Zhu, Ying; Herbert, John M.
2018-01-01
The "real time" formulation of time-dependent density functional theory (TDDFT) involves integration of the time-dependent Kohn-Sham (TDKS) equation in order to describe the time evolution of the electron density following a perturbation. This approach, which is complementary to the more traditional linear-response formulation of TDDFT, is more efficient for computation of broad-band spectra (including core-excited states) and for systems where the density of states is large. Integration of the TDKS equation is complicated by the time-dependent nature of the effective Hamiltonian, and we introduce several predictor/corrector algorithms to propagate the density matrix, one of which can be viewed as a self-consistent extension of the widely used modified-midpoint algorithm. The predictor/corrector algorithms facilitate larger time steps and are shown to be more efficient despite requiring more than one Fock build per time step, and furthermore can be used to detect a divergent simulation on-the-fly, which can then be halted or else the time step modified.
Adams Predictor-Corrector Systems for Solving Fuzzy Differential Equations
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Dequan Shang
2013-01-01
Full Text Available A predictor-corrector algorithm and an improved predictor-corrector (IPC algorithm based on Adams method are proposed to solve first-order differential equations with fuzzy initial condition. These algorithms are generated by updating the Adams predictor-corrector method and their convergence is also analyzed. Finally, the proposed methods are illustrated by solving an example.
A predictor-corrector algorithm to estimate the fractional flow in oil-water models
International Nuclear Information System (INIS)
Savioli, Gabriela B; Berdaguer, Elena M Fernandez
2008-01-01
We introduce a predictor-corrector algorithm to estimate parameters in a nonlinear hyperbolic problem. It can be used to estimate the oil-fractional flow function from the Buckley-Leverett equation. The forward model is non-linear: the sought- for parameter is a function of the solution of the equation. Traditionally, the estimation of functions requires the selection of a fitting parametric model. The algorithm that we develop does not require a predetermined parameter model. Therefore, the estimation problem is carried out over a set of parameters which are functions. The algorithm is based on the linearization of the parameter-to-output mapping. This technique is new in the field of nonlinear estimation. It has the advantage of laying aside parametric models. The algorithm is iterative and is of predictor-corrector type. We present theoretical results on the inverse problem. We use synthetic data to test the new algorithm.
Directory of Open Access Journals (Sweden)
Antonio Roberto Balbo
2012-01-01
Full Text Available This paper proposes a predictor-corrector primal-dual interior point method which introduces line search procedures (IPLS in both the predictor and corrector steps. The Fibonacci search technique is used in the predictor step, while an Armijo line search is used in the corrector step. The method is developed for application to the economic dispatch (ED problem studied in the field of power systems analysis. The theory of the method is examined for quadratic programming problems and involves the analysis of iterative schemes, computational implementation, and issues concerning the adaptation of the proposed algorithm to solve ED problems. Numerical results are presented, which demonstrate improvements and the efficiency of the IPLS method when compared to several other methods described in the literature. Finally, postoptimization analyses are performed for the solution of ED problems.
A Predictor-Corrector Method for Solving Equilibrium Problems
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Zong-Ke Bao
2014-01-01
Full Text Available We suggest and analyze a predictor-corrector method for solving nonsmooth convex equilibrium problems based on the auxiliary problem principle. In the main algorithm each stage of computation requires two proximal steps. One step serves to predict the next point; the other helps to correct the new prediction. At the same time, we present convergence analysis under perfect foresight and imperfect one. In particular, we introduce a stopping criterion which gives rise to Δ-stationary points. Moreover, we apply this algorithm for solving the particular case: variational inequalities.
Dey, C.; Dey, S. K.
1983-01-01
An explicit finite difference scheme consisting of a predictor and a corrector has been developed and applied to solve some hyperbolic partial differential equations (PDEs). The corrector is a convex-type function which is applied at each time level and at each mesh point. It consists of a parameter which may be estimated such that for larger time steps the algorithm should remain stable and generate a fast speed of convergence to the steady-state solution. Some examples have been given.
A predictor-corrector scheme for solving the Volterra integral equation
Al Jarro, Ahmed
2011-08-01
The occurrence of late time instabilities is a common problem of almost all time marching methods developed for solving time domain integral equations. Implicit marching algorithms are now considered stable with various efforts that have been developed for removing low and high frequency instabilities. On the other hand, literature on stabilizing explicit schemes, which might be considered more efficient since they do not require a matrix inversion at each time step, is practically non-existent. In this work, a stable but still explicit predictor-corrector scheme is proposed for solving the Volterra integral equation and its efficacy is verified numerically. © 2011 IEEE.
Three-Step Predictor-Corrector of Exponential Fitting Method for Nonlinear Schroedinger Equations
International Nuclear Information System (INIS)
Tang Chen; Zhang Fang; Yan Haiqing; Luo Tao; Chen Zhanqing
2005-01-01
We develop the three-step explicit and implicit schemes of exponential fitting methods. We use the three-step explicit exponential fitting scheme to predict an approximation, then use the three-step implicit exponential fitting scheme to correct this prediction. This combination is called the three-step predictor-corrector of exponential fitting method. The three-step predictor-corrector of exponential fitting method is applied to numerically compute the coupled nonlinear Schroedinger equation and the nonlinear Schroedinger equation with varying coefficients. The numerical results show that the scheme is highly accurate.
International Nuclear Information System (INIS)
Kiyko, V V; Kislov, V I; Ofitserov, E N
2015-01-01
In the framework of a statistical model of an adaptive optics system (AOS) of phase conjugation, three algorithms based on an integrated mathematical approach are considered, each of them intended for minimisation of one of the following characteristics: the sensor error (in the case of an ideal corrector), the corrector error (in the case of ideal measurements) and the compensation error (with regard to discreteness and measurement noises and to incompleteness of a system of response functions of the corrector actuators). Functional and statistical relationships between the algorithms are studied and a relation is derived to ensure calculation of the mean-square compensation error as a function of the errors of the sensor and corrector with an accuracy better than 10%. Because in adjusting the AOS parameters, it is reasonable to proceed from the equality of the sensor and corrector errors, in the case the Hartmann sensor is used as a wavefront sensor, the required number of actuators in the absence of the noise component in the sensor error turns out 1.5 – 2.5 times less than the number of counts, and that difference grows with increasing measurement noise. (adaptive optics)
Energy Technology Data Exchange (ETDEWEB)
Kiyko, V V; Kislov, V I; Ofitserov, E N [A M Prokhorov General Physics Institute, Russian Academy of Sciences, Moscow (Russian Federation)
2015-08-31
In the framework of a statistical model of an adaptive optics system (AOS) of phase conjugation, three algorithms based on an integrated mathematical approach are considered, each of them intended for minimisation of one of the following characteristics: the sensor error (in the case of an ideal corrector), the corrector error (in the case of ideal measurements) and the compensation error (with regard to discreteness and measurement noises and to incompleteness of a system of response functions of the corrector actuators). Functional and statistical relationships between the algorithms are studied and a relation is derived to ensure calculation of the mean-square compensation error as a function of the errors of the sensor and corrector with an accuracy better than 10%. Because in adjusting the AOS parameters, it is reasonable to proceed from the equality of the sensor and corrector errors, in the case the Hartmann sensor is used as a wavefront sensor, the required number of actuators in the absence of the noise component in the sensor error turns out 1.5 – 2.5 times less than the number of counts, and that difference grows with increasing measurement noise. (adaptive optics)
Directory of Open Access Journals (Sweden)
Vahid Dadashi
2016-02-01
Full Text Available Abstract This paper is dedicated to the introduction a new class of equilibrium problems named generalized multivalued equilibrium-like problems which includes the classes of hemiequilibrium problems, equilibrium-like problems, equilibrium problems, hemivariational inequalities, and variational inequalities as special cases. By utilizing the auxiliary principle technique, some new predictor-corrector iterative algorithms for solving them are suggested and analyzed. The convergence analysis of the proposed iterative methods requires either partially relaxed monotonicity or jointly pseudomonotonicity of the bifunctions involved in generalized multivalued equilibrium-like problem. Results obtained in this paper include several new and known results as special cases.
A Predictor-Corrector Approach for the Numerical Solution of Fractional Differential Equations
Diethelm, Kai; Ford, Neville J.; Freed, Alan D.; Gray, Hugh R. (Technical Monitor)
2002-01-01
We discuss an Adams-type predictor-corrector method for the numerical solution of fractional differential equations. The method may be used both for linear and for nonlinear problems, and it may be extended to multi-term equations (involving more than one differential operator) too.
International Nuclear Information System (INIS)
Li, Xiaoyu; Fan, Guodong; Rizzoni, Giorgio; Canova, Marcello; Zhu, Chunbo; Wei, Guo
2016-01-01
The design of a simplified yet accurate physics-based battery model enables researchers to accelerate the processes of the battery design, aging analysis and remaining useful life prediction. In order to reduce the computational complexity of the Pseudo Two-Dimensional mathematical model without sacrificing the accuracy, this paper proposes a simplified multi-particle model via a predictor-corrector strategy and quasi-linearization. In this model, a predictor-corrector strategy is used for updating two internal states, especially used for solving the electrolyte concentration approximation to reduce the computational complexity and reserve a high accuracy of the approximation. Quasi-linearization is applied to the approximations of the Butler-Volmer kinetics equation and the pore wall flux distribution to predict the non-uniform electrochemical reaction effects without using any nonlinear iterative solver. Simulation and experimental results show that the isothermal model and the model coupled with thermal behavior are greatly improve the computational efficiency with almost no loss of accuracy. - Highlights: • A simplified multi-particle model with high accuracy and computation efficiency is proposed. • The electrolyte concentration is solved based on a predictor-corrector strategy. • The non-uniform electrochemical reaction is solved based on quasi-linearization. • The model is verified by simulations and experiments at various operating conditions.
Energy Technology Data Exchange (ETDEWEB)
Suescun-Diaz, Daniel [Surcolombiana Univ., Neiva (Colombia). Groupo de Fisica Teorica; Narvaez-Paredes, Mauricio [Javeriana Univ., Cali (Colombia). Groupo de Matematica y Estadistica Aplicada Pontificia; Lozano-Parada, Jamie H. [Univ. del Valle, Cali (Colombia). Dept. de Ingenieria
2016-03-15
In this paper, the generalisation of the 4th-order Adams-Bashforth-Moulton predictor-corrector method is proposed to numerically solve the point kinetic equations of the nuclear reactivity calculations without using the nuclear power history. Due to the nature of the point kinetic equations, different predictor modifiers are used in order improve the precision of the approximations obtained. The results obtained with the prediction formulas and generalised corrections improve the precision when compared with previous methods and are valid for various forms of nuclear power and different time steps.
Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism
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Xu Wang
2013-01-01
Full Text Available A differential evolution (DE algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.
A Novel Self-Adaptive Harmony Search Algorithm
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Kaiping Luo
2013-01-01
Full Text Available The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS, improved harmony search (IHS, and other self-adaptive variants for large-scale optimization problems and constrained problems.
Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm
International Nuclear Information System (INIS)
Rao, R.V.; More, K.C.
2017-01-01
Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.
A Self Adaptive Differential Evolution Algorithm for Global Optimization
Kumar, Pravesh; Pant, Millie
This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.
A Monte Carlo implementation of the predictor-corrector Quasi-Static method
International Nuclear Information System (INIS)
Hackemack, M. W.; Ragusa, J. C.; Griesheimer, D. P.; Pounders, J. M.
2013-01-01
The Quasi-Static method (QS) is a useful tool for solving reactor transients since it allows for larger time steps when updating neutron distributions. Because of the beneficial attributes of Monte Carlo (MC) methods (exact geometries and continuous energy treatment), it is desirable to develop a MC implementation for the QS method. In this work, the latest version of the QS method known as the Predictor-Corrector Quasi-Static method is implemented. Experiments utilizing two energy-groups provide results that show good agreement with analytical and reference solutions. The method as presented can easily be implemented in any continuous energy, arbitrary geometry, MC code. (authors)
A chaos wolf optimization algorithm with self-adaptive variable step-size
Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun
2017-10-01
To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.
Self-Adaptive Step Firefly Algorithm
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Shuhao Yu
2013-01-01
Full Text Available In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.
Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation
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Hong SeungHo
2011-01-01
Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.
A chaos wolf optimization algorithm with self-adaptive variable step-size
Directory of Open Access Journals (Sweden)
Yong Zhu
2017-10-01
Full Text Available To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.
A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel
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Quanli Xu
2018-03-01
Full Text Available Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.
Al Jarro, Ahmed
2012-11-01
An explicit marching-on-in-time (MOT) scheme for solving the time domain volume integral equation is presented. The proposed method achieves its stability by employing, at each time step, a corrector scheme, which updates/corrects fields computed by the explicit predictor scheme. The proposedmethod is computationally more efficient when compared to the existing filtering techniques used for the stabilization of explicit MOT schemes. Numerical results presented in this paper demonstrate that the proposed method maintains its stability even when applied to the analysis of electromagnetic wave interactions with electrically large structures meshed using approximately half a million discretization elements.
Al Jarro, Ahmed; Salem, Mohamed; Bagci, Hakan; Benson, Trevor; Sewell, Phillip D.; Vuković, Ana
2012-01-01
An explicit marching-on-in-time (MOT) scheme for solving the time domain volume integral equation is presented. The proposed method achieves its stability by employing, at each time step, a corrector scheme, which updates/corrects fields computed by the explicit predictor scheme. The proposedmethod is computationally more efficient when compared to the existing filtering techniques used for the stabilization of explicit MOT schemes. Numerical results presented in this paper demonstrate that the proposed method maintains its stability even when applied to the analysis of electromagnetic wave interactions with electrically large structures meshed using approximately half a million discretization elements.
Opposition-Based Adaptive Fireworks Algorithm
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Chibing Gong
2016-07-01
Full Text Available A fireworks algorithm (FWA is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA. The purpose of this paper is to add opposition-based learning (OBL to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA. The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA, differential evolution (DE, self-adapting control parameters in differential evolution (jDE, a firefly algorithm (FA, and a standard particle swarm optimization 2011 (SPSO2011 algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.
Institute of Scientific and Technical Information of China (English)
黄静静; 商朋见; 王爱文
2011-01-01
将半定规划(Semidefinite Programming,SDP)的内点算法推广到二次半定规划(QuadraticSemidefinite Programming,QSDP),重点讨论了AHO搜索方向的产生方法.首先利用Wolfe对偶理论推导得到了求解二次半定规划的非线性方程组,利用牛顿法求解该方程组,得到了求解QSDP的内点算法的AHO搜索方向,证明了该搜索方向的存在唯一性,最后给出了求解二次半定规划的预估校正内点算法的具体步骤,并对基于不同搜索方向的内点算法进行了数值实验,结果表明基于NT方向的内点算法最为稳健.%This paper extends the interior point algorithm for solving Semidefinite Programming (SDP) to Quadratic Semidefinite Programming(QSDP) and especially discusses the generation of AHO search direction. Firstly, we derive the nonlinear equations for solving QSDP using Wolfe's dual theorem.The AHO search direction is got by applying Newton' s method to the equations. Then we prove the existence and uniqueness of the search direction, and give the detaied steps of predictor-corrector interior-point algorithm. At last, this paper provides a numerical comparison of the algoritms using three different search directions and suggests the algorithm using NT direction is the most robust.
Design of 2-D Recursive Filters Using Self-adaptive Mutation Differential Evolution Algorithm
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Lianghong Wu
2011-08-01
Full Text Available This paper investigates a novel approach to the design of two-dimensional recursive digital filters using differential evolution (DE algorithm. The design task is reformulated as a constrained minimization problem and is solved by an Self-adaptive Mutation DE algorithm (SAMDE, which adopts an adaptive mutation operator that combines with the advantages of the DE/rand/1/bin strategy and the DE/best/2/bin strategy. As a result, its convergence performance is improved greatly. Numerical experiment results confirm the conclusion. The proposedSAMDE approach is effectively applied to test a numerical example and is compared with previous design methods. The computational experiments show that the SAMDE approach can obtain better results than previous design methods.
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
Energy Technology Data Exchange (ETDEWEB)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-15
A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations.
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
International Nuclear Information System (INIS)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-01
A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations
Childhood Depression: Relation to Adaptive, Clinical and Predictor Variables
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Maite Garaigordobil
2017-05-01
Full Text Available The study had two goals: (1 to explore the relations between self-assessed childhood depression and other adaptive and clinical variables (2 to identify predictor variables of childhood depression. Participants were 420 students aged 7–10 years old (53.3% boys, 46.7% girls. Results revealed: (1 positive correlations between depression and clinical maladjustment, school maladjustment, emotional symptoms, internalizing and externalizing problems, problem behaviors, emotional reactivity, and childhood stress; and (2 negative correlations between depression and personal adaptation, global self-concept, social skills, and resilience (sense of competence and affiliation. Linear regression analysis including the global dimensions revealed 4 predictors of childhood depression that explained 50.6% of the variance: high clinical maladjustment, low global self-concept, high level of stress, and poor social skills. However, upon introducing the sub-dimensions, 9 predictor variables emerged that explained 56.4% of the variance: many internalizing problems, low family self-concept, high anxiety, low responsibility, low personal self-assessment, high social stress, few aggressive behaviors toward peers, many health/psychosomatic problems, and external locus of control. The discussion addresses the importance of implementing prevention programs for childhood depression at early ages.
International Nuclear Information System (INIS)
Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.; Valavi, K.
2013-01-01
Highlights: • SGHS enhanced the convergence rate of LPO using some improvements in comparison to basic HS and GHS. • SGHS optimization algorithm obtained averagely better fitness relative to basic HS and GHS algorithms. • Upshot of the SGHS implementation in the LPO reveals its flexibility, efficiency and reliability. - Abstract: The aim of this work is to apply the new developed optimization algorithm, Self-adaptive Global best Harmony Search (SGHS), for PWRs fuel management optimization. SGHS algorithm has some modifications in comparison with basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms such as dynamically change of parameters. For the demonstration of SGHS ability to find an optimal configuration of fuel assemblies, basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms also have been developed and investigated. For this purpose, Self-adaptive Global best Harmony Search Nodal Expansion package (SGHSNE) has been developed implementing HS, GHS and SGHS optimization algorithms for the fuel management operation of nuclear reactor cores. This package uses developed average current nodal expansion code which solves the multi group diffusion equation by employment of first and second orders of Nodal Expansion Method (NEM) for two dimensional, hexagonal and rectangular geometries, respectively, by one node per a FA. Loading pattern optimization was performed using SGHSNE package for some test cases to present the SGHS algorithm capability in converging to near optimal loading pattern. Results indicate that the convergence rate and reliability of the SGHS method are quite promising and practically, SGHS improves the quality of loading pattern optimization results relative to HS and GHS algorithms. As a result, it has the potential to be used in the other nuclear engineering optimization problems
Institute of Scientific and Technical Information of China (English)
Lili Tao; Bin Xu; Zhihua Hu; Weimin Zhong
2017-01-01
The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta-neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob-lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.
EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal
Chen, Yong; Wu, Chun-ting; Liu, Huan-lin
2017-07-01
Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.
Predictors of Career Adaptability Skill among Higher Education Students in Nigeria
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Amos Shaibu Ebenehi
2016-12-01
Full Text Available This paper examined predictors of career adaptability skill among higher education students in Nigeria. A sample of 603 higher education students randomly selected from six colleges of education in Nigeria participated in this study. A set of self-reported questionnaire was used for data collection, and multiple linear regression analysis was used to analyze the data. Results indicated that 33.3% of career adaptability skill was explained by the model. Four out of the five predictor variables significantly predicted career adaptability skill among higher education students in Nigeria. Among the four predictors, career self-efficacy sources was the most statistically significant predictor of career adaptability skill among higher education students in Nigeria, followed by personal goal orientation, career future concern, and perceived social support respectively. Vocational identity did not statistically predict career adaptability skill among higher education students in Nigeria. The study suggested that similar study should be replicated in other parts of the world in view of the importance of career adaptability skill to the smooth transition of graduates from school to the labor market. The study concluded by requesting stakeholders of higher institutions in Nigeria to provide career exploration database for the students, and encourage career intervention program in order to enhance career adaptability skill among the students.
A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters
Wang, Zhihao; Yi, Jing
2016-01-01
For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291
Institute of Scientific and Technical Information of China (English)
高夫征
2005-01-01
A finite volume element predictor-correetor method for a class of nonlinear parabolic system of equations is presented and analyzed. Suboptimal L2 error estimate for the finite volume element predictor-corrector method is derived. A numerical experiment shows that the numerical results are consistent with theoretical analysis.
Directory of Open Access Journals (Sweden)
Rasim M. Alguliev
2011-01-01
Full Text Available Extractive multidocument summarization is modeled as a modified p-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.
Guidance and Control Algorithms for the Mars Entry, Descent and Landing Systems Analysis
Davis, Jody L.; CwyerCianciolo, Alicia M.; Powell, Richard W.; Shidner, Jeremy D.; Garcia-Llama, Eduardo
2010-01-01
The purpose of the Mars Entry, Descent and Landing Systems Analysis (EDL-SA) study was to identify feasible technologies that will enable human exploration of Mars, specifically to deliver large payloads to the Martian surface. This paper focuses on the methods used to guide and control two of the contending technologies, a mid- lift-to-drag (L/D) rigid aeroshell and a hypersonic inflatable aerodynamic decelerator (HIAD), through the entry portion of the trajectory. The Program to Optimize Simulated Trajectories II (POST2) is used to simulate and analyze the trajectories of the contending technologies and guidance and control algorithms. Three guidance algorithms are discussed in this paper: EDL theoretical guidance, Numerical Predictor-Corrector (NPC) guidance and Analytical Predictor-Corrector (APC) guidance. EDL-SA also considered two forms of control: bank angle control, similar to that used by Apollo and the Space Shuttle, and a center-of-gravity (CG) offset control. This paper presents the performance comparison of these guidance algorithms and summarizes the results as they impact the technology recommendations for future study.
Adaptive algorithms for a self-shielding wavelet-based Galerkin method
International Nuclear Information System (INIS)
Fournier, D.; Le Tellier, R.
2009-01-01
The treatment of the energy variable in deterministic neutron transport methods is based on a multigroup discretization, considering the flux and cross-sections to be constant within a group. In this case, a self-shielding calculation is mandatory to correct sections of resonant isotopes. In this paper, a different approach based on a finite element discretization on a wavelet basis is used. We propose adaptive algorithms constructed from error estimates. Such an approach is applied to within-group scattering source iterations. A first implementation is presented in the special case of the fine structure equation for an infinite homogeneous medium. Extension to spatially-dependent cases is discussed. (authors)
A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm.
Zhang, Weifang; Li, Yingwu; Jin, Bo; Ren, Feifei; Wang, Hongxun; Dai, Wei
2018-04-08
A Fiber Bragg Grating (FBG) interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA) and advanced RISC machine (ARM) platform, tunable Fabry-Perot (F-P) filter and optical switch. To improve system resolution, the F-P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM) of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed.
A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm
Directory of Open Access Journals (Sweden)
Weifang Zhang
2018-04-01
Full Text Available A Fiber Bragg Grating (FBG interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA and advanced RISC machine (ARM platform, tunable Fabry–Perot (F–P filter and optical switch. To improve system resolution, the F–P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed.
Directory of Open Access Journals (Sweden)
Rail M Shamionov
2017-12-01
Full Text Available The article discusses the results of a study on the socio-psychological adaptation predictors of the unemployed in relation to people with regular employment. It is assumed that adaptation of the employed and the unemployed is determined by various socio-psychological phenomena; definition of the phenomena will allow to develop programmes of adaptation for the unemployed with preservation of motivation for self-realization. In total, 362 people (33% of whom were male took part in the study, including 196 unemployed. Standardized methods and scales developed by the authors for assessing the subject position characteristics and adaptive readiness of a person were used. It was found that the unemployed are characterized by lower indicators of socio-psychological adaptation and characteristics that are of paramount importance for adaptation - self-acceptance, acceptance of others, emotional comfort. Socio-demographic characteristics, scales of subjective position, adaptive readiness, subjective well-being and values were consistently introduced to the regression equation. It is shown that adaptive readiness and values are the strongest predictors for the employed, while indicators of subjective well-being and value are more significant for the unemployed. The general predictors of adaptation are the level of education, happiness (positively and negative affect (negatively. In other cases, the predictors are strictly differentiated.
Modeling and Control of Magnetic Fluid Deformable Mirrors for Adaptive Optics Systems
Wu, Zhizheng; Ben Amara, Foued
2013-01-01
Modeling and Control of Magnetic Fluid Deformable Mirrors for Adaptive Optics Systems presents a novel design of wavefront correctors based on magnetic fluid deformable mirrors (MFDM) as well as corresponding control algorithms. The presented wavefront correctors are characterized by their linear, dynamic response. Various mirror surface shape control algorithms are presented along with experimental evaluations of the performance of the resulting adaptive optics systems. Adaptive optics (AO) systems are used in various fields of application to enhance the performance of optical systems, such as imaging, laser, free space optical communication systems, etc. This book is intended for undergraduate and graduate students, professors, engineers, scientists and researchers working on the design of adaptive optics systems and their various emerging fields of application. Zhizheng Wu is an associate professor at Shanghai University, China. Azhar Iqbal is a research associate at the University of Toronto, Canada. Foue...
Encke-Beta Predictor for Orion Burn Targeting and Guidance
Robinson, Shane; Scarritt, Sara; Goodman, John L.
2016-01-01
The state vector prediction algorithm selected for Orion on-board targeting and guidance is known as the Encke-Beta method. Encke-Beta uses a universal anomaly (beta) as the independent variable, valid for circular, elliptical, parabolic, and hyperbolic orbits. The variable, related to the change in eccentric anomaly, results in integration steps that cover smaller arcs of the trajectory at or near perigee, when velocity is higher. Some burns in the EM-1 and EM-2 mission plans are much longer than burns executed with the Apollo and Space Shuttle vehicles. Burn length, as well as hyperbolic trajectories, has driven the use of the Encke-Beta numerical predictor by the predictor/corrector guidance algorithm in place of legacy analytic thrust and gravity integrals.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Wang, Pan; Zhang, Yi; Yan, Dong
2018-05-01
Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.
Design of off-axial Gregory telescope design with freeform mirror corrector
Bazhanov, Yu.; Vlakhko, V.
2017-08-01
In this paper a well-known approach is used for calculation of off-axis three-mirror telescope. It includes usage of conic cross-sections properties, each of the sections forming a stigmatic image. To create a compact optical system, a flat mirror aberration corrector is introduced, which is at later stage transformed into a free-form surface in order to compensate field aberrations. Similarly, one can introduce such a corrector in finalized layout for its further optimization and getting a suitable form, including the conversion of multimirrors axial optical system into decentered one. As an example, off-axial Gregory telescope embodiment is used for infrared waveband region, due to the fact that, unlike the Cassegrain telescope, it provides a real exit pupil, and usage of the mirror corrector brings several advantages. Firstly, this feature may be used to include cold stop or adaptive mirror in the exit pupil, wherein corrector is introduced into a converging beam before the focus of the first mirror. Secondly, when placing corrector in the exit pupil of the optical system it is possible to eliminate high and low order aberrations of center point, which in turn improves optical system f-number, and minimize field aberrations. As another example, off-axial Ritchey-Chretien telescope embodiment is used as a good fit for visible region systems. Analysis and calculation results of optical systems with free-form correctors with surfaces, defined by Power polynomial series are presented in this paper. Advantages of different freeform surfaces usage depends on optical system layouts specifics.
International Nuclear Information System (INIS)
Jiang Chuanwen; Bompard, Etorre
2005-01-01
This paper proposes a short term hydroelectric plant dispatch model based on the rule of maximizing the benefit. For the optimal dispatch model, which is a large scale nonlinear planning problem with multi-constraints and multi-variables, this paper proposes a novel self-adaptive chaotic particle swarm optimization algorithm to solve the short term generation scheduling of a hydro-system better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision. The new method has been examined and tested on a practical hydro-system. The results are promising and show the effectiveness and robustness of the proposed approach in comparison with the traditional particle swarm optimization algorithm
The mechanical design for the WEAVE prime focus corrector system
Abrams, Don Carlos; Dee, Kevin; Agócs, Tibor; Lhome, Emilie; Peñate, José; Jaskó, Attila; Bányai, Evelin; Burgal, José A.; Dalton, Gavin; Middleton, Kevin; Bonifacio, Piercarlo; Aguerri, J. Alfonso L.; Trager, S. C.; Balcells, Marc
WEAVE is the next-generation, wide-field, optical spectroscopy facility for the William Herschel Telescope (WHT) in La Palma, Canary Islands, Spain. The WHT will undergo a significant adaptation to accommodate this facility. A two- degree Prime Focus Corrector (PFC), that includes an Atmospheric
Directory of Open Access Journals (Sweden)
Zhongbo Hu
2014-01-01
Full Text Available Many improved differential Evolution (DE algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.
