Naeem, Huma; Hussain, Mukhtar; Khan, Shoab A
2009-01-01
Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) can fire from a distance of 5Km, but manual TEWA systems are constrained by human vision range and other constraints. Current TEWA systems usually work on target-by-target basis using some type of greedy algorithm thus affecting the optimality of the solution and failing in multi-target scenario. his paper relates to a novel two-staged flexible dynamic decision support based optimal threat evaluation and weapon assignment...
Symplectic algebraic dynamics algorithm
Institute of Scientific and Technical Information of China (English)
2007-01-01
Based on the algebraic dynamics solution of ordinary differential equations andintegration of ,the symplectic algebraic dynamics algorithm sn is designed,which preserves the local symplectic geometric structure of a Hamiltonian systemand possesses the same precision of the na ve algebraic dynamics algorithm n.Computer experiments for the 4th order algorithms are made for five test modelsand the numerical results are compared with the conventional symplectic geometric algorithm,indicating that sn has higher precision,the algorithm-inducedphase shift of the conventional symplectic geometric algorithm can be reduced,and the dynamical fidelity can be improved by one order of magnitude.
Evolutionary Algorithms and Dynamic Programming
Doerr, Benjamin; Eremeev, Anton; Neumann, Frank; Theile, Madeleine; Thyssen, Christian
2013-01-01
Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion. We take a general approach and relate the construction of such algorithms to the development of algorithms using dynamic programming techniques. Thereby, we give general guidelines on how ...
Inventory Management with Asset-Based Financing
John A. Buzacott; Rachel Q. Zhang
2004-01-01
Most of the traditional models in production and inventory control ignore the financial states of an organization and can lead to infeasible practices in real systems. This paper is the first attempt to incorporate asset-based financing into production decisions. Instead of setting a known, exogenously determined budgetary constraint as most existing models suggest, we model the available cash in each period as a function of assets and liabilities that may be updated periodically according to...
Institute of Scientific and Technical Information of China (English)
2007-01-01
Based on the exact analytical solution of ordinary differential equations, a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm. A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models. The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision, and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.
Institute of Scientific and Technical Information of China (English)
WANG ShunJin; ZHANG Hua
2007-01-01
Based on the exact analytical solution of ordinary differential equations,a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm.A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models.The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision,and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.
Control algorithms for dynamic attenuators
Energy Technology Data Exchange (ETDEWEB)
Hsieh, Scott S., E-mail: sshsieh@stanford.edu [Department of Radiology, Stanford University, Stanford, California 94305 and Department of Electrical Engineering, Stanford University, Stanford, California 94305 (United States); Pelc, Norbert J. [Department of Radiology, Stanford University, Stanford California 94305 and Department of Bioengineering, Stanford University, Stanford, California 94305 (United States)
2014-06-15
Purpose: The authors describe algorithms to control dynamic attenuators in CT and compare their performance using simulated scans. Dynamic attenuators are prepatient beam shaping filters that modulate the distribution of x-ray fluence incident on the patient on a view-by-view basis. These attenuators can reduce dose while improving key image quality metrics such as peak or mean variance. In each view, the attenuator presents several degrees of freedom which may be individually adjusted. The total number of degrees of freedom across all views is very large, making many optimization techniques impractical. The authors develop a theory for optimally controlling these attenuators. Special attention is paid to a theoretically perfect attenuator which controls the fluence for each ray individually, but the authors also investigate and compare three other, practical attenuator designs which have been previously proposed: the piecewise-linear attenuator, the translating attenuator, and the double wedge attenuator. Methods: The authors pose and solve the optimization problems of minimizing the mean and peak variance subject to a fixed dose limit. For a perfect attenuator and mean variance minimization, this problem can be solved in simple, closed form. For other attenuator designs, the problem can be decomposed into separate problems for each view to greatly reduce the computational complexity. Peak variance minimization can be approximately solved using iterated, weighted mean variance (WMV) minimization. Also, the authors develop heuristics for the perfect and piecewise-linear attenuators which do not requirea priori knowledge of the patient anatomy. The authors compare these control algorithms on different types of dynamic attenuators using simulated raw data from forward projected DICOM files of a thorax and an abdomen. Results: The translating and double wedge attenuators reduce dose by an average of 30% relative to current techniques (bowtie filter with tube current
Efficient Algorithms for Langevin and DPD Dynamics
Goga, N.; Rzepiela, A. J.; de Vries, A. H.; Marrink, S. J.; Berendsen, H. J. C.
2012-01-01
In this article, we present several algorithms for stochastic dynamics, including Langevin dynamics and different variants of Dissipative Particle Dynamics (DPD), applicable to systems with or without constraints. The algorithms are based on the impulsive application of friction and noise, thus avoi
A Unifying Multibody Dynamics Algorithm Development Workbench
Ziegler, John L.
2005-01-01
The development of new and efficient algorithms for multibody dynamics has been an important research area. These algorithms are used for modeling, simulation, and control of systems such as spacecraft, robotic systems, automotive applications, the human body, manufacturing operations, and micro-electromechanical systems (MEMS). At JPL's Dynamics and Real Time Simulation (DARTS) Laboratory we have developed software that serves as a computational workbench for these algorithms. This software utilizes the mathematical perspective of the spatial operator algebra, which allows the development of dynamics algorithms and new insights into multibody dynamics.
Algebraic dynamics solution and algebraic dynamics algorithm of Burgers equations
Institute of Scientific and Technical Information of China (English)
2008-01-01
Algebraic dynamics solution and algebraic dynamics algorithm of nonlinear partial differential evolution equations in the functional space are applied to Burgers equation. The results indicate that the approach is effective for analytical solutions to Burgers equation, and the algorithm for numerical solutions of Burgers equation is more stable, with higher precision than other existing finite difference algo-rithms.
Dynamic Programming Algorithms in Speech Recognition
Directory of Open Access Journals (Sweden)
Titus Felix FURTUNA
2008-01-01
Full Text Available In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.
Dynamic Streaming Algorithms for Epsilon-Kernels
Chan, Timothy M.
2016-01-01
Introduced by Agarwal, Har-Peled, and Varadarajan [J. ACM, 2004], an epsilon-kernel of a point set is a coreset that can be used to approximate the width, minimum enclosing cylinder, minimum bounding box, and solve various related geometric optimization problems. Such coresets form one of the most important tools in the design of linear-time approximation algorithms in computational geometry, as well as efficient insertion-only streaming algorithms and dynamic (non-streaming) data structures...
Algorithms for Lattice QCD with Dynamical Fermions
International Nuclear Information System (INIS)
We consider recent progress in algorithms for generating gauge field configurations that include the dynamical effects of light fermions. We survey what has been achieved in recent state-of-the-art computations, and examine the trade-offs between performance and control of systematic errors. We briefly review the use of polynomial and rational approximations in Hybrid Monte Carlo algorithms, and some of the theory of on-shell chiral fermions on the lattice. This provides a theoretical framework within which we compare algorithmic alternatives for their implementation; and again we examine the trade-offs between speed and error control
DYNAMIC BANDWIDTH ALLOCATION ALGORITHM UTILIZING FULL BAND
Institute of Scientific and Technical Information of China (English)
Han Guodong; Wen Jianhua; Wu Jiangxing
2006-01-01
A kind of Dynamic Full Bandwidth Utilized (DFBU) allocation algorithm is introduced. This algorithm allows a single link to use bandwidth far beyond its fair share bandwidth in a multi-service packet transporting system. Three important parameters as the bound on maximum and minimum bandwidth, the maximum packet delay and the minimum band width utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system to use all the spare bandwidth (up to full-bandwidth) while the performance of fairness and QoS requirement is still guaranteed.
Cell list algorithms for nonequilibrium molecular dynamics
Dobson, Matthew; Fox, Ian; Saracino, Alexandra
2016-06-01
We present two modifications of the standard cell list algorithm that handle molecular dynamics simulations with deforming periodic geometry. Such geometry naturally arises in the simulation of homogeneous, linear nonequilibrium flow modeled with periodic boundary conditions, and recent progress has been made developing boundary conditions suitable for general 3D flows of this type. Previous works focused on the planar flows handled by Lees-Edwards or Kraynik-Reinelt boundary conditions, while the new versions of the cell list algorithm presented here are formulated to handle the general 3D deforming simulation geometry. As in the case of equilibrium, for short-ranged pairwise interactions, the cell list algorithm reduces the computational complexity of the force computation from O(N2) to O(N), where N is the total number of particles in the simulation box. We include a comparison of the complexity and efficiency of the two proposed modifications of the standard algorithm.
Algorithm for dynamic Speckle pattern processing
Cariñe, J.; Guzmán, R.; Torres-Ruiz, F. A.
2016-07-01
In this paper we present a new algorithm for determining surface activity by processing speckle pattern images recorded with a CCD camera. Surface activity can be produced by motility or small displacements among other causes, and is manifested as a change in the pattern recorded in the camera with reference to a static background pattern. This intensity variation is considered to be a small perturbation compared with the mean intensity. Based on a perturbative method we obtain an equation with which we can infer information about the dynamic behavior of the surface that generates the speckle pattern. We define an activity index based on our algorithm that can be easily compared with the outcomes from other algorithms. It is shown experimentally that this index evolves in time in the same way as the Inertia Moment method, however our algorithm is based on direct processing of speckle patterns without the need for other kinds of post-processes (like THSP and co-occurrence matrix), making it a viable real-time method. We also show how this algorithm compares with several other algorithms when applied to calibration experiments. From these results we conclude that our algorithm offer qualitative and quantitative advantages over current methods.
Local minimization algorithms for dynamic programming equations
Kalise, Dante; Kröner, Axel; Kunisch, Karl
2015-01-01
The numerical realization of the dynamic programming principle for continuous-time optimal control leads to nonlinear Hamilton-Jacobi-Bellman equations which require the minimization of a nonlinear mapping over the set of admissible controls. This minimization is often performed by comparison over a finite number of elements of the control set. In this paper we demonstrate the importance of an accurate realization of these minimization problems and propose algorithms by which this can be achi...
Benchmarking dynamic Bayesian network structure learning algorithms
Trabelsi, Ghada; Leray, Philippe; Ben Ayed, Mounir; Alimi, Adel
2012-01-01
Dynamic Bayesian Networks (DBNs) are probabilistic graphical models dedicated to modeling multivariate time series. Two-time slice BNs (2-TBNs) are the most current type of these models. Static BN structure learning is a well-studied domain. Many approaches have been proposed and the quality of these algorithms has been studied over a range of di erent standard networks and methods of evaluation. To the best of our knowledge, all studies about DBN structure learning use their own benchmarks a...
An Improved Dynamic Bandwidth Allocation Algorithm for Ethernet PON
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
This paper proposes an improved Dynamic Bandwidth Allocation (DBA) algorithm for EPON, which combines static and traditional dynamic allocation schemes. Simulation result shows that the proposed algorithm may effectively improve the performance of packet delay.
Fundamental algorithms in computational fluid dynamics
Pulliam, Thomas H
2014-01-01
Intended as a textbook for courses in computational fluid dynamics at the senior undergraduate or graduate level, this book is a follow-up to the book Fundamentals of Computational Fluid Dynamics by the same authors, which was published in the series Scientific Computation in 2001. Whereas the earlier book concentrated on the analysis of numerical methods applied to model equations, this new book concentrates on algorithms for the numerical solution of the Euler and Navier-Stokes equations. It focuses on some classical algorithms as well as the underlying ideas based on the latest methods. A key feature of the book is the inclusion of programming exercises at the end of each chapter based on the numerical solution of the quasi-one-dimensional Euler equations and the shock-tube problem. These exercises can be included in the context of a typical course, and sample solutions are provided in each chapter, so readers can confirm that they have coded the algorithms correctly.
Enhanced Dynamic Algorithm of Genome Sequence Alignments
Directory of Open Access Journals (Sweden)
Arabi E. keshk
2014-05-01
Full Text Available The merging of biology and computer science has created a new field called computational biology that explore the capacities of computers to gain knowledge from biological data, bioinformatics. Computational biology is rooted in life sciences as well as computers, information sciences, and technologies. The main problem in computational biology is sequence alignment that is a way of arranging the sequences of DNA, RNA or protein to identify the region of similarity and relationship between sequences. This paper introduces an enhancement of dynamic algorithm of genome sequence alignment, which called EDAGSA. It is filling the three main diagonals without filling the entire matrix by the unused data. It gets the optimal solution with decreasing the execution time and therefore the performance is increased. To illustrate the effectiveness of optimizing the performance of the proposed algorithm, it is compared with the traditional methods such as Needleman-Wunsch, Smith-Waterman and longest common subsequence algorithms. Also, database is implemented for using the algorithm in multi-sequence alignments for searching the optimal sequence that matches the given sequence.
Dynamic hierarchical algorithm for accelerated microfossil identification
Wong, Cindy M.; Joseph, Dileepan
2015-02-01
Marine microfossils provide a useful record of the Earth's resources and prehistory via biostratigraphy. To study Hydrocarbon reservoirs and prehistoric climate, geoscientists visually identify the species of microfossils found in core samples. Because microfossil identification is labour intensive, automation has been investigated since the 1980s. With the initial rule-based systems, users still had to examine each specimen under a microscope. While artificial neural network systems showed more promise for reducing expert labour, they also did not displace manual identification for a variety of reasons, which we aim to overcome. In our human-based computation approach, the most difficult step, namely taxon identification is outsourced via a frontend website to human volunteers. A backend algorithm, called dynamic hierarchical identification, uses unsupervised, supervised, and dynamic learning to accelerate microfossil identification. Unsupervised learning clusters specimens so that volunteers need not identify every specimen during supervised learning. Dynamic learning means interim computation outputs prioritize subsequent human inputs. Using a dataset of microfossils identified by an expert, we evaluated correct and incorrect genus and species rates versus simulated time, where each specimen identification defines a moment. The proposed algorithm accelerated microfossil identification effectively, especially compared to benchmark results obtained using a k-nearest neighbour method.
An Improved Artificial Immune Algorithm with a Dynamic Threshold
Institute of Scientific and Technical Information of China (English)
Zhang Qiao; Xu Xu; Liang Yan-chun
2006-01-01
An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody's fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.
Dynamic Traffic Light Sequence Algorithm Using RFID
Directory of Open Access Journals (Sweden)
Khalid A.S. Al-Khateeb
2008-01-01
Full Text Available Problem statement: Traffic congestion and tidal flow management were recognized as major problems in modern urban areas, which have caused much frustration and loss of man hours. Approach: In order to solve the problem an intelligent RFID traffic control has been developed. It has circumvented or avoided the problems that usually arise with systems such as those, which use image processing and beam interruption techniques. RFID technology with appropriate algorithm and data base were applied to a multi vehicle, multi lane and multi road junction area to provide an efficient time management scheme. A dynamic time schedule was worked out for the passage of each column. Results: The simulation has shown that, the dynamic sequence algorithm has the ability to intelligently adjust itself even with the presence of some extreme cases. The real time operation of the system emulated the judgment of a traffic policeman on duty, by considering the number of vehicles in each column and the routing proprieties. Conclusions/Recommendations: RFID together with Internet and GSM technologies are anticipated to create a revolution in traffic management and control systems. The data base contains online statistical information, which can be used by operators and planners to develop better models in the future.
Optimized Dynamical Decoupling via Genetic Algorithms
Quiroz, Gregory
2013-01-01
We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
Directory of Open Access Journals (Sweden)
Tanti Octavia
2003-01-01
Full Text Available A Modified Giffler and Thompson algorithm combined with dynamic slack time is used to allocate machines resources in dynamic nature. It was compared with a Real Time Order Promising (RTP algorithm. The performance of modified Giffler and Thompson and RTP algorithms are measured by mean tardiness. The result shows that modified Giffler and Thompson algorithm combined with dynamic slack time provides significantly better result compared with RTP algorithm in terms of mean tardiness.
Asset-Based Assessment in Educational Psychology: Capturing Perceptions during a Paradigm Shift
Lubbe, Carien; Eloff, Irma
2004-01-01
Several trends are compelling educational psychologists towards a philosophy of assessment that is asset-based and strength focused. This article shares the results from a study that explored perceptions about asset-based assessment in Educational Psychology in South Africa. Three focus groups were held and four main themes emerged from the…
Parallel-Processing Algorithms For Dynamics Of Manipulators
Fijany, Amir; Bejczy, Antal K.
1991-01-01
Class of parallel and parallel/pipeline algorithms presented for more efficient computation of manipulator inertia matrix. Essential for implementing advanced dynamic control schemes as well as dynamic simulation of manipulator motion.
Efficient Intelligent Optimized Algorithm for Dynamic Vehicle Routing Problem
Directory of Open Access Journals (Sweden)
Jiangqing Wang
2011-11-01
Full Text Available In order to solve the dynamic vehicle routing problem (DVRP containing both dynamic network environment and real-time customer requests, an efficient intelligent optimized algorithm called IOA is proposed in this paper, which takes advantages of both global searching ability of evolutionary algorithms and local searching capability of ant colony algorithm. The proposed IOA incorporates ant colony algorithm for exploration and evolutionary algorithm for exploitation, and uses real-time information during the optimization process. In order to discuss the performance of the proposed algorithm, a mixed integral programming model for DVRP is formulated, and benchmark functions are constructed. Detailed simulation results and comparisons with the existed work show that the proposed IOA algorithm can achieve a higher performance gain, and is well suited to problems containing dynamic network environment and real-time customer requests.
On an algorithm for dynamic reconstruction of the input
Blizorukova, M. S.; V.I. Maksimov
2013-01-01
We consider the problem of dynamic reconstruction of the input in a system described by a vector differential equation and nonlinear in the state variable. We indicate an algorithm that is stable under information noises and computational errors and is aimed at infinite system operation time. The algorithm is based on the dynamic regularization method. © 2013 Pleiades Publishing, Ltd.
Algebraic dynamics solution to and algebraic dynamics algorithm for nonlinear advection equation
Institute of Scientific and Technical Information of China (English)
2008-01-01
Algebraic dynamics approach and algebraic dynamics algorithm for the solution of nonlinear partial differential equations are applied to the nonlinear advection equa-tion. The results show that the approach is effective for the exact analytical solu-tion and the algorithm has higher precision than other existing algorithms in nu-merical computation for the nonlinear advection equation.
Intersecting Asset-Based Service, Strengths, and Mentoring for Socially Responsible Leadership.
Hastings, Lindsay
2016-06-01
Grounded in a youth leadership and mentoring program, this chapter discusses the value of asset-based community development from the service-learning literature and the concept of generativity from the leadership development literature. PMID:27150907
Dynamic Uniform Scaling for Multiobjective Genetic Algorithms
DEFF Research Database (Denmark)
Pedersen, Gerulf; Goldberg, David E.
2004-01-01
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....
Dynamic Obfuscation Algorithm based on Demand-Driven Symbolic Execution
Directory of Open Access Journals (Sweden)
Yubo Yang
2014-06-01
Full Text Available Dynamic code obfuscation technique increases the difficulty of dynamically reverse by the runtime confusion. Path explosion directly affects the efficiency and accuracy of dynamic symbolic analysis. Because of the defect, this paper presents a novel algorithm DDD (Demand-Driven Dynamic Obfuscation Algorithm by using the demand-driven theory of symbolic analysis. First, create a large number of invalid paths to mislead the result of symbolic analysis. Second, according to the demand-driven theory, create a specific execution path to protect the security of software. The design and implementation of the algorithm is based on the current popular and mature SMT (satisfiability model theory, and the experimental effects are tested by Z3 - the SMT solver and Pex - the symbolic execution test tools. The experimental results prove that the algorithm enhance the security of the program.
New MPPT algorithm based on hybrid dynamical theory
Elmetennani, Shahrazed
2014-11-01
This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.
Institute of Scientific and Technical Information of China (English)
WANG Shundin; ZHANG Hua
2008-01-01
Using functional derivative technique In quantum field theory,the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations.The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by Introducing the time translation operator.The functional partial differential evolution equations were solved by algebraic dynam-ics.The algebraic dynamics solutions are analytical In Taylor series In terms of both initial functions and time.Based on the exact analytical solutions,a new nu-merical algorithm-algebraic dynamics algorithm was proposed for partial differ-ential evolution equations.The difficulty of and the way out for the algorithm were discussed.The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Dynamic multi DAG scheduling algorithm for optical grid environment
Zhu, Liying; Sun, Zhenyu; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng
2007-11-01
Facing the evolvement of the Optical Grid technology, dynamic task scheduling can largely improve the efficiency of the Grid environment under the real circumstances. We propose a Serve On Time (SOT) algorithm - based on the idea of combining all the dynamic multi tasks so that all the tasks will obtain the rights to be served as soon as possible. We then introduce the basic First Come First Serve (FCFS) algorithm. A simulation will show the advantage of SOT.
Multi-Particle Collision Dynamics Algorithm for Nematic Fluids
Shendruk, Tyler N.; Yeomans, Julia M.
2015-01-01
Research on transport, self-assembly and defect dynamics within confined, flowing liquid crystals requires versatile and computationally efficient mesoscopic algorithms to account for fluctuating nematohydrodynamic interactions. We present a multi-particle collision dynamics (MPCD) based algorithm to simulate liquid-crystal hydrodynamic and director fields in two and three dimensions. The nematic-MPCD method is shown to successfully reproduce the features of a nematic liquid crystal, includin...
Dynamic Skyline Computation with the Skyline Breaker Algorithm
Köppl, Dominik
2014-01-01
Given a sequential data input, we tackle parallel dynamic skyline computation of the read data by means of a spatial tree structure for indexing fine-grained feature vectors. For this purpose, we modified the Skyline Breaker algorithm that solves skyline computation with multiple local split decision trees concurrently. With this approach, we propose an algorithm for dynamic skyline computation that inherits the robustness against the dimension curse and different data distributions.
Using Genetic Algorithms for Navigation Planning in Dynamic Environments
Directory of Open Access Journals (Sweden)
Ferhat Uçan
2012-01-01
Full Text Available Navigation planning can be considered as a combination of searching and executing the most convenient flight path from an initial waypoint to a destination waypoint. Generally the aim is to follow the flight path, which provides minimum fuel consumption for the air vehicle. For dynamic environments, constraints change dynamically during flight. This is a special case of dynamic path planning. As the main concern of this paper is flight planning, the conditions and objectives that are most probable to be used in navigation problem are considered. In this paper, the genetic algorithm solution of the dynamic flight planning problem is explained. The evolutionary dynamic navigation planning algorithm is developed for compensating the existing deficiencies of the other approaches. The existing fully dynamic algorithms process unit changes to topology one modification at a time, but when there are several such operations occurring in the environment simultaneously, the algorithms are quite inefficient. The proposed algorithm may respond to the concurrent constraint updates in a shorter time for dynamic environment. The most secure navigation of the air vehicle is planned and executed so that the fuel consumption is minimum.
Institute of Scientific and Technical Information of China (English)
2008-01-01
Using functional derivative technique in quantum field theory, the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations. The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by introducing the time translation operator. The functional partial differential evolution equations were solved by algebraic dynam-ics. The algebraic dynamics solutions are analytical in Taylor series in terms of both initial functions and time. Based on the exact analytical solutions, a new nu-merical algorithm—algebraic dynamics algorithm was proposed for partial differ-ential evolution equations. The difficulty of and the way out for the algorithm were discussed. The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Dynamic Uniform Scaling for Multiobjective Genetic Algorithms
DEFF Research Database (Denmark)
Pedersen, Gerulf; Goldberg, D.E.
2004-01-01
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front c...
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world''s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. It is accompanied by a CD that contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the important areas.
Dynamic gate algorithm for multimode fiber Bragg grating sensor systems.
Ganziy, D; Jespersen, O; Woyessa, G; Rose, B; Bang, O
2015-06-20
We propose a novel dynamic gate algorithm (DGA) for precise and accurate peak detection. The algorithm uses a threshold-determined detection window and center of gravity algorithm with bias compensation. We analyze the wavelength fit resolution of the DGA for different values of the signal-to-noise ratio and different peak shapes. Our simulations and experiments demonstrate that the DGA method is fast and robust with better stability and accuracy than conventional algorithms. This makes it very attractive for future implementation in sensing systems, especially based on multimode fiber Bragg gratings. PMID:26193010
Synthesizing Dynamic Programming Algorithms from Linear Temporal Logic Formulae
Rosu, Grigore; Havelund, Klaus
2001-01-01
The problem of testing a linear temporal logic (LTL) formula on a finite execution trace of events, generated by an executing program, occurs naturally in runtime analysis of software. We present an algorithm which takes an LTL formula and generates an efficient dynamic programming algorithm. The generated algorithm tests whether the LTL formula is satisfied by a finite trace of events given as input. The generated algorithm runs in linear time, its constant depending on the size of the LTL formula. The memory needed is constant, also depending on the size of the formula.
A dynamic global and local combined particle swarm optimization algorithm
International Nuclear Information System (INIS)
Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.
Effective multicasting algorithm for dynamic membership with delay constraint
Institute of Scientific and Technical Information of China (English)
CHEN Lin; XU Zheng-quan
2006-01-01
This paper proposes an effective heuristic algorithm for dynamic multicast routing with delay-constrained DDMR.The tree constructed by DDMR has the following characteristics: (1) multicast tree changes with the dynamic memberships; (2)the cost of the tree is as small as possible at each node addition/removal event; (3) all of the path delay meet a fixed delay constraint;(4) minimal perturbation to an existing tree. The proposed algorithm is based on "damage" and "usefulness" concepts proposed in previous work, and has a new parameter bf(Balancing Factor) for judging whether or not to rearrange a tree region when membership changes. Mutation operation in Genetic Algorithm (GA) is also employed to find an attached node for a new adding node.Simulation showed that our algorithm performs well and is better than static heuristic algorithms, in term of cost especially.
Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation
Moriarty, K J M; Greenlaw, R
1997-01-01
A parallel algorithm for ``diffusion-limited aggregation'' (DLA) is described and analyzed from the perspective of computational complexity. The dynamic exponent z of the algorithm is defined with respect to the probabilistic parallel random-access machine (PRAM) model of parallel computation according to $T \\sim L^{z}$, where L is the cluster size, T is the running time, and the algorithm uses a number of processors polynomial in D_2 is the second generalized dimension. Simulations of DLA are carried out to measure D_2 and to test scaling assumptions employed in the complexity analysis of the parallel algorithm. It is plausible that the parallel algorithm attains the minimum possible value of the dynamic exponent in which case z characterizes the intrinsic history dependence of DLA.
Parallel algorithms and architecture for computation of manipulator forward dynamics
Fijany, Amir; Bejczy, Antal K.
1989-01-01
Parallel computation of manipulator forward dynamics is investigated. Considering three classes of algorithms for the solution of the problem, that is, the O(n), the O(n exp 2), and the O(n exp 3) algorithms, parallelism in the problem is analyzed. It is shown that the problem belongs to the class of NC and that the time and processors bounds are of O(log2/2n) and O(n exp 4), respectively. However, the fastest stable parallel algorithms achieve the computation time of O(n) and can be derived by parallelization of the O(n exp 3) serial algorithms. Parallel computation of the O(n exp 3) algorithms requires the development of parallel algorithms for a set of fundamentally different problems, that is, the Newton-Euler formulation, the computation of the inertia matrix, decomposition of the symmetric, positive definite matrix, and the solution of triangular systems. Parallel algorithms for this set of problems are developed which can be efficiently implemented on a unique architecture, a triangular array of n(n+2)/2 processors with a simple nearest-neighbor interconnection. This architecture is particularly suitable for VLSI and WSI implementations. The developed parallel algorithm, compared to the best serial O(n) algorithm, achieves an asymptotic speedup of more than two orders-of-magnitude in the computation the forward dynamics.
Multi-particle collision dynamics algorithm for nematic fluids.
Shendruk, Tyler N; Yeomans, Julia M
2015-07-01
Research on transport, self-assembly and defect dynamics within confined, flowing liquid crystals requires versatile and computationally efficient mesoscopic algorithms to account for fluctuating nematohydrodynamic interactions. We present a multi-particle collision dynamics (MPCD) based algorithm to simulate liquid-crystal hydrodynamic and director fields in two and three dimensions. The nematic-MPCD method is shown to successfully reproduce the features of a nematic liquid crystal, including a nematic-isotropic phase transition with hysteresis in 3D, defect dynamics, isotropic Frank elastic coefficients, tumbling and shear alignment regimes and boundary condition-dependent order parameter fields. PMID:26035731
A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments
Institute of Scientific and Technical Information of China (English)
Shengxiang Yang; Renato Tinós
2007-01-01
Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.
Quantum Dynamical Entropies and Gács Algorithmic Entropy
Directory of Open Access Journals (Sweden)
Fabio Benatti
2012-07-01
Full Text Available Several quantum dynamical entropies have been proposed that extend the classical Kolmogorov–Sinai (dynamical entropy. The same scenario appears in relation to the extension of algorithmic complexity theory to the quantum realm. A theorem of Brudno establishes that the complexity per unit time step along typical trajectories of a classical ergodic system equals the KS-entropy. In the following, we establish a similar relation between the Connes–Narnhofer–Thirring quantum dynamical entropy for the shift on quantum spin chains and the Gács algorithmic entropy. We further provide, for the same system, a weaker linkage between the latter algorithmic complexity and a different quantum dynamical entropy proposed by Alicki and Fannes.
Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites
Directory of Open Access Journals (Sweden)
Maocai Wang
2014-01-01
Full Text Available Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.
Institute of Scientific and Technical Information of China (English)
WANG; Shunjin; ZHANG; Hua
2006-01-01
The problem of preserving fidelity in numerical computation of nonlinear ordinary differential equations is studied in terms of preserving local differential structure and approximating global integration structure of the dynamical system.The ordinary differential equations are lifted to the corresponding partial differential equations in the framework of algebraic dynamics,and a new algorithm-algebraic dynamics algorithm is proposed based on the exact analytical solutions of the ordinary differential equations by the algebraic dynamics method.In the new algorithm,the time evolution of the ordinary differential system is described locally by the time translation operator and globally by the time evolution operator.The exact analytical piece-like solution of the ordinary differential equations is expressd in terms of Taylor series with a local convergent radius,and its finite order truncation leads to the new numerical algorithm with a controllable precision better than Runge Kutta Algorithm and Symplectic Geometric Algorithm.
Functional clustering algorithm for the analysis of dynamic network data
Feldt, S.; Waddell, J; Hetrick, V. L.; Berke, J. D.; Żochowski, M
2009-01-01
We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulat...
An Improved Adaptive Dynamic Particle Swarm Optimization Algorithm
Hongbo Zhao; Lina Feng
2014-01-01
In order to overcome the weakness that particle swarm optimization algorithm is likely to fall into local minimum when the complex optimization problems are solved, a new adaptive dynamic particle swarm optimization algorithm is proposed. The paper introduces the evaluation index of particle swarm premature convergence to judge the state of particle swarm in the population space, for the sake of investigates the timing of taking effect of influence function. The influence function is adaptive...
A Dynamic Elimination-Combining Stack Algorithm
Bar-Nissan, Gal; Suissa, Adi
2011-01-01
Two key synchronization paradigms for the construction of scalable concurrent data-structures are software combining and elimination. Elimination-based concurrent data-structures allow operations with reverse semantics (such as push and pop stack operations) to "collide" and exchange values without having to access a central location. Software combining, on the other hand, is effective when colliding operations have identical semantics: when a pair of threads performing operations with identical semantics collide, the task of performing the combined set of operations is delegated to one of the threads and the other thread waits for its operation(s) to be performed. Applying this mechanism iteratively can reduce memory contention and increase throughput. The most highly scalable prior concurrent stack algorithm is the elimination-backoff stack. The elimination-backoff stack provides high parallelism for symmetric workloads in which the numbers of push and pop operations are roughly equal, but its performance d...
An Algorithm of Sensor Management Based on Dynamic Target Detection
Institute of Scientific and Technical Information of China (English)
LIUXianxing; ZHOULin; JINYong
2005-01-01
The probability density of stationary target is only evolved at measurement update, but the probability density of dynamic target is evolved not only at measurement update but also during measurements, this paper researches an algorithm of dynamic targets detection. Firstly, it presents the evolution of probability density at measurement update by Bayes' rule and the evolution of probability density during measurements by Fokker-Planck differential equations, respectively. Secondly, the method of obtaining information entropy by the probability density is given and sensor resources are distributed based on the evolution of information entropy viz. the maximization of information gain. Simulation results show that compared with the algorithm of serial search, this algorithm is feasible and effective when it is used to detect dynamic target.
Crime Busting Model Based on Dynamic Ranking Algorithms
Directory of Open Access Journals (Sweden)
Yang Cao
2013-01-01
Full Text Available This paper proposed a crime busting model with two dynamic ranking algorithms to detect the likelihood of a suspect and the possibility of a leader in a complex social network. Signally, in order to obtain the priority list of suspects, an advanced network mining approach with a dynamic cumulative nominating algorithm is adopted to rapidly reduce computational expensiveness than most other topology-based approaches. Our method can also greatly increase the accuracy of solution with the enhancement of semantic learning filtering at the same time. Moreover, another dynamic algorithm of node contraction is also presented to help identify the leader among conspirators. Test results are given to verify the theoretical results, which show the great performance for either small or large datasets.
Left ventricular border recognition using a dynamic search algorithm
International Nuclear Information System (INIS)
Initial results obtained with a simple, fully automated algorithm for detection of left ventricular boundaries are presented. The strength of this approach is the use of dynamic programming search techniques, which allow determination of local border points to be influenced by the entire global border location. The relative contributions of mask mode subtraction and the dynamic search technique are evaluated with respect to accurate border definition. These computer-determined ventricular borders are compared with hand-traced borders on subtracted and unsubtracted images. The modular dynamic search algorithm is shown to perform better than previously described algorithms, which generally require operator interaction. It is also shown that for both manual and automated techniques, ventricular borders derived from subtracted images may be significantly different from borders derived from nonsubtracted images
Novel algorithm for distributed replicas management based on dynamic programming
Institute of Scientific and Technical Information of China (English)
Wang Tao; Lu Xianliang; Hou Mengshu
2006-01-01
Replicas can improve the data reliability in distributed system. However, the traditional algorithms for replica management are based on the assumption that all replicas have the uniform reliability, which is inaccurate in some actual systems. To address such problem, a novel algorithm is proposed based on dynamic programming to manage the number and distribution of replicas in different nodes. By using Markov model, replicas management is organized as a multi-phase process, and the recursion equations are provided. In this algorithm, the heterogeneity of nodes, the expense for maintaining replicas and the engaged space have been considered. Under these restricted conditions, this algorithm realizes high data reliability in a distributed system. The results of case analysis prove the feasibility of the algorithm.
PACE: A dynamic programming algorithm for hardware/software partitioning
DEFF Research Database (Denmark)
Knudsen, Peter Voigt; Madsen, Jan
1996-01-01
This paper presents the PACE partitioning algorithm which is used in the LYCOS co-synthesis system for partitioning control/dataflow graphs into hardware and software parts. The algorithm is a dynamic programming algorithm which solves both the problem of minimizing system execution time with a...... hardware area constraint and the problem of minimizing hardware area with a system execution time constraint. The target architecture consists of a single microprocessor and a single hardware chip (ASIC, FPGA, etc.) which are connected by a communication channel. The algorithm incorporates a realistic...... communication model and thus attempts to minimize communication overhead. The time-complexity of the algorithm is O(n2·𝒜) and the space-complexity is O(n·𝒜) where 𝒜 is the total area of the hardware chip and n the number of code fragments which may be placed in either hardware or software...
Applying Genetic Algorithm to Dynamic Layout Problem
K.V.Chandratre; K. N. Nandurkar
2011-01-01
In today’s economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in the product mix and demand.[1] Layout design has a significant impact on manufacturing efficiency. Initially, it was treated as a static decision but due to improvements in technology, it is possible to rearrange the manufacturing facilities in different scenarios. The Plant layout affects on the total cost in the industry. Nowadays Dynamic layout is becoming an important issue. Dyna...
New heuristic algorithm for dynamic traffic in WDM optical networks
Directory of Open Access Journals (Sweden)
A. Rodríguez García
2015-12-01
Full Text Available This paper presents the results from the simulation of Snake One, a new heuristic algorithm, and the comparison made between three heuristic algorithms: Genetic Algorithms, Simulated Annealing, and Tabu Search, using blocking probability and network utilization as standard indicators. The simulation exercise was conducted on WDM NSFNET under dynamic traffic conditions. The results show a substantial decrease of blocking. However, this causes a relative network utilization growth. There are also load intervals which lead to performance improvement, decreasing the number of blocked requests.
A Dynamic Hashing Algorithm Suitable for Embedded System
Directory of Open Access Journals (Sweden)
Li Jianwei
2013-06-01
Full Text Available With the increasing of the data numbers, the linear hashing will be a lot of overflow blocks result from Data skew and the index size of extendible hash will surge so as to waste too much memory. This lead to the above two Typical Dynamic hashing algorithm don’t suitable for embedded system that need certain real-time requirements and memory resources are very scarce. To solve this problem, this paper was proposed a dynamic hashing algorithm suitable for embedded system combining with the characteristic of extendible hashing and linear hashing.it is no overflow buckets and the index size is proportional to the adjustment number.
Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning
Smelyanskiy, Vadius; vonToussaint, Udo V.; Timucin, Dogan A.; Clancy, Daniel (Technical Monitor)
2002-01-01
We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum exitation gap, gmin = O(n2(sup -n/2)), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.
Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks
Directory of Open Access Journals (Sweden)
Ruiyun Yu
2014-01-01
Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.
Decentralized Control of Dynamic Routing with a Neural Network Algorithm
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A state-dependent routing algorithm based on the neural network model, which takes advantage of other dynamic routing algorithm for circuit-switched network, is given in [1]. But, the Algorithm in [1] is a centralized control model with complex O (N7), therefore, is difficult to realize by hardware. A simplified algorithm is put forward in this paper, in which routing can be controlled decentralizedly, and its complexity is reduced to O (10N3). Computer simulations are made in a fully connected test network with eight nodes. The results show that the centralized control model has very effective performance that can match RTNR, and the centralized control model is not as good as the centralized one but better than DAR-1.
Parallel conjugate gradient algorithms for manipulator dynamic simulation
Fijany, Amir; Scheld, Robert E.
1989-01-01
Parallel conjugate gradient algorithms for the computation of multibody dynamics are developed for the specialized case of a robot manipulator. For an n-dimensional positive-definite linear system, the Classical Conjugate Gradient (CCG) algorithms are guaranteed to converge in n iterations, each with a computation cost of O(n); this leads to a total computational cost of O(n sq) on a serial processor. A conjugate gradient algorithms is presented that provide greater efficiency using a preconditioner, which reduces the number of iterations required, and by exploiting parallelism, which reduces the cost of each iteration. Two Preconditioned Conjugate Gradient (PCG) algorithms are proposed which respectively use a diagonal and a tridiagonal matrix, composed of the diagonal and tridiagonal elements of the mass matrix, as preconditioners. Parallel algorithms are developed to compute the preconditioners and their inversions in O(log sub 2 n) steps using n processors. A parallel algorithm is also presented which, on the same architecture, achieves the computational time of O(log sub 2 n) for each iteration. Simulation results for a seven degree-of-freedom manipulator are presented. Variants of the proposed algorithms are also developed which can be efficiently implemented on the Robot Mathematics Processor (RMP).
von Maltzahn, Robyn; Durrheim, Kevin
2008-01-01
This paper contests the major emphasis placed on the multidimensional nature of poverty measurement. Instead, it argues that poverty pictures created by different measures and at different units of analysis tend to converge. This argument is derived from a comparison of poverty pictures created using income and asset-based measures at the national…
Dynamic evolutionary community detection algorithms based on the modularity matrix
International Nuclear Information System (INIS)
Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms. (interdisciplinary physics and related areas of science and technology)
A Dynamic Scheduling Algorithm for Divisible Loads in Grid Environments
Directory of Open Access Journals (Sweden)
Said Elnaffar
2007-06-01
Full Text Available
Divisible loads are those workloads that can be partitioned by a scheduler into any arbitrary chunks. The problem of scheduling divisible loads has been defined for a long time, however, a handful of solutions have been proposed. Furthermore, almost all proposed approaches attempt to perform scheduling in dedicated environments such as LANs, whereas scheduling in non-dedicated environments such as Grids remains an open problem. In Grids, the incessant variation of a worker's computing power is a chief difficulty of splitting and distributing workloads to Grid workers efficiently. In this paper, we first introduce a computation model that explains the impact of local (internal tasks and Grid (external tasks that arrive at a given worker. This model helps estimate the available computing power of a worker under the fluctuation of the number of local and Grid applications. Based on this model, we propose the CPU power prediction strategy. Additionally, we build a new dynamic scheduling algorithm by incorporating the prediction strategy into a static scheduling algorithm. Lastly we demonstrate that the proposed dynamic algorithm is superior to the existing dynamic and static algorithms by a comprehensive set of simulations.
An algorithm for the solution of dynamic linear programs
Psiaki, Mark L.
1989-01-01
The algorithm's objective is to efficiently solve Dynamic Linear Programs (DLP) by taking advantage of their special staircase structure. This algorithm constitutes a stepping stone to an improved algorithm for solving Dynamic Quadratic Programs, which, in turn, would make the nonlinear programming method of Successive Quadratic Programs more practical for solving trajectory optimization problems. The ultimate goal is to being trajectory optimization solution speeds into the realm of real-time control. The algorithm exploits the staircase nature of the large constraint matrix of the equality-constrained DLPs encountered when solving inequality-constrained DLPs by an active set approach. A numerically-stable, staircase QL factorization of the staircase constraint matrix is carried out starting from its last rows and columns. The resulting recursion is like the time-varying Riccati equation from multi-stage LQR theory. The resulting factorization increases the efficiency of all of the typical LP solution operations over that of a dense matrix LP code. At the same time numerical stability is ensured. The algorithm also takes advantage of dynamic programming ideas about the cost-to-go by relaxing active pseudo constraints in a backwards sweeping process. This further decreases the cost per update of the LP rank-1 updating procedure, although it may result in more changes of the active set that if pseudo constraints were relaxed in a non-stagewise fashion. The usual stability of closed-loop Linear/Quadratic optimally-controlled systems, if it carries over to strictly linear cost functions, implies that the saving due to reduced factor update effort may outweigh the cost of an increased number of updates. An aerospace example is presented in which a ground-to-ground rocket's distance is maximized. This example demonstrates the applicability of this class of algorithms to aerospace guidance. It also sheds light on the efficacy of the proposed pseudo constraint relaxation
A dynamic inertia weight particle swarm optimization algorithm
International Nuclear Information System (INIS)
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly
A Dynamic Approach to Weighted Suffix Tree Construction Algorithm
Directory of Open Access Journals (Sweden)
Binay Kumar Pandey
2011-02-01
Full Text Available In present time weighted suffix tree is consider as a one of the most important existing data structure used for analyzing molecular weighted sequence. Although a static partitioning based parallel algorithm existed for the construction of weighted suffix tree, but for very long weighted DNA sequences it takes significant amount of time. However, in our implementation of dynamic partition based parallel weighted suffix tree construction algorithm on cluster computing makes it possible to significantly accelerate the construction of weighted suffix tree.
