimal results; however, they guarantee to obtain near-optimal solutions in considerable time. In this paper, an application was performed by using firefly algorithm - one of the heuristic methods. The golden ratio was applied to different steps of firefly algorithm and different parameters of firefly algorithm to develop a new algorithm - called Firefly Algorithm with Golden Ratio (FAGR. It was shown that the golden ratio made firefly algorithm be superior to the firefly algorithm without golden ratio. At this aim, the developed algorithm was applied to WBCD database (breast cancer database to cluster data obtained from breast cancer patients. The highest obtained success rate among all executions is 96% and the highest obtained average success rate in all executions is 94.5%.
Scale-up of nature’s tissue weaving algorithms to engineer advanced functional materials
Ng, Joanna L.; Knothe, Lillian E.; Whan, Renee M.; Knothe, Ulf; Tate, Melissa L. Knothe
2017-01-01
We are literally the stuff from which our tissue fabrics and their fibers are woven and spun. The arrangement of collagen, elastin and other structural proteins in space and time embodies our tissues and organs with amazing resilience and multifunctional smart properties. For example, the periosteum, a soft tissue sleeve that envelops all nonarticular bony surfaces of the body, comprises an inherently “smart” material that gives hard bones added strength under high impact loads. Yet a paucity of scalable bottom-up approaches stymies the harnessing of smart tissues’ biological, mechanical and organizational detail to create advanced functional materials. Here, a novel approach is established to scale up the multidimensional fiber patterns of natural soft tissue weaves for rapid prototyping of advanced functional materials. First second harmonic generation and two-photon excitation microscopy is used to map the microscopic three-dimensional (3D) alignment, composition and distribution of the collagen and elastin fibers of periosteum, the soft tissue sheath bounding all nonarticular bone surfaces in our bodies. Then, using engineering rendering software to scale up this natural tissue fabric, as well as multidimensional weaving algorithms, macroscopic tissue prototypes are created using a computer-controlled jacquard loom. The capacity to prototype scaled up architectures of natural fabrics provides a new avenue to create advanced functional materials.
Indian Academy of Sciences (India)
algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).
Indian Academy of Sciences (India)
algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...
Wu, Jianning; Wu, Bin
2015-01-01
The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of...
Advanced methods in teaching reactor physics
International Nuclear Information System (INIS)
Snoj, Luka; Kromar, Marjan; Zerovnik, Gasper; Ravnik, Matjaz
2011-01-01
Modern computer codes allow detailed neutron transport calculations. In combination with advanced 3D visualization software capable of treating large amounts of data in real time they form a powerful tool that can be used as a convenient modern educational tool for (nuclear power plant) operators, nuclear engineers, students and specialists involved in reactor operation and design. Visualization is applicable not only in education and training, but also as a tool for fuel management, core analysis and irradiation planning. The paper treats the visualization of neutron transport in different moderators, neutron flux and power distributions in two nuclear reactors (TRIGA type research reactor and typical PWR). The distributions are calculated with MCNP and CORD-2 computer codes and presented using Amira software.
Advanced methods in teaching reactor physics
Energy Technology Data Exchange (ETDEWEB)
Snoj, Luka, E-mail: luka.snoj@ijs.s [Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia); Kromar, Marjan, E-mail: marjan.kromar@ijs.s [Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia); Zerovnik, Gasper, E-mail: gasper.zerovnik@ijs.s [Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia); Ravnik, Matjaz [Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia)
2011-04-15
Modern computer codes allow detailed neutron transport calculations. In combination with advanced 3D visualization software capable of treating large amounts of data in real time they form a powerful tool that can be used as a convenient modern educational tool for (nuclear power plant) operators, nuclear engineers, students and specialists involved in reactor operation and design. Visualization is applicable not only in education and training, but also as a tool for fuel management, core analysis and irradiation planning. The paper treats the visualization of neutron transport in different moderators, neutron flux and power distributions in two nuclear reactors (TRIGA type research reactor and typical PWR). The distributions are calculated with MCNP and CORD-2 computer codes and presented using Amira software.
Advances in iterative methods for nonlinear equations
Busquier, Sonia
2016-01-01
This book focuses on the approximation of nonlinear equations using iterative methods. Nine contributions are presented on the construction and analysis of these methods, the coverage encompassing convergence, efficiency, robustness, dynamics, and applications. Many problems are stated in the form of nonlinear equations, using mathematical modeling. In particular, a wide range of problems in Applied Mathematics and in Engineering can be solved by finding the solutions to these equations. The book reveals the importance of studying convergence aspects in iterative methods and shows that selection of the most efficient and robust iterative method for a given problem is crucial to guaranteeing a good approximation. A number of sample criteria for selecting the optimal method are presented, including those regarding the order of convergence, the computational cost, and the stability, including the dynamics. This book will appeal to researchers whose field of interest is related to nonlinear problems and equations...
Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring
Radius, Andrea; Azevedo, Rui; Sapage, Tania; Carmo, Paulo
2013-12-01
During the last years, the increasing pollution occurrence and the alarming deterioration of the environmental health conditions of the sea, lead to the need of global monitoring capabilities, namely for marine environment management in terms of oil spill detection and indication of the suspected polluter. The sensitivity of Synthetic Aperture Radar (SAR) to the different phenomena on the sea, especially for oil spill and vessel detection, makes it a key instrument for global pollution monitoring. The SAR performances in maritime pollution monitoring are being operationally explored by a set of service providers on behalf of the European Maritime Safety Agency (EMSA), which has launched in 2007 the CleanSeaNet (CSN) project - a pan-European satellite based oil monitoring service. EDISOFT, which is from the beginning a service provider for CSN, is continuously investing in R&D activities that will ultimately lead to better algorithms and better performance on oil spill detection from SAR imagery. This strategy is being pursued through EDISOFT participation in the FP7 EC Sea-U project and in the Automatic Oil Spill Detection (AOSD) ESA project. The Sea-U project has the aim to improve the current state of oil spill detection algorithms, through the informative content maximization obtained with data fusion, the exploitation of different type of data/ sensors and the development of advanced image processing, segmentation and classification techniques. The AOSD project is closely related to the operational segment, because it is focused on the automation of the oil spill detection processing chain, integrating auxiliary data, like wind information, together with image and geometry analysis techniques. The synergy between these different objectives (R&D versus operational) allowed EDISOFT to develop oil spill detection software, that combines the operational automatic aspect, obtained through dedicated integration of the processing chain in the existing open source NEST
Advanced finite element method in structural engineering
Long, Yu-Qiu; Long, Zhi-Fei
2009-01-01
This book systematically introduces the research work on the Finite Element Method completed over the past 25 years. Original theoretical achievements and their applications in the fields of structural engineering and computational mechanics are discussed.
Advanced repair methods for enhanced reactor safety
International Nuclear Information System (INIS)
Kornfeldt, H.
1993-01-01
A few innovative concepts are described of the ABB Atom Service Division for repair and mitigation techniques for primary systems in nuclear power plants. The concepts are based on Shape Memory Alloy (SMA) technology. A basic feature of all methods is that welding and component replacement is being avoided and the radiation dose imposed on maintenance personnel reduced. The SMA-based repair methods give plant operators new ways to meet increased safety standards and rising maintenance costs. (Z.S.) 4 figs
A multiple objective magnet sorting algorithm for the Advanced Light Source insertion devices
International Nuclear Information System (INIS)
Humphries, D.; Goetz, F.; Kownacki, P.; Marks, S.; Schlueter, R.
1995-01-01
Insertion devices for the Advanced Light Source (ALS) incorporate large numbers of permanent magnets which have a variety of magnetization orientation errors. These orientation errors can produce field errors which affect both the spectral brightness of the insertion devices and the storage ring electron beam dynamics. A perturbation study was carried out to quantify the effects of orientation errors acting in a hybrid magnetic structure. The results of this study were used to develop a multiple stage sorting algorithm which minimizes undesirable integrated field errors and essentially eliminates pole excitation errors. When applied to a measured magnet population for an existing insertion device, an order of magnitude reduction in integrated field errors was achieved while maintaining near zero pole excitation errors
An Accurate liver segmentation method using parallel computing algorithm
International Nuclear Information System (INIS)
Elbasher, Eiman Mohammed Khalied
2014-12-01
Computed Tomography (CT or CAT scan) is a noninvasive diagnostic imaging procedure that uses a combination of X-rays and computer technology to produce horizontal, or axial, images (often called slices) of the body. A CT scan shows detailed images of any part of the body, including the bones muscles, fat and organs CT scans are more detailed than standard x-rays. CT scans may be done with or without "contrast Contrast refers to a substance taken by mouth and/ or injected into an intravenous (IV) line that causes the particular organ or tissue under study to be seen more clearly. CT scan of the liver and biliary tract are used in the diagnosis of many diseases in the abdomen structures, particularly when another type of examination, such as X-rays, physical examination, and ultra sound is not conclusive. Unfortunately, the presence of noise and artifact in the edges and fine details in the CT images limit the contrast resolution and make diagnostic procedure more difficult. This experimental study was conducted at the College of Medical Radiological Science, Sudan University of Science and Technology and Fidel Specialist Hospital. The sample of study was included 50 patients. The main objective of this research was to study an accurate liver segmentation method using a parallel computing algorithm, and to segment liver and adjacent organs using image processing technique. The main technique of segmentation used in this study was watershed transform. The scope of image processing and analysis applied to medical application is to improve the quality of the acquired image and extract quantitative information from medical image data in an efficient and accurate way. The results of this technique agreed wit the results of Jarritt et al, (2010), Kratchwil et al, (2010), Jover et al, (2011), Yomamoto et al, (1996), Cai et al (1999), Saudha and Jayashree (2010) who used different segmentation filtering based on the methods of enhancing the computed tomography images. Anther
Advanced verification methods for OVI security ink
Coombs, Paul G.; McCaffery, Shaun F.; Markantes, Tom
2006-02-01
OVI security ink +, incorporating OVP security pigment* microflakes, enjoys a history of effective document protection. This security feature provides not only first-line recognition by the person on the street, but also facilitates machine-readability. This paper explores the evolution of OVI reader technology from proof-of-concept to miniaturization. Three different instruments have been built to advance the technology of OVI machine verification. A bench-top unit has been constructed which allows users to automatically verify a multitude of different banknotes and OVI images. In addition, high speed modules were fabricated and tested in a state of the art banknote sorting machine. Both units demonstrate the ability of modern optical components to illuminate and collect light reflected from the interference platelets within OVI ink. Electronic hardware and software convert and process the optical information in milliseconds to accurately determine the authenticity of the security feature. Most recently, OVI ink verification hardware has been miniaturized and simplified providing yet another platform for counterfeit protection. These latest devices provide a tool for store clerks and bank tellers to unambiguously determine the validity of banknotes in the time period it takes the cash drawer to be opened.
Advanced Computational Methods in Bio-Mechanics.
Al Qahtani, Waleed M S; El-Anwar, Mohamed I
2018-04-15
A novel partnership between surgeons and machines, made possible by advances in computing and engineering technology, could overcome many of the limitations of traditional surgery. By extending surgeons' ability to plan and carry out surgical interventions more accurately and with fewer traumas, computer-integrated surgery (CIS) systems could help to improve clinical outcomes and the efficiency of healthcare delivery. CIS systems could have a similar impact on surgery to that long since realised in computer-integrated manufacturing. Mathematical modelling and computer simulation have proved tremendously successful in engineering. Computational mechanics has enabled technological developments in virtually every area of our lives. One of the greatest challenges for mechanists is to extend the success of computational mechanics to fields outside traditional engineering, in particular to biology, the biomedical sciences, and medicine. Biomechanics has significant potential for applications in orthopaedic industry, and the performance arts since skills needed for these activities are visibly related to the human musculoskeletal and nervous systems. Although biomechanics is widely used nowadays in the orthopaedic industry to design orthopaedic implants for human joints, dental parts, external fixations and other medical purposes, numerous researches funded by billions of dollars are still running to build a new future for sports and human healthcare in what is called biomechanics era.
Core design methods for advanced LMFBRs
International Nuclear Information System (INIS)
Chandler, J.C.; Marr, D.R.; McCurry, D.C.; Cantley, D.A.
1977-05-01
The multidiscipline approach to advanced LMFBR core design requires an iterative design procedure to obtain a closely-coupled design. HEDL's philosophy requires that the designs should be coupled to the extent that the design limiting fuel pin, the design limiting duct and the core reactivity lifetime should all be equal and should equal the fuel residence time. The design procedure consists of an iterative loop involving three stages of the design sequence. Stage 1 consists of general mechanical design and reactor physics scoping calculations to arrive at an initial core layout. Stage 2 consists of detailed reactor physics calculations for the core configuration arrived at in Stage 1. Based upon the detailed reactor physics results, a decision is made either to alter the design (Stage 1) or go to Stage 3. Stage 3 consists of core orificing and detailed component mechanical design calculations. At this point, an assessment is made regarding design adequacy. If the design is inadequate the entire procedure is repeated until the design is acceptable
Indian Academy of Sciences (India)
will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...
An advanced computational algorithm for systems analysis of tokamak power plants
International Nuclear Information System (INIS)
Dragojlovic, Zoran; Rene Raffray, A.; Najmabadi, Farrokh; Kessel, Charles; Waganer, Lester; El-Guebaly, Laila; Bromberg, Leslie
2010-01-01
A new computational algorithm for tokamak power plant system analysis is being developed for the ARIES project. The objective of this algorithm is to explore the most influential parameters in the physical, technological and economic trade space related to the developmental transition from experimental facilities to viable commercial power plants. This endeavor is being pursued as a new approach to tokamak systems studies, which examines an expansive, multi-dimensional trade space as opposed to traditional sensitivity analyses about a baseline design point. The new ARIES systems code consists of adaptable modules which are built from a custom-made software toolbox using object-oriented programming. The physics module captures the current tokamak physics knowledge database including modeling of the most-current proposed burning plasma experiment design (FIRE). The engineering model accurately reflects the intent and design detail of the power core elements including accurate and adjustable 3D tokamak geometry and complete modeling of all the power core and ancillary systems. Existing physics and engineering models reflect both near-term as well as advanced technology solutions that have higher performance potential. To fully assess the impact of the range of physics and engineering implementations, the plant cost accounts have been revised to reflect a more functional cost structure, supported by an updated set of costing algorithms for the direct, indirect, and financial cost accounts. All of these features have been validated against the existing ARIES-AT baseline case. The present results demonstrate visualization techniques that provide an insight into trade space assessment of attractive steady-state tokamaks for commercial use.
Energy Technology Data Exchange (ETDEWEB)
Stavrov, Andrei; Yamamoto, Eugene [Rapiscan Systems, Inc., 14000 Mead Street, Longmont, CO, 80504 (United States)
2015-07-01
Radiation Portal Monitors (RPM) with plastic detectors represent the main instruments used for primary border (customs) radiation control. RPM are widely used because they are simple, reliable, relatively inexpensive and have a high sensitivity. However, experience using the RPM in various countries has revealed the systems have some grave shortcomings. There is a dramatic decrease of the probability of detection of radioactive sources under high suppression of the natural gamma background (radiation control of heavy cargoes, containers and, especially, trains). NORM (Naturally Occurring Radioactive Material) existing in objects under control trigger the so-called 'nuisance alarms', requiring a secondary inspection for source verification. At a number of sites, the rate of such alarms is so high it significantly complicates the work of customs and border officers. This paper presents a brief description of new variant of algorithm ASIA-New (New Advanced Source Identification Algorithm), which was developed by the authors and based on some experimental test results. It also demonstrates results of different tests and the capability of a new system to overcome the shortcomings stated above. New electronics and ASIA-New enables RPM to detect radioactive sources under a high background suppression (tested at 15-30%) and to verify the detected NORM (KCl) and the artificial isotopes (Co-57, Ba-133 and other). New variant of ASIA is based on physical principles and does not require a lot of special tests to attain statistical data for its parameters. That is why this system can be easily installed into any RPM with plastic detectors. This algorithm was tested for 1,395 passages of different transports (cars, trucks and trailers) without radioactive sources. It also was tested for 4,015 passages of these transports with radioactive sources of different activity (Co-57, Ba-133, Cs-137, Co-60, Ra-226, Th-232) and these sources masked by NORM (K-40) as well
Advanced and flexible genetic algorithms for BWR fuel loading pattern optimization
International Nuclear Information System (INIS)
Martin-del-Campo, Cecilia; Palomera-Perez, Miguel-Angel; Francois, Juan-Luis
2009-01-01
This work proposes advances in the implementation of a flexible genetic algorithm (GA) for fuel loading pattern optimization for Boiling Water Reactors (BWRs). In order to avoid specific implementations of genetic operators and to obtain a more flexible treatment, a binary representation of the solution was implemented; this representation had to take into account that a little change in the genotype must correspond to a little change in the phenotype. An identifier number is assigned to each assembly by means of a Gray Code of 7 bits and the solution (the loading pattern) is represented by a binary chain of 777 bits of length. Another important contribution is the use of a Fitness Function which includes a Heuristic Function and an Objective Function. The Heuristic Function which is defined to give flexibility on the application of a set of positioning rules based on knowledge, and the Objective Function that contains all the parameters which qualify the neutronic and thermal hydraulic performances of each loading pattern. Experimental results illustrating the effectiveness and flexibility of this optimization algorithm are presented and discussed.
Man/machine interface algorithm for advanced delayed-neutron signal characterization system
International Nuclear Information System (INIS)
Gross, K.C.
1985-01-01
The present failed-element rupture detector (FERD) at Experimental Breeder Reactor II (EBR-II) consists of a single bank of delayed-neutron (DN) detectors at a fixed transit time from the core. Plans are currently under way to upgrade the FERD in 1986 and provide advanced DN signal characterization capability that is embodied in an equivalent-recoil-area (ERA) meter. The new configuration will make available to the operator a wealth of quantitative diagnostic information related to the condition and dynamic evolution of a fuel breach. The diagnostic parameters will include a continuous reading of the ERA value for the breach; the transit time, T/sub tr/, for DN emitters traveling from the core to the FERD; and the isotopic holdup time, T/sub h/, for the source. To enhance the processing, interpretation, and display of these parameters to the reactor operator, a man/machine interface (MMI) algorithm has been developed to run in the background on EBR-II's data acquisition system (DAS). The purpose of this paper is to describe the features and implementation of this newly developed MMI algorithm
Recent advances in coupled-cluster methods
Bartlett, Rodney J
1997-01-01
Today, coupled-cluster (CC) theory has emerged as the most accurate, widely applicable approach for the correlation problem in molecules. Furthermore, the correct scaling of the energy and wavefunction with size (i.e. extensivity) recommends it for studies of polymers and crystals as well as molecules. CC methods have also paid dividends for nuclei, and for certain strongly correlated systems of interest in field theory.In order for CC methods to have achieved this distinction, it has been necessary to formulate new, theoretical approaches for the treatment of a variety of essential quantities
Advanced method for making vitreous waste forms
International Nuclear Information System (INIS)
Pope, J.M.; Harrison, D.E.
1980-01-01
A process is described for making waste glass that circumvents the problems of dissolving nuclear waste in molten glass at high temperatures. Because the reactive mixing process is independent of the inherent viscosity of the melt, any glass composition can be prepared with equal facility. Separation of the mixing and melting operations permits novel glass fabrication methods to be employed
Advanced Testing Method for Ground Thermal Conductivity
Energy Technology Data Exchange (ETDEWEB)
Liu, Xiaobing [ORNL; Clemenzi, Rick [Geothermal Design Center Inc.; Liu, Su [University of Tennessee (UT)
2017-04-01
A new method is developed that can quickly and more accurately determine the effective ground thermal conductivity (GTC) based on thermal response test (TRT) results. Ground thermal conductivity is an important parameter for sizing ground heat exchangers (GHEXs) used by geothermal heat pump systems. The conventional GTC test method usually requires a TRT for 48 hours with a very stable electric power supply throughout the entire test. In contrast, the new method reduces the required test time by 40%–60% or more, and it can determine GTC even with an unstable or intermittent power supply. Consequently, it can significantly reduce the cost of GTC testing and increase its use, which will enable optimal design of geothermal heat pump systems. Further, this new method provides more information about the thermal properties of the GHEX and the ground than previous techniques. It can verify the installation quality of GHEXs and has the potential, if developed, to characterize the heterogeneous thermal properties of the ground formation surrounding the GHEXs.
Advance of core design method for ATR
International Nuclear Information System (INIS)
Maeda, Seiichirou; Ihara, Toshiteru; Iijima, Takashi; Seino, Hideaki; Kobayashi, Tetsurou; Takeuchi, Michio; Sugawara, Satoru; Matsumoto, Mitsuo.
1995-01-01
Core characteristics of ATR demonstration plant has been revised such as increasing the fuel burnup and the channel power, which is achieved by changing the number of fuel rod per fuel assembly from 28 to 36. The research and development concerning the core design method for ATR have been continued. The calculational errors of core analysis code have been evaluated using the operational data of FUGEN and the full scale simulated test results in DCA (Deuterium Critical Assembly) and HTL (Heat Transfer Loop) at O-arai engineering center. It is confirmed that the calculational error of power distribution is smaller than the design value of ATR demonstration plant. Critical heat flux correlation curve for 36 fuel rod cluster has been developed and the probability evaluation method based on its curve, which is more rational to evaluate the fuel dryout, has been adopted. (author)
An advanced method of heterogeneous reactor theory
International Nuclear Information System (INIS)
Kochurov, B.P.
1994-08-01
Recent approaches to heterogeneous reactor theory for numerical applications were presented in the course of 8 lectures given in JAERI. The limitations of initial theory known after the First Conference on Peacefull Uses of Atomic Energy held in Geneva in 1955 as Galanine-Feinberg heterogeneous theory:-matrix from of equations, -lack of consistent theory for heterogeneous parameters for reactor cell, -were overcome by a transformation of heterogeneous reactor equations to a difference form and by a development of a consistent theory for the characteristics of a reactor cell based on detailed space-energy calculations. General few group (G-number of groups) heterogeneous reactor equations in dipole approximation are formulated with the extension of two-dimensional problem to three-dimensions by finite Furie expansion of axial dependence of neutron fluxes. A transformation of initial matrix reactor equations to a difference form is presented. The methods for calculation of heterogeneous reactor cell characteristics giving the relation between vector-flux and vector-current on a cell boundary are based on a set of detailed space-energy neutron flux distribution calculations with zero current across cell boundary and G calculations with linearly independent currents across the cell boundary. The equations for reaction rate matrices are formulated. Specific methods were developed for description of neutron migration in axial and radial directions. The methods for resonance level's approach for numerous high-energy resonances. On the basis of these approaches the theory, methods and computer codes were developed for 3D space-time react or problems including simulation of slow processes with fuel burn-up, control rod movements, Xe poisoning and fast transients depending on prompt and delayed neutrons. As a result reactors with several thousands of channels having non-uniform axial structure can be feasibly treated. (author)
Katsuro-Hopkins, Oksana; Sabbagh, S. A.; Bialek, J. M.; Park, H. K.; Kim, J. Y.; You, K.-I.; Glasser, A. H.; Lao, L. L.
2007-11-01
Stability to ideal MHD kink/ballooning modes and the resistive wall mode (RWM) is investigated for the KSTAR tokamak. Free-boundary equilibria that comply with magnetic field coil current constraints are computed for monotonic and reversed shear safety factor profiles and H-mode tokamak pressure profiles. Advanced tokamak operation at moderate to low plasma internal inductance shows that a factor of two improvement in the plasma beta limit over the no-wall beta limit is possible for toroidal mode number of unity. The KSTAR conducting structure, passive stabilizers, and in-vessel control coils are modeled by the VALEN-3D code and the active RWM stabilization performance of the device is evaluated using both standard and advanced feedback algorithms. Steady-state power and voltage requirements for the system are estimated based on the expected noise on the RWM sensor signals. Using NSTX experimental RWM sensors noise data as input, a reduced VALEN state-space LQG controller is designed to realistically assess KSTAR stabilization system performance.
Indian Academy of Sciences (India)
of programs, illustrate a method of establishing the ... importance of methods of establishing the correctness of .... Thus, the proof will be different for each input ..... Formal methods are pivotal in the design, development, and maintenance of ...
Advanced Topology Optimization Methods for Conceptual Architectural Design
DEFF Research Database (Denmark)
Aage, Niels; Amir, Oded; Clausen, Anders
2015-01-01
This paper presents a series of new, advanced topology optimization methods, developed specifically for conceptual architectural design of structures. The proposed computational procedures are implemented as components in the framework of a Grasshopper plugin, providing novel capacities...
Advanced Topology Optimization Methods for Conceptual Architectural Design
DEFF Research Database (Denmark)
Aage, Niels; Amir, Oded; Clausen, Anders
2014-01-01
This paper presents a series of new, advanced topology optimization methods, developed specifically for conceptual architectural design of structures. The proposed computational procedures are implemented as components in the framework of a Grasshopper plugin, providing novel capacities...
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-08-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.
Polarization control method for UV writing of advanced bragg gratings
DEFF Research Database (Denmark)
Deyerl, Hans-Jürgen; Plougmann, Nikolai; Jensen, Jesper Bo Damm
2002-01-01
We report the application of the polarization control method for the UV writing of advanced fiber Bragg gratings (FBG). We demonstrate the strength of the new method for different apodization profiles, including the Sinc-profile and two designs for dispersion-free square filters. The method has...
Algorithms and Methods for High-Performance Model Predictive Control
DEFF Research Database (Denmark)
Frison, Gianluca
routines employed in the numerical tests. The main focus of this thesis is on linear MPC problems. In this thesis, both the algorithms and their implementation are equally important. About the implementation, a novel implementation strategy for the dense linear algebra routines in embedded optimization...... is proposed, aiming at improving the computational performance in case of small matrices. About the algorithms, they are built on top of the proposed linear algebra, and they are tailored to exploit the high-level structure of the MPC problems, with special care on reducing the computational complexity....
A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm
Directory of Open Access Journals (Sweden)
Wanxing Sheng
2013-01-01
Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.
Hybrid of Natural Element Method (NEM with Genetic Algorithm (GA to find critical slip surface
Directory of Open Access Journals (Sweden)
Shahriar Shahrokhabadi
2014-06-01
Full Text Available One of the most important issues in geotechnical engineering is the slope stability analysis for determination of the factor of safety and the probable slip surface. Finite Element Method (FEM is well suited for numerical study of advanced geotechnical problems. However, mesh requirements of FEM creates some difficulties for solution processing in certain problems. Recently, motivated by these limitations, several new Meshfree methods such as Natural Element Method (NEM have been used to analyze engineering problems. This paper presents advantages of using NEM in 2D slope stability analysis and Genetic Algorithm (GA optimization to determine the probable slip surface and the related factor of safety. The stress field is produced under plane strain condition using natural element formulation to simulate material behavior analysis utilized in conjunction with a conventional limit equilibrium method. In order to justify the preciseness and convergence of the proposed method, two kinds of examples, homogenous and non-homogenous, are conducted and results are compared with FEM and conventional limit equilibrium methods. The results show the robustness of the NEM in slope stability analysis.
Advanced Control Methods for Optimization of Arc Welding
DEFF Research Database (Denmark)
Thomsen, J. S.
Gas Metal Arc Welding (GMAW) is a proces used for joining pieces of metal. Probably, the GMAW process is the most successful and widely used welding method in the industry today. A key issue in welding is the quality of the welds produced. The quality of a weld is influenced by several factors...... in the overall welding process; one of these factors are the ability of the welding machine to control the process. The internal control algorithms in GMAW machines are the topic of this PhD project. Basically, the internal control includes an algorithm which is able to keep the electrode at a given distance...
Laszewski, Audrey; Wichman, Christina L.; Doering, Jennifer J.; Maletta, Kristyn; Hammel, Jennifer
2016-01-01
Early childhood professionals do many things to support young families. This is true now more than ever, as researchers continue to discover the long-term benefits of early, healthy, nurturing relationships. This article provides an overview of the development of an advanced practice perinatal depression algorithm created as a step-by-step guide…
Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A
2015-06-01
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.
[New methodological advances: algorithm proposal for management of Clostridium difficile infection].
González-Abad, María José; Alonso-Sanz, Mercedes
2015-06-01
Clostridium difficile infection (CDI) is considered the most common cause of health care-associated diarrhea and also is an etiologic agent of community diarrhea. The aim of this study was to assess the potential benefit of a test that detects glutamate dehydrogenase (GDH) antigen and C. difficile toxin A/B, simultaneously, followed by detection of C. difficile toxin B (tcdB) gene by PCR as confirmatory assay on discrepant samples, and to propose an algorithm more efficient. From June 2012 to January 2013 at Hospital Infantil Universitario Niño Jesús, Madrid, the stool samples were studied for the simultaneous detection of GDH and toxin A/B, and also for detection of toxin A/B alone. When results between GDH and toxin A/B were discordant, a single sample for patient was selected for detection of C. difficile toxin B (tcdB) gene. A total of 116 samples (52 patients) were tested. Four were positive and 75 negative for toxigenic C. difficile (Toxin A/B, alone or combined with GDH). C. difficile was detected in the remaining 37 samples but not toxin A/B, regardless of the method used, except one. Twenty of the 37 specimens were further tested for C. difficile toxin B (tcdB) gene and 7 were positive. The simultaneous detection of GDH and toxin A/B combined with PCR recovered undiagnosed cases of CDI. In accordance with our data, we propose a two-step algorithm: detection of GDH and PCR (in samples GDH positive). This algorithm could provide a superior cost-benefit ratio in our population.
Directory of Open Access Journals (Sweden)
Wen-Gang Zhou
2015-06-01
Full Text Available With the deep research of genomics and proteomics, the number of new protein sequences has expanded rapidly. With the obvious shortcomings of high cost and low efficiency of the traditional experimental method, the calculation method for protein localization prediction has attracted a lot of attention due to its convenience and low cost. In the machine learning techniques, neural network and support vector machine (SVM are often used as learning tools. Due to its complete theoretical framework, SVM has been widely applied. In this paper, we make an improvement on the existing machine learning algorithm of the support vector machine algorithm, and a new improved algorithm has been developed, combined with Bayesian algorithms. The proposed algorithm can improve calculation efficiency, and defects of the original algorithm are eliminated. According to the verification, the method has proved to be valid. At the same time, it can reduce calculation time and improve prediction efficiency.
Advanced methods of solid oxide fuel cell modeling
Milewski, Jaroslaw; Santarelli, Massimo; Leone, Pierluigi
2011-01-01
Fuel cells are widely regarded as the future of the power and transportation industries. Intensive research in this area now requires new methods of fuel cell operation modeling and cell design. Typical mathematical models are based on the physical process description of fuel cells and require a detailed knowledge of the microscopic properties that govern both chemical and electrochemical reactions. ""Advanced Methods of Solid Oxide Fuel Cell Modeling"" proposes the alternative methodology of generalized artificial neural networks (ANN) solid oxide fuel cell (SOFC) modeling. ""Advanced Methods
Strategy to Promote Active Learning of an Advanced Research Method
McDermott, Hilary J.; Dovey, Terence M.