Uljarević, Mirko; Hedley, Darren; Nevill, Rose; Evans, David W; Cai, Ru Ying; Butter, Eric; Mulick, James A
2018-04-06
The present study examined the link between poor self-regulation (measured by the child behavior checklist dysregulated profile [DP]) and core autism symptoms, as well as with developmental level, in a sample of 107 children with autism spectrum disorder (ASD) aged 19-46 months. We further examined the utility of DP in predicting individual differences in adaptive functioning, relative to the influence of ASD severity, chronological age (CA), and developmental level. Poor self-regulation was unrelated to CA, developmental level, and severity of ADOS-2 restricted and repetitive behaviors, but was associated with lower ADOS-2 social affect severity. Hierarchical regression identified poor self-regulation as a unique independent predictor of adaptive behavior, with more severe dysregulation predicting poorer adaptive functioning. Results highlight the importance of early identification of deficits in self-regulation, and more specifically, of the utility of DP, when designing individually tailored treatments for young children with ASD. Autism Res 2018. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. This study explored the relationship between poor self-regulation and age, verbal and non-verbal developmental level, severity of autism symptoms and adaptive functioning in 107 children with autism under 4 years of age. Poor self-regulation was unrelated to age, developmental level, and severity of restricted and repetitive behaviors but was associated with lower social affect severity. Importantly, more severe self-regulation deficits predicted poorer adaptive functioning. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
An Enhanced Jaya Algorithm with a Two Group Adaption
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Chibing Gong
2017-01-01
Full Text Available This paper proposes a novel performance enhanced Jaya algorithm with a two group adaption (E-Jaya. Two improvements are presented in E-Jaya. First, instead of using the best and the worst values in Jaya algorithm, EJaya separates all candidates into two groups: the better and the worse groups based on their fitness values, then the mean of the better group and the mean of the worse group are used. Second, in order to add non algorithm-specific parameters in E-Jaya, a novel adaptive method of dividing the two groups has been developed. Finally, twelve benchmark functions with different dimensionality, such as 40, 60, and 100, were evaluated using the proposed EJaya algorithm. The results show that E-Jaya significantly outperformed Jaya algorithm in terms of the solution accuracy. Additionally, E-Jaya was also compared with a differential evolution (DE, a self-adapting control parameters in differential evolution (jDE, a firefly algorithm (FA, and a standard particle swarm optimization 2011 (SPSO2011 algorithm. E-Jaya algorithm outperforms all the algorithms.
Maternal-Related Predictors of Self-Regulation among Low-Income Youth
Crossley, Isabelle A.; Buckner, John C.
2012-01-01
The association between self-regulation and various adaptive outcomes has become a topic of growing interest to researchers. Yet, there is not much research on predictors of self-regulation in children. Using a cross-sectional design and an array of psychometrically sound scales and measures from multiple informants, this study examined whether…
Hamming generalized corrector for reactivity calculation
International Nuclear Information System (INIS)
Suescun-Diaz, Daniel; Ibarguen-Gonzalez, Maria C.; Figueroa-Jimenez, Jorge H.
2014-01-01
This work presents the Hamming method generalized corrector for numerically resolving the differential equation of delayed neutron precursor concentration from the point kinetics equations for reactivity calculation, without using the nuclear power history or the Laplace transform. A study was carried out of several correctors with their respective modifiers with different time step calculations, to offer stability and greater precision. Better results are obtained for some correctors than with other existing methods. Reactivity can be calculated with precision of the order h 5 , where h is the time step. (orig.)
Directory of Open Access Journals (Sweden)
Gilberto Herrera-Ruíz
2013-03-01
Full Text Available A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component’s harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.
Gómez-Espinosa, Alfonso; Hernández-Guzmán, Víctor M; Bandala-Sánchez, Manuel; Jiménez-Hernández, Hugo; Rivas-Araiza, Edgar A; Rodríguez-Reséndiz, Juvenal; Herrera-Ruíz, Gilberto
2013-03-19
A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM) Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs) due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component's harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.
Childhood Depression: Relation to Adaptive, Clinical and Predictor Variables
Garaigordobil, Maite; Bernar?s, Elena; Jaureguizar, Joana; Machimbarrena, Juan M.
2017-01-01
The study had two goals: (1) to explore the relations between self-assessed childhood depression and other adaptive and clinical variables (2) to identify predictor variables of childhood depression. Participants were 420 students aged 7–10 years old (53.3% boys, 46.7% girls). Results revealed: (1) positive correlations between depression and clinical maladjustment, school maladjustment, emotional symptoms, internalizing and externalizing problems, problem behaviors, emotional reactivity, and...
Low-energy foil aberration corrector
International Nuclear Information System (INIS)
Aken, R.H. van; Hagen, C.W.; Barth, J.E.; Kruit, P.
2002-01-01
A spherical and chromatic aberration corrector for electron microscopes is proposed, consisting of a thin foil sandwiched between two apertures. The electrons are retarded at the foil to almost zero energy, so that they can travel ballistically through the foil. It is shown that such a low-voltage corrector has a negative spherical aberration for not too large distances between aperture and foil, as well as a negative chromatic aberration. For various distances the third- and fifth-order spherical aberration coefficients and the first- and second-order chromatic aberration coefficients are calculated using ray tracing. Provided that the foils have sufficient electron transmission the corrector is able to correct the third-order spherical aberration and the first-order chromatic aberration of a typical low-voltage scanning electron microscope. Preliminary results show that the fifth-order spherical aberration and the second-order chromatic aberration can be kept sufficiently low
Predictors of Career Adaptability Skill among Higher Education Students in Nigeria
Ebenehi, Amos Shaibu; Rashid, Abdullah Mat; Bakar, Ab Rahim
2016-01-01
This paper examined predictors of career adaptability skill among higher education students in Nigeria. A sample of 603 higher education students randomly selected from six colleges of education in Nigeria participated in this study. A set of self-reported questionnaire was used for data collection, and multiple linear regression analysis was used…
Energy Technology Data Exchange (ETDEWEB)
Subbaraj, P. [Kalasalingam University, Srivilliputhur, Tamilnadu 626 190 (India); Rengaraj, R. [Electrical and Electronics Engineering, S.S.N. College of Engineering, Old Mahabalipuram Road, Thirupporur (T.K), Kalavakkam, Kancheepuram (Dist.) 603 110, Tamilnadu (India); Salivahanan, S. [S.S.N. College of Engineering, Old Mahabalipuram Road, Thirupporur (T.K), Kalavakkam, Kancheepuram (Dist.) 603 110, Tamilnadu (India)
2009-06-15
In this paper, a self adaptive real-coded genetic algorithm (SARGA) is implemented to solve the combined heat and power economic dispatch (CHPED) problem. The self adaptation is achieved by means of tournament selection along with simulated binary crossover (SBX). The selection process has a powerful exploration capability by creating tournaments between two solutions. The better solution is chosen and placed in the mating pool leading to better convergence and reduced computational burden. The SARGA integrates penalty parameterless constraint handling strategy and simultaneously handles equality and inequality constraints. The population diversity is introduced by making use of distribution index in SBX operator to create a better offspring. This leads to a high diversity in population which can increase the probability towards the global optimum and prevent premature convergence. The SARGA is applied to solve CHPED problem with bounded feasible operating region which has large number of local minima. The numerical results demonstrate that the proposed method can find a solution towards the global optimum and compares favourably with other recent methods in terms of solution quality, handling constraints and computation time. (author)
Directory of Open Access Journals (Sweden)
Tinggui Chen
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.
Algorithms for orbit control on SPEAR
International Nuclear Information System (INIS)
Corbett, J.; Keeley, D.; Hettel, R.; Linscott, I.; Sebek, J.
1994-06-01
A global orbit feedback system has been installed on SPEAR to help stabilize the position of the photon beams. The orbit control algorithms depend on either harmonic reconstruction of the orbit or eigenvector decomposition. The orbit motion is corrected by dipole corrector kicks determined from the inverse corrector-to-bpm response matrix. This paper outlines features of these control algorithms as applied to SPEAR
Marzbanrad, Javad; Tahbaz-zadeh Moghaddam, Iman
2016-09-01
The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.
[Self-esteem predictors in adolescents with diabetes].
Małkowska-Szkutnik, Agnieszka; Gajewski, Jakub; Mazur, Joanna; Gajewska, Katarzyna
2012-01-01
Self-esteem is the conviction concerning self-satisfaction and self-assessment of one's competence. It can influence the overall emotional state, and determine the motivation to take actions of characteristic teenagers. Presentation of the results of research on predictors of self-esteem in healthy adolescents and their peers with diabetes. Is was investigated whether there were differences in factors that determine directly and indirectly the self-esteem within these groups. The survey was conducted during the school year 2010/2011 as a part of cultural and linguistic adaptation of the CHIP-AE questionnaire (Child Health and Illness Profile - Adolescent Edition). Data were collected from 1177 students with average age of 15.4 years, who attended junior high and high schools of different types, in five provinces of Poland. In this group there were 117 adolescents with diabetes and 1060 healthy peers. The CHIP-AE questionnaire consists of six main dimensions: satisfaction, complaints, protective factors, risk factors, achievements and illness. Students are asked to respond mostly from the perspective of the last 4 weeks. Predictors of self-esteem were selected from the following fields of CHIP-AE questionnaire: physical health, self-efficiency, limitation of daily activities, academic achievement, burden of school work, social support, capability of solving social problems, family relationships, relationships with peers and with teachers. Multivariate regression models and structural equitation models were estimated for both the healthy and the ill adolescents. It has been proved that self-esteem of healthy adolescents was determined differently than that of their peers with diabetes. The most important elements forming self-esteem of adolescents with diabetes were as follows: self-assessment of physical fitness, academic achievements and social support. In the studied group an indirect impact of limitations of physical activity on self-perceived fitness and
Distinguishing Byproducts from Non-Adaptive Effects of Algorithmic Adaptations
Directory of Open Access Journals (Sweden)
Justin H. Park
2007-01-01
Full Text Available I evaluate the use of the byproduct concept in psychology, particularly the adaptation-byproduct distinction that is commonly invoked in discussions of psychological phenomena. This distinction can be problematic when investigating algorithmic mechanisms and their effects, because although all byproducts may be functionless concomitants of adaptations, not all incidental effects of algorithmic adaptations are byproducts (although they have sometimes been labeled as such. I call attention to Sperber's (1994 distinction between proper domains and actual domains of algorithmic mechanisms. Extending Sperber's distinction, I propose the terms adaptive effects and non-adaptive effects, which more accurately capture the phenomena of interest to psychologists and prevent fruitless adaptation-versus-byproduct debates.
A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks
Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.
2009-01-01
Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207
Opposition-Based Adaptive Fireworks Algorithm
Chibing Gong
2016-01-01
A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based a...
Adaptive Filtering Algorithms and Practical Implementation
Diniz, Paulo S R
2013-01-01
In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are...
Simulations of a phase corrector plate for the National Ignition Facility
International Nuclear Information System (INIS)
Williams, W. H. LLNL
1998-01-01
Simulations are presented on the effect of placing a static phase corrector plate in each beamline of the National Ignition Facility (NIF) to assist the adaptive optic in correcting beam phase aberrations. Results indicate such a plate could significantly improve the focal spot, reducing a 3ω, 80% spot half-angle from 21 to 8 microrad for poorer-qualtiy optics, and 17 to 7 for better optics. Such a plate appears to be within the range of current fabrication technologies. It would have an alignment requiremnt of ±0.5 mm, if placed in the front end. In NIF operation, the occasional replacement of laser slabs would slowly degrade the beam quality for a fixed corrector plate, with the spot size increasing from 8 to 15 microrad after four new slabs for poorer optics, and 7 to 12 microrad for better optics. The energy fraciton clipped on the injection pinhole (±100 microrad) would be <0.5% due to this pre-correction
Predictor-Based Model Reference Adaptive Control
Lavretsky, Eugene; Gadient, Ross; Gregory, Irene M.
2010-01-01
This paper is devoted to the design and analysis of a predictor-based model reference adaptive control. Stable adaptive laws are derived using Lyapunov framework. The proposed architecture is compared with the now classical model reference adaptive control. A simulation example is presented in which numerical evidence indicates that the proposed controller yields improved transient characteristics.
Design of superconducting corrector magnets for LHC
International Nuclear Information System (INIS)
Baynham, D.E.; Coombs, R.C.; Ijspeert, A.; Perin, R.
1994-01-01
The Large Hadron Collider (LHC) will require a range of superconducting corrector magnets. This paper presents the design of sextupole and decapole corrector coils which will be included as spool pieces adjacent to reach main ring dipole. The paper gives detailed 3D field computations of the coil configurations to meet LHC beam dynamics requirements. Coil protection within a long string environment is addressed and mechanical design outlines are presented
Design of superconducting corrector magnets for LHC
Baynham, D. E.; Coombs, R. C.; Ijspeert, A.; Perin, R.
1994-07-01
The Large Hadron Collider (LHC) will require a range of superconducting corrector magnets. This paper presents the design of sextupole and decapole corrector coils which will be included as spool pieces adjacent to each main ring dipole. The paper gives detailed 3D field computations of the coil configurations to meet LHC beam dynamics requirements. Coil protection within a long string environment is addressed and mechanical design outlines are presented.
Neural predictors of sensorimotor adaptation rate and savings.
Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar; Seidler, Rachael
2018-04-01
In this study, we investigate whether individual variability in the rate of visuomotor adaptation and multiday savings is associated with differences in regional gray matter volume and resting-state functional connectivity. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. Functional connectivity strength between sensorimotor, dorsal cingulate, and temporoparietal regions of the brain was found to predict the rate of learning during the early phase of the adaptation task. In contrast, default mode network connectivity strength was found to predict both the rate of learning during the late adaptation phase and savings. As for structural predictors, greater gray matter volume in temporoparietal and occipital regions predicted faster early learning, whereas greater gray matter volume in superior posterior regions of the cerebellum predicted faster late learning. These findings suggest that the offline neural predictors of early adaptation may facilitate the cognitive aspects of sensorimotor adaptation, supported by the involvement of temporoparietal and cingulate networks. The offline neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. © 2017 Wiley Periodicals, Inc.
International Nuclear Information System (INIS)
Majumdar, A.; Makowitz, H.
1987-10-01
With the development of modern vector/parallel supercomputers and their lower performance clones it has become possible to increase computational performance by several orders of magnitude when comparing to the previous generation of scalar computers. These performance gains are not observed when production versions of current thermal-hydraulic codes are implemented on modern supercomputers. It is our belief that this is due in part to the inappropriateness of using old thermal-hydraulic algorithms with these new computer architectures. We believe that a new generation of algorithms needs to be developed for thermal-hydraulics simulation that is optimized for vector/parallel architectures, and not the scalar computers of the previous generation. We have begun a study that will investigate several approaches for designing such optimal algorithms. These approaches are based on the following concepts: minimize recursion; utilize predictor-corrector iterative methods; maximize the convergence rate of iterative methods used; use physical approximations as well as numerical means to accelerate convergence; utilize explicit methods (i.e., marching) where stability will permit. We call this approach the ''EPIC'' methodology (i.e., Explicit Predictor Iterative Corrector methods). Utilizing the above ideas, we have begun our work by investigating the one-dimensional transient heat conduction equation. We have developed several algorithms based on variations of the Hopscotch concept, which we discuss in the body of this report. 14 refs
Directory of Open Access Journals (Sweden)
Wang Wei
2016-01-01
Full Text Available The related theory and algorithm of adaptive inverse control were presented through the research which pointed out the adaptive inverse control strategy could effectively eliminate the noise influence on the system control. Proposed using a frequency domain filter-X LMS adaptive inverse control algorithm, and the control algorithm was applied to the two-exciter hydraulic vibration test system of random shock vibration control process and summarized the process of the adaptive inverse control strategies in the realization of the random shock vibration test. The self-closed-loop and field test show that using the frequency-domain filter-X LMS adaptive inverse control algorithm can realize high precision control of random shock vibration test.
Adaptive control for a PWR using a self-tuning reference model concept
International Nuclear Information System (INIS)
Miley, G.H.; Park, G.T.; Kim, B.S.
1992-01-01
Possible applications of an adaptive control method to a pressurized-water reactor nuclear power plant are investigated. The self-tuning technique with a reference model concept is employed. This control algorithm is developed by combining the self-tuning controller with the model reference adaptive control. This approach overcomes the difficulties in choosing the appropriate weighting polynomials in the cost function of the self-tuning control
A Demosaicking Algorithm with Adaptive Inter-Channel Correlation
Directory of Open Access Journals (Sweden)
Joan Duran
2015-12-01
Full Text Available Most common cameras use a CCD sensor device measuring a single color per pixel. Demosaicking is the interpolation process by which one can infer a full color image from such a matrix of values, thus interpolating the two missing components per pixel. Most demosaicking methods take advantage of inter-channel correlation locally selecting the best interpolation direction. The obtained results look convincing except when local geometry cannot be inferred from neighboring pixels or channel correlation is low. In these cases, these algorithms create interpolation artifacts such as zipper effect or color aliasing. This paper discusses the implementation details of the algorithm proposed in [J. Duran, A. Buades, ``Self-Similarity and Spectral Correlation Adaptive Algorithm for Color Demosaicking'', IEEE Transactions on Image Processing, 23(9, pp. 4031--4040, 2014]. The proposed method involves nonlocal image self-similarity in order to reduce interpolation artifacts when local geometry is ambiguous. It further introduces a clear and intuitive manner of balancing how much channel-correlation must be taken advantage of.
Design and realization of adaptive optical principle system without wavefront sensing
Wang, Xiaobin; Niu, Chaojun; Guo, Yaxing; Han, Xiang'e.
2018-02-01
In this paper, we focus on the performance improvement of the free space optical communication system and carry out the research on wavefront-sensorless adaptive optics. We use a phase only liquid crystal spatial light modulator (SLM) as the wavefront corrector. The optical intensity distribution of the distorted wavefront is detected by a CCD. We develop a wavefront controller based on ARM and a software based on the Linux operating system. The wavefront controller can control the CCD camera and the wavefront corrector. There being two SLMs in the experimental system, one simulates atmospheric turbulence and the other is used to compensate the wavefront distortion. The experimental results show that the performance quality metric (the total gray value of 25 pixels) increases from 3037 to 4863 after 200 iterations. Besides, it is demonstrated that our wavefront-sensorless adaptive optics system based on SPGD algorithm has a good performance in compensating wavefront distortion.
Convergence Performance of Adaptive Algorithms of L-Filters
Directory of Open Access Journals (Sweden)
Robert Hudec
2003-01-01
Full Text Available This paper deals with convergence parameters determination of adaptive algorithms, which are used in adaptive L-filters design. Firstly the stability of adaptation process, convergence rate or adaptation time, and behaviour of convergence curve belong among basic properties of adaptive algorithms. L-filters with variety of adaptive algorithms were used to their determination. Convergence performances finding of adaptive filters is important mainly for their hardware applications, where filtration in real time or adaptation of coefficient filter with low capacity of input data are required.
Active internal corrector coils
International Nuclear Information System (INIS)
Thompson, P.A.; Cottingham, J.; Dahl, P.
1986-01-01
Trim or corrector coils to correct main magnet field errors and provide higher multipole fields for beam optics purposes are a standard feature of superconducting magnet accelerator systems. This paper describes some of the design and construction features of powered internal trim coils and a sampling of the test results obtained
International Nuclear Information System (INIS)
Su Jie; Xia Guoqing; Zhang Wei
2007-01-01
For further improving the dynamic control capabilities of the gas turbine of the nuclear power plant, this paper puts forward to apply the algorithm of global predictive control with self-adaptive in the rotate speed control of the gas turbine, including control structure and the design of controller in the base of expounding the math model of the gas turbine of the nuclear power plant. the simulation results show that the respond of the change of the gas turbine speed under the control algorithm of global predictive control with self-adaptive is ten second faster than that under the PID control algorithm, and the output value of the gas turbine speed under the PID control algorithm is 1%-2% higher than that under the control slgorithm of global predictive control with self-adaptive. It shows that the algorithm of global predictive control with self-adaptive can better control the output of the speed of the gas turbine of the nuclear power plant and get the better control effect. (authors)
Un corrector gramatical basat en cerques per Internet
Directory of Open Access Journals (Sweden)
Joaquim Moré
2006-05-01
Full Text Available En aquest article presentem un corrector gramatical de l'anglès destinat a escriptors no angloparlants. La principal característica d'aquest corrector és l'ús d'un motor de cerca per Internet. Com que hi ha un gran nombre de pàgines web escrites en anglès, el sistema fa la hipòtesi que un segment de text que no és present en cap pàgina web és probablement un segment de text mal escrit. El sistema també fa la hipòtesi que a la Xarxa hi trobarà exemples que ensenyaran a l'usuari com ha d'expressar el contingut del segment de text d'una manera gramatical i idiomàtica. Per tant, un cop el corrector avisa l'usuari que és millor verificar un segment del seu text, el motor cerca contextos que poden ser útils a la persona que escriu a l'hora de decidir si corregeix el segment o no. Gràcies també a l'ús d'un motor de cerca, el corrector suggereix a l'escriptor que utilitzi expressions que són més freqüents a la Xarxa en comptes de l'expressió que ha escrit. Text complet (PDF
A comparison of three self-tuning control algorithms developed for the Bristol-Babcock controller
International Nuclear Information System (INIS)
Tapp, P.A.
1992-04-01
A brief overview of adaptive control methods relating to the design of self-tuning proportional-integral-derivative (PID) controllers is given. The methods discussed include gain scheduling, self-tuning, auto-tuning, and model-reference adaptive control systems. Several process identification and parameter adjustment methods are discussed. Characteristics of the two most common types of self-tuning controllers implemented by industry (i.e., pattern recognition and process identification) are summarized. The substance of the work is a comparison of three self-tuning proportional-plus-integral (STPI) control algorithms developed to work in conjunction with the Bristol-Babcock PID control module. The STPI control algorithms are based on closed-loop cycling theory, pattern recognition theory, and model-based theory. A brief theory of operation of these three STPI control algorithms is given. Details of the process simulations developed to test the STPI algorithms are given, including an integrating process, a first-order system, a second-order system, a system with initial inverse response, and a system with variable time constant and delay. The STPI algorithms' performance with regard to both setpoint changes and load disturbances is evaluated, and their robustness is compared. The dynamic effects of process deadtime and noise are also considered. Finally, the limitations of each of the STPI algorithms is discussed, some conclusions are drawn from the performance comparisons, and a few recommendations are made. 6 refs
A trust region interior point algorithm for optimal power flow problems
Energy Technology Data Exchange (ETDEWEB)
Wang Min [Hefei University of Technology (China). Dept. of Electrical Engineering and Automation; Liu Shengsong [Jiangsu Electric Power Dispatching and Telecommunication Company (China). Dept. of Automation
2005-05-01
This paper presents a new algorithm that uses the trust region interior point method to solve nonlinear optimal power flow (OPF) problems. The OPF problem is solved by a primal/dual interior point method with multiple centrality corrections as a sequence of linearized trust region sub-problems. It is the trust region that controls the linear step size and ensures the validity of the linear model. The convergence of the algorithm is improved through the modification of the trust region sub-problem. Numerical results of standard IEEE systems and two realistic networks ranging in size from 14 to 662 buses are presented. The computational results show that the proposed algorithm is very effective to optimal power flow applications, and favors the successive linear programming (SLP) method. Comparison with the predictor/corrector primal/dual interior point (PCPDIP) method is also made to demonstrate the superiority of the multiple centrality corrections technique. (author)
Self-medication practices and predictors for self-medication with ...
African Journals Online (AJOL)
Background: Self-medication with antimalarials and antibiotics is highly practiced worldwide particularly in developing countries including Tanzania. This study was carried out to determine self-medication practices with antimalarials and antibiotics, and as well as predictors for self-medication among urban communities of ...
Josey, C.; Forget, B.; Smith, K.
2017-12-01
This paper introduces two families of A-stable algorithms for the integration of y‧ = F (y , t) y: the extended predictor-corrector (EPC) and the exponential-linear (EL) methods. The structure of the algorithm families are described, and the method of derivation of the coefficients presented. The new algorithms are then tested on a simple deterministic problem and a Monte Carlo isotopic evolution problem. The EPC family is shown to be only second order for systems of ODEs. However, the EPC-RK45 algorithm had the highest accuracy on the Monte Carlo test, requiring at least a factor of 2 fewer function evaluations to achieve a given accuracy than a second order predictor-corrector method (center extrapolation / center midpoint method) with regards to Gd-157 concentration. Members of the EL family can be derived to at least fourth order. The EL3 and the EL4 algorithms presented are shown to be third and fourth order respectively on the systems of ODE test. In the Monte Carlo test, these methods did not overtake the accuracy of EPC methods before statistical uncertainty dominated the error. The statistical properties of the algorithms were also analyzed during the Monte Carlo problem. The new methods are shown to yield smaller standard deviations on final quantities as compared to the reference predictor-corrector method, by up to a factor of 1.4.
Hardware Acceleration of Adaptive Neural Algorithms.
Energy Technology Data Exchange (ETDEWEB)
James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-11-01
As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.
Classification algorithms using adaptive partitioning
Binev, Peter; Cohen, Albert; Dahmen, Wolfgang; DeVore, Ronald
2014-01-01
© 2014 Institute of Mathematical Statistics. Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335.1353; Mach. Learn. 66 (2007) 209.242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter - of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness β of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.
Classification algorithms using adaptive partitioning
Binev, Peter
2014-12-01
© 2014 Institute of Mathematical Statistics. Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set approximation to the Bayes set and thus fall into the general category of set estimators. In contrast with the most studied tree-based algorithms, which utilize piecewise constant approximation on the generated partition [IEEE Trans. Inform. Theory 52 (2006) 1335.1353; Mach. Learn. 66 (2007) 209.242], we consider decorated trees, which allow us to derive higher order methods. Convergence rates for these methods are derived in terms the parameter - of margin conditions and a rate s of best approximation of the Bayes set by decorated adaptive partitions. They can also be expressed in terms of the Besov smoothness β of the regression function that governs its approximability by piecewise polynomials on adaptive partition. The execution of the algorithms does not require knowledge of the smoothness or margin conditions. Besov smoothness conditions are weaker than the commonly used Holder conditions, which govern approximation by nonadaptive partitions, and therefore for a given regression function can result in a higher rate of convergence. This in turn mitigates the compatibility conflict between smoothness and margin parameters.
DDASAC, Double-Precision Differential or Algebraic Sensitivity Analysis
International Nuclear Information System (INIS)
Caracotsios, M.; Stewart, W.E.; Petzold, L.
1997-01-01
1 - Description of program or function: DDASAC solves nonlinear initial-value problems involving stiff implicit systems of ordinary differential and algebraic equations. Purely algebraic nonlinear systems can also be solved, given an initial guess within the region of attraction of a solution. Options include automatic reconciliation of inconsistent initial states and derivatives, automatic initial step selection, direct concurrent parametric sensitivity analysis, and stopping at a prescribed value of any user-defined functional of the current solution vector. Local error control (in the max-norm or the 2-norm) is provided for the state vector and can include the sensitivities on request. 2 - Method of solution: Reconciliation of initial conditions is done with a damped Newton algorithm adapted from Bain and Stewart (1991). Initial step selection is done by the first-order algorithm of Shampine (1987), extended here to differential-algebraic equation systems. The solution is continued with the DASSL predictor- corrector algorithm (Petzold 1983, Brenan et al. 1989) with the initial acceleration phase detected and with row scaling of the Jacobian added. The backward-difference formulas for the predictor and corrector are expressed in divide-difference form, and the fixed-leading-coefficient form of the corrector (Jackson and Sacks-Davis 1980, Brenan et al. 1989) is used. Weights for error tests are updated in each step with the user's tolerances at the predicted state. Sensitivity analysis is performed directly on the corrector equations as given by Catacotsios and Stewart (1985) and is extended here to the initialization when needed. 3 - Restrictions on the complexity of the problem: This algorithm, like DASSL, performs well on differential-algebraic systems of index 0 and 1 but not on higher-index systems; see Brenan et al. (1989). The user assigns the work array lengths and the output unit. The machine number range and precision are determined at run time by a
PDASAC, Partial Differential Sensitivity Analysis of Stiff System
International Nuclear Information System (INIS)
Caracotsios, M.; Stewart, W.E.