Dynamic bandwidth allocation algorithm for full-band utilization
Institute of Scientific and Technical Information of China (English)
Han Guodong; Wang Hui; Wu Jiangxing
2006-01-01
To improve and optimize the bandwidth utilization for multi-service packet transporting system, a kind of Dynamic Full Bandwidth Utilized (DFBU) allocation algorithm allowing a single link to use far beyond its fair share bandwidth is presented. Three important parameters as the bound on max and minimum bandwidth, the maximum packet delay and the minimum bandwidth utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system use all the spare bandwidth (up to full-bandwidth) while the performance of fairness and QoS requirement is still guaranteed.
Group implicit concurrent algorithms in nonlinear structural dynamics
Ortiz, M.; Sotelino, E. D.
1989-01-01
During the 70's and 80's, considerable effort was devoted to developing efficient and reliable time stepping procedures for transient structural analysis. Mathematically, the equations governing this type of problems are generally stiff, i.e., they exhibit a wide spectrum in the linear range. The algorithms best suited to this type of applications are those which accurately integrate the low frequency content of the response without necessitating the resolution of the high frequency modes. This means that the algorithms must be unconditionally stable, which in turn rules out explicit integration. The most exciting possibility in the algorithms development area in recent years has been the advent of parallel computers with multiprocessing capabilities. So, this work is mainly concerned with the development of parallel algorithms in the area of structural dynamics. A primary objective is to devise unconditionally stable and accurate time stepping procedures which lend themselves to an efficient implementation in concurrent machines. Some features of the new computer architecture are summarized. A brief survey of current efforts in the area is presented. A new class of concurrent procedures, or Group Implicit algorithms is introduced and analyzed. The numerical simulation shows that GI algorithms hold considerable promise for application in coarse grain as well as medium grain parallel computers.
Parameter optimization in molecular dynamics simulations using a genetic algorithm
International Nuclear Information System (INIS)
In this work, we introduce a genetic algorithm for the parameterization of the reactive force field developed by Kieffer . This potential includes directional covalent bonds and dispersion terms. Important features of this force field for simulating systems that undergo significant structural reorganization are (i) the ability to account for the redistribution of electron density upon ionization, formation, or breaking of bonds, through a charge transfer term, and (ii) the fact that the angular constraints dynamically adjust when a change in the coordination number of an atom occurs. In this paper, we present the implementation of the genetic algorithm into the existing code as well as the algorithm efficiency and preliminary results on Si-Si force field optimization. The parameters obtained by this method will be compared to existing parameter sets obtained by a trial-and-error process.
Dynamic Routing and Resource Assignment Algorithm In sloted optical Networks
Directory of Open Access Journals (Sweden)
Bisheng Quan
2013-04-01
Full Text Available All-optical wavelength division multiplexing networks are the most promising candidate for the next generation wideband backbone networks. To improve the utilization of wavelength, time division multiplexing technology is introduced. The routing, wavelength and time-slots assignment problem was studied in such time-space switched networks. Two new dynamic algorithms were proposed which distribute slots of the session request on multiple different wavelengths of single fiber separately based on fixed alternate routing and adaptive routing policy. Especially in LLR-MWLB algorithm, network link weights adjust adaptively with the available time slots of each link and load balancing strategy is adopted. The effectiveness of the proposed algorithms is demonstrated by simulations and results show the better performance.
Iterative Dynamic Diversity Evolutionary Algorithm for Constrained Optimization
Institute of Scientific and Technical Information of China (English)
GAO Wei-Shang; SHAO Cheng
2014-01-01
Evolutionary algorithms (EAs) were shown to be effective for complex constrained optimization problems. However, inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions. In this paper, we propose an iterative dynamic diversity evolutionary algorithm (IDDEA) with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps. In IDDEA, a novel optimum estimation strategy with multi-agents evolving diversely is suggested to eﬃciently compute dominance trend and establish a subregion. In addition, a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration, which is based on a special dominance estimation scheme. Meanwhile, an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents. Furthermore, several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.
Multiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamics
Franz, Benjamin
2013-06-19
Two algorithms that combine Brownian dynami cs (BD) simulations with mean-field partial differential equations (PDEs) are presented. This PDE-assisted Brownian dynamics (PBD) methodology provides exact particle tracking data in parts of the domain, whilst making use of a mean-field reaction-diffusion PDE description elsewhere. The first PBD algorithm couples BD simulations with PDEs by randomly creating new particles close to the interface, which partitions the domain, and by reincorporating particles into the continuum PDE-description when they cross the interface. The second PBD algorithm introduces an overlap region, where both descriptions exist in parallel. It is shown that the overlap region is required to accurately compute variances using PBD simulations. Advantages of both PBD approaches are discussed and illustrative numerical examples are presented. © 2013 Society for Industrial and Applied Mathematics.
Dynamical linked cluster expansions: Algorithmic aspects and applications
International Nuclear Information System (INIS)
Dynamical linked cluster expansions are linked cluster expansions with hopping parameter terms endowed with their own dynamics. They amount to a generalization of series expansions from 2-point to point-link-point interactions. We outline an associated multiple-line graph theory involving extended notions of connectivity and indicate an algorithmic implementation of graphs. Fields of applications are SU(N) gauge Higgs systems within variational estimates, spin glasses and partially annealed neural networks. We present results for the critical line in an SU(2) gauge Higgs model for the electroweak phase transition. The results agree well with corresponding high precision Monte Carlo results
A Bayesian Algorithm for Functional Mapping of Dynamic Complex Traits
Rongling Wu; Tian Liu
2009-01-01
Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML) context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable for analyzing longitudinal data with high dimensions and high measurement errors. In this article, we derive a general functional mapping framework for quantitative trait loc...
Computational Fluid Dynamics. [numerical methods and algorithm development
1992-01-01
This collection of papers was presented at the Computational Fluid Dynamics (CFD) Conference held at Ames Research Center in California on March 12 through 14, 1991. It is an overview of CFD activities at NASA Lewis Research Center. The main thrust of computational work at Lewis is aimed at propulsion systems. Specific issues related to propulsion CFD and associated modeling will also be presented. Examples of results obtained with the most recent algorithm development will also be presented.
A SYMPLECTIC ALGORITHM FOR DYNAMICS OF RIGID BODY
Institute of Scientific and Technical Information of China (English)
LU Ying-jie; REN Ge-xue
2006-01-01
For the dynamics of a rigid body with a fixed point based on the quaternion and the corresponding generalized momenta, a displacement-based symplectic integration scheme for differential-algebraic equations is proposed and applied to the Lagrange's equations based on dependent generalized momenta. Numerical experiments show that the algorithm possesses such characters as high precision and preserving system invariants.More importantly, the generalized momenta based Lagrange's equations show unique advantages over the traditional Lagrange's equations in symplectic integrations.
The δf algorithm for beam dynamics
International Nuclear Information System (INIS)
An algorithm is developed to study particle dynamics of beams including collective interaction with high accuracy and low noise. Particle dynamics with collective interactions is treated through particle simulation, where the main or average distribution f0 and the deviation away from it δf are separately followed. The main distribution f0 is handled by an analytic equilibrium solution and the perturbation away from it δf is followed by the method of characteristics. We call this the δf algorithm. We specifically model a synchrotron collider which includes the collision section where collective effects of collisions are simulated by this δf algorithm and the rest of the collider where single particle dynamics are treated by simple harmonic transport. The most important target of this simulation is to understand and predict the long-time behavior of the beam luminosity and lifetime. The δf method allows the study the effect of small perturbations over long timescales on beam lifetime by eliminating the numerical noise problem inherent in Particle-in-Cell techniques. In the δf code using the reference parameters of the SSC (Superconducting Super Collider), beam blow-up near resonances and oscillations in the tune shift, Δν, far from resonances are observed. In studying long timescale particle diffusion in the phase space of the beams away from resonances, the δf code performance is compared with a tracking code which does not incorporate collective interaction
Dynamic hybrid algorithms for MAP inference in discrete MRFs.
Alahari, Karteek; Kohli, Pushmeet; Torr, Philip H S
2010-10-01
In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multilabel energy functions arising from discrete mrfs or crfs. These methods are motivated by the observations that the performance of minimization algorithms depends on: 1) the initialization used for the primal and dual variables and 2) the number of primal variables involved in the energy function. Our first method (dynamic alpha-expansion) works by "recycling" results from previous problem instances. The second method simplifies the energy function by "reducing" the number of unknown variables present in the problem. Further, we show that it can also be used to generate a good initialization for the dynamic alpha-expansion algorithm by "reusing" dual variables. We test the performance of our methods on energy functions encountered in the problems of stereo matching and color and object-based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of alpha-expansion, as well as other popular algorithms such as sequential tree-reweighted message passing and max-product belief propagation. We also demonstrate the applicability of our schemes for certain higher order energy functions, such as the one described in [1], for interactive texture-based image and video segmentation. In most cases, we achieve a 10-15 times speed-up in the computation time. Our modified alpha-expansion algorithm provides similar performance to Fast-PD, but is conceptually much simpler. Both alpha-expansion and Fast-PD can be made orders of magnitude faster when used in conjunction with the "reduce" scheme proposed in this paper. PMID:20724761
A Dynamic Job Scheduling Algorithm for Parallel System
Institute of Scientific and Technical Information of China (English)
张建; 陆鑫达; 加力
2003-01-01
One of the fundamental problems in parallel and distributed systems is deciding how to allocate jobs toprocessors. The goals of job scheduling in a parallel environment are to minimize the parallel execution time of ajob and try to balance the user's desire with the system's desire. The users always want their jobs be completed asquickly as possible, while the system wants to service as many jobs as possible. In this paper, a dynamic job-scheduling algorithm was introduced. This algorithm tries to utilize the information of a practical system to allo-cate the jobs more evenly. The communication time between the processor and scheduler is overlapped with thecomputation time of the processor. So the communication overhead can be little. The principle of scheduling thejob is based on the desirability of each processor. The scheduler would not allocate a new job to a processor that isalready fully utilized. The execution efficiency of the system will be increased. This algorithm also can be reused inother complex algorithms.
Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms
Knudson, Matthew D.; Colby, Mitchell; Tumer, Kagan
2014-01-01
Dynamic flight environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal flight paths. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance
Algorithms for computational fluid dynamics n parallel processors
International Nuclear Information System (INIS)
A study of parallel algorithms for the numerical solution of partial differential equations arising in computational fluid dynamics is presented. The actual implementation on parallel processors of shared and nonshared memory design is discussed. The performance of these algorithms is analyzed in terms of machine efficiency, communication time, bottlenecks and software development costs. For elliptic equations, a parallel preconditioned conjugate gradient method is described, which has been used to solve pressure equations discretized with high order finite elements on irregular grids. A parallel full multigrid method and a parallel fast Poisson solver are also presented. Hyperbolic conservation laws were discretized with parallel versions of finite difference methods like the Lax-Wendroff scheme and with the Random Choice method. Techniques are developed for comparing the behavior of an algorithm on different architectures as a function of problem size and local computational effort. Effective use of these advanced architecture machines requires the use of machine dependent programming. It is shown that the portability problems can be minimized by introducing high level operations on vectors and matrices structured into program libraries
Dynamic behavior of shortest path routing algorithms for communication networks
Bertsekas, D. P.
1980-06-01
Several proposed routing algorithms for store and forward communication networks, including one currently in operation in the ARPANET, route messages along shortest paths computed by using some set of link lengths. When these lengths depend on current traffic conditions as they must in an adaptive algorithm, dynamic behavior questions such as stability convergence, and speed of convergence are of interest. This paper is the first attempt to analyze systematically these issues. It is shown that minimum queuing delay path algorithms tend to exhibit violent oscillatory behavior in the absence of a damping mechanism. The oscillations can be damped by means of several types of schemes, two of which are analyzed in this paper. In the first scheme a constant bias is added to the queuing delay thereby providing a preference towards paths with a small number of links. In the second scheme the effects of several past routings are averaged as, for example, when the link lengths are computed and communicated asynchronously throughout the network.
Dynamic airspace configuration algorithms for next generation air transportation system
Wei, Jian
The National Airspace System (NAS) is under great pressure to safely and efficiently handle the record-high air traffic volume nowadays, and will face even greater challenge to keep pace with the steady increase of future air travel demand, since the air travel demand is projected to increase to two to three times the current level by 2025. The inefficiency of traffic flow management initiatives causes severe airspace congestion and frequent flight delays, which cost billions of economic losses every year. To address the increasingly severe airspace congestion and delays, the Next Generation Air Transportation System (NextGen) is proposed to transform the current static and rigid radar based system to a dynamic and flexible satellite based system. New operational concepts such as Dynamic Airspace Configuration (DAC) have been under development to allow more flexibility required to mitigate the demand-capacity imbalances in order to increase the throughput of the entire NAS. In this dissertation, we address the DAC problem in the en route and terminal airspace under the framework of NextGen. We develop a series of algorithms to facilitate the implementation of innovative concepts relevant with DAC in both the en route and terminal airspace. We also develop a performance evaluation framework for comprehensive benefit analyses on different aspects of future sector design algorithms. First, we complete a graph based sectorization algorithm for DAC in the en route airspace, which models the underlying air route network with a weighted graph, converts the sectorization problem into the graph partition problem, partitions the weighted graph with an iterative spectral bipartition method, and constructs the sectors from the partitioned graph. The algorithm uses a graph model to accurately capture the complex traffic patterns of the real flights, and generates sectors with high efficiency while evenly distributing the workload among the generated sectors. We further improve
Dynamics of the evolution of learning algorithms by selection
International Nuclear Information System (INIS)
We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results
Integrated dynamic shared protection algorithm for GMPLS networks
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The path protection approach is widely investigated as a survivability solution for GMPLS networks, which has the advantage of efficient capacity utilization. However, there is a problem of the path protection approach that searching a disjoint backup path for a primary path is often unsuccessful. In order to resolve this problem, an integrated dynamic shared protection (IDSP) algorithm is proposed. The main idea of the proposed algorithm is that the path protection approach is first used to establish a backup path for the primary path; if the establishment is unsuccessful, then the primary path is dynamically divided into segments whose hop count are not fixed but not more than the limitation calculated by the equations introduced. In this proposal, backup bandwidth sharing is allowed to improve the capacity utilization ratio, which makes the link cost function quite different from previous ones. Simulation experiments are presented to demonstrate the efficiency of the proposed method compared with previous methods. Numerical results show that IDSP can not only achieve low protection failure probability but can also gain a better tradeoff between the protection overbuild and the average recovery time.
Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization
Directory of Open Access Journals (Sweden)
Weishang Gao
2013-01-01
Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.
Energy Technology Data Exchange (ETDEWEB)
Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
International Nuclear Information System (INIS)
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications
Dynamic characterization of oil fields, complex stratigraphically using genetic algorithms
International Nuclear Information System (INIS)
A novel methodology is presented in this paper for the characterization of highly heterogeneous oil fields by integration of the oil fields dynamic information to the static updated model. The objective of the oil field's characterization process is to build an oil field model, as realistic as possible, through the incorporation of all the available information. The classical approach consists in producing a model based in the oil field's static information, having as the process final stage the validation model with the dynamic information available. It is important to clarify that the term validation implies a punctual process by nature, generally intended to secure the required coherence between productive zones and petrophysical properties. The objective of the proposed methodology is to enhance the prediction capacity of the oil field's model by previously integrating, parameters inherent to the oil field's fluid dynamics by a process of dynamic data inversion through an optimization procedure based on evolutionary computation. The proposed methodology relies on the construction of the oil field's high-resolution static model, escalated by means of hybrid techniques while aiming to preserve the oil field's heterogeneity. Afterwards, using an analytic simulator as reference, the scaled model is methodically modified by means of an optimization process that uses genetic algorithms and production data as conditional information. The process's final product is a model that observes the static and dynamic conditions of the oil field with the capacity to minimize the economic impact that generates production historical adjustments to the simulation tasks. This final model features some petrophysical properties (porosity, permeability and water saturation), as modified to achieve a better adjustment of the simulated production's history versus the real one history matching. Additionally, the process involves a slight modification of relative permeability, which has
A local algorithm for detecting community structures in dynamic networks
Massaro, Emanuele; Guazzini, Andrea; Passarella, Andrea; Bagnoli, Franco
2013-01-01
The emergence and the global adaptation of mobile devices has influenced human interactions at the individual, community, and social levels leading to the so called Cyber-Physical World (CPW) convergence scenario [1]. One of the most important features of CPW is the possibility of exploiting information about the structure of social communities of users, that manifest through joint movement patterns and frequency of physical co-location: mobile devices of users that belong to the same social community are likely to "see" each other (and thus be able to communicate through ad hoc networking techniques) more frequently and regularly than devices outside of the community. In mobile opportunistic networks, this fact can be exploited, for example, to optimize networking operations such as forwarding and dissemination of messages. In this paper we present a novel local cognitive-inspired algorithm for revealing the structure of these dynamic social networks by exploiting information about physical encounters, logge...
RETRACTED ARTICLE: Dynamic voltage restorer controller using grade algorithm
Directory of Open Access Journals (Sweden)
S. Deepa
2015-12-01
Full Text Available This paper deals with the terminology and various issues about power quality problems. This problem occurs owing to voltage sag, swell, harmonics, and surges. The sustained overvoltage and undervoltage originated from power system may often damage/or disrupt computerized process. Voltage sags and harmonics disturb the power quality and this can be overcome by custom power device called dynamic voltage restorer (DVR. The DVR is normally installed between the source voltage and critical or sensitive load. The vital role of DVR depends on the efficiency of the control technique involved in switching circuit of the inverter. In this paper, Combination of improved grade algorithm with fuzzy membership function is used to decide the Proportional-Integral coefficients. The DVR works well both in balanced and unbalanced conditions of voltages. The simulation results show the efficiency of the proposed method.
On the Feasibility of Maintenance Algorithms in Dynamic Graphs
Casteigts, Arnaud; Mathieson, Luke
2011-01-01
Near ubiquitous mobile computing has led to intense interest in dynamic graph theory. This provides a new and challenging setting for algorithmics and complexity theory. For any graph-based problem, the rapid evolution of a (possibly disconnected) graph over time naturally leads to the important complexity question: is it better to calculate a new solution from scratch or to adapt the known solution on the prior graph to quickly provide a solution of guaranteed quality for the changed graph? In this paper, we demonstrate that the former is the best approach in some cases, but that there are cases where the latter is feasible. We prove that, under certain conditions, hard problems cannot even be approximated in any reasonable complexity bound --- i.e., even with a large amount of time, having a solution to a very similar graph does not help in computing a solution to the current graph. To achieve this, we formalize the idea as a maintenance algorithm. Using Dominating Set as the primary example we show that W[...
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
Daqing Wu; Jianguo Zheng
2012-01-01
A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is c...
A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
Nabizadeh, Somayeh; Rezvanian, Alireza; Meybodi, Mohammd Reza
2013-01-01
Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make ...
A Multiagent Dynamic Assessment Approach for Water Quality Based on Improved Q-Learning Algorithm
Jianjun Ni; Li Ren; Minghua Liu; Daqi Zhu
2013-01-01
The dynamic water quality assessment is a challenging and critical issue in water resource management systems. To deal with this complex problem, a dynamic water assessment model based on multiagent technology is proposed, and an improved Q-learning algorithm is used in this paper. In the proposed Q-learning algorithm, a fuzzy membership function and a punishment mechanism are introduced to improve the learning speed of Q-learning algorithm. The dynamic water quality assessment for different ...
AN ADVANCED DYNAMIC FEEDBACK AND RANDOM DISPATCHING LOAD-BALANCING ALGORITHM FOR GMLC IN 3G
Institute of Scientific and Technical Information of China (English)
Liao Jianxin; Zhang Hao; Zhu Xiaomin
2006-01-01
Based on the system architecture and software structure of GMLC (Gateway Mobile Location Center) in 3G (third generation), a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the performance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB (Dynamic FeedBack) algorithm in average throughput and average response time. Experiments results show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condition.
A Dynamic Web Service Composition Algorithm Based on TOPSIS
Directory of Open Access Journals (Sweden)
Longchang Zhang
2011-08-01
Full Text Available Multi-period QoS evaluations have to be considered for obtaining a reliable decision in the service selection process. Besides, open and dynamic Internet environment increased the uncertainty of decision-making. To solve the above difficulties, this paper presents a novel hybrid data type (including real numbers, interval numbers, triangular fuzzy numbers and intuitionistic fuzzy numbers QoS model, multi-period hybrid QoS aggregating operator and a strategy for aggregating composition service QoS firstly. Furthermore, a dynamic Web service composition algorithm based on TOPSIS (DWSCA_TOPSIS is presented to evaluate multi-period hybrid QoS data. DWSCA_TOPSIS includes four main steps: converting hybrid QoS into intervals, calculating weighted normalized decision-matrix, determining the positive-ideal and negative-ideal solution, calculating the close-degrees of candidates. Finally, some experiments are given using actual QoS data to demonstrate the benefits and effectiveness of our approach.
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics
International Nuclear Information System (INIS)
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as “cloning,” in analogy to the “spawning” procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, “trains,” as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics
Makhov, Dmitry V.; Glover, William J.; Martinez, Todd J.; Shalashilin, Dmitrii V.
2014-08-01
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as "cloning," in analogy to the "spawning" procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, "trains," as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions.
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics
Energy Technology Data Exchange (ETDEWEB)
Makhov, Dmitry V.; Shalashilin, Dmitrii V. [Department of Chemistry, University of Leeds, Leeds LS2 9JT (United Kingdom); Glover, William J.; Martinez, Todd J. [Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA and SLAC National Accelerator Laboratory, Menlo Park, California 94025 (United States)
2014-08-07
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as “cloning,” in analogy to the “spawning” procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, “trains,” as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions.
A Bayesian Algorithm for Functional Mapping of Dynamic Complex Traits
Directory of Open Access Journals (Sweden)
Rongling Wu
2009-04-01
Full Text Available Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable for analyzing longitudinal data with high dimensions and high measurement errors. In this article, we derive a general functional mapping framework for quantitative trait locus mapping of dynamic traits within the Bayesian paradigm. Markov chain Monte Carlo techniques were implemented for functional mapping to estimate biologically and statistically sensible parameters that model the structures of time-dependent genetic effects and covariance matrix. The Bayesian approach is useful to handle difficulties in constructing confidence intervals as well as the identifiability problem, enhancing the statistical inference of functional mapping. We have undertaken simulation studies to investigate the statistical behavior of Bayesian-based functional mapping and used a real example with F2 mice to validate the utilization and usefulness of the model.
Dynamic load balancing algorithm for molecular dynamics based on Voronoi cells domain decompositions
Energy Technology Data Exchange (ETDEWEB)
Fattebert, J.-L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Richards, D.F. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Glosli, J.N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2012-12-01
We present a new algorithm for automatic parallel load balancing in classical molecular dynamics. It assumes a spatial domain decomposition of particles into Voronoi cells. It is a gradient method which attempts to minimize a cost function by displacing Voronoi sites associated with each processor/sub-domain along steepest descent directions. Excellent load balance has been obtained for quasi-2D and 3D practical applications, with up to 440·10^{6} particles on 65,536 MPI tasks.
A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset
Directory of Open Access Journals (Sweden)
K. R.S. Kumar
2012-01-01
Full Text Available Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a density based dynamic data clustering algorithm for clustering incremental dataset and compare its performance with full run of normal DBSCAN, Chameleon on the dynamic dataset. Most of the clustering algorithms perform well and will give ideal performance with good accuracy measured with clustering accuracy, which is calculated using the original class labels and the calculated class labels. However, if we measure the performance with a cluster validation metric, then it will give another kind of result. Results: This study addresses the problems of clustering a dynamic dataset in which the data set is increasing in size over time by adding more and more data. So to evaluate the performance of the algorithms, we used Generalized Dunn Index (GDI, Davies-Bouldin index (DB as the cluster validation metric and as well as time taken for clustering. Conclusion: In this study, we have successfully implemented and evaluated the proposed density based dynamic clustering algorithm. The performance of the algorithm was compared with Chameleon and DBSCAN clustering algorithms. The proposed algorithm performed significantly well in terms of clustering accuracy as well as speed.
International Nuclear Information System (INIS)
This paper presents a new optimization technique based on a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints. In the constrained dynamic ED problem, the load demand and spinning reserve capacity as well as some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone are taken into consideration. The MTS algorithm introduces additional mechanisms such as initialization, adaptive searches, multiple searches, crossover and restarting process. To show its efficiency, the MTS algorithm is applied to solve constrained dynamic ED problems of power systems with 6 and 15 units. The results obtained from the MTS algorithm are compared to those achieved from the conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS) algorithm and particle swarm optimization (PSO). The experimental results show that the proposed MTS algorithm approaches is able to obtain higher quality solutions efficiently and with less computational time than the conventional approaches
Energy Technology Data Exchange (ETDEWEB)
Pothiya, Saravuth; Kongprawechnon, Waree [School of Communication, Instrumentation and Control, Sirindhorn International Institute of Technology, Thammasat University, P.O. Box 22, Pathumthani (Thailand); Ngamroo, Issarachai [Electrical Engineering Department, Faculty of Engineering, King Mongkut' s Institute of Technology Ladkrabang, Bangkok (Thailand)
2008-04-15
This paper presents a new optimization technique based on a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints. In the constrained dynamic ED problem, the load demand and spinning reserve capacity as well as some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone are taken into consideration. The MTS algorithm introduces additional mechanisms such as initialization, adaptive searches, multiple searches, crossover and restarting process. To show its efficiency, the MTS algorithm is applied to solve constrained dynamic ED problems of power systems with 6 and 15 units. The results obtained from the MTS algorithm are compared to those achieved from the conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS) algorithm and particle swarm optimization (PSO). The experimental results show that the proposed MTS algorithm approaches is able to obtain higher quality solutions efficiently and with less computational time than the conventional approaches. (author)
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
2007-01-01
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
A parallel clustered dynamic programming algorithm for discrete time optimal control problems
International Nuclear Information System (INIS)
Optimal control of dynamical systems is a problem that arises in many areas of engineering and physical science. Due to the special structure of optimal control problems, currently there is no parallel algorithm that can solve optimal control problems efficiently on computers with a large number of processors. In this paper, we will introduce a new optimal control algorithm that permits massively parallel processing. The proposed algorithm, called Cluster Dynamic Programming, is a combination of two efficient serial algorithms, differential dynamic programming and a stagewise Newton's method. Parallel numerical results on an Intel iPSC/860 will be presented
Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities
Huijuan Guo; Yi Feng; Fei Hao; Shengtong Zhong; Shuai Li
2014-01-01
Genetic Algorithms are traditionally used to solve combinatorial optimization problems. The implementation of Genetic Algorithms involves of using genetic operators (crossover, mutation, selection, etc.). Meanwhile, paramters (such as population size, probabilities of crossover and mutation) of Genetic Algorithm need to be chosen or tuned. In this paper, we propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem. Based on traditional Genetic Alg...
Dynamic Task Scheduling Algorithm based on Ant Colony Scheme
Directory of Open Access Journals (Sweden)
Kamolov Nizomiddin Baxodirjonovich
2015-08-01
Full Text Available Many scientific applications running in Cloud Computing system are workflow applications that contains large number of tasks and in which tasks are connected by precedence relations. Efficient scheduling the workflow tasks become a challenging issue in Cloud Computing environments because the scheduling decides performance of the applications. Unfortunately, finding the optimal scheduling is known as NP-hard. Ant Colony Optimization algorithm can be applied to design efficient scheduling algorithms. Previous scheduling algorithms that use Ant Colony mechanism lack rapid adaptivity. This paper proposes a task scheduling algorithm that uses a modified Ant Colony Optimization. The modified version uses probability in order for ants to decide target machine. The proposed task scheduling algorithm is implemented in WorkflowSim in order to measure performance. The experimental results show that the proposed scheduling algorithm reduce average makespan to about 6.4% compared to a scheduling algorithm that uses basic Ant Colony Optimization scheme.
Maintenance of Process Control Algorithms based on Dynamic Program Slicing
DEFF Research Database (Denmark)
Hansen, Ole Fink; Andersen, Nils Axel; Ravn, Ole
2010-01-01
behavior of a control algorithm, enables maintenance personnel to focus on only relevant parts of the algorithm and semi-automatically locate the part of the algorithm that is responsible for the reduced performance. The solution is tuning-free and can be applied to installed and running systems without......Today’s industrial control systems gradually lose performance after installation and must be regularly maintained by means of adjusting parameters and modifying the control algorithm, in order to regain high performance. Industrial control algorithms are complex software systems, and it is...... particularly difficult to locate causes of performance loss, while readjusting the algorithm once the cause of performance loss is actually realized and found is relatively simple. In this paper we present a software-engineering approach to the maintenance problem, which provides tools for exploring the...
A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset
K. R.S. Kumar; S. A.L. Mary
2012-01-01
Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a ...
Explicit symplectic algorithms based on generating functions for charged particle dynamics
Zhang, Ruili; Tang, Yifa; Liu, Jian; He, Yang; Xiao, Jianyuan
2016-01-01
Dynamics of a charged particle in the canonical coordinates is a Hamiltonian system, and the well-known symplectic algorithm has been regarded as the de facto method for numerical integration of Hamiltonian systems due to its long-term accuracy and fidelity. For long-term simulations with high efficiency, explicit symplectic algorithms are desirable. However, it is widely accepted that explicit symplectic algorithms are only available for sum-separable Hamiltonians, and that this restriction severely limits the application of explicit symplectic algorithms to charged particle dynamics. To overcome this difficulty, we combine the familiar sum-split method and a generating function method to construct second and third order explicit symplectic algorithms for dynamics of charged particle. The generating function method is designed to generate explicit symplectic algorithms for product-separable Hamiltonian with form of $H(\\mathbf{p},\\mathbf{q})=\\mathbf{p}_{i}f(\\mathbf{q})$ or $H(\\mathbf{p},\\mathbf{q})=\\mathbf{q}...
Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities
Feng, Yi
2008-01-01
Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm ...
Qi, Wei; Zhang, Chi; Fu, Guangtao; Zhou, Huicheng
2016-02-01
It is widely recognized that optimization algorithm parameters have significant impacts on algorithm performance, but quantifying the influence is very complex and difficult due to high computational demands and dynamic nature of search parameters. The overall aim of this paper is to develop a global sensitivity analysis based framework to dynamically quantify the individual and interactive influence of algorithm parameters on algorithm performance. A variance decomposition sensitivity analysis method, Analysis of Variance (ANOVA), is used for sensitivity quantification, because it is capable of handling small samples and more computationally efficient compared with other approaches. The Shuffled Complex Evolution method developed at the University of Arizona algorithm (SCE-UA) is selected as an optimization algorithm for investigation, and two criteria, i.e., convergence speed and success rate, are used to measure the performance of SCE-UA. Results show the proposed framework can effectively reveal the dynamic sensitivity of algorithm parameters in the search processes, including individual influences of parameters and their interactive impacts. Interactions between algorithm parameters have significant impacts on SCE-UA performance, which has not been reported in previous research. The proposed framework provides a means to understand the dynamics of algorithm parameter influence, and highlights the significance of considering interactive parameter influence to improve algorithm performance in the search processes.
A HYBRID GRANULARITY PARALLEL ALGORITHM FOR PRECISE INTEGRATION OF STRUCTURAL DYNAMIC RESPONSES
Institute of Scientific and Technical Information of China (English)
Yuanyin Li; Xianlong Jin; Genguo Li
2008-01-01
Precise integration methods to solve structural dynamic responses and the corre-sponding time integration formula are composed of two parts: the multiplication of an exponential matrix with a vector and the integration term. The second term can be solved by the series solu-tion. Two hybrid granularity parallel algorithms are designed, that is, the exponential matrix and the first term are computed by the fine-grained parallel algorithm and the second term is com-puted by the coarse-grained parallel algorithm. Numerical examples show that these two hybrid granularity parallel algorithms obtain higher speedup and parallel efficiency than two existing parallel algorithms.
Efficient conjugate gradient algorithms for computation of the manipulator forward dynamics
Fijany, Amir; Scheid, Robert E.
1989-01-01
The applicability of conjugate gradient algorithms for computation of the manipulator forward dynamics is investigated. The redundancies in the previously proposed conjugate gradient algorithm are analyzed. A new version is developed which, by avoiding these redundancies, achieves a significantly greater efficiency. A preconditioned conjugate gradient algorithm is also presented. A diagonal matrix whose elements are the diagonal elements of the inertia matrix is proposed as the preconditioner. In order to increase the computational efficiency, an algorithm is developed which exploits the synergism between the computation of the diagonal elements of the inertia matrix and that required by the conjugate gradient algorithm.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Directory of Open Access Journals (Sweden)
Zhihua Zhang
2016-01-01
Full Text Available Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO. Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938
Algorithms for New Product Development:. An Exercise in Thought Dynamics
Goldenberg, Y.; Horowitz, R.; Mazursky, D.; Solomon, S.
We generate new product ideas using algorithms which manipulate formally conceptual structures. These structures are abstract closed configurations composed of discrete elements which represent parts or properties of the products and of their immediate environment. The algorithms are assembled of a very limited number of fundamental operations acting on the elements. We present some successful field applications of the method carried out in leading international companies as well as an experiment providing quantitative proof of its superiority over usual ideas search.
Dynamic topology multi force particle swarm optimization algorithm and its application
Chen, Dongning; Zhang, Ruixing; Yao, Chengyu; Zhao, Zheyu
2016-01-01
Particle swarm optimization (PSO) algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization (DTMFPSO) algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization (MFPSO) algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing (FE) topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as µPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.
Institute of Scientific and Technical Information of China (English)
DU Mao-Kang; HE Bo; WANG Yong
2011-01-01
Recently, the cryptosystem based on chaos has attracted much attention. Wang and Yu (Commun. Nonlin. Sci. Numer. Simulat. 14(2009)574) proposed a block encryption algorithm based on dynamic sequences of multiple chaotic systems. We analyze the potential Saws in the algorithm. Then, a chosen-plaintext attack is presented. Some remedial measures are suggested to avoid the flaws effectively. Furthermore, an improved encryption algorithm is proposed to resist the attacks and to keep all the merits of the original cryptosystem.
Dynamic platform-independent meta-algorithms for graph-partitioning
Schwartz, Victor Scott
1998-01-01
Approved for public release, distribution is unlimited A dynamic platform-independent solver is developed for use with network and graph algorithms of operations research. This solver allows analysts to solve a large variety of problems without writing code. Algorithms from a library can be integrated into a meta-algorithm which also provides easy monitoring of solution progress. The solver, DORS, is demonstrated by heuristically solving a graph-partitioning problem to minimize the number ...
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.
Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network
Institute of Scientific and Technical Information of China (English)
Bo Yang; Da-You Liu
2006-01-01
Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104.Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.
Double Four-Bar Crank-Slider Mechanism Dynamic Balancing by Meta-Heuristic Algorithms
Habib Emdadi; Mahsa Yazdanian; Mir Mohammad Ettefagh; Mohammad-Reza Feizi-Derakhshi
2013-01-01
In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by meta- heuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been stu...
Zhihua Zhang; Zheng Sheng; Hanqing Shi; Zhiqiang Fan
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossov...
Behera, H S; Sahu, Sabyasachi; Bhoi, Sourav Kumar
2011-01-01
CPU being considered a primary computer resource, its scheduling is central to operating-system design. A thorough performance evaluation of various scheduling algorithms manifests that Round Robin Algorithm is considered as optimal in time shared environment because the static time is equally shared among the processes. We have proposed an efficient technique in the process scheduling algorithm by using dynamic time quantum in Round Robin. Our approach is based on the calculation of time quantum twice in single round robin cycle. Taking into consideration the arrival time, we implement the algorithm. Experimental analysis shows better performance of this improved algorithm over the Round Robin algorithm and the Shortest Remaining Burst Round Robin algorithm. It minimizes the overall number of context switches, average waiting time and average turn-around time. Consequently the throughput and CPU utilization is better.
Information dynamics algorithm for detecting communities in networks
Massaro, E; Bagnoli, F; Liò, P
2011-01-01
The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network - inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark ...
Battiste, Vernol; Lawton, George; Lachter, Joel; Brandt, Summer; Koteskey, Robert; Dao, Arik-Quang; Kraut, Josh; Ligda, Sarah; Johnson, Walter W.
2012-01-01
Managing the interval between arrival aircraft is a major part of the en route and TRACON controller s job. In an effort to reduce controller workload and low altitude vectoring, algorithms have been developed to allow pilots to take responsibility for, achieve and maintain proper spacing. Additionally, algorithms have been developed to create dynamic weather-free arrival routes in the presence of convective weather. In a recent study we examined an algorithm to handle dynamic re-routing in the presence of convective weather and two distinct spacing algorithms. The spacing algorithms originated from different core algorithms; both were enhanced with trajectory intent data for the study. These two algorithms were used simultaneously in a human-in-the-loop (HITL) simulation where pilots performed weather-impacted arrival operations into Louisville International Airport while also performing interval management (IM) on some trials. The controllers retained responsibility for separation and for managing the en route airspace and some trials managing IM. The goal was a stress test of dynamic arrival algorithms with ground and airborne spacing concepts. The flight deck spacing algorithms or controller managed spacing not only had to be robust to the dynamic nature of aircraft re-routing around weather but also had to be compatible with two alternative algorithms for achieving the spacing goal. Flight deck interval management spacing in this simulation provided a clear reduction in controller workload relative to when controllers were responsible for spacing the aircraft. At the same time, spacing was much less variable with the flight deck automated spacing. Even though the approaches taken by the two spacing algorithms to achieve the interval management goals were slightly different they seem to be simpatico in achieving the interval management goal of 130 sec by the TRACON boundary.
Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.
Xu, Dongpo; Xia, Yili; Mandic, Danilo P
2016-02-01
The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks. PMID:26087504
Dynamic gate algorithm for multimode fiber Bragg grating sensor systems
DEFF Research Database (Denmark)
Ganziy, Denis; Jespersen, O.; Woyessa, Getinet;
2015-01-01
-to-noise ratio and different peak shapes. Our simulations and experiments demonstrate that the DGA method is fast and robust with better stability and accuracy than conventional algorithms. This makes it very attractive for future implementation in sensing systems, especially based on multimode fiber Bragg...
Directory of Open Access Journals (Sweden)
Arild Helseth
2015-12-01
Full Text Available Stochastic dual dynamic programming (SDDP has become a popular algorithm used in practical long-term scheduling of hydropower systems. The SDDP algorithm is computationally demanding, but can be designed to take advantage of parallel processing. This paper presents a novel parallel scheme for the SDDP algorithm, where the stage-wise synchronization point traditionally used in the backward iteration of the SDDP algorithm is partially relaxed. The proposed scheme was tested on a realistic model of a Norwegian water course, proving that the synchronization point relaxation significantly improves parallel efficiency.
A Dynamic Clustering-Based Routing Algorithm for Wireless Senor Networks
Directory of Open Access Journals (Sweden)
Kai Zhou
2008-01-01
Full Text Available Wireless sensor networks (WSN is an energy limited system, thus this study presents an energy-aware quality of service (QoS routing algorithm for it, which can also run efficiently with best-effort traffic. Furthermore, our work differs from existing algorithms in two ways: (1 We improve the first order energy consumption model with dynamic clustering; (2 We use clustering to build the multi-objectives programming model to support QoS. Simulations and comparisons with some typical route algorithms show that our algorithm is robust and effective.
An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics
Charlebois, Daniel A; Fraser, Dawn; Kaern, Mads
2011-01-01
We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell populations. To benchmark performance, we compare simulation results with steadystate and time-dependent analytical solutions for several scenarios, including steadystate and time-dependent gene expression, and the effects on population heterogeneity of cell growth, division, and DNA replication. This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression. We also use the algorithm to model gene expression dynamics within "bet-hedging" cell populations during their adaption to environmental stress. These simulations indicate that the algorithm provides a framework suitable for simulating and ana...
DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks.
Estévez, Francisco José; Glösekötter, Peter; González, Jesús
2016-01-01
The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities. PMID:27347962
DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
Estévez, Francisco José; Glösekötter, Peter; González, Jesús
2016-01-01
The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities. PMID:27347962
Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment
Ataollahi, Iraj
2009-01-01
Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim s...
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
Dynamic Deployment of Wireless Sensor Networks by Biogeography Based Optimization Algorithm
Directory of Open Access Journals (Sweden)
Luo Liu
2012-06-01
Full Text Available As the usage and development of wireless sensor networks increases, problems related to these networks are becoming apparent. Dynamic deployment is one of the main topics that directly affects the performance of the wireless sensor networks. In this paper, biogeography-based optimization is applied to the dynamic deployment of static and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A binary detection model is considered to obtain realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the artificial bee colony algorithm, Homo-H-VFCPSO and stud genetic algorithm that are also population-based optimization algorithms. Results show biogeography-based optimization can be preferable in the dynamic deployment of wireless sensor networks.
Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.
Ozturk, Celal; Karaboga, Dervis; Gorkemli, Beyza
2011-01-01
As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks. PMID:22163942
Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm
Directory of Open Access Journals (Sweden)
Dervis Karaboga
2011-06-01
Full Text Available As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.