2013-01-01
Research methods courses aim to equip students with the knowledge and skills required for research yet seldom include practical aspects of assessment. This reflective practitioner report describes and evaluates an innovative approach to teaching and assessing advanced qualitative research methods to final-year psychology undergraduate students. An…
Advanced computational tools and methods for nuclear analyses of fusion technology systems
International Nuclear Information System (INIS)
Fischer, U.; Chen, Y.; Pereslavtsev, P.; Simakov, S.P.; Tsige-Tamirat, H.; Loughlin, M.; Perel, R.L.; Petrizzi, L.; Tautges, T.J.; Wilson, P.P.H.
2005-01-01
An overview is presented of advanced computational tools and methods developed recently for nuclear analyses of Fusion Technology systems such as the experimental device ITER ('International Thermonuclear Experimental Reactor') and the intense neutron source IFMIF ('International Fusion Material Irradiation Facility'). These include Monte Carlo based computational schemes for the calculation of three-dimensional shut-down dose rate distributions, methods, codes and interfaces for the use of CAD geometry models in Monte Carlo transport calculations, algorithms for Monte Carlo based sensitivity/uncertainty calculations, as well as computational techniques and data for IFMIF neutronics and activation calculations. (author)
Genetic algorithm based optimization of advanced solar cell designs modeled in Silvaco AtlasTM
Utsler, James
2006-01-01
A genetic algorithm was used to optimize the power output of multi-junction solar cells. Solar cell operation was modeled using the Silvaco ATLASTM software. The output of the ATLASTM simulation runs served as the input to the genetic algorithm. The genetic algorithm was run as a diffusing computation on a network of eighteen dual processor nodes. Results showed that the genetic algorithm produced better power output optimizations when compared with the results obtained using the hill cli...
Advanced methods in NDE using machine learning approaches
Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank
2018-04-01
Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability
Methods for studying fuel management in advanced gas cooled reactors
International Nuclear Information System (INIS)
Buckler, A.N.; Griggs, C.F.; Tyror, J.G.
1971-07-01
The methods used for studying fuel and absorber management problems in AGRs are described. The basis of the method is the use of ARGOSY lattice data in reactor calculations performed at successive time steps. These reactor calculations may be quite crude but for advanced design calculations a detailed channel-by-channel representation of the whole core is required. The main emphasis of the paper is in describing such an advanced approach - the ODYSSEUS-6 code. This code evaluates reactor power distributions as a function of time and uses the information to select refuelling moves and determine controller positions. (author)
A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm
Directory of Open Access Journals (Sweden)
Mariana-Eugenia Ilas
2018-03-01
Full Text Available In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs, by considerably reducing the area (thus increasing the level of parallelism, while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.
Faster algorithms for RNA-folding using the Four-Russians method.
Venkatachalam, Balaji; Gusfield, Dan; Frid, Yelena
2014-03-06
The secondary structure that maximizes the number of non-crossing matchings between complimentary bases of an RNA sequence of length n can be computed in O(n3) time using Nussinov's dynamic programming algorithm. The Four-Russians method is a technique that reduces the running time for certain dynamic programming algorithms by a multiplicative factor after a preprocessing step where solutions to all smaller subproblems of a fixed size are exhaustively enumerated and solved. Frid and Gusfield designed an O(n3logn) algorithm for RNA folding using the Four-Russians technique. In their algorithm the preprocessing is interleaved with the algorithm computation. We simplify the algorithm and the analysis by doing the preprocessing once prior to the algorithm computation. We call this the two-vector method. We also show variants where instead of exhaustive preprocessing, we only solve the subproblems encountered in the main algorithm once and memoize the results. We give a simple proof of correctness and explore the practical advantages over the earlier method.The Nussinov algorithm admits an O(n2) time parallel algorithm. We show a parallel algorithm using the two-vector idea that improves the time bound to O(n2logn). We have implemented the parallel algorithm on graphics processing units using the CUDA platform. We discuss the organization of the data structures to exploit coalesced memory access for fast running times. The ideas to organize the data structures also help in improving the running time of the serial algorithms. For sequences of length up to 6000 bases the parallel algorithm takes only about 2.5 seconds and the two-vector serial method takes about 57 seconds on a desktop and 15 seconds on a server. Among the serial algorithms, the two-vector and memoized versions are faster than the Frid-Gusfield algorithm by a factor of 3, and are faster than Nussinov by up to a factor of 20. The source-code for the algorithms is available at http://github.com/ijalabv/FourRussiansRNAFolding.
A novel method to design S-box based on chaotic map and genetic algorithm
International Nuclear Information System (INIS)
Wang, Yong; Wong, Kwok-Wo; Li, Changbing; Li, Yang
2012-01-01
The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.
MRS algorithm: a new method for searching myocardial region in SPECT myocardial perfusion images.
He, Yuan-Lie; Tian, Lian-Fang; Chen, Ping; Li, Bin; Mao, Zhong-Yuan
2005-10-01
First, the necessity of automatically segmenting myocardium from myocardial SPECT image is discussed in Section 1. To eliminate the influence of the background, the optimal threshold segmentation method modified for the MRS algorithm is explained in Section 2. Then, the image erosion structure is applied to identify the myocardium region and the liver region. The contour tracing method is introduced to extract the myocardial contour. To locate the centriod of the myocardium, the myocardial centriod searching method is developed. The protocol of the MRS algorithm is summarized in Section 6. The performance of the MRS algorithm is investigated and the conclusion is drawn in Section 7. Finally, the importance of the MRS algorithm and the improvement of the MRS algorithm are discussed.
A novel method to design S-box based on chaotic map and genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Wang, Yong, E-mail: wangyong_cqupt@163.com [State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China); Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Wong, Kwok-Wo [Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong (Hong Kong); Li, Changbing [Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Li, Yang [Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapping Street, S1 3DJ (United Kingdom)
2012-01-30
The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.
Curing Characterisation of Spruce Tannin-based Foams using the Advanced Isoconversional Method
Directory of Open Access Journals (Sweden)
Matjaž Čop
2014-06-01
Full Text Available The curing kinetics of foam prepared from the tannin of spruce tree bark was investigated using differential scanning calorimetry (DSC and the advanced isoconversional method. An analysis of the formulations with differing amounts of components (furfuryl alcohol, glycerol, tannin, and a catalyst showed that curing was delayed with increasing proportions of glycerol or tannins. An optimum amount of the catalyst constituent was also found during the study. The curing of the foam system was accelerated with increasing temperatures. Finally, the advanced isoconversional method, based on the model-free kinetic algorithm developed by Vyazovkin, appeared to be an appropriate model for the characterisation of the curing kinetics of tannin-based foams.
Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.
1982-02-26
UPGMA ), and Ward’s method. Ling’s papers describe a (k,r) clustering method. Each of these methods have individual characteristics which make them...Reference 7), UPGMA is probably the most frequently used clustering strategy. UPGMA tries to group new points into an existing cluster by using an
A kNN method that uses a non-natural evolutionary algorithm for ...
African Journals Online (AJOL)
We used this algorithm for component selection of a kNN (k Nearest Neighbor) method for breast cancer prognosis. Results with the UCI prognosis data set show that we can find components that help improve the accuracy of kNN by almost 3%, raising it above 79%. Keywords: kNN; classification; evolutionary algorithm; ...
A Teaching Approach from the Exhaustive Search Method to the Needleman-Wunsch Algorithm
Xu, Zhongneng; Yang, Yayun; Huang, Beibei
2017-01-01
The Needleman-Wunsch algorithm has become one of the core algorithms in bioinformatics; however, this programming requires more suitable explanations for students with different major backgrounds. In supposing sample sequences and using a simple store system, the connection between the exhaustive search method and the Needleman-Wunsch algorithm…
A Novel Method to Implement the Matrix Pencil Super Resolution Algorithm for Indoor Positioning
Directory of Open Access Journals (Sweden)
Tariq Jamil Saifullah Khanzada
2011-10-01
Full Text Available This article highlights the estimation of the results for the algorithms implemented in order to estimate the delays and distances for the indoor positioning system. The data sets for the transmitted and received signals are captured at a typical outdoor and indoor area. The estimation super resolution algorithms are applied. Different state of art and super resolution techniques based algorithms are applied to avail the optimal estimates of the delays and distances between the transmitted and received signals and a novel method for matrix pencil algorithm is devised. The algorithms perform variably at different scenarios of transmitted and received positions. Two scenarios are experienced, for the single antenna scenario the super resolution techniques like ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique and theMatrix Pencil algorithms give optimal performance compared to the conventional techniques. In two antenna scenario RootMUSIC and Matrix Pencil algorithm performed better than other algorithms for the distance estimation, however, the accuracy of all the algorithms is worst than the single antenna scenario. In all cases our devised Matrix Pencil algorithm achieved the best estimation results.
Directory of Open Access Journals (Sweden)
D. A. Viattchenin
2009-01-01
Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible clusterization with partial training is proposed in the paper. The method is based on data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.
Epsilon topological accelerating algorithms for difference method for initial-value problems
International Nuclear Information System (INIS)
Hristea, V.; Posirca, M.
1992-01-01
Linear and nonlinear parabolic equations can be solved by discretization methods which lead to linear and nonlinear algebraic systems. The iterative methods (e.g. Gauss - Seidel) show a very slow convergence and instability in the case of nonlinear equations. This paper proposes an ε topological algorithm for accelerating slow iterative methods used in the thermohydraulic code COBRA and the dynamic code ADEP. The results show an executing time approximately ten times lower than original algorithms. (Author)
Method and Tools for Development of Advanced Instructional Systems
Arend, J. van der; Riemersma, J.B.J.
1994-01-01
The application of advanced instructional systems (AISs), like computer-based training systems, intelligent tutoring systems and training simulators, is widely spread within the Royal Netherlands Army. As a consequence there is a growing interest in methods and tools to develop effective and
METHODS ADVANCEMENT FOR MILK ANALYSIS: THE MAMA STUDY
The Methods Advancement for Milk Analysis (MAMA) study was designed by US EPA and CDC investigators to provide data to support the technological and study design needs of the proposed National Children=s Study (NCS). The NCS is a multi-Agency-sponsored study, authorized under the...
Advances in the Analytical Methods for Determining the Antioxidant ...
African Journals Online (AJOL)
Advances in the Analytical Methods for Determining the Antioxidant Properties of Honey: A Review. M Moniruzzaman, MI Khalil, SA Sulaiman, SH Gan. Abstract. Free radicals and reactive oxygen species (ROS) have been implicated in contributing to the processes of aging and disease. In an effort to combat free radical ...
Sastry, Kumara Narasimha
2007-03-01
Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common
Advanced Measuring (Instrumentation Methods for Nuclear Installations: A Review
Directory of Open Access Journals (Sweden)
Wang Qiu-kuan
2012-01-01
Full Text Available The nuclear technology has been widely used in the world. The research of measurement in nuclear installations involves many aspects, such as nuclear reactors, nuclear fuel cycle, safety and security, nuclear accident, after action, analysis, and environmental applications. In last decades, many advanced measuring devices and techniques have been widely applied in nuclear installations. This paper mainly introduces the development of the measuring (instrumentation methods for nuclear installations and the applications of these instruments and methods.
Higher geometry an introduction to advanced methods in analytic geometry
Woods, Frederick S
2005-01-01
For students of mathematics with a sound background in analytic geometry and some knowledge of determinants, this volume has long been among the best available expositions of advanced work on projective and algebraic geometry. Developed from Professor Woods' lectures at the Massachusetts Institute of Technology, it bridges the gap between intermediate studies in the field and highly specialized works.With exceptional thoroughness, it presents the most important general concepts and methods of advanced algebraic geometry (as distinguished from differential geometry). It offers a thorough study
An advanced probabilistic structural analysis method for implicit performance functions
Wu, Y.-T.; Millwater, H. R.; Cruse, T. A.
1989-01-01
In probabilistic structural analysis, the performance or response functions usually are implicitly defined and must be solved by numerical analysis methods such as finite element methods. In such cases, the most commonly used probabilistic analysis tool is the mean-based, second-moment method which provides only the first two statistical moments. This paper presents a generalized advanced mean value (AMV) method which is capable of establishing the distributions to provide additional information for reliability design. The method requires slightly more computations than the second-moment method but is highly efficient relative to the other alternative methods. In particular, the examples show that the AMV method can be used to solve problems involving non-monotonic functions that result in truncated distributions.
Self-organized spectrum chunk selection algorithm for Local Area LTE-Advanced
DEFF Research Database (Denmark)
Kumar, Sanjay; Wang, Yuanye; Marchetti, Nicola
2010-01-01
This paper presents a self organized spectrum chunk selection algorithm in order to minimize the mutual intercell interference among Home Node Bs (HeNBs), aiming to improve the system throughput performance compared to the existing frequency reuse one scheme. The proposed algorithm is useful...
Performance Impact of Rank-Reordering on Advanced Polar Decomposition Algorithms
Esposito, Aniello; Keyes, David E.; Ltaief, Hatem; Sukkari, Dalal
2018-01-01
We demonstrate the importance of both MPI rank reordering and choice of processor grid topology in the context of advanced dense linear algebra (DLA) applications for distributed-memory systems. In particular, we focus on the advanced polar
Advanced non-destructive methods for an efficient service performance
International Nuclear Information System (INIS)
Rauschenbach, H.; Clossen-von Lanken Schulz, M.; Oberlin, R.
2015-01-01
Due to the power generation industry's desire to decrease outage time and extend inspection intervals for highly stressed turbine parts, advanced and reliable Non-destructive methods were developed by Siemens Non-destructive laboratory. Effective outage performance requires the optimized planning of all outage activities as well as modern Non-destructive examination methods, in order to examine the highly stressed components (turbine rotor, casings, valves, generator rotor) reliably and in short periods of access. This paper describes the experience of Siemens Energy with an ultrasonic Phased Array inspection technique for the inspection of radial entry pinned turbine blade roots. The developed inspection technique allows the ultrasonic inspection of steam turbine blades without blade removal. Furthermore advanced Non-destructive examination methods for joint bolts will be described, which offer a significant reduction of outage duration in comparison to conventional inspection techniques. (authors)
Advanced airflow distribution methods for reducing exposure of indoor pollution
DEFF Research Database (Denmark)
Cao, Guangyu; Nielsen, Peter Vilhelm; Melikov, Arsen
2017-01-01
The adverse effect of various indoor pollutants on occupants’ health have been recognized. In public spaces flu viruses may spread from person to person by airflow generated by various traditional ventilation methods, like natural ventilation and mixing ventilation (MV Personalized ventilation (PV......) supplies clean air close to the occupant and directly into the breathing zone. Studies show that it improves the inhaled air quality and reduces the risk of airborne cross-infection in comparison with total volume (TV) ventilation. However, it is still challenging for PV and other advanced air distribution...... methods to reduce the exposure to gaseous and particulate pollutants under disturbed conditions and to ensure thermal comfort at the same time. The objective of this study is to analyse the performance of different advanced airflow distribution methods for protection of occupants from exposure to indoor...
Advanced airflow distribution methods for reducing exposure of indoor pollution
DEFF Research Database (Denmark)
Cao, Guangyu; Nielsen, Peter Vilhelm; Melikov, Arsen Krikor
methods to reduce the exposure to gaseous and particulate pollutants under disturbed conditions and to ensure thermal comfort at the same time. The objective of this study is to analyse the performance of different advanced airflow distribution methods for protection of occupants from exposure to indoor......The adverse effect of various indoor pollutants on occupants’ health have been recognized. In public spaces flu viruses may spread from person to person by airflow generated by various traditional ventilation methods, like natural ventilation and mixing ventilation (MV Personalized ventilation (PV......) supplies clean air close to the occupant and directly into the breathing zone. Studies show that it improves the inhaled air quality and reduces the risk of airborne cross-infection in comparison with total volume (TV) ventilation. However, it is still challenging for PV and other advanced air distribution...
Evaluation of a Didactic Method for the Active Learning of Greedy Algorithms
Esteban-Sánchez, Natalia; Pizarro, Celeste; Velázquez-Iturbide, J. Ángel
2014-01-01
An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of…
A Method Based on Dial's Algorithm for Multi-time Dynamic Traffic Assignment
Directory of Open Access Journals (Sweden)
Rongjie Kuang
2014-03-01
Full Text Available Due to static traffic assignment has poor performance in reflecting actual case and dynamic traffic assignment may incurs excessive compute cost, method of multi-time dynamic traffic assignment combining static and dynamic traffic assignment balances factors of precision and cost effectively. A method based on Dial's logit algorithm is proposed in the article to solve the dynamic stochastic user equilibrium problem in dynamic traffic assignment. Before that, a fitting function that can proximately reflect overloaded traffic condition of link is proposed and used to give corresponding model. Numerical example is given to illustrate heuristic procedure of method and to compare results with one of same example solved by other literature's algorithm. Results show that method based on Dial's algorithm is preferable to algorithm from others.
A fast method to emulate an iterative POCS image reconstruction algorithm.
Zeng, Gengsheng L
2017-10-01
Iterative image reconstruction algorithms are commonly used to optimize an objective function, especially when the objective function is nonquadratic. Generally speaking, the iterative algorithms are computationally inefficient. This paper presents a fast algorithm that has one backprojection and no forward projection. This paper derives a new method to solve an optimization problem. The nonquadratic constraint, for example, an edge-preserving denoising constraint is implemented as a nonlinear filter. The algorithm is derived based on the POCS (projections onto projections onto convex sets) approach. A windowed FBP (filtered backprojection) algorithm enforces the data fidelity. An iterative procedure, divided into segments, enforces edge-enhancement denoising. Each segment performs nonlinear filtering. The derived iterative algorithm is computationally efficient. It contains only one backprojection and no forward projection. Low-dose CT data are used for algorithm feasibility studies. The nonlinearity is implemented as an edge-enhancing noise-smoothing filter. The patient studies results demonstrate its effectiveness in processing low-dose x ray CT data. This fast algorithm can be used to replace many iterative algorithms. © 2017 American Association of Physicists in Medicine.
International Nuclear Information System (INIS)
Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.
2015-01-01
Highlights: • An advanced version of firefly algorithm, EDFA, is proposed for the core pattern optimization problem. • The movement of each firefly toward the best firefly with a dynamic probability is the major improvement of EDFA. • LPO results represent the faster convergence and better performance of EDFA in comparison to CFA and DFA. - Abstract: Inspired by fireflies behavior in nature, a firefly algorithm has been developed for solving optimization problems. In this approach, each firefly movement is based on absorption of the other one. For enhancing the performance of firefly algorithm in the optimization process of nuclear reactor loading pattern optimization (LPO), we introduce a new variant of firefly algorithm, i.e. Effective Discrete Firefly Algorithm (EDFA). In EDFA, a new behavior is the movement of fireflies to current global best position with a dynamic probability, i.e. the movement of each firefly can be determined to be toward the brighter or brightest firefly’s position in any iteration of the algorithm. In this paper, our optimization objectives for the LPO are the maximization of K eff along with the minimization of the power peaking factor (PPF). In order to represent the increase of convergence speed of EDFA, basic firefly algorithms including the continuous firefly algorithm (CFA) and the discrete firefly algorithm (DFA) also have been implemented. Loading pattern optimization results of two well-known problems confirm better performance of EDFA in obtaining nearly optimized fuel arrangements in comparison to CFA and DFA. All in all, we can suggest applying the EDFA to other optimization problems of nuclear engineering field in order to investigate its performance in gaining considered objectives
Chen, Zhangxin; Ewing, Richard E.
1996-01-01
Multigrid algorithms for nonconforming and mixed finite element methods for second order elliptic problems on triangular and rectangular finite elements are considered. The construction of several coarse-to-fine intergrid transfer operators for nonconforming multigrid algorithms is discussed. The equivalence between the nonconforming and mixed finite element methods with and without projection of the coefficient of the differential problems into finite element spaces is described.
Directory of Open Access Journals (Sweden)
Santosh Kumar Singh
2017-06-01
Full Text Available This paper presents a new hybrid method based on Gravity Search Algorithm (GSA and Recursive Least Square (RLS, known as GSA-RLS, to solve the harmonic estimation problems in the case of time varying power signals in presence of different noises. GSA is based on the Newton’s law of gravity and mass interactions. In the proposed method, the searcher agents are a collection of masses that interact with each other using Newton’s laws of gravity and motion. The basic GSA algorithm strategy is combined with RLS algorithm sequentially in an adaptive way to update the unknown parameters (weights of the harmonic signal. Simulation and practical validation are made with the experimentation of the proposed algorithm with real time data obtained from a heavy paper industry. A comparative performance of the proposed algorithm is evaluated with other recently reported algorithms like, Differential Evolution (DE, Particle Swarm Optimization (PSO, Bacteria Foraging Optimization (BFO, Fuzzy-BFO (F-BFO hybridized with Least Square (LS and BFO hybridized with RLS algorithm, which reveals that the proposed GSA-RLS algorithm is the best in terms of accuracy, convergence and computational time.
Carroll, Chester C.; Youngblood, John N.; Saha, Aindam
1987-01-01
Improvements and advances in the development of computer architecture now provide innovative technology for the recasting of traditional sequential solutions into high-performance, low-cost, parallel system to increase system performance. Research conducted in development of specialized computer architecture for the algorithmic execution of an avionics system, guidance and control problem in real time is described. A comprehensive treatment of both the hardware and software structures of a customized computer which performs real-time computation of guidance commands with updated estimates of target motion and time-to-go is presented. An optimal, real-time allocation algorithm was developed which maps the algorithmic tasks onto the processing elements. This allocation is based on the critical path analysis. The final stage is the design and development of the hardware structures suitable for the efficient execution of the allocated task graph. The processing element is designed for rapid execution of the allocated tasks. Fault tolerance is a key feature of the overall architecture. Parallel numerical integration techniques, tasks definitions, and allocation algorithms are discussed. The parallel implementation is analytically verified and the experimental results are presented. The design of the data-driven computer architecture, customized for the execution of the particular algorithm, is discussed.
Improved VMAT planning for head and neck tumors with an advanced optimization algorithm
International Nuclear Information System (INIS)
Klippel, Norbert; Schmuecking, Michael; Terribilini, Dario; Geretschlaeger, Andreas; Aebersold, Daniel M.; Manser, Peter
2015-01-01
In this study, the ''Progressive Resolution Optimizer PRO3'' (Varian Medical Systems) is compared to the previous version PRO2'' with respect to its potential to improve dose sparing to the organs at risk (OAR) and dose coverage of the PTV for head and neck cancer patients. Materials and Methods For eight head and neck cancer patients, volumetric modulated arc therapy (VMAT) treatment plans were generated in this study. All cases have 2-3 phases and the total prescribed dose (PD) was 60-72 Gy in the PTV. The study is mainly focused on the phase 1 plans, which all have an identical PD of 54 Gy, and complex PTV structures with an overlap to the parotids. Optimization was performed based on planning objectives for the PTV according to ICRU83, and with minimal dose to spinal cord, and parotids outside PTV. In order to assess the quality of the optimization algorithms, an identical set of constraints was used for both, PRO2 and PRO3. The resulting treatment plans were investigated with respect to dose distribution based on the analysis of the dose volume histograms. Results For the phase 1 plans (PD = 54 Gy) the near maximum dose D 2% of the spinal cord, could be minimized to 22±5 Gy with PRO3, as compared to 32±12 Gy with PRO2, averaged for all patients. The mean dose to the parotids was also lower in PRO3 plans compared to PRO2, but the differences were less pronounced. A PTV coverage of V 95% = 97±1% could be reached with PRO3, as compared to 86±5% with PRO2. In clinical routine, these PRO2 plans would require modifications to obtain better PTV coverage at the cost of higher OAR doses. Conclusion A comparison between PRO3 and PRO2 optimization algorithms was performed for eight head and neck cancer patients. In general, the quality of VMAT plans for head and neck patients are improved with PRO3 as compared to PRO2. The dose to OARs can be reduced significantly, especially for the spinal cord. These reductions are achieved with better
Advancements in the Development of an Operational Lightning Jump Algorithm for GOES-R GLM
Shultz, Chris; Petersen, Walter; Carey, Lawrence
2011-01-01
Rapid increases in total lightning have been shown to precede the manifestation of severe weather at the surface. These rapid increases have been termed lightning jumps, and are the current focus of algorithm development for the GOES-R Geostationary Lightning Mapper (GLM). Recent lightning jump algorithm work has focused on evaluation of algorithms in three additional regions of the country, as well as, markedly increasing the number of thunderstorms in order to evaluate the each algorithm s performance on a larger population of storms. Lightning characteristics of just over 600 thunderstorms have been studied over the past four years. The 2 lightning jump algorithm continues to show the most promise for an operational lightning jump algorithm, with a probability of detection of 82%, a false alarm rate of 35%, a critical success index of 57%, and a Heidke Skill Score of 0.73 on the entire population of thunderstorms. Average lead time for the 2 algorithm on all severe weather is 21.15 minutes, with a standard deviation of +/- 14.68 minutes. Looking at tornadoes alone, the average lead time is 18.71 minutes, with a standard deviation of +/-14.88 minutes. Moreover, removing the 2 lightning jumps that occur after a jump has been detected, and before severe weather is detected at the ground, the 2 lightning jump algorithm s false alarm rate drops from 35% to 21%. Cold season, low topped, and tropical environments cause problems for the 2 lightning jump algorithm, due to their relative dearth in lightning as compared to a supercellular or summertime airmass thunderstorm environment.
Advances in mixed-integer programming methods for chemical production scheduling.
Velez, Sara; Maravelias, Christos T
2014-01-01
The goal of this paper is to critically review advances in the area of chemical production scheduling over the past three decades and then present two recently proposed solution methods that have led to dramatic computational enhancements. First, we present a general framework and problem classification and discuss modeling and solution methods with an emphasis on mixed-integer programming (MIP) techniques. Second, we present two solution methods: (a) a constraint propagation algorithm that allows us to compute parameters that are then used to tighten MIP scheduling models and (b) a reformulation that introduces new variables, thus leading to effective branching. We also present computational results and an example illustrating how these methods are implemented, as well as the resulting enhancements. We close with a discussion of open research challenges and future research directions.
Directory of Open Access Journals (Sweden)
A. A. Korobko
2017-06-01
Full Text Available Purpose. The main attention is paid to the development and investigation of multifrequency algorithms for the realization of the method of resonance dielcometric measurement of the humidity of emulsions of the type «nonpolar liquid dielectric-water». Multifrequency algorithms take into account the problem of «uncertainty of varieties» and increase the sensitivity of the dielcometric method. Methodology. Multifrequency algorithms are proposed to solve the problem of «uncertainty of varieties» and improve the metrological characteristics of the resonance dielcometric method. The essence of the algorithms is to use a mathematical model of the emulsion and to determine the permittivity of the dehydrated liquid and the emulsion. The task of developing algorithms is to determine and take into account the influence of the parasitic electrical capacitance of the measuring oscillator and the measuring transducer. The essence of the method consists in alternately determining the resonance frequency of the oscillatory circuit with various configurations, which allows to take into account errors from parasitic parameters. The problem of «uncertainty of varieties» is formulated and solved. The metrological characteristics of the resonance dielcometric method are determined using algorithms. Results. Frequency domains of application of mathematical model of an emulsion are defined. An algorithm in a general form with four frequencies suitable for practical implementation in dielcometric resonance measurements is developed. Partial algorithms with three and two frequencies are developed. The systematic values of simulation errors in the emulsion in the microwave range are determined. Generalized metrological characteristics are obtained. The ways of increasing the sensitivity of the dielcometric method are determined. The problem of «uncertainty of varieties» was solved. Experimental data on determination of humidity for the developed algorithms are
Scalable Kernel Methods and Algorithms for General Sequence Analysis
Kuksa, Pavel
2011-01-01
Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…
New or improved computational methods and advanced reactor design
International Nuclear Information System (INIS)
Nakagawa, Masayuki; Takeda, Toshikazu; Ushio, Tadashi
1997-01-01
Nuclear computational method has been studied continuously up to date, as a fundamental technology supporting the nuclear development. At present, research on computational method according to new theory and the calculating method thought to be difficult to practise are also continued actively to find new development due to splendid improvement of features of computer. In Japan, many light water type reactors are now in operations, new computational methods are induced for nuclear design, and a lot of efforts are concentrated for intending to more improvement of economics and safety. In this paper, some new research results on the nuclear computational methods and their application to nuclear design of the reactor were described for introducing recent trend of the nuclear design of the reactor. 1) Advancement of the computational method, 2) Reactor core design and management of the light water reactor, and 3) Nuclear design of the fast reactor. (G.K.)
Advanced hybrid query tree algorithm based on slotted backoff mechanism in RFID
Directory of Open Access Journals (Sweden)
XIE Xiaohui
2013-12-01
Full Text Available The merits of performance quality for a RFID system are determined by the effectiveness of tag anti-collision algorithm.Many algorithms for RFID system of tag identification have been proposed,but they all have obvious weaknesses,such as slow speed of identification,unstable and so on.The existing algorithms can be divided into two groups,one is based on ALOHA and another is based on query tree.This article is based on the hybrid query tree algorithm,combined with a slotted backoff mechanism and a specific encoding (Manchester encoding.The number of value“1” in every three consecutive bits of tags is used to determine the tag response time slots,which will greatly reduce the time slot of the collision and improve the recognition efficiency.
Energy Technology Data Exchange (ETDEWEB)
Morhac, M. E-mail: fyzimiro@savba.skfyzimiro@flnr.jinr.ru; Matousek, V. E-mail: matousek@savba.sk; Kliman, J.; Krupa, L.L.; Jandel, M
2003-04-21
The efficient algorithms to analyze multiparameter {gamma}-ray spectra are presented. They allow to search for peaks, to separate peaks from background, to improve the resolution and to fit 1-, 2-, 3-parameter {gamma}-ray spectra.
Improved algorithm for three-dimensional inverse method
Qiu, Xuwen
An inverse method, which works for full 3D viscous applications in turbomachinery aerodynamic design, is developed. The method takes pressure loading and thickness distribution as inputs and computes the 3D-blade geometry. The core of the inverse method consists of two closely related steps, which are integrated into a time-marching procedure of a Navier-Stokes solver. First, the pressure loading condition is enforced while flow is allowed to cross the blade surfaces. A permeable blade boundary condition is developed here in order to be consistent with the propagation characteristics of the transient Navier-Stokes equations. In the second step, the blade geometry is adjusted so that the flow-tangency condition is satisfied for the new blade. A Non-Uniform Rational B-Spline (NURBS) model is used to represent the span-wise camber curves. The flow-tangency condition is then transformed into a general linear least squares fitting problem, which is solved by a robust Singular Value Decomposition (SVD) scheme. This blade geometry generation scheme allows the designer to have direct control over the smoothness of the calculated blade, and thus ensures the numerical stability during the iteration process. Numerical experiments show that this method is very accurate, efficient and robust. In target-shooting tests, the program was able to converge to the target blade accurately from a different initial blade. The speed of an inverse run is only about 15% slower than its analysis counterpart, which means a complete 3D viscous inverse design can be done in a matter of hours. The method is also proved to work well with the presence of clearance between the blade and the housing, a key factor to be considered in aerodynamic design. The method is first developed for blades without splitters, and is then extended to provide the capability of analyzing and designing machines with splitters. This gives designers an integrated environment where the aerodynamic design of both full
The Global Optimal Algorithm of Reliable Path Finding Problem Based on Backtracking Method
Directory of Open Access Journals (Sweden)
Liang Shen
2017-01-01
Full Text Available There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.