2001-01-01
1 - Description of program or function: PDASAC solves stiff, nonlinear initial-boundary-value problems in a timelike dimension t and a space dimension x. Plane, circular cylindrical or spherical boundaries can be handled. Mixed-order systems of partial differential and algebraic equations can be analyzed with members of order or 0 or 1 in t, 0, 1 or 2 in x. Parametric sensitivities of the calculated states are computed simultaneously on request, via the Jacobian of the state equations. Initial and boundary conditions are efficiently reconciled. Local error control (in the max-norm or the 2-norm) is provided for the state vector and can include the parametric sensitivities if desired. 2 - Method of solution: The method of lines is used, with a user- selected x-grid and a minimum-bandwidth finite-difference approximations of the x-derivatives. Starting conditions are reconciled with a damped Newton algorithm adapted from Bain and Stewart (1991). Initial step selection is done by the first-order algorithms of Shampine (1987), extended here to differential- algebraic equation systems. The solution is continued with the DASSL predictor-corrector algorithm (Petzold 1983, Brenan et al. 1989) with the initial acceleration phase deleted and with row scaling of the Jacobian added. The predictor and corrector are expressed in divided-difference form, with the fixed-leading-coefficient form of corrector (Jackson and Sacks-Davis 1989; Brenan et al. 1989). Weights for the error tests are updated in each step with the user's tolerances at the predicted state. Sensitivity analysis is performed directly on the corrector equations of Caracotsios and Stewart (1985) and is extended here to the initialization when needed. 3 - Restrictions on the complexity of the problem: This algorithm, like DASSL, performs well on differential-algebraic equation systems of index 0 and 1 but not on higher-index systems; see Brenan et al. (1989). The user assigned the work array lengths and the output
First demonstration of the fast-to-slow corrector current shift in the NSLS-II storage ring
Yang, Xi; Tian, Yuke; Yu, Li Hua; Smaluk, Victor
2018-04-01
To realize the full benefits of the high brightness and ultra-small beam sizes of NSLS-II, it is essential that the photon beams are exceedingly stable. In the circumstances of implementing local bumps, changing ID gaps, and long term drifting, the fast orbit feedback (FOFB) requires shifting the fast corrector strengths to the slow correctors to prevent the fast corrector saturation and to make the beam orbit stable in the sub-micron level. As the result, a reliable and precise technique of fast-to-slow corrector strength shift has been developed and tested at NSLS-II. This technique is based on the fast corrector response to the slow corrector change when the FOFB is on. In this article, the shift technique is described and the result of proof-of-principle experiment carried out at NSLS-II is presented. The maximum fast corrector current was reduced from greater than 0.45 A to less than 0.04 A with the orbit perturbation within ±1 μm.
Genetic Algorithms for Case Adaptation
Energy Technology Data Exchange (ETDEWEB)
Salem, A M [Computer Science Dept, Faculty of Computer and Information Sciences, Ain Shams University, Cairo (Egypt); Mohamed, A H [Solid State Dept., (NCRRT), Cairo (Egypt)
2008-07-01
Case based reasoning (CBR) paradigm has been widely used to provide computer support for recalling and adapting known cases to novel situations. Case adaptation algorithms generally rely on knowledge based and heuristics in order to change the past solutions to solve new problems. However, case adaptation has always been a difficult process to engineers within (CBR) cycle. Its difficulties can be referred to its domain dependency; and computational cost. In an effort to solve this problem, this research explores a general-purpose method that applying a genetic algorithm (GA) to CBR adaptation. Therefore, it can decrease the computational complexity of the search space in the problems having a great dependency on their domain knowledge. The proposed model can be used to perform a variety of design tasks on a broad set of application domains. However, it has been implemented for the tablet formulation as a domain of application. The proposed system has improved the performance of the CBR design systems.
Genetic Algorithms for Case Adaptation
International Nuclear Information System (INIS)
Salem, A.M.; Mohamed, A.H.
2008-01-01
Case based reasoning (CBR) paradigm has been widely used to provide computer support for recalling and adapting known cases to novel situations. Case adaptation algorithms generally rely on knowledge based and heuristics in order to change the past solutions to solve new problems. However, case adaptation has always been a difficult process to engineers within (CBR) cycle. Its difficulties can be referred to its domain dependency; and computational cost. In an effort to solve this problem, this research explores a general-purpose method that applying a genetic algorithm (GA) to CBR adaptation. Therefore, it can decrease the computational complexity of the search space in the problems having a great dependency on their domain knowledge. The proposed model can be used to perform a variety of design tasks on a broad set of application domains. However, it has been implemented for the tablet formulation as a domain of application. The proposed system has improved the performance of the CBR design systems
Adaptive sensor fusion using genetic algorithms
International Nuclear Information System (INIS)
Fitzgerald, D.S.; Adams, D.G.
1994-01-01
Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ''fuzzy'' sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Frison, Gianluca; Skajaa, Anders
2015-01-01
We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...... system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that 1) the proposed algorithm...
International Nuclear Information System (INIS)
Niknam, Taher; Golestaneh, Faranak; Shafiei, Mehdi
2013-01-01
Micro Grids (MGs) are clusters of the DER (Distributed Energy Resource) units and loads which can operate in both grid-connected and island modes. This paper addresses a probabilistic cost optimization scheme under uncertain environment for the MGs with several multiple Distributed Generation (DG) units. The purpose of the proposed approach is to make decisions regarding to optimizing the production of the DG units and power exchange with the upstream network for a Combined Heat and Power (CHP) system. A PEMFCPP (Proton Exchange Membrane Fuel cell power plant) is considered as a prime mover of the CHP system. An electrochemical model for representation and performance of the PEMFC is applied. In order to best use of the FCPP, hydrogen production and storage management are carried out. An economic model is organized to calculate the operation cost of the MG based on the electrochemical model of the PEMFC and hydrogen storage. The proposed optimization scheme comprises a self-adaptive Charged System Search (CSS) linked to the 2m + 1 point estimate method. The 2m + 1 point estimate method is employed to cover the uncertainty in the following data: the hourly market tariffs, electrical and thermal load demands, available output power of the PhotoVoltaic (PV) and Wind Turbines (WT) units, fuel prices, hydrogen selling price, operation temperature of the FC and pressure of the reactant gases of FC. The Self-adaptive CSS (SCSS) is organized based on the CSS algorithm and is upgraded by some modification approaches, mainly a self-adaptive reformation approach. In the proposed reformation method, two updating approaches are considered. Each particle based on the ability of those approaches to find optimal solutions in the past iterations, chooses one of them to improve its solution. The effectiveness of the proposed approach is verified on a multiple-DG MG in the grid-connected mode. -- Highlights: ► Consider the effect of Hydrogen produced by PEMFC on MGs. ► Combines
NeatSort - A practical adaptive algorithm
La Rocca, Marcello; Cantone, Domenico
2014-01-01
We present a new adaptive sorting algorithm which is optimal for most disorder metrics and, more important, has a simple and quick implementation. On input $X$, our algorithm has a theoretical $\\Omega (|X|)$ lower bound and a $\\mathcal{O}(|X|\\log|X|)$ upper bound, exhibiting amazing adaptive properties which makes it run closer to its lower bound as disorder (computed on different metrics) diminishes. From a practical point of view, \\textit{NeatSort} has proven itself competitive with (and of...
Self-adaptive Newton-based iteration strategy for the LES of turbulent multi-scale flows
International Nuclear Information System (INIS)
Daude, F.; Mary, I.; Comte, P.
2014-01-01
An improvement of the efficiency of implicit schemes based on Newton-like methods for the simulation of turbulent flows by compressible LES or DNS is proposed. It hinges on a zonal Self-Adaptive Newton method (hereafter denoted SAN), capable of taking advantage of Newton convergence rate heterogeneities in multi-scale flow configurations due to a strong spatial variation of the mesh resolution, such as transitional or turbulent flows controlled by small actuators or passive devices. Thanks to a predictor of the local Newton convergence rate, SAN provides computational savings by allocating resources in regions where they are most needed. The consistency with explicit time integration and the efficiency of the method are checked in three test cases: - The standard test-case of 2-D linear advection of a vortex, on three different two-block grids. - Transition to 3-D turbulence on the lee-side of an airfoil at high angle of attack, which features a challenging laminar separation bubble with a turbulent reattachment. - A passively-controlled turbulent transonic cavity flow, for which the CPU time is reduced by a factor of 10 with respect to the baseline algorithm, illustrates the interest of the proposed algorithm. (authors)
Adaptive Algorithms for Automated Processing of Document Images
2011-01-01
ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University
Further Development of the Sextupole and Decapole Spool Corrector Magnets for the LHC
Allitt, M; Ijspeert, Albert; Karmarkar, M; Karppinen, M; Mazet, J; Pérez, J; Puntambekar, A; Ruwali, K; Salminen, J; Thipsay, A
2000-01-01
In the Large Hadron Collider (LHC) the main dipoles will be equipped with sextupole (MCS) and decapole (MCD) spool correctors to meet the very high demands of field quality required for the satisfactory operation of the machine. Each decapole corrector will in addition have an octupole insert (MCO) and the assembly of the two is designated MCDO. These correctors are needed in relatively large quantities, i.e. 2464 MCS Sextupoles and 1232 MCDO Decapole-Octupole assemblies. Half the number of the required spool correctors will be made in India through a collaboration between CERN and CAT (Centre for Advanced Technology, Indore, India), the other half will be built by European industry. The paper describes final choices concerning design, materials, production techniques, and testing so as to assure economic magnet manufacture but while maintaining a homogenous magnetic quality that results in a robust product.
Goal-Oriented Self-Adaptive hp Finite Element Simulation of 3D DC Borehole Resistivity Simulations
Calo, Victor M.
2011-05-14
In this paper we present a goal-oriented self-adaptive hp Finite Element Method (hp-FEM) with shared data structures and a parallel multi-frontal direct solver. The algorithm automatically generates (without any user interaction) a sequence of meshes delivering exponential convergence of a prescribed quantity of interest with respect to the number of degrees of freedom. The sequence of meshes is generated from a given initial mesh, by performing h (breaking elements into smaller elements), p (adjusting polynomial orders of approximation) or hp (both) refinements on the finite elements. The new parallel implementation utilizes a computational mesh shared between multiple processors. All computational algorithms, including automatic hp goal-oriented adaptivity and the solver work fully in parallel. We describe the parallel self-adaptive hp-FEM algorithm with shared computational domain, as well as its efficiency measurements. We apply the methodology described to the three-dimensional simulation of the borehole resistivity measurement of direct current through casing in the presence of invasion.
Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao
2017-09-01
This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.
A New Adaptive Framework for Collaborative Filtering Prediction.
Almosallam, Ibrahim A; Shang, Yi
2008-06-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.
An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network
Directory of Open Access Journals (Sweden)
Kai Hu
2013-01-01
Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.
An Adaptive Test Sheet Generation Mechanism Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Huan-Yu Lin
2012-01-01
Full Text Available For test-sheet composition systems, it is important to adaptively compose test sheets with diverse conceptual scopes, discrimination and difficulty degrees to meet various assessment requirements during real learning situations. Computation time and item exposure rate also influence performance and item bank security. Therefore, this study proposes an Adaptive Test Sheet Generation (ATSG mechanism, where a Candidate Item Selection Strategy adaptively determines candidate test items and conceptual granularities according to desired conceptual scopes, and an Aggregate Objective Function applies Genetic Algorithm (GA to figure out the approximate solution of mixed integer programming problem for the test-sheet composition. Experimental results show that the ATSG mechanism can efficiently, precisely generate test sheets to meet the various assessment requirements than existing ones. Furthermore, according to experimental finding, Fractal Time Series approach can be applied to analyze the self-similarity characteristics of GA’s fitness scores for improving the quality of the test-sheet composition in the near future.
Object-Oriented Implementation of Adaptive Mesh Refinement Algorithms
Directory of Open Access Journals (Sweden)
William Y. Crutchfield
1993-01-01
Full Text Available We describe C++ classes that simplify development of adaptive mesh refinement (AMR algorithms. The classes divide into two groups, generic classes that are broadly useful in adaptive algorithms, and application-specific classes that are the basis for our AMR algorithm. We employ two languages, with C++ responsible for the high-level data structures, and Fortran responsible for low-level numerics. The C++ implementation is as fast as the original Fortran implementation. Use of inheritance has allowed us to extend the original AMR algorithm to other problems with greatly reduced development time.
Conceptual design of the orbit correctors for D2 and Q4
Rysti, J
2015-01-01
In the luminosity upgrade of the Large Hadron Collider, many dipole, quadrupole, and corrector magnets around the ATLAS and CMS detectors are replaced with larger aperture magnets. The purpose is to reduce the beam size at the interaction point by a factor of two and thus to increase the number of particle collisions. This article presents the results of a preliminary design study of the replacements for double-aperture orbit corrector magnets positioned next to the first matching section quadrupole Q4 and the new correctors to be placed next to the recombination dipole D2. The apertures of the correctors are increased from the current 70 mm diameter to 105 mm. The larger apertures and the fixed 188/194 mm distance between the beams pose design challenges due to magnetic coupling between the apertures. The design proposal described in this report consists of a two-in-one Nb-Ti magnet with one aperture providing horizontal and the other vertical correction. The magnetic forces are taken primarily by stainless ...
Dynamic Self-Adaptive Reliability Control for Electric-Hydraulic Systems
Directory of Open Access Journals (Sweden)
Yi Wan
2015-02-01
Full Text Available The high-speed electric-hydraulic proportional control is a new development of the hydraulic control technique with high reliability, low cost, efficient energy, and easy maintenance; it is widely used in industrial manufacturing and production. However, there are still some unresolved challenges, the most notable being the requirements of high stability and real-time by the classical control algorithm due to its high nonlinear characteristics. We propose a dynamic self-adaptive mixed control method based on the least squares support vector machine (LSSVM and the genetic algorithm for high-speed electric-hydraulic proportional control systems in this paper; LSSVM is used to identify and adjust online a nonlinear electric-hydraulic proportional system, and the genetic algorithm is used to optimize the control law of the controlled system and dynamic self-adaptive internal model control and predictive control are implemented by using the mixed intelligent method. The internal model and the inverse control model are online adjusted together. At the same time, a time-dependent Hankel matrix is constructed based on sample data; thus finite dimensional solution can be optimized on finite dimensional space. The results of simulation experiments show that the dynamic characteristics are greatly improved by the mixed intelligent control strategy, and good tracking and high stability are met in condition of high frequency response.
The role of disability self-concept in adaptation to congenital or acquired disability.
Bogart, Kathleen R
2014-02-01
Current theories of adaptation to disability do not address differences in adaptation to congenital or acquired disability. Although people with congenital disabilities are generally assumed to be better adapted than people with acquired disabilities, few studies have tested this, and even fewer have attempted to explain the mechanisms behind these differences. This study tested the proposition that whether a disability is congenital or acquired plays an important role in the development of the disability self-concept (consisting of disability identity and disability self-efficacy), which in turn, affects satisfaction with life. It was predicted that disability self-concept would be better developed among people with congenital, compared with acquired disabilities, predicting greater satisfaction with life in those with acquired conditions. 226 participants with congenital and acquired mobility disabilities completed a cross-sectional online questionnaire measuring satisfaction with life, self-esteem, disability identity, disability self-efficacy, and demographic information. Self-esteem, disability identity, disability self-efficacy, and income were significant predictors of satisfaction with life. Congenital onset predicted higher satisfaction with life; disability identity and disability self-efficacy, but not self-esteem, partially mediated the relationship. Findings highlight the distinction between adaptation to congenital versus acquired disability and the importance of disability self-concept, which are underresearched constructs. Results suggest that rather than attempting to "normalize" individuals with disabilities, health care professionals should foster their disability self-concept. Possible ways to improve disability self-concept are discussed, such as involvement in the disability community and disability pride. PsycINFO Database Record (c) 2014 APA, all rights reserved.
Adaptive switching gravitational search algorithm: an attempt to ...
Indian Academy of Sciences (India)
Nor Azlina Ab Aziz
An adaptive gravitational search algorithm (GSA) that switches between synchronous and ... genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). ...... heuristic with applications in applied electromagnetics. Prog.
Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...
African Journals Online (AJOL)
This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...
National Aeronautics and Space Administration — The main goal of my research is to develop, implement, verify, and validate an optimal numerical predictor-corrector aerocapture guidance algorithm that is...
Directory of Open Access Journals (Sweden)
Hui Liu
2015-01-01
Full Text Available The key problem of computer-aided diagnosis (CAD of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO pulmonary nodules than other typical algorithms.
Insight, rumination, and self-reflection as predictors of well-being.
Harrington, Rick; Loffredo, Donald A
2011-01-01
Dispositional private self-focused attention variables such as insight, internal self-awareness (ISA), and self-reflectiveness (SR) have been found to relate to well-being. The present study sought to determine which dispositional private self-focused attention variables have the most predictive power for subjective well-being as measured by the Satisfaction With Life Scale (E. Diener, R. A. Emmons, R. J. Larsen, & S. Griffin, 1985) and for a eudaemonic form of well-being as measured by the Psychological Well-Being Scale (C. D. Ryff, 1989). A total of 121 college student participants completed an online version of the Self-Consciousness Scale-Revised, the Rumination-Reflection Questionnaire, the Self-Reflection and Insight Scale, the Satisfaction With Life Scale, and the Psychological WellBeing Scale. Results of a multivariate regression analysis using the Self-Consciousness Scale-Revised's (M. F. Scheier & C. S. Carver, 1985) subfactors of SR and ISA, the Rumination-Reflection Questionnaire's (P. D. Trapnell & J. D. Campbell, 1999) subscales of Rumination and Reflection, and the Self-Reflection and Insight Scale's (A. M. Grant, J. Franklin, & P. Langford, 2002) Self-Reflection and Insight subscales revealed that the Insight subscale was the only statistically significant predictor (a positive predictor) for all 6 dimensions of psychological well-being. Insight was also the only significant positive predictor for satisfaction with life. The Rumination subscale was a significant negative predictor for 3 dimensions of psychological well-being, and the Reflection subscale was a significant positive predictor for 1 dimension. Implications of dispositional self-awareness variables and their relation to dimensions of well-being are discussed.
Directory of Open Access Journals (Sweden)
Wojciszke Bogdan
2014-12-01
Full Text Available Two hypotheses concerning the relative importance of agentic versus communal traits as predictors of selfesteem were tested. The perspective hypothesis assumed that self-esteem is dominated by agency over communion because self-perceptions are formed from the agent (versus recipient perspective. The culture hypothesis assumed that self-esteem is dominated by communal concerns in collectivistic cultures and by agentic concerns in individualistic cultures (echoed by individual differences in self-construal. Study 1 involving three samples from collectivistic countries and three from individualistic ones found that self-esteem was better predicted from self-ratings of agentic than communal traits, with the exception of collectivistic women for whom the two predictors were equal. Study 2 primed the interdependent or independent self and found self-ratings of agency to be better predictors of self-esteem than self-ratings of communion, with the exception of interdependence priming,where the two predictors were equal in strength.
Adaptive discrete-ordinates algorithms and strategies
International Nuclear Information System (INIS)
Stone, J.C.; Adams, M.L.
2005-01-01
We present our latest algorithms and strategies for adaptively refined discrete-ordinates quadrature sets. In our basic strategy, which we apply here in two-dimensional Cartesian geometry, the spatial domain is divided into regions. Each region has its own quadrature set, which is adapted to the region's angular flux. Our algorithms add a 'test' direction to the quadrature set if the angular flux calculated at that direction differs by more than a user-specified tolerance from the angular flux interpolated from other directions. Different algorithms have different prescriptions for the method of interpolation and/or choice of test directions and/or prescriptions for quadrature weights. We discuss three different algorithms of different interpolation orders. We demonstrate through numerical results that each algorithm is capable of generating solutions with negligible angular discretization error. This includes elimination of ray effects. We demonstrate that all of our algorithms achieve a given level of error with far fewer unknowns than does a standard quadrature set applied to an entire problem. To address a potential issue with other algorithms, we present one algorithm that retains exact integration of high-order spherical-harmonics functions, no matter how much local refinement takes place. To address another potential issue, we demonstrate that all of our methods conserve partial currents across interfaces where quadrature sets change. We conclude that our approach is extremely promising for solving the long-standing problem of angular discretization error in multidimensional transport problems. (authors)
Hopp, Toby; Barker, Valerie; Schmitz Weiss, Amy
2015-08-01
This study explored the relationship between interdependent self-construal, video game self-efficacy, massively multiplayer online role-playing game (MMORPG) community involvement, and self-reported learning outcomes. The results suggested that self-efficacy and interdependent self-construal were positive and significant predictors of MMORPG community involvement. For its part, MMORPG community involvement was a positive predictor of self-reported learning in both focused and incidental forms. Supplementary analyses suggested that self-efficacy was a comparatively more robust predictor of MMORPG community involvement when compared to self-construal. Moreover, the present data suggest that community involvement significantly facilitated indirect relationships between self-construal, game-relevant self-efficacy, and both focused and incidental learning.
Adaptive algorithm of magnetic heading detection
Liu, Gong-Xu; Shi, Ling-Feng
2017-11-01
Magnetic data obtained from a magnetic sensor usually fluctuate in a certain range, which makes it difficult to estimate the magnetic heading accurately. In fact, magnetic heading information is usually submerged in noise because of all kinds of electromagnetic interference and the diversity of the pedestrian’s motion states. In order to solve this problem, a new adaptive algorithm based on the (typically) right-angled corridors of a building or residential buildings is put forward to process heading information. First, a 3D indoor localization platform is set up based on MPU9250. Then, several groups of data are measured by changing the experimental environment and pedestrian’s motion pace. The raw data from the attached inertial measurement unit are calibrated and arranged into a time-stamped array and written to a data file. Later, the data file is imported into MATLAB for processing and analysis using the proposed adaptive algorithm. Finally, the algorithm is verified by comparison with the existing algorithm. The experimental results show that the algorithm has strong robustness and good fault tolerance, which can detect the heading information accurately and in real-time.
Optimal Pid Controller Design Using Adaptive Vurpso Algorithm
Zirkohi, Majid Moradi
2015-04-01
The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.
Adaptation of Rejection Algorithms for a Radar Clutter
Directory of Open Access Journals (Sweden)
D. Popov
2017-09-01
Full Text Available In this paper, the algorithms for adaptive rejection of a radar clutter are synthesized for the case of a priori unknown spectral-correlation characteristics at wobbulation of a repetition period of the radar signal. The synthesis of algorithms for the non-recursive adaptive rejection filter (ARF of a given order is reduced to determination of the vector of weighting coefficients, which realizes the best effectiveness index for radar signal extraction from the moving targets on the background of the received clutter. As the effectiveness criterion, we consider the averaged (over the Doppler signal phase shift improvement coefficient for a signal-to-clutter ratio (SCR. On the base of extreme properties of the characteristic numbers (eigennumbers of the matrices, the optimal vector (according to this criterion maximum is defined as the eigenvector of the clutter correlation matrix corresponding to its minimal eigenvalue. The general type of the vector of optimal ARF weighting coefficients is de-termined and specific adaptive algorithms depending upon the ARF order are obtained, which in the specific cases can be reduced to the known algorithms confirming its authenticity. The comparative analysis of the synthesized and known algorithms is performed. Significant bene-fits are established in clutter rejection effectiveness by the offered processing algorithms compared to the known processing algorithms.
Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems
Directory of Open Access Journals (Sweden)
Zahoor Uddin
2018-01-01
Full Text Available Independent component analysis (ICA is a technique of blind source separation (BSS used for separation of the mixed received signals. ICA algorithms are classified into adaptive and batch algorithms. Adaptive algorithms perform well in time-varying scenario with high-computational complexity, while batch algorithms have better separation performance in quasistatic channels with low-computational complexity. Amongst batch algorithms, the gradient-based ICA algorithms perform well, but step size selection is critical in these algorithms. In this paper, an adaptive step size gradient ascent ICA (ASS-GAICA algorithm is presented. The proposed algorithm is free from selection of the step size parameter with improved convergence and separation performance. Different performance evaluation criteria are used to verify the effectiveness of the proposed algorithm. Performance of the proposed algorithm is compared with the FastICA and optimum block adaptive ICA (OBAICA algorithms for quasistatic and time-varying wireless channels. Simulation is performed over quadrature amplitude modulation (QAM and binary phase shift keying (BPSK signals. Results show that the proposed algorithm outperforms the FastICA and OBAICA algorithms for a wide range of signal-to-noise ratio (SNR and input data block lengths.
A study on directional resistivity logging-while-drilling based on self-adaptive hp-FEM
Liu, Dejun; Li, Hui; Zhang, Yingying; Zhu, Gengxue; Ai, Qinghui
2014-12-01
Numerical simulation of resistivity logging-while-drilling (LWD) tool response provides guidance for designing novel logging instruments and interpreting real-time logging data. In this paper, based on self-adaptive hp-finite element method (hp-FEM) algorithm, we analyze LWD tool response against model parameters and briefly illustrate geosteering capabilities of directional resistivity LWD. Numerical simulation results indicate that the change of source spacing is of obvious influence on the investigation depth and detecting precision of resistivity LWD tool; the change of frequency can improve the resolution of low-resistivity formation and high-resistivity formation. The simulation results also indicate that the self-adaptive hp-FEM algorithm has good convergence speed and calculation accuracy to guide the geologic steering drilling and it is suitable to simulate the response of resistivity LWD tools.
International Nuclear Information System (INIS)
Cheng Sheng-Yi; Liu Wen-Jin; Chen Shan-Qiu; Dong Li-Zhi; Yang Ping; Xu Bing
2015-01-01
Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n 2 ) ∼ O(n 3 ) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ∼ (O(n) 3/2 ), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. (paper)
Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data
Gaspar-Cunha, A.; Recio, G.; Costa, L.; Estébanez, C.
2014-01-01
Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201
AMOBH: Adaptive Multiobjective Black Hole Algorithm.
Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen
2017-01-01
This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.
An adaptive inverse kinematics algorithm for robot manipulators
Colbaugh, R.; Glass, K.; Seraji, H.
1990-01-01
An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.
International Nuclear Information System (INIS)
Yu, Kunjie; Chen, Xu; Wang, Xin; Wang, Zhenlei
2017-01-01
Highlights: • SATLBO is proposed to identify the PV model parameters efficiently. • In SATLBO, the learners self-adaptively select different learning phases. • An elite learning is developed in teacher phase to perform local searching. • A diversity learning is proposed in learner phase to maintain population diversity. • SATLBO achieves the first in ranking on overall performance among nine algorithms. - Abstract: Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods.
Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.
Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui
2017-01-01
To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.
Theory of affine projection algorithms for adaptive filtering
Ozeki, Kazuhiko
2016-01-01
This book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important f...
Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...
African Journals Online (AJOL)
This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...
Adaptive protection algorithm and system
Hedrick, Paul [Pittsburgh, PA; Toms, Helen L [Irwin, PA; Miller, Roger M [Mars, PA
2009-04-28
An adaptive protection algorithm and system for protecting electrical distribution systems traces the flow of power through a distribution system, assigns a value (or rank) to each circuit breaker in the system and then determines the appropriate trip set points based on the assigned rank.
A self-learning algorithm for biased molecular dynamics
Tribello, Gareth A.; Ceriotti, Michele; Parrinello, Michele
2010-01-01
A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences. PMID:20876135
An Adaptive Tradeoff Algorithm for Multi-issue SLA Negotiation
Son, Seokho; Sim, Kwang Mong
Since participants in a Cloud may be independent bodies, mechanisms are necessary for resolving different preferences in leasing Cloud services. Whereas there are currently mechanisms that support service-level agreement negotiation, there is little or no negotiation support for concurrent price and timeslot for Cloud service reservations. For the concurrent price and timeslot negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price and timeslot proposal is necessary. The contribution of this work is thus to design an adaptive tradeoff algorithm for multi-issue negotiation mechanism. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed and total utility and to reduce computational load by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using a testbed suggest that due to the concurrent price and timeslot negotiation mechanism with adaptive tradeoff algorithm: 1) both agents achieve the best performance in terms of negotiation speed and utility; 2) the number of evaluations of each proposal is comparatively lower than previous scheme (burst-N).
A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs
Directory of Open Access Journals (Sweden)
Li Liu
2015-01-01
we propose a modified version of hidden Markov model (HMM classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.
Vega, J.; Murari, A.; Dormido-Canto, S.; Moreno, R.; Pereira, A.; Acero, A.; Contributors, JET-EFDA
2014-12-01
The development of accurate real-time disruption predictors is a pre-requisite to any mitigation action. Present theoretical models of disruptions do not reliably cope with the disruption issues. This article deals with data-driven predictors and a review of existing machine learning techniques, from both physics and engineering points of view, is provided. All these methods need large training datasets to develop successful predictors. However, ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor. So far, the attempts to extrapolate predictors between different tokamaks have not shown satisfactory results. In addition, it is not clear how valid this approach can be between present devices and ITER/DEMO, due to the differences in their respective scales and possibly underlying physics. Therefore, this article analyses the requirements to create adaptive predictors from scratch to learn from the data of an individual machine from the beginning of operation. A particular algorithm based on probabilistic classifiers has been developed and it has been applied to the database of the three first ITER-like wall campaigns of JET (1036 non-disruptive and 201 disruptive discharges). The predictions start from the first disruption and only 12 re-trainings have been necessary as a consequence of missing 12 disruptions only. Almost 10 000 different predictors have been developed (they differ in their features) and after the chronological analysis of the 1237 discharges, the predictors recognize 94% of all disruptions with an average warning time (AWT) of 654 ms. This percentage corresponds to the sum of tardy detections (11%), valid alarms (76%) and premature alarms (7%). The false alarm rate is 4%. If only valid alarms are considered, the AWT is 244 ms and the standard deviation is 205 ms. The average probability interval about the reliability and accuracy of all the individual predictions is 0.811 ± 0.189.
Predictors of students' self-esteem: The importance of body self-perception and exercise
Directory of Open Access Journals (Sweden)
Lazarević Ljiljana B.