Zhou, Xu; Liu, Yanheng; Li, Bin; Sun, Geng
2015-10-01
Identifying community structures in static network misses the opportunity to capture the evolutionary patterns. So community detection in dynamic network has attracted many researchers. In this paper, a multiobjective biogeography based optimization algorithm with decomposition (MBBOD) is proposed to solve community detection problem in dynamic networks. In the proposed algorithm, the decomposition mechanism is adopted to optimize two evaluation objectives named modularity and normalized mutual information simultaneously, which measure the quality of the community partitions and temporal cost respectively. A novel sorting strategy for multiobjective biogeography based optimization is presented for comparing quality of habitats to get species counts. In addition, problem-specific migration and mutation model are introduced to improve the effectiveness of the new algorithm. Experimental results both on synthetic and real networks demonstrate that our algorithm is effective and promising, and it can detect communities more accurately in dynamic networks compared with DYNMOGA and FaceNet.
Directory of Open Access Journals (Sweden)
Orly Yadid-Pecht
2013-10-01
Full Text Available Tone mapping algorithms are used to adapt captured wide dynamic range (WDR scenes to the limited dynamic range of available display devices. Although there are several tone mapping algorithms available, most of them require manual tuning of their rendering parameters. In addition, the high complexities of some of these algorithms make it difficult to implement efficient real-time hardware systems. In this work, a real-time hardware implementation of an exponent-based tone mapping algorithm is presented. The algorithm performs a mixture of both global and local compression on colored WDR images. An automatic parameter selector has been proposed for the tone mapping algorithm in order to achieve good tone-mapped images without manual reconfiguration of the algorithm for each WDR image. Both algorithms are described in Verilog and synthesized for a field programmable gate array (FPGA. The hardware architecture employs a combination of parallelism and system pipelining, so as to achieve a high performance in power consumption, hardware resources usage and processing speed. Results show that the hardware architecture produces images of good visual quality that can be compared to software-based tone mapping algorithms. High peak signal-to-noise ratio (PSNR and structural similarity (SSIM scores were obtained when the results were compared with output images obtained from software simulations using MATLAB.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Energy Technology Data Exchange (ETDEWEB)
Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States)
2016-06-21
The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
A local flocking algorithm of multi-agent dynamic systems
Pei, Huiqin; Chen, Shiming; Lai, Qiang
2015-11-01
In this paper, the local flocking of multi-agent systems is investigated, which means all agents form some groups of surrounding multiple targets with the partial information exchange. For the purpose of realising local multi-flocking, a control algorithm of local flocking is proposed, which is a biologically inspired approach that assimilates key characteristics of flocking and anti-flocking. In the process of surrounding mobile targets through the control algorithm, all agents can adaptively choose between two work modes to depend on the variation of visual field and the number of pursuing agents with the mobile target. One is a flocking pursuing mode which is that some agents pursue each mobile target, the other is an anti-flocking searching mode that means with the exception of the pursing agents of mobile targets, other agents respectively hunt for optimal the mobile target with a closest principle between the agent and the target. In two work modes, the agents are controlled severally via the different control protocol. By the Lyapunov theorem, the stability of the second-order multi-agent system is proven in detail. Finally, simulation results verify the effectiveness of the proposed algorithm.
Indian Academy of Sciences (India)
A Rama Mohan Rao; T V S R Appa Rao; B Dattaguru
2004-02-01
The work reported in this paper is motivated by the need to develop portable parallel processing algorithms and codes which can run on a variety of hardware platforms without any modiﬁcations. The prime aim of the research work reported here is to test the portability of the parallel algorithms and also to study and understand the comparative efﬁciencies of three parallel algorithms developed for implicit time integration technique. The standard message passing interface (MPI) is used to develop parallel algorithms for computing nonlinear dynamic response of large structures employing implicit time-marching scheme. The parallel algorithms presented in this paper are developed under the broad framework of non-overlapped domain decomposition technique. Numerical studies indicate that the parallel algorithm devised employing the conventional form of Newmark time integration algorithm is faster than the predictor–corrector form. It is also accurate and highly adaptive to ﬁne grain computations. The group implicit algorithm is found to be extremely superior in performance when compared to the other two parallel algorithms. This algorithm is better suited for large size problems on coarse grain environment as the resulting submeshes will obviously be large and thus permit larger time steps without losing accuracy.
Stable algorithm for event detection in event-driven particle dynamics
Bannerman, Marcus N.; Strobl, Severin; Formella, Arno; Poeschel, Thorsten
2012-01-01
Event-Driven Particle Dynamics is a fast and precise method to simulate particulate systems of all scales. In this work it is demonstrated that, despite the high accuracy of the method, the finite machine precision leads to simulations entering invalid states where the dynamics are undefined. A general event-detection algorithm is proposed which handles these situations in a stable and efficient manner. This requires a definition of the dynamics of invalid states and leads to improved algorit...
DEFF Research Database (Denmark)
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2009-01-01
This paper addresses the unit commitment (UC) in multi-period combined heat and power (CHP) production planning under the deregulated power market. In CHP plants (units), generation of heat and power follows joint characteristics, which implies that it is difficult to determine the relative cost...... efficiency of the plants. We introduce in this paper the DRDP-RSC algorithm, which is a dynamic regrouping based dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units, sequential commitment of units in small groups. Relaxed states of the plants are used to reduce the...
Dynamic Communication Performance Enhancement in Hierarchical Torus Network by Selection Algorithm
Directory of Open Access Journals (Sweden)
MM Hafizur Rahman
2012-03-01
Full Text Available A Hierarchical Torus Network (HTN is a 2D-torus network of multiple basic modules, in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. The static network performance of the HTN and its dynamic communication performance using the deterministic, dimension-order routing algorithm have already been evaluated and shown to be superior to the performance of other conventional and hierarchical interconnection networks. However, the assessment of the dynamic communication performance improvement of HTN by the efficient use of both the physical link and virtual channels has not yet been evaluated. This paper addresses three adaptive routing algorithms -- link-selection, channel-selection, and a combination of link-selection and channel-selection -- for the efficient use of physical links and virtual channels of an HTN to enhance dynamic communication performance. It also proves that the proposed adaptive routing algorithms are deadlock-free with 3 virtual channels. The dynamic communication performances of an HTN is evaluated by using dimension-order routing and proposed adaptive routing algorithms under various traffic patterns. It is found that the dynamic communication performance of an HTN using these adaptive routing algorithms are better than when the dimension-order routing is used, in terms of network throughput.
Evaluation of dynamic bandwidth allocation algorithms in GPON networks
DEFF Research Database (Denmark)
Ozimkiewicz, J.; Ruepp, Sarah Renée; Dittmann, Lars;
2010-01-01
In this paper, two approaches for Dynamic Bandwidth Allocation in GPON networks are proposed, and validated through simulations in the OPNET modeler. One approach address a Status Reporting scheme, where the bandwidth allocation originates from the client request. The second use a centralized Non...... services....
Graph-theoretic algorithm for hierarchial decomposition of dynamic systems
Energy Technology Data Exchange (ETDEWEB)
Pichai, V.; Sezer, M.E.; Siljak, D.D.
1982-03-24
A graph-theoretic scheme is proposed for partitioning of dynamic systems into hierarchially ordered subsystems having independent inputs and outputs. The resulting subsystems are input-output reachable as well as structurally controllable and observable, so that a piece-by-piece design of estimators and controllers can be accomplished for systems with large dimensions without excessive computer requirements.
A new simulation algorithm for lattice QCD with dynamical quarks
Bunk, B.; Jansen, K.; Jegerlehner, B.; Lüscher, M.; Simma, H.; Sommer, R
1994-01-01
A previously introduced multi-boson technique for the simulation of QCD with dynamical quarks is described and some results of first test runs on a $6^3\\times12$ lattice with Wilson quarks and gauge group SU(2) are reported.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
Ghasemian, Amir; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2015-01-01
We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.
A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks
Directory of Open Access Journals (Sweden)
Guoqiang Chen
2013-01-01
Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.
An FDTD algorithm for simulating light propagation in anisotropic dynamic gain media
Al-Jabr, A. A.
2014-05-02
Simulating light propagation in anisotropic dynamic gain media such as semiconductors and solid-state lasers using the finite difference time-domain FDTD technique is a tedious process, as many variables need to be evaluated in the same instant of time. The algorithm has to take care of the laser dynamic gain, rate equations, anisotropy and dispersion. In this paper, to the best of our knowledge, we present the first algorithm that solves this problem. The algorithm is based on separating calculations into independent layers and hence solving each problem in a layer of calculations. The anisotropic gain medium is presented and tested using a one-dimensional set-up. The algorithm is then used for the analysis of a two-dimensional problem.
Bid-based dynamic economic dispatch with an efficient interior point algorithm
International Nuclear Information System (INIS)
An efficient interior point algorithm for solving the bid-based dynamic economic dispatch (BBDED) problem is proposed in this paper. This algorithm is an extension of interior point quadratic programming (IPQP), and is called the predictor-corrector interior point quadratic programming (PCIPQP) algorithm. In a competitive electricity market, the dynamic economic dispatch has evolved to become bid-based framework to maximize the profits and achieve the resource scheduling. To generate a physically feasible dispatch and market spot prices, we form BBDED problem of dealing with various constraints such as ramp rates, transmission line capacity and emission constraints. In this paper, BBDED allows both supply-side and demand-side bids in the spot market, which includes multi-player, multi-period and a large number of constraints. Compared with the pure IPQP algorithm, PCIPQP is more attractive in performance, robustness, and the property of convergence. Many numerical tests reconfirmed the advantages of the predictor-corrector method. (Author)
DEFF Research Database (Denmark)
Hou, Peng; Hu, Weihao; Chen, Cong;
2016-01-01
(MST) algorithm, an improved algorithm, the Dynamic Minimum Spanning Tree (DMST) algorithm is proposed. The current carrying capacity of the cable is considered to be the main constraint and the cable sectional area is changed dynamically. An irregular shaped wind farm is chosen as the studie case...... and the results are compared with the layout obtained by a traditional MST algorithm. Simulation results show that the proposed method is an effective way for offshore wind farm collection system layout design....
A dynamic programming algorithm for the space allocation and aisle positioning problem
Peter Bodnar; Jens Lysgaard
2014-01-01
The space allocation and aisle positioning problem (SAAPP) in a material handling system with gravity flow racks is the problem of minimizing the total number of replenishments over a period subject to practical constraints related to the need for aisles granting safe and easy access to storage locations. In this paper, we develop an exact dynamic programming algorithm for the SAAPP. The computational study shows that our exact algorithm can be used to find optimal solutions for numerous SAAP...
A functional clustering algorithm for the analysis of dynamic network data
Feldt, S.; Waddell, J; Hetrick, V. L.; Berke, J. D.; Zochowski, M.
2008-01-01
We formulate a novel technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both s...
Frigerio, Marco; Buchli, Jonas; Caldwell, Darwin G.
2013-01-01
Rigid body dynamics algorithms play a crucial role in several components of a robot controller and simulations. Real time constraints in high frequency control loops and time requirements of specific applications demand these functions to be very efficient. Despite the availability of established algorithms, their efficient implementation for a specific robot still is a tedious and error-prone task. However, these components are simply necessary to get high performance controllers. To achieve...
Diamantidis A. C.; Papadopoulos C. T.
2004-01-01
In this study, the buffer allocation problem (BAP) in homogeneous, asymptotically reliable serial production lines is considered. A known aggregation method, given by Lim, Meerkov, and Top (1990), for the performance evaluation (i.e., estimation of throughput) of this type of production lines when the buffer allocation is known, is used as an evaluative method in conjunction with a newly developed dynamic programming (DP) algorithm for the BAP. The proposed algorithm is applied to production ...
Point group identification algorithm in dynamic response analysis of nonlinear stochastic systems
Li, Tao; Chen, Jian-bing; Li, Jie
2016-03-01
The point group identification (PGI) algorithm is proposed to determine the representative point sets in response analysis of nonlinear stochastic dynamic systems. The PGI algorithm is employed to identify point groups and their feature points in an initial point set by combining subspace clustering analysis and the graph theory. Further, the representative point set of the random-variate space is determined according to the minimum generalized F-discrepancy. The dynamic responses obtained by incorporating the algorithm PGI into the probability density evolution method (PDEM) are compared with those by the Monte Carlo simulation method. The investigations indicate that the proposed method can reduce the number of the representative points, lower the generalized F-discrepancy of the representative point set, and also ensure the accuracy of stochastic structural dynamic analysis.
Dynamic scheduling study on engineering machinery of clusters using multi-agent system ant algorithm
Gao, Qiang; Wang, Hongli; Guo, Long; Xiang, Jianping
2005-12-01
In the process of road surface construction, dispatchers' scheduling was experiential and blindfold in some degree and static scheduling restricted the continuity of the construction. Serious problems such as labor holdup, material awaiting and scheduling delay could occur when the old scheduling technique was used. This paper presents ant colony algorithm based on MAS that has the abilities of intelligentized modeling and dynamic scheduling. MAS model deals with single agent's communication and corresponding in engineering machinery of clusters firstly, next we apply ant colony algorithm to solve dynamic scheduling in the plant. Ant colony algorithm can optimize the match of agents and make the system dynamic balance. The effectiveness of the proposed method is demonstrated with MATLAB simulations.
Adaptive Dynamic Programming for Control Algorithms and Stability
Zhang, Huaguang; Luo, Yanhong; Wang, Ding
2013-01-01
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-...
Symmetry preserving algorithm for a dynamic contact-impact problem
Czech Academy of Sciences Publication Activity Database
Gabriel, Dušan; Plešek, Jiří
Plzeň : Západočeská univerzita v Plzni, 2004 - (Vimmer, J.), s. 103-108 ISBN 80-7043-314-0. [Conference with international participation Computational mechanics 2004 /20./. Nečtiny (CZ), 08.10.2004-10.10.2004] Institutional research plan: CEZ:AV0Z2076919 Keywords : contact * Gauss point searchl * explicit transient dynamics Subject RIV: JJ - Other Materials
A study on the dynamic tie points ASI algorithm in the Arctic Ocean
Institute of Scientific and Technical Information of China (English)
HAO Guanghua; SU Jie
2015-01-01
Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI (the Arctic Radiation And Turbulence Interaction Study (ARTIST) Sea Ice) retrieval algorithm. Instead of using fixed-point values, we developed ASI algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase (decrease) the sea ice concentrations in low-value (high-value) areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS (Moderate-resolution Imaging Spectroradiometer) data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square (RMS) differences compare with those by the fixed tie points.
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Daqing Wu
2012-01-01
Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.
A Novel Dynamic Bandwidth Assignment Algorithm for Multi-Services EPONs
Institute of Scientific and Technical Information of China (English)
CHEN Xue; ZHANG Yang; HUANG Xiang; DENG Yu; SUN Shu-he
2005-01-01
In this paper we propose a novel Dynamic Bandwidth Assignment (DBA) algorithm for Ethernet-based Passive Optical Networks (EPON) which offers multiple kinds of services. To satisfy crucial Quality of Service (QoS) requirement for Time Division Multiplexing (TDM) service and achieve fair and high bandwidth utilization simultaneously, the algorithm integrates periodic, for TDM service, and polling granting for Ethernet service. Detailed simulation shows that the algorithm guarantees carrier-grade QoS for TDM service, high bandwidth utilization and good fairness of bandwidth assignment among Optical Network Units (ONU).
Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min
2015-12-01
In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.
Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants
International Nuclear Information System (INIS)
A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process
The generation algorithm of arbitrary polygon animation based on dynamic correction
Directory of Open Access Journals (Sweden)
Hou Ya Wei
2016-01-01
Full Text Available This paper, based on the key-frame polygon sequence, proposes a method that makes use of dynamic correction to develop continuous animation. Firstly we use quadratic Bezier curve to interpolate the corresponding sides vector of polygon sequence consecutive frame and realize the continuity of animation sequences. And then, according to Bezier curve characteristic, we conduct dynamic regulation to interpolation parameters and implement the changing smoothness. Meanwhile, we take use of Lagrange Multiplier Method to correct the polygon and close it. Finally, we provide the concrete algorithm flow and present numerical experiment results. The experiment results show that the algorithm acquires excellent effect.
An elementary singularity-free Rotational Brownian Dynamics algorithm for anisotropic particles
Energy Technology Data Exchange (ETDEWEB)
Ilie, Ioana M.; Briels, Wim J. [Computational Biophysics, Faculty of Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Otter, Wouter K. den, E-mail: w.k.denotter@utwente.nl [Computational Biophysics, Faculty of Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Multi Scale Mechanics, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands)
2015-03-21
Brownian Dynamics is the designated technique to simulate the collective dynamics of colloidal particles suspended in a solution, e.g., the self-assembly of patchy particles. Simulating the rotational dynamics of anisotropic particles by a first-order Langevin equation, however, gives rise to a number of complications, ranging from singularities when using a set of three rotational coordinates to subtle metric and drift corrections. Here, we derive and numerically validate a quaternion-based Rotational Brownian Dynamics algorithm that handles these complications in a simple and elegant way. The extension to hydrodynamic interactions is also discussed.
An elementary singularity-free Rotational Brownian Dynamics algorithm for anisotropic particles
International Nuclear Information System (INIS)
Brownian Dynamics is the designated technique to simulate the collective dynamics of colloidal particles suspended in a solution, e.g., the self-assembly of patchy particles. Simulating the rotational dynamics of anisotropic particles by a first-order Langevin equation, however, gives rise to a number of complications, ranging from singularities when using a set of three rotational coordinates to subtle metric and drift corrections. Here, we derive and numerically validate a quaternion-based Rotational Brownian Dynamics algorithm that handles these complications in a simple and elegant way. The extension to hydrodynamic interactions is also discussed
Symetry preserving algorithm for a dynamic contact-impact problem
Czech Academy of Sciences Publication Activity Database
Gabriel, Dušan; Plešek, Jiří; Valeš, František; Okrouhlík, Miloslav
Dordrecht : Springer, 2006 - (Mota Soares, C.; Martins, J.; Rodrigues, H.; Ambrosio, J.). s. 318-318 ISBN 1-4020-4994-3. [European Conference on Computational Mechanics /3./. 05.06.2006-08.06.2006, Lisbon] R&D Projects: GA ČR(CZ) GP101/03/D153; GA ČR(CZ) GA101/06/0914; GA ČR(CZ) GA101/06/0213; GA AV ČR(CZ) 1ET400760509 Institutional research plan: CEZ:AV0Z20760514 Keywords : contact-impact * Gauss point search * explicit dynamics Subject RIV: JJ - Other Materials
Symetry preserving algorithm for a dynamic contact-impact problem
Czech Academy of Sciences Publication Activity Database
Gabriel, Dušan; Plešek, Jiří; Valeš, František; Okrouhlík, Miloslav
Dordrecht : Springer, 2006 - (Mota Soares, C.; Martins, J.; Rodrigues, H.; Ambrosio, J.), s. 1-7 ISBN 1-4020-4994-3. [European Conference on Computational Mechanics /3./. Lisabon (PT), 05.06.2006-08.06.2006] R&D Projects: GA ČR(CZ) GP101/03/D153; GA ČR(CZ) GA101/06/0914; GA ČR(CZ) GA101/06/0213; GA AV ČR(CZ) 1ET400760509 Institutional research plan: CEZ:AV0Z20760514 Keywords : contact-impact * Gaus point search * explicit dynamics Subject RIV: JJ - Other Materials
A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path
Xie, Zhiqiang; Shao, Xia; Xin, Yu
2016-01-01
To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. PMID:27490901
Algorithm to illustrate context using dynamic lighting effects
John, Roshy M.; Balasubramanian, T.
2007-09-01
With the invention of Ultra-Bright LED, solid state lighting has come to something which is much more efficient and energy saving when compared to conventional incandescent or fluorescent lighting. With the use of proper driver electronics now a days it is possible to install solid state lighting systems with the cost same as that of any other lighting technology. This paper is a part of the research project we are doing in our lab, which deals with using ultra bright LEDs of different colors for lighting applications. The driver electronics are made in such a way that, the color and brightness of the lights will change according to context. For instance, if one of the users is reading a story or listening to music in a Personal Computer or in a hand held device such as a PDA, the lighting systems and the HVAC (Heating Ventilation Air-conditioning) systems will change dramatically according to the content of the story or the music. The vulnerability of solid-state lighting helps to accomplish such an effect. Such a type of system will help the reader to feel the story mentally and physically as well. We developed complete driver electronics for the system using multiple microcomputers and a full software suite which uses complex algorithms to decode the context from text or music and synchronize it to lighting and HVAC information. The paper also presents some case-study statistics which shows the advantage of using the system to teach kindergarten children, deaf and dumb children and for language learning classes.
Lu, Jianfeng
2016-01-01
In the spirit of the fewest switches surface hopping, the frozen Gaussian approximation with surface hopping (FGA-SH) method samples a path integral representation of the non-adiabatic dynamics in the semiclassical regime. An improved sampling scheme is developed in this work for FGA-SH based on birth and death branching processes. The algorithm is validated for the standard test examples of non-adiabatic dynamics.
Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm
Dervis Karaboga; Celal Ozturk; Beyza Gorkemli
2011-01-01
As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is ...
Chaudhuri, Arindam
2013-01-01
We present a dynamic algorithm for solving the Longest Common Subsequence Problem using Ant Colony Optimization Technique. The Ant Colony Optimization Technique has been applied to solve many problems in Optimization Theory, Machine Learning and Telecommunication Networks etc. In particular, application of this theory in NP-Hard Problems has a remarkable significance. Given two strings, the traditional technique for finding Longest Common Subsequence is based on Dynamic Programming which cons...
Rogério M. Branco; Antônio S. Coelho; Sérgio F. Mayerle
2016-01-01
This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatch...
Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm
Institute of Scientific and Technical Information of China (English)
Mao-Guo Gong; Ling-Jun Zhang; Jing-Jing Ma; Li-Cheng Jiao
2012-01-01
Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.
Institute of Scientific and Technical Information of China (English)
SUN Fan; DU Wenli; QI Rongbin; QIAN Feng; ZHONG Weimin
2013-01-01
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature.Genetic algorithm(GA)has been proved to be a feasible method when the gradient is difficult to calculate.Its advantage is that the control profiles at all time stages are optimized simultaneously,but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum.In this study,a hybrid improved genetic algorithm(HIGA)for solving dynamic optimization problems is proposed to overcome these defects.Simplex method(SM)is used to perform the local search in the neighborhood of the optimal solution.By using SM,the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved.The hybrid algorithm presents some improvements,such as protecting the best individual,accepting immigrations,as well as employing adaptive crossover and Gaussian mutation operators.The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems.At last,HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
A parallel dynamic programming algorithm for multi-reservoir system optimization
Li, Xiang; Wei, Jiahua; Li, Tiejian; Wang, Guangqian; Yeh, William W.-G.
2014-05-01
This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system. First, a multi-dimensional dynamic programming (DP) model is formulated for a multi-reservoir system. Second, the DP algorithm is parallelized using a peer-to-peer parallel paradigm. The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol. We consider both the distributed computing and distributed computer memory in the parallelization. The parallel paradigm aims at reducing the computation time as well as alleviating the computer memory requirement associated with running a multi-dimensional DP model. Next, we test the parallel DP algorithm on the classic, benchmark four-reservoir problem on a high-performance computing (HPC) system with up to 350 cores. Results indicate that the parallel DP algorithm exhibits good performance in parallel efficiency; the parallel DP algorithm is scalable and will not be restricted by the number of cores. Finally, the parallel DP algorithm is applied to a real-world, five-reservoir system in China. The results demonstrate the parallel efficiency and practical utility of the proposed methodology.
Supercomputer algorithms for reactivity, dynamics and kinetics of small molecules
International Nuclear Information System (INIS)
Even for small systems, the accurate characterization of reactive processes is so demanding of computer resources as to suggest the use of supercomputers having vector and parallel facilities. The full advantages of vector and parallel architectures can sometimes be obtained by simply modifying existing programs, vectorizing the manipulation of vectors and matrices, and requiring the parallel execution of independent tasks. More often, however, a significant time saving can be obtained only when the computer code undergoes a deeper restructuring, requiring a change in the computational strategy or, more radically, the adoption of a different theoretical treatment. This book discusses supercomputer strategies based upon act and approximate methods aimed at calculating the electronic structure and the reactive properties of small systems. The book shows how, in recent years, intense design activity has led to the ability to calculate accurate electronic structures for reactive systems, exact and high-level approximations to three-dimensional reactive dynamics, and to efficient directive and declaratory software for the modelling of complex systems
Dynamic Programming and Genetic Algorithm for Business Processes Optimisation
Directory of Open Access Journals (Sweden)
Mateusz Wibig
2012-12-01
Full Text Available There are many business process modelling techniques, which allow to capture features of those processes, but graphical, diagrammatic models seems to be used most in companies and organizations. Although the modelling notations are more and more mature and can be used not only to visualise the process idea but also to implement it in the workflow solution and although modern software allows us to gather a lot of data for analysis purposes, there is still not much commercial used business process optimisation methods. In this paper the scheduling / optimisation method for automatic task scheduling in business processes models is described. The Petri Net model is used, but it can be easily applied to any other modelling notation, where the process is presented as a set of tasks, i.e. BPMN (Business Process Modelling Notation. The method uses Petri Nets’, business processes’ scalability and dynamic programming concept to reduce the necessary computations, by revising only those parts of the model, to which the change was applied.
A Dynamic Traffic Signal Timing Model and its Algorithm for Junction of Urban Road
DEFF Research Database (Denmark)
Cai, Yanguang; Cai, Hao
2012-01-01
As an important part of Intelligent Transportation System, the scientific traffic signal timing of junction can improve the efficiency of urban transport. This paper presents a novel dynamic traffic signal timing model. According to the characteristics of the model, hybrid chaotic quantum......-time and dynamic signal control of junction. To obtain the optimal solution of the model by hybrid chaotic quantum evolutionary algorithm, the model is converted to an easily solvable form. To simplify calculation, we give the expression of the partial derivative and change rate of the objective function...... such that the implementation of the algorithm only involves function assignments and arithmetic operations and thus avoids complex operations such as integral and differential. Simulation results show that the algorithm has less remain vehicles than Webster method, higher convergence rate and...
Two-stage evolutionary algorithm for dynamic multicast routing in mesh network
Institute of Scientific and Technical Information of China (English)
Li ZHU; Zhi-shu LI; Liang-yin CHEN; Yan-hong CHENG
2008-01-01
In order to share multimedia transmissions in mesh networks and optimize the utilization of network resources, this paper presents a Two-stage Evolutionary Algorithm (TEA), i.e., unicast routing evolution and multicast path composition, for dynamic multicast routing. The TEA uses a novel link-duplicate-degree encoding, which can encode a multicast path in the link-duplicate-degree and decode the path as a link vector easily. A dynamic algorithm for adding nodes to or removing nodes from a multicast group and a repairing algorithm are also covered in this paper. As the TEA is based on global evaluation, the quality of the multicast path remains stabilized without degradation when multicast members change over time. Therefore, it is not necessary to rearrange the multicast path during the life cycle of the multicast sessions. Simulation results show that the TEA is efficient and convergent.
DOUBLE FOUR-BAR CRANK-SLIDER MECHANISM DYNAMIC BALANCING BY META-HEURISTIC ALGORITHMS
Directory of Open Access Journals (Sweden)
Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by metaheuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and runtime values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental doublefour-bar crank-slider mechanism was provided for validating the proposed balancing method practically.
Double Four-Bar Crank-Slider Mechanism Dynamic Balancing by Meta-Heuristic Algorithms
Directory of Open Access Journals (Sweden)
Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by meta-heuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism.Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and run time values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental double four-bar crank-slider mechanism was provided for validating the proposedbalancing method practicall
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.;
2014-01-01
. Accordingly, this paper proposes a dynamic consensus algorithm based distributed optimization method aiming at improving the system efficiency while offering higher expandability and flexibility when compared to centralized control. Hardware-in-the-loop (HIL) results are shown to demonstrate the effectiveness...
Czech Academy of Sciences Publication Activity Database
Larentzos, J.P.; Brennan, J.K.; Moore, J.D.; Lísal, Martin; Mattson, w.D.
2014-01-01
Roč. 185, č. 7 (2014), s. 1987-1998. ISSN 0010-4655 Grant ostatní: ARL(US) W911NF-10-2-0039 Institutional support: RVO:67985858 Keywords : dissipative particle dynamics * shardlow splitting algorithm * numerical integration Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.112, year: 2014
Novotny, M.A.
2010-02-01
The efficiency of dynamic Monte Carlo algorithms for off-lattice systems composed of particles is studied for the case of a single impurity particle. The theoretical efficiencies of the rejection-free method and of the Monte Carlo with Absorbing Markov Chains method are given. Simulation results are presented to confirm the theoretical efficiencies. © 2010.
An Optimal Algorithm towards Successive Location Privacy in Sensor Networks with Dynamic Programming
Zhao, Baokang; Wang, Dan; Shao, Zili; Cao, Jiannong; Chan, Keith C. C.; Su, Jinshu
In wireless sensor networks, preserving location privacy under successive inference attacks is extremely critical. Although this problem is NP-complete in general cases, we propose a dynamic programming based algorithm and prove it is optimal in special cases where the correlation only exists between p immediate adjacent observations.
International Nuclear Information System (INIS)
An anomalous behavior of Gear's predictor-corrector algorithm is observed during a microcanonical molecular dynamics simulation of amorphous silicon. While the amorphous silicon network generated by quenching was being annealed, an anomaly is started: both the total energy and the temperature of the system spontaneously and indefinitely increased, in violation of energy conservation. No such phenomenon was observed when the integrator was switched to the Verlet algorithm. To remove this anomaly with the Gear's algorithm, it was necessary to reduce the size of the time step to 1/32 of the 'usual value (∼ 1/100 of the fastest vibration period in the system)' at least. Our results show that Gear's algorithm may become very unstable in a realistic simulation when the time step is not small enough, that is, orders of magnitude smaller than conventional value.
Genetic Algorithm-based Dynamic Vehicle Route Search using Car-to-Car Communication
Directory of Open Access Journals (Sweden)
KIM, J.
2010-11-01
Full Text Available Suggesting more efficient driving routes generate benefits not only for individuals by saving commute time, but also for society as a whole by reducing accident rates and social costs by lessening traffic congestion. In this paper, we suggest a new route search algorithm based on a genetic algorithm which is more easily installable into mutually communicating car navigation systems, and validate its usefulness through experiments reflecting real-world situations. The proposed algorithm is capable of searching alternative routes dynamically in unexpected events of system malfunctioning or traffic slow-downs due to accidents. Experimental results demonstrate that our algorithm searches the best route more efficiently and evolves with universal adaptability.
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat
Liu, Jian; Li, Dezhang; Liu, Xinzijian
2016-07-01
We introduce a novel simple algorithm for thermostatting path integral molecular dynamics (PIMD) with the Langevin equation. The staging transformation of path integral beads is employed for demonstration. The optimum friction coefficients for the staging modes in the free particle limit are used for all systems. In comparison to the path integral Langevin equation thermostat, the new algorithm exploits a different order of splitting for the phase space propagator associated to the Langevin equation. While the error analysis is made for both algorithms, they are also employed in the PIMD simulations of three realistic systems (the H2O molecule, liquid para-hydrogen, and liquid water) for comparison. It is shown that the new thermostat increases the time interval of PIMD by a factor of 4-6 or more for achieving the same accuracy. In addition, the supplementary material shows the error analysis made for the algorithms when the normal-mode transformation of path integral beads is used.
Directory of Open Access Journals (Sweden)
Chaowei Wang
2013-01-01
Full Text Available This paper proposes an advanced dynamic framed-slotted ALOHA algorithm based on Bayesian estimation and probability response (BE-PDFSA to improve the performance of radio frequency identification (RFID system. The Bayesian estimation is introduced to improve the accuracy of the estimation algorithm for lacking a large number of observations in one query. The probability response is used to adjust responsive probability of the unrecognized tags to make the responsive tag number equal to the frame length. In this way, we can solve the problem of high collision rate with the increase of tag number and improve the throughput of the whole system. From the simulation results, we can see that the algorithm we proposed can greatly improve the stability of RFID system compared with DFSA and other commonly used algorithms.
New dynamic routing algorithm based on MANET in LEO/MEO satellite network
Institute of Scientific and Technical Information of China (English)
LI Zhe; LI Dong-ni; WANG Guang-xing
2006-01-01
The features of low earth orbit/medium earth orbit (LEO/MEO) satellite networks routing algorithm based on inter-satellite link are analyzed and the similarities between satellite networks and mobile Ad Hoc network (MANET) are pointed out.The similar parts in MANET routing protocol are used in the satellite network for reference.A new dynamic routing algorithm based on MANET in LEO/MEO satellite networks,which fits for the LEO/MEO satellite communication system,is proposed.At the same time,the model of the algorithm is simulated and features are analyzed.It is shown that the algorithm has strong adaptability.It can give the network high autonomy,perfect function,low system overhead and great compatibility.
Prasanth, Ravi K.; Klein, Vladislav; Murphy, Patrick C.; Mehra, Raman K.
2005-01-01
This paper describes model structures and parameter estimation algorithms suitable for the identification of unsteady aerodynamic models from input-output data. The model structures presented are state space models and include linear time-invariant (LTI) models and linear parameter-varying (LPV) models. They cover a wide range of local and parameter dependent identification problems arising in unsteady aerodynamics and nonlinear flight dynamics. We present a residue algorithm for estimating model parameters from data. The algorithm can incorporate apriori information and is described in detail. The algorithms are evaluated on the F-16XL wind-tunnel test data from NAS Langley Research Center. Results of numerical evaluation are presented. The paper concludes with a discussion major issues and directions for future work.
A fast algorithm for parallel computation of multibody dynamics on MIMD parallel architectures
Fijany, Amir; Kwan, Gregory; Bagherzadeh, Nader
1993-01-01
In this paper the implementation of a parallel O(LogN) algorithm for computation of rigid multibody dynamics on a Hypercube MIMD parallel architecture is presented. To our knowledge, this is the first algorithm that achieves the time lower bound of O(LogN) by using an optimal number of O(N) processors. However, in addition to its theoretical significance, the algorithm is also highly efficient for practical implementation on commercially available MIMD parallel architectures due to its highly coarse grain size and simple communication and synchronization requirements. We present a multilevel parallel computation strategy for implementation of the algorithm on a Hypercube. This strategy allows the exploitation of parallelism at several computational levels as well as maximum overlapping of computation and communication to increase the performance of parallel computation.
Advanced time integration algorithms for dislocation dynamics simulations of work hardening
Sills, Ryan B.; Aghaei, Amin; Cai, Wei
2016-05-01
Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank–Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relative to traditional schemes. Subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.
Dynamic Crypto Algorithm for Real-Time Applications DCA-RTA, Key Shifting
Directory of Open Access Journals (Sweden)
Ahmad H. Al-Omari
2016-01-01
Full Text Available The need for fast and attack resistance crypto algorithm is challenging issue in the era of the revolution in the information and communication technologies. The previous works presented by the authors “Dynamic Crypto Algorithm for Real-Time Applications DCA_RTA”, still need more enhancements to bring up the DCA_RTA into acceptable security level. In this work, the author added more enhancements on the Transformation-Table that is generated by the Initial-Table IT, which affects the overall encryption/decryption process. The new TT generation proven to be less correlated with the IT than using the previous TT generation processes. The simulated result indicates more randomness in the TT, which means better attack resistance algorithm. More room for algorithm enhancements is still needed.
DEFF Research Database (Denmark)
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2008-01-01
introduce in this paper the DP-RSC1 algorithm, which is a variant of the dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units and sequential commitment of units one by one. The time complexity of DP-RSC1 is proportional to the number of generating units...... in the system, the number of periods over the planning horizon and the time for solving a single-period economic dispatch problem. We have compared the DP-RSC1 algorithm with realistic power plants against the unit decommitment algorithm and the traditional priority listing method. The results show that the DP......-RSC1 algorithm gives somewhat more accurate results (0.08-0.5% on average, maximum 10% for the individual sub-case) and executes 3-5 times faster on average than the unit decommitment algorithm. It is not surprising that the solution quality of the DP-RSC1 algorithm is much better than...
Pluchino, Alessandro; Latora, Vito
2008-01-01
We have recently introduced an efficient method for the detection and identification of modules in complex networks, based on the de-synchronization properties (dynamical clustering) of phase oscillators. In this paper we apply the dynamical clustering tecnique to the identification of communities of marine organisms living in the Chesapeake Bay food web. We show that our algorithm is able to perform a very reliable classification of the real communities existing in this ecosystem by using different kinds of dynamical oscillators. We compare also our results with those of other methods for the detection of community structures in complex networks.
Lin, Yuan; Samei, Ehsan
2016-07-01
Dynamic perfusion imaging can provide the morphologic details of the scanned organs as well as the dynamic information of blood perfusion. However, due to the polyenergetic property of the x-ray spectra, beam hardening effect results in undesirable artifacts and inaccurate CT values. To address this problem, this study proposes a segmentation-free polyenergetic dynamic perfusion imaging algorithm (pDP) to provide superior perfusion imaging. Dynamic perfusion usually is composed of two phases, i.e., a precontrast phase and a postcontrast phase. In the precontrast phase, the attenuation properties of diverse base materials (e.g., in a thorax perfusion exam, base materials can include lung, fat, breast, soft tissue, bone, and metal implants) can be incorporated to reconstruct artifact-free precontrast images. If patient motions are negligible or can be corrected by registration, the precontrast images can then be employed as a priori information to derive linearized iodine projections from the postcontrast images. With the linearized iodine projections, iodine perfusion maps can be reconstructed directly without the influence of various influential factors, such as iodine location, patient size, x-ray spectrum, and background tissue type. A series of simulations were conducted on a dynamic iodine calibration phantom and a dynamic anthropomorphic thorax phantom to validate the proposed algorithm. The simulations with the dynamic iodine calibration phantom showed that the proposed algorithm could effectively eliminate the beam hardening effect and enable quantitative iodine map reconstruction across various influential factors. The error range of the iodine concentration factors ([Formula: see text]) was reduced from [Formula: see text] for filtered back-projection (FBP) to [Formula: see text] for pDP. The quantitative results of the simulations with the dynamic anthropomorphic thorax phantom indicated that the maximum error of iodine concentrations can be reduced from
A dynamic material discrimination algorithm for dual MV energy X-ray digital radiography.
Li, Liang; Li, Ruizhe; Zhang, Siyuan; Zhao, Tiao; Chen, Zhiqiang
2016-08-01
Dual-energy X-ray radiography has become a well-established technique in medical, industrial, and security applications, because of its material or tissue discrimination capability. The main difficulty of this technique is dealing with the materials overlapping problem. When there are two or more materials along the X-ray beam path, its material discrimination performance will be affected. In order to solve this problem, a new dynamic material discrimination algorithm is proposed for dual-energy X-ray digital radiography, which can also be extended to multi-energy X-ray situations. The algorithm has three steps: α-curve-based pre-classification, decomposition of overlapped materials, and the final material recognition. The key of the algorithm is to establish a dual-energy radiograph database of both pure basis materials and pair combinations of them. After the pre-classification results, original dual-energy projections of overlapped materials can be dynamically decomposed into two sets of dual-energy radiographs of each pure material by the algorithm. Thus, more accurate discrimination results can be provided even with the existence of the overlapping problem. Both numerical and experimental results that prove the validity and effectiveness of the algorithm are presented. PMID:27239987
An Event-Driven Hybrid Molecular Dynamics and Direct Simulation Monte Carlo Algorithm
Energy Technology Data Exchange (ETDEWEB)
Donev, A; Garcia, A L; Alder, B J
2007-07-30
A novel algorithm is developed for the simulation of polymer chains suspended in a solvent. The polymers are represented as chains of hard spheres tethered by square wells and interact with the solvent particles with hard core potentials. The algorithm uses event-driven molecular dynamics (MD) for the simulation of the polymer chain and the interactions between the chain beads and the surrounding solvent particles. The interactions between the solvent particles themselves are not treated deterministically as in event-driven algorithms, rather, the momentum and energy exchange in the solvent is determined stochastically using the Direct Simulation Monte Carlo (DSMC) method. The coupling between the solvent and the solute is consistently represented at the particle level, however, unlike full MD simulations of both the solvent and the solute, the spatial structure of the solvent is ignored. The algorithm is described in detail and applied to the study of the dynamics of a polymer chain tethered to a hard wall subjected to uniform shear. The algorithm closely reproduces full MD simulations with two orders of magnitude greater efficiency. Results do not confirm the existence of periodic (cycling) motion of the polymer chain.
A Superstabilizing $\\log(n)$-Approximation Algorithm for Dynamic Steiner Trees
Blin, Lélia; Rovedakis, Stephane
2009-01-01
In this paper we design and prove correct a fully dynamic distributed algorithm for maintaining an approximate Steiner tree that connects via a minimum-weight spanning tree a subset of nodes of a network (referred as Steiner members or Steiner group) . Steiner trees are good candidates to efficiently implement communication primitives such as publish/subscribe or multicast, essential building blocks for the new emergent networks (e.g. P2P, sensor or adhoc networks). The cost of the solution returned by our algorithm is at most $\\log |S|$ times the cost of an optimal solution, where $S$ is the group of members. Our algorithm improves over existing solutions in several ways. First, it tolerates the dynamism of both the group members and the network. Next, our algorithm is self-stabilizing, that is, it copes with nodes memory corruption. Last but not least, our algorithm is \\emph{superstabilizing}. That is, while converging to a correct configuration (i.e., a Steiner tree) after a modification of the network, it...
Directory of Open Access Journals (Sweden)
Jingjing Ma
2014-01-01
Full Text Available Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.
An adaptive mass algorithm for Car-Parrinello and Ehrenfest ab initio molecular dynamics
Kadir, Ashraful; Szepessy, Anders
2014-01-01
Ehrenfest and Car-Parrinello molecular dynamics are computational alternatives to approximate Born-Oppenheimer molecular dynamics without solving the electron eigenvalue problem at each time-step. A non-trivial issue is to choose the artificial electron mass parameter appearing in the Car-Parrinello method to achieve both good accuracy and high computational efficiency. In this paper, we propose an algorithm, motivated by the Landau-Zener probability, to systematically choose an artificial mass dynamically, which makes the Car-Parrinello and Ehrenfest molecular dynamics methods dependent only on the problem data. Numerical experiments for simple model problems show that the time-dependent adaptive artificial mass parameter improves the efficiency of the Car-Parrinello and Ehrenfest molecular dynamics.
A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm
DEFF Research Database (Denmark)
Bork, Lasse
This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time...... series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors. This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics...... as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic...