Improved Expectation Maximization Algorithm for Gaussian Mixed Model Using the Kernel Method
Directory of Open Access Journals (Sweden)
Mohd Izhan Mohd Yusoff
2013-01-01
Full Text Available Fraud activities have contributed to heavy losses suffered by telecommunication companies. In this paper, we attempt to use Gaussian mixed model, which is a probabilistic model normally used in speech recognition to identify fraud calls in the telecommunication industry. We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. Firstly, we look at a mechanism for the determination of the initial number of Gaussian components and the choice of the initial values of the algorithm using the kernel method. We show via simulation that the technique improves the performance of the algorithm. Secondly, we developed a procedure for determining the order of the Gaussian mixed model using the log-likelihood function and the Akaike information criteria. Finally, for illustration, we apply the improved algorithm to real telecommunication data. The modified method will pave the way to introduce a comprehensive method for detecting fraud calls in future work.
Research of beam hardening correction method for CL system based on SART algorithm
International Nuclear Information System (INIS)
Cao Daquan; Wang Yaxiao; Que Jiemin; Sun Cuili; Wei Cunfeng; Wei Long
2014-01-01
Computed laminography (CL) is a non-destructive testing technique for large objects, especially for planar objects. Beam hardening artifacts were wildly observed in the CL system and significantly reduce the image quality. This study proposed a novel simultaneous algebraic reconstruction technique (SART) based beam hardening correction (BHC) method for the CL system, namely the SART-BHC algorithm in short. The SART-BHC algorithm took the polychromatic attenuation process in account to formulate the iterative reconstruction update. A novel projection matrix calculation method which was different from the conventional cone-beam or fan-beam geometry was also studied for the CL system. The proposed method was evaluated with simulation data and experimental data, which was generated using the Monte Carlo simulation toolkit Geant4 and a bench-top CL system, respectively. All projection data were reconstructed with SART-BHC algorithm and the standard filtered back projection (FBP) algorithm. The reconstructed images show that beam hardening artifacts are greatly reduced with the SART-BHC algorithm compared to the FBP algorithm. The SART-BHC algorithm doesn't need any prior know-ledge about the object or the X-ray spectrum and it can also mitigate the interlayer aliasing. (authors)
Balouchestani, Mohammadreza; Krishnan, Sridhar
2014-01-01
Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.
Development and application of advanced methods for electronic structure calculations
DEFF Research Database (Denmark)
Schmidt, Per Simmendefeldt
. For this reason, part of this thesis relates to developing and applying a new method for constructing so-called norm-conserving PAW setups, that are applicable to GW calculations by using a genetic algorithm. The effect of applying the new setups significantly affects the absolute band positions, both for bulk......This thesis relates to improvements and applications of beyond-DFT methods for electronic structure calculations that are applied in computational material science. The improvements are of both technical and principal character. The well-known GW approximation is optimized for accurate calculations...... of electronic excitations in two-dimensional materials by exploiting exact limits of the screened Coulomb potential. This approach reduces the computational time by an order of magnitude, enabling large scale applications. The GW method is further improved by including so-called vertex corrections. This turns...
Methods and Algorithms for Detecting Objects in Video Files
Directory of Open Access Journals (Sweden)
Nguyen The Cuong
2018-01-01
Full Text Available Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.
Semi-definite Programming: methods and algorithms for energy management
International Nuclear Information System (INIS)
Gorge, Agnes
2013-01-01
The present thesis aims at exploring the potentialities of a powerful optimization technique, namely Semi-definite Programming, for addressing some difficult problems of energy management. We pursue two main objectives. The first one consists of using SDP to provide tight relaxations of combinatorial and quadratic problems. A first relaxation, called 'standard' can be derived in a generic way but it is generally desirable to reinforce them, by means of tailor-made tools or in a systematic fashion. These two approaches are implemented on different models of the Nuclear Outages Scheduling Problem, a famous combinatorial problem. We conclude this topic by experimenting the Lasserre's hierarchy on this problem, leading to a sequence of semi-definite relaxations whose optimal values tends to the optimal value of the initial problem. The second objective deals with the use of SDP for the treatment of uncertainty. We investigate an original approach called 'distributionally robust optimization', that can be seen as a compromise between stochastic and robust optimization and admits approximations under the form of a SDP. We compare the benefits of this method w.r.t classical approaches on a demand/supply equilibrium problem. Finally, we propose a scheme for deriving SDP relaxations of MISOCP and we report promising computational results indicating that the semi-definite relaxation improves significantly the continuous relaxation, while requiring a reasonable computational effort. SDP therefore proves to be a promising optimization method that offers great opportunities for innovation in energy management. (author)
Method and Excel VBA Algorithm for Modeling Master Recession Curve Using Trigonometry Approach.
Posavec, Kristijan; Giacopetti, Marco; Materazzi, Marco; Birk, Steffen
2017-11-01
A new method was developed and implemented into an Excel Visual Basic for Applications (VBAs) algorithm utilizing trigonometry laws in an innovative way to overlap recession segments of time series and create master recession curves (MRCs). Based on a trigonometry approach, the algorithm horizontally translates succeeding recession segments of time series, placing their vertex, that is, the highest recorded value of each recession segment, directly onto the appropriate connection line defined by measurement points of a preceding recession segment. The new method and algorithm continues the development of methods and algorithms for the generation of MRC, where the first published method was based on a multiple linear/nonlinear regression model approach (Posavec et al. 2006). The newly developed trigonometry-based method was tested on real case study examples and compared with the previously published multiple linear/nonlinear regression model-based method. The results show that in some cases, that is, for some time series, the trigonometry-based method creates narrower overlaps of the recession segments, resulting in higher coefficients of determination R 2 , while in other cases the multiple linear/nonlinear regression model-based method remains superior. The Excel VBA algorithm for modeling MRC using the trigonometry approach is implemented into a spreadsheet tool (MRCTools v3.0 written by and available from Kristijan Posavec, Zagreb, Croatia) containing the previously published VBA algorithms for MRC generation and separation. All algorithms within the MRCTools v3.0 are open access and available free of charge, supporting the idea of running science on available, open, and free of charge software. © 2017, National Ground Water Association.
International Nuclear Information System (INIS)
Dessì, Alessia; Pani, Danilo; Raffo, Luigi
2014-01-01
Non-invasive fetal electrocardiography is still an open research issue. The recent publication of an annotated dataset on Physionet providing four-channel non-invasive abdominal ECG traces promoted an international challenge on the topic. Starting from that dataset, an algorithm for the identification of the fetal QRS complexes from a reduced number of electrodes and without any a priori information about the electrode positioning has been developed, entering into the top ten best-performing open-source algorithms presented at the challenge. In this paper, an improved version of that algorithm is presented and evaluated exploiting the same challenge metrics. It is mainly based on the subtraction of the maternal QRS complexes in every lead, obtained by synchronized averaging of morphologically similar complexes, the filtering of the maternal P and T waves and the enhancement of the fetal QRS through independent component analysis (ICA) applied on the processed signals before a final fetal QRS detection stage. The RR time series of both the mother and the fetus are analyzed to enhance pseudoperiodicity with the aim of correcting wrong annotations. The algorithm has been designed and extensively evaluated on the open dataset A (N = 75), and finally evaluated on datasets B (N = 100) and C (N = 272) to have the mean scores over data not used during the algorithm development. Compared to the results achieved by the previous version of the algorithm, the current version would mark the 5th and 4th position in the final ranking related to the events 1 and 2, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. On dataset A, the algorithm achieves 0.982 median sensitivity and 0.976 median positive predictivity. (paper)
Digital spectral analysis parametric, non-parametric and advanced methods
Castanié, Francis
2013-01-01
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a
Advanced method of double contrast examination of the stomach
International Nuclear Information System (INIS)
Vlasov, P.V.; Yakimenko, V.F.
1981-01-01
An advanced method of double contrast examination of the stomach with the use of high concentrated barium suspension is described. It is shown that concentration of barium suspension must be not less than 200 mass/volume per cent to obtain the sharp image of the mucosal microrelief 6 standard position are recommended for the double contrast examination of all stomach walls. 200 patients with different digestive system diseases are examined with the help of developed methods. The sharp image of the mucosal microrelief is obtained in 70% cases [ru
Performance Assessment Method for a Forged Fingerprint Detection Algorithm
Shin, Yong Nyuo; Jun, In-Kyung; Kim, Hyun; Shin, Woochang
The threat of invasion of privacy and of the illegal appropriation of information both increase with the expansion of the biometrics service environment to open systems. However, while certificates or smart cards can easily be cancelled and reissued if found to be missing, there is no way to recover the unique biometric information of an individual following a security breach. With the recognition that this threat factor may disrupt the large-scale civil service operations approaching implementation, such as electronic ID cards and e-Government systems, many agencies and vendors around the world continue to develop forged fingerprint detection technology, but no objective performance assessment method has, to date, been reported. Therefore, in this paper, we propose a methodology designed to evaluate the objective performance of the forged fingerprint detection technology that is currently attracting a great deal of attention.
Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue
2018-04-01
The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.
Advances in Statistical Methods for Substance Abuse Prevention Research
MacKinnon, David P.; Lockwood, Chondra M.
2010-01-01
The paper describes advances in statistical methods for prevention research with a particular focus on substance abuse prevention. Standard analysis methods are extended to the typical research designs and characteristics of the data collected in prevention research. Prevention research often includes longitudinal measurement, clustering of data in units such as schools or clinics, missing data, and categorical as well as continuous outcome variables. Statistical methods to handle these features of prevention data are outlined. Developments in mediation, moderation, and implementation analysis allow for the extraction of more detailed information from a prevention study. Advancements in the interpretation of prevention research results include more widespread calculation of effect size and statistical power, the use of confidence intervals as well as hypothesis testing, detailed causal analysis of research findings, and meta-analysis. The increased availability of statistical software has contributed greatly to the use of new methods in prevention research. It is likely that the Internet will continue to stimulate the development and application of new methods. PMID:12940467
Penalty Algorithm Based on Conjugate Gradient Method for Solving Portfolio Management Problem
Directory of Open Access Journals (Sweden)
Wang YaLin
2009-01-01
Full Text Available A new approach was proposed to reformulate the biobjectives optimization model of portfolio management into an unconstrained minimization problem, where the objective function is a piecewise quadratic polynomial. We presented some properties of such an objective function. Then, a class of penalty algorithms based on the well-known conjugate gradient methods was developed to find the solution of portfolio management problem. By implementing the proposed algorithm to solve the real problems from the stock market in China, it was shown that this algorithm is promising.
Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.
2018-05-01
This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.
A method for classification of network traffic based on C5.0 Machine Learning Algorithm
DEFF Research Database (Denmark)
Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup
2012-01-01
current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown...... and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options...
An algorithm of α-and γ-mode eigenvalue calculations by Monte Carlo method
International Nuclear Information System (INIS)
Yamamoto, Toshihiro; Miyoshi, Yoshinori
2003-01-01
A new algorithm for Monte Carlo calculation was developed to obtain α- and γ-mode eigenvalues. The α is a prompt neutron time decay constant measured in subcritical experiments, and the γ is a spatial decay constant measured in an exponential method for determining the subcriticality. This algorithm can be implemented into existing Monte Carlo eigenvalue calculation codes with minimum modifications. The algorithm was implemented into MCNP code and the performance of calculating the both mode eigenvalues were verified through comparison of the calculated eigenvalues with the ones obtained by fixed source calculations. (author)
Ogawa, Takahiro; Haseyama, Miki
2013-03-01
A missing texture reconstruction method based on an error reduction (ER) algorithm, including a novel estimation scheme of Fourier transform magnitudes is presented in this brief. In our method, Fourier transform magnitude is estimated for a target patch including missing areas, and the missing intensities are estimated by retrieving its phase based on the ER algorithm. Specifically, by monitoring errors converged in the ER algorithm, known patches whose Fourier transform magnitudes are similar to that of the target patch are selected from the target image. In the second approach, the Fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors. Consequently, by using the ER algorithm, we can estimate both the Fourier transform magnitudes and phases to reconstruct the missing areas.
NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM
Institute of Scientific and Technical Information of China (English)
ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi
2005-01-01
Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.
Directory of Open Access Journals (Sweden)
Keivan Borna
2015-12-01
Full Text Available Traveling salesman problem (TSP is a well-established NP-complete problem and many evolutionary techniques like particle swarm optimization (PSO are used to optimize existing solutions for that. PSO is a method inspired by the social behavior of birds. In PSO, each member will change its position in the search space, according to personal or social experience of the whole society. In this paper, we combine the principles of PSO and crossover operator of genetic algorithm to propose a heuristic algorithm for solving the TSP more efficiently. Finally, some experimental results on our algorithm are applied in some instances in TSPLIB to demonstrate the effectiveness of our methods which also show that our algorithm can achieve better results than other approaches.
Xiao, Zhongxiu
2018-04-01
A Method of Measuring and Correcting Tilt of Anti - vibration Wind Turbines Based on Screening Algorithm is proposed in this paper. First of all, we design a device which the core is the acceleration sensor ADXL203, the inclination is measured by installing it on the tower of the wind turbine as well as the engine room. Next using the Kalman filter algorithm to filter effectively by establishing a state space model for signal and noise. Then we use matlab for simulation. Considering the impact of the tower and nacelle vibration on the collected data, the original data and the filtering data are classified and stored by the Screening algorithm, then filter the filtering data to make the output data more accurate. Finally, we eliminate installation errors by using algorithm to achieve the tilt correction. The device based on this method has high precision, low cost and anti-vibration advantages. It has a wide range of application and promotion value.
Directory of Open Access Journals (Sweden)
Qiuhong Sun
2014-04-01
Full Text Available Based on the data mining research, the data mining based on genetic algorithm method, the genetic algorithm is briefly introduced, while the genetic algorithm based on two important theories and theoretical templates principle implicit parallelism is also discussed. Focuses on the application of genetic algorithms for association rule mining method based on association rule mining, this paper proposes a genetic algorithm fitness function structure, data encoding, such as the title of the improvement program, in particular through the early issues study, proposed the improved adaptive Pc, Pm algorithm is applied to the genetic algorithm, thereby improving efficiency of the algorithm. Finally, a genetic algorithm based association rule mining algorithm, and be applied in sea water samples database in data mining and prove its effective.
Nuclear power reactor analysis, methods, algorithms and computer programs
International Nuclear Information System (INIS)
Matausek, M.V
1981-01-01
Full text: For a developing country buying its first nuclear power plants from a foreign supplier, disregarding the type and scope of the contract, there is a certain number of activities which have to be performed by local stuff and domestic organizations. This particularly applies to the choice of the nuclear fuel cycle strategy and the choice of the type and size of the reactors, to bid parameters specification, bid evaluation and final safety analysis report evaluation, as well as to in-core fuel management activities. In the Nuclear Engineering Department of the Boris Kidric Institute of Nuclear Sciences (NET IBK) the continual work is going on, related to the following topics: cross section and resonance integral calculations, spectrum calculations, generation of group constants, lattice and cell problems, criticality and global power distribution search, fuel burnup analysis, in-core fuel management procedures, cost analysis and power plant economics, safety and accident analysis, shielding problems and environmental impact studies, etc. The present paper gives the details of the methods developed and the results achieved, with the particular emphasis on the NET IBK computer program package for the needs of planning, construction and operation of nuclear power plants. The main problems encountered so far were related to small working team, lack of large and powerful computers, absence of reliable basic nuclear data and shortage of experimental and empirical results for testing theoretical models. Some of these difficulties have been overcome thanks to bilateral and multilateral cooperation with developed countries, mostly through IAEA. It is the authors opinion, however, that mutual cooperation of developing countries, having similar problems and similar goals, could lead to significant results. Some activities of this kind are suggested and discussed. (author)
Optimization of advanced gas-cooled reactor fuel performance by a stochastic method
International Nuclear Information System (INIS)
Parks, G.T.
1987-01-01
A brief description is presented of a model representing the in-core behaviour of a single advanced gas-cooled reactor fuel channel, developed specifically for optimization studies. The performances of the only suitable Numerical Algorithms Group (NAG) library package and a Metropolis algorithm routine on this problem are discussed and contrasted. It is concluded that, for the problem in question, the stochastic Metropolis algorithm has distinct advantages over the deterministic NAG routine. (author)
Finite element methods for viscous incompressible flows a guide to theory, practice, and algorithms
Gunzburger, Max D
2012-01-01
In this book, the author examines mathematical aspects of finite element methods for the approximate solution of incompressible flow problems. The principal goal is to present some of the important mathematical results that are relevant to practical computations. In so doing, useful algorithms are also discussed. Although rigorous results are stated, no detailed proofs are supplied; rather, the intention is to present these results so that they can serve as a guide for the selection and, in certain respects, the implementation of algorithms.
1984-01-01
That there have been remarkable advances in the field of molecular electronic structure during the last decade is clear not only to those working in the field but also to anyone else who has used quantum chemical results to guide their own investiga tions. The progress in calculating the electronic structures of molecules has occurred through the truly ingenious theoretical and methodological developments that have made computationally tractable the underlying physics of electron distributions around a collection of nuclei. At the same time there has been consider able benefit from the great advances in computer technology. The growing sophistication, declining costs and increasing accessibi lity of computers have let theorists apply their methods to prob lems in virtually all areas of molecular science. Consequently, each year witnesses calculations on larger molecules than in the year before and calculations with greater accuracy and more com plete information on molecular properties. We can surel...
A novel orthoimage mosaic method using a weighted A∗ algorithm - Implementation and evaluation
Zheng, Maoteng; Xiong, Xiaodong; Zhu, Junfeng
2018-04-01
The implementation and evaluation of a weighted A∗ algorithm for orthoimage mosaic with UAV (Unmanned Aircraft Vehicle) imagery is proposed. The initial seam-line network is firstly generated by standard Voronoi Diagram algorithm; an edge diagram is generated based on DSM (Digital Surface Model) data; the vertices (conjunction nodes of seam-lines) of the initial network are relocated if they are on high objects (buildings, trees and other artificial structures); and the initial seam-lines are refined using the weighted A∗ algorithm based on the edge diagram and the relocated vertices. Our method was tested with three real UAV datasets. Two quantitative terms are introduced to evaluate the results of the proposed method. Preliminary results show that the method is suitable for regular and irregular aligned UAV images for most terrain types (flat or mountainous areas), and is better than the state-of-the-art method in both quality and efficiency based on the test datasets.
An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm
Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin
2018-04-01
Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.
International Nuclear Information System (INIS)
Zhan, Shuyue; Wang, Xiaoping; Liu, Yuling
2011-01-01
To simplify the algorithm for determining the surface plasmon resonance (SPR) angle for special applications and development trends, a fast method for determining an SPR angle, called the fixed-boundary centroid algorithm, has been proposed. Two experiments were conducted to compare three centroid algorithms from the aspects of the operation time, sensitivity to shot noise, signal-to-noise ratio (SNR), resolution, and measurement range. Although the measurement range of this method was narrower, the other performance indices were all better than the other two centroid methods. This method has outstanding performance, high speed, good conformity, low error and a high SNR and resolution. It thus has the potential to be widely adopted
Borodinov, A. A.; Myasnikov, V. V.
2018-04-01
The present work is devoted to comparing the accuracy of the known qualification algorithms in the task of recognizing local objects on radar images for various image preprocessing methods. Preprocessing involves speckle noise filtering and normalization of the object orientation in the image by the method of image moments and by a method based on the Hough transform. In comparison, the following classification algorithms are used: Decision tree; Support vector machine, AdaBoost, Random forest. The principal component analysis is used to reduce the dimension. The research is carried out on the objects from the base of radar images MSTAR. The paper presents the results of the conducted studies.
International Nuclear Information System (INIS)
Deng, Zhongwei; Yang, Lin; Cai, Yishan; Deng, Hao; Sun, Liu
2016-01-01
The key technology of a battery management system is to online estimate the battery states accurately and robustly. For lithium iron phosphate battery, the relationship between state of charge and open circuit voltage has a plateau region which limits the estimation accuracy of voltage-based algorithms. The open circuit voltage hysteresis requires advanced online identification algorithms to cope with the strong nonlinear battery model. The available capacity, as a crucial parameter, contributes to the state of charge and state of health estimation of battery, but it is difficult to predict due to comprehensive influence by temperature, aging and current rates. Aim at above problems, the ampere-hour counting with current correction and the dual adaptive extended Kalman filter algorithms are combined to estimate model parameters and state of charge. This combination presents the advantages of less computation burden and more robustness. Considering the influence of temperature and degradation, the data-driven algorithm namely least squares support vector machine is implemented to predict the available capacity. The state estimation and capacity prediction methods are coupled to improve the estimation accuracy at different temperatures among the lifetime of battery. The experiment results verify the proposed methods have excellent state and available capacity estimation accuracy. - Highlights: • A dual adaptive extended Kalman filter is used to estimate parameters and states. • A correction term is introduced to consider the effect of current rates. • The least square support vector machine is used to predict the available capacity. • The experiment results verify the proposed state and capacity prediction methods.
Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
V. D. Sulimov
2014-01-01
Full Text Available Modern methods for optimization investigation of complex systems are based on development and updating the mathematical models of systems because of solving the appropriate inverse problems. Input data desirable for solution are obtained from the analysis of experimentally defined consecutive characteristics for a system or a process. Causal characteristics are the sought ones to which equation coefficients of mathematical models of object, limit conditions, etc. belong. The optimization approach is one of the main ones to solve the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. Global optimization methods are widely used in problems of identification and computation diagnosis system as well as in optimal control, computing to-mography, image restoration, teaching the neuron networks, other intelligence technologies. Increasingly complicated systems of optimization observed during last decades lead to more complicated mathematical models, thereby making solution of appropriate extreme problems significantly more difficult. A great deal of practical applications may have the problem con-ditions, which can restrict modeling. As a consequence, in inverse problems the criterion functions can be not everywhere differentiable and noisy. Available noise means that calculat-ing the derivatives is difficult and unreliable. It results in using the optimization methods without calculating the derivatives.An efficiency of deterministic algorithms of global optimization is significantly restrict-ed by their dependence on the extreme problem dimension. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algo-rithms that combine a stochastic algorithm for scanning the variable space with deterministic local search
International Nuclear Information System (INIS)
Coelho, Pedro J.
2014-01-01
Many methods are available for the solution of radiative heat transfer problems in participating media. Among these, the discrete ordinates method (DOM) and the finite volume method (FVM) are among the most widely used ones. They provide a good compromise between accuracy and computational requirements, and they are relatively easy to integrate in CFD codes. This paper surveys recent advances on these numerical methods. Developments concerning the grid structure (e.g., new formulations for axisymmetrical geometries, body-fitted structured and unstructured meshes, embedded boundaries, multi-block grids, local grid refinement), the spatial discretization scheme, and the angular discretization scheme are described. Progress related to the solution accuracy, solution algorithm, alternative formulations, such as the modified DOM and FVM, even-parity formulation, discrete-ordinates interpolation method and method of lines, and parallelization strategies is addressed. The application to non-gray media, variable refractive index media, and transient problems is also reviewed. - Highlights: • We survey recent advances in the discrete ordinates and finite volume methods. • Developments in spatial and angular discretization schemes are described. • Progress in solution algorithms and parallelization methods is reviewed. • Advances in the transient solution of the radiative transfer equation are appraised. • Non-gray media and variable refractive index media are briefly addressed
Ameneiros-Lago, E; Carballada-Rico, C; Garrido-Sanjuán, J A; García Martínez, A
2015-01-01
Decision making in the patient with chronic advanced disease is especially complex. Health professionals are obliged to prevent avoidable suffering and not to add any more damage to that of the disease itself. The adequacy of the clinical interventions consists of only offering those diagnostic and therapeutic procedures appropriate to the clinical situation of the patient and to perform only those allowed by the patient or representative. In this article, the use of an algorithm is proposed that should serve to help health professionals in this decision making process. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.
A Numerical Algorithm and a Graphical Method to Size a Heat Exchanger
DEFF Research Database (Denmark)
Berning, Torsten
2011-01-01
This paper describes the development of a numerical algorithm and a graphical method that can be employed in order to determine the overall heat transfer coefficient inside heat exchangers. The method is based on an energy balance and utilizes the spreadsheet application software Microsoft ExcelTM...
Lanying Lin; Sheng He; Feng Fu; Xiping Wang
2015-01-01
Wood failure percentage (WFP) is an important index for evaluating the bond strength of plywood. Currently, the method used for detecting WFP is visual inspection, which lacks efficiency. In order to improve it, image processing methods are applied to wood failure detection. The present study used thresholding and K-means clustering algorithms in wood failure detection...
Beacon- and Schema-Based Method for Recognizing Algorithms from Students' Source Code
Taherkhani, Ahmad; Malmi, Lauri
2013-01-01
In this paper, we present a method for recognizing algorithms from students programming submissions coded in Java. The method is based on the concept of "programming schemas" and "beacons". Schemas are high-level programming knowledge with detailed knowledge abstracted out, and beacons are statements that imply specific…
A Numerical Algorithm and a Graphical Method to Size a Heat Exchanger
DEFF Research Database (Denmark)
Berning, Torsten
2011-01-01
This paper describes the development of a numerical algorithm and a graphical method that can be employed in order to determine the overall heat transfer coefficient inside heat exchangers. The method is based on an energy balance and utilizes the spreadsheet application software Microsoft Excel...
Advanced codes and methods supporting improved fuel cycle economics - 5493
International Nuclear Information System (INIS)
Curca-Tivig, F.; Maupin, K.; Thareau, S.
2015-01-01
AREVA's code development program was practically completed in 2014. The basic codes supporting a new generation of advanced methods are the followings. GALILEO is a state-of-the-art fuel rod performance code for PWR and BWR applications. Development is completed, implementation started in France and the U.S.A. ARCADIA-1 is a state-of-the-art neutronics/ thermal-hydraulics/ thermal-mechanics code system for PWR applications. Development is completed, implementation started in Europe and in the U.S.A. The system thermal-hydraulic codes S-RELAP5 and CATHARE-2 are not really new but still state-of-the-art in the domain. S-RELAP5 was completely restructured and re-coded such that its life cycle increases by further decades. CATHARE-2 will be replaced in the future by the new CATHARE-3. The new AREVA codes and methods are largely based on first principles modeling with an extremely broad international verification and validation data base. This enables AREVA and its customers to access more predictable licensing processes in a fast evolving regulatory environment (new safety criteria, requests for enlarged qualification databases, statistical applications, uncertainty propagation...). In this context, the advanced codes and methods and the associated verification and validation represent the key to avoiding penalties on products, on operational limits, or on methodologies themselves
Advances in product family and product platform design methods & applications
Jiao, Jianxin; Siddique, Zahed; Hölttä-Otto, Katja
2014-01-01
Advances in Product Family and Product Platform Design: Methods & Applications highlights recent advances that have been made to support product family and product platform design and successful applications in industry. This book provides not only motivation for product family and product platform design—the “why” and “when” of platforming—but also methods and tools to support the design and development of families of products based on shared platforms—the “what”, “how”, and “where” of platforming. It begins with an overview of recent product family design research to introduce readers to the breadth of the topic and progresses to more detailed topics and design theory to help designers, engineers, and project managers plan, architect, and implement platform-based product development strategies in their companies. This book also: Presents state-of-the-art methods and tools for product family and product platform design Adopts an integrated, systems view on product family and pro...
Combinatorial methods for advanced materials research and development
Energy Technology Data Exchange (ETDEWEB)
Cremer, R.; Dondorf, S.; Hauck, M.; Horbach, D.; Kaiser, M.; Krysta, S.; Kyrylov, O.; Muenstermann, E.; Philipps, M.; Reichert, K.; Strauch, G. [Rheinisch-Westfaelische Technische Hochschule Aachen (Germany). Lehrstuhl fuer Theoretische Huettenkunde
2001-10-01
The applicability of combinatorial methods in developing advanced materials is illustrated presenting four examples for the deposition and characterization of one- and two-dimensionally laterally graded coatings, which were deposited by means of (reactive) magnetron sputtering and plasma-enhanced chemical vapor deposition. To emphasize the advantages of combinatorial approaches, metastable hard coatings like (Ti,Al)N and (Ti,Al,Hf)N respectively, as well as Ge-Sb-Te based films for rewritable optical data storage were investigated with respect to the relations between structure, composition, and the desired materials properties. (orig.)
Is STAPLE algorithm confident to assess segmentation methods in PET imaging?
Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien
2015-12-01
Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.
Is STAPLE algorithm confident to assess segmentation methods in PET imaging?
International Nuclear Information System (INIS)
Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Vermandel, Maximilien; Baillet, Clio
2015-01-01
Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging.Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used.Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results.The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging. (paper)
Gong, Chunye; Bao, Weimin; Tang, Guojian; Jiang, Yuewen; Liu, Jie
2014-01-01
It is very time consuming to solve fractional differential equations. The computational complexity of two-dimensional fractional differential equation (2D-TFDE) with iterative implicit finite difference method is O(M(x)M(y)N(2)). In this paper, we present a parallel algorithm for 2D-TFDE and give an in-depth discussion about this algorithm. A task distribution model and data layout with virtual boundary are designed for this parallel algorithm. The experimental results show that the parallel algorithm compares well with the exact solution. The parallel algorithm on single Intel Xeon X5540 CPU runs 3.16-4.17 times faster than the serial algorithm on single CPU core. The parallel efficiency of 81 processes is up to 88.24% compared with 9 processes on a distributed memory cluster system. We do think that the parallel computing technology will become a very basic method for the computational intensive fractional applications in the near future.
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.
Tian, Yuling; Zhang, Hongxian
2016-01-01
For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.
Zhang, B.; Sang, Jun; Alam, Mohammad S.
2013-03-01
An image hiding method based on cascaded iterative Fourier transform and public-key encryption algorithm was proposed. Firstly, the original secret image was encrypted into two phase-only masks M1 and M2 via cascaded iterative Fourier transform (CIFT) algorithm. Then, the public-key encryption algorithm RSA was adopted to encrypt M2 into M2' . Finally, a host image was enlarged by extending one pixel into 2×2 pixels and each element in M1 and M2' was multiplied with a superimposition coefficient and added to or subtracted from two different elements in the 2×2 pixels of the enlarged host image. To recover the secret image from the stego-image, the two masks were extracted from the stego-image without the original host image. By applying public-key encryption algorithm, the key distribution was facilitated, and also compared with the image hiding method based on optical interference, the proposed method may reach higher robustness by employing the characteristics of the CIFT algorithm. Computer simulations show that this method has good robustness against image processing.