2017-01-01
Full Text Available The goal of this study was to explore the predictive validity of physical self-efficacy, social physique anxiety, and physical activity in the self-esteem of students, as well as to investigate potential gender differences. The Rosenberg's Self-Esteem Scale (SES, Physical Self-Efficacy Scale (PSES, Social Physique Anxiety Scale (SPAS, and a short questionnaire about physical activity were administered to a sample of 232 university students. The overall results show that students are moderately physically active (on the average, 2.75 times per week, have moderately high selfesteem and physical self-efficacy and lower social physique anxiety. No gender differences were detected in self-esteem. In other variables, gender differences are significant and mostly in favour of males. The analyses showed that self-esteem correlated positively with physical self-efficacy and physical activity, and negatively with social physique anxiety. The regression analyses indicated that physical selfefficacy, social physique anxiety and female gender were significant predictors of self-esteem. Physical activity was not a significant predictor of self-esteem. Future studies should investigate the relations of body self-perceptions, physical exercise, and domain-specific self-esteem.
DEFF Research Database (Denmark)
Markham, Annette
This paper takes an actor network theory approach to explore some of the ways that algorithms co-construct identity and relational meaning in contemporary use of social media. Based on intensive interviews with participants as well as activity logging and data tracking, the author presents a richly...... layered set of accounts to help build our understanding of how individuals relate to their devices, search systems, and social network sites. This work extends critical analyses of the power of algorithms in implicating the social self by offering narrative accounts from multiple perspectives. It also...... contributes an innovative method for blending actor network theory with symbolic interaction to grapple with the complexity of everyday sensemaking practices within networked global information flows....
Health Value and Self-Esteem as Predictors of Wellness Behaviors
1988-08-25
tes for example, may be more related to environmental influences than to low self - esteem . Moreover, the findings that these respondents’ percent...ii iI.-’ HhUU-"Y ,I. HEALTH VALUE AND SELF - ESTEEM - AS PREDICTORS OF WELLNESS BEHAVIORS CDi DTIC cF.- I- ECTE JUL 1 2) 1990 tib D. A. ABOOD T. L...COMND% NAVAL MEDICAL RESSARCHf AN~D DEVELOP491NT COMMANDt " BETHElDA, MARYLANO, Health Value and Self - Esteem as Predictors of Wellness Behaviors
Time Optimized Algorithm for Web Document Presentation Adaptation
DEFF Research Database (Denmark)
Pan, Rong; Dolog, Peter
2010-01-01
Currently information on the web is accessed through different devices. Each device has its own properties such as resolution, size, and capabilities to display information in different format and so on. This calls for adaptation of information presentation for such platforms. This paper proposes...... content-optimized and time-optimized algorithms for information presentation adaptation for different devices based on its hierarchical model. The model is formalized in order to experiment with different algorithms.......Currently information on the web is accessed through different devices. Each device has its own properties such as resolution, size, and capabilities to display information in different format and so on. This calls for adaptation of information presentation for such platforms. This paper proposes...
Directory of Open Access Journals (Sweden)
Wen-Tsai Sung
2013-12-01
Full Text Available This work aims to develop a smart LED lighting system, which is remotely controlled by Android apps via handheld devices, e.g., smartphones, tablets, and so forth. The status of energy use is reflected by readings displayed on a handheld device, and it is treated as a criterion in the lighting mode design of a system. A multimeter, a wireless light dimmer, an IR learning remote module, etc. are connected to a server by means of RS 232/485 and a human computer interface on a touch screen. The wireless data communication is designed to operate in compliance with the ZigBee standard, and signal processing on sensed data is made through a self adaptive weighted data fusion algorithm. A low variation in data fusion together with a high stability is experimentally demonstrated in this work. The wireless light dimmer as well as the IR learning remote module can be instructed directly by command given on the human computer interface, and the reading on a multimeter can be displayed thereon via the server. This proposed smart LED lighting system can be remotely controlled and self learning mode can be enabled by a single handheld device via WiFi transmission. Hence, this proposal is validated as an approach to power monitoring for home appliances, and is demonstrated as a digital home network in consideration of energy efficiency.
Sung, Wen-Tsai; Lin, Jia-Syun
2013-01-01
This work aims to develop a smart LED lighting system, which is remotely controlled by Android apps via handheld devices, e.g., smartphones, tablets, and so forth. The status of energy use is reflected by readings displayed on a handheld device, and it is treated as a criterion in the lighting mode design of a system. A multimeter, a wireless light dimmer, an IR learning remote module, etc. are connected to a server by means of RS 232/485 and a human computer interface on a touch screen. The wireless data communication is designed to operate in compliance with the ZigBee standard, and signal processing on sensed data is made through a self adaptive weighted data fusion algorithm. A low variation in data fusion together with a high stability is experimentally demonstrated in this work. The wireless light dimmer as well as the IR learning remote module can be instructed directly by command given on the human computer interface, and the reading on a multimeter can be displayed thereon via the server. This proposed smart LED lighting system can be remotely controlled and self learning mode can be enabled by a single handheld device via WiFi transmission. Hence, this proposal is validated as an approach to power monitoring for home appliances, and is demonstrated as a digital home network in consideration of energy efficiency.
Implementation and analysis of an adaptive multilevel Monte Carlo algorithm
Hoel, Hakon; Von Schwerin, Erik; Szepessy, Anders; Tempone, Raul
2014-01-01
We present an adaptive multilevel Monte Carlo (MLMC) method for weak approximations of solutions to Itô stochastic dierential equations (SDE). The work [11] proposed and analyzed an MLMC method based on a hierarchy of uniform time discretizations and control variates to reduce the computational effort required by a single level Euler-Maruyama Monte Carlo method from O(TOL-3) to O(TOL-2 log(TOL-1)2) for a mean square error of O(TOL2). Later, the work [17] presented an MLMC method using a hierarchy of adaptively re ned, non-uniform time discretizations, and, as such, it may be considered a generalization of the uniform time discretizationMLMC method. This work improves the adaptiveMLMC algorithms presented in [17] and it also provides mathematical analysis of the improved algorithms. In particular, we show that under some assumptions our adaptive MLMC algorithms are asymptotically accurate and essentially have the correct complexity but with improved control of the complexity constant factor in the asymptotic analysis. Numerical tests include one case with singular drift and one with stopped diusion, where the complexity of a uniform single level method is O(TOL-4). For both these cases the results con rm the theory, exhibiting savings in the computational cost for achieving the accuracy O(TOL) from O(TOL-3) for the adaptive single level algorithm to essentially O(TOL-2 log(TOL-1)2) for the adaptive MLMC algorithm. © 2014 by Walter de Gruyter Berlin/Boston 2014.
Cheng, Sheng-Yi; Liu, Wen-Jin; Chen, Shan-Qiu; Dong, Li-Zhi; Yang, Ping; Xu, Bing
2015-08-01
Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ˜ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ˜ (O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. Project supported by the National Key Scientific and Research Equipment Development Project of China (Grant No. ZDYZ2013-2), the National Natural Science Foundation of China (Grant No. 11173008), and the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program, China (Grant No. 2012JQ0012).
Adaptive symbiotic organisms search (SOS algorithm for structural design optimization
Directory of Open Access Journals (Sweden)
Ghanshyam G. Tejani
2016-07-01
Full Text Available The symbiotic organisms search (SOS algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.
New time-saving predictor algorithm for multiple breath washout in adolescents
DEFF Research Database (Denmark)
Grønbæk, Jonathan; Hallas, Henrik Wegener; Arianto, Lambang
2016-01-01
BACKGROUND: Multiple breath washout (MBW) is an informative but time-consuming test. This study evaluates the uncertainty of a time-saving predictor algorithm in adolescents. METHODS: Adolescents were recruited from the Copenhagen Prospective Study on Asthma in Childhood (COPSAC2000) birth cohort...
International Nuclear Information System (INIS)
Niknam, Taher; Azadfarsani, Ehsan; Jabbari, Masoud
2012-01-01
Highlights: ► Network reconfiguration is a very important way to save the electrical energy. ► This paper proposes a new algorithm to solve the DFR. ► The algorithm combines NFAPSO with NM. ► The proposed algorithm is tested on two distribution test feeders. - Abstract: Network reconfiguration for loss reduction in distribution system is a very important way to save the electrical energy. This paper proposes a new hybrid evolutionary algorithm to solve the Distribution Feeder Reconfiguration problem (DFR). The algorithm is based on combination of a New Fuzzy Adaptive Particle Swarm Optimization (NFAPSO) and Nelder–Mead simplex search method (NM) called NFAPSO–NM. In the proposed algorithm, a new fuzzy adaptive particle swarm optimization includes two parts. The first part is Fuzzy Adaptive Binary Particle Swarm Optimization (FABPSO) that determines the status of tie switches (open or close) and second part is Fuzzy Adaptive Discrete Particle Swarm Optimization (FADPSO) that determines the sectionalizing switch number. In other side, due to the results of binary PSO(BPSO) and discrete PSO(DPSO) algorithms highly depends on the values of their parameters such as the inertia weight and learning factors, a fuzzy system is employed to adaptively adjust the parameters during the search process. Moreover, the Nelder–Mead simplex search method is combined with the NFAPSO algorithm to improve its performance. Finally, the proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization.
A theoretical analysis of the median LMF adaptive algorithm
DEFF Research Database (Denmark)
Bysted, Tommy Kristensen; Rusu, C.
1999-01-01
Higher order adaptive algorithms are sensitive to impulse interference. In the case of the LMF (Least Mean Fourth), an easy and effective way to reduce this is to median filter the instantaneous gradient of the LMF algorithm. Although previous published simulations have indicated that this reduces...... the speed of convergence, no analytical studies have yet been made to prove this. In order to enhance the usability, this paper presents a convergence and steady-state analysis of the median LMF adaptive algorithm. As expected this proves that the median LMF has a slower convergence and a lower steady...
An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps
Directory of Open Access Journals (Sweden)
Jun Liu
2016-01-01
Full Text Available Virtual machines (VM on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.
Spontaneous Self-Distancing and Adaptive Self-Reflection Across Adolescence.
White, Rachel E; Kross, Ethan; Duckworth, Angela L
2015-07-01
Experiments performed primarily with adults show that self-distancing facilitates adaptive self-reflection. However, no research has investigated whether adolescents spontaneously engage in this process or whether doing so is linked to adaptive outcomes. In this study, 226 African American adolescents, aged 11-20, reflected on an anger-related interpersonal experience. As expected, spontaneous self-distancing during reflection predicted lower levels of emotional reactivity by leading adolescents to reconstrue (rather than recount) their experience and blame their partner less. Moreover, the inverse relation between self-distancing and emotional reactivity strengthened with age. These findings highlight the role that self-distancing plays in fostering adaptive self-reflection in adolescence, and begin to elucidate the role that development plays in enhancing the benefits of engaging in this process. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.
Robust stability analysis of adaptation algorithms for single perceptron.
Hui, S; Zak, S H
1991-01-01
The problem of robust stability and convergence of learning parameters of adaptation algorithms in a noisy environment for the single preceptron is addressed. The case in which the same input pattern is presented in the adaptation cycle is analyzed. The algorithm proposed is of the Widrow-Hoff type. It is concluded that this algorithm is robust. However, the weight vectors do not necessarily converge in the presence of measurement noise. A modified version of this algorithm in which the reduction factors are allowed to vary with time is proposed, and it is shown that this algorithm is robust and that the weight vectors converge in the presence of bounded noise. Only deterministic-type arguments are used in the analysis. An ultimate bound on the error in terms of a convex combination of the initial error and the bound on the noise is obtained.
Adaptive Gradient Multiobjective Particle Swarm Optimization.
Han, Honggui; Lu, Wei; Zhang, Lu; Qiao, Junfei
2017-10-09
An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO. Attributed to the stocktickerMOG method and the self-adaptive flight parameters mechanism, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy, but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front.
[Self-acceptance as adaptively resigning the self to low self-evaluation].
Ueda, T
1996-10-01
In past studies, the concept of self-acceptance has often been confused with self-evaluation or self-esteem. The purpose of this study was to distinguish these concepts, and operationally define self-acceptance as Carl Rogers proposed: feeling all right toward the self when self-evaluation was low. Self-acceptance as adaptive resignation, a moderating variable, therefore should raise self-esteem of only those people with low self-evaluation. Self-acceptance was measured in the study as affirmative evaluation of own self-evaluation. Two hundred and forty college students, 120 each for men and women, completed a questionnaire of self-evaluative consciousness and self-esteem scales. Results of statistical analyses showed that among subjects with low self-evaluation, the higher self-acceptance, the higher the person's self-esteem. The same relation was not observed among those with high self-evaluation. Thus, it may be concluded that self-acceptance was adaptive resignation, and therefore meaningful to only those with low self-evaluation.
Partially Adaptive STAP Algorithm Approaches to functional MRI
Huang, Lejian; Thompson, Elizabeth A.; Schmithorst, Vincent; Holland, Scott K.; Talavage, Thomas M.
2008-01-01
In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing tim...
International Nuclear Information System (INIS)
Park, H.; De Oliveira, C. R. E.
2007-01-01
This paper describes the verification of the recently developed space-angle self-adaptive algorithm for the finite element-spherical harmonics method via the Method of Manufactured Solutions. This method provides a simple, yet robust way for verifying the theoretical properties of the adaptive algorithm and interfaces very well with the underlying second-order, even-parity transport formulation. Simple analytic solutions in both spatial and angular variables are manufactured to assess the theoretical performance of the a posteriori error estimates. The numerical results confirm reliability of the developed space-angle error indicators. (authors)
Adaptive sampling algorithm for detection of superpoints
Institute of Scientific and Technical Information of China (English)
CHENG Guang; GONG Jian; DING Wei; WU Hua; QIANG ShiQiang
2008-01-01
The superpoints are the sources (or the destinations) that connect with a great deal of destinations (or sources) during a measurement time interval, so detecting the superpoints in real time is very important to network security and management. Previous algorithms are not able to control the usage of the memory and to deliver the desired accuracy, so it is hard to detect the superpoints on a high speed link in real time. In this paper, we propose an adaptive sampling algorithm to detect the superpoints in real time, which uses a flow sample and hold module to reduce the detection of the non-superpoints and to improve the measurement accuracy of the superpoints. We also design a data stream structure to maintain the flow records, which compensates for the flow Hash collisions statistically. An adaptive process based on different sampling probabilities is used to maintain the recorded IP ad dresses in the limited memory. This algorithm is compared with the other algo rithms by analyzing the real network trace data. Experiment results and mathematic analysis show that this algorithm has the advantages of both the limited memory requirement and high measurement accuracy.
Design and development of a bipolar power supply for APS storage ring correctors
International Nuclear Information System (INIS)
Kang, Y.G.
1993-01-01
The Advanced Photon Source (APS) requires a number of correction magnets. Basically, two different types of bipolar power supplies (BPS) will be used for all the correction magnets. One requires dc correction only, and the other requires dc and ac correction. For the storage ring horizontal/vertical (H/V) correctors, the BPS should be able to supply dc and ac current. This paper describes the design aspects and considerations for a bipolar power supply for the APS storage ring H/V correctors
Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm
International Nuclear Information System (INIS)
Sun, Zhe; Wang, Ning; Bi, Yunrui; Srinivasan, Dipti
2015-01-01
In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance. - Highlights: • We propose a hybrid adaptive differential evolution algorithm (HADE). • The search efficiency is enhanced in low and high dimension search space. • The effectiveness is confirmed by testing benchmark functions. • The identification of the PEMFC model is conducted by adopting HADE.
Performance of the Superconducting Corrector Magnet Circuits during the Commissioning of the LHC
Venturini-Delsolaro, W; Ballarino, A; Bellesia, B; Bordry, Frederick; Cantone, A; Casas Lino, M; Castaneda Serra, A; Castillo Trello, C; Catalan-Lasheras, N; Charifoulline, Z; Charrondiere, C; Dahlerup-Petersen, K; D'Angelo, G; Denz, R; Fehér, S; Flora, R; Gruwé, M; Kain, V; Karppinen, M; Khomenko, B; Kirby, G; MacPherson, A; Marqueta Barbero, A; Mess, K H; Modena, M; Mompo, R; Montabonnet, V; le Naour, S; Nisbet, D; Parma, V; Pojer, M; Ponce, L; Raimondo, A; Redaelli, S; Remondino, V; Reymond, H; de Rijk, G; Rijllart, A; Romera Ramirez, I; Saban, R; Sanfilippo, S; Schirm, K; Schmidt, R; Siemko, A; Solfaroli Camillocci, M; Thurel, Y; Thiesen, H; Vergara Fernandez, A; Verweij, A; Wolf, R; Zerlauth, M
2008-01-01
The LHC is a complex machine requiring more than 7400 superconducting corrector magnets distributed along a circumference of 26.7 km. These magnets are powered in 1446 different electrical circuits at currents ranging from 60Â A up to 600 A. Among the corrector circuits the 600 A corrector magnets form the most diverse and differentiated group. All together, about 60000 high current connections had to be made. A fault in a circuit or one of the superconducting connections would have severe consequences for the accelerator operation. All magnets are wound from various types of Nb-Ti superconducting strands, and many contain parallel protection resistors to by-pass the current still flowing in the other magnets of the same circuit when they quench. In this paper the performance of these magnet circuits is presented, focussing on the quench behaviour of the magnets. Quench detection and the performance of the electrical interconnects will be dealt with. The results as measured on the entire circuits are compar...
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
A Self-embedding Robust Digital Watermarking Algorithm with Blind Detection
Directory of Open Access Journals (Sweden)
Gong Yunfeng
2014-08-01
Full Text Available In order to achieve the perfectly blind detection of robustness watermarking algorithm, a novel self-embedding robust digital watermarking algorithm with blind detection is proposed in this paper. Firstly the original image is divided to not overlap image blocks and then decomposable coefficients are obtained by lifting-based wavelet transform in every image blocks. Secondly the low-frequency coefficients of block images are selected and then approximately represented as a product of a base matrix and a coefficient matrix using NMF. Then the feature vector represent original image is obtained by quantizing coefficient matrix, and finally the adaptive quantization of the robustness watermark is embedded in the low-frequency coefficients of LWT. Experimental results show that the scheme is robust against common signal processing attacks, meanwhile perfect blind detection is achieve.
The Predictors for Maternal Self-efficacy in Early Parenthood
Directory of Open Access Journals (Sweden)
Elham Azmoude
2015-04-01
Full Text Available Background & aim: Many parents do not believe in their ability to fulfill their parental responsibilities. Parental self-efficacy is crucial to parents’ sense of well-being and is considered a predictor for quality of life. However, evidence is scarce on the factors that influence parents’ perception of efficacy. Therefore, this study aimed to investigate the predictors for parental self-efficacy in the early postpartum period. Methods:This descriptive analytical study was conducted on 150 primiparous women referring to the health care centers of Mashhad during their early postpartum months. For data collection, we used demographic questionnaires, Bates’ Infant Characteristics Questionnaire (ICQ, Scale of Perceived Social Support, Reece’s parent expectations survey (PES, and Edinburgh Postnatal Depression Scale (EPDS. For data analysis, independent T-test, one-way ANOVA, Pearson’s correlation coefficient, and stepwise regression were performed, using SPSS version 16. Results: In this study, a significant association was observed between self-efficacy scores and the parents’ income, educational status, depression, and infant’s gender. Furthermore, there was a significant correlation between self-efficacy scores and infant’s characteristics, mother’s satisfaction with childbirth experience, perceived support from friends, infant’s perceived temperament, infant’s gender, mother’s educational level, and depression, which could predict 26.1% of parental self-efficacy. Conclusion: According to the results of this study, the most significant predictors of maternal self-efficacy during the early postpartum months were maternal depression and educational status, infant’s gender, and infant’s characteristics.
Fast algorithm of adaptive Fourier series
Gao, You; Ku, Min; Qian, Tao
2018-05-01
Adaptive Fourier decomposition (AFD, precisely 1-D AFD or Core-AFD) was originated for the goal of positive frequency representations of signals. It achieved the goal and at the same time offered fast decompositions of signals. There then arose several types of AFDs. AFD merged with the greedy algorithm idea, and in particular, motivated the so-called pre-orthogonal greedy algorithm (Pre-OGA) that was proven to be the most efficient greedy algorithm. The cost of the advantages of the AFD type decompositions is, however, the high computational complexity due to the involvement of maximal selections of the dictionary parameters. The present paper offers one formulation of the 1-D AFD algorithm by building the FFT algorithm into it. Accordingly, the algorithm complexity is reduced, from the original $\\mathcal{O}(M N^2)$ to $\\mathcal{O}(M N\\log_2 N)$, where $N$ denotes the number of the discretization points on the unit circle and $M$ denotes the number of points in $[0,1)$. This greatly enhances the applicability of AFD. Experiments are carried out to show the high efficiency of the proposed algorithm.
A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao
2016-01-01
The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361
Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System
Directory of Open Access Journals (Sweden)
Zhang Yulin
2015-01-01
Full Text Available To address the limitation of conventional adaptive algorithm used for active noise control (ANC system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE, which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS and Decomposition and Reconstruction LMS algorithm (DR-LMS based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.
International Nuclear Information System (INIS)
Vega, J.; Moreno, R.; Pereira, A.; Acero, A.; Murari, A.; Dormido-Canto, S.
2014-01-01
The development of accurate real-time disruption predictors is a pre-requisite to any mitigation action. Present theoretical models of disruptions do not reliably cope with the disruption issues. This article deals with data-driven predictors and a review of existing machine learning techniques, from both physics and engineering points of view, is provided. All these methods need large training datasets to develop successful predictors. However, ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor. So far, the attempts to extrapolate predictors between different tokamaks have not shown satisfactory results. In addition, it is not clear how valid this approach can be between present devices and ITER/DEMO, due to the differences in their respective scales and possibly underlying physics. Therefore, this article analyses the requirements to create adaptive predictors from scratch to learn from the data of an individual machine from the beginning of operation. A particular algorithm based on probabilistic classifiers has been developed and it has been applied to the database of the three first ITER-like wall campaigns of JET (1036 non-disruptive and 201 disruptive discharges). The predictions start from the first disruption and only 12 re-trainings have been necessary as a consequence of missing 12 disruptions only. Almost 10 000 different predictors have been developed (they differ in their features) and after the chronological analysis of the 1237 discharges, the predictors recognize 94% of all disruptions with an average warning time (AWT) of 654 ms. This percentage corresponds to the sum of tardy detections (11%), valid alarms (76%) and premature alarms (7%). The false alarm rate is 4%. If only valid alarms are considered, the AWT is 244 ms and the standard deviation is 205 ms. The average probability interval about the reliability and accuracy of all the individual predictions is 0.811 ± 0.189. (paper)
An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel
2016-01-01
Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. PMID:27043559
An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
Directory of Open Access Journals (Sweden)
Bruno Srbinovski
2016-03-01
Full Text Available Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind. Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources and power hungry sensors (ultrasonic wind sensor and gas sensors. The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.
Link adaptation algorithm for distributed coded transmissions in cooperative OFDMA systems
DEFF Research Database (Denmark)
Varga, Mihaly; Badiu, Mihai Alin; Bota, Vasile
2015-01-01
This paper proposes a link adaptation algorithm for cooperative transmissions in the down-link connection of an OFDMA-based wireless system. The algorithm aims at maximizing the spectral efficiency of a relay-aided communication link, while satisfying the block error rate constraints at both...... adaptation algorithm has linear complexity with the number of available resource blocks, while still provides a very good performance, as shown by simulation results....
QoS-aware self-adaptation of communication protocols in a pervasive service middleware
DEFF Research Database (Denmark)
Zhang, Weishan; Hansen, Klaus Marius; Fernandes, João
2010-01-01
Pervasive computing is characterized by heterogeneous devices that usually have scarce resources requiring optimized usage. These devices may use different communication protocols which can be switched at runtime. As different communication protocols have different quality of service (Qo......S) properties, this motivates optimized self-adaption of protocols for devices, e.g., considering power consumption and other QoS requirements, e.g. round trip time (RTT) for service invocations, throughput, and reliability. In this paper, we present an extensible approach for self-adaptation of communication...... protocols for pervasive web services, where protocols are designed as reusable connectors and our middleware infrastructure can hide the complexity of using different communication protocols to upper layers. We also propose to use Genetic Algorithms (GAs) to find optimized configurations at runtime...
H-convergence for quasi-linear elliptic equations under natural hypotheses on the correctors
International Nuclear Information System (INIS)
Bensoussan, A.; Boccardo, L.; Dall'Aglio, A.; Murat, F.
1995-01-01
In this paper we study the behavior of the solutions of quasi-linear Dirichlet problems when the principal parts H-converge and when the lower order terms have quadratic growth with respect to the gradient. We show that the limit problem consists of a principal part which is the H-limit of the principal parts and of the lower order term which is constructed from the corresponding terms by using a linear corrector result. We assume only natural hypotheses on the correctors (i.e. L 2 equi-integrability and not L ∞ boundedness). (author)
Partially Adaptive STAP Algorithm Approaches to functional MRI
Huang, Lejian; Thompson, Elizabeth A.; Schmithorst, Vincent; Holland, Scott K.; Talavage, Thomas M.
2010-01-01
In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis. PMID:19272913
PID-controller with predictor and auto-tuning algorithm: study of efficiency for thermal plants
Kuzishchin, V. F.; Merzlikina, E. I.; Hoang, Van Va
2017-09-01
The problem of efficiency estimation of an automatic control system (ACS) with a Smith predictor and PID-algorithm for thermal plants is considered. In order to use the predictor, it is proposed to include an auto-tuning module (ATC) into the controller; the module calculates parameters for a second-order plant module with a time delay. The study was conducted using programmable logical controllers (PLC), one of which performed control, ATC, and predictor functions. A simulation model was used as a control plant, and there were two variants of the model: one of them was built on the basis of a separate PLC, and the other was a physical model of a thermal plant in the form of an electrical heater. Analysis of the efficiency of the ACS with the predictor was carried out for several variants of the second order plant model with time delay, and the analysis was performed on the basis of the comparison of transient processes in the system when the set point was changed and when a disturbance influenced the control plant. The recommendations are given on correction of the PID-algorithm parameters when the predictor is used by means of using the correcting coefficient k for the PID parameters. It is shown that, when the set point is changed, the use of the predictor is effective taking into account the parameters correction with k = 2. When the disturbances influence the plant, the use of the predictor is doubtful, because the transient process is too long. The reason for this is that, in the neighborhood of the zero frequency, the amplitude-frequency characteristic (AFC) of the system with the predictor has an ascent in comparison with the AFC of the system without the predictor.
Adaptive firefly algorithm: parameter analysis and its application.
Directory of Open Access Journals (Sweden)
Ngaam J Cheung
Full Text Available As a nature-inspired search algorithm, firefly algorithm (FA has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa. There are three strategies in AdaFa including (1 a distance-based light absorption coefficient; (2 a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3 five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.
Algorithms for adaptive nonlinear pattern recognition
Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary
2011-09-01
In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral
Self-adaptive numerical integrator for analytic functions
International Nuclear Information System (INIS)
Garribba, S.; Quartapelle, L.; Reina, G.
1978-01-01
A new adaptive algorithm for the integration of analytical functions is presented. The algorithm processes the integration interval by generating local subintervals whose length is controlled through a feedback loop. The control is obtained by means of a relation derived on an analytical basis and valid for an arbitrary integration rule: two different estimates of an integral are used to compute the interval length necessary to obtain an integral estimate with accuracy within the assigned error bounds. The implied method for local generation of subintervals and an effective assumption of error partition among subintervals give rise to an adaptive algorithm provided with a highly accurate and very efficient integration procedure. The particular algorithm obtained by choosing the 6-point Gauss-Legendre integration rule is considered and extensive comparisons are made with other outstanding integration algorithms
Performance of the Superconducting Corrector Magnet Circuits during the Commissioning of the LHC
International Nuclear Information System (INIS)
Venturini Delsolaro, W.; Baggiolini, V.; Ballarino, A.; Bellesia, B.; Bordry, F.; Cantone, A.; Casas Lino, M.P.; CastilloTrello, C.; Catalan-Lasheras, N.; Charifoulline, Zinour; Charrondiere, C.; CERN; Madrid, CIEMAT; Fermilab
2008-01-01
The LHC is a complex machine requiring more than 7400 superconducting corrector magnets distributed along a circumference of 26.7 km. These magnets are powered in 1446 different electrical circuits at currents ranging from 60 A up to 600 A. Among the corrector circuits the 600 A corrector magnets form the most diverse and differentiated group. All together, about 60000 high current connections had to be made. A fault in a circuit or one of the superconducting connections would have severe consequences for the accelerator operation. All magnets are wound from various types of Nb-Ti superconducting strands, and many contain parallel protection resistors to by-pass the current still flowing in the other magnets of the same circuit when they quench. In this paper the performance of these magnet circuits is presented, focusing on the quench behavior of the magnets. Quench detection and the performance of the electrical interconnects will be dealt with. The results as measured on the entire circuits are compared to the test results obtained at the reception of the individual magnets
Takano, Keisuke; Tanno, Yoshihiko
2009-03-01
Self-focused attention has adaptive and maladaptive aspects: self-reflection and self-rumination [Trapnell, P. D., & Campbell, J. D. (1999). Private self-consciousness and the Five-Factor Model of personality: distinguishing rumination from reflection. Journal of Personality and Social Psychology, 76, 284-304]. Although reflection is thought to be associated with problem solving and the promotion of mental health, previous researches have shown that reflection does not always have an adaptive effect on depression. Authors have examined the causes behind this inconsistency by modeling the relationships among self-reflection, self-rumination, and depression. One hundred and eleven undergraduates (91 men and 20 women) participated in a two-time point assessment with a 3-week interval. Statistical analysis with structural equation modeling showed that self-reflection significantly predicted self-rumination, whereas self-rumination did not predict self-reflection. With regard to depression, self-reflection was associated with a lower level of depression; self-rumination, with a higher level of depression. The total effect of self-reflection on depression was almost zero. This result indicates that self-reflection per se has an adaptive effect, which is canceled out by the maladaptive effect of self-rumination, because reflectors are likely to ruminate and reflect simultaneously.