Dynamic learning rates algorithm for BPNN to forecast time series of dam security
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Time series data of dam security have a large number of observed values and should be forecasted accurately in time.Neural networks have the powerful approach ablilities of arbitrary functions and have been broadly utilized in many domains.In this paper,a dynamic learning rate training algorithm of hack-propagation neural networks for time series forecasting is proposed and the networks with this algorithm are built to forecast time series of dam security.The application results demonostrate the efficiency of modelling and the effictiveness of forecasting.
A Dynamic Framed Slotted ALOHA Algorithm Using Collision Factor for RFID Identification
Choi, Seung Sik; Kim, Sangkyung
In RFID systems, collision resolution is a significant issue in fast tag identification. This letter presents a dynamic frame-slotted ALOHA algorithm that uses a collision factor (DFSA-CF). This method enables fast tag identification by estimating the next frame size with the collision factor in the current frame. Simulation results show that the proposed method reduces slot times Required for RFID identification. When the number of tags is larger than the frame size, the efficiency of the proposed method is greater than those of conventional algorithms.
Extracting quantum dynamics from genetic learning algorithms through principal control analysis
International Nuclear Information System (INIS)
Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Standardi, Laura; Edlund, Kristian;
2014-01-01
This paper presents a warm-started Dantzig–Wolfe decomposition algorithm tailored to economic model predictive control of dynamically decoupled subsystems. We formulate the constrained optimal control problem solved at each sampling instant as a linear program with state space constraints, input...... limits, input rate limits, and soft output limits. The objective function of the linear program is related directly to the cost of operating the subsystems, and the cost of violating the soft output constraints. Simulations for large-scale economic power dispatch problems show that the proposed algorithm...
Kumar, Rohit
2016-05-01
We discuss the stability of fragments identified by secondary algorithms used to construct fragments within quantum molecular dynamics model. For this purpose we employ three different algorithms for fragment identification. 1) The conventional minimum spanning tree (MST) method based on the spatial correlations, 2) an improved version of MST with additional binding energy constraints of cold nuclear matter, 3) and that of hot matter. We find significant role of thermal binding energies over cold matter binding energies. Significant role is observed for fragment multiplicities and stopping of fragments. Whereas insignificant effect is observed on fragment's flow.
DEFF Research Database (Denmark)
Hou, Peng; Hu, Weihao; Chen, Zhe
2015-01-01
Anew approach, Dynamic Minimal Spanning Tree (DMST) algorithm, whichisbased on the MST algorithm isproposed in this paper to optimizethe cable connectionlayout for large scale offshore wind farm collection system. The current carrying capacity of the cable is considered as the main constraint....... Thedynamic changing of the cable capacity, therefore, the cost during the searching process is presented in this work. Twowind farms arechosen as the studied case and the final results showthat the proposed methodcan save the investment on cables 1.07% and 6.10% respectively compared with MST method...
Dynamic Crypto Algorithm for Real-Time Applications DCA-RTA, Key Shifting
Ahmad H. Al-Omari
2016-01-01
The need for fast and attack resistance crypto algorithm is challenging issue in the era of the revolution in the information and communication technologies. The previous works presented by the authors “Dynamic Crypto Algorithm for Real-Time Applications DCA_RTA”, still need more enhancements to bring up the DCA_RTA into acceptable security level. In this work, the author added more enhancements on the Transformation-Table that is generated by the Initial-Table IT, which affects the overall e...
Dynamic Routing Algorithm Based on the Channel Quality Control for Farmland Sensor Networks
Directory of Open Access Journals (Sweden)
Dongfeng Xu
2014-04-01
Full Text Available This article reports a Dynamic Routing Algorithm for Farmland Sensor Networks (DRA-FSN based on channel quality control to improve energy efficiency, which combines the distance and communication characteristics of farmland wireless sensor network. The functional architecture of the DRA-FSN algorithm, routing establish the mechanisms, the communication transmission mechanism, the global routing beacon return mechanism, abnormal node handling mechanism and sensor networks timing control mechanisms were designed in detail in this article. This article also evaluates and simulated the performance of DRA-FSN algorithm in different conditions from energy efficiency, packet energy consumption and packet distribution balance by comparing DRA-FSN algorithm with DSDV, EAP algorithm. Simulations showed that the DRA-FSN was more energy efficient than EAP and DSDV, the DRA-FSN algorithm overcame the shortcoming that capacity and bandwidth of the routing table correspondingly increase as more and more nodes joining the network. It has better performance in scalability and network loading balance
International Nuclear Information System (INIS)
The purpose of this study was to develop a leaf-setting algorithm for Dynamic Multileaf Collimator-Intensity-Modulated Radiation Therapy (DMLC-IMRT) for optimal marker visibility. Here, a leaf-setting algorithm (called a Delta algorithm) was developed with the objective of maximizing marker visibility so as to improve the tracking effectiveness of fiducial markers during treatment delivery. The initial leaf trajectories were generated using a typical leaf-setting algorithm, then the leaf trajectories were adjusted by Delta algorithm operations (analytical computations and a series of matrix calculations) to achieve the optimal solution. The performance of the Delta algorithm was evaluated with six test fields (with randomly generated intensity profiles) and 15 clinical fields from IMRT plans of three prostate cancer patients. Compared with the initial solution, the Delta algorithm kept the total delivered intensities (TDIs) constant (without increasing the beam delivery time), but improved marker visibility (the percentage ratio of marker visibility time to beam delivery time). For the artificial fields (with three markers), marker visibility increased from 68.00-72.00% for a small field (5 x 5), from 38.46-43.59% for a medium field (10 x 10), and from 28.57-37.14% for a large field (20 x 20). For the 15 clinical fields, marker visibility increased 6-30% for eight fields and > 50% for two fields but did not change for five fields. A Delta algorithm was proposed to maximize marker visibility for DMLC-IMRT without increasing beam delivery time, and this will provide theoretical fundamentals for future studies of 4D DMLC tracking radiotherapy. (author)
A Dynamic Programming Algorithm for Optimal Design of Tidal Power Plants
Nag, B.
2013-03-01
A dynamic programming algorithm is proposed and demonstrated on a test case to determine the optimum operating schedule of a barrage tidal power plant to maximize the energy generation over a tidal cycle. Since consecutive sets of high and low tides can be predicted accurately for any tidal power plant site, this algorithm can be used to calculate the annual energy generation for different technical configurations of the plant. Thus an optimal choice of a tidal power plant design can be made from amongst different design configurations yielding the least cost of energy generation. Since this algorithm determines the optimal time of operation of sluice gate opening and turbine gates opening to maximize energy generation over a tidal cycle, it can also be used to obtain the annual schedule of operation of a tidal power plant and the minute-to-minute energy generation, for dissemination amongst power distribution utilities.
International Nuclear Information System (INIS)
Dynamic economic dispatch (DED) problem is one of the optimization issues in power system operation. In this paper, an improved chaotic particle swarm optimization (ICPSO) algorithm is proposed to solve DED with value-point effects. In proposed ICPSO, chaotic mutation is embedded to overcome the drawback of premature in PSO. What's more, enhanced heuristic strategies are proposed to handling the various constraints of DED problem effectively. Comparing with penalty function method, the proposed constraints handling method can guide the population to feasible region without violating any constraints. Moreover, the effects of two crucial parameters on the performance of proposed ICPSO are also studied. Finally, the ICPSO algorithm is validated for two test systems consisting of 10 and extended 30 generators. While compared with other method reported in this literature, the experimental results demonstrated the high feasibility and effectiveness of proposed algorithm.
Noon, Abbas; Kadry, Seifedine
2011-01-01
Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.
Directory of Open Access Journals (Sweden)
Diamantidis A. C.
2004-01-01
Full Text Available In this study, the buffer allocation problem (BAP in homogeneous, asymptotically reliable serial production lines is considered. A known aggregation method, given by Lim, Meerkov, and Top (1990, for the performance evaluation (i.e., estimation of throughput of this type of production lines when the buffer allocation is known, is used as an evaluative method in conjunction with a newly developed dynamic programming (DP algorithm for the BAP. The proposed algorithm is applied to production lines where the number of machines is varying from four up to a hundred machines. The proposed algorithm is fast because it reduces the volume of computations by rejecting allocations that do not lead to maximization of the line's throughput. Numerical results are also given for large production lines.
Phase based stiffness tuning algorithm for a magnetorheological elastomer dynamic vibration absorber
International Nuclear Information System (INIS)
This paper presents a phase based stiffness tuning algorithm to overcome the uncertainty of the relation between the magnetic current and the natural frequency for magnetorheological elastomer (MRE) dynamic vibration absorbers (DVA) caused by the nonlinearity of the MRE. The phase difference of the relative acceleration of the DVA mass and the absolute acceleration of the primary system was used to check whether the natural frequency of the DVA is adjusted to the excitation frequency. The magnetic current was controlled by the phase difference, which made the proposed algorithm not rely on the model of the MRE DVA. Both the simulation and the experiment demonstrate that the proposed algorithm is efficient for MRE DVA in rapidly tracking the excitation frequency. (paper)
Weeks, Cindy Lou
1986-01-01
Experiments were conducted at NASA Ames Research Center to define multi-tasking software requirements for multiple-instruction, multiple-data stream (MIMD) computer architectures. The focus was on specifying solutions for algorithms in the field of computational fluid dynamics (CFD). The program objectives were to allow researchers to produce usable parallel application software as soon as possible after acquiring MIMD computer equipment, to provide researchers with an easy-to-learn and easy-to-use parallel software language which could be implemented on several different MIMD machines, and to enable researchers to list preferred design specifications for future MIMD computer architectures. Analysis of CFD algorithms indicated that extensions of an existing programming language, adaptable to new computer architectures, provided the best solution to meeting program objectives. The CoFORTRAN Language was written in response to these objectives and to provide researchers a means to experiment with parallel software solutions to CFD algorithms on machines with parallel architectures.
Kodali, Anuradha
In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a
Dynamic Routing Algorithm for Reliability and Energy Efficiency in Wireless Sensor Networks
Choi, Seong-Yong; Kim, Jin-Su; Han, Seung-Jin; Choi, Jun-Hyeog; Rim, Kee-Wook; Lee, Jung-Hyun
What are important in wireless sensor networks are energy efficiency, reliable data transmission, and topological adaptation to the change of external environment. This study proposes dynamic routing algorithm that satisfies the above-mentioned conditions at the same time using a dynamic single path in wireless sensor networks. In our proposed algorithm, each node transmits data through the optimal single path using hop count to the sink and node average energy according to the change of external environment. For reliable data transmission, each node monitors its own transmission process. If a node detects a damaged path, it switches from the damaged path to the optimal path and, by doing so, enhances network reliability. In case of a topological change, only the changed part is reconstructed instead of the whole network, and this enhances the energy efficiency of the network.
Tracking and Following Algorithms of Mobile Robots for Service Activities in Dynamic Environments
Directory of Open Access Journals (Sweden)
Feng-Li Lian
2015-02-01
Full Text Available By providing the capability of following a human target in an appropriate manner, the robot can assist people in various ways under different environments. One of the main difficulties when performing human tracking and following is the occlusion problem caused by static as well as dynamic obstacles. The aim of the paper is to tackle the occlusion problem by planning a robotic trajectory of maximizing target visibility and following the moving target. Initially, a laser range finder is used to detect the human target and then robustly track the target using the Kalman filter. Afterward, a human following algorithm based on a look-ahead algorithm, DWA*, is implemented to pursue the target while avoiding any static or dynamic obstacles. Fundamental experiments have been extensively tested to evaluate robot maneuvers and several field tests are conducted in more complex environments such as student cafeteria, computer center, and university library.
Thickness determination in textile material design: dynamic modeling and numerical algorithms
International Nuclear Information System (INIS)
Textile material design is of paramount importance in the study of functional clothing design. It is therefore important to determine the dynamic heat and moisture transfer characteristics in the human body–clothing–environment system, which directly determine the heat–moisture comfort level of the human body. Based on a model of dynamic heat and moisture transfer with condensation in porous fabric at low temperature, this paper presents a new inverse problem of textile thickness determination (IPTTD). Adopting the idea of the least-squares method, we formulate the IPTTD into a function minimization problem. By means of the finite-difference method, quasi-solution method and direct search method for one-dimensional minimization problems, we construct iterative algorithms of the approximated solution for the IPTTD. Numerical simulation results validate the formulation of the IPTTD and demonstrate the effectiveness of the proposed numerical algorithms. (paper)
Spectrum of the Dirac operator and multigrid algorithm with dynamical staggered fermions
International Nuclear Information System (INIS)
Complete spectra of the staggered Dirac operator D are determined in quenched four-dimensional SU(2) gauge fields, and also in the presence of dynamical fermions. Periodic as well as antiperiodic boundary conditions are used. An attempt is made to relate the performance of multigrid (MG) and conjugate gradient (CG) algorithms for propagators with the distribution of the eigenvalues of D. The convergence of the CG algorithm is determined only by the condition number k and by the lattice size. Since k's do not vary signigicantly when quarks become dynamic, CG convergence in unquenched fields can be predicted from quenched simulations. On the other hand, MG convergence is not affected by k but depends on the spectrum in a more subtle way. (orig.)
Study on algorithm of dynamic uncalibrated eye-in-hand visual servoing system
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Currently, most visual servoing system must be calibrated, while it is impossible to calibrate cameras and robot models precisely in industrial practice, so a novel dynamic uncalibrated eye-in-hand visual servoing system of tracking a moving target is proposed. The method does not require calibration of camera and robot kinematic models. Vision guided algorithm for tracking dynamic image is developed through minimizing nonlinear objective function. For the large residual has not been approximated in dynamic environment and the change of composite image Jacobian with time increment has not been computed in visual servoing system now,large residuals are dynamic approximated and the change of composite image Jacobian at each iterative step is computed.Simulation results demonstrate the validity of these approaches.
Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems
DEFF Research Database (Denmark)
Lissovoi, Andrei
exist more complex oscillations that cannot be tracked with a polynomial-size colony. MMAS and (μ+1) EA on Maze We analyse the behaviour of a (μ + 1) EA with genotype diversity on a dynamic fitness function Maze, extended to a finite-alphabet search space. We prove that the (μ + 1) EA is able to track...... the dynamic optimum for finite alphabets up to size μ, while MMAS is able to do so for any finite alphabet size. Parallel Evolutionary Algorithms on Maze. We prove that while a (1 + λ) EA is unable to track the optimum of the dynamic fitness function Maze for offspring population size up to λ = O(n1-ε...... analysis showing how closely the EA can track the dynamically moving optimum over time. These results are also extended to a finite-alphabet search space....
Dynamic Fuzzy Controlled RWA Algorithm for IP/GMPLS over WDM Networks
Institute of Scientific and Technical Information of China (English)
I-Shyan Hwang; I-Feng Huang; Shin-Cheng Yu
2005-01-01
This paper proposes a dynamic RWA scheme using fuzzy logic control on IP/GMPLS over WDM networks to achieve the best quality of network transmission. The proposed algorithm dynamically allocates network resources and reserves partial bandwidth based on the current network status, which includes the request bandwidth, average utilization for each wavelength and its coefficient of variance (C.V.) of data traffic, to determine whether the connection can be set up. Five fuzzy sets for request bandwidth, average rate and C.V. of data traffic are used to divide the variable space: very large (LP), large (SP), normal (ZE), small (SN), and very small (LN). Setting the fuzzy limit is a key part in the proposed algorithm. The simulation of scenarios in this paper has two steps. In the first step, the adaptive fuzzy limits are evaluated based on average transmission cost pertaining to ten network statuses. The second step is to compare the proposed algorithm with periodic measurement of traffic (PMT) in ATM networks in six network situations to show that the proposed FC-RWA algorithm can provide better network transmission.
Institute of Scientific and Technical Information of China (English)
LIU Xiao; WANG Cheng-en
2005-01-01
This paper addresses a single item dynamic lot-sizing model with inventory capacity and out-sourcing. The goal is to minimize the total costs of production, setup, inventory holding and out-sourcing. Two versions of an out-sourcing model with time-varying costs are considered: stock out case and conservation case. Zero Inventory Order property has been found and some new properties are obtained in an optimal solution. Dynamic programming algorithms are developed to solve the problem in strongly polynomial time respectively. Furthermore, some numerical results demonstrate that the approach proposed is efficient and applicable.
Lelu, Alain; Cuxac, Pascal
2008-01-01
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the data-vectors stream, 2) the cognitive challenge: we have implemented a stringent selection process of association rules between clusters at time t-1 and time t for directly generating the main conclusions about the dynamics of a data-stream. We illustrate these points with an application to a two years and 2600 documents scientific information database.
Institute of Scientific and Technical Information of China (English)
LI Qiang; WU Jianxin; SUN Yan
2009-01-01
Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm(EPSA), is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimization. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.
A Generalized Speckle Tracking Algorithm for Ultrasonic Strain Imaging Using Dynamic Programming
Jiang, Jingfeng; Hall, Timothy J.
2009-01-01
This study developed an improved motion estimation algorithm for ultrasonic strain imaging that employs a dynamic programming technique. In this paper, we model the motion estimation task as an optimization problem. Since tissue motion under external mechanical stimuli often should be reasonably continuous, a set of cost functions combining correlation and various levels of motion continuity constraint were used to regularize the motion estimation. To solve the optimization problem with a rea...
Nair, T. R. Gopalakrishnan; Sooda, Kavitha; Yashoda, M. B.
2011-01-01
In Internet Routing, the static shortest path (SP) problem has been addressed using well known intelligent optimization techniques like artificial neural networks, genetic algorithms (GAs) and particle swarm optimization. Advancement in wireless communication lead more and more mobile wireless networks, such as mobile networks [mobile ad hoc networks (MANETs)] and wireless sensor networks. Dynamic nature of the network is the main characteristic of MANET. Therefore, the SP routing problem in ...
Directory of Open Access Journals (Sweden)
Stanimirović Ivan
2009-01-01
Full Text Available We introduce a heuristic method for the single resource constrained project scheduling problem, based on the dynamic programming solution of the knapsack problem. This method schedules projects with one type of resources, in the non-preemptive case: once started an activity is not interrupted and runs to completion. We compare the implementation of this method with well-known heuristic scheduling method, called Minimum Slack First (known also as Gray-Kidd algorithm, as well as with Microsoft Project.
Yunlong Yu; Le Ru; Sheng Mao; Kangning Sun; Qiangqiang Yu; Kun Fang
2016-01-01
Airborne highly dynamic ad hoc UAV network has features of high node mobility, fast changing network topology, and complex application environment. The performance of traditional routing algorithms is so poor over aspects such as end to end delay, data packet delivery ratio, and routing overhead that they cannot provide efficient communication for multi-UAVs carrying out missions synergistically. A bionic optimization based stability and congestion aware routing algorithm—BSCAR algorithm—is p...
DYNAMIC K-MEANS ALGORITHM FOR OPTIMIZED ROUTING IN MOBILE AD HOC NETWORKS
Directory of Open Access Journals (Sweden)
Zahra Zandieh Shirazi
2016-04-01
Full Text Available In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks (MANETs is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can operate without using focal access points, pre-existing infrastructures, or a centralized management point. In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead to various problems in the routing process such as increase of the overhead massages and inefficient routing between nodes of network. A large variety of clustering methods have been developed for establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having significant impact on MANETs performance. The K-means algorithm is one of the effective clustering methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption. This paper proposed a new K-means clustering algorithm to find out optimal path from source node to destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the performance of routing process in Mobile ad-hoc networks.
Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks
Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2016-07-01
The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.
A new second-order integration algorithm for simulating mechanical dynamic systems
Howe, R. M.
1989-01-01
A new integration algorithm which has the simplicity of Euler integration but exhibits second-order accuracy is described. In fixed-step numerical integration of differential equations for mechanical dynamic systems the method represents displacement and acceleration variables at integer step times and velocity variables at half-integer step times. Asymptotic accuracy of the algorithm is twice that of trapezoidal integration and ten times that of second-order Adams-Bashforth integration. The algorithm is also compatible with real-time inputs when used for a real-time simulation. It can be used to produce simulation outputs at double the integration frame rate, i.e., at both half-integer and integer frame times, even though it requires only one evaluation of state-variable derivatives per integration step. The new algorithm is shown to be especially effective in the simulation of lightly-damped structural modes. Both time-domain and frequency-domain accuracy comparisons with traditional integration methods are presented. Stability of the new algorithm is also examined.
A new algorithm for combined dynamic economic emission dispatch with security constraints
International Nuclear Information System (INIS)
The primary objective of CDEED (combined dynamic economic emission dispatch) problem is to determine the optimal power generation schedule for the online generating units over a time horizon considered and simultaneously minimizing the emission level and satisfying the generators and system constraints. The CDEED problem is bi-objective optimization problem, where generation cost and emission are considered as two competing objective functions. This bi-objective CDEED problem is represented as a single objective optimization problem by assigning different weights for each objective functions. The weights are varied in steps and for each variation one compromise solution are generated and finally fuzzy based selection method is used to select the best compromise solution from the set of compromise solutions obtained. In order to reflect the test systems considered as real power system model, the security constraints are also taken into account. Three new versions of DHS (differential harmony search) algorithms have been proposed to solve the CDEED problems. The feasibility of the proposed algorithms is demonstrated on IEEE-26 and IEEE-39 bus systems. The result obtained by the proposed CSADHS (chaotic self-adaptive differential harmony search) algorithm is found to be better than EP (evolutionary programming), DHS, and the other proposed algorithms in terms of solution quality, convergence speed and computation time. - Highlights: • In this paper, three new algorithms CDHS, SADHS and CSADHS are proposed. • To solve DED with emission, poz's, spinning reserve and security constraints. • Results obtained by the proposed CSADHS algorithm are better than others. • The proposed CSADHS algorithm has fast convergence characteristic than others
Jackson, Jennifer N; Hass, Chris J; Fregly, Benjamin J
2015-11-01
Patient-specific gait optimizations capable of predicting post-treatment changes in joint motions and loads could improve treatment design for gait-related disorders. To maximize potential clinical utility, such optimizations should utilize full-body three-dimensional patient-specific musculoskeletal models, generate dynamically consistent gait motions that reproduce pretreatment marker measurements closely, and achieve accurate foot motion tracking to permit deformable foot-ground contact modeling. This study enhances an existing residual elimination algorithm (REA) Remy, C. D., and Thelen, D. G., 2009, “Optimal Estimation of Dynamically Consistent Kinematics and Kinetics for Forward Dynamic Simulation of Gait,” ASME J. Biomech. Eng., 131(3), p. 031005) to achieve all three requirements within a single gait optimization framework. We investigated four primary enhancements to the original REA: (1) manual modification of tracked marker weights, (2) automatic modification of tracked joint acceleration curves, (3) automatic modification of algorithm feedback gains, and (4) automatic calibration of model joint and inertial parameter values. We evaluated the enhanced REA using a full-body three-dimensional dynamic skeletal model and movement data collected from a subject who performed four distinct gait patterns: walking, marching, running, and bounding. When all four enhancements were implemented together, the enhanced REA achieved dynamic consistency with lower marker tracking errors for all segments, especially the feet (mean root-mean-square (RMS) errors of 3.1 versus 18.4 mm), compared to the original REA. When the enhancements were implemented separately and in combinations, the most important one was automatic modification of tracked joint acceleration curves, while the least important enhancement was automatic modification of algorithm feedback gains. The enhanced REA provides a framework for future gait optimization studies that seek to predict subject
Dynamic Air-Route Adjustments - Model,Algorithm,and Sensitivity Analysis
Institute of Scientific and Technical Information of China (English)
GENG Rui; CHENG Peng; CUI Deguang
2009-01-01
Dynamic airspace management (DAM) is an important approach to extend limited air space resources by using them more efficiently and flexibly.This paper analyzes the use of the dynamic air-route adjustment (DARA) method as a core procedure in DAM systems.DARA method makes dynamic decisions on when and how to adjust the current air-route network with the minimum cost.This model differs from the air traffic flow management (ATFM) problem because it considers dynamic opening and closing of air-route segments instead of only arranging flights on a given air traffic network and it takes into account several new constraints,such as the shortest opening time constraint.The DARA problem is solved using a two-step heuristic algorithm.The sensitivities of important coefficients in the model are analyzed to determine proper values for these coefficients.The computational results based on practical data from the Beijing ATC region show that the two-step heuristic algorithm gives as good results as the CPLEX in less or equal time in most cases.
Nair, T R Gopalakrishnan; Yashoda, M B
2011-01-01
In Internet Routing, the static shortest path (SP) problem has been addressed using well known intelligent optimization techniques like artificial neural networks, genetic algorithms (GAs) and particle swarm optimization. Advancement in wireless communication lead more and more mobile wireless networks, such as mobile networks [mobile ad hoc networks (MANETs)] and wireless sensor networks. Dynamic nature of the network is the main characteristic of MANET. Therefore, the SP routing problem in MANET turns into dynamic optimization problem (DOP). Here the nodes ae made aware of the environmental condition, thereby making it intelligent, which goes as the input for GA. The implementation then uses GAs with immigrants and memory schemes to solve the dynamic SP routing problem (DSPRP) in MANETS. In our paper, once the network topology changes, the optimal solutions in the new environment can be searched using the new immigrants or the useful information stored in the memory. Results shows GA with new immigrants sho...
Vijay Alagappan, A.; Narasimha Rao, K. V.; Krishna Kumar, R.
2015-02-01
Tyre models are a prerequisite for any vehicle dynamics simulation. Tyre models range from the simplest mathematical models that consider only the cornering stiffness to a complex set of formulae. Among all the steady-state tyre models that are in use today, the Magic Formula tyre model is unique and most popular. Though the Magic Formula tyre model is widely used, obtaining the model coefficients from either the experimental or the simulation data is not straightforward due to its nonlinear nature and the presence of a large number of coefficients. A common procedure used for this extraction is the least-squares minimisation that requires considerable experience for initial guesses. Various researchers have tried different algorithms, namely, gradient and Newton-based methods, differential evolution, artificial neural networks, etc. The issues involved in all these algorithms are setting bounds or constraints, sensitivity of the parameters, the features of the input data such as the number of points, noisy data, experimental procedure used such as slip angle sweep or tyre measurement (TIME) procedure, etc. The extracted Magic Formula coefficients are affected by these variants. This paper highlights the issues that are commonly encountered in obtaining these coefficients with different algorithms, namely, least-squares minimisation using trust region algorithms, Nelder-Mead simplex, pattern search, differential evolution, particle swarm optimisation, cuckoo search, etc. A key observation is that not all the algorithms give the same Magic Formula coefficients for a given data. The nature of the input data and the type of the algorithm decide the set of the Magic Formula tyre model coefficients.
Hilburn, Jeremy
2015-01-01
In this qualitative collective case study with 6 high school civics teachers, I found that using an asset-based approach to teaching civics for, with, and by immigrant students enriched teaching and learning for immigrant and native-born students, although participants missed some opportunities for deeper exploration. I used a combined theoretical…
DLJ：A Dynamic Line—Justification Algorithm for Test Generation
Institute of Scientific and Technical Information of China (English)
陈庆方; 魏道政
1993-01-01
Lne justification is a basic factor in affecting the efficiency of algorithms for test generation.The existence of reconvergent fanouts in the circuit under test results in backtracks in the process of line justification.In order to reduce the number of backtracks and shorten the processing time between backtracks,we present a new algorithm called DLJ(dynamic line justification)in whic two techniques are employed.1.A cost function called “FOCOST” is proposed as heuristic information to represent the cost of justifying a certain line.Whn the relations among the lines being justified are“and”,the line having the highest FOCOST should be chosen.When the relations are“or”,the line having the lowest FOCOST should be chosen.The computing of the FOCOST of lines is very simple.2.Disjoint justification cubes dynamically generated to perform backtracks make the backtrack number of the algorithm minimal.When the backtrace with cube C1 does not yield a solution,the next cube to be chosen is C'2=C2-{C1,C2}.Experimental results demonstrate that the combination of the two techniques effectively reduces the backtracks and accelerates the test generation.
Directory of Open Access Journals (Sweden)
Yanhua Jiang
2014-09-01
Full Text Available This paper presents a monocular visual odometry algorithm that incorporates a wheeled vehicle model for ground vehicles. The main innovation of this algorithm is to use the single-track bicycle model to interpret the relationship between the yaw rate and side slip angle, which are the two most important parameters that describe the motion of a wheeled vehicle. Additionally, the pitch angle is also considered since the planar-motion hypothesis often fails due to the dynamic characteristics of wheel suspensions and tires in real-world environments. Linearization is used to calculate a closed-form solution of the motion parameters that works as a hypothesis generator in a RAndom SAmple Consensus (RANSAC scheme to reduce the complexity in solving equations involving trigonometric. All inliers found are used to refine the winner solution through minimizing the reprojection error. Finally, the algorithm is applied to real-time on-board visual localization applications. Its performance is evaluated by comparing against the state-of-the-art monocular visual odometry methods using both synthetic data and publicly available datasets over several kilometers in dynamic outdoor environments.
Directory of Open Access Journals (Sweden)
Rogério M. Branco
2016-07-01
Full Text Available This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatching rules when forming each individual from a defined set of available machines and, also make the scheduling for each of these individuals created. The chromosome codifies a route, or the selected machines, and also an order to process the operations. In essence , each individual needs to be decoded by the scheduler to evaluate its time of completion, so the fitness function of the genetic algorithm, applying the modified Giffler and Thomson’s algorithm, obtains a scheduling of the selected routes in a given planning horizon. The scheduler considers the preparation time between operations on the machines and can manage operations exchange respecting the route and the order given by the chromosome. The best results in the evolutionary process are individuals with routes and processing orders optimized for this type of problema.
An Efficient Algorithm for Contact Angle Estimation in Molecular Dynamics Simulations
Directory of Open Access Journals (Sweden)
Sumith YD
2015-01-01
Full Text Available It is important to find contact angle for a liquid to understand its wetting properties, capillarity and surface interaction energy with a surface. The estimation of contact angle from Non Equilibrium Molecular Dynamics (NEMD, where we need to track the changes in contact angle over a period of time is challenging compared to the estimation from a single image from an experimental measurement. Often such molecular simulations involve finite number of molecules above some metallic or non-metallic substrates and coupled to a thermostat. The identification of profile of the droplet formed during this time will be difficult and computationally expensive to process as an image. In this paper a new algorithm is explained which can efficiently calculate time dependent contact angle from a NEMD simulation just by processing the molecular coordinates. The algorithm implements many simple yet accurate mathematical methods available, especially to remove the vapor molecules and noise data and thereby calculating the contact angle with more accuracy. To further demonstrate the capability of the algorithm a simulation study has been reported which compares the contact angle influence with different thermostats in the Molecular Dynamics (MD simulation of water over platinum surface.
Fijany, Amir
1993-01-01
In this paper parallel 0(log N) algorithms for dynamic simulation of single closed-chain rigid multibody system as specialized to the case of a robot manipulatoar in contact with the environment are developed.
Staged optimization algorithms based MAC dynamic bandwidth allocation for OFDMA-PON
Liu, Yafan; Qian, Chen; Cao, Bingyao; Dun, Han; Shi, Yan; Zou, Junni; Lin, Rujian; Wang, Min
2016-06-01
Orthogonal frequency division multiple access passive optical network (OFDMA-PON) has being considered as a promising solution for next generation PONs due to its high spectral efficiency and flexible bandwidth allocation scheme. In order to take full advantage of these merits of OFDMA-PON, a high-efficiency medium access control (MAC) dynamic bandwidth allocation (DBA) scheme is needed. In this paper, we propose two DBA algorithms which can act on two different stages of a resource allocation process. To achieve higher bandwidth utilization and ensure the equity of ONUs, we propose a DBA algorithm based on frame structure for the stage of physical layer mapping. Targeting the global quality of service (QoS) of OFDMA-PON, we propose a full-range DBA algorithm with service level agreement (SLA) and class of service (CoS) for the stage of bandwidth allocation arbitration. The performance of the proposed MAC DBA scheme containing these two algorithms is evaluated using numerical simulations. Simulations of a 15 Gbps network with 1024 sub-carriers and 32 ONUs demonstrate the maximum network throughput of 14.87 Gbps and the maximum packet delay of 1.45 ms for the highest priority CoS under high load condition.
A Dynamic Noise Level Algorithm for Spectral Screening of Peptide MS/MS Spectra
Directory of Open Access Journals (Sweden)
Xu Hua
2010-08-01
Full Text Available Abstract Background High-throughput shotgun proteomics data contain a significant number of spectra from non-peptide ions or spectra of too poor quality to obtain highly confident peptide identifications. These spectra cannot be identified with any positive peptide matches in some database search programs or are identified with false positives in others. Removing these spectra can improve the database search results and lower computational expense. Results A new algorithm has been developed to filter tandem mass spectra of poor quality from shotgun proteomic experiments. The algorithm determines the noise level dynamically and independently for each spectrum in a tandem mass spectrometric data set. Spectra are filtered based on a minimum number of required signal peaks with a signal-to-noise ratio of 2. The algorithm was tested with 23 sample data sets containing 62,117 total spectra. Conclusions The spectral screening removed 89.0% of the tandem mass spectra that did not yield a peptide match when searched with the MassMatrix database search software. Only 6.0% of tandem mass spectra that yielded peptide matches considered to be true positive matches were lost after spectral screening. The algorithm was found to be very effective at removal of unidentified spectra in other database search programs including Mascot, OMSSA, and X!Tandem (75.93%-91.00% with a small loss (3.59%-9.40% of true positive matches.
International Nuclear Information System (INIS)
Positron-Emission-Tomography (PET) is a modern medicine technology to gain information about the distribution of biochemical substances in the living human. The substances used for the examination are called tracers and show the metabolic behavior of the substance. The measurement is quantitative so that the dynamics of the metabolization can be calculated by recording a series of pictures. Because of the complex structure and limited spatial and temporal resolution of PET, simplifications have to be introduced to analyze the data. Biomathematical models are used to perform this task. The definition of appropriate models and the estimation of related parameters is called modelling. Its goal is to extract relevant metabolic parameters showing differences in normal or diseased tissue. Models for PET data, especially the mainly used compartment models, are discussed in this paper. Different methods for parameter extraction, i.e. graphical and algebraic nonlinear algorithms, are described in detail. A common problem of the nonlinear parameter estimation is its dependence on starting values. Another problem is the uniqueness of the estimated parameters. These problems are addressed by two new algorithms. They are based on the stochastic approach of simulated annealing. Comparisons to conventional algorithms known from literature are given. The new algorithms show independence of starting values and a better numerical stability. Furthermore, they help with the identification of the parameters. In this way the proposed procedures are regarded as important for the development of new tracers and models. (orig.)
COStar: a D-star Lite-based dynamic search algorithm for codon optimization.
Liu, Xiaowu; Deng, Riqiang; Wang, Jinwen; Wang, Xunzhang
2014-03-01
Codon optimized genes have two major advantages: they simplify de novo gene synthesis and increase the expression level in target hosts. Often they achieve this by altering codon usage in a given gene. Codon optimization is complex because it usually needs to achieve multiple opposing goals. In practice, finding an optimal sequence from the massive number of possible combinations of synonymous codons that can code for the same amino acid sequence is a challenging task. In this article, we introduce COStar, a D-star Lite-based dynamic search algorithm for codon optimization. The algorithm first maps the codon optimization problem into a weighted directed acyclic graph using a sliding window approach. Then, the D-star Lite algorithm is used to compute the shortest path from the start site to the target site in the resulting graph. Optimizing a gene is thus converted to a search in real-time for a shortest path in a generated graph. Using in silico experiments, the performance of the algorithm was shown by optimizing the different genes including the human genome. The results suggest that COStar is a promising codon optimization tool for de novo gene synthesis and heterologous gene expression. PMID:24316385
QoS-aware dynamic bandwidth allocation algorithm for Gigabit-capable PONS
Institute of Scientific and Technical Information of China (English)
WANG Xin; ZHAO Yang; GU Wanyi
2007-01-01
The Gigabit-capable passive optical network (GPON)technology is being considered as a promising solution for the next-generation broadband access network.Since the network topology of the GPON is point-to-multipoint,a media access control called dynamic bandwidth allocation(DBA)algorithm is an important factor for determining the performance of the GPON.In this paper,we propose a new DBA algorithm to effectively and fairly allocate bandwidths among end users.This DBA algorithm supports difierentiated services-a cmcial requirement for a converged broadband access network with heterogeneous traffic.In this article we first reviewed the signaling and configuration of the DBA,and then proposed a new DBA scheme that implemented QoS-based priority for this need to maximally satisfy the requirements of all optical network units(ONUs)and provide difierentiated services.Analyses and simulation results show that the new algorithm can improve the bandwidth utilization and realize the fairness for both different ONUs and services.
A DYNAMIC FAULT TOLERANT ALGORITHM FOR IMPROVISING PERFORMANCE OF MULTIMEDIA SERVICES
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Multimedia Services has drawn much attention from both industrial and academic researchers due to the emerging consumer market, how to provide High-Availability service is one of most important issues to take into account. In this paper, a dynamic fault tolerant algorithm is presented for highly available distributed multimedia service, then by introducing SLB(server load balancing) into fault tolerance and switching servers in different ways according to their functions, the proposed schema can preserve reliability and real-time of the system .The analysis and experiments indicate that resuming server's faulty by this method is smooth and transparent to the client The proposed algorithm is effectively improving the reliability of the multimedia service.
Directory of Open Access Journals (Sweden)
Eskandar Gholipour
2013-02-01
Full Text Available Power-system dynamic stability improvement by a static synchronous series compensator (SSSC based damping controller is thoroughly investigated in this paper. In order to design the optimal parameters of the controller, Imperialist Competitive Algorithm (ICA is employed to search for the optimal controller parameters. Both local and remote signals are considered in the present study and the performance of the proposed controllers with variations in the signal transmission delays has been investigated. The performances of the proposed controllers are evaluated under different disturbances for both single-machine-infinite-bus and multi-machine power systems. Finally, the results of ICA method are compared with the results of Genetic Algorithm (GA.
Bhole, Gaurav; Anjusha, V. S.; Mahesh, T. S.
2016-04-01
A robust control over quantum dynamics is of paramount importance for quantum technologies. Many of the existing control techniques are based on smooth Hamiltonian modulations involving repeated calculations of basic unitaries resulting in time complexities scaling rapidly with the length of the control sequence. Here we show that bang-bang controls need one-time calculation of basic unitaries and hence scale much more efficiently. By employing a global optimization routine such as the genetic algorithm, it is possible to synthesize not only highly intricate unitaries, but also certain nonunitary operations. We demonstrate the unitary control through the implementation of the optimal fixed-point quantum search algorithm in a three-qubit nuclear magnetic resonance (NMR) system. Moreover, by combining the bang-bang pulses with the crusher gradients, we also demonstrate nonunitary transformations of thermal equilibrium states into effective pure states in three- as well as five-qubit NMR systems.
Dynamics Assessment of Grid-Synchronization Algorithms for Single-Phase Grid-Connected Converters
DEFF Research Database (Denmark)
Han, Yang; Luo, Mingyu; Guerrero, Josep M.
2015-01-01
Several advanced phase-lock-loop (PLL) algorithms have been proposed for single-phase power electronic systems. Among these algorithms, the orthogonal signal generators (OSGs) are widely utilized to generate a set of in-quadrature signals, owing to its benefit of simple digital implementation and...... low computational burden. Meanwhile, some other techniques have been proposed to enhance system robustness and stability characteristics. In this paper, a comprehensive comparison among the OSG-based PLLs and the advanced single-phase PLLs is presented when the grid voltage undergoes disturbances, and...... the performance comparison of transient response and disturbance rejection capabilities are presented. Finally, the moving average filter (MAF) is applied to enhance steady state and dynamic response of the delayed-type PLL, derivator-based PLL and the complex-coefficient filter (CCF-PLL) under grid...
DEFF Research Database (Denmark)
Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão;
2013-01-01
activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells. A preliminary evaluation of the algorithm performance is provided considering its live execution on a software defined radio network testbed. The......Next generation wireless networks aim at a significant improvement of the spectral efficiency in order to meet the dramatic increase in data service demand. In local area scenarios user-deployed base stations are expected to take place, thus making the centralized planning of frequency resources...... among the cells, a non-viable solution. Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) are the research paradigms which are expected to provide the network nodes the capabilities for an autonomous and efficient selection of the spectrum resources. In this paper we present the first experimental...
Directory of Open Access Journals (Sweden)
Y. Li
2015-01-01
Full Text Available 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 CHAMP and 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 in random errors (standard deviations of optimized bending angles down to about two-thirds 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; (4 produces 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.
A graph based algorithm for adaptable dynamic airspace configuration for NextGen
Savai, Mehernaz P.
The National Airspace System (NAS) is a complicated large-scale aviation network, consisting of many static sectors wherein each sector is controlled by one or more controllers. The main purpose of the NAS is to enable safe and prompt air travel in the U.S. However, such static configuration of sectors will not be able to handle the continued growth of air travel which is projected to be more than double the current traffic by 2025. Under the initiative of the Next Generation of Air Transportation system (NextGen), the main objective of Adaptable Dynamic Airspace Configuration (ADAC) is that the sectors should change to the changing traffic so as to reduce the controller workload variance with time while increasing the throughput. Change in the resectorization should be such that there is a minimal increase in exchange of air traffic among controllers. The benefit of a new design (improvement in workload balance, etc.) should sufficiently exceed the transition cost, in order to deserve a change. This leads to the analysis of the concept of transition workload which is the cost associated with a transition from one sectorization to another. Given two airspace configurations, a transition workload metric which considers the air traffic as well as the geometry of the airspace is proposed. A solution to reduce this transition workload is also discussed. The algorithm is specifically designed to be implemented for the Dynamic Airspace Configuration (DAC) Algorithm. A graph model which accurately represents the air route structure and air traffic in the NAS is used to formulate the airspace configuration problem. In addition, a multilevel graph partitioning algorithm is developed for Dynamic Airspace Configuration which partitions the graph model of airspace with given user defined constraints and hence provides the user more flexibility and control over various partitions. In terms of air traffic management, vertices represent airports and waypoints. Some of the major
Institute of Scientific and Technical Information of China (English)
LIN Xiangguo; LIANG Yong
2005-01-01
The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years.As a result, many linear methods and nonlinear methods have been developed.But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed.A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.