Wang, Z.
2015-12-01
For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.
A novel orthoimage mosaic method using the weighted A* algorithm for UAV imagery
Zheng, Maoteng; Zhou, Shunping; Xiong, Xiaodong; Zhu, Junfeng
2017-12-01
A weighted A* algorithm is proposed to select optimal seam-lines in orthoimage mosaic for UAV (Unmanned Aircraft Vehicle) imagery. The whole workflow includes four steps: the initial seam-line network is firstly generated by standard Voronoi Diagram algorithm; an edge diagram is then detected based on DSM (Digital Surface Model) data; the vertices (conjunction nodes) of initial network are relocated since some of them are on the high objects (buildings, trees and other artificial structures); and, the initial seam-lines are finally refined using the weighted A* algorithm based on the edge diagram and the relocated vertices. The method was tested with two real UAV datasets. Preliminary results show that the proposed method produces acceptable mosaic images in both the urban and mountainous areas, and is better than the result of the state-of-the-art methods on the datasets.
Development of CAD implementing the algorithm of boundary elements’ numerical analytical method
Directory of Open Access Journals (Sweden)
Yulia V. Korniyenko
2015-03-01
Full Text Available Up to recent days the algorithms for numerical-analytical boundary elements method had been implemented with programs written in MATLAB environment language. Each program had a local character, i.e. used to solve a particular problem: calculation of beam, frame, arch, etc. Constructing matrices in these programs was carried out “manually” therefore being time-consuming. The research was purposed onto a reasoned choice of programming language for new CAD development, allows to implement algorithm of numerical analytical boundary elements method and to create visualization tools for initial objects and calculation results. Research conducted shows that among wide variety of programming languages the most efficient one for CAD development, employing the numerical analytical boundary elements method algorithm, is the Java language. This language provides tools not only for development of calculating CAD part, but also to build the graphic interface for geometrical models construction and calculated results interpretation.
New algorithms derived from the synthesis method. Application to diffusion problems
International Nuclear Information System (INIS)
Rouzaud, Philippe.
1976-05-01
Two algorithms to compute the neutron distribution in a nuclear reactor are presented. These algorithms, the iterative synthesis method (MSI) and the synthesis method by deflation (MSD), are derived from the classical synthesis method (MSC). They retain the most important advantages of MSC (computing time and memory storage reduced with regard to finite difference methods) and avoid its drawbacks: choice of trial functions; choice of weighting functions; choice of the number of terms (for MSD only). Extensive numerical checks of the three methods (MSC, MSI, MSD) were carried out on two fast reactor configurations described in plane geometry (X,Y). Monoenergetic and multigroup theories were successively used. The use of MSI and MSD allows a significant reduction of the discrepancies between the finite difference method and the synthesis method for the reactivity values and the flux distribution [fr
Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models
Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.
2017-12-01
Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream
Directory of Open Access Journals (Sweden)
Ari Shawakat Tahir
2015-12-01
Full Text Available The Steganography is an art and science of hiding information by embedding messages within other, seemingly harmless messages and lots of researches are working in it. Proposed system is using AES Algorithm and Lossy technique to overcome the limitation of previous work and increasing the process’s speed. The sender uses AES Algorithm to encrypt message and image, then using LSB technique to hide encrypted data in encrypted message. The receive get the original data using the keys that had been used in encryption process. The proposed system has been implemented in NetBeans 7.3 software uses image and data in different size to find the system’s speed.
Directory of Open Access Journals (Sweden)
Nurmaulidar Nurmaulidar
2015-04-01
Full Text Available Travelling Salesman Problem (TSP is one of complex optimization problem that is difficult to be solved, and require quite a long time for a large number of cities. Evolutionary algorithm is a precise algorithm used in solving complex optimization problem as it is part of heuristic method. Evolutionary algorithm, like many other algorithms, also experiences a premature convergence phenomenon, whereby variation is eliminated from a population of fairly fit individuals before a complete solution is achieved. Therefore it requires a method to delay the convergence. A specific method of fitness sharing called phenotype fitness sharing has been used in this research. The aim of this research is to find out whether fitness sharing in evolutionary algorithm is able to optimize TSP. There are two concepts of evolutionary algorithm being used in this research. the first one used single elitism and the other one used federated solution. The two concepts had been tested to the method of fitness sharing by using the threshold of 0.25, 0.50 and 0.75. The result was then compared to a non fitness sharing method. The result in this study indicated that by using single elitism concept, fitness sharing was able to give a more optimum result for the data of 100-1000 cities. On the other hand, by using federation solution concept, fitness sharing can yield a more optimum result for the data above 1000 cities, as well as a better solution of data-spreading compared to the method without fitness sharing.
Application aspects of advanced antenna diagnostics with the 3D reconstruction algorithm
DEFF Research Database (Denmark)
Cappellin, Cecilia; Pivnenko, Sergey
2015-01-01
This paper focuses on two important applications of the 3D reconstruction algorithm of the commercial software DIATOOL for antenna diagnostics. The first one is the accurate and detailed identification of array malfunctioning, thanks to the available enhanced spatial resolution of the reconstruct...... fields and currents. The second one is the filtering of the scattering from support structures and feed network leakage. Representative experimental results are presented and guidelines on the recommended measurement parameters for obtaining the best diagnostics results are provided....
Directory of Open Access Journals (Sweden)
Adnan Kiayani
2012-01-01
Full Text Available Direct-conversion architecture-based orthogonal frequency division multiplexing (OFDM systems are troubled by impairments such as in-phase and quadrature-phase (I/Q imbalance and carrier frequency offset (CFO. These impairments are unavoidable in any practical implementation and severely degrade the obtainable link performance. In this contribution, we study the joint impact of frequency-selective I/Q imbalance at both transmitter and receiver together with channel distortions and CFO error. Two estimation and compensation structures based on different pilot patterns are proposed for coping with such impairments. The first structure is based on preamble pilot pattern while the second one assumes a sparse pilot pattern. The proposed estimation/compensation structures are able to separate the individual impairments, which are then compensated in the reverse order of their appearance at the receiver. We present time-domain estimation and compensation algorithms for receiver I/Q imbalance and CFO and propose low-complexity algorithms for the compensation of channel distortions and transmitter IQ imbalance. The performance of the compensation algorithms is investigated with computer simulations as well as with practical radio frequency (RF measurements. The performance results indicate that the proposed techniques provide close to the ideal performance both in simulations and measurements.
Advanced methods for fabrication of PHWR and LMFBR fuels
International Nuclear Information System (INIS)
Ganguly, C.
1988-01-01
For self-reliance in nuclear power, the Department of Atomic Energy (DAE), India is pursuing two specific reactor systems, namely the pressurised heavy water reactors (PHWR) and the liquid metal cooled fast breeder reactors (LMFBR). The reference fuel for PHWR is zircaloy-4 clad high density (≤ 96 per cent T.D.) natural UO 2 pellet-pins. The advanced PHWR fuels are UO 2 -PuO 2 (≤ 2 per cent), ThO 2 -PuO 2 (≤ 4 per cent) and ThO 2 -U 233 O 2 (≤ 2 per cent). Similarly, low density (≤ 85 per cent T.D.) (UPu)O 2 pellets clad in SS 316 or D9 is the reference fuel for the first generation of prototype and commercial LMFBRs all over the world. However, (UPu)C and (UPu)N are considered as advanced fuels for LMFBRs mainly because of their shorter doubling time. The conventional method of fabrication of both high and low density oxide, carbide and nitride fuel pellets starting from UO 2 , PuO 2 and ThO 2 powders is 'powder metallurgy (P/M)'. The P/M route has, however, the disadvantage of generation and handling of fine powder particles of the fuel and the associated problem of 'radiotoxic dust hazard'. The present paper summarises the state-of-the-art of advanced methods of fabrication of oxide, carbide and nitride fuels and highlights the author's experience on sol-gel-microsphere-pelletisation (SGMP) route for preparation of these materials. The SGMP process uses sol gel derived, dust-free and free-flowing microspheres of oxides, carbide or nitride for direct pelletisation and sintering. Fuel pellets of both low and high density, excellent microhomogeneity and controlled 'open' or 'closed' porosity could be fabricated via the SGMP route. (author). 5 tables, 14 figs., 15 refs
Advanced methods for image registration applied to JET videos
Energy Technology Data Exchange (ETDEWEB)
Craciunescu, Teddy, E-mail: teddy.craciunescu@jet.uk [EURATOM-MEdC Association, NILPRP, Bucharest (Romania); Murari, Andrea [Consorzio RFX, Associazione EURATOM-ENEA per la Fusione, Padova (Italy); Gelfusa, Michela [Associazione EURATOM-ENEA – University of Rome “Tor Vergata”, Roma (Italy); Tiseanu, Ion; Zoita, Vasile [EURATOM-MEdC Association, NILPRP, Bucharest (Romania); Arnoux, Gilles [EURATOM/CCFE Fusion Association, Culham Science Centre, Abingdon, Oxon (United Kingdom)
2015-10-15
Graphical abstract: - Highlights: • Development of an image registration method for JET IR and fast visible cameras. • Method based on SIFT descriptors and coherent point drift points set registration technique. • Method able to deal with extremely noisy images and very low luminosity images. • Computation time compatible with the inter-shot analysis. - Abstract: The last years have witnessed a significant increase in the use of digital cameras on JET. They are routinely applied for imaging in the IR and visible spectral regions. One of the main technical difficulties in interpreting the data of camera based diagnostics is the presence of movements of the field of view. Small movements occur due to machine shaking during normal pulses while large ones may arise during disruptions. Some cameras show a correlation of image movement with change of magnetic field strength. For deriving unaltered information from the videos and for allowing correct interpretation an image registration method, based on highly distinctive scale invariant feature transform (SIFT) descriptors and on the coherent point drift (CPD) points set registration technique, has been developed. The algorithm incorporates a complex procedure for rejecting outliers. The method has been applied for vibrations correction to videos collected by the JET wide angle infrared camera and for the correction of spurious rotations in the case of the JET fast visible camera (which is equipped with an image intensifier). The method has proved to be able to deal with the images provided by this camera frequently characterized by low contrast and a high level of blurring and noise.
Energy Technology Data Exchange (ETDEWEB)
Williams, P. T. [Univ. of Tennessee, Knoxville, TN (United States)
1993-09-01
As the field of computational fluid dynamics (CFD) continues to mature, algorithms are required to exploit the most recent advances in approximation theory, numerical mathematics, computing architectures, and hardware. Meeting this requirement is particularly challenging in incompressible fluid mechanics, where primitive-variable CFD formulations that are robust, while also accurate and efficient in three dimensions, remain an elusive goal. This dissertation asserts that one key to accomplishing this goal is recognition of the dual role assumed by the pressure, i.e., a mechanism for instantaneously enforcing conservation of mass and a force in the mechanical balance law for conservation of momentum. Proving this assertion has motivated the development of a new, primitive-variable, incompressible, CFD algorithm called the Continuity Constraint Method (CCM). The theoretical basis for the CCM consists of a finite-element spatial semi-discretization of a Galerkin weak statement, equal-order interpolation for all state-variables, a 0-implicit time-integration scheme, and a quasi-Newton iterative procedure extended by a Taylor Weak Statement (TWS) formulation for dispersion error control. Original contributions to algorithmic theory include: (a) formulation of the unsteady evolution of the divergence error, (b) investigation of the role of non-smoothness in the discretized continuity-constraint function, (c) development of a uniformly H^{1} Galerkin weak statement for the Reynolds-averaged Navier-Stokes pressure Poisson equation, (d) derivation of physically and numerically well-posed boundary conditions, and (e) investigation of sparse data structures and iterative methods for solving the matrix algebra statements generated by the algorithm.
An Initialization Method Based on Hybrid Distance for k-Means Algorithm.
Yang, Jie; Ma, Yan; Zhang, Xiangfen; Li, Shunbao; Zhang, Yuping
2017-11-01
The traditional [Formula: see text]-means algorithm has been widely used as a simple and efficient clustering method. However, the performance of this algorithm is highly dependent on the selection of initial cluster centers. Therefore, the method adopted for choosing initial cluster centers is extremely important. In this letter, we redefine the density of points according to the number of its neighbors, as well as the distance between points and their neighbors. In addition, we define a new distance measure that considers both Euclidean distance and density. Based on that, we propose an algorithm for selecting initial cluster centers that can dynamically adjust the weighting parameter. Furthermore, we propose a new internal clustering validation measure, the clustering validation index based on the neighbors (CVN), which can be exploited to select the optimal result among multiple clustering results. Experimental results show that the proposed algorithm outperforms existing initialization methods on real-world data sets and demonstrates the adaptability of the proposed algorithm to data sets with various characteristics.
The application of advanced rotor (performance) methods for design calculations
Energy Technology Data Exchange (ETDEWEB)
Bussel, G.J.W. van [Delft Univ. of Technology, Inst. for Wind Energy, Delft (Netherlands)
1997-08-01
The calculation of loads and performance of wind turbine rotors has been a topic for research over the last century. The principles for the calculation of loads on rotor blades with a given specific geometry, as well as the development of optimal shaped rotor blades have been published in the decades that significant aircraft development took place. Nowadays advanced computer codes are used for specific problems regarding modern aircraft, and application to wind turbine rotors has also been performed occasionally. The engineers designing rotor blades for wind turbines still use methods based upon global principles developed in the beginning of the century. The question what to expect in terms of the type of methods to be applied in a design environment for the near future is addressed here. (EG) 14 refs.
Methods and Systems for Advanced Spaceport Information Management
Fussell, Ronald M. (Inventor); Ely, Donald W. (Inventor); Meier, Gary M. (Inventor); Halpin, Paul C. (Inventor); Meade, Phillip T. (Inventor); Jacobson, Craig A. (Inventor); Blackwell-Thompson, Charlie (Inventor)
2007-01-01
Advanced spaceport information management methods and systems are disclosed. In one embodiment, a method includes coupling a test system to the payload and transmitting one or more test signals that emulate an anticipated condition from the test system to the payload. One or more responsive signals are received from the payload into the test system and are analyzed to determine whether one or more of the responsive signals comprises an anomalous signal. At least one of the steps of transmitting, receiving, analyzing and determining includes transmitting at least one of the test signals and the responsive signals via a communications link from a payload processing facility to a remotely located facility. In one particular embodiment, the communications link is an Internet link from a payload processing facility to a remotely located facility (e.g. a launch facility, university, etc.).
GPU-based parallel algorithm for blind image restoration using midfrequency-based methods
Xie, Lang; Luo, Yi-han; Bao, Qi-liang
2013-08-01
GPU-based general-purpose computing is a new branch of modern parallel computing, so the study of parallel algorithms specially designed for GPU hardware architecture is of great significance. In order to solve the problem of high computational complexity and poor real-time performance in blind image restoration, the midfrequency-based algorithm for blind image restoration was analyzed and improved in this paper. Furthermore, a midfrequency-based filtering method is also used to restore the image hardly with any recursion or iteration. Combining the algorithm with data intensiveness, data parallel computing and GPU execution model of single instruction and multiple threads, a new parallel midfrequency-based algorithm for blind image restoration is proposed in this paper, which is suitable for stream computing of GPU. In this algorithm, the GPU is utilized to accelerate the estimation of class-G point spread functions and midfrequency-based filtering. Aiming at better management of the GPU threads, the threads in a grid are scheduled according to the decomposition of the filtering data in frequency domain after the optimization of data access and the communication between the host and the device. The kernel parallelism structure is determined by the decomposition of the filtering data to ensure the transmission rate to get around the memory bandwidth limitation. The results show that, with the new algorithm, the operational speed is significantly increased and the real-time performance of image restoration is effectively improved, especially for high-resolution images.
Bestvina, Christine M; Wroblewski, Kristen E; Daly, Bobby; Beach, Brittany; Chow, Selina; Hantel, Andrew; Malec, Monica; Huber, Michael T; Polite, Blase N
2018-03-13
Accurate understanding of the prognosis of an advanced cancer patient can lead to decreased aggressive care at the end of life and earlier hospice enrollment. Our goal was to determine the association between high-risk clinical events identified by a simple, rules-based algorithm and decreased overall survival, to target poor prognosis cancer patients who would urgently benefit from advanced care planning. A retrospective analysis was performed on outpatient oncology patients with an index visit from April 1, 2015, through June 30, 2015. We examined a three-month window for "high-risk events," defined as (1) change in chemotherapy, (2) emergency department (ED) visit, and (3) hospitalization. Patients were followed until January 31, 2017. A total of 219 patients receiving palliative chemotherapy at the University of Chicago Medicine with a prognosis of ≤12 months were included. The main outcome was overall survival, and each "high-risk event" was treated as a time-varying covariate in a Cox proportional hazards regression model to calculate a hazard ratio (HR) of death. A change in chemotherapy regimen, ED visit, hospitalization, and at least one high-risk event occurred in 54% (118/219), 10% (22/219), 26% (57/219), and 67% (146/219) of patients, respectively. The adjusted HR of death for patients with a high-risk event was 1.72 (95% confidence interval [CI] 1.19-2.46, p = 0.003), with hospitalization reaching significance (HR 2.74, 95% CI 1.84-4.09, p rules-based algorithm identified those with the greatest risk of death among a poor prognosis patient group. Implementation of this algorithm in the electronic health record can identify patients with increased urgency to address goals of care.
Calculation methods for advanced concept light water reactor lattices
International Nuclear Information System (INIS)
Carmona, S.
1986-01-01
In the last few years s several advanced concepts for fuel rod lattices have been studied. Improved fuel utilization is one of the major aims in the development of new fuel rod designs and lattice modifications. By these changes s better performance in fuel economics s fuel burnup and material endurance can be achieved in the frame of the well-known basic Light Water Reactor technology. Among the new concepts involved in these studies that have attracted serious attention are lattices consisting of arrays of annular rods duplex pellet rods or tight multicells. These new designs of fuel rods and lattices present several computational problems. The treatment of resonance shielded cross sections is a crucial point in the analyses of these advanced concepts . The purpose of this study was to assess adequate approximation methods for calculating as accurately as possible, resonance shielding for these new lattices. Although detailed and exact computational methods for the evaluation of the resonance shielding in these lattices are possible, they are quite inefficient when used in lattice codes. The computer time and memory required for this kind of computations are too large to be used in an acceptable routine manner. In order to over- come these limitations and to make the analyses possible with reasonable use of computer resources s approximation methods are necessary. Usual approximation methods, for the resonance energy regions used in routine lattice computer codes, can not adequately handle the evaluation of these new fuel rod lattices. The main contribution of the present work to advanced lattice concepts is the development of an equivalence principle for the calculation of resonance shielding in the annular fuel pellet zone of duplex pellets; the duplex pellet in this treatment consists of two fuel zones with the same absorber isotope in both regions. In the transition from a single duplex rod to an infinite array of this kind of fuel rods, the similarity of the
Time-domain hybrid method for simulating large amplitude motions of ships advancing in waves
Directory of Open Access Journals (Sweden)
Shukui Liu
2011-03-01
Full Text Available Typical results obtained by a newly developed, nonlinear time domain hybrid method for simulating large amplitude motions of ships advancing with constant forward speed in waves are presented. The method is hybrid in the way of combining a time-domain transient Green function method and a Rankine source method. The present approach employs a simple double integration algorithm with respect to time to simulate the free-surface boundary condition. During the simulation, the diffraction and radiation forces are computed by pressure integration over the mean wetted surface, whereas the incident wave and hydrostatic restoring forces/moments are calculated on the instantaneously wetted surface of the hull. Typical numerical results of application of the method to the seakeeping performance of a standard containership, namely the ITTC S175, are herein presented. Comparisons have been made between the results from the present method, the frequency domain 3D panel method (NEWDRIFT of NTUA-SDL and available experimental data and good agreement has been observed for all studied cases between the results of the present method and comparable other data.
Ferraro, Ralph; Beauchamp, James; Cecil, Dan; Heymsfeld, Gerald
2015-01-01
In previous studies published in the open literature, a strong relationship between the occurrence of hail and the microwave brightness temperatures (primarily at 37 and 85 GHz) was documented. These studies were performed with the Nimbus-7 SMMR, the TRMM Microwave Imager (TMI) and most recently, the Aqua AMSR-E sensor. This lead to climatologies of hail frequency from TMI and AMSR-E, however, limitations include geographical domain of the TMI sensor (35 S to 35 N) and the overpass time of the Aqua satellite (130 am/pm local time), both of which reduce an accurate mapping of hail events over the global domain and the full diurnal cycle. Nonetheless, these studies presented exciting, new applications for passive microwave sensors. Since 1998, NOAA and EUMETSAT have been operating the AMSU-A/B and the MHS on several operational satellites: NOAA-15 through NOAA-19; MetOp-A and -B. With multiple satellites in operation since 2000, the AMSU/MHS sensors provide near global coverage every 4 hours, thus, offering a much larger time and temporal sampling than TRMM or AMSR-E. With similar observation frequencies near 30 and 85 GHz and additionally three at the 183 GHz water vapor band, the potential to detect strong convection associated with severe storms on a more comprehensive time and space scale exists. In this study, we develop a prototype AMSU-based hail detection algorithm through the use of collocated satellite and surface hail reports over the continental U.S. for a 12-year period (2000-2011). Compared with the surface observations, the algorithm detects approximately 40 percent of hail occurrences. The simple threshold algorithm is then used to generate a hail climatology that is based on all available AMSU observations during 2000-11 that is stratified in several ways, including total hail occurrence by month (March through September), total annual, and over the diurnal cycle. Independent comparisons are made compared to similar data sets derived from other
An NMR log echo data de-noising method based on the wavelet packet threshold algorithm
International Nuclear Information System (INIS)
Meng, Xiangning; Xie, Ranhong; Li, Changxi; Hu, Falong; Li, Chaoliu; Zhou, Cancan
2015-01-01
To improve the de-noising effects of low signal-to-noise ratio (SNR) nuclear magnetic resonance (NMR) log echo data, this paper applies the wavelet packet threshold algorithm to the data. The principle of the algorithm is elaborated in detail. By comparing the properties of a series of wavelet packet bases and the relevance between them and the NMR log echo train signal, ‘sym7’ is found to be the optimal wavelet packet basis of the wavelet packet threshold algorithm to de-noise the NMR log echo train signal. A new method is presented to determine the optimal wavelet packet decomposition scale; this is within the scope of its maximum, using the modulus maxima and the Shannon entropy minimum standards to determine the global and local optimal wavelet packet decomposition scales, respectively. The results of applying the method to the simulated and actual NMR log echo data indicate that compared with the wavelet threshold algorithm, the wavelet packet threshold algorithm, which shows higher decomposition accuracy and better de-noising effect, is much more suitable for de-noising low SNR–NMR log echo data. (paper)
Advances in the Surface Renewal Flux Measurement Method
Shapland, T. M.; McElrone, A.; Paw U, K. T.; Snyder, R. L.
2011-12-01
The measurement of ecosystem-scale energy and mass fluxes between the planetary surface and the atmosphere is crucial for understanding geophysical processes. Surface renewal is a flux measurement technique based on analyzing the turbulent coherent structures that interact with the surface. It is a less expensive technique because it does not require fast-response velocity measurements, but only a fast-response scalar measurement. It is therefore also a useful tool for the study of the global cycling of trace gases. Currently, surface renewal requires calibration against another flux measurement technique, such as eddy covariance, to account for the linear bias of its measurements. We present two advances in the surface renewal theory and methodology that bring the technique closer to becoming a fully independent flux measurement method. The first advance develops the theory of turbulent coherent structure transport associated with the different scales of coherent structures. A novel method was developed for identifying the scalar change rate within structures at different scales. Our results suggest that for canopies less than one meter in height, the second smallest coherent structure scale dominates the energy and mass flux process. Using the method for resolving the scalar exchange rate of the second smallest coherent structure scale, calibration is unnecessary for surface renewal measurements over short canopies. This study forms the foundation for analysis over more complex surfaces. The second advance is a sensor frequency response correction for measuring the sensible heat flux via surface renewal. Inexpensive fine-wire thermocouples are frequently used to record high frequency temperature data in the surface renewal technique. The sensible heat flux is used in conjunction with net radiation and ground heat flux measurements to determine the latent heat flux as the energy balance residual. The robust thermocouples commonly used in field experiments
Recent advances in computational structural reliability analysis methods
Thacker, Ben H.; Wu, Y.-T.; Millwater, Harry R.; Torng, Tony Y.; Riha, David S.
1993-10-01
The goal of structural reliability analysis is to determine the probability that the structure will adequately perform its intended function when operating under the given environmental conditions. Thus, the notion of reliability admits the possibility of failure. Given the fact that many different modes of failure are usually possible, achievement of this goal is a formidable task, especially for large, complex structural systems. The traditional (deterministic) design methodology attempts to assure reliability by the application of safety factors and conservative assumptions. However, the safety factor approach lacks a quantitative basis in that the level of reliability is never known and usually results in overly conservative designs because of compounding conservatisms. Furthermore, problem parameters that control the reliability are not identified, nor their importance evaluated. A summary of recent advances in computational structural reliability assessment is presented. A significant level of activity in the research and development community was seen recently, much of which was directed towards the prediction of failure probabilities for single mode failures. The focus is to present some early results and demonstrations of advanced reliability methods applied to structural system problems. This includes structures that can fail as a result of multiple component failures (e.g., a redundant truss), or structural components that may fail due to multiple interacting failure modes (e.g., excessive deflection, resonate vibration, or creep rupture). From these results, some observations and recommendations are made with regard to future research needs.
A meta-heuristic method for solving scheduling problem: crow search algorithm
Adhi, Antono; Santosa, Budi; Siswanto, Nurhadi
2018-04-01
Scheduling is one of the most important processes in an industry both in manufacturingand services. The scheduling process is the process of selecting resources to perform an operation on tasks. Resources can be machines, peoples, tasks, jobs or operations.. The selection of optimum sequence of jobs from a permutation is an essential issue in every research in scheduling problem. Optimum sequence becomes optimum solution to resolve scheduling problem. Scheduling problem becomes NP-hard problem since the number of job in the sequence is more than normal number can be processed by exact algorithm. In order to obtain optimum results, it needs a method with capability to solve complex scheduling problems in an acceptable time. Meta-heuristic is a method usually used to solve scheduling problem. The recently published method called Crow Search Algorithm (CSA) is adopted in this research to solve scheduling problem. CSA is an evolutionary meta-heuristic method which is based on the behavior in flocks of crow. The calculation result of CSA for solving scheduling problem is compared with other algorithms. From the comparison, it is found that CSA has better performance in term of optimum solution and time calculation than other algorithms.
A modified 4D ROOSTER method using the Chambolle-Pock algorithm
Mory, Cyril; Jacques, Laurent; The Third International Conference on Image Formation in X-Ray Computed Tomography
2014-01-01
The 4D RecOnstructiOn using Spatial and TEmpo- ral Regularization method is a recent 4D cone beam computed tomography algorithm. 4D ROOSTER has not been rigorously proved to converge. This paper aims to reformulate it using the Chambolle & Pock primal-dual optimization scheme. The convergence of this reformulated 4D ROOSTER is therefore guaranteed.
Directory of Open Access Journals (Sweden)
Shao-Fei Jiang
2014-01-01
Full Text Available Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO. This paper presents an improved MPSCO algorithm (IMPSCO firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA. The results show threefold: (1 the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2 the damage location can be accurately detected using the damage threshold proposed in this paper; and (3 compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.
Some aspects of Trim-algorithm modernization for Monte-Carlo method
International Nuclear Information System (INIS)
Dovnar, S.V.; Grigor'ev, V.V.; Kamyshan, M.A.; Leont'ev, A.V.; Yanusko, S.V.
2001-01-01
Some aspects of Trim-algorithm modernization in Monte-Carlo method are discussed. This modification permits to raise the universality of program work with various potentials of ion-atom interactions and to improve the calculation precision for scattering angle θ c
Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method
Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen
2008-01-01
In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…
Directory of Open Access Journals (Sweden)
ELİF BULUT
2013-06-01
Full Text Available Partial Least Squares Regression (PLSR is a multivariate statistical method that consists of partial least squares and multiple linear regression analysis. Explanatory variables, X, having multicollinearity are reduced to components which explain the great amount of covariance between explanatory and response variable. These components are few in number and they don’t have multicollinearity problem. Then multiple linear regression analysis is applied to those components to model the response variable Y. There are various PLSR algorithms. In this study NIPALS and PLS-Kernel algorithms will be studied and illustrated on a real data set.
Chen, Kun; Zhang, Hongyuan; Wei, Haoyun; Li, Yan
2014-08-20
In this paper, we propose an improved subtraction algorithm for rapid recovery of Raman spectra that can substantially reduce the computation time. This algorithm is based on an improved Savitzky-Golay (SG) iterative smoothing method, which involves two key novel approaches: (a) the use of the Gauss-Seidel method and (b) the introduction of a relaxation factor into the iterative procedure. By applying a novel successive relaxation (SG-SR) iterative method to the relaxation factor, additional improvement in the convergence speed over the standard Savitzky-Golay procedure is realized. The proposed improved algorithm (the RIA-SG-SR algorithm), which uses SG-SR-based iteration instead of Savitzky-Golay iteration, has been optimized and validated with a mathematically simulated Raman spectrum, as well as experimentally measured Raman spectra from non-biological and biological samples. The method results in a significant reduction in computing cost while yielding consistent rejection of fluorescence and noise for spectra with low signal-to-fluorescence ratios and varied baselines. In the simulation, RIA-SG-SR achieved 1 order of magnitude improvement in iteration number and 2 orders of magnitude improvement in computation time compared with the range-independent background-subtraction algorithm (RIA). Furthermore the computation time of the experimentally measured raw Raman spectrum processing from skin tissue decreased from 6.72 to 0.094 s. In general, the processing of the SG-SR method can be conducted within dozens of milliseconds, which can provide a real-time procedure in practical situations.
International Nuclear Information System (INIS)
Duo, J. I.; Azmy, Y. Y.
2007-01-01
A new method, the Singular Characteristics Tracking algorithm, is developed to account for potential non-smoothness across the singular characteristics in the exact solution of the discrete ordinates approximation of the transport equation. Numerical results show improved rate of convergence of the solution to the discrete ordinates equations in two spatial dimensions with isotropic scattering using the proposed methodology. Unlike the standard Weighted Diamond Difference methods, the new algorithm achieves local convergence in the case of discontinuous angular flux along the singular characteristics. The method also significantly reduces the error for problems where the angular flux presents discontinuous spatial derivatives across these lines. For purposes of verifying the results, the Method of Manufactured Solutions is used to generate analytical reference solutions that permit estimating the local error in the numerical solution. (authors)
Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei
2011-04-01
An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.