Adaptive Multigrid Algorithm for the Lattice Wilson-Dirac Operator
International Nuclear Information System (INIS)
Babich, R.; Brower, R. C.; Rebbi, C.; Brannick, J.; Clark, M. A.; Manteuffel, T. A.; McCormick, S. F.; Osborn, J. C.
2010-01-01
We present an adaptive multigrid solver for application to the non-Hermitian Wilson-Dirac system of QCD. The key components leading to the success of our proposed algorithm are the use of an adaptive projection onto coarse grids that preserves the near null space of the system matrix together with a simplified form of the correction based on the so-called γ 5 -Hermitian symmetry of the Dirac operator. We demonstrate that the algorithm nearly eliminates critical slowing down in the chiral limit and that it has weak dependence on the lattice volume.
A new adaptive blind channel identification algorithm
International Nuclear Information System (INIS)
Peng Dezhong; Xiang Yong; Yi Zhang
2009-01-01
This paper addresses the blind identification of single-input multiple-output (SIMO) finite-impulse-response (FIR) systems. We first propose a new adaptive algorithm for the blind identification of SIMO FIR systems. Then, its convergence property is analyzed systematically. It is shown that under some mild conditions, the proposed algorithm is guaranteed to converge in the mean to the true channel impulse responses in both noisy and noiseless cases. Simulations are carried out to demonstrate the theoretical results.
A predictor-corrector scheme for solving the Volterra integral equation
Al Jarro, Ahmed; Bagci, Hakan
2011-01-01
The occurrence of late time instabilities is a common problem of almost all time marching methods developed for solving time domain integral equations. Implicit marching algorithms are now considered stable with various efforts that have been
Scalable Algorithms for Adaptive Statistical Designs
Directory of Open Access Journals (Sweden)
Robert Oehmke
2000-01-01
Full Text Available We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning algorithms for a stochastic environment, and we focus on the problem of optimally assigning patients to treatments in clinical trials. While adaptive designs have significant ethical and cost advantages, they are rarely utilized because of the complexity of optimizing and analyzing them. Computational challenges include massive memory requirements, few calculations per memory access, and multiply-nested loops with dynamic indices. We analyze the effects of various parallelization options, and while standard approaches do not work well, with effort an efficient, highly scalable program can be developed. This allows us to solve problems thousands of times more complex than those solved previously, which helps make adaptive designs practical. Further, our work applies to many other problems involving neighbor recurrences, such as generalized string matching.
Cheung, Y M; Leung, W M; Xu, L
1997-01-01
We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.
Design and implementation of adaptive inverse control algorithm for a micro-hand control system
Directory of Open Access Journals (Sweden)
Wan-Cheng Wang
2014-01-01
Full Text Available The Letter proposes an online tuned adaptive inverse position control algorithm for a micro-hand. First, the configuration of the micro-hand is discussed. Next, a kinematic analysis of the micro-hand is investigated and then the relationship between the rotor position of micro-permanent magnet synchronous motor and the tip of the micro-finger is derived. After that, an online tuned adaptive inverse control algorithm, which includes an adaptive inverse model and an adaptive inverse control, is designed. The online tuned adaptive inverse control algorithm has better performance than the proportional–integral control algorithm does. In addition, to avoid damaging the object during the grasping process, an online force control algorithm is proposed here as well. An embedded micro-computer, cRIO-9024, is used to realise the whole position control algorithm and the force control algorithm by using software. As a result, the hardware circuit is very simple. Experimental results show that the proposed system can provide fast transient responses, good load disturbance responses, good tracking responses and satisfactory grasping responses.
The Role of Item Feedback in Self-Adapted Testing.
Roos, Linda L.; And Others
1997-01-01
The importance of item feedback in self-adapted testing was studied by comparing feedback and no feedback conditions for computerized adaptive tests and self-adapted tests taken by 363 college students. Results indicate that item feedback is not necessary to realize score differences between self-adapted and computerized adaptive testing. (SLD)
Domain Adaptation for Opinion Classification: A Self-Training Approach
Directory of Open Access Journals (Sweden)
Yu, Ning
2013-03-01
Full Text Available Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.
Adaptive phase k-means algorithm for waveform classification
Song, Chengyun; Liu, Zhining; Wang, Yaojun; Xu, Feng; Li, Xingming; Hu, Guangmin
2018-01-01
Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.
Adaptive Control Algorithm of the Synchronous Generator
Directory of Open Access Journals (Sweden)
Shevchenko Victor
2017-01-01
Full Text Available The article discusses the the problem of controlling a synchronous generator, namely, maintaining the stability of the control object in the conditions of occurrence of noise and disturbances in the regulatory process. The model of a synchronous generator is represented by a system of differential equations of Park-Gorev, where state variables are computed relative to synchronously rotating d, q-axis. Management of synchronous generator is proposed to organize on the basis of the position-path control using algorithms to adapt with the reference model. Basic control law directed on the stabilizing indicators the frequency generated by the current and the required power level, which is achieved by controlling the mechanical torque on the shaft of the turbine and the value of the excitation voltage of the synchronous generator. Modification of the classic adaptation algorithm using the reference model, allowing to minimize the error of the reference regulation and the model under investigation within the prescribed limits, produced by means of the introduction of additional variables controller adaptation in the model. Сarried out the mathematical modeling of control provided influence on the studied model of continuous nonlinear and unmeasured the disturbance. Simulation results confirm the high level accuracy of tracking and adaptation investigated model with respect to the reference, and the present value of the loop error depends on parameters performance of regulator.
Active vibration suppression of self-excited structures using an adaptive LMS algorithm
Danda Roy, Indranil
The purpose of this investigation is to study the feasibility of an adaptive feedforward controller for active flutter suppression in representative linear wing models. The ability of the controller to suppress limit-cycle oscillations in wing models having root springs with freeplay nonlinearities has also been studied. For the purposes of numerical simulation, mathematical models of a rigid and a flexible wing structure have been developed. The rigid wing model is represented by a simple three-degree-of-freedom airfoil while the flexible wing is modelled by a multi-degree-of-freedom finite element representation with beam elements for bending and rod elements for torsion. Control action is provided by one or more flaps attached to the trailing edge and extending along the entire wing span for the rigid model and a fraction of the wing span for the flexible model. Both two-dimensional quasi-steady aerodynamics and time-domain unsteady aerodynamics have been used to generate the airforces in the wing models. An adaptive feedforward controller has been designed based on the filtered-X Least Mean Squares (LMS) algorithm. The control configuration for the rigid wing model is single-input single-output (SISO) while both SISO and multi-input multi-output (MIMO) configurations have been applied on the flexible wing model. The controller includes an on-line adaptive system identification scheme which provides the LMS controller with a reasonably accurate model of the plant. This enables the adaptive controller to track time-varying parameters in the plant and provide effective control. The wing models in closed-loop exhibit highly damped responses at airspeeds where the open-loop responses are destructive. Simulations with the rigid and the flexible wing models in a time-varying airstream show a 63% and 53% increase, respectively, over their corresponding open-loop flutter airspeeds. The ability of the LMS controller to suppress wing store flutter in the two models has
International Nuclear Information System (INIS)
Cho, Bumhee; Cho, Nam Zin
2015-01-01
In this study, the steady-state p-CMFD adjoint flux is used as the weighting function to obtain PK parameters instead of the computationally expensive transport adjoint angular flux. Several numerical problems are investigated to see the capability of the PCQS method applied to the NLG iteration. CRX-2K adopts the nonoverlapping local/global (NLG) iterative method with the 2-D/1-D fusion transport kernel and the global p-CMFD wrapper. The parallelization of the NLG iteration has been recently implemented in CRX-2K and several numerical results are reported in a companion paper. However, the direct time discretization leads to a fine time step size to acquire an accurate transient solution, and the step size involved in the transport transient calculations is millisecond-order. Therefore, the transient calculations need much longer computing time than the steady-state calculation. To increase the time step size, Predictor-Corrector Quasi-Static (PCQS) method can be one option to apply to the NLG iteration. The PCQS method is a linear algorithm, so the shape function does not need to be updated more than once at a specific time step like a conventional quasi-static (QS) family such as Improved Quasi-Static (IQS) method. Moreover, the shape function in the PCQS method directly comes from the direct transport calculation (with a large time step), so one can easily implement the PCQS method in an existing transient transport code. Any QS method needs to solve the amplitude function in the form of the point kinetics (PK) equations, and accurate PK parameters can be obtained by the transport steady-state adjoint angular flux as a weighting function. The PCQS method is applied to the transient NLG iteration with the 2-D/1-D fusion transport kernel and the global p-CMFD wrapper, and has been implemented in CRX-2K. In the numerical problems, the PCQS method with the NLG iteration shows more accurate solutions compared to the direct transient calculations with large time step
Automatic synthesis of MEMS devices using self-adaptive hybrid metaheuristics
DEFF Research Database (Denmark)
Tutum, Cem Celal; Fan, Zhun
2011-01-01
- multaneous minimization of size and power input of a MEMS device, while investigating optimum geometrical conguration as the main concern. The major contribution of this paper is the application of self-adaptive memetic computing in MEMS design. An evolutionary multi-objective optimization (EMO) technique......, in particular non-dominated sorting genetic algorithm (NSGA-II), has been applied to- gether with a pattern recognition statistical tool, i.e. Principal Component Analysis (PCA), to nd multiple trade-o solutions in an ecient manner. Following this, a gradient- based local search, i.e. sequential quadratic...
The MMPI as a Predictor of Psychosocial Adaptation to Cancer.
Sobel, Harry J.; Worden, J. William
1979-01-01
Examined utility of the Minnesota Multiphasic Personality Inventory (MMPI) as a longitudinal predictor of psychosocial adaptation to cancer. A post hoc discriminant analysis revealed that 75% of all patients could have been correctly classified into a high-distressed v a low-distressed cancer patient group using only the MMPI. (Author)
International Nuclear Information System (INIS)
Zu Yun-Xiao; Zhou Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. (geophysics, astronomy, and astrophysics)
PCX, Interior-Point Linear Programming Solver
International Nuclear Information System (INIS)
Czyzyk, J.
2004-01-01
1 - Description of program or function: PCX solves linear programming problems using the Mehrota predictor-corrector interior-point algorithm. PCX can be called as a subroutine or used in stand-alone mode, with data supplied from an MPS file. The software incorporates modules that can be used separately from the linear programming solver, including a pre-solve routine and data structure definitions. 2 - Methods: The Mehrota predictor-corrector method is a primal-dual interior-point method for linear programming. The starting point is determined from a modified least squares heuristic. Linear systems of equations are solved at each interior-point iteration via a sparse Cholesky algorithm native to the code. A pre-solver is incorporated in the code to eliminate inefficiencies in the user's formulation of the problem. 3 - Restriction on the complexity of the problem: There are no size limitations built into the program. The size of problem solved is limited by RAM and swap space on the user's computer
An Adaptive Motion Estimation Scheme for Video Coding
Directory of Open Access Journals (Sweden)
Pengyu Liu
2014-01-01
Full Text Available The unsymmetrical-cross multihexagon-grid search (UMHexagonS is one of the best fast Motion Estimation (ME algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised.
Hi-Corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data.
Li, Wenyuan; Gong, Ke; Li, Qingjiao; Alber, Frank; Zhou, Xianghong Jasmine
2015-03-15
Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, resulting in very large matrices of chromatin contacts. Such large-size matrices, however, pose a great challenge on the memory usage and speed of its normalization. Therefore, there is an urgent need for fast and memory-efficient methods for normalization of Hi-C data. We developed Hi-Corrector, an easy-to-use, open source implementation of the Hi-C data normalization algorithm. Its salient features are (i) scalability-the software is capable of normalizing Hi-C data of any size in reasonable times; (ii) memory efficiency-the sequential version can run on any single computer with very limited memory, no matter how little; (iii) fast speed-the parallel version can run very fast on multiple computing nodes with limited local memory. The sequential version is implemented in ANSI C and can be easily compiled on any system; the parallel version is implemented in ANSI C with the MPI library (a standardized and portable parallel environment designed for solving large-scale scientific problems). The package is freely available at http://zhoulab.usc.edu/Hi-Corrector/. © The Author 2014. Published by Oxford University Press.
A software sampling frequency adaptive algorithm for reducing spectral leakage
Institute of Scientific and Technical Information of China (English)
PAN Li-dong; WANG Fei
2006-01-01
Spectral leakage caused by synchronous error in a nonsynchronous sampling system is an important cause that reduces the accuracy of spectral analysis and harmonic measurement.This paper presents a software sampling frequency adaptive algorithm that can obtain the actual signal frequency more accurately,and then adjusts sampling interval base on the frequency calculated by software algorithm and modifies sampling frequency adaptively.It can reduce synchronous error and impact of spectral leakage;thereby improving the accuracy of spectral analysis and harmonic measurement for power system signal where frequency changes slowly.This algorithm has high precision just like the simulations show,and it can be a practical method in power system harmonic analysis since it can be implemented easily.
An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt
Directory of Open Access Journals (Sweden)
Qingming Zhan
2017-08-01
Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.
Comparative study of adaptive-noise-cancellation algorithms for intrusion detection systems
International Nuclear Information System (INIS)
Claassen, J.P.; Patterson, M.M.
1981-01-01
Some intrusion detection systems are susceptible to nonstationary noise resulting in frequent nuisance alarms and poor detection when the noise is present. Adaptive inverse filtering for single channel systems and adaptive noise cancellation for two channel systems have both demonstrated good potential in removing correlated noise components prior detection. For such noise susceptible systems the suitability of a noise reduction algorithm must be established in a trade-off study weighing algorithm complexity against performance. The performance characteristics of several distinct classes of algorithms are established through comparative computer studies using real signals. The relative merits of the different algorithms are discussed in the light of the nature of intruder and noise signals
Peters, F.; Sahasrabudhe, P.; Gross-Wilde, H.; Kleizen, Bertrand; Conrath, K.; Braakman, I.
2017-01-01
The current therapeutic strategy to repair cystic fibrosis-causing defects in the chloride channel CFTR is to develop novel and better correctors (to improve folding) and potentiators (to improve function). Galapagos- AbbVie identified C2 correctors by high-throughput compound screening and Med Chem
Adaptive Proximal Point Algorithms for Total Variation Image Restoration
Directory of Open Access Journals (Sweden)
Ying Chen
2015-02-01
Full Text Available Image restoration is a fundamental problem in various areas of imaging sciences. This paper presents a class of adaptive proximal point algorithms (APPA with contraction strategy for total variational image restoration. In each iteration, the proposed methods choose an adaptive proximal parameter matrix which is not necessary symmetric. In fact, there is an inner extrapolation in the prediction step, which is followed by a correction step for contraction. And the inner extrapolation is implemented by an adaptive scheme. By using the framework of contraction method, global convergence result and a convergence rate of O(1/N could be established for the proposed methods. Numerical results are reported to illustrate the efficiency of the APPA methods for solving total variation image restoration problems. Comparisons with the state-of-the-art algorithms demonstrate that the proposed methods are comparable and promising.
Predictors of weight loss success. Exercise vs. dietary self-efficacy and treatment attendance.
Byrne, Shannon; Barry, Danielle; Petry, Nancy M
2012-04-01
Pre-treatment diet and exercise self-efficacies can predict weight loss success. Changes in diet self-efficacy across treatment appear to be even stronger predictors than baseline levels, but research on changes in exercise self-efficacy is lacking. Using data from a pilot study evaluating tangible reinforcement for weight loss (N=30), we examined the impact of changes in diet and exercise self-efficacy on outcomes. Multiple regression analyses indicated that treatment attendance and changes in exercise self-efficacy during treatment were the strongest predictors of weight loss. Developing weight loss programs that foster the development of exercise self-efficacy may enhance participants' success. Published by Elsevier Ltd.
EFFICIENT ADAPTIVE STEGANOGRAPHY FOR COLOR IMAGESBASED ON LSBMR ALGORITHM
Directory of Open Access Journals (Sweden)
B. Sharmila
2012-02-01
Full Text Available Steganography is the art of hiding the fact that communication is taking place, by hiding information in other medium. Many different carrier file formats can be used, but digital images are the most popular because of their frequent use on the Internet. For hiding secret information in images, there exists a large variety of steganographic techniques. The Least Significant Bit (LSB based approach is a simplest type of steganographic algorithm. In all the existing approaches, the decision of choosing the region within a cover image is performed without considering the relationship between image content and the size of secret message. Thus, the plain regions in the cover will be ruin after data hiding even at a low data rate. Hence choosing the edge region for data hiding will be a solution. Many algorithms are deal with edges in images for data hiding. The Paper 'Edge adaptive image steganography based on LSBMR algorithm' is a LSB steganography presented the results of algorithms on gray-scale images only. This paper presents the results of analyzing the performance of edge adaptive steganography for colored images (JPEG. The algorithms have been slightly modified for colored image implementation and are compared on the basis of evaluation parameters like peak signal noise ratio (PSNR and mean square error (MSE. This method can select the edge region depending on the length of secret message and difference between two consecutive bits in the cover image. For length of message is short, only small edge regions are utilized while on leaving other region as such. When the data rate increases, more regions can be used adaptively for data hiding by adjusting the parameters. Besides this, the message is encrypted using efficient cryptographic algorithm which further increases the security.
International Nuclear Information System (INIS)
Chen, Xia; Hu, Hong-li; Liu, Fei; Gao, Xiang Xiang
2011-01-01
The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality
An objective procedure for evaluation of adaptive antifeedback algorithms in hearing aids.
Freed, Daniel J; Soli, Sigfrid D
2006-08-01
This study evaluated the performance of nine adaptive antifeedback algorithms. There were two goals: first, to identify objective procedures that are useful for evaluating these algorithms, and second, to identify strengths and weaknesses of existing algorithms. The algorithms were evaluated in behind-the-ear implementations on the Knowles Electronics Manikin for Acoustic Research (KEMAR). Different acoustic conditions were created by placing a telephone handset or a hat on KEMAR. Electroacoustic techniques were devised to measure the following performance aspects of each algorithm: (1) additional gain made available before oscillation, (2) gain lost in specific frequency regions, (3) reduction of suboscillatory peaks in the frequency response, (4) speed of adaptation to changing acoustic conditions, and (5) robustness in the presence of tonal input signals. For each measurement, performance varied widely across algorithms. No single algorithm was clearly superior or inferior to the others. Generally, the feedback cancellation algorithms were less likely to sacrifice gain in specific frequency regions and better at reducing suboscillatory peaks, whereas the algorithms that used noncancellation techniques were more tolerant of tonal input signals. For those algorithms equipped with special operational modes intended for music listening, the music mode improved the response to tonal inputs but sometimes sacrificed other performance aspects. Algorithms that required an acoustic measurement for initialization purposes tended to perform poorly in acoustic conditions dissimilar to the condition in which initialization was performed. The objective methods devised for this study appear useful for evaluating the performance of adaptive antifeedback algorithms. Currently available algorithms demonstrate a wide range of performance, and further research is required to develop new algorithms that combine the best features of existing algorithms.
Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method
Zhang, Lijuan; Li, Dongming; Su, Wei; Yang, Jinhua; Jiang, Yutong
2014-01-01
To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. Firstly, we need to make a mathematical model for the degenerate multiframe adaptive optics images. The function model is deduced for the points that spread with time based on phase error. The AO images are denoised using the image power spectral density and support constrain...
VLSI PARTITIONING ALGORITHM WITH ADAPTIVE CONTROL PARAMETER
Directory of Open Access Journals (Sweden)
P. N. Filippenko
2013-03-01
Full Text Available The article deals with the problem of very large-scale integration circuit partitioning. A graph is selected as a mathematical model describing integrated circuit. Modification of ant colony optimization algorithm is presented, which is used to solve graph partitioning problem. Ant colony optimization algorithm is an optimization method based on the principles of self-organization and other useful features of the ants’ behavior. The proposed search system is based on ant colony optimization algorithm with the improved method of the initial distribution and dynamic adjustment of the control search parameters. The experimental results and performance comparison show that the proposed method of very large-scale integration circuit partitioning provides the better search performance over other well known algorithms.
International Nuclear Information System (INIS)
Shivaie, Mojtaba; Ameli, Mohammad T.; Sepasian, Mohammad S.; Weinsier, Philip D.; Vahidinasab, Vahid
2015-01-01
In this paper, the authors present a new multistage framework for reliability-based Distribution Expansion Planning (DEP) in which expansion options are a reinforcement and/or installation of substations, feeders, and Distributed Generations (DGs). The proposed framework takes into account not only costs associated with investment, maintenance, and operation, but also expected customer interruption cost in the optimization as four problem objectives. At the same time, operational restrictions, Kirchhoff's laws, radial structure limitation, voltage limits, and capital expenditure budget restriction are considered as problem constraints. The proposed model is a non-convex optimization problem having a non-linear, mixed-integer nature. Hence, a hybrid Self-adaptive Global-based Harmony Search Algorithm (SGHSA) and Optimal Power Flow (OPF) were used and followed by a fuzzy satisfying method in order to obtain the final optimal solution. The SGHSA is a recently developed optimization algorithm which imitates the music improvisation process. In this process, the harmonists improvise their instrument pitches, searching for the perfect state of harmony. The planning methodology was demonstrated on the 27-node, 13.8-kV test system in order to demonstrate the feasibility and capability of the proposed model. Simulation results illustrated the sufficiency and profitableness of the newly developed framework, when compared with other methods. - Highlights: • A new multistage framework is presented for reliability-based DEP problem. • In this paper, DGs are considered as an expansion option to increase the flexibility of the proposed model. • In this paper, effective factors of DEP problem are incorporated as a multi-objective model. • In this paper, three new algorithms HSA, IHSA and SGHSA are proposed. • Results obtained by the proposed SGHSA algorithm are better than others
DEFF Research Database (Denmark)
Wang, Yong; Cai, Zixing; Zhou, Yuren
2009-01-01
A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...
Adaptive Watermarking Algorithm in DCT Domain Based on Chaos
Directory of Open Access Journals (Sweden)
Wenhao Wang
2013-05-01
Full Text Available In order to improve the security, robustness and invisibility of the digital watermarking, a new adaptive watermarking algorithm is proposed in this paper. Firstly, this algorithm uses chaos sequence, which Logistic chaotic mapping produces, to encrypt the watermark image. And then the original image is divided into many sub-blocks and discrete cosine transform (DCT.The watermark information is embedded into sub-blocks medium coefficients. With the features of Human Visual System (HVS and image texture sufficiently taken into account during embedding, the embedding intensity of watermark is able to adaptively adjust according to HVS and texture characteristic. The watermarking is embedded into the different sub-blocks coefficients. Experiment results haven shown that the proposed algorithm is robust against the attacks of general image processing methods, such as noise, cut, filtering and JPEG compression, and receives a good tradeoff between invisible and robustness, and better security.
Self-Adaptive Systems for Machine Intelligence
He, Haibo
2011-01-01
This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application
The optimal algorithm for Multi-source RS image fusion.
Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan
2016-01-01
In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.
A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Baoguo Yu
2016-01-01
Full Text Available In the wireless sensor network (WSN localization methods based on Received Signal Strength Indicator (RSSI, it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.
Energy Technology Data Exchange (ETDEWEB)
Rattá, G.A., E-mail: giuseppe.ratta@ciemat.es [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Vega, J. [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Murari, A. [Consorzio RFX, Associazione EURATOM/ENEA per la Fusione, Padua (Italy); Dormido-Canto, S. [Dpto. de Informática y Automática, Universidad Nacional de Educación a Distancia, Madrid (Spain); Moreno, R. [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain)
2016-11-15
Highlights: • A global optimization method based on genetic algorithms was developed. • It allowed improving the prediction of disruptions using APODIS architecture. • It also provides the potential opportunity to develop a spectrum of future predictors using different training datasets. • The future analysis of how their structures reassemble and evolve in each test may help to improve the development of disruption predictors for ITER. - Abstract: Since year 2010, the APODIS architecture has proven its accuracy predicting disruptions in JET tokamak. Nevertheless, it has shown margins for improvements, fact indisputable after the enhanced performances achieved in posterior upgrades. In this article, a complete optimization driven by Genetic Algorithms (GA) is applied to it aiming at considering all possible combination of signals, signal features, quantity of models, their characteristics and internal parameters. This global optimization targets the creation of the best possible system with a reduced amount of required training data. The results harbor no doubts about the reliability of the global optimization method, allowing to outperform the ones of previous versions: 91.77% of predictions (89.24% with an anticipation higher than 10 ms) with a 3.55% of false alarms. Beyond its effectiveness, it also provides the potential opportunity to develop a spectrum of future predictors using different training datasets.
International Nuclear Information System (INIS)
Rattá, G.A.; Vega, J.; Murari, A.; Dormido-Canto, S.; Moreno, R.
2016-01-01
Highlights: • A global optimization method based on genetic algorithms was developed. • It allowed improving the prediction of disruptions using APODIS architecture. • It also provides the potential opportunity to develop a spectrum of future predictors using different training datasets. • The future analysis of how their structures reassemble and evolve in each test may help to improve the development of disruption predictors for ITER. - Abstract: Since year 2010, the APODIS architecture has proven its accuracy predicting disruptions in JET tokamak. Nevertheless, it has shown margins for improvements, fact indisputable after the enhanced performances achieved in posterior upgrades. In this article, a complete optimization driven by Genetic Algorithms (GA) is applied to it aiming at considering all possible combination of signals, signal features, quantity of models, their characteristics and internal parameters. This global optimization targets the creation of the best possible system with a reduced amount of required training data. The results harbor no doubts about the reliability of the global optimization method, allowing to outperform the ones of previous versions: 91.77% of predictions (89.24% with an anticipation higher than 10 ms) with a 3.55% of false alarms. Beyond its effectiveness, it also provides the potential opportunity to develop a spectrum of future predictors using different training datasets.
Agent-based station for on-line diagnostics by self-adaptive laser Doppler vibrometry
Serafini, S.; Paone, N.; Castellini, P.
2013-12-01
A self-adaptive diagnostic system based on laser vibrometry is proposed for quality control of mechanical defects by vibration testing; it is developed for appliances at the end of an assembly line, but its characteristics are generally suited for testing most types of electromechanical products. It consists of a laser Doppler vibrometer, equipped with scanning mirrors and a camera, which implements self-adaptive bahaviour for optimizing the measurement. The system is conceived as a Quality Control Agent (QCA) and it is part of a Multi Agent System that supervises all the production line. The QCA behaviour is defined so to minimize measurement uncertainty during the on-line tests and to compensate target mis-positioning under guidance of a vision system. Best measurement conditions are reached by maximizing the amplitude of the optical Doppler beat signal (signal quality) and consequently minimize uncertainty. In this paper, the optimization strategy for measurement enhancement achieved by the down-hill algorithm (Nelder-Mead algorithm) and its effect on signal quality improvement is discussed. Tests on a washing machine in controlled operating conditions allow to evaluate the efficacy of the method; significant reduction of noise on vibration velocity spectra is observed. Results from on-line tests are presented, which demonstrate the potential of the system for industrial quality control.
Agent-based station for on-line diagnostics by self-adaptive laser Doppler vibrometry.
Serafini, S; Paone, N; Castellini, P
2013-12-01
A self-adaptive diagnostic system based on laser vibrometry is proposed for quality control of mechanical defects by vibration testing; it is developed for appliances at the end of an assembly line, but its characteristics are generally suited for testing most types of electromechanical products. It consists of a laser Doppler vibrometer, equipped with scanning mirrors and a camera, which implements self-adaptive bahaviour for optimizing the measurement. The system is conceived as a Quality Control Agent (QCA) and it is part of a Multi Agent System that supervises all the production line. The QCA behaviour is defined so to minimize measurement uncertainty during the on-line tests and to compensate target mis-positioning under guidance of a vision system. Best measurement conditions are reached by maximizing the amplitude of the optical Doppler beat signal (signal quality) and consequently minimize uncertainty. In this paper, the optimization strategy for measurement enhancement achieved by the down-hill algorithm (Nelder-Mead algorithm) and its effect on signal quality improvement is discussed. Tests on a washing machine in controlled operating conditions allow to evaluate the efficacy of the method; significant reduction of noise on vibration velocity spectra is observed. Results from on-line tests are presented, which demonstrate the potential of the system for industrial quality control.