An Efficient Dynamic Programming Algorithm for STR-IC-SEQ-EC-LCS Problem
Zhu, Daxin; Wang, Lei; Wu, Yingjie; Wang, Xiaodong
2015-01-01
In this paper, we consider a generalized longest common subsequence problem, in which a constraining sequence of length $s$ must be included as a substring and the other constraining sequence of length $t$ must be excluded as a subsequence of two main sequences and the length of the result must be maximal. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and the given two constraining sequences of length $s$ and $t$, we present an $O(nmst)$ time dynamic programming algorithm fo...
Binary interaction algorithms for the simulation of flocking and swarming dynamics
Albi, Giacomo
2012-01-01
Microscopic models of flocking and swarming takes in account large numbers of interacting individ- uals. Numerical resolution of large flocks implies huge computational costs. Typically for $N$ interacting individuals we have a cost of $O(N^2)$. We tackle the problem numerically by considering approximated binary interaction dynamics described by kinetic equations and simulating such equations by suitable stochastic methods. This approach permits to compute approximate solutions as functions of a small scaling parameter $\\varepsilon$ at a reduced complexity of O(N) operations. Several numerical results show the efficiency of the algorithms proposed.
An efficient dissipative particle dynamics-based algorithm for simulating electrolyte solutions
Medina, Stefan; Wang, Zhen-Gang; Schmid, Friederike
2014-01-01
We propose an efficient simulation algorithm based on the dissipative particle dynamics (DPD) method for studying electrohydrodynamic phenomena in electrolyte fluids. The fluid flow is mimicked with DPD particles while the evolution of the concentration of the ionic species is described using Brownian pseudo particles. The method is designed especially for systems with high salt concentrations, as explicit treatment of the salt ions becomes computationally expensive. For illustration, we apply the method to electro-osmotic flow over patterned, superhydrophobic surfaces. The results are in good agreement with recent theoretical predictions.
Genetic algorithm-fuzzy based dynamic motion planning approach for a mobile robot
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Presents the mobile robots dynamic motion planning problem with a task to find an obstacle-free route that requires minimum travel time from the start point to the destination point in a changing environment, due to the obstacle's moving. An Genetic Algorithm fuzzy(GA-Fuzzy)based optimal approach proposed to find any obstacle-free path and the GA used to select the optimal one, points ont that using this learned knowledge off line, a mobile robot can navigate to its goal point when it faces new scenario on-line. Concludes with the opti mal rule base given and the simulation results showing its effectiveness.
Jain, A.; Man, G. K.
1993-01-01
This paper describes the Dynamics Algorithms for Real-Time Simulation (DARTS) real-time hardware-in-the-loop dynamics simulator for the National Aeronautics and Space Administration's Cassini spacecraft. The spacecraft model consists of a central flexible body with a number of articulated rigid-body appendages. The demanding performance requirements from the spacecraft control system require the use of a high fidelity simulator for control system design and testing. The DARTS algorithm provides a new algorithmic and hardware approach to the solution of this hardware-in-the-loop simulation problem. It is based upon the efficient spatial algebra dynamics for flexible multibody systems. A parallel and vectorized version of this algorithm is implemented on a low-cost, multiprocessor computer to meet the simulation timing requirements.
An Optimized Dynamic Scene Change Detection Algorithm for H.264/AVC Encoded Video Sequences
Directory of Open Access Journals (Sweden)
Giorgio Rascioni
2010-01-01
Full Text Available Scene change detection plays an important role in a number of video applications, including video indexing, semantic features extraction, and, in general, pre- and post-processing operations. This paper deals with the design and performance evaluation of a dynamic scene change detector optimized for H.264/AVC encoded video sequences. The detector is based on a dynamic threshold that adaptively tracks different features of the video sequence, to increase the whole scheme accuracy in correctly locating true scene changes. The solution has been tested on suitable video sequences resembling real-world videos thanks to a number of different motion features, and has provided good performance without requiring an increase in decoder complexity. This is a valuable issue, considering the possible application of the proposed algorithm in post-processing operations, such as error concealment for video decoding in typical error prone video transmission environments, such as wireless networks.
An Analytical Measuring Rectification Algorithm of Monocular Systems in Dynamic Environment
Directory of Open Access Journals (Sweden)
Deshi Li
2016-01-01
Full Text Available Range estimation is crucial for maintaining a safe distance, in particular for vision navigation and localization. Monocular autonomous vehicles are appropriate for outdoor environment due to their mobility and operability. However, accurate range estimation using vision system is challenging because of the nonholonomic dynamics and susceptibility of vehicles. In this paper, a measuring rectification algorithm for range estimation under shaking conditions is designed. The proposed method focuses on how to estimate range using monocular vision when a shake occurs and the algorithm only requires the pose variations of the camera to be acquired. Simultaneously, it solves the problem of how to assimilate results from different kinds of sensors. To eliminate measuring errors by shakes, we establish a pose-range variation model. Afterwards, the algebraic relation between distance increment and a camera’s poses variation is formulated. The pose variations are presented in the form of roll, pitch, and yaw angle changes to evaluate the pixel coordinate incensement. To demonstrate the superiority of our proposed algorithm, the approach is validated in a laboratory environment using Pioneer 3-DX robots. The experimental results demonstrate that the proposed approach improves in the range accuracy significantly.
Directory of Open Access Journals (Sweden)
Prof. Dibyendu Barman,
2013-03-01
Full Text Available In multitasking and time sharing operating system the performance of the CPU depends on waiting time, response time, turnaround time, context switches from the process mainly depends on the scheduling algorithm. Round Robin is most widely used scheduling algorithm but this algorithm has some disadvantages. Here Time Quantum play very impartment role. If the time quantum is too large then it works like FCFS (First cum First Serve scheduling algorithm and if time quantum is too small then more context switches is occur which decrease the performance of the CPU. In this paper based on the experiments and calculations a new scheduling algorithm is introduced. In this algorithm the main idea is to adjust time quantum dynamically depending upon arrival time and burst time of the processes
Chen, Yunjie; Kale, Seyit; Weare, Jonathan; Dinner, Aaron R; Roux, Benoît
2016-04-12
A multiple time-step integrator based on a dual Hamiltonian and a hybrid method combining molecular dynamics (MD) and Monte Carlo (MC) is proposed to sample systems in the canonical ensemble. The Dual Hamiltonian Multiple Time-Step (DHMTS) algorithm is based on two similar Hamiltonians: a computationally expensive one that serves as a reference and a computationally inexpensive one to which the workload is shifted. The central assumption is that the difference between the two Hamiltonians is slowly varying. Earlier work has shown that such dual Hamiltonian multiple time-step schemes effectively precondition nonlinear differential equations for dynamics by reformulating them into a recursive root finding problem that can be solved by propagating a correction term through an internal loop, analogous to RESPA. Of special interest in the present context, a hybrid MD-MC version of the DHMTS algorithm is introduced to enforce detailed balance via a Metropolis acceptance criterion and ensure consistency with the Boltzmann distribution. The Metropolis criterion suppresses the discretization errors normally associated with the propagation according to the computationally inexpensive Hamiltonian, treating the discretization error as an external work. Illustrative tests are carried out to demonstrate the effectiveness of the method. PMID:26918826
Zhang, Sheng-Fei; Xu, Jun-Bo; Wen, Hao; Bhattacharjee, Subir
2011-08-01
Heavy crude oil consists of thousands of compounds, a significant fraction of which have fairly large molecular weights and complex structures. Our work aims at constructing a meso-scale platform to explore this complex fluid in terms of microstructure, phase behavior, stability and rheology. In the present study, we focus on the treatment of the structures of fused aromatic rings as rigid body fragments in fractions such as asphaltenes and resins. To derive the rotational motion of rigid bodies in a non-conservative force field, we conduct a comparison of three rigid body rotational algorithms integrated into a standard dissipative particle dynamics (DPD) simulation. The simulation results confirm the superiority of the Quaternion method. To ease any doubt concerning the introduction of rigid bodies into DPD, the performance of the Quaternion method was tested carefully. Finally, the aggregation dynamics of asphaltene in very diluted toluene was investigated. The nanoaggregates are found to experience forming, breaking up and reforming. The sizes of the asphaltene monomer and nanoaggregate are identified. The diffusion coefficient of diluted asphaltene in toluene is similar to that found experimentally. All these results verify the rotational algorithm and encourage us to extend this platform to study the rheological and colloidal characteristics of heavy crude oils in the future.
FPGA implementation of dynamic channel assignment algorithm for cognitive wireless sensor networks
Martínez, Daniela M.; Andrade, Ángel G.
2015-07-01
The reliability of wireless sensor networks (WSNs) in industrial applications can be thwarted due to multipath fading, noise generated by industrial equipment or heavy machinery and particularly by the interference generated from other wireless devices operating in the same spectrum band. Recently, cognitive WSNs (CWSNs) were proposed to improve the performance and reliability of WSNs in highly interfered and noisy environments. In this class of WSN, the nodes are spectrum aware, that is, they monitor the radio spectrum to find channels available for data transmission and dynamically assign and reassign nodes to low-interference condition channels. In this work, we present the implementation of a channel assignment algorithm in a field-programmable gate array, which dynamically assigns channels to sensor nodes based on the interference and noise levels experimented in the network. From the results obtained from the performance evaluation of the CWSN when the channel assignment algorithm is considered, it is possible to identify how many channels should be available in the network in order to achieve a desired percentage of successful transmissions, subject to constraints on the signal-to-interference plus noise ratio on each active link.
A matrix product algorithm for stochastic dynamics on locally tree-like graphs
Barthel, Thomas; de Bacco, Caterina; Franz, Silvio
In this talk, I describe a novel algorithm for the efficient simulation of generic stochastic dynamics of classical degrees of freedom defined on the vertices of locally tree-like graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon the cavity method and ideas from quantum many-body theory, the algorithm is based on a matrix product approximation of the so-called edge messages - conditional probabilities of vertex variable trajectories. The matrix product edge messages (MPEM) are constructed recursively. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the MPEM in truncations. In contrast to Monte Carlo simulations, the approach has a better error scaling and works for both, single instances as well as the thermodynamic limit. Due to the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations with unprecedented accuracy. The method is demonstrated for the prototypical non-equilibrium Glauber dynamics of an Ising spin system. Reference: arXiv:1508.03295.
Graph Theoretic Foundations of Multibody Dynamics Part II: Analysis and Algorithms.
Jain, Abhinandan
2011-10-01
This second, of a two part paper, uses concepts from graph theory to obtain a deeper understanding of the mathematical foundations of multibody dynamics. The first part [7] established the block-weighted adjacency (BWA) matrix structure of spatial operators associated with serial and tree topology multibody system dynamics, and introduced the notions of spatial kernel operators (SKO) and spatial propagation operators (SPO). This paper builds upon these connections to show that key analytical results and computational algorithms are a direct consequence of these structural properties and require minimal assumptions about the specific nature of the underlying multibody system. We formalize this notion by introducing the notion of SKO models for general tree-topology multibody systems. We show that key analytical results, including mass matrix factorization, inversion, and decomposition hold for all SKO models. It is also shown that key low-order scatter/gather recursive computational algorithms follow directly from these abstract-level analytical results. Application examples to illustrate the concrete application of these general results are provided. The paper also describes a general recipe for developing SKO models. The abstract nature of SKO models allows the application of these techniques to a very broad class of multibody systems. PMID:22102791
A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm
Wang, Zhongbin; Xu, Xihua; Si, Lei; Ji, Rui; Liu, Xinhua; Tan, Chao
2016-01-01
In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others. PMID:27123002
Renison, C Alicia; Fernandes, Kyle D; Naidoo, Kevin J
2015-07-01
This article describes an extension of the quantum supercharger library (QSL) to perform quantum mechanical (QM) gradient and optimization calculations as well as hybrid QM and molecular mechanical (QM/MM) molecular dynamics simulations. The integral derivatives are, after the two-electron integrals, the most computationally expensive part of the aforementioned calculations/simulations. Algorithms are presented for accelerating the one- and two-electron integral derivatives on a graphical processing unit (GPU). It is shown that a Hartree-Fock ab initio gradient calculation is up to 9.3X faster on a single GPU compared with a single central processing unit running an optimized serial version of GAMESS-UK, which uses the efficient Schlegel method for s- and l-orbitals. Benchmark QM and QM/MM molecular dynamics simulations are performed on cellobiose in vacuo and in a 39 Å water sphere (45 QM atoms and 24843 point charges, respectively) using the 6-31G basis set. The QSL can perform 9.7 ps/day of ab initio QM dynamics and 6.4 ps/day of QM/MM dynamics on a single GPU in full double precision. © 2015 Wiley Periodicals, Inc. PMID:25975864
A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm
Directory of Open Access Journals (Sweden)
Zhongbin Wang
2016-01-01
Full Text Available In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN and support vector machine (SVM methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.
International Nuclear Information System (INIS)
We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 106-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of
Shimojo, Fuyuki; Hattori, Shinnosuke; Kalia, Rajiv K.; Kunaseth, Manaschai; Mou, Weiwei; Nakano, Aiichiro; Nomura, Ken-ichi; Ohmura, Satoshi; Rajak, Pankaj; Shimamura, Kohei; Vashishta, Priya
2014-05-01
We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 106-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of techniques
Al-Jabr, Ahmad Ali
2013-01-01
This paper presents methods of simulating gain media in the finite difference time-domain (FDTD) algorithm utilizing a generalized polarization formulation. The gain can be static or dynamic. For static gain, Lorentzian and non-Lorentzian models are presented and tested. For the dynamic gain, rate equations for two-level and four-level models are incorporated in the FDTD scheme. The simulation results conform with the expected behavior of wave amplification and dynamic population inversion.
An algorithm for the calculation of dose distributions from dynamic wedges
International Nuclear Information System (INIS)
The Clinac 2100C of Varian has the option of a dynamic wedge. The control of the dynamic wedge is done by means of a segmentation table, which specifies a number of asymmetric fields with corresponding numbers of monitor units. Advantages of this dynamic wedge are the possibility to have more wedge angles and an increased flexibility of operation; a disadvantage is that the measurement of the data needed for a planning system may be time consuming. For the calculation of the dose distribution, we have developed an algorithm which eradicates this disadvantage because it only uses measured data from square fields of the open beam: central axis depth-doses, profiles and Peak Scatter Factors. It is based on a previously developed method which separates the dose into three factors: depth-dose, boundary distribution and envelope profile, and computes the dose of irregular field as well. Depth-dose and boundary distribution are computed by convolution of a field intensity with 'scatter' and 'boundary' pencil beam kernels respectively. For a rectangular field, the field intensity is computed by a weighted sum of intensity matrices, derived from the segmentation table. The matrix elements are equal to 1 within the asymmetric fields and equal to an effective transmission factor under the jaws. The weighting factors in the sum take into account the number of monitor units and the collimator scatter back to the monitor. For an irregular field, the blocked parts of the field are modified, taking into account the transmission through the blocks. This algorithm has been implemented in CADPLAN (Varian-Dosetek) and has been compared with measurements. The results are in good agreement with the international requirements on dose accuracy
Directory of Open Access Journals (Sweden)
H.Z. Igamberdiyev
2014-07-01
Full Text Available Dynamic systems condition estimation regularization algorithms in the conditions of signals and hindrances statistical characteristics aprioristic uncertainty are offered. Regular iterative algorithms of strengthening matrix factor elements of the Kalman filter, allowing to adapt the filter to changing hindrance-alarm conditions are developed. Steady adaptive estimation algorithms of a condition vector in the aprioristic uncertainty conditions of covariance matrixes of object noise and the measurements hindrances providing a certain roughness of filtration process in relation to changing statistical characteristics of signals information parameters are offered. Offered practical realization results of the dynamic systems condition estimation algorithms are given at the adaptive management systems synthesis problems solution by technological processes of granulation drying of an ammophos pulp and receiving ammonia.
Dynamic Multi-Stage Placement of Phasor Measurement Units using Bat Optimization Algorithm
Directory of Open Access Journals (Sweden)
S. E. Razavi Asfali
2014-07-01
Full Text Available In recent years, utilization of phasor measurement units (PMUs has increased in monitoring, control and protection of power systems. In reality, power systems are large scale, accordingly, financial limitations (due to PMU cost and technical problems are avoiding to install all necessary PMUs in one stage. Therefore, the PMUs usually are installed in several stages. This paper proposes a new dynamic multi-stage PMU placement approach by introducing a new index related to network observability in planning stages. Despite of conventional methods, the proposed multi-stage PMU placement is investigated dependently, simultaneously, and dynamically. Moreover, the phasing of PMUs of all stages is achieved in a single optimization process. Furthermore, in order to consider the practical aspect, the channel and communication limitations are covered in this study. According to the complexity of the proposed model, Bat Algorithm is used as an optimization tool to solve the proposed dynamic multi-stage PMU placement model. The proposed approach is applied on standard IEEE 14-, 57- and 118- bus test systems as well as Iranian 230- and 400-kV transmission network. Finally, the obtained results are compared with the results of conventional methods and ability of the proposed approach is investigated.
Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm
Grippa, M.; Mognard, N.; Le, Toan T.; Josberger, E.G.
2004-01-01
One of the major challenges in determining snow depth (SD) from passive microwave measurements is to take into account the spatiotemporal variations of the snow grain size. Static algorithms based on a constant snow grain size cannot provide accurate estimates of snow pack thickness, particularly over large regions where the snow pack is subjected to big spatial temperature variations. A recent dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from the Special Sensor Microwave/Imager (SSM/I) over the Northern Great Plains (NGP) in the US. In this paper, we develop a combined dynamic and static algorithm to estimate snow depth from 13 years of SSM/I observations over Central Siberia. This region is characterised by extremely cold surface air temperatures and by the presence of permafrost that significantly affects the ground temperature. The dynamic algorithm is implemented to take into account these effects and it yields accurate snow depths early in the winter, when thin snowpacks combine with cold air temperatures to generate rapid crystal growth. However, it is not applicable later in the winter when the grain size growth slows. Combining the dynamic algorithm to a static algorithm, with a temporally constant but spatially varying coefficient, we obtain reasonable snow depth estimates throughout the entire snow season. Validation is carried out by comparing the satellite snow depth monthly averages to monthly climatological data. We show that the location of the snow depth maxima and minima is improved when applying the combined algorithm, since its dynamic portion explicitly incorporate the thermal gradient through the snowpack. The results obtained are presented and evaluated for five different vegetation zones of Central Siberia. Comparison with in situ measurements is also shown and discussed. ?? 2004 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
A chaotic sequence based differential evolution (DE) approach for solving the dynamic economic dispatch problem (DEDP) with valve-point effect is presented in this paper. The proposed method combines the DE algorithm with the local search technique to improve the performance of the algorithm. DE is the main optimizer, while an approximated model for local search is applied to fine tune in the solution of the DE run. To accelerate convergence of DE, a series of constraints handling rules are adopted. An initial population obtained by using chaotic sequence exerts optimal performance of the proposed algorithm. The combined algorithm is validated for two test systems consisting of 10 and 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other algorithms reported in literatures for DEDP considering valve-point effects.
Maes, K.; Iliopoulos, A.; Weijtjens, W.; Devriendt, C.; Lombaert, G.
2016-08-01
Offshore wind turbines are exposed to continuous wind and wave excitation. The monitoring of high periodic strains at critical locations is important to assess the remaining lifetime of the structure. At some critical locations below the water level, direct measurements of the strains are not feasible. Response estimation techniques can then be used to estimate the strains from a limited set of response measurements and a system model. This paper compares a Kalman filtering algorithm, a joint input-state estimation algorithm, and a modal expansion algorithm, for the estimation of dynamic strains in the tower of an offshore monopile wind turbine. The algorithms make use of a model of the structure and a limited number of response measurements for the prediction of the strain responses. The strain signals obtained from the response estimation algorithms are compared to the actual measured strains in the tower.
AbouEisha, Hassan M.
2014-06-06
In this paper we present a dynamic programming algorithm for finding optimal elimination trees for computational grids refined towards point or edge singularities. The elimination tree is utilized to guide the multi-frontal direct solver algorithm. Thus, the criterion for the optimization of the elimination tree is the computational cost associated with the multi-frontal solver algorithm executed over such tree. We illustrate the paper with several examples of optimal trees found for grids with point, isotropic edge and anisotropic edge mixed with point singularity. We show the comparison of the execution time of the multi-frontal solver algorithm with results of MUMPS solver with METIS library, implementing the nested dissection algorithm.
A Fair Dynamic Quantum Algorithm%一种公平的动态轮转算法
Institute of Scientific and Technical Information of China (English)
胡赛; 赵碧海; 熊慧军
2012-01-01
时间片轮转算法作为一种经典的调度算法得到了广泛的应用.针对时间片轮转算法的调度策略和时间片长度的选取等问题开展深入的研究,提出了一种改进的动态轮转算法,算法是短作业优先算法、多级队列算法和时间片轮转算法的综合和发展.利用生灭过程理论建立了时间片轮转算法和动态轮转算法的性能模型,分析了两种算法的平均等待时间和平均周转时间,引入性能提高百分比的概念对比两种算法的差异.实验结果和理论分析均表明改进算法的性能优于传统的时间片轮转算法.%As a classic scheduling algorithm, round robin algorithm has been widely applied. According to the scheduling policy and the selection of the length of time slice for the round robin algorithm, a large number of in-depth studies are carried out. An improved dynamic quantum algorithm is proposed. The algorithm is the development and integration of shortest job first algorithm, multi-level queue scheduling algorithm and round robin algorithm. The performance models of the round robin algorithm and the dynamic quantum algorithm are built by using the theory of birth-death process, including average waiting time and average turnaround time of the two algorithms. The concept of performance improvement percentage is introduced in order to show the differences between the two algorithms. Both theoretical analysis and experimental results indicate that the performance of the improved algorithm is better than the traditional algorithm. The advantages of the improved algorithm become more obviously when the number of tasks increase and the rate of service improves.
Muda, Lindasalwa; Elamvazuthi, I
2010-01-01
Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. Several methods such as Liner Predictive Predictive Coding (LPC), Hidden Markov Model (HMM), Artificial Neural Network (ANN) and etc are evaluated with a view to identify a straight forward and effective method for voice signal. The extraction and matching process is implemented right after the Pre Processing or filtering signal is performed. The non-parametric method for modelling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) are utilize as extraction techniques. The non linear sequence alignment known as Dynamic Time Warping (DTW) intro...
SMACK: A NEW ALGORITHM FOR MODELING COLLISIONS AND DYNAMICS OF PLANETESIMALS IN DEBRIS DISKS
International Nuclear Information System (INIS)
We present the Superparticle-Method/Algorithm for Collisions in Kuiper belts and debris disks (SMACK), a new method for simultaneously modeling, in three dimensions, the collisional and dynamical evolution of planetesimals in a debris disk with planets. SMACK can simulate azimuthal asymmetries and how these asymmetries evolve over time. We show that SMACK is stable to numerical viscosity and numerical heating over 107 yr and that it can reproduce analytic models of disk evolution. We use SMACK to model the evolution of a debris ring containing a planet on an eccentric orbit. Differential precession creates a spiral structure as the ring evolves, but collisions subsequently break up the spiral, leaving a narrower eccentric ring
Metric-tensor flexible-cell algorithm for isothermal-isobaric molecular dynamics simulations
Hernández, E
2001-01-01
An extended Hamiltonian approach to conduct isothermal-isobaric molecular dynamics simulations with full cell flexibility is presented. The components of the metric tensor are used as the fictitious degrees of freedom for the cell, thus avoiding the problem of spurious cell rotations and artificial symmetry breaking effects present in the original Parrinello-Rahman scheme. This is complemented by the Nose-Poincare approach for isothermal sampling. The combination of these two approaches leads to equations of motion that are Hamiltonian in structure, and which can therefore be solved numerically using recently developed powerful symplectic integrators. One such integrator, the generalised leap-frog, is employed to provide a numerical algorithm for integrating the isothermal-isobaric equations of motion obtained.
A new graph-matching-based algorithm to study dynamical processes
Martelli, Fausto; Ko, Hsin-Yu; Car, Roberto
We present a new algorithm to identify and quantify the degree of local order in dynamical systems. To each particle site we associate a given number of neighboring sites the positions of which define the nodes of a pattern graph. We match this graph with a graph describing the geometry of an ordered reference system. The degree of overlap is obtained by recursively maximizing a score function having a value ranging from 0 (in the case of a completely disordered system) to 1 (in the case of a perfect crystal). While typically order parameters are tailored to specific cases, our approach is general and could be applied to different areas of condensed matter physics. Here we illustrate the approach with applications to atomic and molecular fluids, namely melting of Lennard Jones particles, direct crystallization of supercooled water and melting of Yukawa crystals. Scientific Discovery through Advanced Computing (SciDAC)program; the Department of Energy (DOE), Grant Number DESC0008626.
Rocha, M. C.; Saraiva, J. T.
2012-10-01
The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.
Tang, Yu-Hang
2013-01-01
We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10-30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases ...
A novel dynamic frame rate control algorithm for H.264 low-bit-rate video coding
Institute of Scientific and Technical Information of China (English)
Yang Jing; Fang Xiangzhong
2007-01-01
The goal of this paper is to improve human visual perceptual quality as well as coding efficiency of H.264 video at low bit rate conditions by adaptively adjusting the number of skipped frames. The encoding frames ale selected according to the motion activity of each frame and the motion accumulation of successive frames. The motion activity analysis is based on the statistics of motion vectors and with consideration of the characteristics of H. 264 coding standard. A prediction model of motion accumulation is proposed to reduce complex computation of motion estimation. The dynamic encoding frame rate control algorithm is applied to both the frame level and the GOB (Group of Macroblocks) level. Simulation is done to compare the performance of JM76 with the proposed frame level scheme and GOB level scheme.
MODIFIED DYNAMIC ONLINE ROUTING ALGORITHM AND REGENERATOR PLACEMENT IN WDM NETWORKS
Directory of Open Access Journals (Sweden)
S. Indira Gandhi
2011-03-01
Full Text Available Dynamic Online Routing Algorithm (DORA has been already proposed for conventional networks to distribute load evenly across the network. However, the exponential growth of the internet has placed heavy burdens on network management and control application when the existing protocols are used. Adding more resources to the network may temporarily remove congestion conditions. However, it is not a cost-effective solution in solving resource contention problems in the long run. The network providers are facing problems in setting up on-demand network tunnels in backbone or transport networks. The MDORA proposed in this work is useful to decide the optimum number of regenerators to be placed on each node. Placement of regenerator in all the nodes is not cost effective and hence in this work, techniques have been proposed to place the regenerators only in selected nodes. Node selection for regenerator placement is performed using fuzzy logic.
Directory of Open Access Journals (Sweden)
Saraiva J. T.
2012-10-01
Full Text Available The basic objective of Transmission Expansion Planning (TEP is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.
A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles
Taheri, Nicole; Ye, Yinyu
2011-01-01
Plug-in Electric Vehicles (PEVs) are a rapidly developing technology that can reduce greenhouse gas emissions and change the way vehicles obtain power. PEV charging stations will most likely be available at home and at work, and occasionally be publicly available, offering flexible charging options. Ideally, each vehicle will charge during periods when electricity prices are relatively low, to minimize the cost to the consumer and maximize societal benefits. A Demand Response (DR) service for a fleet of PEVs could yield such charging schedules by regulating consumer electricity use during certain time periods, in order to meet an obligation to the market. We construct an automated DR mechanism for a fleet of PEVs that facilitates vehicle charging to ensure the demands of the vehicles and the market are met. Our dynamic algorithm depends only on the knowledge of a few hundred driving behaviors from a previous similar day, and uses a simple adjusted pricing scheme to instantly assign feasible and satisfactory c...
An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
Bliss, Catherine A; Danforth, Christopher M; Dodds, Peter Sheridan
2013-01-01
Many real world, complex phenomena have underlying structures of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of user-specific metadata and usage patterns can improve link prediction, however methodologies for doing so in a systematic way are largely unexplored in the literature. Here, we provide an approach to predicting future links by applying an evolutionary algorithm to weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine Twitter reciprocal reply networks constructed at the time scale of weeks, both as a test of our general method and as a...
Parameter Matching Analysis of Hydraulic Hybrid Excavators Based on Dynamic Programming Algorithm
Directory of Open Access Journals (Sweden)
Wei Shen
2013-01-01
Full Text Available In order to meet the energy saving requirement of the excavator, hybrid excavators are becoming the hot spot for researchers. The initial problem is to match the parameter of each component, because the system is tending to be more complicated due to the introduction of the accumulator. In this paper, firstly, a new architecture is presented which is hydraulic hybrid excavator based on common pressure rail combined switched function (HHES. Secondly, the general principle of dynamic programming algorithm (DPA is explained. Then, the method by using DPA for parameter matching of HHES is described in detail. Furthermore, the DPA is translated into the M language for simulation. Finally, the calculation results are analyzed, and the optimal matching group is obtained.
Directory of Open Access Journals (Sweden)
Xiaochen Zhang
2015-01-01
Full Text Available To evaluate the performance of ball screw, screw performance degradation assessment technology based on quantum genetic algorithm (QGA and dynamic fuzzy neural network (DFNN is studied. The ball screw of the CINCINNATIV5-3000 machining center is treated as the study object. Two Kistler 8704B100M1 accelerometers and a Kistler 8765A250M5 three-way accelerometer are installed to monitor the degradation trend of screw performance. First, screw vibration signal features are extracted both in time domain and frequency domain. Then the feature vectors can be obtained by principal component analysis (PCA. Second, the initialization parameters of the DFNN are optimized by means of QGA. Finally, the feature vectors are inputted to DFNN for training and then get the screw performance degradation model. The experiment results show that the screw performance degradation model could effectively evaluate the performance of NC machine screw.
DEFF Research Database (Denmark)
Meng, Lexuan; Zhao, Xin; Tang, Fen;
2016-01-01
impedance, can be applied to help the positive sequence active and reactive power sharing. Secondary level is used to assist voltage unbalance compensation. However, if distribution line differences are considered, the negative sequence current cannot be well shared among DGs. In order to overcome this......In islanded microgrids (MGs), distributed generators (DGs) can be employed as distributed compensators for improving the power quality in the consumer side. Two-level hierarchical control can be used for voltage unbalance compensation. Primary level, consisting of droop control and virtual...... problem, this paper proposes a distributed negative sequence current sharing method by using a dynamic consensus algorithm (DCA). In clear contrast with the previously proposed methods, this approach does not require a dedicated central controller and the communication links are only required between...
Energy Technology Data Exchange (ETDEWEB)
Chandrasekhar Potluri,; Madhavi Anugolu; Marco P. Schoen; D. Subbaram Naidu
2013-08-01
In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy, the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusionbased approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.
A Novel Dynamic Algorithm for IT Outsourcing Risk Assessment Based on Transaction Cost Theory
Directory of Open Access Journals (Sweden)
Guodong Cong
2015-01-01
Full Text Available With the great risk exposed in IT outsourcing, how to assess IT outsourcing risk becomes a critical issue. However, most of approaches to date need to further adapt to the particular complexity of IT outsourcing risk for either falling short in subjective bias, inaccuracy, or efficiency. This paper proposes a dynamic algorithm of risk assessment. It initially forwards extended three layers (risk factors, risks, and risk consequences of transferring mechanism based on transaction cost theory (TCT as the framework of risk analysis, which bridges the interconnection of components in three layers with preset transferring probability and impact. Then, it establishes an equation group between risk factors and risk consequences, which assures the “attribution” more precisely to track the specific sources that lead to certain loss. Namely, in each phase of the outsourcing lifecycle, both the likelihood and the loss of each risk factor and those of each risk are acquired through solving equation group with real data of risk consequences collected. In this “reverse” way, risk assessment becomes a responsive and interactive process with real data instead of subjective estimation, which improves the accuracy and alleviates bias in risk assessment. The numerical case proves the effectiveness of the algorithm compared with the approach forwarded by other references.
A Novel Dynamic Adjusting Algorithm for Load Balancing and Handover Co-Optimization in LTE SON
Institute of Scientific and Technical Information of China (English)
Wen-Yu Li; Xiang Zhang; Shu-Cong Jia; Xin-Yu Gu; Lin Zhang; Xiao-Yu Duan; Jia-Ru Lin
2013-01-01
With the development of mobile internet and multi-media service,advanced techniques need to be applied in wireless network to improve user experience.Long term evolution (LTE) systems,which can offer up to 100Mbps downlink date rates,have been deployed in USA and Korea.However,because plenty of complex physical layer algorithms are utilized,network planning and optimization become heavy burdens for LTE network operators.Self-organizing network (SON) is a promising method to overcome this problem by automatically selecting and adjusting key parameters in LTE systems.In this paper,we present a dynamic adjusting algorithm to improve both handover and load balancing performance by introducing a weighted co-satisfaction factor (CSF).Analysis and system level simulation are conducted to exhibit the performance improvement of the proposed scheme.Results show that the proposed method outperforms the conventional solutions in terms of the network handover success ratio and load balancing gains significantly.
A Dynamic Era-Based Time-Symmetric Block Time-Step Algorithm with Parallel Implementations
Kaplan, M
2012-01-01
The time-symmetric block time--step (TSBTS) algorithm is a newly developed efficient scheme for $N$--body integrations. It is constructed on an era-based iteration. In this work, we re-designed the TSBTS integration scheme with dynamically changing era size. A number of numerical tests were performed to show the importance of choosing the size of the era, especially for long time integrations. Our second aim was to show that the TSBTS scheme is as suitable as previously known schemes for developing parallel $N$--body codes. In this work, we relied on a parallel scheme using the copy algorithm for the time-symmetric scheme. We implemented a hybrid of data and task parallelization for force calculation to handle load balancing problems that can appear in practice. Using the Plummer model initial conditions for different numbers of particles, we obtained the expected efficiency and speedup for a small number of particles. Although parallelization of the direct $N$--body codes is negatively affected by the commun...
Setiawan, A.; Wangsaputra, R.; Martawirya, Y. Y.; Halim, A. H.
2016-02-01
This paper deals with Flexible Manufacturing System (FMS) production rescheduling due to unavailability of cutting tools caused either of cutting tool failure or life time limit. The FMS consists of parallel identical machines integrated with an automatic material handling system and it runs fully automatically. Each machine has a same cutting tool configuration that consists of different geometrical cutting tool types on each tool magazine. The job usually takes two stages. Each stage has sequential operations allocated to machines considering the cutting tool life. In the real situation, the cutting tool can fail before the cutting tool life is reached. The objective in this paper is to develop a dynamic scheduling algorithm when a cutting tool is broken during unmanned and a rescheduling needed. The algorithm consists of four steps. The first step is generating initial schedule, the second step is determination the cutting tool failure time, the third step is determination of system status at cutting tool failure time and the fourth step is the rescheduling for unfinished jobs. The approaches to solve the problem are complete-reactive scheduling and robust-proactive scheduling. The new schedules result differences starting time and completion time of each operations from the initial schedule.
A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers
International Nuclear Information System (INIS)
This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input–output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results. (paper)
Institute of Scientific and Technical Information of China (English)
王雅琳; 陈冬冬; 陈晓方; 蔡国民; 阳春华
2015-01-01
Region partition (RP) is the key technique to the finite element parallel computing (FEPC), and its performance has a decisive influence on the entire process of analysis and computation. The performance evaluation index of RP method for the three-dimensional finite element model (FEM) has been given. By taking the electric field of aluminum reduction cell (ARC) as the research object, the performance of two classical RP methods, which are Al-NASRA and NGUYEN partition (ANP) algorithm and the multi-level partition (MLP) method, has been analyzed and compared. The comparison results indicate a sound performance of ANP algorithm, but to large-scale models, the computing time of ANP algorithm increases notably. This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration. To obtain the satisfied speed and the precision, an improved dynamic self-adaptive ANP (DSA-ANP) algorithm has been proposed. With consideration of model scale, complexity and sub-RP stage, the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight, and then dynamically adds these connected elements. The proposed algorithm has been applied to the finite element analysis (FEA) of the electric field simulation of ARC. Compared with the traditional ANP algorithm, the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s. This proves the superiority of the improved algorithm on computing time performance.
Van Woerkom, P. Th. L. M.; de Boer, A.
1995-01-01
Robotic manipulators designed to operate on-board spacecraft and Space Stations are characterized by large spatial dimensions. The structural flexibility inherent in such manipulators introduces a noticeable and undesirable modification of the traditional rigid-body manipulator dynamics. As a result, the dynamics of the complete system comprising a flexible spacecraft or Space Station as a manipulator base, and an attached flexible manipulator, are also modified. Operational requirements related to high manoeuvre accuracy and modest manoeuvre duration, create the need for careful modelling and simulation of the dynamics of such systems. The objective of this paper is to outline the development and validation of an advanced algorithm for the simulation of the dynamics of such flexible spacecraft/space manipulator systems. The requirements imposed during the development of the present prototype dynamics simulator led to the modification and implementation of an existing linear recursive algorithm ("Order- N" algorithm), which requires a computational effort proportional to the number of component bodies in the system. Starting with the Lagrange form of the d'Alembert principle, we first deduce a parametric form which is found to yield—amongst others—the basic forms of the Newton-Euler, the d'Alembert and the Gauss dynamics principles. It is then shown how the application of each of the latter three principles can be made to lead graciously to the desired Order- N algorithm for the flexible multi-body system. The Order- N algorithm thus obtained and validated analytically, forms the basis for the prototype simulator REALDYN, designed to permit numerical simulation of the algorithm on UNIX workstations. Verification, numerical integration and further validation tests have been carried out. Some of the results obtained during the validation exercises could not be explained readily, even in the case of simple multi-body systems. The use of test tools and physical
International Nuclear Information System (INIS)
The use of evolutionary algorithms has been popular in recent years for solving the inverse problem of identifying system parameters given the chaotic response of a dynamical system. The inverse problem is reformulated as a minimization problem and population-based optimizers such as evolutionary algorithms have been shown to be efficient solvers of the minimization problem. However, to the best of our knowledge, there has been no published work that evaluates the efficacy of using the two most popular evolutionary techniques – particle swarm optimization and differential evolution algorithm, on a wide range of parameter estimation problems. In this paper, the two methods along with their variants (for a total of seven algorithms) are applied to fifteen different parameter estimation problems of varying degrees of complexity. Estimation results are analyzed using nonparametric statistical methods to identify if an algorithm is statistically superior to others over the class of problems analyzed. Results based on parameter estimation quality suggest that there are significant differences between the algorithms with the newer, more sophisticated algorithms performing better than their canonical versions. More importantly, significant differences were also found among variants of the particle swarm optimizer and the best performing differential evolution algorithm
Hybrid methods in planetesimal dynamics (I) : Description of a new composite algorithm
Glaschke, Patrick; Spurzem, Rainer
2011-01-01
The formation and evolution of protoplanetary systems, the breeding grounds of planet formation, is a complex dynamical problem that involves many orders of magnitudes. To serve this purpose, we present a new hybrid algorithm that combines a Fokker-Planck approach with the advantages of a pure direct-summation N-body scheme, with a very accurate integration of close encounters for the orbital evolution of the larger bodies with a statistical model, envisaged to simulate the very large number of smaller planetesimals in the disc. Direct-summation techniques have been historically developped for the study of dense stellar systems such as open and globular clusters and, within some limits imposed by the number of stars, of galactic nuclei. The number of modifications to adapt direct-summation N-body techniques to planetary dynamics is not undemanding and requires modifications. These include the way close encounters are treated, as well as the selection process for the "neighbour radius" of the particles and the...
International Nuclear Information System (INIS)
In this paper, a stochastic model is presented to simulate the flow of gases, which are not in thermodynamic equilibrium, like in rarefied or micro situations. For the interaction of a particle with others, statistical moments of the local ensemble have to be evaluated, but unlike in molecular dynamics simulations or DSMC, no collisions between computational particles are considered. In addition, a novel integration technique allows for time steps independent of the stochastic time scale. The stochastic model represents a Fokker-Planck equation in the kinetic description, which can be viewed as an approximation to the Boltzmann equation. This allows for a rigorous investigation of the relation between the new model and classical fluid and kinetic equations. The fluid dynamic equations of Navier-Stokes and Fourier are fully recovered for small relaxation times, while for larger values the new model extents into the kinetic regime. Numerical studies demonstrate that the stochastic model is consistent with Navier-Stokes in that limit, but also that the results become significantly different, if the conditions for equilibrium are invalid. The application to the Knudsen paradox demonstrates the correctness and relevance of this development, and comparisons with existing kinetic equations and standard solution algorithms reveal its advantages. Moreover, results of a test case with geometrically complex boundaries are presented.
Sun, Lin; Wei, Jing; Wang, Jian; Mi, Xueting; Guo, Yamin; Lv, Yang; Yang, Yikun; Gan, Ping; Zhou, Xueying; Jia, Chen; Tian, Xinpeng
2016-06-01
Conventional cloud detection methods are easily affected by mixed pixels, complex surface structures, and atmospheric factors, resulting in poor cloud detection results. To minimize these problems, a new Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a priori surface reflectance database is proposed in this paper. A monthly surface reflectance database is constructed using long-time-sequenced MODerate resolution Imaging Spectroradiometer surface reflectance product (MOD09A1) to provide the surface reflectance of the underlying surfaces. The relationships between the apparent reflectance changes and the surface reflectance are simulated under different observation and atmospheric conditions with the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model, and the dynamic threshold cloud detection models are developed. Two typical remote sensing data with important application significance and different sensor parameters, MODIS and Landsat 8, are selected for cloud detection experiments. The results were validated against the visual interpretation of clouds and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud measurements. The results showed that the UDTCDA can obtain a high precision in cloud detection, correctly identifying cloudy pixels and clear-sky pixels at rates greater than 80% with error rate and missing rate of less than 20%. The UDTCDA cloud product overall shows less estimation uncertainty than the current MODIS cloud mask products. Moreover, the UDTCDA can effectively reduce the effects of atmospheric factors and mixed pixels and can be applied to different satellite sensors to realize long-term, large-scale cloud detection operations.