International Nuclear Information System (INIS)
Wang Hu; Qi Guangcai; Li Shaohua; Li Changjian
2011-01-01
Because it is difficulty to accurately determine the extraction steam turbine enthalpy and the exhaust enthalpy, the calculated result from the conventional equivalent enthalpy drop method of PWR nuclear steam turbine is not accurate. This paper presents the improved algorithm on the equivalent enthalpy drop method of PWR nuclear steam turbine to solve this problem and takes the secondary circuit thermal system calculation of 1000 MW PWR as an example. The results show that, comparing with the design value, the error of actual thermal efficiency of the steam turbine cycle obtained by the improved algorithm is within the allowable range. Since the improved method is based on the isentropic expansion process, the extraction steam turbine enthalpy and the exhaust enthalpy can be determined accurately, which is more reasonable and accurate compared to the traditional equivalent enthalpy drop method. (authors)
The use of genetic algorithms with niching methods in nuclear reactor related problems
International Nuclear Information System (INIS)
Sacco, Wagner Figueiredo
2000-03-01
Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to test existing niching techniques and two methods introduced herein, bearing in mind their eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. Tests are performed using widely known test functions and their results show that the new methods are quite promising, specially in real world problems like the nuclear reactor core reload. (author)
Utilization of niching methods of genetic algorithms in nuclear reactor problems optimization
International Nuclear Information System (INIS)
Sacco, Wagner Figueiredo; Schirru, Roberto
2000-01-01
Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to test existing niching techniques and two methods introduced herein, bearing in mind their eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. Tests are performed using widely known test functions and their results show that the new methods are quite promising, specially in real world problems like the nuclear reactor core reload. (author)
Directory of Open Access Journals (Sweden)
Yeo Beom Yoon
2014-04-01
Full Text Available Windows are the primary aperture to introduce solar radiation to the interior space of a building. This experiment explores the use of EnergyPlus software for analyzing the illuminance level on the floor of a room with reference to its distance from the window. For this experiment, a double clear glass window has been used. The preliminary modelling in EnergyPlus showed a consistent result with the experimentally monitored data in real time. EnergyPlus has two mainly used daylighting algorithms: DElight method employing radiosity technique and Detailed method employing split-flux technique. Further analysis for illuminance using DElight and Detailed methods showed significant difference in the results. Finally, we compared the algorithms of the two analysis methods in EnergyPlus.
Advanced methods for the study of PWR cores
International Nuclear Information System (INIS)
Lambert, M.; Salvatores, St.; Ferrier, A.; Pelet, J.; Nicaise, N.; Pouliquen, J.Y.; Foret, F.; Chauliac, C.; Johner, J.; Cohen, Ch.
2003-01-01
This document gathers the transparencies presented at the 6. technical session of the French nuclear energy society (SFEN) in October 2003. The transparencies of the annual meeting are presented in the introductive part: 1 - status of the French nuclear park: nuclear energy results, management of an exceptional climatic situation: the heat wave of summer 2003 and the power generation (J.C. Barral); 2 - status of the research on controlled thermonuclear fusion (J. Johner). Then follows the technical session about the advanced methods for the study of PWR reactor cores: 1 - the evolution approach of study methodologies (M. Lambert, J. Pelet); 2 - the point of view of the nuclear safety authority (D. Brenot); 3 - the improved decoupled methodology for the steam pipe rupture (S. Salvatores, J.Y. Pouliquen); 4 - the MIR method for the pellet-clad interaction (renovated IPG methodology) (E. Baud, C. Royere); 5 - the improved fuel management (IFM) studies for Koeberg (C. Cohen); 6 - principle of the methods of accident study implemented for the European pressurized reactor (EPR) (F. Foret, A. Ferrier); 7 - accident studies with the EPR, steam pipe rupture (N. Nicaise, S. Salvatores); 8 - the co-development platform, a new generation of software tools for the new methodologies (C. Chauliac). (J.S.)
Directory of Open Access Journals (Sweden)
Aliasghar Baziar
2015-03-01
Full Text Available Abstract In order to handle large scale problems this study has used shuffled frog leaping algorithm. This algorithm is an optimization method based on natural memetics that uses a new two-phase modification to it to have a better search in the problem space. The suggested algorithm is evaluated by comparing to some well known algorithms using several benchmark optimization problems. The simulation results have clearly shown the superiority of this algorithm over other well-known methods in the area.
Zhenying, Xu; Jiandong, Zhu; Qi, Zhang; Yamba, Philip
2018-06-01
Metallographic microscopy shows that the vast majority of metal materials are composed of many small grains; the grain size of a metal is important for determining the tensile strength, toughness, plasticity, and other mechanical properties. In order to quantitatively evaluate grain size in metals, grain boundaries must be identified in metallographic images. Based on the phenomenon of grain boundary blurring or disconnection in metallographic images, this study develops an algorithm based on regional separation for automatically extracting grain boundaries by an improved mean shift method. Experimental observation shows that the grain boundaries obtained by the proposed algorithm are highly complete and accurate. This research has practical value because the proposed algorithm is suitable for grain boundary extraction from most metallographic images.
A Derandomized Algorithm for RP-ADMM with Symmetric Gauss-Seidel Method
Xu, Jinchao; Xu, Kailai; Ye, Yinyu
2017-01-01
For multi-block alternating direction method of multipliers(ADMM), where the objective function can be decomposed into multiple block components, we show that with block symmetric Gauss-Seidel iteration, the algorithm will converge quickly. The method will apply a block symmetric Gauss-Seidel iteration in the primal update and a linear correction that can be derived in view of Richard iteration. We also establish the linear convergence rate for linear systems.
International Nuclear Information System (INIS)
Kutlakhmedov, Y.; Zezina, N.; Micheev, A.; Jouve, A.; Perepelyatnikov, G.
1996-01-01
This paper represents our data of comparative analysis of efficacy of different countermeasures in decontamination of soils in Ukraine in total and in case study Milyachi. On this base it was created of optimal algorithm of strategy of decontamination of soils which is based on method of usage turf harvester for unploughed soils and method of phytodesactivation for ploughed soils of Ukraine after Chernobyl accident
Directory of Open Access Journals (Sweden)
B. Y. Qu
2017-01-01
Full Text Available Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.
Movia, A.; Beinat, A.; Crosilla, F.
2015-04-01
The recognition of vegetation by the analysis of very high resolution (VHR) aerial images provides meaningful information about environmental features; nevertheless, VHR images frequently contain shadows that generate significant problems for the classification of the image components and for the extraction of the needed information. The aim of this research is to classify, from VHR aerial images, vegetation involved in the balance process of the environmental biochemical cycle, and to discriminate it with respect to urban and agricultural features. Three classification algorithms have been experimented in order to better recognize vegetation, and compared to NDVI index; unfortunately all these methods are conditioned by the presence of shadows on the images. Literature presents several algorithms to detect and remove shadows in the scene: most of them are based on the RGB to HSI transformations. In this work some of them have been implemented and compared with one based on RGB bands. Successively, in order to remove shadows and restore brightness on the images, some innovative algorithms, based on Procrustes theory, have been implemented and applied. Among these, we evaluate the capability of the so called "not-centered oblique Procrustes" and "anisotropic Procrustes" methods to efficiently restore brightness with respect to a linear correlation correction based on the Cholesky decomposition. Some experimental results obtained by different classification methods after shadows removal carried out with the innovative algorithms are presented and discussed.
Skinnider, Michael A; Dejong, Chris A; Franczak, Brian C; McNicholas, Paul D; Magarvey, Nathan A
2017-08-16
Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics.
Directory of Open Access Journals (Sweden)
Wenyu Zhang
2016-01-01
Full Text Available With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition.
A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms.
Caldas, Rafael; Mundt, Marion; Potthast, Wolfgang; Buarque de Lima Neto, Fernando; Markert, Bernd
2017-09-01
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects. Copyright © 2017 Elsevier B.V. All rights reserved.
Schönberger, Jörn
2005-01-01
The modern freight carrier business requires a sophisticated automatic decision support in order to ensure the efficiency and reliability and therefore the survival of transport service providers. This book addresses these challenges and provides generic decision models for the short-term operations planning as well as advanced metaheuristics to obtain efficient operation plans. After a thorough analysis of the operations planning in the freight carrier business, decision models are derived. Their suitability is proven within a large number of numerical experiments, in which a new class of hybrid genetic search approaches demonstrate their appropriateness.
Advanced display object selection methods for enhancing user-computer productivity
Osga, Glenn A.
1993-01-01
The User-Interface Technology Branch at NCCOSC RDT&E Division has been conducting a series of studies to address the suitability of commercial off-the-shelf (COTS) graphic user-interface (GUI) methods for efficiency and performance in critical naval combat systems. This paper presents an advanced selection algorithm and method developed to increase user performance when making selections on tactical displays. The method has also been applied with considerable success to a variety of cursor and pointing tasks. Typical GUI's allow user selection by: (1) moving a cursor with a pointing device such as a mouse, trackball, joystick, touchscreen; and (2) placing the cursor on the object. Examples of GUI objects are the buttons, icons, folders, scroll bars, etc. used in many personal computer and workstation applications. This paper presents an improved method of selection and the theoretical basis for the significant performance gains achieved with various input devices tested. The method is applicable to all GUI styles and display sizes, and is particularly useful for selections on small screens such as notebook computers. Considering the amount of work-hours spent pointing and clicking across all styles of available graphic user-interfaces, the cost/benefit in applying this method to graphic user-interfaces is substantial, with the potential for increasing productivity across thousands of users and applications.
A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm.
Mascolo-Fortin, Julia; Matenine, Dmitri; Archambault, Louis; Després, Philippe
2018-01-01
Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time. The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV). Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs. All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality. The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.
Quantitative Imaging Biomarkers: A Review of Statistical Methods for Computer Algorithm Comparisons
2014-01-01
Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research. PMID:24919829
Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons.
Obuchowski, Nancy A; Reeves, Anthony P; Huang, Erich P; Wang, Xiao-Feng; Buckler, Andrew J; Kim, Hyun J Grace; Barnhart, Huiman X; Jackson, Edward F; Giger, Maryellen L; Pennello, Gene; Toledano, Alicia Y; Kalpathy-Cramer, Jayashree; Apanasovich, Tatiyana V; Kinahan, Paul E; Myers, Kyle J; Goldgof, Dmitry B; Barboriak, Daniel P; Gillies, Robert J; Schwartz, Lawrence H; Sullivan, Daniel C
2015-02-01
Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Directory of Open Access Journals (Sweden)
Hasmukh J Prajapati
Full Text Available To develop the treatment algorithm from multivariate survival analyses (MVA in patients with Barcelona clinic liver cancer (BCLC C (advanced Hepatocellular carcinoma (HCC patients treated with Trans-arterial Chemoembolization (TACE.Consecutive unresectable and non-tranplantable patients with advanced HCC, who received DEB TACE were studied. A total of 238 patients (mean age, 62.4yrs was included in the study. Survivals were analyzed according to different parameters from the time of the 1st DEB TACE. Kaplan Meier and Cox Proportional Hazard model were used for survival analysis. The SS was constructed from MVA and named BCLC C HCC Prognostic (BCHP staging system (SS.Overall median survival (OS was 16.2 months. In HCC patients with venous thrombosis (VT of large vein [main portal vein (PV, right or left PV, hepatic vein, inferior vena cava] (22.7% versus small vein (segmental/subsegmental PV (9.7% versus no VT had OSs of 6.4 months versus 20 months versus 22.8 months respectively (p<0.001. On MVA, the significant independent prognostic factors (PFs of survival were CP class, eastern cooperative oncology group (ECOG performance status (PS, single HCC<5 cm, site of VT, metastases, serum creatinine and serum alpha-feto protein. Based on these PFs, the BCHP staging system was constructed. The OSs of stages I, II and III were 28.4 months, 11.8 months and 2.4 months accordingly (p<0.001. The treatment plan was proposed according to the different stages.On MVA of patients with advanced HCC treated with TACE, significant independent prognostic factors (PFs of survival were CP class, ECOG PS, single HCC<5 cm or others, site of VT, metastases, serum creatinine and serum alpha-feto protein. New BCHP SS was proposed based on MVA data to identify the suitable advanced HCC patients for TACE treatments.
Susyanto, N.; Veldhuis, R.N.J.; Spreeuwers, L.J.; Klaassen, C.A.J.; Fierrez, J.; Li, S.Z.; Ross, A.; Veldhuis, R.; Alonso-Fernandez, F.; Bigun, J.
2016-01-01
We propose a new method for combining multi-algorithm score-based face recognition systems, which we call the two-step calibration method. Typically, algorithms for face recognition systems produce dependent scores. The two-step method is based on parametric copulas to handle this dependence. Its
An improved Four-Russians method and sparsified Four-Russians algorithm for RNA folding.
Frid, Yelena; Gusfield, Dan
2016-01-01
The basic RNA secondary structure prediction problem or single sequence folding problem (SSF) was solved 35 years ago by a now well-known [Formula: see text]-time dynamic programming method. Recently three methodologies-Valiant, Four-Russians, and Sparsification-have been applied to speedup RNA secondary structure prediction. The sparsification method exploits two properties of the input: the number of subsequence Z with the endpoints belonging to the optimal folding set and the maximum number base-pairs L. These sparsity properties satisfy [Formula: see text] and [Formula: see text], and the method reduces the algorithmic running time to O(LZ). While the Four-Russians method utilizes tabling partial results. In this paper, we explore three different algorithmic speedups. We first expand the reformulate the single sequence folding Four-Russians [Formula: see text]-time algorithm, to utilize an on-demand lookup table. Second, we create a framework that combines the fastest Sparsification and new fastest on-demand Four-Russians methods. This combined method has worst-case running time of [Formula: see text], where [Formula: see text] and [Formula: see text]. Third we update the Four-Russians formulation to achieve an on-demand [Formula: see text]-time parallel algorithm. This then leads to an asymptotic speedup of [Formula: see text] where [Formula: see text] and [Formula: see text] the number of subsequence with the endpoint j belonging to the optimal folding set. The on-demand formulation not only removes all extraneous computation and allows us to incorporate more realistic scoring schemes, but leads us to take advantage of the sparsity properties. Through asymptotic analysis and empirical testing on the base-pair maximization variant and a more biologically informative scoring scheme, we show that this Sparse Four-Russians framework is able to achieve a speedup on every problem instance, that is asymptotically never worse, and empirically better than achieved by
Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study.
Grossi, Enzo; Olivieri, Chiara; Buscema, Massimo
2017-04-01
Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease. The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones. Fifteen definite ASD subjects (13 males; 2 females; age range 7-14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7-12; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers. The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age
A human-machine cooperation route planning method based on improved A* algorithm
Zhang, Zhengsheng; Cai, Chao
2011-12-01
To avoid the limitation of common route planning method to blindly pursue higher Machine Intelligence and autoimmunization, this paper presents a human-machine cooperation route planning method. The proposed method includes a new A* path searing strategy based on dynamic heuristic searching and a human cooperated decision strategy to prune searching area. It can overcome the shortage of A* algorithm to fall into a local long term searching. Experiments showed that this method can quickly plan a feasible route to meet the macro-policy thinking.
International Nuclear Information System (INIS)
Zhao Yi; Small, Michael; Coward, David; Howell, Eric; Zhao Chunnong; Ju Li; Blair, David
2006-01-01
We describe the application of complexity estimation and the surrogate data method to identify deterministic dynamics in simulated gravitational wave (GW) data contaminated with white and coloured noises. The surrogate method uses algorithmic complexity as a discriminating statistic to decide if noisy data contain a statistically significant level of deterministic dynamics (the GW signal). The results illustrate that the complexity method is sensitive to a small amplitude simulated GW background (SNR down to 0.08 for white noise and 0.05 for coloured noise) and is also more robust than commonly used linear methods (autocorrelation or Fourier analysis)
Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm
Karaca, Yeliz; Cattani, Carlo
Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.
An effective trust-based recommendation method using a novel graph clustering algorithm
Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin
2015-10-01
Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.
Advanced density matrix renormalization group method for nuclear structure calculations
Czech Academy of Sciences Publication Activity Database
Legeza, Ö.; Veis, Libor; Poves, A.; Dukelsky, J.
2015-01-01
Roč. 92, č. 5 (2015), 051303 ISSN 0556-2813 Institutional support: RVO:61388955 Keywords : INITIO QUANTUM- CHEMISTRY * GROUP ALGORITHM * SHELL-MODEL Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.146, year: 2015
Berg, W. K.
2016-12-01
The Global Precipitation Mission (GPM) Core Observatory, which was launched in February of 2014, provides a number of advances for satellite monitoring of precipitation including a dual-frequency radar, high frequency channels on the GPM Microwave Imager (GMI), and coverage over middle and high latitudes. The GPM concept, however, is about producing unified precipitation retrievals from a constellation of microwave radiometers to provide approximately 3-hourly global sampling. This involves intercalibration of the input brightness temperatures from the constellation radiometers, development of an apriori precipitation database using observations from the state-of-the-art GPM radiometer and radars, and accounting for sensor differences in the retrieval algorithm in a physically-consistent way. Efforts by the GPM inter-satellite calibration working group, or XCAL team, and the radiometer algorithm team to create unified precipitation retrievals from the GPM radiometer constellation were fully implemented into the current version 4 GPM precipitation products. These include precipitation estimates from a total of seven conical-scanning and six cross-track scanning radiometers as well as high spatial and temporal resolution global level 3 gridded products. Work is now underway to extend this unified constellation-based approach to the combined TRMM/GPM data record starting in late 1997. The goal is to create a long-term global precipitation dataset employing these state-of-the-art calibration and retrieval algorithm approaches. This new long-term global precipitation dataset will incorporate the physics provided by the combined GPM GMI and DPR sensors into the apriori database, extend prior TRMM constellation observations to high latitudes, and expand the available TRMM precipitation data to the full constellation of available conical and cross-track scanning radiometers. This combined TRMM/GPM precipitation data record will thus provide a high-quality high
An evolutionary method for synthesizing technological planning and architectural advance
Cole, Bjorn Forstrom
In the development of systems with ever-increasing performance and/or decreasing drawbacks, there inevitably comes a point where more progress is available by shifting to a new set of principles of use. This shift marks a change in architecture, such as between the piston-driven propeller and the jet engine. The shift also often involves an abandonment of previous competencies that have been developed with great effort, and so a foreknowledge of these shifts can be advantageous. A further motivation for this work is the consideration of the Micro Autonomous Systems and Technology (MAST) project, which aims to develop very small (thesis provide context and a philosophical background to the studies and research that was conducted. In particular, the idea that technology progresses in a fundamentally gradual way is developed and supported with previous historical research. The import of this is that the future can to some degree be predicted by the past, provided that the appropriate technological antecedents are accounted for in developing the projection. The third chapter of the thesis compiles a series of observations and philosophical considerations into a series of research questions. Some research questions are then answered with further thought, observation, and reading, leading to conjectures on the problem. The remainder require some form of experimentation, and so are used to formulate hypotheses. Falsifiability conditions are then generated from those hypotheses, and used to get the development of experiments to be performed, in this case on a computer upon various conditions of use of a genetic algorithm. The fourth chapter of the thesis walks through the formulation of a method to attack the problem of strategically choosing an architecture. This method is designed to find the optimum architecture under multiple conditions, which is required for the ability to play the "what if" games typically undertaken in strategic situations. The chapter walks through
Combination of Rivest-Shamir-Adleman Algorithm and End of File Method for Data Security
Rachmawati, Dian; Amalia, Amalia; Elviwani
2018-03-01
Data security is one of the crucial issues in the delivery of information. One of the ways which used to secure the data is by encoding it into something else that is not comprehensible by human beings by using some crypto graphical techniques. The Rivest-Shamir-Adleman (RSA) cryptographic algorithm has been proven robust to secure messages. Since this algorithm uses two different keys (i.e., public key and private key) at the time of encryption and decryption, it is classified as asymmetric cryptography algorithm. Steganography is a method that is used to secure a message by inserting the bits of the message into a larger media such as an image. One of the known steganography methods is End of File (EoF). In this research, the cipher text resulted from the RSA algorithm is compiled into an array form and appended to the end of the image. The result of the EoF is the image which has a line with black gradations under it. This line contains the secret message. This combination of cryptography and steganography in securing the message is expected to increase the security of the message, since the message encryption technique (RSA) is mixed with the data hiding technique (EoF).
Liu, Ke; Chen, Xiaojing; Li, Limin; Chen, Huiling; Ruan, Xiukai; Liu, Wenbin
2015-02-09
The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. Copyright © 2014 Elsevier B.V. All rights reserved.
Advanced order management in ERM systems: the tic-tac-toe algorithm
Badell, Mariana; Fernandez, Elena; Puigjaner, Luis
2000-10-01
The concept behind improved enterprise resource planning systems (ERP) systems is the overall integration of the whole enterprise functionality into the management systems through financial links. Converting current software into real management decision tools requires crucial changes in the current approach to ERP systems. This evolution must be able to incorporate the technological achievements both properly and in time. The exploitation phase of plants needs an open web-based environment for collaborative business-engineering with on-line schedulers. Today's short lifecycles of products and processes require sharp and finely tuned management actions that must be guided by scheduling tools. Additionally, such actions must be able to keep track of money movements related to supply chain events. Thus, the necessary outputs require financial-production integration at the scheduling level as proposed in the new approach of enterprise management systems (ERM). Within this framework, the economical analysis of the due date policy and its optimization become essential to manage dynamically realistic and optimal delivery dates with price-time trade-off during the marketing activities. In this work we propose a scheduling tool with web-based interface conducted by autonomous agents when precise economic information relative to plant and business actions and their effects are provided. It aims to attain a better arrangement of the marketing and production events in order to face the bid/bargain process during e-commerce. Additionally, management systems require real time execution and an efficient transaction-oriented approach capable to dynamically adopt realistic and optimal actions to support marketing management. To this end the TicTacToe algorithm provides sequence optimization with acceptable tolerances in realistic time.
Advances in Time Estimation Methods for Molecular Data.
Kumar, Sudhir; Hedges, S Blair
2016-04-01
Molecular dating has become central to placing a temporal dimension on the tree of life. Methods for estimating divergence times have been developed for over 50 years, beginning with the proposal of molecular clock in 1962. We categorize the chronological development of these methods into four generations based on the timing of their origin. In the first generation approaches (1960s-1980s), a strict molecular clock was assumed to date divergences. In the second generation approaches (1990s), the equality of evolutionary rates between species was first tested and then a strict molecular clock applied to estimate divergence times. The third generation approaches (since ∼2000) account for differences in evolutionary rates across the tree by using a statistical model, obviating the need to assume a clock or to test the equality of evolutionary rates among species. Bayesian methods in the third generation require a specific or uniform prior on the speciation-process and enable the inclusion of uncertainty in clock calibrations. The fourth generation approaches (since 2012) allow rates to vary from branch to branch, but do not need prior selection of a statistical model to describe the rate variation or the specification of speciation model. With high accuracy, comparable to Bayesian approaches, and speeds that are orders of magnitude faster, fourth generation methods are able to produce reliable timetrees of thousands of species using genome scale data. We found that early time estimates from second generation studies are similar to those of third and fourth generation studies, indicating that methodological advances have not fundamentally altered the timetree of life, but rather have facilitated time estimation by enabling the inclusion of more species. Nonetheless, we feel an urgent need for testing the accuracy and precision of third and fourth generation methods, including their robustness to misspecification of priors in the analysis of large phylogenies and data
Advances in Airborne and Ground Geophysical Methods for Uranium Exploration
International Nuclear Information System (INIS)
2013-01-01
through the use of effective exploration techniques. Geophysical methods with the capability of mapping surface and subsurface parameters in relation to uranium deposition and accumulation are proving to be vital components of current exploration efforts around the world. There is continuous development and improvement of technical and scientific disciplines using measuring instruments and spatially referenced data processing techniques. Newly designed geophysical instruments and their applications in uranium exploration are contributing to an increased probability of successful discoveries. Dissemination of information on advances in geophysical techniques encourages new strategies and promotes new approaches toward uranium exploration. Meetings and conferences organized by the IAEA, collecting the experience of participating countries, as well as its publications and the International Nuclear Information System, play an important role in the dissemination of knowledge of all aspects of the nuclear fuel cycle. The purpose of this report is to highlight advances in airborne and ground geophysical techniques, succinctly describing modern geophysical methods and demonstrating the application of techniques through examples. The report also provides some basic concepts of radioactivity, nuclear radiation and interaction with matter.
Microwave imaging of dielectric cylinder using level set method and conjugate gradient algorithm
International Nuclear Information System (INIS)
Grayaa, K.; Bouzidi, A.; Aguili, T.
2011-01-01
In this paper, we propose a computational method for microwave imaging cylinder and dielectric object, based on combining level set technique and the conjugate gradient algorithm. By measuring the scattered field, we tried to retrieve the shape, localisation and the permittivity of the object. The forward problem is solved by the moment method, while the inverse problem is reformulate in an optimization one and is solved by the proposed scheme. It found that the proposed method is able to give good reconstruction quality in terms of the reconstructed shape and permittivity.
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xuyang Wang
2012-05-01
Full Text Available A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots. To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion.
State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xuyang Wang
2008-11-01
Full Text Available A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots.To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion.
Nofriansyah, Dicky; Defit, Sarjon; Nurcahyo, Gunadi W.; Ganefri, G.; Ridwan, R.; Saleh Ahmar, Ansari; Rahim, Robbi
2018-01-01
Cybercrime is one of the most serious threats. Efforts are made to reduce the number of cybercrime is to find new techniques in securing data such as Cryptography, Steganography and Watermarking combination. Cryptography and Steganography is a growing data security science. A combination of Cryptography and Steganography is one effort to improve data integrity. New techniques are used by combining several algorithms, one of which is the incorporation of hill cipher method and Morse code. Morse code is one of the communication codes used in the Scouting field. This code consists of dots and lines. This is a new modern and classic concept to maintain data integrity. The result of the combination of these three methods is expected to generate new algorithms to improve the security of the data, especially images.
Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network
International Nuclear Information System (INIS)
Wang Xiaojia; Mao Qirong; Zhan Yongzhao
2008-01-01
There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions. The experiments show that this method can improve the recognition rate and the time of feature extraction
Energy Technology Data Exchange (ETDEWEB)
Chen, Zaigao; Wang, Jianguo [Key Laboratory for Physical Electronics and Devices of the Ministry of Education, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China); Northwest Institute of Nuclear Technology, P.O. Box 69-12, Xi' an, Shaanxi 710024 (China); Wang, Yue; Qiao, Hailiang; Zhang, Dianhui [Northwest Institute of Nuclear Technology, P.O. Box 69-12, Xi' an, Shaanxi 710024 (China); Guo, Weijie [Key Laboratory for Physical Electronics and Devices of the Ministry of Education, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China)
2013-11-15
Optimal design method of high-power microwave source using particle simulation and parallel genetic algorithms is presented in this paper. The output power, simulated by the fully electromagnetic particle simulation code UNIPIC, of the high-power microwave device is given as the fitness function, and the float-encoding genetic algorithms are used to optimize the high-power microwave devices. Using this method, we encode the heights of non-uniform slow wave structure in the relativistic backward wave oscillators (RBWO), and optimize the parameters on massively parallel processors. Simulation results demonstrate that we can obtain the optimal parameters of non-uniform slow wave structure in the RBWO, and the output microwave power enhances 52.6% after the device is optimized.
A New Waveform Signal Processing Method Based on Adaptive Clustering-Genetic Algorithms
International Nuclear Information System (INIS)
Noha Shaaban; Fukuzo Masuda; Hidetsugu Morota
2006-01-01
We present a fast digital signal processing method for numerical analysis of individual pulses from CdZnTe compound semiconductor detectors. Using Maxi-Mini Distance Algorithm and Genetic Algorithms based discrimination technique. A parametric approach has been used for classifying the discriminated waveforms into a set of clusters each has a similar signal shape with a corresponding pulse height spectrum. A corrected total pulse height spectrum was obtained by applying a normalization factor for the full energy peak for each cluster with a highly improvements in the energy spectrum characteristics. This method applied successfully for both simulated and real measured data, it can be applied to any detector suffers from signal shape variation. (authors)
PMU Placement Methods in Power Systems based on Evolutionary Algorithms and GPS Receiver
Directory of Open Access Journals (Sweden)
M. R. Mosavi
2013-06-01
Full Text Available In this paper, optimal placement of Phasor Measurement Unit (PMU using Global Positioning System (GPS is discussed. Ant Colony Optimization (ACO, Simulated Annealing (SA, Particle Swarm Optimization (PSO and Genetic Algorithm (GA are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modified algorithm overcomes the ACO in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. We also compare this simulink with SA, PSO and GA that to find capability of ACO in the search of optimal solution. The fitness function includes observability, redundancy and number of PMU. Logarithmic Least Square Method (LLSM is used to calculate the weights of fitness function. The suggested optimization method is applied in 30-bus IEEE system and the simulation results show modified ACO find results better than PSO and SA, but same result with GA.
Directory of Open Access Journals (Sweden)
M. Soleimanpour-moghadam
2013-06-01
Full Text Available This paper devotes itself to the study of secret message delivery using cover image and introduces a novel steganographic technique based on genetic algorithm to find a near-optimum structure for the pair-wise least-significant-bit (LSB matching scheme. A survey of the related literatures shows that the LSB matching method developed by Mielikainen, employs a binary function to reduce the number of changes of LSB values. This method verifiably reduces the probability of detection and also improves the visual quality of stego images. So, our proposal draws on the Mielikainen's technique to present an enhanced dual-state scoring model, structured upon genetic algorithm which assesses the performance of different orders for LSB matching and searches for a near-optimum solution among all the permutation orders. Experimental results confirm superiority of the new approach compared to the Mielikainen’s pair-wise LSB matching scheme.
Approximate k-NN delta test minimization method using genetic algorithms: Application to time series
Mateo, F; Gadea, Rafael; Sovilj, Dusan
2010-01-01
In many real world problems, the existence of irrelevant input variables (features) hinders the predictive quality of the models used to estimate the output variables. In particular, time series prediction often involves building large regressors of artificial variables that can contain irrelevant or misleading information. Many techniques have arisen to confront the problem of accurate variable selection, including both local and global search strategies. This paper presents a method based on genetic algorithms that intends to find a global optimum set of input variables that minimize the Delta Test criterion. The execution speed has been enhanced by substituting the exact nearest neighbor computation by its approximate version. The problems of scaling and projection of variables have been addressed. The developed method works in conjunction with MATLAB's Genetic Algorithm and Direct Search Toolbox. The goodness of the proposed methodology has been evaluated on several popular time series examples, and also ...