Algorithms for adaptive histogram equalization
International Nuclear Information System (INIS)
Pizer, S.M.; Austin, J.D.; Cromartie, R.; Geselowitz, A.; Ter Haar Romeny, B.; Zimmerman, J.B.; Zuiderveld, K.
1986-01-01
Adaptive histogram equalization (ahe) is a contrast enhancement method designed to be broadly applicable and having demonstrated effectiveness [Zimmerman, 1985]. However, slow speed and the overenhancement of noise it produces in relatively homogeneous regions are two problems. The authors summarize algorithms designed to overcome these and other concerns. These algorithms include interpolated ahe, to speed up the method on general purpose computers; a version of interpolated ahe designed to run in a few seconds on feedback processors; a version of full ahe designed to run in under one second on custom VLSI hardware; and clipped ahe, designed to overcome the problem of overenhancement of noise contrast. The authors conclude that clipped ahe should become a method of choice in medical imaging and probably also in other areas of digital imaging, and that clipped ahe can be made adequately fast to be routinely applied in the normal display sequence
Kleier, Jo Ann; Dittman, Patricia Welch
2014-01-01
Diabetes mellitus is a leading cause of end stage renal disease among African Americans. The complications associated with diabetes can largely be reduced with effective diabetes self-management. Selected variables were tested as predictors of self-reported self-care, and self-reported self-care was tested as a predictor of A1C among 100 African-American individuals with diabetes. Participants scored high on their understanding of diabetes, its treatment, and engagement in self-care activities, but this was not reflected in their body mass index levels or A IC values.
A new adaptive GMRES algorithm for achieving high accuracy
Energy Technology Data Exchange (ETDEWEB)
Sosonkina, M.; Watson, L.T.; Kapania, R.K. [Virginia Polytechnic Inst., Blacksburg, VA (United States); Walker, H.F. [Utah State Univ., Logan, UT (United States)
1996-12-31
GMRES(k) is widely used for solving nonsymmetric linear systems. However, it is inadequate either when it converges only for k close to the problem size or when numerical error in the modified Gram-Schmidt process used in the GMRES orthogonalization phase dramatically affects the algorithm performance. An adaptive version of GMRES (k) which tunes the restart value k based on criteria estimating the GMRES convergence rate for the given problem is proposed here. The essence of the adaptive GMRES strategy is to adapt the parameter k to the problem, similar in spirit to how a variable order ODE algorithm tunes the order k. With FORTRAN 90, which provides pointers and dynamic memory management, dealing with the variable storage requirements implied by varying k is not too difficult. The parameter k can be both increased and decreased-an increase-only strategy is described next followed by pseudocode.
Emotional and attentional predictors of self-regulation in early childhood
Directory of Open Access Journals (Sweden)
Stępień-Nycz Małgorzata
2015-09-01
Full Text Available The development of self-regulation in early childhood is related to development of emotional regulation and attention, in particular executive attention (Feldman, 2009; Posner & Rothbart, 1998. As the ability to self-regulate is crucial in life (Casey et al., 2011, it is important to reveal early predictors of self-regulation. The aim of the paper is to present the results of longitudinal studies on the relationships between the functioning of attention, regulation of emotion and later self-regulatory abilities. 310 children were assessed at three time points. At 12 months of age emotional regulation in situation of frustration and attention regulation were assessed. At 18 and 24 months behavioral-emotional regulation in the Snack Delay Task was measured. Additionally parents assessed executive attention using The Early Childhood Behavior Questionnaire when children were 26 months old. Structural equation modelling revealed two different paths to development of self-regulatory abilities at 18 months: emotional (reactive system and emotionalattentional and only one emotional-attentional path at 24 months. The early ability to focus attention and later executive attention functioning revealed to be important predictors of self-regulatory abilities both at 18 and 24 months of age.
International Timetabling Competition 2011: An Adaptive Large Neighborhood Search algorithm
DEFF Research Database (Denmark)
Sørensen, Matias; Kristiansen, Simon; Stidsen, Thomas Riis
2012-01-01
An algorithm based on Adaptive Large Neighborhood Search (ALNS) for solving the generalized High School Timetabling problem in XHSTT-format (Post et al (2012a)) is presented. This algorithm was among the nalists of round 2 of the International Timetabling Competition 2011 (ITC2011). For problem...
Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method
Directory of Open Access Journals (Sweden)
Lijuan Zhang
2014-01-01
Full Text Available To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. Firstly, we need to make a mathematical model for the degenerate multiframe adaptive optics images. The function model is deduced for the points that spread with time based on phase error. The AO images are denoised using the image power spectral density and support constraint. Secondly, the EM algorithm is improved by combining the AO imaging system parameters and regularization technique. A cost function for the joint-deconvolution multiframe AO images is given, and the optimization model for their parameter estimations is built. Lastly, the image-restoration experiments on both analog images and the real AO are performed to verify the recovery effect of our algorithm. The experimental results show that comparing with the Wiener-IBD or RL-IBD algorithm, our iterations decrease 14.3% and well improve the estimation accuracy. The model distinguishes the PSF of the AO images and recovers the observed target images clearly.
Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm
Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong
2018-06-01
The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.
Physiological Self-Regulation and Adaptive Automation
Prinzell, Lawrence J.; Pope, Alan T.; Freeman, Frederick G.
2007-01-01
Adaptive automation has been proposed as a solution to current problems of human-automation interaction. Past research has shown the potential of this advanced form of automation to enhance pilot engagement and lower cognitive workload. However, there have been concerns voiced regarding issues, such as automation surprises, associated with the use of adaptive automation. This study examined the use of psychophysiological self-regulation training with adaptive automation that may help pilots deal with these problems through the enhancement of cognitive resource management skills. Eighteen participants were assigned to 3 groups (self-regulation training, false feedback, and control) and performed resource management, monitoring, and tracking tasks from the Multiple Attribute Task Battery. The tracking task was cycled between 3 levels of task difficulty (automatic, adaptive aiding, manual) on the basis of the electroencephalogram-derived engagement index. The other two tasks remained in automatic mode that had a single automation failure. Those participants who had received self-regulation training performed significantly better and reported lower National Aeronautics and Space Administration Task Load Index scores than participants in the false feedback and control groups. The theoretical and practical implications of these results for adaptive automation are discussed.
Self-efficacy as predictor of job performance of public secondary ...
African Journals Online (AJOL)
The study investigated the level of job performance and self-efficacy of public secondary school teachers in Osun State. It also examined self-efficacy as a predictor of teachers' job performance with a view to enhancing job productivity. The study adopted survey design. The population consisted of public secondary school ...
Directory of Open Access Journals (Sweden)
Qiguang Zhu
2014-05-01
Full Text Available To resolve the difficulty in establishing accurate priori noise model for the extended Kalman filtering algorithm, propose the fractional-order Darwinian particle swarm optimization (PSO algorithm has been proposed and introduced into the fuzzy adaptive extended Kalman filtering algorithm. The natural selection method has been adopted to improve the standard particle swarm optimization algorithm, which enhanced the diversity of particles and avoided the premature. In addition, the fractional calculus has been used to improve the evolution speed of particles. The PSO algorithm after improved has been applied to train fuzzy adaptive extended Kalman filter and achieve the simultaneous localization and mapping. The simulation results have shown that compared with the geese particle swarm optimization training of fuzzy adaptive extended Kalman filter localization and mapping algorithm, has been greatly improved in terms of localization and mapping.
An Adaptive Pruning Algorithm for the Discrete L-Curve Criterion
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Toke Koldborg; Rodriguez, Giuseppe
2004-01-01
SVD or regularizing CG iterations). Our algorithm needs no pre-defined parameters, and in order to capture the global features of the curve in an adaptive fashion, we use a sequence of pruned L-curves that correspond to considering the curves at different scales. We compare our new algorithm...
Model-Free Adaptive Control Algorithm with Data Dropout Compensation
Directory of Open Access Journals (Sweden)
Xuhui Bu
2012-01-01
Full Text Available The convergence of model-free adaptive control (MFAC algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations. It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained.
Zhao, Z.-G.; Zhou, L.-J.; Zhang, J.-T.; Zhu, Q.; Hedrick, J.-K.
2017-05-01
Considering the controllability and observability of the braking torques of the hub motor, Integrated Starter Generator (ISG), and hydraulic brake for four-wheel drive (4WD) hybrid electric cars, a distributed and self-adaptive vehicle speed estimation algorithm for different braking situations has been proposed by fully utilising the Electronic Stability Program (ESP) sensor signals and multiple powersource signals. Firstly, the simulation platform of a 4WD hybrid electric car was established, which integrates an electronic-hydraulic composited braking system model and its control strategy, a nonlinear seven degrees-of-freedom vehicle dynamics model, and the Burckhardt tyre model. Secondly, combining the braking torque signals with the ESP signals, self-adaptive unscented Kalman sub-filter and main-filter adaptable to the observation noise were, respectively, designed. Thirdly, the fusion rules for the sub-filters and master filter were proposed herein, and the estimation results were compared with the simulated value of a real vehicle speed. Finally, based on the hardware in-the-loop platform and by picking up the regenerative motor torque signals and wheel cylinder pressure signals, the proposed speed estimation algorithm was tested under the case of moderate braking on the highly adhesive road, and the case of Antilock Braking System (ABS) action on the slippery road, as well as the case of ABS action on the icy road. Test results show that the presented vehicle speed estimation algorithm has not only a high precision but also a strong adaptability in the composite braking case.
Parallel Algorithm for Adaptive Numerical Integration
International Nuclear Information System (INIS)
Sujatmiko, M.; Basarudin, T.
1997-01-01
This paper presents an automation algorithm for integration using adaptive trapezoidal method. The interval is adaptively divided where the width of sub interval are different and fit to the behavior of its function. For a function f, an integration on interval [a,b] can be obtained, with maximum tolerance ε, using estimation (f, a, b, ε). The estimated solution is valid if the error is still in a reasonable range, fulfil certain criteria. If the error is big, however, the problem is solved by dividing it into to similar and independent sub problem on to separate [a, (a+b)/2] and [(a+b)/2, b] interval, i. e. ( f, a, (a+b)/2, ε/2) and (f, (a+b)/2, b, ε/2) estimations. The problems are solved in two different kinds of processor, root processor and worker processor. Root processor function ti divide a main problem into sub problems and distribute them to worker processor. The division mechanism may go further until all of the sub problem are resolved. The solution of each sub problem is then submitted to the root processor such that the solution for the main problem can be obtained. The algorithm is implemented on C-programming-base distributed computer networking system under parallel virtual machine platform
A neural learning classifier system with self-adaptive constructivism for mobile robot control.
Hurst, Jacob; Bull, Larry
2006-01-01
For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.
Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.
Mei, Gang; Xu, Nengxiong; Xu, Liangliang
2016-01-01
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.
Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo
2008-03-01
In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.
Modified Projection Algorithms for Solving the Split Equality Problems
Directory of Open Access Journals (Sweden)
Qiao-Li Dong
2014-01-01
proposed a CQ algorithm for solving it. In this paper, we propose a modification for the CQ algorithm, which computes the stepsize adaptively and performs an additional projection step onto two half-spaces in each iteration. We further propose a relaxation scheme for the self-adaptive projection algorithm by using projections onto half-spaces instead of those onto the original convex sets, which is much more practical. Weak convergence results for both algorithms are analyzed.
High order aberrations calculation of a hexapole corrector using a differential algebra method
Energy Technology Data Exchange (ETDEWEB)
Kang, Yongfeng, E-mail: yfkang@mail.xjtu.edu.cn [Key Laboratory for Physical Electronics and Devices of the Ministry of Education, Xi' an Jiaotong University, Xi' an 710049 (China); Liu, Xing [Key Laboratory for Physical Electronics and Devices of the Ministry of Education, Xi' an Jiaotong University, Xi' an 710049 (China); Zhao, Jingyi, E-mail: jingyi.zhao@foxmail.com [School of Science, Chang’an University, Xi’an 710064 (China); Tang, Tiantong [Key Laboratory for Physical Electronics and Devices of the Ministry of Education, Xi' an Jiaotong University, Xi' an 710049 (China)
2017-02-21
A differential algebraic (DA) method is proved as an unusual and effective tool in numerical analysis. It implements conveniently differentiation up to arbitrary high order, based on the nonstandard analysis. In this paper, the differential algebra (DA) method has been employed to compute the high order aberrations up to the fifth order of a practical hexapole corrector including round lenses and hexapole lenses. The program has been developed and tested as well. The electro-magnetic fields of arbitrary point are obtained by local analytic expressions, then field potentials are transformed into new forms which can be operated in the DA calculation. In this paper, the geometric and chromatic aberrations up to fifth order of a practical hexapole corrector system are calculated by the developed program.
Liquid-crystal intraocular adaptive lens with wireless control
Simonov, A.N.; Vdovine, G.V.; Loktev, M.
2007-01-01
We present a prototype of an adaptive intraocular lens based on a modal liquid-crystal spatial phase modulator with wireless control. The modal corrector consists of a nematic liquid-crystal layer sandwiched between two glass substrates with transparent low- and high-ohmic electrodes, respectively.
DEFF Research Database (Denmark)
Conrad, Finn; Zhou, Jianjun; Gabacik, Andrzej
1998-01-01
Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control.......Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control....
He, Ye; Chen, Xiaoan; Liu, Zhi; Qin, Yi
2018-06-01
The motorized spindle is the core component of CNC machine tools, and the vibration of it reduces the machining precision and service life of the machine tools. Owing to the fast response, large output force, and displacement of the piezoelectric stack, it is often used as the actuator in the active vibration control of the spindle. A piezoelectric self-sensing actuator (SSA) can reduce the cost of the active vibration control system and simplify the structure by eliminating the use of a sensor, because a SSA can have both actuating and sensing functions at the same time. The signal separation method of a SSA based on a bridge circuit is widely applied because of its simple principle and easy implementation. However, it is difficult to maintain dynamic balance of the circuit. Prior research has used adaptive algorithm to balance of the bridge circuit on the flexible beam dynamically, but those algorithms need no correlation between sensing and control voltage, which limit the applications of SSA in the vibration control of the rotor-bearing system. Here, the electromechanical coupling model of the piezoelectric stack is established, followed by establishment of the dynamic model of the spindle system. Next, a new adaptive signal separation method based on the bridge circuit is proposed, which can separate relative small sensing voltage from related mixed voltage adaptively. The experimental results show that when the self-sensing signal obtained from the proposed method is used as a displacement signal, the vibration of the motorized spindle can be suppressed effectively through a linear quadratic Gaussian (LQG) algorithm.
Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm
Directory of Open Access Journals (Sweden)
Manuel Prado-Velasco
2013-10-01
Full Text Available Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions. The algorithm works together with an unsupervised real-time learning technique that addresses the adaptive capability, and this is also presented. The work demonstrates the robustness and reliability of our new algorithm, which will be the basis of a smart falling monitor. This is shown in this work to underline the relevance of the results.
Statistical Algorithm for the Adaptation of Detection Thresholds
DEFF Research Database (Denmark)
Stotsky, Alexander A.
2008-01-01
Many event detection mechanisms in spark ignition automotive engines are based on the comparison of the engine signals to the detection threshold values. Different signal qualities for new and aged engines necessitate the development of an adaptation algorithm for the detection thresholds...... remains constant regardless of engine age and changing detection threshold values. This, in turn, guarantees the same event detection performance for new and aged engines/sensors. Adaptation of the engine knock detection threshold is given as an example. Udgivelsesdato: 2008...
An adaptive occlusion culling algorithm for use in large ves
DEFF Research Database (Denmark)
Bormann, Karsten
2000-01-01
The Hierarchical Occlusion Map algorithm is combined with Frustum Slicing to give a simpler occlusion-culling algorithm that more adequately caters to large, open VEs. The algorithm adapts to the level of visual congestion and is well suited for use with large, complex models with long mean free ...... line of sight ('the great outdoors'), models for which it is not feasible to construct, or search, a database of occluders to be rendered each frame....
Stability of Parental Nurturance as a Salient Predictor of Self-Esteem.
Buri, John R.
In the recent past there has been a growing interest in the investigation of the self. A primary area of investigation has revolved around the question of the stability of the self-concept. This study investigated parental nurturance as a stable predictor of self-esteem across adolescent and young adult age groups. Subjects (N=784) were students…
Self-* and Adaptive Mechanisms for Large Scale Distributed Systems
Fragopoulou, P.; Mastroianni, C.; Montero, R.; Andrjezak, A.; Kondo, D.
Large-scale distributed computing systems and infrastructure, such as Grids, P2P systems and desktop Grid platforms, are decentralized, pervasive, and composed of a large number of autonomous entities. The complexity of these systems is such that human administration is nearly impossible and centralized or hierarchical control is highly inefficient. These systems need to run on highly dynamic environments, where content, network topologies and workloads are continuously changing. Moreover, they are characterized by the high degree of volatility of their components and the need to provide efficient service management and to handle efficiently large amounts of data. This paper describes some of the areas for which adaptation emerges as a key feature, namely, the management of computational Grids, the self-management of desktop Grid platforms and the monitoring and healing of complex applications. It also elaborates on the use of bio-inspired algorithms to achieve self-management. Related future trends and challenges are described.
DEFF Research Database (Denmark)
Muller, Laurent Flindt
2009-01-01
We present an application of an Adaptive Large Neighborhood Search (ALNS) algorithm to the Resource-constrained Project Scheduling Problem (RCPSP). The ALNS framework was first proposed by Pisinger and Røpke [19] and can be described as a large neighborhood search algorithm with an adaptive layer......, where a set of destroy/repair neighborhoods compete to modify the current solution in each iteration of the algorithm. Experiments are performed on the wellknown J30, J60 and J120 benchmark instances, which show that the proposed algorithm is competitive and confirms the strength of the ALNS framework...
"Accelerated Perceptron": A Self-Learning Linear Decision Algorithm
Zuev, Yu. A.
2003-01-01
The class of linear decision rules is studied. A new algorithm for weight correction, called an "accelerated perceptron", is proposed. In contrast to classical Rosenblatt's perceptron this algorithm modifies the weight vector at each step. The algorithm may be employed both in learning and in self-learning modes. The theoretical aspects of the behaviour of the algorithm are studied when the algorithm is used for the purpose of increasing the decision reliability by means of weighted voting. I...
Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
M. Udin Harun Al Rasyid
2014-12-01
Full Text Available Wireless sensor network (WSN uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as power‐efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with non‐adaptive scheme. From the simulation results, our proposed idea has good‐quality data transmission and more efficient in energy consumption, but it has higher delay than that of non‐adaptive scheme. Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order.
Hierarchical Threshold Adaptive for Point Cloud Filter Algorithm of Moving Surface Fitting
Directory of Open Access Journals (Sweden)
ZHU Xiaoxiao
2018-02-01
Full Text Available In order to improve the accuracy,efficiency and adaptability of point cloud filtering algorithm,a hierarchical threshold adaptive for point cloud filter algorithm of moving surface fitting was proposed.Firstly,the noisy points are removed by using a statistic histogram method.Secondly,the grid index is established by grid segmentation,and the surface equation is set up through the lowest point among the neighborhood grids.The real height and fit are calculated.The difference between the elevation and the threshold can be determined.Finally,in order to improve the filtering accuracy,hierarchical filtering is used to change the grid size and automatically set the neighborhood size and threshold until the filtering result reaches the accuracy requirement.The test data provided by the International Photogrammetry and Remote Sensing Society (ISPRS is used to verify the algorithm.The first and second error and the total error are 7.33%,10.64% and 6.34% respectively.The algorithm is compared with the eight classical filtering algorithms published by ISPRS.The experiment results show that the method has well-adapted and it has high accurate filtering result.
Directory of Open Access Journals (Sweden)
Lun-Hui Xu
2013-01-01
Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.
Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants
International Nuclear Information System (INIS)
Husam Fayiz, Al Masri
2017-01-01
The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms. (paper)
Shin, Yun Mi; Chung, Young Ki; Lim, Ki Young; Lee, Young Moon; Oh, Eun Young
2009-01-01
The aim of this study was to investigate predictors of adolescence suicidality in a longitudinal study. Additionally, the prevalence of deliberate self-harm behavior and suicide ideation at age 7 and during middle school were examined. Initial assessment data was obtained from 1998 to 2000, and a follow-up assessment was performed in 2006 when the original subjects became middle school students. The addresses and names of 1,857 subjects were located from the original data; they were 910 boys and 947 girls. The subjects were evaluated with the Korean version of the Child Behavior Checklist (K-CBCL), which was administered by the parents of the children, and by various demographic and psychosocial factors. They were reassessed using self reports on the Korea Youth Self Report (K-YSR); in particular, replies to items related to self-harm behavior and suicide ideation were recorded. A logistic regression analysis showed that the factors of gender, economic status, the overall amount of behavior problems, the tendency to internalizing and externalizing problems, somatic problems, thought problems, delinquent behavior, and aggressive behavior were independent predictors of adolescent suicide ideation and self-harm behavior. The importance of total behavior problems suggested that adolescent difficulty is a consequence of an accumulation of various risk factors. Accordingly, clinicians must consider a range of internalizing and externalizing issues, especially overall adaptation, for suicide intervention. PMID:19399261
Research on EMI Reduction of Multi-stage Interleaved Bridgeless Power Factor Corrector
DEFF Research Database (Denmark)
Li, Qingnan; Thomsen, Ole Cornelius; Andersen, Michael A. E.
2012-01-01
Working as an electronic pollution eliminator, the Power Factor Corrector's (PFC) own Electromagnetic Interference (EMI) problems have been blocking its performance improvement for long. In this paper, a systematic research on EMI generation of a multi-stage Two-Boost-Circuit Interleaved Bridgeless...
A New Adaptive Hungarian Mating Scheme in Genetic Algorithms
Directory of Open Access Journals (Sweden)
Chanju Jung
2016-01-01
Full Text Available In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes.
An adaptive tensor voting algorithm combined with texture spectrum
Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi
2015-01-01
An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.
Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.
Piastra, Marco
2013-05-01
Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.
An adaptive clustering algorithm for image matching based on corner feature
Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song
2018-04-01
The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.
Smith, J E
2012-01-01
Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes
Energy Technology Data Exchange (ETDEWEB)
Pereira, Augusto, E-mail: augusto.pereira@ciemat.es [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Vega, Jesús; Moreno, Raúl [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Dormido-Canto, Sebastián [Dpto. Informática y Automática – UNED, Madrid (Spain); Rattá, Giuseppe A. [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Pavón, Fernando [Dpto. Informática y Automática – UNED, Madrid (Spain)
2015-10-15
Recently, a probabilistic classifier has been developed at JET to be used as predictor from scratch. It has been applied to a database of 1237 JET ITER-like wall (ILW) discharges (of which 201 disrupted) with good results: success rate of 94% and false alarm rate of 4.21%. A combinatorial analysis between 14 features to ensure the selection of the best ones to achieve good enough results in terms of success rate and false alarm rate was performed. All possible combinations with a number of features between 2 and 7 were tested and 9893 different predictors were analyzed. An important drawback in this analysis was the time required to compute the results that can be estimated in 1731 h (∼2.4 months). Genetic algorithms (GA) are searching algorithms that simulate the process of natural selection. In this article, the GA and the Venn predictors are combined with the objective not only of finding good enough features within the 14 available ones but also of reducing the computational time requirements. Five different performance metrics as measures of the GA fitness function have been evaluated. The best metric was the measurement called Informedness, with just 6 generations (168 predictors at 29.4 h).
International Nuclear Information System (INIS)
Pereira, Augusto; Vega, Jesús; Moreno, Raúl; Dormido-Canto, Sebastián; Rattá, Giuseppe A.; Pavón, Fernando
2015-01-01
Recently, a probabilistic classifier has been developed at JET to be used as predictor from scratch. It has been applied to a database of 1237 JET ITER-like wall (ILW) discharges (of which 201 disrupted) with good results: success rate of 94% and false alarm rate of 4.21%. A combinatorial analysis between 14 features to ensure the selection of the best ones to achieve good enough results in terms of success rate and false alarm rate was performed. All possible combinations with a number of features between 2 and 7 were tested and 9893 different predictors were analyzed. An important drawback in this analysis was the time required to compute the results that can be estimated in 1731 h (∼2.4 months). Genetic algorithms (GA) are searching algorithms that simulate the process of natural selection. In this article, the GA and the Venn predictors are combined with the objective not only of finding good enough features within the 14 available ones but also of reducing the computational time requirements. Five different performance metrics as measures of the GA fitness function have been evaluated. The best metric was the measurement called Informedness, with just 6 generations (168 predictors at 29.4 h).
Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift
Ortíz Díaz, Agustín; Ramos-Jiménez, Gonzalo; Frías Blanco, Isvani; Caballero Mota, Yailé; Morales-Bueno, Rafael
2015-01-01
The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts. PMID:25879051
Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET
Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Vega, J.; Dormido-Canto, S.; Baruzzo, M.; Gelfusa, M.; Contributors, JET
2018-05-01
Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy ‘from scratch’ has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.
An adaptation of Krylov subspace methods to path following
Energy Technology Data Exchange (ETDEWEB)
Walker, H.F. [Utah State Univ., Logan, UT (United States)
1996-12-31
Krylov subspace methods at present constitute a very well known and highly developed class of iterative linear algebra methods. These have been effectively applied to nonlinear system solving through Newton-Krylov methods, in which Krylov subspace methods are used to solve the linear systems that characterize steps of Newton`s method (the Newton equations). Here, we will discuss the application of Krylov subspace methods to path following problems, in which the object is to track a solution curve as a parameter varies. Path following methods are typically of predictor-corrector form, in which a point near the solution curve is {open_quotes}predicted{close_quotes} by some easy but relatively inaccurate means, and then a series of Newton-like corrector iterations is used to return approximately to the curve. The analogue of the Newton equation is underdetermined, and an additional linear condition must be specified to determine corrector steps uniquely. This is typically done by requiring that the steps be orthogonal to an approximate tangent direction. Augmenting the under-determined system with this orthogonality condition in a straightforward way typically works well if direct linear algebra methods are used, but Krylov subspace methods are often ineffective with this approach. We will discuss recent work in which this orthogonality condition is imposed directly as a constraint on the corrector steps in a certain way. The means of doing this preserves problem conditioning, allows the use of preconditioners constructed for the fixed-parameter case, and has certain other advantages. Experiments on standard PDE continuation test problems indicate that this approach is effective.
Exploring self-efficacy as a predictor of disease management.
Clark, N M; Dodge, J A
1999-02-01
Self-efficacy is posited in social cognitive theory as fundamental to behavior change. Few health behavior studies have examined self-efficacy prospectively, viewed it as part of a reciprocal behavioral process, or compared self-efficacy beliefs in the same population across different behaviors. This article first discusses self-efficacy in its theoretical context and reviews the available prospective studies. Second, it explores self-efficacy as a predictor of disease management behaviors in 570 older women with heart disease. Although the R2 statistics in each case were modest, the construct is shown to be a statistically significant (pmanagement behaviors: using medicine as prescribed, getting adequate exercise, managing stress, and following a recommended diet. Building self-efficacy is likely a reasonable starting point for interventions aiming to enhance heart disease management behaviors of mature female patients.
Performance study of LMS based adaptive algorithms for unknown system identification
Energy Technology Data Exchange (ETDEWEB)
Javed, Shazia; Ahmad, Noor Atinah [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Penang (Malaysia)
2014-07-10
Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.
Performance study of LMS based adaptive algorithms for unknown system identification
International Nuclear Information System (INIS)
Javed, Shazia; Ahmad, Noor Atinah
2014-01-01
Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment
Patterns and predictors of self-medication in northern Uganda.
Directory of Open Access Journals (Sweden)
Moses Ocan
Full Text Available Self-medication with antimicrobial agents is a common form of self-care among patients globally with the prevalence and nature differing from country to country. Here we assessed the prevalence and predictors of antimicrobial self-medication in post-conflict northern Uganda. A cross-sectional study was carried out using structured interviews on 892 adult (≥18 years participants. Information on drug name, prescriber, source, cost, quantity of drug obtained, and drug use was collected. Households were randomly selected using multistage cluster sampling method. One respondent who reported having an illness within three months in each household was recruited. In each household, information was obtained from only one adult individual. Data was analyzed using STATA at 95% level of significance. The study found that a high proportion (75.7% of the respondents practiced antimicrobial self-medication. Fever, headache, lack of appetite and body weakness were the disease symptoms most treated through self-medication (30.3%. The commonly self-medicated antimicrobials were coartem (27.3%, amoxicillin (21.7%, metronidazole (12.3%, and cotrimoxazole (11.6%. Drug use among respondents was mainly initiated by self-prescription (46.5% and drug shop attendants (57.6%. On average, participants obtained 13.9±8.8 (95%CI: 12.6-13.8 tablets/capsules of antimicrobial drugs from drug shops and drugs were used for an average of 3.7±2.8 days (95%CI: 3.3-3.5. Over half (68.2% of the respondents would recommend self-medication to another sick person. A high proportion (76% of respondents reported that antimicrobial self-medication had associated risks such as wastage of money (42.1%, drug resistance (33.2%, and masking symptoms of underlying disease (15.5%. Predictors of self-medication with antimicrobial agents included gender, drug knowledge, drug leaflets, advice from friends, previous experience, long waiting time, and distance to the health facility. Despite
Pipeline Implementation of Polyphase PSO for Adaptive Beamforming Algorithm
Directory of Open Access Journals (Sweden)
Shaobing Huang
2017-01-01
Full Text Available Adaptive beamforming is a powerful technique for anti-interference, where searching and tracking optimal solutions are a great challenge. In this paper, a partial Particle Swarm Optimization (PSO algorithm is proposed to track the optimal solution of an adaptive beamformer due to its great global searching character. Also, due to its naturally parallel searching capabilities, a novel Field Programmable Gate Arrays (FPGA pipeline architecture using polyphase filter bank structure is designed. In order to perform computations with large dynamic range and high precision, the proposed implementation algorithm uses an efficient user-defined floating-point arithmetic. In addition, a polyphase architecture is proposed to achieve full pipeline implementation. In the case of PSO with large population, the polyphase architecture can significantly save hardware resources while achieving high performance. Finally, the simulation results are presented by cosimulation with ModelSim and SIMULINK.