高动态星跟踪方法%Highly dynamic star tracking algorithm
Institute of Scientific and Technical Information of China (English)
金雁; 江洁; 张广军
2013-01-01
阐述了高动态星敏感器星图的特点,指出了目前星跟踪方法的不足.针对这些不足,提出了一种基于卡尔曼预测的高动态星跟踪方法.根据高动态星敏感器运动特性,建立了星体目标在图像坐标系下运动模型,根据星体运动模型,对卡尔曼滤波器进行了自适应修正.利用经自适应修正的卡尔曼滤波器预测出参考星位置,再利用临星逼近法进行跟踪匹配.最后给出了利用上述方法进行星体位置预测及星跟踪结果.实验结果表明,在5(°)/s动态条件下星体位置预测偏差小于5像素,星跟踪成功率高于95％,并且载体动态特性的变化对星体跟踪成功率影响较小.%The character of high dynamic star sensor' s sky image and the deficiency of existing star tracking algorithm at home and abroad were presented.Aiming at these deficiencies, a new star tracking algorithm based on Kalman prediction was put forward.The model of stars' movement was set up based on the character of the star sensor' s movement.The adaptive Kalman filter was used to predict the position of the reference stars.The star was matched and tracked by Star Neighborhood Approach.At the end of the article, the prediction and tracking results were presented.The experiment results indicate that the star position prediction errors are less than 5 pixels under the dynamic condition of 5 (° )/s, and the success rate of tracking is up to 95%.The method can adapt for high dynamic star sensor and improve the success rates of tracking availably.
The settling dynamics of flocculating mud-sand mixtures: Part 1—Empirical algorithm development
Manning, Andrew James; Baugh, John V.; Spearman, Jeremy R.; Pidduck, Emma L.; Whitehouse, Richard J. S.
2011-03-01
European estuaries tend to be regarded as being either predominantly muddy or sandy. In some estuaries, the cohesive and non-cohesive fractions can become segregated. However, recent laboratory tests have revealed that mud and sand from many coastal locations can exhibit some degree of flocculation. A clear understanding of the dynamic behaviour of sediments in the nearshore region is of particular importance for estuarine management groups who want to be able to accurately predict the transportation routes and fate of the suspended sediments. To achieve this goal, numerical computer simulations are usually the chosen tools. In order to use these models with any degree of confidence, the user must be able to provide the model with a reasonable mathematical description of spatial and temporal mass settling fluxes. However, the majority of flocculation models represent purely muddy suspensions. This paper assesses the settling characteristics of flocculating mixed-sediment suspensions through the synthesis of data, which was presented as a series of algorithms. Collectively, the algorithms were referred to as the mixed-sediment settling velocity (MSSV) empirical model and could estimate the mass settling flux of mixed suspensions. The MSSV was based entirely on the settling and mass distribution patterns demonstrated by experimental observations, as opposed to pure physical theory. The selection of the algorithm structure was based on the concept of macroflocs—the larger aggregate structures—and smaller microflocs, representing constituent particles of the macroflocs. The floc data was generated using annular flume simulations and the floc properties measured using the video-based LabSFLOC instrumentation. The derived algorithms are valid for suspended sediment concentrations and turbulent shear stress values ranging between 0.2-5 g l-1 and 0.06-0.9 Pa, respectively. However, the MSSV algorithms were principally derived using manufactured mixtures of Tamar
International Nuclear Information System (INIS)
This HDR is dedicated to the research in the framework of fast transient dynamics for industrial fluid-structure systems carried in the Laboratory of Dynamic Studies from CEA, implementing new numerical methods for the modelling of complex systems and the parallel solution of large coupled problems on supercomputers. One key issue for the proposed approaches is the limitation to its minimum of the number of non-physical parameters, to cope with constraints arising from the area of usage of the concepts: safety for both nuclear applications (CEA, EDF) and aeronautics (ONERA), protection of the citizen (EC/JRC) in particular. Kinematic constraints strongly coupling structures (namely through unilateral contact) or fluid and structures (with both conformant or non-conformant meshes depending on the geometrical situation) are handled through exact methods including Lagrange Multipliers, with consequences on the solution strategy to be dealt with. This latter aspect makes EPX, the simulation code where the methods are integrated, a singular tool in the community of fast transient dynamics software. The document mainly relies on a description of the modelling needs for industrial fast transient scenarios, for nuclear applications in particular, and the proposed solutions built in the framework of the collaboration between CEA, EDF (via the LaMSID laboratory) and the LaMCoS laboratory from INSA Lyon. The main considered examples are the tearing of the fluid-filled tank after impact, the Code Disruptive Accident for a Generation IV reactor or the ruin of reinforced concrete structures under impact. Innovative models and parallel algorithms are thus proposed, allowing to carry out with robustness and performance the corresponding simulations on supercomputers made of interconnected multi-core nodes, with a strict preservation of the quality of the physical solution. This was particularly the main point of the ANR RePDyn project (2010-2013), with CEA as the pilot. (author)
International Nuclear Information System (INIS)
In this paper, an improved algorithm based on Pattern Search method (PS) to solve the Dynamic Economic Dispatch is proposed. The algorithm maintains the essential unit ramp rate constraint, along with all other necessary constraints, not only for the time horizon of operation (24 h), but it preserves these constraints through the transaction period to the next time horizon (next day) in order to avoid the discontinuity of the power system operation. The Dynamic Economic and Emission Dispatch problem (DEED) is also considered. The load balance constraints, operating limits, valve-point loading and network losses are included in the models of both DED and DEED. The numerical results clarify the significance of the improved algorithm and verify its performance.
Institute of Scientific and Technical Information of China (English)
范勤勤; 王循华; 颜学峰
2015-01-01
A modified harmony search algorithm with co-evolutional control parameters (DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual (i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
Directory of Open Access Journals (Sweden)
Syed Mahamud Hossein
2013-09-01
Full Text Available Storing, transmitting and security of DNA sequences are well known research challenge. The problem has got magnified with increasing discovery and availability of DNA sequences. We have represent DNA sequence compression algorithm based on Dynamic Look Up Table (DLUT and modified Huffman technique. DLUT consists of 43(64 bases that are 64 sub-stings, each sub-string is of 3 bases long. Each sub-string are individually coded by single ASCII code from 33(! to 96(` and vice versa. Encode depends on encryption key choose by user from four base pair {a,t.g and c}and decode also require decryption key provide by the encoded user. Decoding must require authenticate input for encode the data. The sub-strings are combined into a Dynamic Look up Table based pre-coding routine. This algorithm is tested on reverse; complement & reverse complement the DNA sequences and also test on artificial DNA sequences of equivalent length. Speed of encryption and security levels are two important measurements for evaluating any encryption system. Due to proliferate of ubiquitous computing system, where digital contents are accessible through resource constraint biological database security concern is very important issue. A lot of research has been made to find an encryption system which can be run effectively in those biological databases. Information security is the most challenging question to protect the data from unauthorized user. The proposed method may protect the data from hackers. It can provide the three tier security, in tier one is ASCII code, in tier two is nucleotide (a,t,g and c choice by user and tier three is change of label or change of node position in Huffman Tree. Compression of the genome sequences will help to increase the efficiency of their use. The greatest advantage of this algorithm is fast execution, small memory occupation and easy implementation. Since the program to implement the technique have been written originally in the C language
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.
A Scalable O(N) Algorithm for Large-Scale Parallel First-Principles Molecular Dynamics Simulations
Energy Technology Data Exchange (ETDEWEB)
Osei-Kuffuor, Daniel [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Fattebert, Jean-Luc [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2014-01-01
Traditional algorithms for first-principles molecular dynamics (FPMD) simulations only gain a modest capability increase from current petascale computers, due to their O(N^{3}) complexity and their heavy use of global communications. To address this issue, we are developing a truly scalable O(N) complexity FPMD algorithm, based on density functional theory (DFT), which avoids global communications. The computational model uses a general nonorthogonal orbital formulation for the DFT energy functional, which requires knowledge of selected elements of the inverse of the associated overlap matrix. We present a scalable algorithm for approximately computing selected entries of the inverse of the overlap matrix, based on an approximate inverse technique, by inverting local blocks corresponding to principal submatrices of the global overlap matrix. The new FPMD algorithm exploits sparsity and uses nearest neighbor communication to provide a computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic orbitals are confined, and a cutoff beyond which the entries of the overlap matrix can be omitted when computing selected entries of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to O(100K) atoms on O(100K) processors, with a wall-clock time of O(1) minute per molecular dynamics time step.
Enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm
Wang, Minmin; Zhou, Canlin; Zhang, Chaorui; Si, Shuchun; Li, Hui; Lei, Zhenkun; Li, YanJie
2016-01-01
It is a challenge for Phase Measurement Profilometry (PMP) to measure objects with a large range of reflectivity variation across the surface. Saturated or dark pixels in the deformed fringe patterns captured by the camera will lead to phase fluctuations and errors. Jiang et al. proposed a high dynamic range real-time 3D shape measurement method without changing camera exposures. Three inverted phase-shifted fringe patterns are used to complement three regular phase-shifted fringe patterns for phase retrieval when any of the regular fringe patterns are saturated. But Jiang's method still has some drawbacks: (1) The phases in saturated pixels are respectively estimated by different formulas for different cases. It is shortage of an universal formula; (2) it cannot be extended to four-step phase-shifting algorithm because inverted fringe patterns are the repetition of regular fringe patterns; (3) only three unsaturated intensity values at every pixel of fringe patterns are chosen for phase demodulation, lying i...
Directory of Open Access Journals (Sweden)
E.Kayalvizhi
2015-08-01
Full Text Available Mitigation of global warming gases from burning gasoline for transportation in vehicles is one of the biggest and most complex issues the world has ever faced. In an intention to eradicate the environmental crisis caused due to global warming, electric vehicles were been introduced that are powered by electric motor which works on the energy stored in a battery pack. Inspired by the research on power management in electric vehicles, this paper focuses on the development of an energy management system for electric vehicles (EMSEV to optimally balance the energy from battery pack. The proposed methodology uses firefly optimization algorithm to optimize the power consumption of the devices like electric motor, power steering, air conditioner, power window, automatic door locks, radio, speaker, horn, wiper, GPS, internal and external lights etc., from the battery in electric vehicles. Depending upon the distance to cover and the battery availability, the devices are made to switch down automatically through dynamic EDF scheduling. CAN protocol is used for effective communication between the devices and the controller. Simulation results are obtained using MATLAB.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A global optimization approach to turbine blade design based on hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs) coupled with Reynolds-averaged Navier-Stokes (RANS) equation is presented. In order to meet the search theory of GAs and the aerodynamic performances of turbine, Bezier curve is adopted to parameterize the turbine blade profile, and a fitness function pertaining to optimization is designed. The design variables are the control points' ordinates of characteristic polygon of Bezier curve representing the turbine blade profile. The object function is the maximum lift-drag ratio of the turbine blade. The constraint conditions take into account the leading and trailing edge metal angle, and the strength and aerodynamic performances of turbine blade. And the treatment method of the constraint conditions is the flexible penalty function. The convergence history of test function indicates that HFCDN-GAs can locate the global optimum within a few search steps and have high robustness. The lift-drag ratio of the optimized blade is 8.3% higher than that of the original one. The results show that the proposed global optimization approach is effective for turbine blade.
Polynomially scaling spin dynamics simulation algorithm based on adaptive state space restriction
Kuprov, I; Hore, P J; Kuprov, Ilya; Wagner-Rundell, Nicola
2007-01-01
The conventional spin dynamics simulations are performed in direct products of state spaces of individual spins. In a general system of n spins, the total number of elements in the state basis is >4^n. A system propagation step requires an action by an operator on the state vector and thus requires >4^2n multiplications. It is obvious that with current computers there is no way beyond about ten spins, and the calculation complexity scales exponentially with the spin system size. We demonstrate that a polynomially scaling algorithm can be obtained if the state space is reduced by neglecting unimportant or unpopulated spin states. The class of such states is surprisingly wide. In particular, there are indications that very high multi-spin orders can be dropped completely, as can all the orders linking the spins that are remote on the interaction graph. The computational cost of the propagation step for a ktuples-restricted densely connected n-spin system with k<
A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem
Moradgholi, Mostafa; Paydar, Mohammad Mahdi; Mahdavi, Iraj; Jouzdani, Javid
2016-05-01
Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The bi-objective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method.
International Nuclear Information System (INIS)
In this work, we show that the set of primes can be obtained through dynamical processes. Indeed, we see that behind their generation there is an apparent stochastic process; this is obtained with the combination of two processes: a 'zig-zag' between two classes of primes and an intermittent process (that is a selection rule to exclude some prime candidates of the classes). Although we start with a stochastic process, the knowledge of its inner properties in terms of zig-zagging and intermittent processes gives us a deterministic and analytic way to generate the distribution of prime numbers. Thanks to genetic algorithms and evolution systems, as we will see, we answer some of most relevant questions of the last two centuries, that is 'How can we know a priori if a number is prime or not? Or similarly, does the generation of number primes follow a specific rule and if yes what is its form? Moreover, has it a deterministic or stochastic form?' To reach these results we start to analyze prime numbers by using binary representation and building a hierarchy among derivative classes. We obtain for the first time an explicit relation for generating the full set Pn of prime numbers smaller than n or equal to n
An Adaptive RFID Anti-Collision Algorithm Based on Dynamic Framed ALOHA
Lee, Chang Woo; Cho, Hyeonwoo; Kim, Sang Woo
The collision of ID signals from a large number of colocated passive RFID tags is a serious problem; to realize a practical RFID systems we need an effective anti-collision algorithm. This letter presents an adaptive algorithm to minimize the total time slots and the number of rounds required for identifying the tags within the RFID reader's interrogation zone. The proposed algorithm is based on the framed ALOHA protocol, and the frame size is adaptively updated each round. Simulation results show that our proposed algorithm is more efficient than the conventional algorithms based on the framed ALOHA.
International Nuclear Information System (INIS)
This work has been consecrated to the modular simulation of a PWR 925 MWe power plant's dynamic and to the design of a multivariable algorithm control: a mathematical model of a plant type was developed. The programs were written on a structured manner in order to maximize flexibility. A multivariable control algorithm based on pole placement with output feedback was elaborated together with its correspondent program. The simulation results for different normal transients were shown and the capabilities of the new method of multivariable control are illustrated through many examples
Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm
Hong, Yoon-Seok Timothy; White, Paul A.
2009-01-01
This paper introduces the dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system with on-line and local learning algorithm for complex dynamic hydrological modeling tasks. Our DNFLMS is aimed to implement a fast training speed with the capability of on-line simulation where model adaptation occurs at the arrival of each new item of hydrological data. The DNFLMS applies an on-line, one-pass, training procedure to create and update fuzzy local models dynamically. The extended Kalman filtering algorithm is then implemented to optimize the parameters of the consequence part of each fuzzy model during the training phase. Local generalization in the DNFLMS is employed to optimize the parameters of each fuzzy model separately, region-by-region, using subsets of training data rather than all training data. The proposed DNFLMS is applied to develop a model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatt Cobb hydropower station on spring flow. It is demonstrated that the proposed DNFLMS is superior in terms of model complexity and computational efficiency when compared with models that adopt global generalization such as a multi-layer perceptron (MLP) trained with the back propagation learning algorithm and the well-known adaptive neural-fuzzy system (ANFIS).
International Nuclear Information System (INIS)
A novel combined Event-Driven/Time-Driven (ED/TD) algorithm to speed-up the Molecular Dynamics simulation of rarefied gases using realistic spherically symmetric soft potentials is presented. Due to the low density regime, the proposed method correctly identifies the time that must elapse before the next interaction occurs, similarly to Event-Driven Molecular Dynamics. However, each interaction is treated using Time-Driven Molecular Dynamics, thereby integrating Newton's Second Law using the sufficiently small time step needed to correctly resolve the atomic motion. Although infrequent, many-body interactions are also accounted for with a small approximation. The combined ED/TD method is shown to correctly reproduce translational relaxation in argon, described using the Lennard-Jones potential. For densities between ρ=10-4kg/m3 and ρ=10-1kg/m3, comparisons with kinetic theory, Direct Simulation Monte Carlo, and pure Time-Driven Molecular Dynamics demonstrate that the ED/TD algorithm correctly reproduces the proper collision rates and the evolution toward thermal equilibrium. Finally, the combined ED/TD algorithm is applied to the simulation of a Mach 9 shock wave in rarefied argon. Density and temperature profiles as well as molecular velocity distributions accurately match DSMC results, and the shock thickness is within the experimental uncertainty. For the problems considered, the ED/TD algorithm ranged from several hundred to several thousand times faster than conventional Time-Driven MD. Moreover, the force calculation to integrate the molecular trajectories is found to contribute a negligible amount to the overall ED/TD simulation time. Therefore, this method could pave the way for the application of much more refined and expensive interatomic potentials, either classical or first-principles, to Molecular Dynamics simulations of shock waves in rarefied gases, involving vibrational nonequilibrium and chemical reactivity.
Directory of Open Access Journals (Sweden)
R. Sukumar
2009-01-01
Full Text Available Problem statement: In order to minimize the overall network traffic in a multiserver system, the number of users served by each server (and hence the group size should remain constant. As the underlying traffic fluctuates, a split and merge scheme is implemented in a physical server to achieve load balancing. Approach: Minimizing the number of servers during the merge operation is NP hard and to achieve these two algorithms namely FFD bin packing algorithm and LL algorithm were proposed to find the near optimal values of destination servers. Results: The performance of these algorithms were analyzed and compared based on several parameters. Conclusion: Results showed that LL algorithm outperforms FFD algorithm.
Nonomura, Yoshihiko
2014-01-01
Nonequilibrium relaxation behaviors in the Ising model on a square lattice based on the Wolff algorithm are totally different from those based on local-update algorithms. In particular, the critical relaxation is described by the stretched-exponential decay. We propose a novel scaling procedure to connect nonequilibrium and equilibrium behaviors continuously, and find that the stretched-exponential scaling region in the Wolff algorithm is as wide as the power-law scaling region in local-updat...
Li, Liang
2016-01-01
In coherent detection employing digital signal processing, chromatic dispersion (CD) can be compensated effectively in the electrical domain. In practical optical transport networks, the signal lightpaths between two terminal nodes can be different due to current network conditions. Accordingly, the transmission distance and the accumulated dispersion in the lightpath cannot be predicted. Therefore, the adaptive compensation of dynamic dispersion such as the use of least-mean-square (LMS) algorithm is necessary in such optical fiber networks to enable a flexible routing and switching. In this paper, we present a detailed analysis on the adaptive dispersion compensation using the LMS algorithms in coherent optical transmission networks. Numerical simulations have been carried out accordingly. It can be found that the variable-step-size LMS equalizer can achieve the adaptive CD equalization with a lower complexity, compared to the traditional LMS algorithm.
Study on Contact Algorithm of Dynamic Explicit FEM for Sheet Forming Simulation
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Based on existing algorithms, a newly developed contact search algorithm is proposed. The new algorithm consists of global search, local searching, local tracking and penetration calculation processes. It requires no iteration steps. It can deal with not only general tool surfaces with vertical walls, but also tool surfaces meshed with elements having very poor aspect ratios. It is demonstrated that the FE code employing this new contact search algorithm becomes more reliable, efficient and accurate for sheet metal forming sim ulation than conventional ones.
Stick-slip algorithm in a tangential contact force model for multi-body system dynamics
International Nuclear Information System (INIS)
Contact force of Multi-body dynamics (MBD) system can be classified two parts. First is a normal force and the other is a tangential force called friction force. And the friction force can be represented by two states such as stick and slip. The stick-slip phenomenon is simply described as a simple contact model which is a rigid body contacted on a sloped surface. If the calculated friction coefficient between the body and sloped surface is less than the static friction coefficient, the body should be stuck. If the calculated friction coefficient is greater than the static friction coefficient, the body will be sliding along the surface. The phenomenon is called as stick and slip state of friction, respectively. Usually many researchers and commercial MBD software used a coulomb friction force model which is defined with an only function of relative velocity. This kind of friction force model will be called a conventional friction force model in this paper. A big problem of the conventional model can not describe a stick state of friction phenomenon. In the case of conventional friction force model, the body will be sliding even though friction state is stick. Because, the relative velocity must have a non-zero value in order to generate the friction force. To solve this kind of problem, we propose a stick-slip friction force model including a spring like force. In the case of stick-slip friction force model, the body can be stuck on the sloped surface because the friction force will be a non-zero value, even though the relative velocity approaches zero. We defined a relative displacement variable called stiction deformation. In this paper, the stick-slip friction model is proposed and applied in the contact algorithm of MBD system. And then two friction models are compared with numerical examples. With the proposed stick-slip friction model, more realistic results are achieved
Chaotic algorithms: A numerical exploration of the dynamics of a stiff photoconductor model
International Nuclear Information System (INIS)
The photoconducting property of semiconductors leads, in general, to a very complex kinetics for the charge carriers due to the non-equilibrium processes involved. In a semi-conductor with one type of trap, the dynamics of the photo-conducting process are described by a set of ordinary coupled non-linear differential equations given by where n and p are the free electron and hole densities, and m the trapped electron density at time t. So far, there is no known closed solution for these set of non-linear differential equations, and therefore, numerical integration techniques have to be employed, as, for example, the standard procedure of the Runge-Kutta (RK) method. Now then, each one of the mechanisms of generation, recombination, and trapping has its own lifetime, which means that different time constants are to be expected in the time dependent behavior of the photocurrent. Thus, depending on the parameters of the model, the system may become stiff if the time scales between n, m, and p separate considerably. This situation may impose a considerable stress upon a fixed step numerical algorithm as the RK, which may produce then unreliable results, and other methods have to be considered. Therefore, the purpose of this note is to examine, for a critical range of parameters, the results of the numerical integration of the stiff system obtained by standard numerical schemes, such as the single-step fourth-order Runge-Kutta method and the multistep Gear method, the latter being appropriate for a rigid system of equations. 7 refs., 2 figs
Jou, Jonathan D; Jain, Swati; Georgiev, Ivelin S; Donald, Bruce R
2016-06-01
Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem. PMID:26744898
de Souza, Isaac D T; Silva, Sergio N; Teles, Rafael M; Fernandes, Marcelo A C
2014-01-01
The development of new embedded algorithms for automation and control of industrial equipment usually requires the use of real-time testing. However, the equipment required is often expensive, which means that such tests are often not viable. The objective of this work was therefore to develop an embedded platform for the distributed real-time simulation of dynamic systems. This platform, called the Real-Time Simulator for Dynamic Systems (RTSDS), could be applied in both industrial and academic environments. In industrial applications, the RTSDS could be used to optimize embedded control algorithms. In the academic sphere, it could be used to support research into new embedded solutions for automation and control and could also be used as a tool to assist in undergraduate and postgraduate teaching related to the development of projects concerning on-board control systems. PMID:25320906
Acharya, Ayan; Konar, Amit; Janarthanan, Ramadoss
2008-01-01
Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable p...
Platform for Real-Time Simulation of Dynamic Systems and Hardware-in-the-Loop for Control Algorithms
Directory of Open Access Journals (Sweden)
Isaac D. T. de Souza
2014-10-01
Full Text Available The development of new embedded algorithms for automation and control of industrial equipment usually requires the use of real-time testing. However, the equipment required is often expensive, which means that such tests are often not viable. The objective of this work was therefore to develop an embedded platform for the distributed real-time simulation of dynamic systems. This platform, called the Real-Time Simulator for Dynamic Systems (RTSDS, could be applied in both industrial and academic environments. In industrial applications, the RTSDS could be used to optimize embeddedcontrol algorithms. In the academic sphere, it could be used to support research into newembedded solutions for automation and control and could also be used as a tool to assist in undergraduate and postgraduate teaching related to the development of projects concerningon-board control systems.
Mántaras, Daniel A.; Luque, Pablo; Nava, Javier A.; Riva, Paolo; Girón, Pablo; Compadre, Diego; Ferran, Jordi
2013-10-01
A key factor to understand the vehicle dynamic behaviour is to know as accurately as possible the interaction that occurs between the tyre and the road, since it depends on many factors that influence the dynamic response of the vehicle. This paper aims to develop a methodology in order to characterise the tyre-road behaviour, applying it to obtain the tyre-road grip coefficient. This methodology is based on the use of dynamic simulation of a virtual model, integrated into a genetic algorithm that identifies the tyre-road friction coefficient in order to adjust the response obtained by simulation to real data. The numerical model was developed in collaboration with SEAT Technical Centre and it was implemented in multibody dynamic simulation software Adams®, from MSC®.
Directory of Open Access Journals (Sweden)
mahmood Joorabian
2013-02-01
Full Text Available This study presents a dynamically adapted bacterial foraging algorithm (BFA to solve the economic dispatch (ED problem considering valve-point effects and power losses. In addition, wind power is included in the problem formulation. Renewable sources and wind energy in particular have recently been getting more interest because of various environmental and economical considerations. The original BFA is a recently developed evolutionary optimisation technique inspired by the foraging behaviour of the Escherichia coli bacteria. The basic BFA has been successfully implemented to solve small optimisation problems; however, it shows poor convergence characteristics for larger constrained problems. To deal with the complexity and highdimensioned search space of the ED problem, essential modifications are introduced to enhance the performance of the algorithm. The basic chemotactic step is adjusted to have a dynamic non-linear behavior in order to improve balancing the global and local search. The stopping criterion of the original BFA is also modified to be adaptive depending on the solution improvement instead of the preset maximum number of iterations. The proposed algorithm is validated using several test systems. The results are compared with those obtained by other algorithms previously applied to solve the problem considering valve-point effects and power losses in addition to wind power.
Rhode, Kawal; Lambrou, Tryphon; Hawkes, David J.; Hamilton, George; Seifalian, Alexander M.
2000-06-01
We have developed a weighted optical flow algorithm for the extraction of instantaneous blood velocity from dynamic digital x-ray images of blood vessels. We have carried out in- vitro validation of this technique. A pulsatile physiological blood flow circuit was constructed using sections of silicone tubing to simulate blood vessels with whole blood as the fluid. Instantaneous recording of flow from an electromagnetic flow meter (EMF) provided the gold standard measurement. Biplanar dynamic digital x-ray images of the blood vessel with injection of contrast medium were acquired at 25 fps using a PC frame capture card. Imaging of a Perspex calibration cube allowed 3D reconstruction of the vessel and determination of true dimensions. Blood flow waveforms were calculated off-line on a Sun workstation using the new algorithm. The correlation coefficient between instantaneous blood flow values obtained from the EMF and the x-ray method was r equals 0.871, n equals 1184, p less than 0.0001. The correlation coefficient for average blood flow was r equals 0.898, n equals 16, p less than 0.001. We have successfully demonstrated that our new algorithm can measure pulsatile blood flow in a vessel phantom. We aim to use this algorithm to measure blood flow clinically in patients undergoing vascular interventional procedures.
Ramos, A; Talaia, P; Queirós de Melo, F J
2016-01-01
The main goal of this work was to develop an approached model to study dynamic behavior and prediction of the stress distribution in an in vitro Charnley cemented hip arthroplasty. An alternative version of the described pseudo-dynamic procedure is proposed by using the time integration Newmark algorithm. An internal restoring force vector is numerically calculated from the displacement, velocity, and acceleration vectors. A numerical model of hip replacement was developed to analyze the deformation of a dynamically stressed structure for all time steps. The experimental measurement of resulting internal forces generated in the structure (internal restoring force vector) is the second fundamental step of the pseudo-dynamic procedure. These data (as a feedback) are used by the time integration algorithm, which allows updating of the structure's shape for the next displacement, velocity, and acceleration vectors. In the field of Biomechanics, the potentialities of this method contribute to the determination of a dynamically equivalent in vitro stress field of a cemented hip prosthesis; implant fitted in patients with a normal mobility or practice sports. Consequences of the stress distribution in the implant zone that underwent cyclic fatigue loads were also discussed by using a finite element model. Application of this method in Biomechanics appears as a useful tool in the approximate stress field characterization of the peak stress state. Results show a peak value around two times the static situation, more for making possible the prediction of future damage and a programed clinical examination in patients using hip prosthesis. PMID:25483822
Pioldi, Fabio; Ferrari, Rosalba; Rizzi, Egidio
2016-02-01
The present paper deals with the seismic modal dynamic identification of frame structures by a refined Frequency Domain Decomposition (rFDD) algorithm, autonomously formulated and implemented within MATLAB. First, the output-only identification technique is outlined analytically and then employed to characterize all modal properties. Synthetic response signals generated prior to the dynamic identification are adopted as input channels, in view of assessing a necessary condition for the procedure's efficiency. Initially, the algorithm is verified on canonical input from random excitation. Then, modal identification has been attempted successfully at given seismic input, taken as base excitation, including both strong motion data and single and multiple input ground motions. Rather than different attempts investigating the role of seismic response signals in the Time Domain, this paper considers the identification analysis in the Frequency Domain. Results turn-out very much consistent with the target values, with quite limited errors in the modal estimates, including for the damping ratios, ranging from values in the order of 1% to 10%. Either seismic excitation and high values of damping, resulting critical also in case of well-spaced modes, shall not fulfill traditional FFD assumptions: this shows the consistency of the developed algorithm. Through original strategies and arrangements, the paper shows that a comprehensive rFDD modal dynamic identification of frames at seismic input is feasible, also at concomitant high damping.
Yang, Yun; Kimura, Shinji
This paper proposes an efficient design algorithm for power/ground (P/G) network synthesis with dynamic signal consideration, which is mainly caused by Ldi/dt noise and Cdv/dt decoupling capacitance (DECAP) current in the distribution network. To deal with the nonlinear global optimization under synthesis constraints directly, the genetic algorithm (GA) is introduced. The proposed GA-based synthesis method can avoid the linear transformation loss and the restraint condition complexity in current SLP, SQP, ICG, and random-walk methods. In the proposed Hybrid Grid Synthesis algorithm, the dynamic signal is simulated in the gene disturbance process, and Trapezoidal Modified Euler (TME) method is introduced to realize the precise dynamic time step process. We also use a hybrid-SLP method to reduce the genetic execute time and increase the network synthesis efficiency. Experimental results on given power distribution network show the reduction on layout area and execution time compared with current P/G network synthesis methods.
Guo, Jie; Zhu, Chang`an
2016-01-01
The development of optics and computer technologies enables the application of the vision-based technique that uses digital cameras to the displacement measurement of large-scale structures. Compared with traditional contact measurements, vision-based technique allows for remote measurement, has a non-intrusive characteristic, and does not necessitate mass introduction. In this study, a high-speed camera system is developed to complete the displacement measurement in real time. The system consists of a high-speed camera and a notebook computer. The high-speed camera can capture images at a speed of hundreds of frames per second. To process the captured images in computer, the Lucas-Kanade template tracking algorithm in the field of computer vision is introduced. Additionally, a modified inverse compositional algorithm is proposed to reduce the computing time of the original algorithm and improve the efficiency further. The modified algorithm can rapidly accomplish one displacement extraction within 1 ms without having to install any pre-designed target panel onto the structures in advance. The accuracy and the efficiency of the system in the remote measurement of dynamic displacement are demonstrated in the experiments on motion platform and sound barrier on suspension viaduct. Experimental results show that the proposed algorithm can extract accurate displacement signal and accomplish the vibration measurement of large-scale structures.
Dynamic adaptation cuckoo search algorithm%动态适应布谷鸟搜索算法
Institute of Scientific and Technical Information of China (English)
张永韡; 汪镭; 吴启迪
2014-01-01
介绍一种新的生物启发算法--布谷鸟搜索(CS)及其相关的Lévy飞行搜索机制。为了进一步提高算法的适应性，将反馈引入算法框架，建立了CS算法参数的闭环控制系统。将Rechenberg的1/5法则作为进化的评价指标，引入学习因子平衡种群的多样性和集中性，提出动态适应布谷鸟算法(DACS)。最后，通过数值实验验证了所提出算法的有效性。%A novel bio-inspired algorithm, cuckoo search(CS), is introduced along with the related Lévy flight mechanism. In order to improve the adaptation of this algorithm, a feedback control scheme of algorithm parameters is adopted in CS. By utilizing Rechenberg’s 1/5 criteria to evaluate evolution process, and introducing the learning factor, the diversification and intensification of population are well balanced. The dynamic adaptation cuckoo search(DACS) algorithm is proposed. Finally, numerical experiment results show the effectiveness of the proposed algorithm.
A Dynamic Resource Synchronizer Mutual Exclusion Algorithm for Wired/Wireless Distributed Systems
Directory of Open Access Journals (Sweden)
Ahmed Sharieh
2008-01-01
Full Text Available A mobile host has small memory, relatively slow processor, low power batteries, and communicate over low bandwidth wireless communication links. Existing mutual exclusion algorithms for distributed systems are not enough for mobile systems because of several limitations. In this study, a mutual exclusion algorithm that is more suitable for mobile computer systems is developed. The algorithm tends to minimize the number of messages needed to be transmitted in the system, by reducing the number of sites involved in the mutual exclusion decision, and reducing the amount of storage needed at different sites of the system.
The Dynamics of Entanglement in the Adiabatic Search and Deutsch Algorithms
Choy, K; Ahrensmeier, D; Carrington, M E; Fugleberg, T; Kobes, R; Kunstatter, G
2006-01-01
The goal of this paper is to study the effect of entanglement on the running time of a quantum computation. Adiabatic quantum computation is suited to this kind of study, since it allows us to explicitly calculate the time evolution of the entanglement throughout the calculation. On the other hand however, the adiabatic formalism makes it impossible to study the roles of entanglement and fidelity separately, which means that results have to be interpreted carefully. We study two algorithms: the search algorithm and the Deutsch-Jozsa algorithm. We find some evidence that entanglement can be considered a resource in quantum computation.
Directory of Open Access Journals (Sweden)
Farzin Piltan
2011-12-01
Full Text Available Refer to the research, design a novel SISO adaptive fuzzy sliding algorithm inverse dynamic like method (NAIDLC and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in inverse dynamic controller, fuzzy logic controller and self tuning fuzzy sliding method, the output has improved. The main objective in this research is analyses and design of the adaptive robust controller based on artificial intelligence and nonlinear control. Robot manipulator is nonlinear, time variant and a number of parameters are uncertain, so design the best controller for this plant is the main target. Although inverse dynamic controller have acceptable performance with known dynamic parameters but regarding to uncertainty, this controller\\'s output has fairly fluctuations. In order to solve this problem this research is focoused on two methodology the first one is design a fuzzy inference system as a estimate nonlinear part of main controller but this method caused to high computation load in fuzzy rule base and the second method is focused on design novel adaptive method to reduce the computation in fuzzy algorithm.
Halder, Amiya
2012-01-01
This paper proposes a Genetic Algorithm based segmentation method that can automatically segment gray-scale images. The proposed method mainly consists of spatial unsupervised grayscale image segmentation that divides an image into regions. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, Fuzzy Hopfield Neural Network (FHNN) clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the optimum number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.
C-Strategy: A Dynamic Adaptive Strategy for the CLONALG Algorithm
Riff, Maria Cristina; Montero, Elizabeth; Neveu, Bertrand
2010-01-01
The control of parameters during the execution of bioinspired algorithms is an open research area. In this paper, we propose a new parameter control strategy for the immune algorithm CLONALG. Our approach is based on reinforcement learning ideas. We focus our attention on controlling the number of clones. Our approach provides an efficient and low cost adaptive technique for parameter control. We use instances of the Travelling Salesman Problem. The results obtained are very encouraging.
A Routing Algorithm Based on Dynamic Forecast of Vehicle Speed and Position in VANET
Shukui Zhang; Haojing Huang
2013-01-01
Considering city road environment as the background, by researching GPSR greedy algorithm and the movement characteristics of vehicle nodes in VANET, this paper proposes the concept of circle changing trends angle in vehicle speed fluctuation curve and the movement domain and designs an SWF routing algorithm based on the vehicle speed point forecasted and the changing trends time computation. Simulation experiments are carried out through using a combination of NS-2 and VanetMobiSim software....
International Nuclear Information System (INIS)
July 1999 The Netherlands Electricity Regulatory Service (DtE) published an Information and Consultation Document on the subject of 'Price Cap Regulation in the Dutch Electricity Sector'. By means of price cap regulation tariffs are determined such that businesses are stimulated continuously to organize their total processes and operation as efficient as possible. In the consultation document a large number of questions with respect to the future organization and planning of the system of economic regulation of the electricity sector in the Netherlands can be found. Many reactions and answers were received, compiled and analyzed. The results are presented in the main report, which forms the framework for the DtE to shape the economic regulation of the Dutch electricity sector. In this background document attention is paid to a method to determine the Regulatory Asset Base (RAB)
Institute of Scientific and Technical Information of China (English)
FANG Ming-chung; LEE Zi-yi
2013-01-01
This paper develops a nonlinear mathematical model to simulate the dynamic motion behavior of the barge equipped with the portable outboard Dynamic Positioning (DP) system in short-crested waves.The self-tuning Proportional-Derivative (PD) controller based on the neural network algorithm is applied to control the thrusters for optimal adjustment of the barge position in waves.In addition to the wave,the current,the wind and the nonlinear drift force are also considered in the calculations.The time domain simulations for the six-degree-of-freedom motions of the barge with the DP system are solved by the 4th order Runge-Kutta method which can compromise the efficiency and the accuracy of the simulations.The technique of the portable alternative DP system developed here can serve as a practical tool to assist those ships without being equipped with the DP facility while the dynamic positioning missions are needed.
Li, Zhi-Hui; Ma, Qiang; Cui, Junzhi
2016-06-01
The new second-order two-scale (SOTS) finite element algorithm is developed for the dynamic thermo-mechanical coupling problems in axisymmetric and spherical symmetric structures made of composite materials. The axisymmetric structure considered is periodic in both radial and axial directions and homogeneous in circumferential direction. The spherical symmetric structure is only periodic in radial direction. The dynamic thermo-mechanical coupling model is presented and the equivalent compact form is derived. Then, the cell problems, effective material coefficients and the homogenized thermo-mechanical coupling problem are obtained successively by the second-order asymptotic expansion of the temperature increment and displacement. The homogenized material obtained is manifested with the anisotropic property in the circumferential direction. The explicit expressions of the homogenized coefficients in the plane axisymmetric and spherical symmetric cases are given and both the derivation of the analytical solutions of the cell functions and the quasi-static thermoelasticity problems are discussed. Based on the SOTS method, the corresponding finite-element procedure is presented and the unconditionally stable implicit algorithm is established. Some numerical examples are solved and the mutual interaction between the temperature and displacement field is studied under the condition of structural vibration. The computational results demonstrate that the second-order asymptotic analysis finite-element algorithm is feasible and effective in simulating and predicting the dynamic thermo-mechanical behaviors of the composite materials with small periodic configurations in axisymmetric and spherical symmetric structures. This may provide a vital computational tool for analyzing composite material internal temperature distribution and structural deformation induced by the dynamic thermo-mechanical coupling response under strong aerothermodynamic environment.
International Nuclear Information System (INIS)
Purpose: In dynamic multileaf collimator (MLC) motion tracking with complex intensity-modulated radiation therapy (IMRT) fields, target motion perpendicular to the MLC leaf travel direction can cause beam holds, which increase beam delivery time by up to a factor of 4. As a means to balance delivery efficiency and accuracy, a moving average algorithm was incorporated into a dynamic MLC motion tracking system (i.e., moving average tracking) to account for target motion perpendicular to the MLC leaf travel direction. The experimental investigation of the moving average algorithm compared with real-time tracking and no compensation beam delivery is described. Methods: The properties of the moving average algorithm were measured and compared with those of real-time tracking (dynamic MLC motion tracking accounting for both target motion parallel and perpendicular to the leaf travel direction) and no compensation beam delivery. The algorithm was investigated using a synthetic motion trace with a baseline drift and four patient-measured 3D tumor motion traces representing regular and irregular motions with varying baseline drifts. Each motion trace was reproduced by a moving platform. The delivery efficiency, geometric accuracy, and dosimetric accuracy were evaluated for conformal, step-and-shoot IMRT, and dynamic sliding window IMRT treatment plans using the synthetic and patient motion traces. The dosimetric accuracy was quantified via a γ-test with a 3%/3 mm criterion. Results: The delivery efficiency ranged from 89 to 100% for moving average tracking, 26%-100% for real-time tracking, and 100% (by definition) for no compensation. The root-mean-square geometric error ranged from 3.2 to 4.0 mm for moving average tracking, 0.7-1.1 mm for real-time tracking, and 3.7-7.2 mm for no compensation. The percentage of dosimetric points failing the γ-test ranged from 4 to 30% for moving average tracking, 0%-23% for real-time tracking, and 10%-47% for no compensation. Conclusions
Li, Shuo; Peng, Jun; Liu, Weirong; Zhu, Zhengfa; Lin, Kuo-Chi
2013-01-01
Recent research has indicated that using the mobility of the actuator in wireless sensor and actuator networks (WSANs) to achieve mobile data collection can greatly increase the sensor network lifetime. However, mobile data collection may result in unacceptable collection delays in the network if the path of the actuator is too long. Because real-time network applications require meeting data collection delay constraints, planning the path of the actuator is a very important issue to balance the prolongation of the network lifetime and the reduction of the data collection delay. In this paper, a multi-hop routing mobile data collection algorithm is proposed based on dynamic polling point selection with delay constraints to address this issue. The algorithm can actively update the selection of the actuator's polling points according to the sensor nodes' residual energies and their locations while also considering the collection delay constraint. It also dynamically constructs the multi-hop routing trees rooted by these polling points to balance the sensor node energy consumption and the extension of the network lifetime. The effectiveness of the algorithm is validated by simulation. PMID:24451455
Directory of Open Access Journals (Sweden)
2015-03-01
Full Text Available The dynamic economic load dispatch is one of the main problems of power systems generation and operation. The objective is to schedule power generation for units over a certain period of time, while satisfying operating constraints and load demand in each interval. Wind farms, as renewable energy resources are playing an increasing role in electricity generation. In this paper, a computational framework is presented to solve the dynamic economic emission dispatch problem with inclusion of wind farms considering their associated constraints. An optimization algorithm called modified co-evolutionary particle swarm optimization (MCPSO is proposed to solve the problem. In the proposed algorithm, two kinds of swarms evolve interactively where one of them is used to calculate the penalty factors (constraints handling and the other is used for searching good solutions (optimization process. In addition, some modifications such as using an inertia weight that decreases linearly during the simulation are made to improve the performance of the algorithm. Finally, the validity and superiority of the proposed method are demonstrated by simulation results on a modiﬁed IEEE benchmark system including six thermal units and two wind farms.