Advanced methods of quality control in nuclear fuel fabrication
International Nuclear Information System (INIS)
Onoufriev, Vladimir
2004-01-01
Under pressure of current economic and electricity market situation utilities implement more demanding fuel utilization schemes including higher burn ups and thermal rates, longer fuel cycles and usage of Mo fuel. Therefore, fuel vendors have recently initiated new R and D programmes aimed at improving fuel quality, design and materials to produce robust and reliable fuel. In the beginning of commercial fuel fabrication, emphasis was given to advancements in Quality Control/Quality Assurance related mainly to product itself. During recent years, emphasis was transferred to improvements in process control and to implementation of overall Total Quality Management (TQM) programmes. In the area of fuel quality control, statistical control methods are now widely implemented replacing 100% inspection. This evolution, some practical examples and IAEA activities are described in the paper. The paper presents major findings of the latest IAEA Technical Meetings (TMs) and training courses in the area with emphasis on information received at the TM and training course held in 1999 and other latest publications to provide an overview of new developments in process/quality control, their implementation and results obtained including new approaches to QC
Underwater Photosynthesis of Submerged Plants – Recent Advances and Methods
Pedersen, Ole; Colmer, Timothy D.; Sand-Jensen, Kaj
2013-01-01
We describe the general background and the recent advances in research on underwater photosynthesis of leaf segments, whole communities, and plant dominated aquatic ecosystems and present contemporary methods tailor made to quantify photosynthesis and carbon fixation under water. The majority of studies of aquatic photosynthesis have been carried out with detached leaves or thalli and this selectiveness influences the perception of the regulation of aquatic photosynthesis. We thus recommend assessing the influence of inorganic carbon and temperature on natural aquatic communities of variable density in addition to studying detached leaves in the scenarios of rising CO2 and temperature. Moreover, a growing number of researchers are interested in tolerance of terrestrial plants during flooding as torrential rains sometimes result in overland floods that inundate terrestrial plants. We propose to undertake studies to elucidate the importance of leaf acclimation of terrestrial plants to facilitate gas exchange and light utilization under water as these acclimations influence underwater photosynthesis as well as internal aeration of plant tissues during submergence. PMID:23734154
Striking against bioterrorism with advanced proteomics and reference methods.
Armengaud, Jean
2017-01-01
The intentional use by terrorists of biological toxins as weapons has been of great concern for many years. Among the numerous toxins produced by plants, animals, algae, fungi, and bacteria, ricin is one of the most scrutinized by the media because it has already been used in biocrimes and acts of bioterrorism. Improving the analytical toolbox of national authorities to monitor these potential bioweapons all at once is of the utmost interest. MS/MS allows their absolute quantitation and exhibits advantageous sensitivity, discriminative power, multiplexing possibilities, and speed. In this issue of Proteomics, Gilquin et al. (Proteomics 2017, 17, 1600357) present a robust multiplex assay to quantify a set of eight toxins in the presence of a complex food matrix. This MS/MS reference method is based on scheduled SRM and high-quality standards consisting of isotopically labeled versions of these toxins. Their results demonstrate robust reliability based on rather loose scheduling of SRM transitions and good sensitivity for the eight toxins, lower than their oral median lethal doses. In the face of an increased threat from terrorism, relevant reference assays based on advanced proteomics and high-quality companion toxin standards are reliable and firm answers. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Advanced algorithms for ionosphere modelling in GNSS applications within AUDITOR project
Goss, Andreas; Erdogan, Eren; Schmidt, Michael; Garcia-Rigo, Alberto; Hernandez-Pajares, Manuel; Lyu, Haixia; Nohutcu, Metin
2017-04-01
The H2020 project AUDITOR of the European Union started on January 1st 2016, with the participation of several European institutions and universities. The goal of the project is the implementation of a novel precise positioning technique, based on augmentation data in a customized GNSS receiver. Therefore more sophisticated ionospheric models have to be developed and implemented to increase the accuracy in real-time at the user side. Since the service should be available for the public, we use public data from GNSS networks (e.g. IGS, EUREF). The contributions of DGFI-TUM and UPC are focusing on the development of high accuracy GNSS algorithms to provide enhanced ionospheric corrections. This includes two major issues: 1. The existing mapping function to convert the slant total electron content (STEC) measurable by GNSS into the vertical total electron content (VTEC) is based on a so called single layer model (SLM), where all electrons are concentrated on an infinitesimal thin layer with fixed height (between 350 and 450 kilometers). This quantity is called the effective ionospheric height (EIH). An improvement of the mapping function shall be achieved by estimating more realistic numerical values for the EIH by means of a voxel-based tomographic model (TOMION). 2. The ionospheric observations are distributed rather unevenly over the globe and within specific regions. This inhomogeneous distribution is handled by data adaptive B-Spline approaches, with polynomial and trigonometric functions used for the latitude and longitude representations to provide high resolution VTEC maps for global and regional purposes. A Kalman filter is used as sequential estimator. The unknown parameters of the filter state vector are composed of the B-spline coefficients as well as the satellite and receiver DCBs. The resulting high accuracy ionosphere products will be disseminated to the users via downlink from a dedicated server to a receiver site. In this context, an appropriate
Advanced Monte Carlo methods for thermal radiation transport
Wollaber, Allan B.
During the past 35 years, the Implicit Monte Carlo (IMC) method proposed by Fleck and Cummings has been the standard Monte Carlo approach to solving the thermal radiative transfer (TRT) equations. However, the IMC equations are known to have accuracy limitations that can produce unphysical solutions. In this thesis, we explicitly provide the IMC equations with a Monte Carlo interpretation by including particle weight as one of its arguments. We also develop and test a stability theory for the 1-D, gray IMC equations applied to a nonlinear problem. We demonstrate that the worst case occurs for 0-D problems, and we extend the results to a stability algorithm that may be used for general linearizations of the TRT equations. We derive gray, Quasidiffusion equations that may be deterministically solved in conjunction with IMC to obtain an inexpensive, accurate estimate of the temperature at the end of the time step. We then define an average temperature T* to evaluate the temperature-dependent problem data in IMC, and we demonstrate that using T* is more accurate than using the (traditional) beginning-of-time-step temperature. We also propose an accuracy enhancement to the IMC equations: the use of a time-dependent "Fleck factor". This Fleck factor can be considered an automatic tuning of the traditionally defined user parameter alpha, which generally provides more accurate solutions at an increased cost relative to traditional IMC. We also introduce a global weight window that is proportional to the forward scalar intensity calculated by the Quasidiffusion method. This weight window improves the efficiency of the IMC calculation while conserving energy. All of the proposed enhancements are tested in 1-D gray and frequency-dependent problems. These enhancements do not unconditionally eliminate the unphysical behavior that can be seen in the IMC calculations. However, for fixed spatial and temporal grids, they suppress them and clearly work to make the solution more
Research on GPU-accelerated algorithm in 3D finite difference neutron diffusion calculation method
International Nuclear Information System (INIS)
Xu Qi; Yu Ganglin; Wang Kan; Sun Jialong
2014-01-01
In this paper, the adaptability of the neutron diffusion numerical algorithm on GPUs was studied, and a GPU-accelerated multi-group 3D neutron diffusion code based on finite difference method was developed. The IAEA 3D PWR benchmark problem was calculated in the numerical test. The results demonstrate both high efficiency and adequate accuracy of the GPU implementation for neutron diffusion equation. (authors)
Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?
Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W
2018-03-01
The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.
International Nuclear Information System (INIS)
Kirk, B.L.; Azmy, Y.Y.
1992-01-01
In this paper the one-group, steady-state neutron diffusion equation in two-dimensional Cartesian geometry is solved using the nodal integral method. The discrete variable equations comprise loosely coupled sets of equations representing the nodal balance of neutrons, as well as neutron current continuity along rows or columns of computational cells. An iterative algorithm that is more suitable for solving large problems concurrently is derived based on the decomposition of the spatial domain and is accelerated using successive overrelaxation. This algorithm is very well suited for parallel computers, especially since the spatial domain decomposition occurs naturally, so that the number of iterations required for convergence does not depend on the number of processors participating in the calculation. Implementation of the authors' algorithm on the Intel iPSC/2 hypercube and Sequent Balance 8000 parallel computer is presented, and measured speedup and efficiency for test problems are reported. The results suggest that the efficiency of the hypercube quickly deteriorates when many processors are used, while the Sequent Balance retains very high efficiency for a comparable number of participating processors. This leads to the conjecture that message-passing parallel computers are not as well suited for this algorithm as shared-memory machines
Projected role of advanced computational aerodynamic methods at the Lockheed-Georgia company
Lores, M. E.
1978-01-01
Experience with advanced computational methods being used at the Lockheed-Georgia Company to aid in the evaluation and design of new and modified aircraft indicates that large and specialized computers will be needed to make advanced three-dimensional viscous aerodynamic computations practical. The Numerical Aerodynamic Simulation Facility should be used to provide a tool for designing better aerospace vehicles while at the same time reducing development costs by performing computations using Navier-Stokes equations solution algorithms and permitting less sophisticated but nevertheless complex calculations to be made efficiently. Configuration definition procedures and data output formats can probably best be defined in cooperation with industry, therefore, the computer should handle many remote terminals efficiently. The capability of transferring data to and from other computers needs to be provided. Because of the significant amount of input and output associated with 3-D viscous flow calculations and because of the exceedingly fast computation speed envisioned for the computer, special attention should be paid to providing rapid, diversified, and efficient input and output.
Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna
2017-12-01
The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.
Burger, Jessica L.
2015-07-16
© This article not subject to U.S. Copyright. Published 2015 by the American Chemical Society. Incremental but fundamental changes are currently being made to fuel composition and combustion strategies to diversify energy feedstocks, decrease pollution, and increase engine efficiency. The increase in parameter space (by having many variables in play simultaneously) makes it difficult at best to propose strategic changes to engine and fuel design by use of conventional build-and-test methodology. To make changes in the most time- and cost-effective manner, it is imperative that new computational tools and surrogate fuels are developed. Currently, sets of fuels are being characterized by industry groups, such as the Coordinating Research Council (CRC) and other entities, so that researchers in different laboratories have access to fuels with consistent properties. In this work, six gasolines (FACE A, C, F, G, I, and J) are characterized by the advanced distillation curve (ADC) method to determine the composition and enthalpy of combustion in various distillate volume fractions. Tracking the composition and enthalpy of distillate fractions provides valuable information for determining structure property relationships, and moreover, it provides the basis for the development of equations of state that can describe the thermodynamic properties of these complex mixtures and lead to development of surrogate fuels composed of major hydrocarbon classes found in target fuels.
Meerts, W.L.; Schmitt, M.
2006-01-01
This paper describes a numerical technique that has recently been developed to automatically assign and fit high-resolution spectra. The method makes use of genetic algorithms (GA). The current algorithm is compared with previously used analysing methods. The general features of the GA and its
A scalable method for parallelizing sampling-based motion planning algorithms
Jacobs, Sam Ade; Manavi, Kasra; Burgos, Juan; Denny, Jory; Thomas, Shawna; Amato, Nancy M.
2012-01-01
This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.
A scalable method for parallelizing sampling-based motion planning algorithms
Jacobs, Sam Ade
2012-05-01
This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.
Specific algorithm method of scoring the Clock Drawing Test applied in cognitively normal elderly
Directory of Open Access Journals (Sweden)
Liana Chaves Mendes-Santos
Full Text Available The Clock Drawing Test (CDT is an inexpensive, fast and easily administered measure of cognitive function, especially in the elderly. This instrument is a popular clinical tool widely used in screening for cognitive disorders and dementia. The CDT can be applied in different ways and scoring procedures also vary. OBJECTIVE: The aims of this study were to analyze the performance of elderly on the CDT and evaluate inter-rater reliability of the CDT scored by using a specific algorithm method adapted from Sunderland et al. (1989. METHODS: We analyzed the CDT of 100 cognitively normal elderly aged 60 years or older. The CDT ("free-drawn" and Mini-Mental State Examination (MMSE were administered to all participants. Six independent examiners scored the CDT of 30 participants to evaluate inter-rater reliability. RESULTS AND CONCLUSION: A score of 5 on the proposed algorithm ("Numbers in reverse order or concentrated", equivalent to 5 points on the original Sunderland scale, was the most frequent (53.5%. The CDT specific algorithm method used had high inter-rater reliability (p<0.01, and mean score ranged from 5.06 to 5.96. The high frequency of an overall score of 5 points may suggest the need to create more nuanced evaluation criteria, which are sensitive to differences in levels of impairment in visuoconstructive and executive abilities during aging.
Multi-objective genetic algorithm based innovative wind farm layout optimization method
International Nuclear Information System (INIS)
Chen, Ying; Li, Hua; He, Bang; Wang, Pengcheng; Jin, Kai
2015-01-01
Highlights: • Innovative optimization procedures for both regular and irregular shape wind farm. • Using real wind condition and commercial wind turbine parameters. • Using multiple-objective genetic algorithm optimization method. • Optimize the selection of different wind turbine types and their hub heights. - Abstract: Layout optimization has become one of the critical approaches to increase power output and decrease total cost of a wind farm. Previous researches have applied intelligent algorithms to optimizing the wind farm layout. However, those wind conditions used in most of previous research are simplified and not accurate enough to match the real world wind conditions. In this paper, the authors propose an innovative optimization method based on multi-objective genetic algorithm, and test it with real wind condition and commercial wind turbine parameters. Four case studies are conducted to investigate the number of wind turbines needed in the given wind farm. Different cost models are also considered in the case studies. The results clearly demonstrate that the new method is able to optimize the layout of a given wind farm with real commercial data and wind conditions in both regular and irregular shapes, and achieve a better result by selecting different type and hub height wind turbines.
2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm
Directory of Open Access Journals (Sweden)
Patrik Kamencay
2014-03-01
Full Text Available This paper presents a proposed methodology for face recognition based on an information theory approach to coding and decoding face images. In this paper, we propose a 2D-3D face-matching method based on a principal component analysis (PCA algorithm using canonical correlation analysis (CCA to learn the mapping between a 2D face image and 3D face data. This method makes it possible to match a 2D face image with enrolled 3D face data. Our proposed fusion algorithm is based on the PCA method, which is applied to extract base features. PCA feature-level fusion requires the extraction of different features from the source data before features are merged together. Experimental results on the TEXAS face image database have shown that the classification and recognition results based on the modified CCA-PCA method are superior to those based on the CCA method. Testing the 2D-3D face match results gave a recognition rate for the CCA method of a quite poor 55% while the modified CCA method based on PCA-level fusion achieved a very good recognition score of 85%.
A Modified AH-FDTD Unconditionally Stable Method Based on High-Order Algorithm
Directory of Open Access Journals (Sweden)
Zheng Pan
2017-01-01
Full Text Available The unconditionally stable method, Associated-Hermite FDTD, has attracted more and more attentions in computational electromagnetic for its time-frequency compact property. Because of the fewer orders of AH basis needed in signal reconstruction, the computational efficiency can be improved further. In order to further improve the accuracy of the traditional AH-FDTD, a high-order algorithm is introduced. Using this method, the dispersion error induced by the space grid can be reduced, which makes it possible to set coarser grid. The simulation results show that, on the condition of coarse grid, the waveforms obtained from the proposed method are matched well with the analytic result, and the accuracy of the proposed method is higher than the traditional AH-FDTD. And the efficiency of the proposed method is higher than the traditional FDTD method in analysing 2D waveguide problems with fine-structure.
New Design Methods And Algorithms For High Energy-Efficient And Low-cost Distillation Processes
Energy Technology Data Exchange (ETDEWEB)
Agrawal, Rakesh [Purdue Univ., West Lafayette, IN (United States)
2013-11-21
This project sought and successfully answered two big challenges facing the creation of low-energy, cost-effective, zeotropic multi-component distillation processes: first, identification of an efficient search space that includes all the useful distillation configurations and no undesired configurations; second, development of an algorithm to search the space efficiently and generate an array of low-energy options for industrial multi-component mixtures. Such mixtures are found in large-scale chemical and petroleum plants. Commercialization of our results was addressed by building a user interface allowing practical application of our methods for industrial problems by anyone with basic knowledge of distillation for a given problem. We also provided our algorithm to a major U.S. Chemical Company for use by the practitioners. The successful execution of this program has provided methods and algorithms at the disposal of process engineers to readily generate low-energy solutions for a large class of multicomponent distillation problems in a typical chemical and petrochemical plant. In a petrochemical complex, the distillation trains within crude oil processing, hydrotreating units containing alkylation, isomerization, reformer, LPG (liquefied petroleum gas) and NGL (natural gas liquids) processing units can benefit from our results. Effluents from naphtha crackers and ethane-propane crackers typically contain mixtures of methane, ethylene, ethane, propylene, propane, butane and heavier hydrocarbons. We have shown that our systematic search method with a more complete search space, along with the optimization algorithm, has a potential to yield low-energy distillation configurations for all such applications with energy savings up to 50%.
METHODS OF ASSESSING THE DEGREE OF DESTRUCTION OF RUBBER PRODUCTS USING COMPUTER VISION ALGORITHMS
Directory of Open Access Journals (Sweden)
A. A. Khvostov
2015-01-01
Full Text Available For technical inspection of rubber products are essential methods of improving video scopes analyzing the degree of destruction and aging of rubber in an aggressive environment. The main factor determining the degree of destruction of the rubber product, the degree of coverage is cracked, which can be described as the amount of the total area, perimeter cracks, geometric shapes and other parameters. In the process of creating a methodology for assessing the degree of destruction of rubber products arises the problem of the development of machine vision algorithm for estimating the degree of coverage of the sample fractures and fracture characterization. For the development of image processing algorithm performed experimental studies on the artificial aging of several samples of products that are made from different rubbers. In the course of the experiments it was obtained several samples of shots vulcanizates in real time. To achieve the goals initially made light stabilization of array images using Gaussian filter. Thereafter, for each image binarization operation is applied. To highlight the contours of the surface damage of the sample is used Canny algorithm. The detected contours are converted into an array of pixels. However, a crack may be allocated to several contours. Therefore, an algorithm was developed by combining contours criterion of minimum distance between them. At the end of the calculation is made of the morphological features of each contour (area, perimeter, length, width, angle of inclination, the At the end of the calculation is made of the morphological features of each contour (area, perimeter, length, width, angle of inclination, the Minkowski dimension. Show schedule obtained by the method parameters destruction of samples of rubber products. The developed method allows you to automate assessment of the degree of aging of rubber products in telemetry systems, to study the dynamics of the aging process of polymers to
Xia, Youshen; Kamel, Mohamed S
2007-06-01
Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.
Recognition algorithms in knot theory
International Nuclear Information System (INIS)
Dynnikov, I A
2003-01-01
In this paper the problem of constructing algorithms for comparing knots and links is discussed. A survey of existing approaches and basic results in this area is given. In particular, diverse combinatorial methods for representing links are discussed, the Haken algorithm for recognizing a trivial knot (the unknot) and a scheme for constructing a general algorithm (using Haken's ideas) for comparing links are presented, an approach based on representing links by closed braids is described, the known algorithms for solving the word problem and the conjugacy problem for braid groups are described, and the complexity of the algorithms under consideration is discussed. A new method of combinatorial description of knots is given together with a new algorithm (based on this description) for recognizing the unknot by using a procedure for monotone simplification. In the conclusion of the paper several problems are formulated whose solution could help to advance towards the 'algorithmization' of knot theory
ADVANCED SEISMIC BASE ISOLATION METHODS FOR MODULAR REACTORS
Energy Technology Data Exchange (ETDEWEB)
E. Blanford; E. Keldrauk; M. Laufer; M. Mieler; J. Wei; B. Stojadinovic; P.F. Peterson
2010-09-20
Advanced technologies for structural design and construction have the potential for major impact not only on nuclear power plant construction time and cost, but also on the design process and on the safety, security and reliability of next generation of nuclear power plants. In future Generation IV (Gen IV) reactors, structural and seismic design should be much more closely integrated with the design of nuclear and industrial safety systems, physical security systems, and international safeguards systems. Overall reliability will be increased, through the use of replaceable and modular equipment, and through design to facilitate on-line monitoring, in-service inspection, maintenance, replacement, and decommissioning. Economics will also receive high design priority, through integrated engineering efforts to optimize building arrangements to minimize building heights and footprints. Finally, the licensing approach will be transformed by becoming increasingly performance based and technology neutral, using best-estimate simulation methods with uncertainty and margin quantification. In this context, two structural engineering technologies, seismic base isolation and modular steel-plate/concrete composite structural walls, are investigated. These technologies have major potential to (1) enable standardized reactor designs to be deployed across a wider range of sites, (2) reduce the impact of uncertainties related to site-specific seismic conditions, and (3) alleviate reactor equipment qualification requirements. For Gen IV reactors the potential for deliberate crashes of large aircraft must also be considered in design. This report concludes that base-isolated structures should be decoupled from the reactor external event exclusion system. As an example, a scoping analysis is performed for a rectangular, decoupled external event shell designed as a grillage. This report also reviews modular construction technology, particularly steel-plate/concrete construction using
ADVANCED SEISMIC BASE ISOLATION METHODS FOR MODULAR REACTORS
International Nuclear Information System (INIS)
Blanford, E.; Keldrauk, E.; Laufer, M.; Mieler, M.; Wei, J.; Stojadinovic, B.; Peterson, P.F.
2010-01-01
Advanced technologies for structural design and construction have the potential for major impact not only on nuclear power plant construction time and cost, but also on the design process and on the safety, security and reliability of next generation of nuclear power plants. In future Generation IV (Gen IV) reactors, structural and seismic design should be much more closely integrated with the design of nuclear and industrial safety systems, physical security systems, and international safeguards systems. Overall reliability will be increased, through the use of replaceable and modular equipment, and through design to facilitate on-line monitoring, in-service inspection, maintenance, replacement, and decommissioning. Economics will also receive high design priority, through integrated engineering efforts to optimize building arrangements to minimize building heights and footprints. Finally, the licensing approach will be transformed by becoming increasingly performance based and technology neutral, using best-estimate simulation methods with uncertainty and margin quantification. In this context, two structural engineering technologies, seismic base isolation and modular steel-plate/concrete composite structural walls, are investigated. These technologies have major potential to (1) enable standardized reactor designs to be deployed across a wider range of sites, (2) reduce the impact of uncertainties related to site-specific seismic conditions, and (3) alleviate reactor equipment qualification requirements. For Gen IV reactors the potential for deliberate crashes of large aircraft must also be considered in design. This report concludes that base-isolated structures should be decoupled from the reactor external event exclusion system. As an example, a scoping analysis is performed for a rectangular, decoupled external event shell designed as a grillage. This report also reviews modular construction technology, particularly steel-plate/concrete construction using
Advanced Methods for Direct Ink Write Additive Manufacturing
Energy Technology Data Exchange (ETDEWEB)
Compel, W. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Lewicki, J. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2018-01-24
Lawrence Livermore National Laboratory is one of the world’s premier labs for research and development of additive manufacturing processes. Out of these many processes, direct ink write (DIW) is arguably one of the most relevant for the manufacture of architected polymeric materials, components and hardware. However, a bottleneck in this pipeline that has largely been ignored to date is the lack of advanced software implementation with respect to toolpath execution. There remains to be a convenient, automated method to design and produce complex parts that is user-friendly and enabling for the realization of next generation designs and structures. For a material to be suitable as a DIW ink it must possess the appropriate rheological properties for this process. Most importantly, the material must exhibit shear-thinning in order to extrude through a print head and have a rapid recovery of its static shear modulus. This makes it possible for the extrudate to be self-supporting upon exiting the print head. While this and other prerequisites narrow the scope of ‘offthe- shelf’ printable materials directly amenable to DIW, the process still tolerates a wide range of potential feedstock materials. These include metallic alloys, inorganic solvent borne dispersions, polymeric melts, filler stabilized monomer compositions, pre-elastomeric feedstocks and thermoset resins each of which requires custom print conditions tailored to the individual ink. As such, an ink perfectly suited for DIW may be prematurely determined to be undesirable for the process if printed under the wrong conditions. Defining appropriate print conditions such as extrusion rate, layer height, and maximum bridge length is a vital first step in validating an ink’s DIW capability.
A method to quantitate regional wall motion in left ventriculography using Hildreth algorithm
Energy Technology Data Exchange (ETDEWEB)
Terashima, Mikio [Hyogo Red Cross Blood Center (Japan); Naito, Hiroaki; Sato, Yoshinobu; Tamura, Shinichi; Kurosawa, Tsutomu
1998-06-01
Quantitative measurement of ventricular wall motion is indispensable for objective evaluation of cardiac function associated with coronary artery disease. We have modified the Hildreth`s algorithm to estimate excursions of the ventricular wall on left ventricular images yielded by various imaging techniques. Tagging cine-MRI was carried out on 7 healthy volunteers. The original Hildreth method, the modified Hildreth method and the centerline method were applied to the outlines of the images obtained, to estimate excursion of the left ventricular wall and regional shortening and to evaluate the accuracy of these methods when measuring these parameters, compared to the values of these parameters measured actually using the attached tags. The accuracy of the original Hildreth method was comparable to that of the centerline method, while the modified Hildreth method was significantly more accurate than the centerline method (P<0.05). Regional shortening as estimated using the modified Hildreth method differed less from the actually measured regional shortening than did the shortening estimated using the centerline method (P<0.05). The modified Hildreth method allowed reasonable estimation of left ventricular wall excursion in all cases where it was applied. These results indicate that when applied to left ventriculograms for ventricular wall motion analysis, the modified Hildreth method is more useful than the original Hildreth method. (author)
Method of transient identification based on a possibilistic approach, optimized by genetic algorithm
International Nuclear Information System (INIS)
Almeida, Jose Carlos Soares de
2001-02-01
This work develops a method for transient identification based on a possible approach, optimized by Genetic Algorithm to optimize the number of the centroids of the classes that represent the transients. The basic idea of the proposed method is to optimize the partition of the search space, generating subsets in the classes within a partition, defined as subclasses, whose centroids are able to distinguish the classes with the maximum correct classifications. The interpretation of the subclasses as fuzzy sets and the possible approach provided a heuristic to establish influence zones of the centroids, allowing to achieve the 'don't know' answer for unknown transients, that is, outside the training set. (author)
Method of fault diagnosis in nuclear power plant base on genetic algorithm and knowledge base
International Nuclear Information System (INIS)
Zhou Yangping; Zhao Bingquan
2000-01-01
Via using the knowledge base, combining Genetic Algorithm and classical probability and contraposing the characteristic of the fault diagnosis of NPP. The authors put forward a method of fault diagnosis. In the process of fault diagnosis, this method contact the state of NPP with the colony in GA and transform the colony to get the individual that adapts to the condition. On the 950MW full size simulator in Beijing NPP simulation training center, experimentation shows it has comparative adaptability to the imperfection of expert knowledge, illusive signal and other instance
Yang, Yan-Pu
2017-01-01
Consumers' opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers' preference. However, how to identify and improve the reliability of consumers' Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers' opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers' opinions. Furthermore, the process of the proposed method is presented and the details are illustrated using an example of electronic scooter design evaluation. The case study reveals that the proposed method is promising for reaching a consensus through searching optimal solutions by PSO and improving the reliability of consumers' evaluation opinions toward design alternatives according to Kansei indexes.
Directory of Open Access Journals (Sweden)
JingRui Zhang
2015-03-01
Full Text Available In this article, we focus on safe and effective completion of a rendezvous and docking task by looking at planning approaches and control with fuel-optimal rendezvous for a target spacecraft running on a near-circular reference orbit. A variety of existent practical path constraints are considered, including the constraints of field of view, impulses, and passive safety. A rendezvous approach is calculated by using a hybrid genetic algorithm with those constraints. Furthermore, a control method of trajectory tracking is adopted to overcome the external disturbances. Based on Clohessy–Wiltshire equations, we first construct the mathematical model of optimal planning approaches of multiple impulses with path constraints. Second, we introduce the principle of hybrid genetic algorithm with both stronger global searching ability and local searching ability. We additionally explain the application of this algorithm in the problem of trajectory planning. Then, we give three-impulse simulation examples to acquire an optimal rendezvous trajectory with the path constraints presented in this article. The effectiveness and applicability of the tracking control method are verified with the optimal trajectory above as control objective through the numerical simulation.
A method for the interpretation of flow cytometry data using genetic algorithms
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Cesar Angeletti
2018-01-01
Full Text Available Background: Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described. Subjects and Methods: Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system. Results: Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC curve of 0.912. Conclusions: The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.
EVALUATION OF WEB SEARCHING METHOD USING A NOVEL WPRR ALGORITHM FOR TWO DIFFERENT CASE STUDIES
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V. Lakshmi Praba
2012-04-01
Full Text Available The World-Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to web data and documents. Web content mining and web structure mining have important roles in identifying the relevant web page. Relevancy of web page denotes how well a retrieved web page or set of web pages meets the information need of the user. Page Rank, Weighted Page Rank and Hypertext Induced Topic Selection (HITS are existing algorithms which considers only web structure mining. Vector Space Model (VSM, Cover Density Ranking (CDR, Okapi similarity measurement (Okapi and Three-Level Scoring method (TLS are some of existing relevancy score methods which consider only web content mining. In this paper, we propose a new algorithm, Weighted Page with Relevant Rank (WPRR which is blend of both web content mining and web structure mining that demonstrates the relevancy of the page with respect to given query for two different case scenarios. It is shown that WPRR’s performance is better than the existing algorithms.
Directory of Open Access Journals (Sweden)
Oleksandr B. Yashchyk
2016-05-01
Full Text Available The article discusses the importance of studying the notion of algorithm and its formal specification using Turing machines. In the article it was identified the basic hypothesis of the theory of algorithms for Turing as well as reviewed scientific research of modern scientists devoted to this issue and found the main principles of the Turing machine as an abstract mathematical model. The process of forming information competencies components, information culture and students` logical thinking development with the inclusion of the topic “Study and Application of Turing machine as Universal Algorithm Executor” in the course of Informatics was analyzed.
A study on the performance advancement of teat algorithm for defects in semiconductor packages
Energy Technology Data Exchange (ETDEWEB)
Kim, Jae Yeol; Kim, Chang Hyun; Yang, Dong Jo; Ko, Myung Soo [Chosun University, Gwangju (Korea, Republic of); You, Sin [Computer Added Mechanical Engineering, Mokpo Science College, Mokpo (Korea, Republic of)
2002-11-15
In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, tile pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.
The methods and algorithms for designing complex three-dimensional robots
International Nuclear Information System (INIS)
Solovjev, A.E.; Naumov, V.B.