Energy Technology Data Exchange (ETDEWEB)
Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
International Nuclear Information System (INIS)
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao
2014-01-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm
A kernel adaptive algorithm for quaternion-valued inputs.
Paul, Thomas K; Ogunfunmi, Tokunbo
2015-10-01
The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.
Disruption prediction with adaptive neural networks for ASDEX Upgrade
International Nuclear Information System (INIS)
Cannas, B.; Fanni, A.; Pautasso, G.; Sias, G.
2011-01-01
In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the 'novelty' of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated.
A locally adaptive algorithm for shadow correction in color images
Karnaukhov, Victor; Kober, Vitaly
2017-09-01
The paper deals with correction of color images distorted by spatially nonuniform illumination. A serious distortion occurs in real conditions when a part of the scene containing 3D objects close to a directed light source is illuminated much brighter than the rest of the scene. A locally-adaptive algorithm for correction of shadow regions in color images is proposed. The algorithm consists of segmentation of shadow areas with rank-order statistics followed by correction of nonuniform illumination with human visual perception approach. The performance of the proposed algorithm is compared to that of common algorithms for correction of color images containing shadow regions.
An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient
Nobile, Fabio; Tamellini, Lorenzo; Tesei, Francesco; Tempone, Raul
2016-01-01
In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature), obtaining an enhanced version capable of using non-nested collocation points, and supporting quadrature
Gender, g, gender identity concepts, and self-constructs as predictors of the self-estimated IQ.
Storek, Josephine; Furnham, Adrian
2013-01-01
In all 102 participants completed 2 intelligence tests, a self-estimated domain-masculine (DMIQ) intelligence rating (which is a composite of self-rated mathematical-logical and spatial intelligence), a measure of self-esteem, and of self-control. The aim was to confirm and extend previous findings about the role of general intelligence and gender identity in self-assessed intelligence. It aimed to examine further correlates of the Hubris-Humility Effect that shows men believe they are more intelligent than women. The DMIQ scores were correlated significantly with gender, psychometrically assessed IQ, and masculinity but not self-esteem or self-control. Stepwise regressions indicated that gender and gender role were the strongest predictors of DMIQ accounting for a third of the variance.
Gender, g, Gender Identity Concepts, and Self-Constructs as Predictors of the Self-Estimated IQ
Storek, Josephine
2013-01-01
In all 102 participants completed 2 intelligence tests, a self-estimated domain-masculine (DMIQ) intelligence rating (which is a composite of self-rated mathematical–logical and spatial intelligence), a measure of self-esteem, and of self-control. The aim was to confirm and extend previous findings about the role of general intelligence and gender identity in self-assessed intelligence. It aimed to examine further correlates of the Hubris–Humility Effect that shows men believe they are more intelligent than women. The DMIQ scores were correlated significantly with gender, psychometrically assessed IQ, and masculinity but not self-esteem or self-control. Stepwise regressions indicated that gender and gender role were the strongest predictors of DMIQ accounting for a third of the variance. PMID:24303578
Social adaptability and substance abuse: Predictors of depression among hemodialysis patients?
2013-01-01
Background Several aspects linked to social are involved in the onset of depressive feelings. We aimed to find out if social adaptability and substance abuse predict depression among end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). Methods We included 145 ESRD patients undergoing HD. Social adaptability was estimated by the Social Adaptability Index (SAI). Substance abuse was defined according to SAI. We screened for depression by applying the 20-item version of the Center for Epidemiologic Studies Depression Scale. A score ≥ 24 classified the patients as depressed. Comparisons between depressed and non-depressed patients were carried out and logistic regression was performed to test gender, age, total SAI, SAI without the substance abuse item, only the substance abuse score and substance abuse as a categorical variable (yes/no) as predictors of depression. Results There were 36 (24.8%) depressed patients. There were no differences regarding demographic and laboratory data between the depressed and non-depressed patients. Mean SAI among depressed and non-depressed patients was, respectively, 6.1 ± 1.6 vs. 6.2 ± 1.9 (p=0.901). The percentage of patients with or without substance abuse among depressed patients was, respectively, 13.8% vs. 13.9% (p=1.000). Gender, age, total SAI, SAI without the substance abuse item, only the substance abuse score and substance abuse as a categorical variable did not predict depression. Conclusions Social adaptability and substance abuse did not predict depression in HD patients. We propose that aspects related to socioeconomic status not comprised in SAI items should be ruled out as predictors of depression. PMID:23320829
Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao
2018-06-01
To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.
Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning
2015-03-01
ARL-SR-0318 ● MAR 2015 US Army Research Laboratory Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated...Adaptive Intelligent Tutoring Systems for Self-Regulated Learning by Robert A Sottilare Human Research and Engineering Directorate, ARL...TITLE AND SUBTITLE Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c
Frequency-Domain Adaptive Algorithm for Network Echo Cancellation in VoIP
Directory of Open Access Journals (Sweden)
Patrick A. Naylor
2008-05-01
Full Text Available We propose a new low complexity, low delay, and fast converging frequency-domain adaptive algorithm for network echo cancellation in VoIP exploiting MMax and sparse partial (SP tap-selection criteria in the frequency domain. We incorporate these tap-selection techniques into the multidelay filtering (MDF algorithm in order to mitigate the delay inherent in frequency-domain algorithms. We illustrate two such approaches and discuss their tradeoff between convergence performance and computational complexity. Simulation results show an improvement in convergence rate for the proposed algorithm over MDF and significantly reduced complexity. The proposed algorithm achieves a convergence performance close to that of the recently proposed, but substantially more complex improved proportionate MDF (IPMDF algorithm.
Evaluation of the global orbit correction algorithm for the APS real-time orbit feedback system
International Nuclear Information System (INIS)
Carwardine, J.; Evans, K. Jr.
1997-01-01
The APS real-time orbit feedback system uses 38 correctors per plane and has available up to 320 rf beam position monitors. Orbit correction is implemented using multiple digital signal processors. Singular value decomposition is used to generate a correction matrix from a linear response matrix model of the storage ring lattice. This paper evaluates the performance of the APS system in terms of its ability to correct localized and distributed sources of orbit motion. The impact of regulator gain and bandwidth, choice of beam position monitors, and corrector dynamics are discussed. The weighted least-squares algorithm is reviewed in the context of local feedback
A Self-adaptive Dynamic Evaluation Model for Diabetes Mellitus, Based on Evolutionary Strategies
Directory of Open Access Journals (Sweden)
An-Jiang Lu
2016-03-01
Full Text Available In order to evaluate diabetes mellitus objectively and accurately, this paper builds a self-adaptive dynamic evaluation model for diabetes mellitus, based on evolutionary strategies. First of all, on the basis of a formalized description of the evolutionary process of diabetes syndromes, using a state transition function, it judges whether a disease is evolutionary, through an excitation parameter. It then, provides evidence for the rebuilding of the evaluation index system. After that, by abstracting and rebuilding the composition of evaluation indexes, it makes use of a heuristic algorithm to determine the composition of the evolved evaluation index set of diabetes mellitus, It then, calculates the weight of each index in the evolved evaluation index set of diabetes mellitus by building a dependency matrix and realizes the self-adaptive dynamic evaluation of diabetes mellitus under an evolutionary environment. Using this evaluation model, it is possible to, quantify all kinds of diagnoses and treatment experiences of diabetes and finally to adopt ideal diagnoses and treatment measures for different patients with diabetics.
The Pointing Self-calibration Algorithm for Aperture Synthesis Radio Telescopes
Energy Technology Data Exchange (ETDEWEB)
Bhatnagar, S.; Cornwell, T. J., E-mail: sbhatnag@nrao.edu [National Radio Astronomy Observatory, 1003 Lopezville Road, Socorro, NM 87801 (United States)
2017-11-01
This paper is concerned with algorithms for calibration of direction-dependent effects (DDE) in aperture synthesis radio telescopes (ASRT). After correction of direction-independent effects (DIE) using self-calibration, imaging performance can be limited by the imprecise knowledge of the forward gain of the elements in the array. In general, the forward gain pattern is directionally dependent and varies with time due to a number of reasons. Some factors, such as rotation of the primary beam with Parallactic Angle for Azimuth–Elevation mount antennas are known a priori. Some, such as antenna pointing errors and structural deformation/projection effects for aperture-array elements cannot be measured a priori. Thus, in addition to algorithms to correct for DD effects known a priori, algorithms to solve for DD gains are required for high dynamic range imaging. Here, we discuss a mathematical framework for antenna-based DDE calibration algorithms and show that this framework leads to computationally efficient optimal algorithms that scale well in a parallel computing environment. As an example of an antenna-based DD calibration algorithm, we demonstrate the Pointing SelfCal (PSC) algorithm to solve for the antenna pointing errors. Our analysis show that the sensitivity of modern ASRT is sufficient to solve for antenna pointing errors and other DD effects. We also discuss the use of the PSC algorithm in real-time calibration systems and extensions for antenna Shape SelfCal algorithm for real-time tracking and corrections for pointing offsets and changes in antenna shape.
The Pointing Self-calibration Algorithm for Aperture Synthesis Radio Telescopes
Bhatnagar, S.; Cornwell, T. J.
2017-11-01
This paper is concerned with algorithms for calibration of direction-dependent effects (DDE) in aperture synthesis radio telescopes (ASRT). After correction of direction-independent effects (DIE) using self-calibration, imaging performance can be limited by the imprecise knowledge of the forward gain of the elements in the array. In general, the forward gain pattern is directionally dependent and varies with time due to a number of reasons. Some factors, such as rotation of the primary beam with Parallactic Angle for Azimuth-Elevation mount antennas are known a priori. Some, such as antenna pointing errors and structural deformation/projection effects for aperture-array elements cannot be measured a priori. Thus, in addition to algorithms to correct for DD effects known a priori, algorithms to solve for DD gains are required for high dynamic range imaging. Here, we discuss a mathematical framework for antenna-based DDE calibration algorithms and show that this framework leads to computationally efficient optimal algorithms that scale well in a parallel computing environment. As an example of an antenna-based DD calibration algorithm, we demonstrate the Pointing SelfCal (PSC) algorithm to solve for the antenna pointing errors. Our analysis show that the sensitivity of modern ASRT is sufficient to solve for antenna pointing errors and other DD effects. We also discuss the use of the PSC algorithm in real-time calibration systems and extensions for antenna Shape SelfCal algorithm for real-time tracking and corrections for pointing offsets and changes in antenna shape.
Hirschi, Andreas
2009-01-01
This longitudinal panel study investigated predictors of career adaptability development and its effect on development of sense of power and experience of life satisfaction among 330 Swiss eighth graders. A multivariate measure of career adaptability consisting of career choice readiness, planning, exploration, and confidence was applied. Based on…
Robust Self Tuning Controllers
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad
1985-01-01
The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...... has several operation modes and a detector for controlling the mode. A special self tuning controller has been developed to regulate plant with changing time delay.......The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...
A procedure for empirical initialization of adaptive testing algorithms
van der Linden, Willem J.
1997-01-01
In constrained adaptive testing, the numbers of constraints needed to control the content of the tests can easily run into the hundreds. Proper initialization of the algorithm becomes a requirement because the presence of large numbers of constraints slows down the convergence of the ability
Self-efficacy, emotional intelligence and birth order as predictors of ...
African Journals Online (AJOL)
Self-efficacy, emotional intelligence and birth order as predictors of academic performance among secondary school students in Kajola Local Government ... standardized scales and the data obtained was analyzed using Pearson Product Moment Correlation (PPMC) and Multiple Regression Statistical analysis of SPSS.
A hydraulic hybrid propulsion method for automobiles with self-adaptive system
International Nuclear Information System (INIS)
Wu, Wei; Hu, Jibin; Yuan, Shihua; Di, Chongfeng
2016-01-01
A hydraulic hybrid vehicle with the self-adaptive system is proposed. The mode-switching between the driving mode and the hydraulic regenerative braking mode is realised by the pressure cross-feedback control. Extensive simulated and tested results are presented. The control parameters are reduced and the energy efficiency can be increased by the self-adaptive system. The mode-switching response is fast. The response time can be adjusted by changing the controlling spool diameter of the hydraulic operated check valve in the self-adaptive system. The closing of the valve becomes faster with a smaller controlling spool diameter. The hydraulic regenerative braking mode can be achieved by changing the hydraulic transformer controlled angle. Compared with the convention electric-hydraulic system, the self-adaptive system for the hydraulic hybrid vehicle mode-switching has a higher reliability and a lower cost. The efficiency of the hydraulic regenerative braking is also increased. - Highlights: • A new hybrid system with a self-adaptive system for automobiles is presented. • The mode-switching is realised by the pressure cross-feedback control. • The energy efficiency can be increased with the self-adaptive system. • The control parameters are reduced with the self-adaptive system.
Wojciszke Bogdan; Bialobrzeska Olga
2014-01-01
Two hypotheses concerning the relative importance of agentic versus communal traits as predictors of selfesteem were tested. The perspective hypothesis assumed that self-esteem is dominated by agency over communion because self-perceptions are formed from the agent (versus recipient) perspective. The culture hypothesis assumed that self-esteem is dominated by communal concerns in collectivistic cultures and by agentic concerns in individualistic cultures (echoed by individual differences in s...
Adaptive Numerical Algorithms in Space Weather Modeling
Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.;
2010-01-01
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical
Control algorithms and applications of the wavefront sensorless adaptive optics
Ma, Liang; Wang, Bin; Zhou, Yuanshen; Yang, Huizhen
2017-10-01
Compared with the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system need not to measure the wavefront and reconstruct it. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. Based on the analysis of principle and system model of the WFSless AO system, wavefront correction methods of the WFSless AO system were divided into two categories: model-free-based and model-based control algorithms. The WFSless AO system based on model-free-based control algorithms commonly considers the performance metric as a function of the control parameters and then uses certain control algorithm to improve the performance metric. The model-based control algorithms include modal control algorithms, nonlinear control algorithms and control algorithms based on geometrical optics. Based on the brief description of above typical control algorithms, hybrid methods combining the model-free-based control algorithm with the model-based control algorithm were generalized. Additionally, characteristics of various control algorithms were compared and analyzed. We also discussed the extensive applications of WFSless AO system in free space optical communication (FSO), retinal imaging in the human eye, confocal microscope, coherent beam combination (CBC) techniques and extended objects.
On flexible CAD of adaptive control and identification algorithms
DEFF Research Database (Denmark)
Christensen, Anders; Ravn, Ole
1988-01-01
a total redesign of the system within each sample. The necessary design parameters are evaluated and a decision vector is defined, from which the identification algorithm can be generated by the program. Using the decision vector, a decision-node tree structure is built up, where the nodes define......SLLAB is a MATLAB-family software package for solving control and identification problems. This paper concerns the planning of a general-purpose subroutine structure for solving identification and adaptive control problems. A general-purpose identification algorithm is suggested, which allows...
Genetic Algorithms for Case Adaptation
International Nuclear Information System (INIS)
Salem, A.M.; Mohamed, A.H.
2008-01-01
Case adaptation is the core of case based reasoning (CBR) approach that can modify the past solutions to solve new problems. It generally relies on the knowledge base and heuristics in order to achieve the required changes. It has always been a difficult process to designers within (CBR) cycle. Its difficulties can be referred to the large effort, and computational analysis needed for acquiring the knowledge's domain. To solve these problems, this research explores a new method that applying a genetic algorithm (GA) to CBR adaptation. However, it can decrease the computational complexity of determining the required changes of the problems especially those having a great amount of domain knowledge. besides, it can decrease the required time by dividing the design task into sub tasks those can be solved at the same time. Therefore, the proposed system can he practically applied for solving the complex problems. It can be used to perform a variety of design tasks on a broad set of application domains. However, it has been implemented for the tablet formulation as a domain of application. Proposed system has improved the accuracy performance of the CBR design systems
Unsupervised Classification Using Immune Algorithm
Al-Muallim, M. T.; El-Kouatly, R.
2012-01-01
Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...
A Line-Based Adaptive-Weight Matching Algorithm Using Loopy Belief Propagation
Directory of Open Access Journals (Sweden)
Hui Li
2015-01-01
Full Text Available In traditional adaptive-weight stereo matching, the rectangular shaped support region requires excess memory consumption and time. We propose a novel line-based stereo matching algorithm for obtaining a more accurate disparity map with low computation complexity. This algorithm can be divided into two steps: disparity map initialization and disparity map refinement. In the initialization step, a new adaptive-weight model based on the linear support region is put forward for cost aggregation. In this model, the neural network is used to evaluate the spatial proximity, and the mean-shift segmentation method is used to improve the accuracy of color similarity; the Birchfield pixel dissimilarity function and the census transform are adopted to establish the dissimilarity measurement function. Then the initial disparity map is obtained by loopy belief propagation. In the refinement step, the disparity map is optimized by iterative left-right consistency checking method and segmentation voting method. The parameter values involved in this algorithm are determined with many simulation experiments to further improve the matching effect. Simulation results indicate that this new matching method performs well on standard stereo benchmarks and running time of our algorithm is remarkably lower than that of algorithm with rectangle-shaped support region.
Ogawa, Masakatsu; Hiraguri, Takefumi; Nishimori, Kentaro; Takaya, Kazuhiro; Murakawa, Kazuo
This paper proposes and investigates a distributed adaptive contention window adjustment algorithm based on the transmission history for wireless LANs called the transmission-history-based distributed adaptive contention window adjustment (THAW) algorithm. The objective of this paper is to reduce the transmission delay and improve the channel throughput compared to conventional algorithms. The feature of THAW is that it adaptively adjusts the initial contention window (CWinit) size in the binary exponential backoff (BEB) algorithm used in the IEEE 802.11 standard according to the transmission history and the automatic rate fallback (ARF) algorithm, which is the most basic algorithm in automatic rate controls. This effect is to keep CWinit at a high value in a congested state. Simulation results show that the THAW algorithm outperforms the conventional algorithms in terms of the channel throughput and delay, even if the timer in the ARF is changed.
From a distance: implications of spontaneous self-distancing for adaptive self-reflection.
Ayduk, Ozlem; Kross, Ethan
2010-05-01
Although recent experimental work indicates that self-distancing facilitates adaptive self-reflection, it remains unclear (a) whether spontaneous self-distancing leads to similar adaptive outcomes, (b) how spontaneous self-distancing relates to avoidance, and (c) how this strategy impacts interpersonal behavior. Three studies examined these issues demonstrating that the more participants spontaneously self-distanced while reflecting on negative memories, the less emotional (Studies 1-3) and cardiovascular (Study 2) reactivity they displayed in the short term. Spontaneous self-distancing was also associated with lower emotional reactivity and intrusive ideation over time (Study 1). The negative association between spontaneous self-distancing and emotional reactivity was mediated by how participants construed their experience (i.e., less recounting relative to reconstruing) rather than avoidance (Studies 1-2). In addition, spontaneous self-distancing was associated with more problem-solving behavior and less reciprocation of negativity during conflicts among couples in ongoing relationships (Study 3). Although spontaneous self-distancing was empirically related to trait rumination, it explained unique variance in predicting key outcomes. 2010 APA, all rights reserved
Liu, Chong-xin; Liu, Bo; Zhang, Li-jia; Xin, Xiang-jun; Tian, Qing-hua; Tian, Feng; Wang, Yong-jun; Rao, Lan; Mao, Yaya; Li, Deng-ao
2018-01-01
During the last decade, the orthogonal frequency division multiplexing radio-over-fiber (OFDM-ROF) system with adaptive modulation technology is of great interest due to its capability of raising the spectral efficiency dramatically, reducing the effects of fiber link or wireless channel, and improving the communication quality. In this study, according to theoretical analysis of nonlinear distortion and frequency selective fading on the transmitted signal, a low-complexity adaptive modulation algorithm is proposed in combination with sub-carrier grouping technology. This algorithm achieves the optimal performance of the system by calculating the average combined signal-to-noise ratio of each group and dynamically adjusting the origination modulation format according to the preset threshold and user's requirements. At the same time, this algorithm takes the sub-carrier group as the smallest unit in the initial bit allocation and the subsequent bit adjustment. So, the algorithm complexity is only 1 /M (M is the number of sub-carriers in each group) of Fischer algorithm, which is much smaller than many classic adaptive modulation algorithms, such as Hughes-Hartogs algorithm, Chow algorithm, and is in line with the development direction of green and high speed communication. Simulation results show that the performance of OFDM-ROF system with the improved algorithm is much better than those without adaptive modulation, and the BER of the former achieves 10e1 to 10e2 times lower than the latter when SNR values gets larger. We can obtain that this low complexity adaptive modulation algorithm is extremely useful for the OFDM-ROF system.
Further Development of the Sextupole Dipole Corrector (MSCB) Magnet for the LHC
Ang, Z; Bajko, M; Bottura, L; Coxill, D; Giloux, C; Ijspeert, Albert; Karppinen, M; Landgrebe, D; Walckiers, L
2000-01-01
Combined sextupole-dipole corrector magnets (MSCB) will be mounted in each half cell of the new Large Hadron Collider (LHC) being built at CERN. The dipole part, used for particle orbit corrections, will be powered individually and is designed for low current, originally 30 A but now 55 A. The sextupole part, used for chromaticity corrections, is connected via cold busbars in families of 12 or 13 magnets and is powered with 550 A. Several versions of this corrector magnet were tested as model magnets in order to develop the final design for the series. In the first design the coils are nested, with the dipole coil wound around the sextupole coil to obtain as short a magnet as possible, accepting the slight cross-talk between the coils due to persistent currents, and increased saturation effects. The design has evolved and an alternative design, in which the dipole and sextupole coils are separated, is now favored. Tests at 4.5 K and at 1.9 K were conducted to determine the training behavior, the field qualit...
Design, fabrication and cold tests of a super ferric octupole corrector for the LHC
International Nuclear Information System (INIS)
Garcia-Tabares, L.; Calero, J.; Laurent, G.; Russenschuck, S.; Siegel, N.; Traveria, M.; Aguirre, P.; Etxeandia, J.; Garcia, J.
1996-01-01
In the corrections scheme of the LHC it is planed to install octupole corrector magnets in the short straight section of the lattice. Initially these correctors were distributed windings on the cold bore tube nested in the tuning quadrupoles. The latter being suppressed a new compact super ferric design was chosen for the octupole prototype, suitable for a two-in-one configuration. This prototype was designed by CERN and CEDEX/Spain, built at INDAR/Spain and tested at CEDEX. The paper reports on the design of the prototype, describes the fabrication and assembly and presents the measurement results. Special interest has been taken to design a simple and compact magnet, easy to fabricate and training free below nominal field. First results show the feasibility of the solution wich will be finally confirmed by magnetic measurement. (Author) 4 refs
Automatically tuned adaptive differencing algorithm for 3-D SN implemented in PENTRAN
International Nuclear Information System (INIS)
Sjoden, G.; Courau, T.; Manalo, K.; Yi, C.
2009-01-01
We present an adaptive algorithm with an automated tuning feature to augment optimum differencing scheme selection for 3-D S N computations in Cartesian geometry. This adaptive differencing scheme has been implemented in the PENTRAN parallel S N code. Individual fixed zeroth spatial transport moment based schemes, including Diamond Zero (DZ), Directional Theta Weighted (DTW), and Exponential Directional Iterative (EDI) 3-D S N methods were evaluated and compared with solutions generated using a code-tuned adaptive algorithm. Model problems considered include a fixed source slab problem (using reflected y- and z-axes) which contained mixed shielding and diffusive regions, and a 17 x 17 PWR assembly eigenvalue test problem; these problems were benchmarked against multigroup MCNP5 Monte Carlo computations. Both problems were effective in highlighting the performance of the adaptive scheme compared to single schemes, and demonstrated that the adaptive tuning handles exceptions to the standard DZ-DTW-EDI adaptive strategy. The tuning feature includes special scheme selection provisions for optically thin cells, and incorporates the ratio of the angular source density relative to the total angular collision density to best select the differencing method. Overall, the adaptive scheme demonstrated the best overall solution accuracy in the test problems. (authors)
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.
Self-Tuning Insulin Adjustment Algorithm for Type 1 Diabetic Patients based on Multi-Doses Regime
Directory of Open Access Journals (Sweden)
D. U. Campos-Delgado
2005-01-01
Full Text Available A self-tuning algorithm is presented for on-line insulin dosage adjustment in type 1 diabetic patients (chronic stage. The algorithm suggested does not need information of the patient insulin–glucose dynamics (model-free. Three doses are programmed daily, where a combination of two types of insulin: rapid/short and intermediate/long acting is injected into the patient through a subcutaneous route. The doses adaptation is performed by reducing the error in the blood glucose level from euglycemics. In this way, a total of five doses are tuned per day: three rapid/short and two intermediate/long, where there is large penalty to avoid hypoglycemic scenarios. Closed-loop simulation results are illustrated using a detailed nonlinear model of the subcutaneous insulin–glucose dynamics in a type 1 diabetic patient with meal intake.
General purpose graphic processing unit implementation of adaptive pulse compression algorithms
Cai, Jingxiao; Zhang, Yan
2017-07-01
This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.
An Adaptive Connectivity-based Centroid Algorithm for Node Positioning in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Aries Pratiarso
2015-06-01
Full Text Available In wireless sensor network applications, the position of nodes is randomly distributed following the contour of the observation area. A simple solution without any measurement tools is provided by range-free method. However, this method yields the coarse estimating position of the nodes. In this paper, we propose Adaptive Connectivity-based (ACC algorithm. This algorithm is a combination of Centroid as range-free based algorithm, and hop-based connectivity algorithm. Nodes have a possibility to estimate their own position based on the connectivity level between them and their reference nodes. Each node divides its communication range into several regions where each of them has a certain weight depends on the received signal strength. The weighted value is used to obtain the estimated position of nodes. Simulation result shows that the proposed algorithm has up to 3 meter error of estimated position on 100x100 square meter observation area, and up to 3 hop counts for 80 meters' communication range. The proposed algorithm performs an average error positioning up to 10 meters better than Weighted Centroid algorithm. Keywords: adaptive, connectivity, centroid, range-free.
A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling
Tong, Cao; Gong, Haili
2018-03-01
This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.
Smart Electrochemical Energy Storage Devices with Self-Protection and Self-Adaptation Abilities.
Yang, Yun; Yu, Dandan; Wang, Hua; Guo, Lin
2017-12-01
Currently, with booming development and worldwide usage of rechargeable electrochemical energy storage devices, their safety issues, operation stability, service life, and user experience are garnering special attention. Smart and intelligent energy storage devices with self-protection and self-adaptation abilities aiming to address these challenges are being developed with great urgency. In this Progress Report, we highlight recent achievements in the field of smart energy storage systems that could early-detect incoming internal short circuits and self-protect against thermal runaway. Moreover, intelligent devices that are able to take actions and self-adapt in response to external mechanical disruption or deformation, i.e., exhibiting self-healing or shape-memory behaviors, are discussed. Finally, insights into the future development of smart rechargeable energy storage devices are provided. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Directory of Open Access Journals (Sweden)
Wang Chao
2016-03-01
Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.
Self-adaptive phosphor coating technology for wafer-level scale chip packaging
International Nuclear Information System (INIS)
Zhou Linsong; Rao Haibo; Wang Wei; Wan Xianlong; Liao Junyuan; Wang Xuemei; Zhou Da; Lei Qiaolin
2013-01-01
A new self-adaptive phosphor coating technology has been successfully developed, which adopted a slurry method combined with a self-exposure process. A phosphor suspension in the water-soluble photoresist was applied and exposed to LED blue light itself and developed to form a conformal phosphor coating with self-adaptability to the angular distribution of intensity of blue light and better-performing spatial color uniformity. The self-adaptive phosphor coating technology had been successfully adopted in the wafer surface to realize a wafer-level scale phosphor conformal coating. The first-stage experiments show satisfying results and give an adequate demonstration of the flexibility of self-adaptive coating technology on application of WLSCP. (semiconductor devices)
Directory of Open Access Journals (Sweden)
Masoumeh Soflaei
2014-01-01
Full Text Available One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA and the family of selective partial update APA (SPU-APA which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE of SR-APA and SPU-APA decrease by 5.8 (dB and 5.5 (dB, respectively, in comparison with least mean square (LMS algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm.