Shivakumar, R
2010-01-01
The Application of Bio Inspired Algorithms to complicated Power System Stability Problems has recently attracted the researchers in the field of Artificial Intelligence. Low frequency oscillations after a disturbance in a Power system, if not sufficiently damped, can drive the system unstable. This paper provides a systematic procedure to damp the low frequency oscillations based on Bio Inspired Genetic (GA) and Particle Swarm Optimization (PSO) algorithms. The proposed controller design is based on formulating a System Damping ratio enhancement based Optimization criterion to compute the optimal controller parameters for better stability. The Novel and contrasting feature of this work is the mathematical modeling and simulation of the Synchronous generator model including the Steam Governor Turbine (GT) dynamics. To show the robustness of the proposed controller, Non linear Time domain simulations have been carried out under various system operating conditions. Also, a detailed Comparative study has been don...
Yu, Xiao; Yuan, Ximei; Dong, Enzeng; Ríha, Kamil
2016-07-01
Banded blurred Infrared image segmentation is a challenging topic since banded blurred infrared images are characterized by high noise, low contrast, and weak edges. Based on the interconnected and networked collaborative mechanism between innate immune factors and adaptive immune factors, this paper presents an immune dynamical algorithm with two-dimensional minimum distance immune field to solve this puzzle. Firstly, using the original characteristics as antigen surface molecular patterns, innate immune factors in the first layer of immune dynamical network extract banded blurred regions from the whole banded blurred infrared image region. Secondly, innate immune factors in the second layer of immune dynamical network extract new characteristics to design the complex of major histocompatibility complex (MHC) and antigen peptide. Lastly, adaptive immune factors in the last layer will extract object and background antigens from all the banded blurred image antigens, and design the optimal immune field of every adaptive immune factors. Experimental results on hand trace infrared images verified that the proposed algorithm could efficiently extract targets from images, and produce better extraction accuracy.
Directory of Open Access Journals (Sweden)
Xiaoyan Lei
2016-01-01
Full Text Available A model for dynamic analysis of the vehicle-track nonlinear coupling system is established by the finite element method. The whole system is divided into two subsystems: the vehicle subsystem and the track subsystem. Coupling of the two subsystems is achieved by equilibrium conditions for wheel-to-rail nonlinear contact forces and geometrical compatibility conditions. To solve the nonlinear dynamics equations for the vehicle-track coupling system, a cross iteration algorithm and a relaxation technique are presented. Examples of vibration analysis of the vehicle and slab track coupling system induced by China’s high speed train CRH3 are given. In the computation, the influences of linear and nonlinear wheel-to-rail contact models and different train speeds are considered. It is found that the cross iteration algorithm and the relaxation technique have the following advantages: simple programming; fast convergence; shorter computation time; and greater accuracy. The analyzed dynamic responses for the vehicle and the track with the wheel-to-rail linear contact model are greater than those with the wheel-to-rail nonlinear contact model, where the increasing range of the displacement and the acceleration is about 10%, and the increasing range of the wheel-to-rail contact force is less than 5%.
International Nuclear Information System (INIS)
Highlights: • Dynamic load economic dispatch with wind power (DLEDW) model is established. • Markov chains combined with scenario analysis method are used to predict wind power. • Chance constrained technique is used to simulate the impacts of wind forecast error. • Improved artificial physical optimization algorithm is proposed to solve DLEDW. • Heuristic search strategies are applied to handle the constraints of DLEDW. - Abstract: Wind power, a kind of promising renewable energy resource, has recently been getting more attractive because of various environmental and economic considerations. But the penetration of wind power with its fluctuation nature has made the operation of power system more intractable. To coordinate the reliability and operation cost, this paper established a stochastic model of dynamic load economic dispatch with wind integration (DLEDW). In this model, constraints such as ramping up/down capacity, prohibited operating zone are considered and effects of valve-point are taken into account. Markov chains combined with scenario analysis method is used to generate predictive values of wind power and chance constrained programming (CCP) is applied to simulate the impacts of wind power fluctuation on system operation. An improved artificial physical optimization algorithm is presented to solve the DLEDW problem. Heuristic strategies based on the priority list and stochastic simulation techniques are proposed to handle the constraints. In addition, a local chaotic mutation strategy is applied to overcome the disadvantage of premature convergence of artificial physical optimization algorithm. Two test systems with and without wind power integration are used to verify the feasibility and effectiveness of the proposed method and the results are compared with those of gravitational search algorithm, particle swarm optimization and standard artificial physical optimization. The simulation results demonstrate that the proposed method has a
Energy Technology Data Exchange (ETDEWEB)
Silveira, L.M.; Kamon, M.; Elfadel, I.; White, J. [Massachusetts Inst. of Technology, Cambridge, MA (United States)
1996-12-31
Model order reduction based on Krylov subspace iterative methods has recently emerged as a major tool for compressing the number of states in linear models used for simulating very large physical systems (VLSI circuits, electromagnetic interactions). There are currently two main methods for accomplishing such a compression: one is based on the nonsymmetric look-ahead Lanczos algorithm that gives a numerically stable procedure for finding Pade approximations, while the other is based on a less well characterized Arnoldi algorithm. In this paper, we show that for certain classes of generalized state-space systems, the reduced-order models produced by a coordinate-transformed Arnoldi algorithm inherit the stability of the original system. Complete Proofs of our results will be given in the final paper.
Parallel algorithm of trigonometric collocation method in nonlinear dynamics of rotors
Directory of Open Access Journals (Sweden)
Musil T.
2007-11-01
Full Text Available A parallel algorithm of a numeric procedure based on a method of trigonometric collocation is presented for investigating an unbalance response of a rotor supported by journal bearings. After a condensation process the trigonometric collocation method results in a set of nonlinear algebraic equations which is solved by the Newton-Raphson method. The order of the set is proportional to the number of nonlinear bearing coordinates and terms of the finite Fourier series. The algorithm, realized in the MATLAB parallel computing environment (DCT/DCE, uses message passing technique for interacting among processes on nodes of a parallel computer. This technique enables portability of the source code both on parallel computers with distributed and shared memory. Tests, made on a Beowulf cluster and a symmetric multiprocessor, have revealed very good speed-up and scalability of this algorithm.
A Bee Optimization Algorithm for Scheduling a Job Dynamically in Grid Environment
Directory of Open Access Journals (Sweden)
P. Rajeswari M. Prakash
2011-12-01
Full Text Available Grid computing is based on large scale resources sharing in a widely connected network. Grid scheduling is defined as the process of making scheduling decisions involving allocating jobs to resources over multiple administrative domains. Scheduling is the one of the key issues in the research. Matchmaking is a key aspect in the grid environment. Matching a job with available suitable resources has to satisfy certain constraints. Resource discovery is one of the key issues for job scheduling in the grid environment. The proposed Bee optimization algorithm is to analyze Quality of Service (QoS metrics such as service class, job type in the heterogeneous grid environment. QoS parameters play a major role in selecting grid resources and optimizing resources effectively and efficiently. The output of the proposed algorithm is compared with max-min and min-min algorithm.
Stable algorithm for event detection in event-driven particle dynamics: logical states
Strobl, Severin; Bannerman, Marcus N.; Pöschel, Thorsten
2016-07-01
Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.
Stable algorithm for event detection in event-driven particle dynamics: Logical states
Strobl, Severin; Poeschel, Thorsten
2015-01-01
Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.
Tang, Yu-Hang; Karniadakis, George Em
2014-11-01
We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10-30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases simulating Poiseuille flow and spontaneous vesicle formation. Computer benchmarks demonstrate the speedup of our implementation over the CPU implementation as well as strong and weak scalability. A large-scale simulation of spontaneous vesicle formation consisting of 128 million particles was conducted to further illustrate the practicality of our code in real-world applications. Catalogue identifier: AETN_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AETN_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 1 602 716 No. of bytes in distributed program, including test data, etc.: 26 489 166 Distribution format: tar.gz Programming language: C/C++, CUDA C/C++, MPI. Computer: Any computers having nVidia GPGPUs with compute capability 3.0. Operating system: Linux. Has the code been
Zheleznyakova, A. L.
2015-05-01
A new computational approach for automated triangulation of Computer-Aided Design (CAD) surface models, applicable to various CFD (Computational Fluid Dynamics) problems of practical interest is proposed. The complex shaped product configurations are represented by a set of Non-Uniform Rational B-Splines (NURBS) surface patches. The suggested technique is based on the molecular dynamics method. The main idea of the approach is that the mesh nodes are considered as similarly charged interacting particles which move within the region to be meshed under the influence of internal (such as particle-particle interaction forces) and external forces as well as optional additional forces. Moreover, the particles experience a medium resistance due to which the system comes to equilibrium within a relatively short period of time. The proposed 3D surface mesh generation algorithm uses a parametric NURBS representation as initial definition of the domain boundary. This method first distributes the interacting nodes into optimal locations in the parametric domain of the NURBS surface patch using molecular dynamics simulation. Then, the well-shaped triangles can be created after connecting the nodes by Delaunay triangulation. Finally, the mapping from parametric space to 3D physical space is performed. Since the presented interactive algorithm allows to control the distance between a pair of nodes depending on the curvature of the NURBS surface, the method generates high quality triangular mesh. The algorithm enables to produce uniform mesh, as well as anisotropic adaptive mesh with refinement in the large gradient regions. The mesh generation approach has the abilities to preserve the representation accuracy of the input geometry model, create a close relationship between geometry modeling and grid generation process, be automated to a large degree. Some examples are considered in order to illustrate the method's ability to generate a surface mesh for a complicated CAD model.
Matthews, A P; Garenne, Michel
2013-01-01
The matching algorithm in a dynamic marriage market model is described in this first of two companion papers. Iterative Proportional Fitting is used to find a marriage function (an age distribution of new marriages for both sexes), in a stable reference population, that is consistent with the one-sex age distributions of new marriages, and includes age preference. The one-sex age distributions (which are the marginals of the two-sex distribution) are based on the Picrate model, and age prefer...
Jiang, Xiaoming; Van den Broek, Wouter; Koch, Christoph T
2016-04-01
Inverse dynamical photon scattering (IDPS), an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy, is introduced. Because the inverse problem entails numerical minimization of an explicit error metric, it becomes possible to freely choose a more robust metric, to introduce regularization of the solution, and to retrieve unknown experimental settings or microscope values, while the starting guess is simply set to zero. The regularization is accomplished through an alternate directions augmented Lagrangian approach, implemented on a graphics processing unit. These improvements are demonstrated on open source experimental data, retrieving three-dimensional amplitude and phase for a thick specimen. PMID:27136994
Sadeghi, Mehdi; Parto, Sahar; Arab, Shahriar; Ranjbar, Bijan
2005-06-20
We have used a statistical approach for protein secondary structure prediction based on information theory and simultaneously taking into consideration pairwise residue types and conformational states. Since the prediction of residue secondary structure by one residue window sliding make ambiguity in state prediction, we used a dynamic programming algorithm to find the path with maximum score. A score system for residue pairs in particular conformations is derived for adjacent neighbors up to ten residue apart in sequence. The three state overall per-residue accuracy, Q3, of this method in a jackknife test with dataset created from PDBSELECT is more than 70%. PMID:15936021
Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.
2015-01-01
This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.
Energy Efficient Routing Algorithms in Dynamic Optical Core Networks with Dual Energy Sources
DEFF Research Database (Denmark)
Wang, Jiayuan; Fagertun, Anna Manolova; Ruepp, Sarah Renée; Dittmann, Lars
This paper proposes new energy efficient routing algorithms in optical core networks, with the application of solar energy sources and bundled links. A comprehensive solar energy model is described in the proposed network scenarios. Network performance in energy savings, connection blocking...
DEFF Research Database (Denmark)
Neumann, Frank; Witt, Carsten
combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very...
A phi-competitive algorithm for collecting items with increasing weights from a dynamic queue
Czech Academy of Sciences Publication Activity Database
Bienkowski, M.; Chrobak, M.; Dürr, Ch.; Hurand, M.; Jeż, A.; Jeż, Łukasz; Stachowiak, G.
2013-01-01
Roč. 475, 4 March (2013), s. 92-102. ISSN 0304-3975 Institutional support: RVO:67985840 Keywords : online algorithms * competitive analysis * buffer management Subject RIV: BA - General Mathematics Impact factor: 0.516, year: 2013 http://www.sciencedirect.com/science/article/pii/S0304397513000121
An improved bi-level algorithm for partitioning dynamic grid hierarchies.
Energy Technology Data Exchange (ETDEWEB)
Deiterding, Ralf (California Institute of Technology, Pasadena, CA); Johansson, Henrik (Uppsala University, Uppsala, Sweden); Steensland, Johan; Ray, Jaideep
2006-05-01
Structured adaptive mesh refinement methods are being widely used for computer simulations of various physical phenomena. Parallel implementations potentially offer realistic simulations of complex three-dimensional applications. But achieving good scalability for large-scale applications is non-trivial. Performance is limited by the partitioner's ability to efficiently use the underlying parallel computer's resources. Designed on sound SAMR principles, Nature+Fable is a hybrid, dedicated SAMR partitioning tool that brings together the advantages of both domain-based and patch-based techniques while avoiding their drawbacks. But the original bi-level partitioning approach in Nature+Fable is insufficient as it for realistic applications regards frequently occurring bi-levels as ''impossible'' and fails. This document describes an improved bi-level partitioning algorithm that successfully copes with all possible bi-levels. The improved algorithm uses the original approach side-by-side with a new, complementing approach. By using a new, customized classification method, the improved algorithm switches automatically between the two approaches. This document describes the algorithms, discusses implementation issues, and presents experimental results. The improved version of Nature+Fable was found to be able to handle realistic applications and also to generate less imbalances, similar box count, but more communication as compared to the native, domain-based partitioner in the SAMR framework AMROC.
AN EFFICIENT, BOX SHAPE INDEPENDENT NONBONDED FORCE AND VIRIAL ALGORITHM FOR MOLECULAR-DYNAMICS
Bekker, H.; Dijkstra, E.J; Renardus, M.K.R.; Berendsen, H.J.C.
1995-01-01
A notation is introduced and used to transform a conventional specification of the non-bonded force and virial algorithm in the case of periodic boundary conditions into an alternative specification. The implementation of the transformed specification is simpler and typically a factor of 1.5 faster
Synthesizing mixed H2/H-infinity dynamic controller using evolutionary algorithms
DEFF Research Database (Denmark)
Pedersen, Gerulf; Langballe, A.S.; Wisniewski, Rafal
2001-01-01
This paper covers the design of an Evolutionary Algorithm (EA), which should be able to synthesize a mixed H2/H-infinity. It will be shown how a system can be expressed as Matrix Inequalities (MI) and these will then be used in the design of the EA. The main objective is to examine whether a mixed...
An evolutionary Algorithm for Structural Subjected to Dynamic Loading With Random Excitation
International Nuclear Information System (INIS)
This paper presents an evolutionary algorithm for optimization of structures subjected to random excitation. The new iteration scheme in conjunction with multi-population genetic strategy, entropy-based searching technique with narrowing down space and the quasi-exactness penalty function is developed to ensure rapid and steady convergence. Numerical example shows that proposed method has good accuracy and efficiency
Synthesizing multi-objective H2/H-infinity dynamic controller using evolutionary algorithms
DEFF Research Database (Denmark)
Pedersen, Gerulf; Langballe, A.S.; Wisniewski, Rafal
This paper covers the design of an Evolutionary Algorithm (EA), which should be able to synthesize a mixed H2/H-infinity. It will be shown how a system can be expressed as Matrix Inequalities (MI) and these will then be used in the design of the EA. The main objective is to examine whether a mixed...
Optimization algorithms and weighting factors for analysis of dynamic PET studies
International Nuclear Information System (INIS)
Positron emission tomography (PET) pharmacokinetic analysis involves fitting of measured PET data to a PET pharmacokinetic model. The fitted parameters may, however, suffer from bias or be unrealistic, especially in the case of noisy data. There are many optimization algorithms, each having different characteristics. The purpose of the present study was to evaluate (1) the performance of different optimization algorithms and (2) the effects of using incorrect weighting factors during optimization in terms of both accuracy and reproducibility of fitted PET pharmacokinetic parameters. In this study, the performance of commonly used optimization algorithms (i.e. interior-reflective Newton methods) and a simulated annealing (SA) method was evaluated. This SA algorithm, known as basin hopping, was modified for the present application. In addition, optimization was performed using various weighting factors. Algorithms and effects of using incorrect weighting factors were studied using both simulated and clinical time-activity curves (TACs). Input data, taken from [15O]H2O, [11C]flumazenil and [11C](R)-PK11195 studies, were used to simulate time-activity curves at various variance levels (0-15% COV). Clinical evaluation was based on studies with the same three tracers. SA was able to produce accurate results without the need for selecting appropriate starting values for (kinetic) parameters, in contrast to the interior-reflective Newton method. The latter gave biased results unless it was modified to allow for a range of starting values for the different parameters. For patient studies, where large variability is expected, both SA and the extended Newton method provided accurate results. Simulations and clinical assessment showed similar results for the evaluation of different weighting models in that small to intermediate mismatches between data variance and weighting factors did not significantly affect the outcome of the fits. Large errors were observed only when the
Pallab Banerjee; Talat Zabin; ShwetaKumai; Pushpa Kumari
2015-01-01
Round Robin Scheduling algorithm is designed especially for Real Time Operating system (RTOS). It is a preemptive CPU scheduling algorithm which switches between the processes when static time Quantum expires .Existing Round Robin CPU scheduling algorithm cannot be implemented in real time operating system due to their high context switch rates, large waiting time, large response time, large turnaround time and less throughput . In this paper a new algorithm is presented called Best Performan...
Naeem, Huma; Hussain, Mukhtar; Khan, Shoab A
2009-01-01
This paper presents a novel two-stage flexible dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi-target air-borne threats. The algorithm provides flexibility and optimality by swapping between two objective functions, i.e. the preferential and subtractive defense strategies as and when required. To further enhance the solution quality, it outlines and divides the critical parameters used in Threat Evaluation and Weapon Assignment (TEWA) into three broad categories (Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution for a range of scenarios. Analysis part of this paper presents ...
International Nuclear Information System (INIS)
Dynamic X ray tomography is a new medical imaging modality. In conventional scanner, a static cross-section of the patient is reconstructed from a set of x-ray projection acquired from the rotation around the patient of a X ray source and a set of detectors. In dynamic tomography a set of successive cross sections is reconstructed enabling the imaging of naturally moving organs such as the heart or organs in dynamic interaction with exterior elements. This is the case for CAS (computer assisted surgery) where we would like to control under CT imaging the progression of surgical tools (example: peri-cardiac punction). The diffusion of a contrast product (ex in angiography), or of a marker in nuclear imaging can also be studied dynamically. Thus dynamic tomography is becoming a very promising tool for the diagnostic of disease, the study of their evolution but also for guiding surgeons during CAS. (author)
Directory of Open Access Journals (Sweden)
Ximing Wang
2015-04-01
Full Text Available To explore the problems associated with applying dynamic programming (DP in the energy management strategies of plug-in hybrid electric vehicles (PHEVs, a plug-in hybrid bus powertrain is introduced and its dynamic control model is constructed. The numerical issues, including the discretization resolution of the relevant variables and the boundary issue of their feasible regions, were considered when implementing DP to solve the optimal control problem of PHEVs. The tradeoff between the optimization accuracy when using the DP algorithm and the computational burden was systematically investigated. As a result of overcoming the numerical issues, the DP-based approach has the potential to improve the fuel-savings potential of PHEVs. The results from comparing the DP-based strategy and the traditional control strategy indicate that there is an approximately 20% improvement in fuel economy.
International Nuclear Information System (INIS)
We develop two classes of quasi-classical dynamics that are shown to conserve the initial quantum ensemble when used in combination with the Feynman-Kleinert approximation of the density operator. These dynamics are used to improve the Feynman-Kleinert implementation of the classical Wigner approximation for the evaluation of quantum time correlation functions known as Feynman-Kleinert linearized path-integral. As shown, both classes of dynamics are able to recover the exact classical and high temperature limits of the quantum time correlation function, while a subset is able to recover the exact harmonic limit. A comparison of the approximate quantum time correlation functions obtained from both classes of dynamics is made with the exact results for the challenging model problems of the quartic and double-well potentials. It is found that these dynamics provide a great improvement over the classical Wigner approximation, in which purely classical dynamics are used. In a special case, our first method becomes identical to centroid molecular dynamics
Directory of Open Access Journals (Sweden)
Mohameed Sarhan Al_Duais
2015-01-01
Full Text Available The drawback of the Back Propagation (BP algorithm is slow training and easily convergence to the local minimum and suffers from saturation training. To overcome those problems, we created a new dynamic function for each training rate and momentum term. In this study, we presented the (BPDRM algorithm, which training with dynamic training rate and momentum term. Also in this study, a new strategy is proposed, which consists of multiple steps to avoid inflation in the gross weight when adding each training rate and momentum term as a dynamic function. In this proposed strategy, fitting is done by making a relationship between the dynamic training rate and the dynamic momentum. As a result, this study placed an implicit dynamic momentum term in the dynamic training rate. This αdmic = f(1/&etadmic . This procedure kept the weights as moderate as possible (not to small or too large. The 2-dimensional XOR problem and buba data were used as benchmarks for testing the effects of the ‘new strategy’. All experiments were performed on Matlab software (2012a. From the experiment’s results, it is evident that the dynamic BPDRM algorithm provides a superior performance in terms of training and it provides faster training compared to the (BP algorithm at same limited error.
Wang, Yong; Ma, Xiaolei; Liu, Yong; Gong, Ke; Henrickson, Kristian C; Henricakson, Kristian C; Xu, Maozeng; Wang, Yinhai
2016-01-01
This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers' route choice behavior. PMID:26761209
Directory of Open Access Journals (Sweden)
R. Lakshmipathi
2010-01-01
Full Text Available The Application of Bio Inspired Algorithms to complicated Power System Stability Problems has recently attracted the researchers in the field of Artificial Intelligence. Low frequency oscillations after a disturbance in a Power system, if not sufficiently damped, can drive the system unstable. This paper provides a systematic procedure to damp the low frequency oscillations based on Bio Inspired Genetic (GA and Particle Swarm Optimization (PSO algorithms. The proposed controller design is based on formulating a System Damping ratio enhancement based Optimization criterion to compute the optimal controller parameters for better stability. The Novel and contrasting feature of this work is the mathematical modeling and simulation of the Synchronous generator model including the Steam Governor Turbine (GT dynamics. To show the robustness of the proposed controller, Non linear Time domain simulations have been carried out under various system operating conditions. Also, a detailed Comparative study has been done to show the superiority of the Bio inspired algorithm based controllers over the Conventional Lead lag controller.
International Nuclear Information System (INIS)
This paper presents an algorithm for the efficient numerical analysis and simulation of modest to heavily constrained multi-rigid-body dynamic systems. The algorithm can accommodate the spatial motion of general multi-rigid-body systems containing arbitrarily many closed loops in O(n + m)operations overall for systems containing n generalized coordinates, and m independent algebraic constraints. The presented approach does not suffer from the performance (speed) penalty encountered by most other of the so-called 'O(n)' state-space formulations, when dealing with constraints which tend to actually show O(n + m + nm + nm2+ m3) performance. Additionally, these latter formulations may require additional constraint violation stabilization procedures (e.g. Baumgarte's method, coordinate partitioning, etc.) which can contribute significant additional computation. The presented method suffers less from this difficulty because the loop closure constraints at both the velocity and acceleration level are directly embedded within the formulation. Due to these characteristics, the presented algorithm offers superior computing performance relative to other methods in situations involving both large n and m
Shin, Kwang Cheol; Park, Seung Bo; Jo, Geun Sik
2009-01-01
In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID) is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA) based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm. PMID:22399942
Directory of Open Access Journals (Sweden)
Kwang Cheol Shin
2009-02-01
Full Text Available In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm.
Shin, Kwang Cheol; Park, Seung Bo; Jo, Geun Sik
2009-01-01
In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID) is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA) based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm. PMID:22399942
Wang, Yong; Ma, Xiaolei; Liu, Yong; Gong, Ke; Henricakson, Kristian C.; Xu, Maozeng; Wang, Yinhai
2016-01-01
This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers’ route choice behavior. PMID:26761209
Dynamic Head Cluster Election Algorithm for Clustered Ad-Hoc Networks
Arwa Zabian; Ahmed Ibrahim; Fadi Al-Kalani
2008-01-01
In distributed system, the concept of clustering consists on dividing the geographical area covered by a set of nodes into small zones. In mobile network, the clustering mechanism varied due to the mobility of the nodes any time in any direction. That causes the partitioning of the network or the joining of nodes. Several existing centralized or globalized algorithm have been proposed for clustering technique, in a manner that no one node becomes isolated and no cluster becomes overloaded. A ...
Stable algorithm for event detection in event-driven particle dynamics: logical states
Strobl, Severin; Bannerman, Marcus N.; Poeschel, Thorsten
2015-01-01
Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and i...
Kanojia Sindhuben Babulal; Rajiv Ranjan Tewari
2010-01-01
The main focus of this article is to achieve prolonged network lifetime with overall energy efficiency in wireless sensor networks through controlled utilization of limited energy. Major percentage of energy in wireless sensor network is consumed during routing from source to destination, retransmission of data on packet loss. For improvement, cross layered algorithm is proposed for routing and retransmission scheme. Simulation and results shows that this approach can save the overall energy ...
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
Chein-I Chang; Chuin-Mu Wang; Yen-Chieh Ouyang; Cheng-Yi Yu
2010-01-01
Abstract Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used fo...
The Stochastic Evolution of a Protocell: The Gillespie Algorithm in a Dynamically Varying Volume
Carletti, T.; Filisetti, A.
2012-01-01
In the present paper we propose an improvement of the Gillespie algorithm allowing us to study the time evolution of an ensemble of chemical reactions occurring in a varying volume, whose growth is directly related to the amount of some specific molecules, belonging to the reactions set. This allows us to study the stochastic evolution of a protocell, whose volume increases because of the production of container molecules. Several protocells models are considered and compared with the determi...
The stochastic evolution of a protocell. The Gillespie algorithm in a dynamically varying volume
Carletti, Timoteo
2011-01-01
In the present paper we propose an improvement of the Gillespie algorithm allowing us to study the time evolution of an ensemble of chemical reactions occurring in a varying volume, whose growth is directly related to the amount of some specific molecules, belonging to the reactions set. This allows us to study the stochastic evolution of a protocell, whose volume increases because of the production of container molecules. Several protocells models are considered and compared with the deterministic models.
Institute of Scientific and Technical Information of China (English)
WANG Chong; LI Jun; JING Ning; WANG Jun; CHEN Hao
2011-01-01
Traditionally,heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites.However,the traditional heuristic strategies depend on the concrete tasks,which often affect the result's optimality.Noticing that the historical information of cooperative task planning will impact the latter planning results,we propose a hybrid learning algorithrn for dynamic multi-satellite task planning,which is based on the multi-agent reinforcement learning of policy iteration and the transfer learning.The reinforcement learning strategy of each satellite is described with neural networks.The policy neural network individuals with the best topological structure and weights are found by applying co-evolutionary search iteratively.To avoid the failure of the historical learning caused by the randomly occurring observation requests,a novel approach is proposed to balance the quality and efficiency of the task planning,which converts the historical leaming strategy to the current initial learning strategy by applying the transfer learning algorithm.The simulations and analysis show the feasibility and adaptability of the proposed approach especially for the situation with randomly occurring observation requests.
International Nuclear Information System (INIS)
This paper proposes a hybrid model based on genetic algorithm (GA) and system dynamics (SD) for coal production–environmental pollution load in China. GA has been utilized in the optimization of the parameters of the SD model to reduce implementation subjectivity. The chain of “Economic development–coal demand–coal production–environmental pollution load” of China in 2030 was predicted, and scenarios were analyzed. Results show that: (1) GA performs well in optimizing the parameters of the SD model objectively and in simulating the historical data; (2) The demand for coal energy continuously increases, although the coal intensity has actually decreased because of China's persistent economic development. Furthermore, instead of reaching a turning point by 2030, the environmental pollution load continuously increases each year even under the scenario where coal intensity decreased by 20% and investment in pollution abatement increased by 20%; (3) For abating the amount of “three types of wastes”, reducing the coal intensity is more effective than reducing the polluted production per tonne of coal and increasing investment in pollution control. - Highlights: ► We propos a GA-SD model for China's coal production-pollution prediction. ► Genetic algorithm (GA) can objectively and accurately optimize parameters of system dynamics (SD) model. ► Environmental pollution in China is projected to grow in our scenarios by 2030. ► The mechanism of reducing waste production per tonne of coal mining is more effective than others.
Osei-Kuffuor, Daniel; Fattebert, Jean-Luc
2014-03-01
We present a truly scalable First-Principles Molecular Dynamics algorithm with O(N) complexity and fully controllable accuracy, capable of simulating systems of sizes that were previously impossible with this degree of accuracy. By avoiding global communication, we have extended W. Kohn's condensed matter ``nearsightedness'' principle to a practical computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic wavefunctions are confined, and a cutoff beyond which the components of the overlap matrix can be omitted when computing selected elements of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to 100,000 atoms on 100,000 processors, with a wall-clock time of the order of one minute per molecular dynamics time step. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
Yu, Cheng-Yi; Ouyang, Yen-Chieh; Wang, Chuin-Mu; Chang, Chein-I.
2010-12-01
Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
Directory of Open Access Journals (Sweden)
Wang Chuin-Mu
2010-01-01
Full Text Available Abstract Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
Directory of Open Access Journals (Sweden)
Chein-I Chang
2010-01-01
Full Text Available Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.
International Nuclear Information System (INIS)
We apply the Feynman-Kleinert Quasi-Classical Wigner (FK-QCW) method developed in our previous work [Smith et al., J. Chem. Phys. 142, 244112 (2015)] for the determination of the dynamic structure factor of liquid para-hydrogen and ortho-deuterium at state points of (T = 20.0 K, n = 21.24 nm−3) and (T = 23.0 K, n = 24.61 nm−3), respectively. When applied to this challenging system, it is shown that this new FK-QCW method consistently reproduces the experimental dynamic structure factor reported by Smith et al. [J. Chem. Phys. 140, 034501 (2014)] for all momentum transfers considered. This shows that FK-QCW provides a substantial improvement over the Feynman-Kleinert linearized path-integral method, in which purely classical dynamics are used. Furthermore, for small momentum transfers, it is shown that FK-QCW provides nearly the same results as ring-polymer molecular dynamics (RPMD), thus suggesting that FK-QCW provides a potentially more appealing algorithm than RPMD since it is not formally limited to correlation functions involving linear operators
Fast Forward Dynamics Algorithm for Robot Arms Using Multi-Processing
Zomaya, A.Y.; A S Morris
1989-01-01
The computation of the direct dynamics problem (forward dynamics) plays a major role in the real-time computer modelling and simulation of robot manipulators. The efficient and computationally inexpensive solution of this problem facilitates the design of real-time robot simulators. In addition, it allows for a better understanding of the key elements affecting robot operations. This work proposes to solve this problem by employing parallel and distributed processing techniques. First, a para...
Energy Technology Data Exchange (ETDEWEB)
Williams, P.T.
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{sup 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.
Institute of Scientific and Technical Information of China (English)
马超
2012-01-01
针对遗传算法和Dijkstra算法在求解动态权值系统中最短路径时的性能问题,采用比较法,将两种算法应用在同一个实际游戏模型中,对其算法的稳定性、智能性、时间复杂度进行对比测试.游戏模型模拟了各种条件下的动态权值系统.为了使遗传算法更加可靠,通过优化其变异过程使得收敛速度更快,可靠性更高.实验数据表明,遗传算法在每张地图上的得分数以及算法所用时间普遍高于Dijkstra算法,从而得出遗传算法在求解动态权值系统中最短路径问题时稳定性和预期效果明显好于Dijkstra算法,但其时间复杂度较高的结论.%Used a comparative approach to compare the performance of the genetic algorithm with the Dijkstra algorithm when solve the shortest path problem in the dynamic weight system. Did an experiment in the actual model with these two algorithms in order to test their stability, intelligence and time complexity. The game model makes" many kinds of dynamic weight system. In order to make the genetic algorithm more reliable, the new algorithm gets a way to optimize the process of mutation to make the speed of the genetic algorithm faster and the reliability better. The experiment data shows that most data of the genetic algorithm is higher than the Dijkstra algorithm. The experiment makes a conclusion that the stability and expected result of the genetic algorithm is better than the Dijkstra algorithm in the dynamic weight system,but the time complexity of algorithm is higher than the Dijkstra algorithm.
Solano, Carlos J F; Pothula, Karunakar R; Prajapati, Jigneshkumar D; De Biase, Pablo M; Noskov, Sergei Yu; Kleinekathöfer, Ulrich
2016-05-10
All-atom molecular dynamics simulations have a long history of applications studying ion and substrate permeation across biological and artificial pores. While offering unprecedented insights into the underpinning transport processes, MD simulations are limited in time-scales and ability to simulate physiological membrane potentials or asymmetric salt solutions and require substantial computational power. While several approaches to circumvent all of these limitations were developed, Brownian dynamics simulations remain an attractive option to the field. The main limitation, however, is an apparent lack of protein flexibility important for the accurate description of permeation events. In the present contribution, we report an extension of the Brownian dynamics scheme which includes conformational dynamics. To achieve this goal, the dynamics of amino-acid residues was incorporated into the many-body potential of mean force and into the Langevin equations of motion. The developed software solution, called BROMOCEA, was applied to ion transport through OmpC as a test case. Compared to fully atomistic simulations, the results show a clear improvement in the ratio of permeating anions and cations. The present tests strongly indicate that pore flexibility can enhance permeation properties which will become even more important in future applications to substrate translocation. PMID:27088446
An Approach for State Observation in Dynamical Systems Based on the Twisting Algorithm
DEFF Research Database (Denmark)
Schmidt, Lasse; Andersen, Torben Ole; Pedersen, Henrik C.
2013-01-01
for the observation, not directly implying its applicability for state observation. However it is shown that in the discrete case this approach may be applied to obtain a real time derivative using an intermediate integral process. Furthermore, different types of gains for the algorithm are suggested...... and analyzed via Lyapunov arguments, and a special version of the observer is proposed specifically for hydraulic drives utilizing online available plant information. The proposed observer with different observer gains are subjected to simulation studies in order to evaluate the exact properties, and...
Institute of Scientific and Technical Information of China (English)
Huang; Ling; Liu; Yang; Xu; Jianfeng
2015-01-01
With the transformation of the Chinese economy from an extensive growth to intensive development, city development is also gradually turning from incremental construction to stock management. Community, as a basic unit of human settlements, is an important platform to build and improve the social governance capability. In 2013, Shiyoulu Jiedao Offi ce of Yuzhong District led the 1st urban community development planning, which was a milestone of Chongqing’s city regeneration and governance innovation. This paper focuses on two key issues: how to understand the community values and make the community development planning based on the above, and how to integrate with the local forces so that the community development planning can be integrated into the action plan. Combined with the practice of Minlecun Community Development Planning, using the concept of asset-based community development, a comprehensive survey is conducted on community assets(including three aspects of physical, human, and social capital), and a community comprehensive planning strategy is formulated which covers two parts: the optimization of community spaces and the upgrading of community governance. The paper explores the local-based community planning theories and methods from such aspects as value attitude, public participation, role transformation of urban planners, and others.
Special algorithm of enhancing underwater target-radiated dynamic line spectrum
Institute of Scientific and Technical Information of China (English)
Guo Yecai; Zhao Junwei; Chen Huawei
2005-01-01
Performance of traditional adaptive line enhancer (ALE) in suppressing Gaussian noise is low and can get worse at low input signal-to-noise ratio(SNR). For greatly overcoming these disadvantages, feature of fourth-order cumulant(FOC) different slices for quasi-stationary random process is analyzed, fourth order cumulant(FOC) different slice-based adaptive dynamic line enhancer is presented, and output SNR of the proposed enhancer is derived and bigger than that of the ALE via theoretical analysis. Simulation tests with the underwater moving target-radiated data have shown that the proposed enhancer outperforms the ALE in suppressing Gaussian noise and enhancing dynamic line spectrum feature.
Peralta, Richard C.; Forghani, Ali; Fayad, Hala
2014-04-01
Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.
The adaptive dynamic community detection algorithm based on the non-homogeneous random walking
Xin, Yu; Xie, Zhi-Qiang; Yang, Jing
2016-05-01
With the changing of the habit and custom, people's social activity tends to be changeable. It is required to have a community evolution analyzing method to mine the dynamic information in social network. For that, we design the random walking possibility function and the topology gain function to calculate the global influence matrix of the nodes. By the analysis of the global influence matrix, the clustering directions of the nodes can be obtained, thus the NRW (Non-Homogeneous Random Walk) method for detecting the static overlapping communities can be established. We design the ANRW (Adaptive Non-Homogeneous Random Walk) method via adapting the nodes impacted by the dynamic events based on the NRW. The ANRW combines the local community detection with dynamic adaptive adjustment to decrease the computational cost for ANRW. Furthermore, the ANRW treats the node as the calculating unity, thus the running manner of the ANRW is suitable to the parallel computing, which could meet the requirement of large dataset mining. Finally, by the experiment analysis, the efficiency of ANRW on dynamic community detection is verified.
Naeem, Huma; Masood, Asif; Hussain, Mukhtar; Shoab A. Khan
2009-01-01
This paper presents a novel two-stage flexible dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi-target air-borne threats. The algorithm provides flexibility and optimality by swapping between two objective functions, i.e. the preferential and subtractive defense strategies as and when required. To further enhance the solution quality, it outlines and divides the critical parameters used in Threat Evaluation and Weapon Assignment (T...
Maric, Tomislav; Marschall, Holger; Bothe, Dieter
2013-01-01
A new parallelized unsplit geometrical Volume of Fluid (VoF) algorithm with support for arbitrary unstructured meshes and dynamic local Adaptive Mesh Refinement (AMR), as well as for two and three dimensional computation is developed. The geometrical VoF algorithm supports arbitrary unstructured meshes in order to enable computations involving flow domains of arbitrary geometrical complexity. The implementation of the method is done within the framework of the OpenFOAM library for Computation...
Dynamic Head Cluster Election Algorithm for Clustered Ad-Hoc Networks
Directory of Open Access Journals (Sweden)
Arwa Zabian
2008-01-01
Full Text Available In distributed system, the concept of clustering consists on dividing the geographical area covered by a set of nodes into small zones. In mobile network, the clustering mechanism varied due to the mobility of the nodes any time in any direction. That causes the partitioning of the network or the joining of nodes. Several existing centralized or globalized algorithm have been proposed for clustering technique, in a manner that no one node becomes isolated and no cluster becomes overloaded. A particular node called head cluster or leader is elected, has the role to organize the distribution of nodes in clusters. We propose a distributed clustering and leader election mechanism for Ad-Hoc mobile networks, in which the leader is a mobile node. Our results show that, in the case of leader mobility the time needed to elect a new leader is smaller than the time needed a significant topological change in the network is happens.
Dynamic Consensus Algorithm based Distributed Voltage Harmonic Compensation in Islanded Microgrids
DEFF Research Database (Denmark)
Meng, Lexuan; Tang, Fen; Firoozabadi, Mehdi Savaghebi;
2015-01-01
control to realize voltage harmonic compensation and accurate current sharing in multi-bus islanded microgrids. Low order harmonic components are considered as examples in this paper. Harmonic current sharing is also realized among distributed generators by applying the proposed methods. Plug......In islanded microgrids, the existence of nonlinear electric loads may cause voltage distortion and affect the performance of power quality sensitive equipment. Thanks to the prevalent utilization of interfacing power electronic devices and information/communication technologies, distributed...... generators can be employed as compensators to enhance the power quality on consumer side. However, conventional centralized control is facing obstacles because of the distributed fashion of generation and consumption. Accordingly, this paper proposes a consensus algorithm based distributed hierarchical...
A parallel neural network training algorithm for control of discrete dynamical systems.
Energy Technology Data Exchange (ETDEWEB)
Gordillo, J. L.; Hanebutte, U. R.; Vitela, J. E.
1998-01-20
In this work we present a parallel neural network controller training code, that uses MPI, a portable message passing environment. A comprehensive performance analysis is reported which compares results of a performance model with actual measurements. The analysis is made for three different load assignment schemes: block distribution, strip mining and a sliding average bin packing (best-fit) algorithm. Such analysis is crucial since optimal load balance can not be achieved because the work load information is not available a priori. The speedup results obtained with the above schemes are compared with those corresponding to the bin packing load balance scheme with perfect load prediction based on a priori knowledge of the computing effort. Two multiprocessor platforms: a SGI/Cray Origin 2000 and a IBM SP have been utilized for this study. It is shown that for the best load balance scheme a parallel efficiency of over 50% for the entire computation is achieved by 17 processors of either parallel computers.
Dynamic Topology Re-Configuration in Multihop Cellular Networks Using Sequential Genetic Algorithm
Directory of Open Access Journals (Sweden)
B.Shantha Kumari
2014-10-01
Full Text Available Cellular communications has experienced explosive growth in the past two decades. Today millions of people around the world use cellular phones. Cellular phones allow a person to make or receive a call from almost anywhere. Likewise, a person is allowed to continue the phone conversation while on the move. Cellular communications is supported by an infrastructure called a cellular network, which integrates cellular phones into the public switched telephone network. The cellular network has gone through three generations.The first generation of cellular networks is analog in nature. To accommodate more cellular phone subscribers, digital TDMA (time division multiple access and CDMA (code division multiple access technologies are used in the second generation (2G to increase the network capacity. With digital technologies, digitized voice can be coded and encrypted. Therefore, the 2G cellular network is also more secure. The third generation (3G integrates cellular phones into the Internet world by providing highspeed packet-switching data transmission in addition to circuit-switching voice transmission. The 3G cellular networks have been deployed in some parts of Asia, Europe, and the United States since 2002 and will be widely deployed in the coming years. The high increase in traffic and data rate for future generations of mobile communication systems, with simultaneous requirement for reduced power consumption, makes Multihop Cellular Networks (MCNs an attractive technology. To exploit the potentials of MCNs a new network paradigm is proposed in this paper. In addition, a novel sequential genetic algorithm (SGA is proposed as a heuristic approximation to reconfigure the optimum relaying topology as the network traffic changes. Network coding is used to combine the uplink and downlink transmissions, and incorporate it into the optimum bidirectional relaying with ICI awareness. Numerical results have shown that the algorithms suggested in this
Dynamic video encryption algorithm for H.264/AVC based on a spatiotemporal chaos system.