1996-01-01
For automation designing by the Robotics laboratory were executed some fundamental and applied researches. This researching allowed to create rational mathematical model for numeric modeling with real-time simulation. In the mathematical model used set of equations of rigid body's motion in Lagrange's form and set of Appel's equations taking into consideration holonomic and non-holonomic connections. In present article are considered methods and algorithms of dynamic modeling of a system of rigid bodies for robotics task and brief description of the package Computer Aided Engineering for Industrial Robots, based on considered algorithms. So far as, in researching of robots the dynamic tasks (direct and inverse) are more interesting than another tasks, authors pay attention just on these problems
PARALLEL ALGORITHM FOR THREE-DIMENSIONAL STOKES FLOW SIMULATION USING BOUNDARY ELEMENT METHOD
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D. G. Pribytok
2016-01-01
Full Text Available Parallel computing technique for modeling three-dimensional viscous flow (Stokes flow using direct boundary element method is presented. The problem is solved in three phases: sampling and construction of system of linear algebraic equations (SLAE, its decision and finding the velocity of liquid at predetermined points. For construction of the system and finding the velocity, the parallel algorithms using graphics CUDA cards programming technology have been developed and implemented. To solve the system of linear algebraic equations the implemented software libraries are used. A comparison of time consumption for three main algorithms on the example of calculation of viscous fluid motion in three-dimensional cavity is performed.
International Nuclear Information System (INIS)
Coulomb, F.
1989-06-01
The aim of this work is to study methods for solving the diffusion equation, based on a primal or mixed-dual finite elements discretization and well suited for use on multiprocessors computers; domain decomposition methods are the subject of the main part of this study, the linear systems being solved by the block-Jacobi method. The origin of the diffusion equation is explained in short, and various variational formulations are reminded. A survey of iterative methods is given. The elemination of the flux or current is treated in the case of a mixed method. Numerical tests are performed on two examples of reactors, in order to compare mixed elements and Lagrange elements. A theoretical study of domain decomposition is led in the case of Lagrange finite elements, and convergence conditions for the block-Jacobi method are derived; the dissection decomposition is previously the purpose of a particular numerical analysis. In the case of mixed-dual finite elements, a study is led on examples and is confirmed by numerical tests performed for the dissection decomposition; furthermore, after being justified, decompositions along axes of symmetry are numerically tested. In the case of a decomposition into two subdomains, the dissection decomposition and the decomposition with an integrated interface are compared. Alternative directions methods are defined; the convergence of those relative to Lagrange elements is shown; in the case of mixed elements, convergence conditions are found [fr
Hashimoto, Hiroyuki; Takaguchi, Yusuke; Nakamura, Shizuka
Instability of calculation process and increase of calculation time caused by increasing size of continuous optimization problem remain the major issues to be solved to apply the technique to practical industrial systems. This paper proposes an enhanced quadratic programming algorithm based on interior point method mainly for improvement of calculation stability. The proposed method has dynamic estimation mechanism of active constraints on variables, which fixes the variables getting closer to the upper/lower limit on them and afterwards releases the fixed ones as needed during the optimization process. It is considered as algorithm-level integration of the solution strategy of active-set method into the interior point method framework. We describe some numerical results on commonly-used bench-mark problems called “CUTEr” to show the effectiveness of the proposed method. Furthermore, the test results on large-sized ELD problem (Economic Load Dispatching problems in electric power supply scheduling) are also described as a practical industrial application.
Bu, Sunyoung; Huang, Jingfang; Boyer, Treavor H.; Miller, Cass T.
2010-07-01
The focus of this work is on the modeling of an ion exchange process that occurs in drinking water treatment applications. The model formulation consists of a two-scale model in which a set of microscale diffusion equations representing ion exchange resin particles that vary in size and age are coupled through a boundary condition with a macroscopic ordinary differential equation (ODE), which represents the concentration of a species in a well-mixed reactor. We introduce a new age-averaged model (AAM) that averages all ion exchange particle ages for a given size particle to avoid the expensive Monte-Carlo simulation associated with previous modeling applications. We discuss two different numerical schemes to approximate both the original Monte-Carlo algorithm and the new AAM for this two-scale problem. The first scheme is based on the finite element formulation in space coupled with an existing backward difference formula-based ODE solver in time. The second scheme uses an integral equation based Krylov deferred correction (KDC) method and a fast elliptic solver (FES) for the resulting elliptic equations. Numerical results are presented to validate the new AAM algorithm, which is also shown to be more computationally efficient than the original Monte-Carlo algorithm. We also demonstrate that the higher order KDC scheme is more efficient than the traditional finite element solution approach and this advantage becomes increasingly important as the desired accuracy of the solution increases. We also discuss issues of smoothness, which affect the efficiency of the KDC-FES approach, and outline additional algorithmic changes that would further improve the efficiency of these developing methods for a wide range of applications.
International Nuclear Information System (INIS)
Satake, Shin-ichi; Kunugi, Tomoaki
2006-01-01
Scientific computational methods have advanced remarkably with the progress of nuclear development. They have played the role of weft connecting each realm of nuclear engineering and then an introductory course of advanced scientific computational methods and their applications to nuclear technologies were prepared in serial form. This is the third issue showing the introduction of continuum simulation methods and their applications. Spectral methods and multi-interface calculation methods in fluid dynamics are reviewed. (T. Tanaka)
Interpretation of gypsy moth frontal advance using meteorology in a conditional algorithm.
Frank, K L; Tobin, P C; Thistle, H W; Kalkstein, Laurence S
2013-05-01
The gypsy moth, Lymantria dispar, is a non-native species that continues to invade areas in North America. It spreads generally through stratified dispersal where local growth and diffusive spread are coupled with long-distance jumps ahead of the leading edge. Long-distance jumps due to anthropogenic movement of life stages is a well-documented spread mechanism. Another mechanism is the atmospheric transport of early instars and adult males, believed to occur over short distances. However, empirical gypsy moth population data continue to support the possibility of alternative methods of long-range dispersal. Such dispersal events seemed to have occurred in the mid- to late-1990s with spread across Lake Michigan to Wisconsin. Such dispersal would be against the prevailing wind flow for the area and would have crossed a significant physical barrier (Lake Michigan). The climatology of the region shows that vigorous cyclones can result in strong easterly winds in the area at the time when early instars are present. It is hypothesized that these storms would enable individuals to be blown across the Lake and explain the appearance of new population centers observed at several locations on the western shore of Lake Michigan nearly simultaneously. A synoptic climatology model coupled with population dynamics data from the area was parameterized to show an association between transport events and population spread from 1996 to 2007. This work highlights the importance of atmospheric transport events relative to the invasion dynamics of the gypsy moth, and serves as a model for understanding this mechanism of spread in other related biological invasions.
International Nuclear Information System (INIS)
Zhu Shuchai; Li Ren; Li Juan; Qiu Rong; Han Chun; Wan Jun
2004-01-01
Objective: To explore the clinical staging of moderately advanced and advanced thoracic esophageal carcinoma by evaluating the prognosis and provide criteria for individual treatment. Methods: The authors retrospectively analyzed 500 patients with moderately advanced and advanced thoracic esophageal carcinoma treated by radiotherapy alone. According to the primary lesion length by barium meal X-ray film, the invasion range and the relation between location and the surrounding organs by CT scans the disease category was classified by a 6 stage method and a 4 stage method. With the primary lesion divide into T1, T2a, T2b, T3a, T3b and T4 incorporating the locregional lymph node metastasis, a 6 stage system was obtained, I, IIa , IIb, IIIa, IIIb and IV. The results of this as compared with those of 4 stage system, the following data were finally arrived at. Results: Among the 500 cases, there were T1 23, T2a 111, T2b 157, T3a 84, T3b 82 and T4 43. The survival rates of these six categories showed significant differences (χ 2 =63.32, P 2 =56.29, P 2 =94.29, P 2 =83.48, P<0.05). Conclusions: Both the 6 stage and 4 stage systems are adaptable to predict prognosis of moderately advanced and advanced esophageal carcinoma treated by radiotherapy alone. For simplicity and convenience, the 4 stage classification is recommended. (authors)
Alteration of Box-Jenkins methodology by implementing genetic algorithm method
Ismail, Zuhaimy; Maarof, Mohd Zulariffin Md; Fadzli, Mohammad
2015-02-01
A time series is a set of values sequentially observed through time. The Box-Jenkins methodology is a systematic method of identifying, fitting, checking and using integrated autoregressive moving average time series model for forecasting. Box-Jenkins method is an appropriate for a medium to a long length (at least 50) time series data observation. When modeling a medium to a long length (at least 50), the difficulty arose in choosing the accurate order of model identification level and to discover the right parameter estimation. This presents the development of Genetic Algorithm heuristic method in solving the identification and estimation models problems in Box-Jenkins. Data on International Tourist arrivals to Malaysia were used to illustrate the effectiveness of this proposed method. The forecast results that generated from this proposed model outperformed single traditional Box-Jenkins model.
A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.
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Daniel M de Brito
Full Text Available Genomic Islands (GIs are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.
A diabetic retinopathy detection method using an improved pillar K-means algorithm.
Gogula, Susmitha Valli; Divakar, Ch; Satyanarayana, Ch; Rao, Allam Appa
2014-01-01
The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.
Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng
2015-01-01
The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.
Recent advances in the modeling of plasmas with the Particle-In-Cell methods
Vay, Jean-Luc; Lehe, Remi; Vincenti, Henri; Godfrey, Brendan; Lee, Patrick; Haber, Irv
2015-11-01
The Particle-In-Cell (PIC) approach is the method of choice for self-consistent simulations of plasmas from first principles. The fundamentals of the PIC method were established decades ago but improvements or variations are continuously being proposed. We report on several recent advances in PIC related algorithms, including: (a) detailed analysis of the numerical Cherenkov instability and its remediation, (b) analytic pseudo-spectral electromagnetic solvers in Cartesian and cylindrical (with azimuthal modes decomposition) geometries, (c) arbitrary-order finite-difference and generalized pseudo-spectral Maxwell solvers, (d) novel analysis of Maxwell's solvers' stencil variation and truncation, in application to domain decomposition strategies and implementation of Perfectly Matched Layers in high-order and pseudo-spectral solvers. Work supported by US-DOE Contracts DE-AC02-05CH11231 and the US-DOE SciDAC program ComPASS. Used resources of NERSC, supported by US-DOE Contract DE-AC02-05CH11231.
Advancing multilevel thinking and methods in HRM research
Renkema, Maarten; Meijerink, Jeroen Gerard; Bondarouk, Tatiana
2016-01-01
Purpose Despite the growing belief that multilevel research is necessary to advance HRM understanding, there remains a lack of multilevel thinking – the application of principles for multilevel theory building. The purpose of this paper is to propose a systematic approach for multilevel HRM
Directory of Open Access Journals (Sweden)
Masataka Uehara
2015-01-01
Full Text Available The nonsurgical strategies for locally advanced oral cancer are desirable. Superselective intra-arterial infusion with radiotherapy was utilized for this purpose, and there are two types of superselective intra-arterial infusion methods: The Seldinger method and the retrograde superselective intra-arterial chemotherapy (HFT method. In one case, the HFT method was applied to locally advanced tongue cancer, and the Seldinger method was used for additional administration of cisplatin (CDDP to compensate for a lack of drug flow in the HFT method. In another case, the HFT method was applied to locally advanced lower gingival cancer. The Seldinger method was applied to metastatic lymph nodes. In both cases, additional administration of CDDP using the Seldinger method resulted in a complete response. The combination of the HFT and Seldinger methods was useful to eradicate locally advanced oral cancer because each method compensated for the defects of the other.
Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.
2016-04-01
Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the
Biazzo, Indaco; Braunstein, Alfredo; Zecchina, Riccardo
2012-08-01
We study the behavior of an algorithm derived from the cavity method for the prize-collecting steiner tree (PCST) problem on graphs. The algorithm is based on the zero temperature limit of the cavity equations and as such is formally simple (a fixed point equation resolved by iteration) and distributed (parallelizable). We provide a detailed comparison with state-of-the-art algorithms on a wide range of existing benchmarks, networks, and random graphs. Specifically, we consider an enhanced derivative of the Goemans-Williamson heuristics and the dhea solver, a branch and cut integer linear programming based approach. The comparison shows that the cavity algorithm outperforms the two algorithms in most large instances both in running time and quality of the solution. Finally we prove a few optimality properties of the solutions provided by our algorithm, including optimality under the two postprocessing procedures defined in the Goemans-Williamson derivative and global optimality in some limit cases.
Directory of Open Access Journals (Sweden)
Murray Christopher JL
2011-08-01
Full Text Available Abstract Background Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results into meaningful information for health workers and policymakers, however, are often costly or complicated to use. We present a simple additive algorithm, the Tariff Method (termed Tariff, which can be used for assigning individual cause of death and for determining cause-specific mortality fractions (CSMFs from verbal autopsy data. Methods Tariff calculates a score, or "tariff," for each cause, for each sign/symptom, across a pool of validated verbal autopsy data. The tariffs are summed for a given response pattern in a verbal autopsy, and this sum (score provides the basis for predicting the cause of death in a dataset. We implemented this algorithm and evaluated the method's predictive ability, both in terms of chance-corrected concordance at the individual cause assignment level and in terms of CSMF accuracy at the population level. The analysis was conducted separately for adult, child, and neonatal verbal autopsies across 500 pairs of train-test validation verbal autopsy data. Results Tariff is capable of outperforming physician-certified verbal autopsy in most cases. In terms of chance-corrected concordance, the method achieves 44.5% in adults, 39% in children, and 23.9% in neonates. CSMF accuracy was 0.745 in adults, 0.709 in children, and 0.679 in neonates. Conclusions Verbal autopsies can be an efficient means of obtaining cause of death data, and Tariff provides an intuitive, reliable method for generating individual cause assignment and CSMFs. The method is transparent and flexible and can be readily implemented by users without training in statistics or computer science.
International Nuclear Information System (INIS)
Lenoir, A.
2008-01-01
We focus in this thesis, on the optimization process of large systems under uncertainty, and more specifically on solving the class of so-called deterministic equivalents with the help of splitting methods. The underlying application we have in mind is the electricity unit commitment problem under climate, market and energy consumption randomness, arising at EDF. We set the natural time-space-randomness couplings related to this application and we propose two new discretization schemes to tackle the randomness one, each of them based on non-parametric estimation of conditional expectations. This constitute an alternative to the usual scenario tree construction. We use the mathematical model consisting of the sum of two convex functions, a separable one and a coupling one. On the one hand, this simplified model offers a general framework to study decomposition-coordination algorithms by elapsing technicality due to a particular choice of subsystems. On the other hand, the convexity assumption allows to take advantage of monotone operators theory and to identify proximal methods as fixed point algorithms. We underlie the differential properties of the generalized reactions we are looking for a fixed point in order to derive bounds on the speed of convergence. Then we examine two families of decomposition-coordination algorithms resulting from operator splitting methods, namely Forward-Backward and Rachford methods. We suggest some practical method of acceleration of the Rachford class methods. To this end, we analyze the method from a theoretical point of view, furnishing as a byproduct explanations to some numerical observations. Then we propose as a response some improvements. Among them, an automatic updating strategy of scaling factors can correct a potential bad initial choice. The convergence proof is made easier thanks to stability results of some operator composition with respect to graphical convergence provided before. We also submit the idea of introducing
Genetic Algorithm (GA Method for Optimization of Multi-Reservoir Systems Operation
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Shervin Momtahen
2006-01-01
Full Text Available A Genetic Algorithm (GA method for optimization of multi-reservoir systems operation is proposed in this paper. In this method, the parameters of operating policies are optimized using system simulation results. Hence, any operating problem with any sort of objective function, constraints and structure of operating policy can be optimized by GA. The method is applied to a 3-reservoir system and is compared with two traditional methods of Stochastic Dynamic Programming and Dynamic Programming and Regression. The results show that GA is superior both in objective function value and in computational speed. The proposed method is further improved using a mutation power updating rule and a varying period simulation method. The later is a novel procedure proposed in this paper that is believed to help in solving computational time problem in large systems. These revisions are evaluated and proved to be very useful in converging to better solutions in much less time. The final GA method is eventually evaluated as a very efficient procedure that is able to solve problems of large multi-reservoir system which is usually impossible by traditional methods. In fact, the real performance of the GA method starts where others fail to function.
Trobec, Roman
2015-01-01
This book is concentrated on the synergy between computer science and numerical analysis. It is written to provide a firm understanding of the described approaches to computer scientists, engineers or other experts who have to solve real problems. The meshless solution approach is described in more detail, with a description of the required algorithms and the methods that are needed for the design of an efficient computer program. Most of the details are demonstrated on solutions of practical problems, from basic to more complicated ones. This book will be a useful tool for any reader interes
Manchanda, Pammy; Bhardwaj, Rashmi
2015-01-01
The present volume contains invited talks of 11th biennial conference on “Emerging Mathematical Methods, Models and Algorithms for Science and Technology”. The main message of the book is that mathematics has a great potential to analyse and understand the challenging problems of nanotechnology, biotechnology, medical science, oil industry and financial technology. The book highlights all the features and main theme discussed in the conference. All contributing authors are eminent academicians, scientists, researchers and scholars in their respective fields, hailing from around the world.
Reactor Network Synthesis Using Coupled Genetic Algorithm with the Quasi-linear Programming Method
Soltani, H.; Shafiei, S.; Edraki, J.
2016-01-01
This research is an attempt to develop a new procedure for the synthesis of reactor networks (RNs) using a genetic algorithm (GA) coupled with the quasi-linear programming (LP) method. The GA is used to produce structural configuration, whereas continuous variables are handled using a quasi-LP formulation for finding the best objective function. Quasi-LP consists of LP together with a search loop to find the best reactor conversions (xi), as well as split and recycle ratios (yi). Quasi-LP rep...
Adaptive algorithms for a self-shielding wavelet-based Galerkin method
International Nuclear Information System (INIS)
Fournier, D.; Le Tellier, R.
2009-01-01
The treatment of the energy variable in deterministic neutron transport methods is based on a multigroup discretization, considering the flux and cross-sections to be constant within a group. In this case, a self-shielding calculation is mandatory to correct sections of resonant isotopes. In this paper, a different approach based on a finite element discretization on a wavelet basis is used. We propose adaptive algorithms constructed from error estimates. Such an approach is applied to within-group scattering source iterations. A first implementation is presented in the special case of the fine structure equation for an infinite homogeneous medium. Extension to spatially-dependent cases is discussed. (authors)
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Yukai Yao
2015-01-01
Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.
International Nuclear Information System (INIS)
Sergeev, V.A.; Malkov, M.; Mursula, K.
1993-01-01
This paper describes tests done on one model system for studying the magnetic field in the magneotail, called the isotropic boundary algorithm method. The tail field lines map into the ionosphere, and there have been two direct methods applied to study tail fields, one a global model, and the other a local model. The global models are so broad in scope that they have a hard time dealing with specific field configurations at some time and some location. Local models rely upon field measurements being simultaneously available over a large region of space to study simultaneously the field configurations. In general this is either very fortuitous or very expensive. The isotropic boundary algorithm method relys upon measuring energetic particles, here protons with energies greater than 30 keV, in the isotropic boundary at low altitudes and interpreting them as representing the boundary between stochastic and adiabatic particle motion regions in the equatorial tail current sheet. The authors have correlated particle measurements by NOAA spacecraft to study the isotropic boundary, with magnetic measurements of tail magnetic fields by the geostationary GOES 2 spacecraft. Positive correlations are observed
A high-resolution neutron spectra unfolding method using the Genetic Algorithm technique
Mukherjee, B
2002-01-01
The Bonner sphere spectrometers (BSS) are commonly used to determine the neutron spectra within various nuclear facilities. Sophisticated mathematical tools are used to unfold the neutron energy distribution from the output data of the BSS. This paper highlights a novel high-resolution neutron spectra-unfolding method using the Genetic Algorithm (GA) technique. The GA imitates the biological evolution process prevailing in the nature to solve complex optimisation problems. The GA method was utilised to evaluate the neutron energy distribution, average energy, fluence and equivalent dose rates at important work places of a DIDO class research reactor and a high-energy superconducting heavy ion cyclotron. The spectrometer was calibrated with a sup 2 sup 4 sup 1 Am/Be (alpha,n) neutron standard source. The results of the GA method agreed satisfactorily with the results obtained by using the well-known BUNKI neutron spectra unfolding code.
Construction Method of Display Proposal for Commodities in Sales Promotion by Genetic Algorithm
Yumoto, Masaki
In a sales promotion task, wholesaler prepares and presents the display proposal for commodities in order to negotiate with retailer's buyers what commodities they should sell. For automating the sales promotion tasks, the proposal has to be constructed according to the target retailer's buyer. However, it is difficult to construct the proposal suitable for the target retail store because of too much combination of commodities. This paper proposes a construction method by Genetic algorithm (GA). The proposed method represents initial display proposals for commodities with genes, improve ones with the evaluation value by GA, and rearrange one with the highest evaluation value according to the classification of commodity. Through practical experiment, we can confirm that display proposal by the proposed method is similar with the one constructed by a wholesaler.
Application of Computer Vision Methods and Algorithms in Documentation of Cultural Heritage
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David Káňa
2012-12-01
Full Text Available The main task of this paper is to describe methods and algorithms used in computer vision for fully automatic reconstruction of exterior orientation in ordered and unordered sets of images captured by digital calibrated cameras without prior informations about camera positions or scene structure. Attention will be paid to the SIFT interest operator for finding key points clearly describing the image areas with respect to scale and rotation, so that these areas could be compared to the regions in other images. There will also be discussed methods of matching key points, calculation of the relative orientation and strategy of linking sub-models to estimate the parameters entering complex bundle adjustment. The paper also compares the results achieved with above system with the results obtained by standard photogrammetric methods in processing of project documentation for reconstruction of the Žinkovy castle.
Integral equation models for image restoration: high accuracy methods and fast algorithms
International Nuclear Information System (INIS)
Lu, Yao; Shen, Lixin; Xu, Yuesheng
2010-01-01
Discrete models are consistently used as practical models for image restoration. They are piecewise constant approximations of true physical (continuous) models, and hence, inevitably impose bottleneck model errors. We propose to work directly with continuous models for image restoration aiming at suppressing the model errors caused by the discrete models. A systematic study is conducted in this paper for the continuous out-of-focus image models which can be formulated as an integral equation of the first kind. The resulting integral equation is regularized by the Lavrentiev method and the Tikhonov method. We develop fast multiscale algorithms having high accuracy to solve the regularized integral equations of the second kind. Numerical experiments show that the methods based on the continuous model perform much better than those based on discrete models, in terms of PSNR values and visual quality of the reconstructed images
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Pereira Ana Paula Esteves
2013-02-01
Full Text Available Abstract Background A valid, accurate method for determining gestational age (GA is crucial in classifying early and late prematurity, and it is a relevant issue in perinatology. This study aimed at assessing the validity of different measures for approximating GA, and it provides an insight into the development of algorithms that can be adopted in places with similar characteristics to Brazil. A follow-up study was carried out in two cities in southeast Brazil. Participants were interviewed in the first trimester of pregnancy and in the postpartum period, with a final sample of 1483 participants after exclusions. The distribution of GA estimates at birth using ultrasound (US at 21–28 weeks, US at 29+ weeks, last menstrual period (LMP, and the Capurro method were compared with GA estimates at birth using the reference US (at 7–20 weeks of gestation. Kappa, sensitivity, and specificity tests were calculated for preterm (=42 weeks birth rates. The difference in days in the GA estimates between the reference US and the LMP and between the reference US and the Capurro method were evaluated in terms of maternal and infant characteristics, respectively. Results For prematurity, US at 21–28 weeks had the highest sensitivity (0.84 and the Capurro method the highest specificity (0.97. For postmaturity, US at 21–28 weeks and the Capurro method had a very high sensitivity (0.98. All methods of GA estimation had a very low specificity (≤0.50 for postmaturity. GA estimates at birth with the algorithm and the reference US produced very similar results, with a preterm birth rate of 12.5%. Conclusions In countries such as Brazil, where there is less accurate information about the LMP and lower coverage of early obstetric US examinations, we recommend the development of algorithms that enable the use of available information using methodological strategies to reduce the chance of errors with GA. Thus, this study calls into attention the care needed
Liu, Xiaofeng; Bai, Fang; Ouyang, Sisheng; Wang, Xicheng; Li, Honglin; Jiang, Hualiang
2009-03-31
Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of
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Li Honglin
2009-03-01
Full Text Available Abstract Background Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. Results The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105–112. Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 Å to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 ± 0.18 seconds per molecule renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. Conclusion On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms
Nurdiyanto, Heri; Rahim, Robbi; Wulan, Nur
2017-12-01
Symmetric type cryptography algorithm is known many weaknesses in encryption process compared with asymmetric type algorithm, symmetric stream cipher are algorithm that works on XOR process between plaintext and key, to improve the security of symmetric stream cipher algorithm done improvisation by using Triple Transposition Key which developed from Transposition Cipher and also use Base64 algorithm for encryption ending process, and from experiment the ciphertext that produced good enough and very random.
Statistical methods applied to gamma-ray spectroscopy algorithms in nuclear security missions.
Fagan, Deborah K; Robinson, Sean M; Runkle, Robert C
2012-10-01
Gamma-ray spectroscopy is a critical research and development priority to a range of nuclear security missions, specifically the interdiction of special nuclear material involving the detection and identification of gamma-ray sources. We categorize existing methods by the statistical methods on which they rely and identify methods that have yet to be considered. Current methods estimate the effect of counting uncertainty but in many cases do not address larger sources of decision uncertainty, which may be significantly more complex. Thus, significantly improving algorithm performance may require greater coupling between the problem physics that drives data acquisition and statistical methods that analyze such data. Untapped statistical methods, such as Bayes Modeling Averaging and hierarchical and empirical Bayes methods, could reduce decision uncertainty by rigorously and comprehensively incorporating all sources of uncertainty. Application of such methods should further meet the needs of nuclear security missions by improving upon the existing numerical infrastructure for which these analyses have not been conducted. Copyright © 2012 Elsevier Ltd. All rights reserved.
Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
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Muneki Yasuda
2018-04-01
Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.
International Nuclear Information System (INIS)
Qi, Zhipeng; Li, Xiu; Lu, Xushan; Zhang, Yingying; Yao, Weihua
2015-01-01
We introduce a new and potentially useful method for wave field inverse transformation and its application in transient electromagnetic method (TEM) 3D interpretation. The diffusive EM field is known to have a unique integral representation in terms of a fictitious wave field that satisfies a wave equation. The continuous imaging of TEM can be accomplished using the imaging methods in seismic interpretation after the diffusion equation is transformed into a fictitious wave equation. The interpretation method based on the imaging of a fictitious wave field could be used as a fast 3D inversion method. Moreover, the fictitious wave field possesses some wave field features making it possible for the application of a wave field interpretation method in TEM to improve the prospecting resolution.Wave field transformation is a key issue in the migration imaging of a fictitious wave field. The equation in the wave field transformation belongs to the first class Fredholm integration equation, which is a typical ill-posed equation. Additionally, TEM has a large dynamic time range, which also facilitates the weakness of this ill-posed problem. The wave field transformation is implemented by using pre-conditioned regularized conjugate gradient method. The continuous imaging of a fictitious wave field is implemented by using Kirchhoff integration. A synthetic aperture and deconvolution algorithm is also introduced to improve the interpretation resolution. We interpreted field data by the method proposed in this paper, and obtained a satisfying interpretation result. (paper)
Gamshadzaei, Mohammad Hossein; Rahimzadegan, Majid
2017-10-01
Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.
Exploring biomolecular dynamics and interactions using advanced sampling methods
International Nuclear Information System (INIS)
Luitz, Manuel; Bomblies, Rainer; Ostermeir, Katja; Zacharias, Martin
2015-01-01
Molecular dynamics (MD) and Monte Carlo (MC) simulations have emerged as a valuable tool to investigate statistical mechanics and kinetics of biomolecules and synthetic soft matter materials. However, major limitations for routine applications are due to the accuracy of the molecular mechanics force field and due to the maximum simulation time that can be achieved in current simulations studies. For improving the sampling a number of advanced sampling approaches have been designed in recent years. In particular, variants of the parallel tempering replica-exchange methodology are widely used in many simulation studies. Recent methodological advancements and a discussion of specific aims and advantages are given. This includes improved free energy simulation approaches and conformational search applications. (topical review)
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Yang Bai
2015-04-01
Full Text Available As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST retrieved from Thermal Infra-Red (TIR images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD and absolute average difference (AAD are smaller than 1.7 K, where the correlation coefficient (CC and root-mean-square error (RMSE are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.
Machine Learning an algorithmic perspective
Marsland, Stephen
2009-01-01
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le
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Lee, K; Leung, R; Law, G; Wong, M; Lee, V; Tung, S; Cheung, S; Chan, M [Tuen Mun Hospital, Hong Kong (Hong Kong)
2016-06-15
Background: Commercial treatment planning system Pinnacle3 (Philips, Fitchburg, WI, USA) employs a convolution-superposition algorithm for volumetric-modulated arc radiotherapy (VMAT) optimization and dose calculation. Study of Monte Carlo (MC) dose recalculation of VMAT plans for advanced-stage nasopharyngeal cancers (NPC) is currently limited. Methods: Twenty-nine VMAT prescribed 70Gy, 60Gy, and 54Gy to the planning target volumes (PTVs) were included. These clinical plans achieved with a CS dose engine on Pinnacle3 v9.0 were recalculated by the Monaco TPS v5.0 (Elekta, Maryland Heights, MO, USA) with a XVMC-based MC dose engine. The MC virtual source model was built using the same measurement beam dataset as for the Pinnacle beam model. All MC recalculation were based on absorbed dose to medium in medium (Dm,m). Differences in dose constraint parameters per our institution protocol (Supplementary Table 1) were analyzed. Results: Only differences in maximum dose to left brachial plexus, left temporal lobe and PTV54Gy were found to be statistically insignificant (p> 0.05). Dosimetric differences of other tumor targets and normal organs are found in supplementary Table 1. Generally, doses outside the PTV in the normal organs are lower with MC than with CS. This is also true in the PTV54-70Gy doses but higher dose in the nasal cavity near the bone interfaces is consistently predicted by MC, possibly due to the increased backscattering of short-range scattered photons and the secondary electrons that is not properly modeled by the CS. The straight shoulders of the PTV dose volume histograms (DVH) initially resulted from the CS optimization are merely preserved after MC recalculation. Conclusion: Significant dosimetric differences in VMAT NPC plans were observed between CS and MC calculations. Adjustments of the planning dose constraints to incorporate the physics differences from conventional CS algorithm should be made when VMAT optimization is carried out directly
Analyses of Methods and Algorithms for Modelling and Optimization of Biotechnological Processes
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Stoyan Stoyanov
2009-08-01
Full Text Available A review of the problems in modeling, optimization and control of biotechnological processes and systems is given in this paper. An analysis of existing and some new practical optimization methods for searching global optimum based on various advanced strategies - heuristic, stochastic, genetic and combined are presented in the paper. Methods based on the sensitivity theory, stochastic and mix strategies for optimization with partial knowledge about kinetic, technical and economic parameters in optimization problems are discussed. Several approaches for the multi-criteria optimization tasks are analyzed. The problems concerning optimal controls of biotechnological systems are also discussed.