Xu, Hui; Tong, Xiao-Jun; Zhang, Miao; Wang, Zhu; Li, Ling-Hao
2016-06-01
Video encryption schemes mostly employ the selective encryption method to encrypt parts of important and sensitive video information, aiming to ensure the real-time performance and encryption efficiency. The classic block cipher is not applicable to video encryption due to the high computational overhead. In this paper, we propose the encryption selection control module to encrypt video syntax elements dynamically which is controlled by the chaotic pseudorandom sequence. A novel spatiotemporal chaos system and binarization method is used to generate a key stream for encrypting the chosen syntax elements. The proposed scheme enhances the resistance against attacks through the dynamic encryption process and high-security stream cipher. Experimental results show that the proposed method exhibits high security and high efficiency with little effect on the compression ratio and time cost. PMID:27409446
Institute of Scientific and Technical Information of China (English)
ZHANG Li-wei; FENG Xiao-bo; WANG Chang-de
2005-01-01
On the basis of analysis the governing process of downstream water level gates AVIO and AVIS, a mathematical model for simulation of dynamic operation process of hydraulically automated irrigation canals installed with AVIO and AVIS gates is presented. the main point of this mathematical model is firstly applying a set of unsteady flow equations (St. Venant equations here) and treating the condition of gate movement as its dynamic boundary, and then decoupling this interaction of gate movement with the change of canal flow. In this process, it is necessary to give the gates' open-loop transfer function whose input is water level deviation and output is gate discharge. The result of this simulation for a practical reach has shown it has satisfactory accuracy.
A Component Mode Synthesis Algorithm for Multibody Dynamics of Wind Turbines
DEFF Research Database (Denmark)
Holm-Jørgensen, Kristian; Nielsen, Søren R.K.
2009-01-01
motion of the substructure. The system reduction is based on a component mode synthesis method, where the response of the internal degrees of freedom of the substructure is described as the quasi-static response induced by the boundary degrees of freedom via the constraint modes superimposed in...... combination to a dynamic component induced by inertial effects and internal loads. The latter component is modelled by a truncated modal expansion in fixed interface undamped eigenmodes. The selected modal vector base for the internal dynamics ensures that the boundary degrees of freedom account for the rigid...... fixed-free method due to incompatible displacements and rotations at the interface between the two substructures. Moreover, the results from the reduced model by use of constant constraint modes and constant fixed interface modes over a large operating areal for the wind turbine blade are almost...
A Data Transmission Algorithm Based on Dynamic Grid Division for Coal Goaf Temperature Monitoring
Directory of Open Access Journals (Sweden)
Qingsong Hu
2014-01-01
Full Text Available WSN (wireless sensor network is a perfect tool of temperature monitoring in coal goaf. Based on the three-zone theory of goaf, the GtmWSN model is proposed, and its dynamic features are analyzed. Accordingly, a data transmission scheme, named DTDGD, is worked out. Firstly, sink nodes conduct dynamic grid division on the GtmWSN according to virtual semicircle. Secondly, each node will confirm to which grid it belongs based on grid number. Finally, data will be delivered to sink nodes with greedy forward and hole avoidance. Simulation results and field data showed that the GtmWSN and DTDGD satisfied the lifetime need of goaf temperature monitoring.
A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems
Sharafi, Masoud; Elmekkawy, Tarek Y.
2014-01-01
In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO-simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examine...
Distributed Dynamic Memetic Algorithm Based Coding Aware Routing for Wireless Mesh Sensor Networks
Saeed Hamam; Ahmad S. Almogren
2016-01-01
Network coding has been confirmed as a potential technology to improve performance of wireless mesh networks (WMNs); network coding has great advantages for sensor networks like minimization of communication needed to collect sensor data and error recovery. A few network coding aware routings have been proposed. However, these mechanisms detect coding opportunities through local traffic pattern checking, which hardly obtains optimal routes. This paper proposes a Distributed Dynamic Memetic Al...
A component mode synthesis algorithm for multibody dynamics of wind turbines
Holm-Jørgensen, K.; Nielsen, S. R. K.
2009-10-01
A system reduction scheme related to a multibody formulation of wind turbine dynamics is devised. Each substructure is described in its own frame of reference, which is moving freely in the vicinity of the moving substructure, in principle without any constraints to the rigid body part of the motion of the substructure. The system reduction is based on a component mode synthesis method, where the response of the internal degrees of freedom of the substructure is described as the quasi-static response induced by the boundary degrees of freedom via the constraint modes superimposed in combination to a dynamic component induced by inertial effects and internal loads. The latter component is modelled by a truncated modal expansion in fixed interface undamped eigenmodes. The selected modal vector base for the internal dynamics ensures that the boundary degrees of freedom account for the rigid-body dynamics of the substructure, and explicitly represent the coupling degrees of freedom at the interface to the adjacent substructures. The method has been demonstrated for a blade structure, which has been modelled as two substructures. Two modelling methods have been examined where the first is by use of fixed-fixed eigenmodes for the innermost substructure and fixed-free eigenmodes for the outermost substructure. The other approach is by use of fixed-free eigenmodes for both substructures. The fixed-fixed method shows good correspondence with the full FE model which is not the case for the fixed-free method due to incompatible displacements and rotations at the interface between the two substructures. Moreover, the results from the reduced model by use of constant constraint modes and constant fixed interface modes over a large operating area for the wind turbine blade are almost identical to the full FE model.
Dynamic Multi-Stage Placement of Phasor Measurement Units using Bat Optimization Algorithm
S. E. Razavi Asfali; H. Falaghi
2014-01-01
In recent years, utilization of phasor measurement units (PMUs) has increased in monitoring, control and protection of power systems. In reality, power systems are large scale, accordingly, financial limitations (due to PMU cost) and technical problems are avoiding to install all necessary PMUs in one stage. Therefore, the PMUs usually are installed in several stages. This paper proposes a new dynamic multi-stage PMU placement approach by introducing a new index related to network observabili...
Parameter matching analysis of hydraulic hybrid excavators based on dynamic programming algorithm
Wei Shen; Jihai Jiang; Xiaoyu Su; Hamid Reza Karimi
2013-01-01
In order to meet the energy saving requirement of the excavator, hybrid excavators are becoming the hot spot for researchers. The initial problem is to match the parameter of each component, because the system is tending to be more complicated due to the introduction of the accumulator. In this paper, firstly, a new architecture is presented which is hydraulic hybrid excavator based on common pressure rail combined switched function (HHES). Secondly, the general principle of dynamic programmi...
An Investigation on Optimal Designing of Dynamic Vibration Absorbers Using Genetic Algorithm
ABDOLLAHI KAMRAN, Mehdi; Rezazadeh, Ghader; GHAFFARI, Shiva
2015-01-01
Abstract. In this study reducing of the unwanted phenomenon in machine tools, Vibration, has been investigated. The machine tools has been simulated as a five-degree-of-freedom discrete system consisting of cutting tool, work piece, body, head and cantilever of the machine. In order to reduce the undesired vibrations, a dynamic vibration absorber (DVA) with three unknown parameters (mass absorber, damping coefficient , stiffness absorber has been added to the system in different positions. Ut...
Mori, Takaharu; Miyashita, Naoyuki; Im, Wonpil; Feig, Michael; Sugita, Yuji
2016-07-01
This paper reviews various enhanced conformational sampling methods and explicit/implicit solvent/membrane models, as well as their recent applications to the exploration of the structure and dynamics of membranes and membrane proteins. Molecular dynamics simulations have become an essential tool to investigate biological problems, and their success relies on proper molecular models together with efficient conformational sampling methods. The implicit representation of solvent/membrane environments is reasonable approximation to the explicit all-atom models, considering the balance between computational cost and simulation accuracy. Implicit models can be easily combined with replica-exchange molecular dynamics methods to explore a wider conformational space of a protein. Other molecular models and enhanced conformational sampling methods are also briefly discussed. As application examples, we introduce recent simulation studies of glycophorin A, phospholamban, amyloid precursor protein, and mixed lipid bilayers and discuss the accuracy and efficiency of each simulation model and method. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov. PMID:26766517
Luo, Dan; Ohya, Jun
2009-01-01
To achieve environments in which humans and mobile robots co-exist, technologies for recognizing hand gestures from the video sequence acquired by a dynamic camera could be useful for human-to-robot interface systems. Most of conventional hand gesture technologies deal with only still camera images. This paper proposes a very simple and stable method for extracting hand motion trajectories based on the Human-Following Local Coordinate System (HFLC System), which is obtained from the located human face and both hands. Then, we apply Condensation Algorithm to the extracted hand trajectories so that the hand motion is recognized. We demonstrate the effectiveness of the proposed method by conducting experiments on 35 kinds of sign language based hand gestures.
International Nuclear Information System (INIS)
This paper illustrates a method for choosing the optimal mix of wind capacity at several geographically dispersed locations. The method is based on a dynamic fuzzy search algorithm that can be applied to different optimization targets. We illustrate the method using two objective functions for the optimization: maximum economic benefit and maximum reliability. We also illustrate the sensitivity of the fuzzy economic benefit solutions to small perturbations of the capacity selections at each wind site. We find that small changes in site capacity and/or location have small effects on the economic benefit provided by wind power plants. We use electric load and generator data from Iowa, along with high-quality wind-speed data collected by the Iowa Wind Energy Institute
A DYNAMIC FOOD CHAIN MODEL FOR HONG KONG BASED ON RADFOOD MODEL AND BIRCHALL—JAMES ALGORITHM
Institute of Scientific and Technical Information of China (English)
余君岳; 吴国斌; 等
1995-01-01
In this paper a dynamic food chain model for Hong Kong which simulates the transfer of radioactive substances from a fallout deposition via the food chain into the human bodies is built.The model is based on the RADFOOD model and the Birchall-James algorithm.The radionuclides 131I and 90Sr representing the short-term and long-term risk situations have been studied as sample cases.Various types of crops,and the dietary pattern of the public have been considered.The resulting internal radiation doses have been calculated.The results are obtained for food consumption starting at various time after the fallout deposition and for different consumption durations.
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
We give an equivalent construction of the infinitesimal time translation operator for partial differential evolution equation in the algebraic dynamics algorithm proposed by Shun-Jin Wang and his students. Our construction involves only simple partial differentials and avoids the derivative terms of δ function which appear in the course of computation by means of Wang-Zhang operator. We prove Wang’s equivalent theorem which says that our construction and Wang-Zhang’s are equivalent. We use our construction to deal with several typical equations such as nonlinear advection equation, Burgers equation, nonlinear Schrodinger equation, KdV equation and sine-Gordon equation, and obtain at least second order approximate solutions to them. These equations include the cases of real and complex field variables and the cases of the first and the second order time derivatives.
International Nuclear Information System (INIS)
This paper concerns the implementation of the Kalman Filter on an FPGA. This provides the reader with three major results. The Kalman Filter is based on matrix and vector operations. We have transformed it to a system where matrix inputs are avoided. From that model and floating point in random integer generation we implemented the Kalman Filter on an FPGA using the SPARTAN-3E board. Secondly, investigations on dynamic partial reconfiguration [DPR] were made and led to identifying operations that are involved in DPR. This paper provides the inventory of these operations. Thirdly, looking for the reconfiguration time of the Kalman Filter, we can state in seconds the clock cycle of any algorithm.
Xiao, Li; Cai, Qin; Li, Zhilin; Zhao, Hongkai; Luo, Ray
2014-11-25
A multi-scale framework is proposed for more realistic molecular dynamics simulations in continuum solvent models by coupling a molecular mechanics treatment of solute with a fluid mechanics treatment of solvent. This article reports our initial efforts to formulate the physical concepts necessary for coupling the two mechanics and develop a 3D numerical algorithm to simulate the solvent fluid via the Navier-Stokes equation. The numerical algorithm was validated with multiple test cases. The validation shows that the algorithm is effective and stable, with observed accuracy consistent with our design. PMID:25404761
Hu, T C
2002-01-01
Newly enlarged, updated second edition of a valuable text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. 153 black-and-white illus. 23 tables.Newly enlarged, updated second edition of a valuable, widely used text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discussed are binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. New to this edition: Chapter 9
Directory of Open Access Journals (Sweden)
Meihong Wu
2016-01-01
Full Text Available Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn and average stride interval (ASI parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney U test (p<0.01 in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth. The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years, middle (aged 6–8 years, and elder (aged 10–14 years children. Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems. In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%, recall (≥0.8, and precision (≥0.8077.
Li, Liang; Jia, Gang; Chen, Jie; Zhu, Hongjun; Cao, Dongpu; Song, Jian
2015-08-01
Direct yaw moment control (DYC), which differentially brakes the wheels to produce a yaw moment for the vehicle stability in a steering process, is an important part of electric stability control system. In this field, most control methods utilise the active brake pressure with a feedback controller to adjust the braked wheel. However, the method might lead to a control delay or overshoot because of the lack of a quantitative project relationship between target values from the upper stability controller to the lower pressure controller. Meanwhile, the stability controller usually ignores the implementing ability of the tyre forces, which might be restrained by the combined-slip dynamics of the tyre. Therefore, a novel control algorithm of DYC based on the hierarchical control strategy is brought forward in this paper. As for the upper controller, a correctional linear quadratic regulator, which not only contains feedback control but also contains feed forward control, is introduced to deduce the object of the stability yaw moment in order to guarantee the yaw rate and side-slip angle stability. As for the medium and lower controller, the quantitative relationship between the vehicle stability object and the target tyre forces of controlled wheels is proposed to achieve smooth control performance based on a combined-slip tyre model. The simulations with the hardware-in-the-loop platform validate that the proposed algorithm can improve the stability of the vehicle effectively.
Mundhenk, Terrell N.; Dhavale, Nitin; Marmol, Salvador; Calleja, Elizabeth; Navalpakkam, Vidhya; Bellman, Kirstie; Landauer, Chris; Arbib, Michael A.; Itti, Laurent
2003-10-01
In view of the growing complexity of computational tasks and their design, we propose that certain interactive systems may be better designed by utilizing computational strategies based on the study of the human brain. Compared with current engineering paradigms, brain theory offers the promise of improved self-organization and adaptation to the current environment, freeing the programmer from having to address those issues in a procedural manner when designing and implementing large-scale complex systems. To advance this hypothesis, we discus a multi-agent surveillance system where 12 agent CPUs each with its own camera, compete and cooperate to monitor a large room. To cope with the overload of image data streaming from 12 cameras, we take inspiration from the primate"s visual system, which allows the animal to operate a real-time selection of the few most conspicuous locations in visual input. This is accomplished by having each camera agent utilize the bottom-up, saliency-based visual attention algorithm of Itti and Koch (Vision Research 2000;40(10-12):1489-1506) to scan the scene for objects of interest. Real time operation is achieved using a distributed version that runs on a 16-CPU Beowulf cluster composed of the agent computers. The algorithm guides cameras to track and monitor salient objects based on maps of color, orientation, intensity, and motion. To spread camera view points or create cooperation in monitoring highly salient targets, camera agents bias each other by increasing or decreasing the weight of different feature vectors in other cameras, using mechanisms similar to excitation and suppression that have been documented in electrophysiology, psychophysics and imaging studies of low-level visual processing. In addition, if cameras need to compete for computing resources, allocation of computational time is weighed based upon the history of each camera. A camera agent that has a history of seeing more salient targets is more likely to obtain
AN IMPROVED CONTROL ALGORITHM FOR «DYNAMIC CAPACITOR» VAR COMPENSATOR
Directory of Open Access Journals (Sweden)
S.K. Podnebennaya
2015-11-01
Full Text Available Purpose. Modern approaches of VAR compensation are: using compensators with stepped regulation, STATCOMs, active power filters. Recently, more attention is paid to VAR compensator’s design based on the direct AC / AC converters, which are called dynamic capacitors. Methodology. The dynamic capacitor (D-CAP is the capacitor bank, which is connected to the mains through direct AC / AC buck converter. By varying the duty cycle of bidirectional switches, smooth control of reactive power can be achieved. However, in case of distorted mains voltage, D-CAP mains current will have a high THD. This is due to the fact that the D-CAP affects the frequency response of electric grid thus leading to the appearance of resonances. With non-sinusoidal mains voltage, capacitors are affected by harmonics. This reduces the reliability of the D-CAP, increasing the probability of their failure. To eliminate these drawbacks it is suggested to improve the D-CAP control system so that the input current of the dynamic capacitor is forced to be close to sinusoidal. This can be achieved if the duty cycle of the switching bi-directional switches is changed according to the proposed expression. Results. The research is done on a single-phase D-CAP with the proposed control system, its input current diagrams are shown. In contrast to the D-CAP with a constant duty cycle control, the resulting THD of its input current is much lower. Thus, the control system provides a form of the input current that is close to a sine wave. This reduces the influence of mains voltage harmonics on the D-CAP operation, increases its reliability and improves power quality. Originality. The proposed D-CAP control system ensures reliable operation with non-sinusoidal mains voltage. Practical value. Application of D-CAPs with the proposed control system allows for improved energy efficiency of electrical mains by providing VAR compensation and improving power quality.
A Pre-compensation Fuzzy Logic Algorithm Designed for the Dynamic Compensation Robotic System
Directory of Open Access Journals (Sweden)
Shouren Huang
2015-01-01
Full Text Available This paper deals with the issue of non-model-based position regulation for the dynamic compensation robotic system (DCRS, which has been proposed for cooperating with the existing main robotic systems, such as the common serial robotic arms, to accomplish high-speed and accurate manipulations. The dynamic compensation concept is realized by fusing a high-speed & light-weight compensation actuator as well as endpoint closed loop (ECL configured high-speed cameras. Within the context of the DCRS, the coarse motion, which is realized by the main robotic system, usually gives rise to negative dynamic impact on the compensation actuator that is configured to accomplish the fine motion. Through the analysis of a simplified model for the coupled two-plant system, relative velocity information between the two plants is found to play a role in the first order derivative of the displacement error. With the use of the relative position information from high-speed visual feedback, this paper proposes a new pre- compensation fuzzy logic control (PFLC approach for control of the compensation actuator. The PFLC method is model-independent and is realized with a cascade fuzzy inference structure that conveniently integrates the relative velocity term between the two plants into the error regulation, and therefore realizes the partial counteraction of the disturbance from the main robot easily without knowing the explicit mathematical models of the system. Comparison works between the proposed PFLC and approaches that take no consideration of the relative velocity information, such as proportional-derivative (PD control and conventional fuzzy logic control, are conducted. Simulations and experiments show the consistent effectiveness of the proposed approach.
Zhu, Daxin; Wang, Lei; Wu, Yingjie; Wang, Xiaodong
2015-01-01
In this paper, we consider a generalized longest common subsequence problem with multiple substring inclusive constraints. For the two input sequences $X$ and $Y$ of lengths $n$ and $m$, and a set of $d$ constraints $P=\\{P_1,\\cdots,P_d\\}$ of total length $r$, the problem is to find a common subsequence $Z$ of $X$ and $Y$ including each of constraint string in $P$ as a substring and the length of $Z$ is maximized. A new dynamic programming solution to this problem is presented in this paper. T...
A Service Ratio-Based Dynamic Fair Queueing Algorithm for Packet Switching Networks
Institute of Scientific and Technical Information of China (English)
YIN De-bin; XIE Jian-ying; ZHANG Yan; WU Jian-zhen; SUN Hua-li
2008-01-01
A new weighted fair queeetng algodthm is proposed,which uses the novel flow-based service ratio parameters to schedule flows.This solves the main drawback of traditional weighted fair quoneing algorithmsthe packet-based calculation of the weight parameters.In addition,this paper proposes a novel service ratio calculation method and a queue management techaology.The former adjusts the service ratio parameters adaptively based on the dynamics of the packet lengths and then solves the unfairness problem induced by the variable packet length.The latter impgoves the utilization of the server's queue buffeg and reduces the delay jitter throegh restricting the buffer length for each flow.
International Nuclear Information System (INIS)
Background: In radioactive nuclides atmospheric diffusion models, the empirical dispersion coefficients were deduced under certain experiment conditions, whose difference with nuclear accident conditions is a source of deviation. A better estimation of the radioactive nuclide's actual dispersion process could be done by correcting dispersion coefficients with observation data, and Genetic Algorithm (GA) is an appropriate method for this correction procedure. Purpose: This study is to analyze the fitness functions' influence on the correction procedure and the forecast ability of diffusion model. Methods: GA, coupled with Lagrange dispersion model, was used in a numerical simulation to compare 4 fitness functions' impact on the correction result. Results: In the numerical simulation, the fitness function with observation deviation taken into consideration stands out when significant deviation exists in the observed data. After performing the correction procedure on the Kincaid experiment data, a significant boost was observed in the diffusion model's forecast ability. Conclusion: As the result shows, in order to improve dispersion models' forecast ability using GA, observation data should be given different weight in the fitness function corresponding to their error. (authors)
Chen, Jun; Luo, Chaomin; Krishnan, Mohan; Paulik, Mark; Tang, Yipeng
2010-01-01
An enhanced dynamic Delaunay Triangulation-based (DT) path planning approach is proposed for mobile robots to plan and navigate a path successfully in the context of the Autonomous Challenge of the Intelligent Ground Vehicle Competition (www.igvc.org). The Autonomous Challenge course requires the application of vision techniques since it involves path-based navigation in the presence of a tightly clustered obstacle field. Course artifacts such as switchbacks, ramps, dashed lane lines, trap etc. are present which could turn the robot around or cause it to exit the lane. The main contribution of this work is a navigation scheme based on dynamic Delaunay Triangulation (DDT) that is heuristically enhanced on the basis of a sense of general lane direction. The latter is computed through a "GPS (Global Positioning System) tail" vector obtained from the immediate path history of the robot. Using processed data from a LADAR, camera, compass and GPS unit, a composite local map containing both obstacles and lane line segments is built up and Delaunay Triangulation is continuously run to plan a path. This path is heuristically corrected, when necessary, by taking into account the "GPS tail" . With the enhancement of the Delaunay Triangulation by using the "GPS tail", goal selection is successfully achieved in a majority of situations. The robot appears to follow a very stable path while navigating through switchbacks and dashed lane line situations. The proposed enhanced path planning and GPS tail technique has been successfully demonstrated in a Player/Stage simulation environment. In addition, tests on an actual course are very promising and reveal the potential for stable forward navigation.
Sheong, Fu Kit; Silva, Daniel-Adriano; Meng, Luming; Zhao, Yutong; Huang, Xuhui
2015-01-13
The conformational dynamics of multibody systems plays crucial roles in many important problems. Markov state models (MSMs) are powerful kinetic network models that can predict long-time-scale dynamics using many short molecular dynamics simulations. Although MSMs have been successfully applied to conformational changes of individual proteins, the analysis of multibody systems is still a challenge because of the complexity of the dynamics that occur on a mixture of drastically different time scales. In this work, we have developed a new algorithm, automatic state partitioning for multibody systems (APM), for constructing MSMs to elucidate the conformational dynamics of multibody systems. The APM algorithm effectively addresses different time scales in the multibody systems by directly incorporating dynamics into geometric clustering when identifying the metastable conformational states. We have applied the APM algorithm to a 2D potential that can mimic a protein-ligand binding system and the aggregation of two hydrophobic particles in water and have shown that it can yield tremendous enhancements in the computational efficiency of MSM construction and the accuracy of the models. PMID:26574199
Directory of Open Access Journals (Sweden)
Anna Bourmistrova
2011-02-01
Full Text Available The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS vehicle, though it is also applicable to two-wheel-steering (TWS vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while the dynamic center of rotation is the output of dynamic equations of motion of the vehicle using steering angle and velocity measurements as inputs. We use kinematic condition of steering to set the steering angles in such a way that the kinematic center of rotation of the vehicle sits at a desired point. At low speeds the ideal and actual paths of the vehicle are very close. With increase of forward speed the road and tire characteristics, along with the motion dynamics of the vehicle cause the vehicle to turn about time-varying points. By adjusting the steering angles, our algorithm controls the dynamic turning center of the vehicle so that it coincides with the road curvature center, hence keeping the vehicle on a given road autonomously. The position and orientation errors are used as feedback signals in a closed loop control to adjust the steering angles. The application of the presented autodriver algorithm demonstrates reliable performance under different driving conditions.
Dejean, Alain; Azémar, Frédéric; Céréghino, Régis; Leponce, Maurice; Corbara, Bruno; Orivel, Jérôme; Compin, Arthur
2016-08-01
Ants, the most abundant taxa among canopy-dwelling animals in tropical rainforests, are mostly represented by territorially dominant arboreal ants (TDAs) whose territories are distributed in a mosaic pattern (arboreal ant mosaics). Large TDA colonies regulate insect herbivores, with implications for forestry and agronomy. What generates these mosaics in vegetal formations, which are dynamic, still needs to be better understood. So, from empirical research based on 3 Cameroonian tree species (Lophira alata, Ochnaceae; Anthocleista vogelii, Gentianaceae; and Barteria fistulosa, Passifloraceae), we used the Self-Organizing Map (SOM, neural network) to illustrate the succession of TDAs as their host trees grow and age. The SOM separated the trees by species and by size for L. alata, which can reach 60 m in height and live several centuries. An ontogenic succession of TDAs from sapling to mature trees is shown, and some ecological traits are highlighted for certain TDAs. Also, because the SOM permits the analysis of data with many zeroes with no effect of outliers on the overall scatterplot distributions, we obtained ecological information on rare species. Finally, the SOM permitted us to show that functional groups cannot be selected at the genus level as congeneric species can have very different ecological niches, something particularly true for Crematogaster spp., which include a species specifically associated with B. fistulosa, nondominant species and TDAs. Therefore, the SOM permitted the complex relationships between TDAs and their growing host trees to be analyzed, while also providing new information on the ecological traits of the ant species involved. PMID:25684460
Maric, Tomislav; Bothe, Dieter
2013-01-01
A new parallelized unsplit geometrical Volume of Fluid (VoF) algorithm with support for arbitrary unstructured meshes and dynamic local Adaptive Mesh Refinement (AMR), as well as for two and three dimensional computation is developed. The geometrical VoF algorithm supports arbitrary unstructured meshes in order to enable computations involving flow domains of arbitrary geometrical complexity. The implementation of the method is done within the framework of the OpenFOAM library for Computational Continuum Mechanics (CCM) using the C++ programming language with modern policy based design for high program code modularity. The development of the geometrical VoF algorithm significantly extends the method base of the OpenFOAM library by geometrical volumetric flux computation for two-phase flow simulations. For the volume fraction advection, a novel unsplit geometrical algorithm is developed, which inherently sustains volume conservation utilizing unique Lagrangian discrete trajectories located in the mesh points. ...
Shrestha, Kalyan; Mompean, Gilmar; Calzavarini, Enrico
2016-02-01
A finite-volume (FV) discretization method for the lattice Boltzmann (LB) equation, which combines high accuracy with limited computational cost is presented. In order to assess the performance of the FV method we carry out a systematic comparison, focused on accuracy and computational performances, with the standard streaming lattice Boltzmann equation algorithm. In particular we aim at clarifying whether and in which conditions the proposed algorithm, and more generally any FV algorithm, can be taken as the method of choice in fluid-dynamics LB simulations. For this reason the comparative analysis is further extended to the case of realistic flows, in particular thermally driven flows in turbulent conditions. We report the successful simulation of high-Rayleigh number convective flow performed by a lattice Boltzmann FV-based algorithm with wall grid refinement. PMID:26986438
Semantics-based metrics and algorithms for dynamic content in web database applications
Papastavrou, Stavros C.; Παπασταύρου, Σταύρος Κ.
2009-01-01
Η Τεχνολογία Δυναμικού Περιεχομένου (Dynamic Web Content) συσχετίζει τις Παραδοσιακές Βάσεις Δεδομένων (Traditional Databases) με το Παγκόσμιο Πλέγμα Πληροφοριών (World Wide Web), επιτρέποντας την επισκόπηση και ενημέρωση των βάσεων δεδομένων μέσω δυναμικών ιστοσελίδων. Με την εμφάνιση του Common Gateway Interface (CGI), η τεχνολογία δυναμικού περιεχομένου έχει υποβοηθήσει στην μεταφορά των παραδοσιακών δημοφιλών εφαρμογών στο διαδικτυακό κόσμο. Παραδείγματα δημοφιλών εφαρμογών αποτελούν τα δ...
Huang, Wen-Cheng; Yuan, Lun-Chin; Lee, Chi-Ming
2002-12-01
The objective of this paper is to present a genetic algorithm-based stochastic dynamic programming (GA-based SDP) to cope with the dimensionality problem of a multiple-reservoir system. The joint long-term operation of a parallel reservoir system in the Feitsui and Shihmen reservoirs in northern Taiwan demonstrates the successful application of the proposed GA-based SDP model. Within the case study system it is believed that GA is a useful technique in supporting optimization. Though the employment of GA-based SDP may be time consuming as it proceeds through generation by generation, the model can overcome the "dimensionality curse" in searching solutions. Simulation results show Feitsui's surplus water can be utilized efficiently to fill Shihmen's deficit water without affecting Feitsui's main purpose as Taipei city's water supply. The optimal joint operation suggests that Feitsui, on average, can provide 650,000 m3/day and 920,000 m3/day to Shihmen during the wet season and dry season, respectively.
Kazachenko, Sergey; Giovinazzo, Mark; Hall, Kyle Wm; Cann, Natalie M
2015-09-15
A custom code for molecular dynamics simulations has been designed to run on CUDA-enabled NVIDIA graphics processing units (GPUs). The double-precision code simulates multicomponent fluids, with intramolecular and intermolecular forces, coarse-grained and atomistic models, holonomic constraints, Nosé-Hoover thermostats, and the generation of distribution functions. Algorithms to compute Lennard-Jones and Gay-Berne interactions, and the electrostatic force using Ewald summations, are discussed. A neighbor list is introduced to improve scaling with respect to system size. Three test systems are examined: SPC/E water; an n-hexane/2-propanol mixture; and a liquid crystal mesogen, 2-(4-butyloxyphenyl)-5-octyloxypyrimidine. Code performance is analyzed for each system. With one GPU, a 33-119 fold increase in performance is achieved compared with the serial code while the use of two GPUs leads to a 69-287 fold improvement and three GPUs yield a 101-377 fold speedup. PMID:26174435
Institute of Scientific and Technical Information of China (English)
王剑
2011-01-01
For traditional monetary policy instruments,there are many problems about dealing with more and more complicated economic conditions.Asset-based reserve requirements（ABRR） are the feasible choice for monetary policy innovation,which has significant advant%传统的货币政策工具已难以应对日益复杂的经济形势,基于资产的准备金制度在宏观调控、结构调整以及宏观审慎监管等方面都具有显著优势,是货币政策工具创新的一个可行选择。本文对该制度的作用机理、优势与局限性等问题进行了研究,并结合我国货币调控的实践得出一些启示与思考。
International Nuclear Information System (INIS)
An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research. -- Research highlights: → Quantum Genetic Algorithm can effectively improve the global search ability. → It can achieve the real objective of the global optimal solutions. → The CPU computation time is less than that other algorithms adopted in this paper.
International Nuclear Information System (INIS)
The simplicity in coding the heuristic judgment of experienced operator by means of fuzzy logic can be exploited for enhancement of energy efficiency. Fuzzy logic has been used as an effective tool for scheduling conventional PID controllers gain coefficients (F-PID). However, to search for the most desirable fuzzy system characteristics that allow for best performance of the energy system with minimum energy input, optimization techniques such as genetic algorithm (GA) could be utilized and the control methodology is identified as GA-based F-PID (GA-F-PID). The objective of this study is to examine the performance of PID, F-PID, and GA-F-PID controllers for enhancement of energy efficiency of a dynamic energy system. The performance evaluation of the controllers is accomplished by means of two cost functions that are based on the quadratic forms of the energy input and deviation from a setpoint temperature, referred to as energy and comfort costs, respectively. The GA-F-PID controller is examined in two different forms, namely, global form and local form. For the global form, all possible combinations of fuzzy system characteristics in the search domain are explored by GA for finding the fittest chromosome for all discrete time intervals during the entire operation period. For the local form, however, GA is used in each discrete time interval to find the fittest chromosome for implementation. The results show that the global form GA-F-PID and local form GA-F-PID control methodologies, in comparison with PID controller, achieve higher energy efficiency by lowering energy costs by 51.2%, and 67.8%, respectively. Similarly, the comfort costs for deviation from setpoint are enhanced by 54.4%, and 62.4%, respectively. It is determined that GA-F-PID performs better in local from than global form.
Directory of Open Access Journals (Sweden)
2014-05-01
Full Text Available Dynamic economic dispatch deals with the scheduling of online generator outputs with predicted load demands over a certain period of time so that an electric power system operates most economically. This paper proposes a hybrid methodology integrating Bacterial Foraging Optimization Algorithm (BFOA with sequential quadratic programming (SQP for solving dynamic economic dispatch problem of generating units considering valve-point effects. This hybrid method incorporates Bacterial Foraging Optimization Algorithm as a base level search which can give a good direction to the optimal region and sequential quadratic programming as a local search procedure which is used to fine tune that region for achieving the final solution. Numerical results of a ten-unit system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared with those obtained from hybrid of particle swarm optimization and sequential quadratic programming and hybrid of evolutionary programming and sequential quadratic programming.
Institute of Scientific and Technical Information of China (English)
Yujie Wei; Yongheng Jiang; Dexian Huang⁎
2014-01-01
This paper introduces a practical solving scheme of gradetransition trajectory optimization (GTTO) problems under typical certificate-checking–updating framework. Due to complicated kinetics of polymerization, differential/algebraic equations (DAEs) always cause great computational burden and system non-linearity usual y makes GTTO non-convex bearing multiple optima. Therefore, coupled with the three-stage decomposi-tion model, a three-section algorithm of dynamic programming (TSDP) is proposed based on the general iteration mechanism of iterative programming (IDP) and incorporated with adaptivegrid allocation scheme and heuristic modifications. The algorithm iteratively performs dynamic programming with heuristic modifications under constant calculation loads and adaptively allocates the valued computational resources to the regions that can further improve the optimality under the guidance of local error estimates. TSDP is finally compared with IDP and interior point method (IP) to verify its efficiency of computation.
International Nuclear Information System (INIS)
In this paper, we study a multi-state weighted k-out-of-n:G system model in a dynamic setup. In particular, we study the random time spent by the system with a minimum performance level of k. Our method is based on ordering the lifetimes of the system's components in different state subsets. Using this ordering along with the Monte-Carlo simulation algorithm, we obtain estimates of the mean and survival function of the time spent by the system in state k or above. We present illustrative computational results when the degradation in the components follows a Markov process. - Highlights: • A multi-state weighted k-out-of-n:G system is studied. • A Monte-Carlo simulation algorithm is provided for the dynamic analysis. • Numerics are presented when the components' degradation follow the Markov process
Moerkotte, Guido; Neumann, Thomas; Dayal, Umeshwar; Whang, Kyu-Young; Lomet, David B.; Alonso, Gustavo; Lohman, Guy M.; Kersten, Martin L.; Cha, Sang Kyun; Kim, Young-Kuk
2006-01-01
Two approaches to derive dynamic programming algorithms for constructing join trees are described in the literature. We show analytically and experimentally that these two variants exhibit vastly diverging runtime behaviors for different query graphs. More specifically, each variant is superior to the other for one kind of query graph (chain or clique), but fails for the other. Moreover, neither of them handles star queries well. This motivates us to derive an algorith...
Abánades, Miguel A.; Botana, Francisco
2013-01-01
An enhancement of the dynamic geometry system GeoGebra for the automatic symbolic computation of algebraic loci and envelopes is presented. Given a GeoGebra construction, the prototype, after rewriting the construction as a polynomial system in terms of variables and parameters, uses an implementation of the recent Gr\\"obnerCover algorithm to obtain the algebraic description of the sought locus/envelope as a locally closed set. The prototype shows the applicability of these techniques in gene...
Burnham-Slipper, Hugh
2009-01-01
Improved cooking stoves can bring significant benefits to women and children in rural African situations, due to reduced fuel consumption and improved indoor air quality. This investigation focuses on the use of Computational Fluid Dynamics (CFD) and Genetic Algorithms (GAs) to optimise a stove for Eritrea. Initial work focussed on developing a model of wood combustion in a fixed bed. An experimental investigation was carried out on regular wood cribs to determine the burn rate and temper...
I. Rafols; L. Leydesdorff
2009-01-01
The aggregated journal-journal citation matrix—based on the Journal Citation Reports (JCR) of the Science Citation Index—can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two c
Behera, H S; Panda, Bijayalakshmi; 10.5120/2037-2648
2011-01-01
The main objective of the paper is to improve the Round Robin (RR) algorithm using dynamic ITS by coalescing it with Shortest Remaining Time Next (SRTN) algorithm thus reducing the average waiting time, average turnaround time and the number of context switches. The original time slice has been calculated for each process based on its burst time.This is mostly suited for soft real time systems where meeting of deadlines is desirable to increase its performance. The advantage is that processes that are closer to their remaining completion time will get more chances to execute and leave the ready queue. This will reduce the number of processes in the ready queue by knocking out short jobs relatively faster in a hope to reduce the average waiting time, turn around time and number of context switches. This paper improves the algorithm [8] and the experimental analysis shows that the proposed algorithm performs better than algorithm [6] and [8] when the processes are having an increasing order, decreasing order an...
International Nuclear Information System (INIS)
Purpose: Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. Methods: An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered at points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. Results: The performances of the authors’ method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors’ method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the
Energy Technology Data Exchange (ETDEWEB)
Yi, Jianbing, E-mail: yijianbing8@163.com [College of Information Engineering, Shenzhen University, Shenzhen, Guangdong 518000, China and College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000 (China); Yang, Xuan, E-mail: xyang0520@263.net; Li, Yan-Ran, E-mail: lyran@szu.edu.cn [College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518000 (China); Chen, Guoliang, E-mail: glchen@szu.edu.cn [National High Performance Computing Center at Shenzhen, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518000 (China)
2015-10-15
Purpose: Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. Methods: An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered at points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. Results: The performances of the authors’ method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors’ method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the
Rafols, Ismael
2008-01-01
The aggregated journal-journal citation matrix -based on the Journal Citation Reports (JCR) of the Science Citation Index- can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glaenzel & Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. The consequent indexer effects are significant in sparse areas of the matrix more than in denser ones. Algorithmic decompositions, on the other hand, are more heavily ...
Directory of Open Access Journals (Sweden)
Suresh Chandra Maurya
2011-07-01
Full Text Available In this paper DCA using modified Genetic Algorithm (MGA is applied to a Broadband Fixed Wireless Access (BFWA network that provides data services. The performance of the modified GA is compared using a simulation with other DCAs namely the Least Interfered (LI and Channel Segregation (CS and simple genetic algorithm. It is shown that the modified GA has the highest throughput and is more capable of adapting to interference and reconverging to a new stable state.
Directory of Open Access Journals (Sweden)
Ray Debraj
2015-01-01
Full Text Available Speeded Up Robust Feature (SURF is used to position a robot with respect to an environment and aid in vision-based robotic navigation. During the course of navigation irregularities in the terrain, especially in an outdoor environment may deviate a robot from the track. Another reason for deviation can be unequal speed of the left and right robot wheels. Hence it is essential to detect such deviations and perform corrective operations to bring the robot back to the track. In this paper we propose a novel algorithm that uses image matching using SURF to detect deviation of a robot from the trajectory and subsequent restoration by corrective operations. This algorithm is executed in parallel to positioning and navigation algorithms by distributing tasks among different CPU cores using Open Multi-Processing (OpenMP API.
Dall'Anese, Emiliano; Dhople, Sairaj; Giannakis, Georgios B.
2014-01-01
This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of network-wide constrained optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual epsilon-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference...
The Variable Size Memory-based Evolutionary Algorithm in Dynamic Environments%动态环境下基于可变记忆的进化算法
Institute of Scientific and Technical Information of China (English)
关守平; 尹晓峰
2011-01-01
常规基于记忆的进化算法在动态环境中往往达不到期望的效果,这主要是由于记忆体大小的限制.为此提出了动态环境下基于可变记忆的进化算法(IMEEA),其核心思想是算法中拥有两个种群,即搜索种群和记忆种群,同时采用过度变异策略来增加种群的多样性.算法中的两个种群有最小和最大的允许长度,并且种群的大小根据进化过程的进行而不断变化.仿真结果表明,在动态环境中IMEEA算法的跟踪误差要小于常规的记忆提高进化算法(MEEA),从而证明了所提算法的有效性.%Traditional memory-based evolutionary algorithms often may not achieve the desired performances in dynamic environments, which is mainly due to the fixed memory size. A variable size memory-based evolutionary algorithm is proposed. The improved memory enhanced evolutionary algorithm (IMEEA), which combines memory population and search population,and hyper-mutation is used to promote and maintain diversity. The two populations have minimum and maximum sizes allowed that change according to the stage of the evolutionary process. Simulation results show that the tracking error of the IMEEA is less than the memory enhanced evolutionary algorithm (MEEA), and then prove the effectiveness of this new algorithm.
Song, Hairong; Ferrer, Emilio
2009-01-01
This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a…
Nagarajan, Karthik; Judge, Jasmeet; Graham, Wendy D.; Monsivais-Huertero, Alejandro
2011-04-01
In this study, we implement Particle Filter (PF)-based assimilation algorithms to improve root-zone soil moisture (RZSM) estimates from a coupled SVAT-vegetation model during a growing season of sweet corn in North Central Florida. The results from four different PF algorithms were compared with those from the Ensemble Kalman Filter (EnKF) when near-surface soil moisture was assimilated every 3 days using both synthetic and field observations. In the synthetic case, the PF algorithm with the best performance used residual resampling of the states and obtained resampled parameters from a uniform distribution and provided reductions of 76% in root mean square error (RMSE) over the openloop estimates. The EnKF provided the RZSM and parameter estimates that were closer to the truth than the PF with an 84% reduction in RMSE. When field observations were assimilated, the PF algorithm that maintained maximum parameter diversity offered the largest reduction of 16% in root mean square difference (RMSD) over the openloop estimates. Minimal differences were observed in the overall performance of the EnKF and PF using field observations since errors in model physics affected both the filters in a similar manner, with maximum reductions in RMSD compared to the openloop during the mid and reproductive stages.