Abbiati, Giuseppe; La Salandra, Vincenzo; Bursi, Oreste S.; Caracoglia, Luca
2018-02-01
Successful online hybrid (numerical/physical) dynamic substructuring simulations have shown their potential in enabling realistic dynamic analysis of almost any type of non-linear structural system (e.g., an as-built/isolated viaduct, a petrochemical piping system subjected to non-stationary seismic loading, etc.). Moreover, owing to faster and more accurate testing equipment, a number of different offline experimental substructuring methods, operating both in time (e.g. the impulse-based substructuring) and frequency domains (i.e. the Lagrange multiplier frequency-based substructuring), have been employed in mechanical engineering to examine dynamic substructure coupling. Numerous studies have dealt with the above-mentioned methods and with consequent uncertainty propagation issues, either associated with experimental errors or modelling assumptions. Nonetheless, a limited number of publications have systematically cross-examined the performance of the various Experimental Dynamic Substructuring (EDS) methods and the possibility of their exploitation in a complementary way to expedite a hybrid experiment/numerical simulation. From this perspective, this paper performs a comparative uncertainty propagation analysis of three EDS algorithms for coupling physical and numerical subdomains with a dual assembly approach based on localized Lagrange multipliers. The main results and comparisons are based on a series of Monte Carlo simulations carried out on a five-DoF linear/non-linear chain-like systems that include typical aleatoric uncertainties emerging from measurement errors and excitation loads. In addition, we propose a new Composite-EDS (C-EDS) method to fuse both online and offline algorithms into a unique simulator. Capitalizing from the results of a more complex case study composed of a coupled isolated tank-piping system, we provide a feasible way to employ the C-EDS method when nonlinearities and multi-point constraints are present in the emulated system.
A method of estimating GPS instrumental biases with a convolution algorithm
Li, Qi; Ma, Guanyi; Lu, Weijun; Wan, Qingtao; Fan, Jiangtao; Wang, Xiaolan; Li, Jinghua; Li, Changhua
2018-03-01
This paper presents a method of deriving the instrumental differential code biases (DCBs) of GPS satellites and dual frequency receivers. Considering that the total electron content (TEC) varies smoothly over a small area, one ionospheric pierce point (IPP) and four more nearby IPPs were selected to build an equation with a convolution algorithm. In addition, unknown DCB parameters were arranged into a set of equations with GPS observations in a day unit by assuming that DCBs do not vary within a day. Then, the DCBs of satellites and receivers were determined by solving the equation set with the least-squares fitting technique. The performance of this method is examined by applying it to 361 days in 2014 using the observation data from 1311 GPS Earth Observation Network (GEONET) receivers. The result was crosswise-compared with the DCB estimated by the mesh method and the IONEX products from the Center for Orbit Determination in Europe (CODE). The DCB values derived by this method agree with those of the mesh method and the CODE products, with biases of 0.091 ns and 0.321 ns, respectively. The convolution method's accuracy and stability were quite good and showed improvements over the mesh method.
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Jing Xu
2016-07-01
Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.
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KULANTHAISAMY, A.
2014-05-01
Full Text Available This paper presents a Multi- objective Optimal Placement of Phasor Measurement Units (MOPP method in large electric transmission systems. It is proposed for minimizing the number of Phasor Measurement Units (PMUs for complete system observability and maximizing the measurement redundancy of the system, simultaneously. The measurement redundancy means that number of times a bus is able to monitor more than once by PMUs set. A higher level of measurement redundancy can maximize the total system observability and it is desirable for a reliable power system state estimation. Therefore, simultaneous optimization of the two conflicting objectives are performed using a binary coded Artificial Bee Colony (ABC algorithm. The complete observability of the power system is first prepared and then, single line loss contingency condition is considered to the main model. The efficiency of the proposed method is validated on IEEE 14, 30, 57 and 118 bus test systems. The valuable approach of ABC algorithm is demonstrated in finding the optimal number of PMUs and their locations by comparing the performance with earlier works.
A stereo remote sensing feature selection method based on artificial bee colony algorithm
Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi
2014-05-01
To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.
Joux, Antoine
2009-01-01
Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic
International Nuclear Information System (INIS)
Ganjaei, A. A.; Nourazar, S. S.
2009-01-01
A new algorithm, the modified direct simulation Monte-Carlo (MDSMC) method, for the simulation of Couette- Taylor gas flow problem is developed. The Taylor series expansion is used to obtain the modified equation of the first order time discretization of the collision equation and the new algorithm, MDSMC, is implemented to simulate the collision equation in the Boltzmann equation. In the new algorithm (MDSMC) there exists a new extra term which takes in to account the effect of the second order collision. This new extra term has the effect of enhancing the appearance of the first Taylor instabilities of vortices streamlines. In the new algorithm (MDSMC) there also exists a second order term in time step in the probabilistic coefficients which has the effect of simulation with higher accuracy than the previous DSMC algorithm. The appearance of the first Taylor instabilities of vortices streamlines using the MDSMC algorithm at different ratios of ω/ν (experimental data of Taylor) occurred at less time-step than using the DSMC algorithm. The results of the torque developed on the stationary cylinder using the MDSMC algorithm show better agreement in comparison with the experimental data of Kuhlthau than the results of the torque developed on the stationary cylinder using the DSMC algorithm
2014-01-01
Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism
Directory of Open Access Journals (Sweden)
Yongwei Zhang
2017-01-01
Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.
Soft-tissue injuries of the fingertip: methods of evaluation and treatment. An algorithmic approach.
Lemmon, Joshua A; Janis, Jeffrey E; Rohrich, Rod J
2008-09-01
After studying this article, the participant should be able to: 1. Understand the anatomy of the fingertip. 2. Describe the methods of evaluating fingertip injuries. 3. Discuss reconstructive options for various tip injuries. The fingertip is the most commonly injured part of the hand, and therefore fingertip injuries are among the most frequent injuries that plastic surgeons are asked to treat. Although microsurgical techniques have enabled replantation of even very distal tip amputations, it is relatively uncommon that a distal tip injury will be appropriate for replantation. In the event that replantation is not pursued, options for distal tip soft-tissue reconstruction must be considered. This review presents a straightforward method for evaluating fingertip injuries and provides an algorithm for fingertip reconstruction.
Nour, Abdoulshakour M.
Oil and gas exploration professionals have long recognized the importance of predicting pore pressure before drilling wells. Pre-drill pore pressure estimation not only helps with drilling wells safely but also aids in the determination of formation fluids migration and seal integrity. With respect to the hydrocarbon reservoirs, the appropriate drilling mud weight is directly related to the estimated pore pressure in the formation. If the mud weight is lower than the formation pressure, a blowout may occur, and conversely, if it is higher than the formation pressure, the formation may suffer irreparable damage due to the invasion of drilling fluids into the formation. A simple definition of pore pressure is the pressure of the pore fluids in excess of the hydrostatic pressure. In this thesis, I investigated the utility of advance computer algorithm called Support Vector Machine (SVM) to learn the pattern of high pore pressure regime, using seismic attributes such as Instantaneous phase, t*Attenuation, Cosine of Phase, Vp/Vs ratio, P-Impedance, Reflection Acoustic Impedance, Dominant frequency and one well attribute (Mud-Weigh) as the learning dataset. I applied this technique to the over pressured Qalibah formation of Northwest Saudi Arabia. The results of my research revealed that in the Qalibah formation of Northwest Saudi Arabia, the pore pressure trend can be predicted using SVM with seismic and well attributes as the learning dataset. I was able to show the pore pressure trend at any given point within the geographical extent of the 3D seismic data from which the seismic attributes were derived. In addition, my results surprisingly showed the subtle variation of pressure within the thick succession of shale units of the Qalibah formation.
International Nuclear Information System (INIS)
Mohorianu, S.; Lozovan, M.; Rusu, F.-V.
2009-01-01
Nanostructured materials with tailored properties are now essential for future applications in the current industrial manufacturing. Extracting valuable information from data by using the distributed computer processing and storage technologies, as well the Artificial Neural Network (ANN) and the development of advanced algorithms for knowledge discovery are the purpose of our work. We describe how a Simulation and Design Method (SDM) attempt, based on our last results, is applied on two perovskites type materials, La 0.7 Ca 0.3 MnO 3 and La 0.7 Sr 0.3 MnO 3 in order to study the Anomalous Hall Effect (AHE). Our new ANN model, is intended to contribute to the effort to improve some properties of new materials. It implements and uses the basic building blocks of neural computation, such as multi-layer perceptrons. ANN can learn associative patterns and approximate the functional relationship between a set of input and output. Modeling and simulation techniques affect all stages in the development and improvement of new materials, from the initial formation of concepts to synthesis and characterization of properties. A new SDM with ANN for some nanomagnetic materials was given. Neural networks have been applied successfully in the identification and classification of some nanomagnetic characteristics from a large amount of data. (authors)
Experiences from introduction of peer-to-peer teaching methods in Advanced Biochemistry E2010
DEFF Research Database (Denmark)
Brodersen, Ditlev; Etzerodt, Michael; Rasmussen, Jan Trige
2012-01-01
During the autumn semester 2010, we experimented with a range of active teaching methods on the course, Advanced Biochemistry, at the Department of Molecular Biology and Genetics.......During the autumn semester 2010, we experimented with a range of active teaching methods on the course, Advanced Biochemistry, at the Department of Molecular Biology and Genetics....
Advanced methods of analysis variance on scenarios of nuclear prospective
International Nuclear Information System (INIS)
Blazquez, J.; Montalvo, C.; Balbas, M.; Garcia-Berrocal, A.
2011-01-01
Traditional techniques of propagation of variance are not very reliable, because there are uncertainties of 100% relative value, for this so use less conventional methods, such as Beta distribution, Fuzzy Logic and the Monte Carlo Method.
Advanced construction methods for new nuclear power plants
International Nuclear Information System (INIS)
Bilbao y Leon, Sama; Cleveland, John; Moon, Seong-Gyun; Tyobeka, Bismark
2009-01-01
The length of the construction and commissioning phases of nuclear power plants have historically been longer than for conventional fossil fuelled plants, often having a record of delays and cost overruns as a result from several factors including legal interventions and revisions of safety regulations. Recent nuclear construction projects however, have shown that long construction periods for nuclear power plants are no longer the norm. While there are several inter-related factors that influence the construction time, the use of advanced construction techniques has contributed significantly to reducing the construction length of recent nuclear projects. (author)
Advanced 3D inverse method for designing turbomachine blades
Energy Technology Data Exchange (ETDEWEB)
Dang, T. [Syracuse Univ., NY (United States)
1995-10-01
To meet the goal of 60% plant-cycle efficiency or better set in the ATS Program for baseload utility scale power generation, several critical technologies need to be developed. One such need is the improvement of component efficiencies. This work addresses the issue of improving the performance of turbo-machine components in gas turbines through the development of an advanced three-dimensional and viscous blade design system. This technology is needed to replace some elements in current design systems that are based on outdated technology.
Advances in beam position monitoring methods at GSI synchrotrons
Energy Technology Data Exchange (ETDEWEB)
Singh, Rahul; Reiter, Andreas; Forck, Peter; Kowina, Piotr; Lang, Kevin; Miedzik, Piotr [GSI, Darmstadt (Germany)
2016-07-01
At the GSI synchrotron facilities, capacitive beam pick-up signals for position evaluation are immediately digitized within the acquisition electronics due to availability of reliable, fast and high resolution ADCs. The signal processing aspects are therefore fully dealt with in the digital domain. Novel digital techniques for asynchronous and synchronous (bunch-by-bunch) beam position estimation have been developed at GSI SIS-18 and CRYRING as part of FAIR development program. This contribution will highlight the advancements and its impact on the operational ease and high availability of the BPM systems.
Nonlinear dynamics of rotating shallow water methods and advances
Zeitlin, Vladimir
2007-01-01
The rotating shallow water (RSW) model is of wide use as a conceptual tool in geophysical fluid dynamics (GFD), because, in spite of its simplicity, it contains all essential ingredients of atmosphere and ocean dynamics at the synoptic scale, especially in its two- (or multi-) layer version. The book describes recent advances in understanding (in the framework of RSW and related models) of some fundamental GFD problems, such as existence of the slow manifold, dynamical splitting of fast (inertia-gravity waves) and slow (vortices, Rossby waves) motions, nonlinear geostrophic adjustment and wa
Directory of Open Access Journals (Sweden)
Mehdi Neshat
2015-11-01
Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.
Directory of Open Access Journals (Sweden)
Yidong Xu
2017-10-01
Full Text Available A novel localization method based on multiple signal classification (MUSIC algorithm is proposed for positioning an electric dipole source in a confined underwater environment by using electric dipole-receiving antenna array. In this method, the boundary element method (BEM is introduced to analyze the boundary of the confined region by use of a matrix equation. The voltage of each dipole pair is used as spatial-temporal localization data, and it does not need to obtain the field component in each direction compared with the conventional fields based localization method, which can be easily implemented in practical engineering applications. Then, a global-multiple region-conjugate gradient (CG hybrid search method is used to reduce the computation burden and to improve the operation speed. Two localization simulation models and a physical experiment are conducted. Both the simulation results and physical experiment result provide accurate positioning performance, with the help to verify the effectiveness of the proposed localization method in underwater environments.
Development of Nuclear Power Plant Safety Evaluation Method for the Automation Algorithm Application
Energy Technology Data Exchange (ETDEWEB)
Kim, Seung Geun; Seong, Poong Hyun [KAIST, Daejeon (Korea, Republic of)
2016-10-15
It is commonly believed that replacing human operators to the automated system would guarantee greater efficiency, lower workloads, and fewer human error. Conventional machine learning techniques are considered as not capable to handle complex situations in NPP. Due to these kinds of issues, automation is not actively adopted although human error probability drastically increases during abnormal situations in NPP due to overload of information, high workload, and short time available for diagnosis. Recently, new machine learning techniques, which are known as ‘deep learning’ techniques have been actively applied to many fields, and the deep learning technique-based artificial intelligences (AIs) are showing better performance than conventional AIs. In 2015, deep Q-network (DQN) which is one of the deep learning techniques was developed and applied to train AI that automatically plays various Atari 2800 games, and this AI surpassed the human-level playing in many kind of games. Also in 2016, ‘Alpha-Go’, which was developed by ‘Google Deepmind’ based on deep learning technique to play the game of Go (i.e. Baduk), was defeated Se-dol Lee who is the World Go champion with score of 4:1. By the effort for reducing human error in NPPs, the ultimate goal of this study is the development of automation algorithm which can cover various situations in NPPs. As the first part, quantitative and real-time NPP safety evaluation method is being developed in order to provide the training criteria for automation algorithm. For that, EWS concept of medical field was adopted, and the applicability is investigated in this paper. Practically, the application of full automation (i.e. fully replaces human operators) may requires much more time for the validation and investigation of side-effects after the development of automation algorithm, and so the adoption in the form of full automation will take long time.
Development of Nuclear Power Plant Safety Evaluation Method for the Automation Algorithm Application
International Nuclear Information System (INIS)
Kim, Seung Geun; Seong, Poong Hyun
2016-01-01
It is commonly believed that replacing human operators to the automated system would guarantee greater efficiency, lower workloads, and fewer human error. Conventional machine learning techniques are considered as not capable to handle complex situations in NPP. Due to these kinds of issues, automation is not actively adopted although human error probability drastically increases during abnormal situations in NPP due to overload of information, high workload, and short time available for diagnosis. Recently, new machine learning techniques, which are known as ‘deep learning’ techniques have been actively applied to many fields, and the deep learning technique-based artificial intelligences (AIs) are showing better performance than conventional AIs. In 2015, deep Q-network (DQN) which is one of the deep learning techniques was developed and applied to train AI that automatically plays various Atari 2800 games, and this AI surpassed the human-level playing in many kind of games. Also in 2016, ‘Alpha-Go’, which was developed by ‘Google Deepmind’ based on deep learning technique to play the game of Go (i.e. Baduk), was defeated Se-dol Lee who is the World Go champion with score of 4:1. By the effort for reducing human error in NPPs, the ultimate goal of this study is the development of automation algorithm which can cover various situations in NPPs. As the first part, quantitative and real-time NPP safety evaluation method is being developed in order to provide the training criteria for automation algorithm. For that, EWS concept of medical field was adopted, and the applicability is investigated in this paper. Practically, the application of full automation (i.e. fully replaces human operators) may requires much more time for the validation and investigation of side-effects after the development of automation algorithm, and so the adoption in the form of full automation will take long time
Hybrid Genetic Algorithm - Local Search Method for Ground-Water Management
Chiu, Y.; Nishikawa, T.; Martin, P.
2008-12-01
Ground-water management problems commonly are formulated as a mixed-integer, non-linear programming problem (MINLP). Relying only on conventional gradient-search methods to solve the management problem is computationally fast; however, the methods may become trapped in a local optimum. Global-optimization schemes can identify the global optimum, but the convergence is very slow when the optimal solution approaches the global optimum. In this study, we developed a hybrid optimization scheme, which includes a genetic algorithm and a gradient-search method, to solve the MINLP. The genetic algorithm identifies a near- optimal solution, and the gradient search uses the near optimum to identify the global optimum. Our methodology is applied to a conjunctive-use project in the Warren ground-water basin, California. Hi- Desert Water District (HDWD), the primary water-manager in the basin, plans to construct a wastewater treatment plant to reduce future septic-tank effluent from reaching the ground-water system. The treated wastewater instead will recharge the ground-water basin via percolation ponds as part of a larger conjunctive-use strategy, subject to State regulations (e.g. minimum distances and travel times). HDWD wishes to identify the least-cost conjunctive-use strategies that control ground-water levels, meet regulations, and identify new production-well locations. As formulated, the MINLP objective is to minimize water-delivery costs subject to constraints including pump capacities, available recharge water, water-supply demand, water-level constraints, and potential new-well locations. The methodology was demonstrated by an enumerative search of the entire feasible solution and comparing the optimum solution with results from the branch-and-bound algorithm. The results also indicate that the hybrid method identifies the global optimum within an affordable computation time. Sensitivity analyses, which include testing different recharge-rate scenarios, pond
Methods and Algorithms for Solving Inverse Problems for Fractional Advection-Dispersion Equations
Aldoghaither, Abeer
2015-11-12
Fractional calculus has been introduced as an e cient tool for modeling physical phenomena, thanks to its memory and hereditary properties. For example, fractional models have been successfully used to describe anomalous di↵usion processes such as contaminant transport in soil, oil flow in porous media, and groundwater flow. These models capture important features of particle transport such as particles with velocity variations and long-rest periods. Mathematical modeling of physical phenomena requires the identification of pa- rameters and variables from available measurements. This is referred to as an inverse problem. In this work, we are interested in studying theoretically and numerically inverse problems for space Fractional Advection-Dispersion Equation (FADE), which is used to model solute transport in porous media. Identifying parameters for such an equa- tion is important to understand how chemical or biological contaminants are trans- ported throughout surface aquifer systems. For instance, an estimate of the di↵eren- tiation order in groundwater contaminant transport model can provide information about soil properties, such as the heterogeneity of the medium. Our main contribution is to propose a novel e cient algorithm based on modulat-ing functions to estimate the coe cients and the di↵erentiation order for space FADE, which can be extended to general fractional Partial Di↵erential Equation (PDE). We also show how the method can be applied to the source inverse problem. This work is divided into two parts: In part I, the proposed method is described and studied through an extensive numerical analysis. The local convergence of the proposed two-stage algorithm is proven for 1D space FADE. The properties of this method are studied along with its limitations. Then, the algorithm is generalized to the 2D FADE. In part II, we analyze direct and inverse source problems for a space FADE. The problem consists of recovering the source term using final
Advances in Probes and Methods for Clinical EPR Oximetry
Hou, Huagang; Khan, Nadeem; Jarvis, Lesley A.; Chen, Eunice Y.; Williams, Benjamin B.; Kuppusamy, Periannan
2015-01-01
EPR oximetry, which enables reliable, accurate, and repeated measurements of the partial pressure of oxygen in tissues, provides a unique opportunity to investigate the role of oxygen in the pathogenesis and treatment of several diseases including cancer, stroke, and heart failure. Building on significant advances in the in vivo application of EPR oximetry for small animal models of disease, we are developing suitable probes and instrumentation required for use in human subjects. Our laboratory has established the feasibility of clinical EPR oximetry in cancer patients using India ink, the only material presently approved for clinical use. We now are developing the next generation of probes, which are both superior in terms of oxygen sensitivity and biocompatibility including an excellent safety profile for use in humans. Further advances include the development of implantable oxygen sensors linked to an external coupling loop for measurements of deep-tissue oxygenations at any depth, overcoming the current limitation of 10 mm. This paper presents an overview of recent developments in our ability to make meaningful measurements of oxygen partial pressures in human subjects under clinical settings. PMID:24729217
Directory of Open Access Journals (Sweden)
Luman Zhao
2015-01-01
Full Text Available A thrust allocation method was proposed based on a hybrid optimization algorithm to efficiently and dynamically position a semisubmersible drilling rig. That is, the thrust allocation was optimized to produce the generalized forces and moment required while at the same time minimizing the total power consumption under the premise that forbidden zones should be taken into account. An optimization problem was mathematically formulated to provide the optimal thrust allocation by introducing the corresponding design variables, objective function, and constraints. A hybrid optimization algorithm consisting of a genetic algorithm and a sequential quadratic programming (SQP algorithm was selected and used to solve this problem. The proposed method was evaluated by applying it to a thrust allocation problem for a semisubmersible drilling rig. The results indicate that the proposed method can be used as part of a cost-effective strategy for thrust allocation of the rig.
Directory of Open Access Journals (Sweden)
Erik Cuevas
2015-01-01
Full Text Available In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC algorithm and the evolutionary method harmony search (HS. With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.
Status and prospects of activities on algorithms and methods in WWER-1000 core control
International Nuclear Information System (INIS)
Filimonov, P.; Krainov, Y.; Proselkov, V.
1994-01-01
On the basis of long-term operational experience and investigations the problems of WWER-1000 reactor control are discussed. Such control is needed for WWER-1000, as well as for its Western analog PWR, for suppressing the axially instable power density field resulted from non-equilibrium redistribution of Xe-135 nuclei in the reactor core. It has been found that an adequate assessment of the reactor state and the prediction of its response to various control actions is essential for the control of power density distribution. For this purpose a computerized operator's adviser with a reactor simulator realizing a physical reactor model based on BIPR-7 code is used. The operation experience of WWER-1000 shows that the available control algorithms allow, with a fair degree of assurance, the prevention of intensive xenon oscillations and the stabilization of the axial offset. But in connection with the renunciation of half-length control rods a new algorithm is under development which makes use of full-length control rods for suppressing the intensive xenon oscillations in the descending phase. A new method based on BIPR-7 and PERMAK codes is also being developed for estimating the value and rate of linear power rating change of the fuel elements in power cycling. 12 figs., 7 refs
Proposed algorithm to improve job shop production scheduling using ant colony optimization method
Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari
2017-12-01
This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.
A Multiuser Manufacturing Resource Service Composition Method Based on the Bees Algorithm
Directory of Open Access Journals (Sweden)
Yongquan Xie
2015-01-01
Full Text Available In order to realize an optimal resource service allocation in current open and service-oriented manufacturing model, multiuser resource service composition (RSC is modeled as a combinational and constrained multiobjective problem. The model takes into account both subjective and objective quality of service (QoS properties as representatives to evaluate a solution. The QoS properties aggregation and evaluation techniques are based on existing researches. The basic Bees Algorithm is tailored for finding a near optimal solution to the model, since the basic version is only proposed to find a desired solution in continuous domain and thus not suitable for solving the problem modeled in our study. Particular rules are designed for handling the constraints and finding Pareto optimality. In addition, the established model introduces a trusted service set to each user so that the algorithm could start by searching in the neighbor of more reliable service chains (known as seeds than those randomly generated. The advantages of these techniques are validated by experiments in terms of success rate, searching speed, ability of avoiding ingenuity, and so forth. The results demonstrate the effectiveness of the proposed method in handling multiuser RSC problems.
Directory of Open Access Journals (Sweden)
Q. Zhou
2017-07-01
Full Text Available Visual Odometry (VO is a critical component for planetary robot navigation and safety. It estimates the ego-motion using stereo images frame by frame. Feature points extraction and matching is one of the key steps for robotic motion estimation which largely influences the precision and robustness. In this work, we choose the Oriented FAST and Rotated BRIEF (ORB features by considering both accuracy and speed issues. For more robustness in challenging environment e.g., rough terrain or planetary surface, this paper presents a robust outliers elimination method based on Euclidean Distance Constraint (EDC and Random Sample Consensus (RANSAC algorithm. In the matching process, a set of ORB feature points are extracted from the current left and right synchronous images and the Brute Force (BF matcher is used to find the correspondences between the two images for the Space Intersection. Then the EDC and RANSAC algorithms are carried out to eliminate mismatches whose distances are beyond a predefined threshold. Similarly, when the left image of the next time matches the feature points with the current left images, the EDC and RANSAC are iteratively performed. After the above mentioned, there are exceptional remaining mismatched points in some cases, for which the third time RANSAC is applied to eliminate the effects of those outliers in the estimation of the ego-motion parameters (Interior Orientation and Exterior Orientation. The proposed approach has been tested on a real-world vehicle dataset and the result benefits from its high robustness.
Status and prospects of activities on algorithms and methods in WWER-1000 core control
Energy Technology Data Exchange (ETDEWEB)
Filimonov, P; Krainov, Y; Proselkov, V [Russian Research Centre Kurchatov Inst., Moscow (Russian Federation)
1994-12-31
On the basis of long-term operational experience and investigations the problems of WWER-1000 reactor control are discussed. Such control is needed for WWER-1000, as well as for its Western analog PWR, for suppressing the axially instable power density field resulted from non-equilibrium redistribution of Xe-135 nuclei in the reactor core. It has been found that an adequate assessment of the reactor state and the prediction of its response to various control actions is essential for the control of power density distribution. For this purpose a computerized operator`s adviser with a reactor simulator realizing a physical reactor model based on BIPR-7 code is used. The operation experience of WWER-1000 shows that the available control algorithms allow, with a fair degree of assurance, the prevention of intensive xenon oscillations and the stabilization of the axial offset. But in connection with the renunciation of half-length control rods a new algorithm is under development which makes use of full-length control rods for suppressing the intensive xenon oscillations in the descending phase. A new method based on BIPR-7 and PERMAK codes is also being developed for estimating the value and rate of linear power rating change of the fuel elements in power cycling. 12 figs., 7 refs.
Novel image reconstruction algorithm for multi-phase flow tomography system using γ ray method
International Nuclear Information System (INIS)
Hao Kuihong; Wang Huaxiang; Gao Mei
2007-01-01
After analyzing the reason of image reconstructed algorithm by using the conventional back projection (IBP) is prone to produce spurious line, and considering the characteristic of multi-phase flow tomography, a novel image reconstruction algorithm is proposed, which carries out the intersection calculation using back projection data. This algorithm can obtain a perfect system point spread function, and can eliminate spurious line better. Simulating results show that the algorithm is effective for identifying multi-phase flow pattern. (authors)
A new method for data assimilation: the back and forth nudging algorithm
Auroux , Didier; Blum , Jacques; Nodet , Maëlle
2013-01-01
International audience; In this paper, we propose an improvement to the Back and Forth Nudging algorithm for handling diffusion in the context of geophysical data assimilation. We detail the Diffusive Back and Forth Nudging algorithm, in which the sign of the diffusion term is changed in the backward integrations. We study the convergence of this algorithm, in particular for linear transport equations.
Advanced methods of microscope control using μManager software.
Edelstein, Arthur D; Tsuchida, Mark A; Amodaj, Nenad; Pinkard, Henry; Vale, Ronald D; Stuurman, Nico
μManager is an open-source, cross-platform desktop application, to control a wide variety of motorized microscopes, scientific cameras, stages, illuminators, and other microscope accessories. Since its inception in 2005, μManager has grown to support a wide range of microscopy hardware and is now used by thousands of researchers around the world. The application provides a mature graphical user interface and offers open programming interfaces to facilitate plugins and scripts. Here, we present a guide to using some of the recently added advanced μManager features, including hardware synchronization, simultaneous use of multiple cameras, projection of patterned light onto a specimen, live slide mapping, imaging with multi-well plates, particle localization and tracking, and high-speed imaging.
Proceedings of national workshop on advanced methods for materials characterization
International Nuclear Information System (INIS)
2004-10-01
During the past two decades there had been tremendous growth in the field of material science and a variety of new materials with user specific properties have been developed such as smart shape memory alloys, hybrid materials like glass-ceramics, cermets, met-glasses, inorganic- organic composite layered structures, mixed oxides with negative thermal expansion, functional polymer materials etc. Study of nano-particles and the materials assembled from such particles is another area of active research being pursued all over the world. Preparation and characterization of nano-sized materials is a challenge because of their dimensions and size dependent properties. This has led to the emergence of a variety of advanced techniques, which need to be brought to the attention of the researchers working in the field of material science which requires the expertise of physics, chemistry and process engineering. This volume deals with above aspects and papers relevant to INIS are indexed separately
Recent advances in neutral particle transport methods and codes
International Nuclear Information System (INIS)
Azmy, Y.Y.
1996-01-01
An overview of ORNL's three-dimensional neutral particle transport code, TORT, is presented. Special features of the code that make it invaluable for large applications are summarized for the prospective user. Advanced capabilities currently under development and installation in the production release of TORT are discussed; they include: multitasking on Cray platforms running the UNICOS operating system; Adjacent cell Preconditioning acceleration scheme; and graphics codes for displaying computed quantities such as the flux. Further developments for TORT and its companion codes to enhance its present capabilities, as well as expand its range of applications are disucssed. Speculation on the next generation of neutron particle transport codes at ORNL, especially regarding unstructured grids and high order spatial approximations, are also mentioned
Advanced methods of microscope control using μManager software
Directory of Open Access Journals (Sweden)
Arthur D Edelstein
2014-07-01
Full Text Available µManager is an open-source, cross-platform desktop application, to control a wide variety of motorized microscopes, scientific cameras, stages, illuminators, and other microscope accessories. Since its inception in 2005, µManager has grown to support a wide range of microscopy hardware and is now used by thousands of researchers around the world. The application provides a mature graphical user interface and offers open programming interfaces to facilitate plugins and scripts. Here, we present a guide to using some of the recently added advanced µManager features, including hardware synchronization, simultaneous use of multiple cameras, projection of patterned light onto a specimen, live slide mapping, imaging with multi-well plates, particle localization and tracking, and high-speed imaging.
Advances in surface wave methods: Cascaded MASW-SASW
Westerhoff, R.S.; Brouwer, J.H.; Meekes, J.A.C.
2005-01-01
The application of the MASW method in areas that show strong lateral variations in subsurface properties is limited. Traditional SASW may yield a better lateral resolution but the dispersion curves (and thus the subsurface models) obtained with the method may be poor. The joint application of MASW