An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning
Directory of Open Access Journals (Sweden)
Xiaohui Yan
2012-01-01
Full Text Available Bacterial Foraging Algorithm (BFO is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.
International Nuclear Information System (INIS)
Pyragas, V.; Pyragas, K.
2011-01-01
We propose a simple adaptive delayed feedback control algorithm for stabilization of unstable periodic orbits with unknown periods. The state dependent time delay is varied continuously towards the period of controlled orbit according to a gradient-descent method realized through three simple ordinary differential equations. We demonstrate the efficiency of the algorithm with the Roessler and Mackey-Glass chaotic systems. The stability of the controlled orbits is proven by computation of the Lyapunov exponents of linearized equations. -- Highlights: → A simple adaptive modification of the delayed feedback control algorithm is proposed. → It enables the control of unstable periodic orbits with unknown periods. → The delay time is varied continuously according to a gradient descend method. → The algorithm is embodied by three simple ordinary differential equations. → The validity of the algorithm is proven by computation of the Lyapunov exponents.
Clark, T. K.; Peters, B.; Gadd, N. E.; De Dios, Y. E.; Wood, S.; Bloomberg, J. J.; Mulavara, A. P.
2016-01-01
Introduction: During space exploration missions astronauts are exposed to a series of novel sensorimotor environments, requiring sensorimotor adaptation. Until adaptation is complete, sensorimotor decrements occur, affecting critical tasks such as piloted landing or docking. Of particularly interest are locomotion tasks such as emergency vehicle egress or extra-vehicular activity. While nearly all astronauts eventually adapt sufficiently, it appears there are substantial individual differences in how quickly and effectively this adaptation occurs. These individual differences in capacity for sensorimotor adaptation are poorly understood. Broadly, we aim to identify measures that may serve as pre-flight predictors of and individual's adaptation capacity to spaceflight-induced sensorimotor changes. As a first step, since spaceflight is thought to involve a reinterpretation of graviceptor cues (e.g. otolith cues from the vestibular system) we investigate the relationships between various measures of vestibular function in humans. Methods: In a set of 15 ground-based control subjects, we quantified individual differences in vestibular function using three measures: 1) ocular vestibular evoked myogenic potential (oVEMP), 2) computerized dynamic posturography and 3) vestibular perceptual thresholds. oVEMP responses are elicited using a mechanical stimuli approach. Computerized dynamic posturography was used to quantify Sensory Organization Tests (SOTs), including SOT5M which involved performing pitching head movements while balancing on a sway-reference support surface with eyes closed. We implemented a vestibular perceptual threshold task using the tilt capabilities of the Tilt-Translation Sled (TTS) at JSC. On each trial, the subject was passively roll-tilted left ear down or right ear down in the dark and verbally provided a forced-choice response regarding which direction they felt tilted. The motion profile was a single-cycle sinusoid of angular acceleration with a
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.
Dang, Michelle T
2014-03-01
The purpose of this cross-sectional study was to explore social connectedness and self-esteem as predictors of resilience among homeless youth with histories of maltreatment. Connectedness variables included family connectedness, school connectedness, and affiliation with prosocial peers. The sample included 150 homeless youth aged 14 to 21 (mean age = 18 years) with the majority being an ethnic minority. Participants completed surveys using audio-CASI. Results revealed that youth with higher levels of social connectedness and self-esteem reported lower levels of psychological distress. When all predictor variables were controlled in the analysis, self-esteem remained significant for predicting better mental health.
Directory of Open Access Journals (Sweden)
Fuqing Zhao
2016-01-01
Full Text Available A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.
Yook, Sunhyun; Nam, Kyoung Won; Kim, Heepyung; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young
2015-04-01
In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.
Childhood Predictors of Adolescent Competence and Self-Worth in Rural Youth
Rew, Lynn; Grady, Matthew W.; Spoden, Micajah
2012-01-01
Problem Urban children who become competent adults despite circumstances that place their development and mental health at risk are considered to be resilient. Less is known about the risk and protective factors that characterize resilience among Hispanic/Latinos living in rural areas. Methods Data for regression analyses were collected when children (N = 603; 54% Hispanic/Latino) enrolled in the study in fifth grade, (M=10.4 years of age) and again five years later when they were in high school (M=15 years of age). Findings Statistically significant predictors of competence and self-worth in high schoolers included gender, ethnicity, and mother’s education, as well as stress, temperament (task persistence), and competences measured in grade school. Conclusions Parents’ perceptions of child’s temperament is a significant predictor of future competence and self-worth among rural adolescents. PMID:23121139
Childhood predictors of adolescent competence and self-worth in rural youth.
Rew, Lynn; Grady, Matthew W; Spoden, Micajah
2012-11-01
Urban children who become competent adults despite circumstances that place their development and mental health at risk are considered to be resilient. Less is known about the risk and protective factors that characterize resilience among Hispanic/Latinos living in rural areas. Data for regression analyses were collected when children (n = 603; 54% Hispanic/Latino) enrolled in the study in fifth grade (M = 10.4 years of age), and again 5 years later when they were in high school (M = 15 years of age). Statistically significant predictors of competence and self-worth in high schoolers included gender, ethnicity, and mother's education, as well as stress, temperament (task persistence), and competences measured in grade school. Parents' perception of child's temperament is a significant predictor of future competence and self-worth among rural adolescents. © 2012 Wiley Periodicals, Inc.
Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm
Directory of Open Access Journals (Sweden)
Amjad Mahmood
2017-04-01
Full Text Available In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.
Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm
Directory of Open Access Journals (Sweden)
S. Radhika
2016-04-01
Full Text Available Maximum correntropy criterion (MCC based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters.
DySOA : Making service systems self-adaptive
Siljee, J; Bosloper, [No Value; Nijhuis, J; Hammer, D; Benatallah, B; Casati, F; Traverso, P
2005-01-01
Service-centric systems exist in a very dynamic environment. This requires these systems to adapt at runtime in order to keep fulfilling their QoS. In order to create self-adaptive service systems, developers should not only design the service architecture, but also need to design the
Predictors of psychosocial adaptation among elderly residents in long-term care settings.
Chao, Shu-Yuan; Lan, Yii-Hwei; Tso, Hsiu-Ching; Chung, Chao-Ming; Neim, Yum-Mei; Clark, Mary Jo
2008-06-01
This cross-sectional descriptive study explored psychosocial adaptation and its determinants among elderly residents of long-term care facilities. A convenience sample of 165 elderly residents was recruited from two nursing homes and two assisted living institutions in the Taichung area. All residents who met the criteria for this study were interviewed individually from April through June 2006. A structured questionnaire was used to collect data on participant demographic characteristics, admission conditions, functional status, perceived family support, life attitudes, and psychosocial adaptation. The adaptation inventory incorporated three aspects of adaptation, including (1) sense of self-value, (2) sense of belonging and (3) sense of continuity. Findings, in general, did not indicate participants had achieved a high level of overall adaptation or significant adaptation in any of the three aspects targeted. Most participants were female. More than half were widowed and unable to fully finance their own institutional care. Nearly one-third was not admitted voluntarily. Having adequate funding for admission, voluntary admission, and number of roommates were the three most influential factors affecting overall adaptation, explaining 54% of variance. Study findings reflect the importance to residents' adaptation of self-determination, autonomy, and pre-institutionalization preparation and are intended to provide guidance for nursing intervention and social welfare policy making.
Predictors of Weight Loss Success: Exercise vs. Dietary Self-Efficacy and Treatment Attendance
Byrne, Shannon; Barry, Danielle; Petry, Nancy M.
2012-01-01
Pre-treatment diet and exercise self-efficacies can predict weight loss success. Changes in diet self-efficacy across treatment appear to be even stronger predictors than baseline levels, but research on changes in exercise self-efficacy is lacking. Using data from a pilot study evaluating tangible reinforcement for weight loss (N = 30), we examined the impact of changes in diet and exercise self-efficacy on outcomes. Multiple regression analyses indicated that treatment attendance and change...
Simulation of the 2-dimensional Drude’s model using molecular dynamics method
Energy Technology Data Exchange (ETDEWEB)
Naa, Christian Fredy; Amin, Aisyah; Ramli,; Suprijadi,; Djamal, Mitra [Theoretical High Energy Physics and Instrumentation Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132 (Indonesia); Wahyoedi, Seramika Ari; Viridi, Sparisoma, E-mail: viridi@cphys.fi.itb.ac.id [Nuclear and Biophysics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132 (Indonesia)
2015-04-16
In this paper, we reported the results of the simulation of the electronic conduction in solids. The simulation is based on the Drude’s models by applying molecular dynamics (MD) method, which uses the fifth-order predictor-corrector algorithm. A formula of the electrical conductivity as a function of lattice length and ion diameter τ(L, d) cand be obtained empirically based on the simulation results.
Strong coupling in electromechanical computation
Fuezi, J
2000-01-01
A method is presented to carry out simultaneously electromagnetic field and force computation, electrical circuit analysis and mechanical computation to simulate the dynamic operation of electromagnetic actuators. The equation system is solved by a predictor-corrector scheme containing a Powell error minimization algorithm which ensures that every differential equation (coil current, field strength rate, flux rate, speed of the keeper) is fulfilled within the same time step.
Strong coupling in electromechanical computation
Energy Technology Data Exchange (ETDEWEB)
Fuezi, J. E-mail: fuzi@leda.unitbv.rofuzi@evtsz.bme.hu
2000-06-02
A method is presented to carry out simultaneously electromagnetic field and force computation, electrical circuit analysis and mechanical computation to simulate the dynamic operation of electromagnetic actuators. The equation system is solved by a predictor-corrector scheme containing a Powell error minimization algorithm which ensures that every differential equation (coil current, field strength rate, flux rate, speed of the keeper) is fulfilled within the same time step.
Dim small targets detection based on self-adaptive caliber temporal-spatial filtering
Fan, Xiangsuo; Xu, Zhiyong; Zhang, Jianlin; Huang, Yongmei; Peng, Zhenming
2017-09-01
To boost the detect ability of dim small targets, this paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HQS). Finally, on the basis of image pre-processing, to address the problem of missed and wrong detection caused by fixed caliber of traditional pipeline filtering, this paper used targets' multi-frame movement correlation in the time-space domain, combined with the scale-space theory, to propose a temporal-spatial filtering algorithm which allows the caliber to make self-adaptive changes according to the changes of the targets' scale, effectively solving the detection-related issues brought by unchanged caliber and decreased/increased size of the targets. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HQS significantly increased the signal-noise ratio of images; when the signal-noise ratio was lower than 2.6 dB, this detection algorithm could effectively eliminate noise and detect targets. For the algorithm, the lowest signal-to-noise ratio of the detectable target is 0.37.
Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.
Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong
2016-01-01
In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.
Cutural Predictors of Self-Esteem: A Study of Chinese American Female and Male Young Adults.
Tsai, Jeanne L.; Ying, Yu-Wen; Lee, Peter Allen
2001-01-01
Domains of cultural orientation such as language, social affiliation, and cultural pride, were examined in Chinese American college students (N=353) to see how they related to self-esteem. Cultural orientation significantly predicted self-esteem differences. Cultural predictors of self-esteem varied by gender; self-esteem was mainly related to…
Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang
2018-05-01
Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.
The effect of self-distancing on adaptive versus maladaptive self-reflection in children.
Kross, Ethan; Duckworth, Angela; Ayduk, Ozlem; Tsukayama, Eli; Mischel, Walter
2011-10-01
Although children and adolescents vary in their chronic tendencies to adaptively versus maladaptively reflect over negative feelings, the psychological mechanisms underlying these different types of self-reflection among youngsters are unknown. We addressed this issue in the present research by examining the role that self-distancing plays in distinguishing adaptive versus maladaptive self-reflection among an ethnically and socioeconomically diverse sample of fifth-grade public schoolchildren. Children were randomly assigned to analyze their feelings surrounding a recent anger-related interpersonal experience from either a self-immersed or self-distanced perspective. They then rated their negative affect and described in writing the stream of thoughts they experienced when they analyzed their feelings. Children's stream-of-thought essays were content analyzed for the presence of recounting statements, reconstruing statements, and blame attributions. Path analyses indicated that children who analyzed their feelings from a self-distanced perspective focused significantly less on recounting the "hot," emotionally arousing features of their memory (i.e., what happened to me?) and relatively more on reconstruing their experience. This shift in thought content--less recounting and more reconstruing--led children in the self-distanced group to blame the other person involved in their recalled experience significantly less, which in turn led them to display significantly lower levels of emotional reactivity. These findings help delineate the psychological mechanisms that distinguish adaptive versus maladaptive forms of self-reflection over anger experiences in children. Their basic findings and clinical implications are discussed.
Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments
Directory of Open Access Journals (Sweden)
Kairong Duan
2018-05-01
Full Text Available Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs. When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA. We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.
International Nuclear Information System (INIS)
Yasin, M; Akhtar, Pervez; Pathan, Amir Hassan
2013-01-01
In this paper, we analyze the performance of adaptive blind algorithms – i.e. Kaiser Constant Modulus Algorithm (KCMA), Hamming CMA (HAMCMA) – with CMA in a wireless cellular communication system using digital modulation technique. These blind algorithms are used in digital signal processor of adaptive antenna to make it smart and change weights of the antenna array system dynamically. The simulation results revealed that KCMA and HAMCMA provide minimum mean square error (MSE) with 1.247 dB and 1.077 dB antenna gain enhancement, 75% reduction in bit error rate (BER) respectively over that of CMA. Therefore, KCMA and HAMCMA algorithms give a cost effective solution for a communication system
Strong discontinuity with cam clay under large deformations
DEFF Research Database (Denmark)
Katic, Natasa; Hededal, Ole
2008-01-01
The work shows simultaneous implementation of Strong discontinuity approach (SDA) by means of Enhanced Assumed Strain (EAS) and Critical State Soil Mechanics CSSM) in large strain regime. The numerical model is based on an additive decomposition of the displacement gradient into a conforming and ...... and an enhanced part. The localized deformations are approximated by means of a discontinuous displacement field. The applied algorithm leads to a predictor/corrector procedure which is formally identical to the returnmapping algorithm of classical (local and continuous) Cam clay model....
Adaptive MPC based on MIMO ARX-Laguerre model.
Ben Abdelwahed, Imen; Mbarek, Abdelkader; Bouzrara, Kais
2017-03-01
This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Huang, Yu-Hsin; Liu, Hui-Ching; Sun, Fang-Ju; Tsai, Fang-Ju; Huang, Kuo-Yang; Chen, Ting-Chun; Huang, Yo-Ping; Liu, Shen-Ing
2017-05-01
Data on the incidence of deliberate self-harm (DSH) and suicide attempts (SAs) are lacking in non-Western adolescents, and no studies have investigated differences in incident DSH and SA worldwide. This study aimed to investigate the incidence rates and relationships between predictors in DSH and SA. The Taiwanese Adolescent Self-Harm Project was a longitudinal study of DSH among adolescents. We recruited 5,879 students from 14 senior high schools in northern Taiwan. Online questionnaires on sociodemographic data, suicidality, depressive symptoms, self-esteem, social support, family discord, impulsivity, and alcohol and tobacco use were assessed at baseline (T1) and at 1 year of follow-up (T2). Logistic regression analyses were performed to evaluate the predictors of incident DSH and SA. The mean age was 16.02 years, and 56.73% of the cohort was female. At T1, the lifetime prevalence rates of DSH and SA were 25.04% and 3.50%, respectively. At T2, 4,331 (73.67%) students had completed follow-up assessments. The 1-year incidence rates of DSH and SA were 4.04% and 1.53%, respectively. The predictors of incident DSH included perceived family discord and more depressive symptoms at T1. The predictors of incident SA were lifetime suicide ideation, more depressive symptoms, and tobacco use at T1. The incidence rates of DSH and SA were similar to those reported in Western countries. The predictors of incident DSH and SA were similar but not identical. Our results highlight the risk factors which should be considered in terms of early identification and intervention among adolescents to prevent suicidality. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.
Predictors of suicidal ideation in a community sample: roles of anger, self-esteem, and depression.
Jang, Jin-Mahn; Park, Jong-Il; Oh, Keun-Young; Lee, Keon-Hak; Kim, Myung Sig; Yoon, Myeong-Sook; Ko, Sung-Hee; Cho, Hye-Chung; Chung, Young-Chul
2014-04-30
The objective of this cross-sectional study was to investigate the relationships of anger, self-esteem, and depression with suicidal ideation. A survey was conducted in a wide range of community areas across Jeollabuk-do Province, Korea. A total of 2964 subjects (mean age=44.4yr) participated in this study. Hierarchical regression was used to investigate predictors of suicidal ideation in terms of their sociodemographic characteristics, depression, self-esteem, and anger. Hierarchical regression analyses revealed that anger and self-esteem were significantly associated with suicidal ideation regardless of age and after controlling for depression. Moderation analysis showed that the impact of anger on suicidal ideation was significantly greater among females than males in adolescents, but not in other age groups. Additionally, there were some differences in sociodemographic predictors of suicidal ideation among age groups. Predictors included gender and family harmony in adolescents, marital status and family harmony in middle-aged individuals, and economic status and family harmony in elderly individuals. Our results revealed that anger and self-esteem play important roles in suicidal ideation beyond the effect of depression. Development and implementation of preventive strategies, including management of anger and self-esteem, could possibly reduce suicidal ideation and subsequent suicide attempts. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Sistemas Correctores de Campo Para EL Telescopio Cassegrain IAC80
Galan, M. J.; Cobos, F. J.
1987-05-01
El proyecto de instrumentación de mayor importancia que ha tenido el Instituto de Astrofisica de Canarias en los últimos afios ha sido el diseflo y construcción del te1escopio IAC8O. Este requería del esfuerzo con junto en mec´nica, óptica y electrónica, lo que facilitó la estructuración y el crecimiento de los respectivos grupos de trabajo, que posteriormente se integraron en departamentos En su origen (1977), el telescopio IAC80 fue concebido como un sistema clásico tipo Cassegrain, con una razón focal F/i 1.3 para el sistema Casse grain y una razón focal F/20 para el sistema Coudé. Posteriormente, aunque se mantuvo la filosofia de que el sistema básico fuera el F/11.3, se consideró conveniente el diseño de secundarios para razones focales F/16 y F/32, y se eliminó el de F/20. Sin embargo, dada la importancia relativa que un foco estrictamente fotográfico tiene en un telescopio moderno, diseñado básicamente para fotometría fotoeléctrica y con un campo util mínimamente de 40 minutos de arco, se decídió Ilevar a cabo el diseño de un secundario F/8 con un sistema corrector de campo, pero que estuviera formado únicamente por lentes con superficies esféricas para que asl su construcción fuera posible en España ó en México. La creciente utilización de detectores bidimensionales para fines de investigación astron6mica y la viabilidad de que en un futuro cercano éstos tengan un área sensible cada vez mayor, hicieron atractiva la idea de tener diseñado un sistema corrector de campo para el foco primario (F/3), con un campo útil mínimo de un grado, y también con la limitante de que sus componentes tuvieron sólamente supérficies esféricas. Ambos diseños de los sis-temas correctores de campo se llevaron a cabo, en gran medida, como parte de un proyecto de colaboración e intercambio en el área de diseño y evaluación de sistemas ópticos.
Directory of Open Access Journals (Sweden)
Raimundi, María Julia
2011-12-01
Full Text Available The perception of the value of themselves as individuals is a very important outcome in childhood. The aim of this paper is to study the influence of specific domain self-perception on the self-esteem of school age children from Buenos Aires City/AR (CABA. The Self-Perception Profile for Children was administered to 219 children of both genders (mean age = 10.34; SD = 1.77 from a private school from CABA. Multiple lineal regression analysis were performed. School grade and sex differences were taken into account. The main predictor of self-esteem for the boys is the self-perception of physical appearance and for the girls the self-perception of social acceptance. Considering grade differences, the main predictor of self-esteem for children from third to fifth grade is the self-perception of physical appearance and for sixth and seventh graders the self-perception of social acceptance and behavior.
Parallel Monitors for Self-adaptive Sessions
Directory of Open Access Journals (Sweden)
Mario Coppo
2016-06-01
Full Text Available The paper presents a data-driven model of self-adaptivity for multiparty sessions. System choreography is prescribed by a global type. Participants are incarnated by processes associated with monitors, which control their behaviour. Each participant can access and modify a set of global data, which are able to trigger adaptations in the presence of critical changes of values. The use of the parallel composition for building global types, monitors and processes enables a significant degree of flexibility: an adaptation step can dynamically reconfigure a set of participants only, without altering the remaining participants, even if the two groups communicate.
Longitudinal predictors of psychological distress and self-esteem in people with ALS.
Goldstein, L H; Atkins, L; Landau, S; Brown, R G; Leigh, P N
2006-11-14
To identify predictors of psychological distress (measured by anxiety and depression) and low self-esteem and to determine whether these change over time in people with ALS. We interviewed 50 patients with ALS living with a spouse/partner; further interviews were held at median intervals of 6 and then 5 months. Although carers were interviewed, we report the patients' data. Patients completed measures about their social support and marital relationship; the functional impact of ALS; everyday cognitive, emotional, and behavioral changes; symptoms of anxiety and depression; and self-esteem. The ALS Severity Scale was also completed. From the initial sample of 50, 26 patients were interviewed on all three occasions. At the first interview, negative social support and bulbar impairment were most predictive of psychological distress; pre-illness marital intimacy was the best predictor of patients' self-esteem. Over time, negative social support and pre-illness marital intimacy retained an ability to predict patients' affective state and self-esteem. Social factors are important in determining longer-term psychological well-being in people with ALS who are in the relatively early stages of the disease.
Jones, Lenette M; Veinot, Tiffany; Pressler, Susan J; Coleman-Burns, Patricia; McCall, Alecia
2018-06-01
Self-management of hypertension requires patients to find, understand, and use information to lower their blood pressure. Little is known about information use among African American women with hypertension, therefore the purpose of this study was to examine predictors of self-reported information use to self-manage blood pressure. Ninety-four Midwestern African American women (mean age = 59) completed questionnaires about information behaviors (seeking, sharing, use) and personal beliefs (attitude, social norms) related to self-management of blood pressure. Linear regression was used to identify significant predictors of information use. The total variance explained by the model was 36%, F(7, 79) = 6.29, p < .001. Information sharing was the only significant predictor (beta = .46, p < .001). These results provide evidence that information sharing is a potential health behavior to support intervention strategies for African American women with hypertension.
McDowell, M. W.; Klee, H. W.
1986-02-01
The use of the zero power corrector concept has been extended to the design of objective lenses and magnifiers suitable for use in night vision goggles. A novel design which can be used as either an f/1.2 objective or an f/2 magnifier is also described.
Trojahn, Melina Maria; Ruschel, Karen Brasil; Nogueira de Souza, Emiliane; Mussi, Cláudia Motta; Naomi Hirakata, Vânia; Nogueira Mello Lopes, Alexandra; Rabelo-Silva, Eneida Rejane
2013-01-01
This study aimed to examine the predictors of better self-care behavior in patients with heart failure (HF) in a home visiting program. This is a longitudinal study nested in a randomized controlled trial (ISRCTN01213862) in which the home-based educational intervention consisted of a six-month followup that included four home visits by a nurse, interspersed with four telephone calls. The self-care score was measured at baseline and at six months using the Brazilian version of the European Heart Failure Self-Care Behaviour Scale. The associations included eight variables: age, sex, schooling, having received the intervention, social support, income, comorbidities, and symptom severity. A simple linear regression model was developed using significant variables (P ≤ 0.20), followed by a multivariate model to determine the predictors of better self-care. One hundred eighty-eight patients completed the study. A better self-care behavior was associated with patients who received intervention (P < 0.001), had more years of schooling (P = 0.016), and had more comorbidities (P = 0.008). Having received the intervention (P < 0.001) and having a greater number of comorbidities (P = 0.038) were predictors of better self-care. In the multivariate regression model, being in the intervention group and having more comorbidities were a predictor of better self-care. PMID:24083023
AN ALGORITHM OF ADAPTIVE TORQUE CONTROL IN INJECTOR INTERNAL COMBUSTION ENGINE
Directory of Open Access Journals (Sweden)
D. N. Gerasimov
2015-07-01
Full Text Available Subject of Research. Internal combustion engine as a plant is a highly nonlinear complex system that works mostly in dynamic regimes in the presence of noise and disturbances. A number of engine characteristics and parameters is not known or known approximately due to the complex structure and multimode operating of the engine. In this regard the problem of torque control is not trivial and motivates the use of modern techniques of control theory that give the possibility to overcome the mentioned problems. As a consequence, a relatively simple algorithm of adaptive torque control of injector engine is proposed in the paper. Method. Proposed method is based on nonlinear dynamic model with parametric and functional uncertainties (static characteristics which are suppressed by means of adaptive control algorithm with single adjustable parameter. The algorithm is presented by proportional control law with adjustable feedback gain and provides the exponential convergence of the control error to the neighborhood of zero equilibrium. It is shown that the radius of the neighborhood can be arbitrary reduced by the change of controller design parameters. Main Results. A dynamical nonlinear model of the engine has been designed for the purpose of control synthesis and simulation of the closed-loop system. The parameters and static functions of the model are identified with the use of data aquired during Federal Test Procedure (USA of Chevrolet Tahoe vehicle with eight cylinders 5,7L engine. The algorithm of adaptive torque control is designed, and the properties of the closed-loop system are analyzed with the use of Lyapunov functions approach. The closed-loop system operating is verified by means of simulation in the MatLab/Simulink environment. Simulation results show that the controller provides the boundedness of all signals and convergence of the control error to the neighborhood of zero equilibrium despite significant variations of engine speed. The
Adaptive discrete cosine transform coding algorithm for digital mammography
Baskurt, Atilla M.; Magnin, Isabelle E.; Goutte, Robert
1992-09-01
The need for storage, transmission, and archiving of medical images has led researchers to develop adaptive and efficient data compression techniques. Among medical images, x-ray radiographs of the breast are especially difficult to process because of their particularly low contrast and very fine structures. A block adaptive coding algorithm based on the discrete cosine transform to compress digitized mammograms is described. A homogeneous repartition of the degradation in the decoded images is obtained using a spatially adaptive threshold. This threshold depends on the coding error associated with each block of the image. The proposed method is tested on a limited number of pathological mammograms including opacities and microcalcifications. A comparative visual analysis is performed between the original and the decoded images. Finally, it is shown that data compression with rather high compression rates (11 to 26) is possible in the mammography field.
Adaptation in the fuzzy self-organising controller
DEFF Research Database (Denmark)
Jantzen, Jan; Poulsen, Niels Kjølstad
2003-01-01
This simulation study provides an analysis of the adaptation mechanism in the self-organising fuzzy controller, SOC. The approach is to apply a traditional adaptive control viewpoint. A simplified performance measure in the SOC controller is used in a loss function, and thus the MIT rule implies...... an update mechanism similar to the SOC update mechanism. Two simulations of proportionally controlled systems show the behaviour of the proportional gain as it adapts to a specified behaviour....
Fuzzy model predictive control algorithm applied in nuclear power plant
International Nuclear Information System (INIS)
Zuheir, Ahmad
2006-01-01
The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)
The mechanical design for the WEAVE prime focus corrector system
Abrams, Don Carlos; Dee, Kevin; Agócs, Tibor; Lhome, Emilie; Peñate, José; Jaskó, Attila; Bányai, Evelin; Burgal, José A.; Dalton, Gavin; Middleton, Kevin; Bonifacio, Piercarlo; Aguerri, J. Alfonso L.; Trager, S. C.; Balcells, Marc
2014-08-01
WEAVE is the next-generation, wide-field, optical spectroscopy facility for the William Herschel Telescope (WHT) in La Palma, Canary Islands, Spain. The WHT will undergo a significant adaptation to accommodate this facility. A two- degree Prime Focus Corrector (PFC), that includes an Atmospheric Dispersion Compensator, is being planned and is currently in its final design phase. To compensate for the effects of temperature-induced image degradation, the entire PFC system will be translated along the telescope optical axis. The optical system comprises six lenses, the largest of which will have a diameter of 1.1m. Now that the optical elements are in production, the designs for the lens cells and the mounting arrangements are being analysed to ensure that the image quality of the complete system is better than 1.0 arcsec (80% encircled energy diameter) over the full field of view. The new PFC system is designed to be routinely interchanged with the existing top-end ring. This will maximise the versatility of the WHT and allow the two top-end systems to be interchanged as dictated by the scientific needs of the astronomers that will use WEAVE and other instruments on the telescope. This manuscript describes the work that has been carried out in developing the designs for the mechanical subsystems and the plans for mounting the lenses to attain an optical performance that is commensurate with the requirements derived from planning the WEAVE surveys.
Conforming to interface structured adaptive mesh refinement: 3D algorithm and implementation
Nagarajan, Anand; Soghrati, Soheil
2018-03-01
A new non-iterative mesh generation algorithm named conforming to interface structured adaptive mesh refinement (CISAMR) is introduced for creating 3D finite element models of problems with complex geometries. CISAMR transforms a structured mesh composed of tetrahedral elements into a conforming mesh with low element aspect ratios. The construction of the mesh begins with the structured adaptive mesh refinement of elements in the vicinity of material interfaces. An r-adaptivity algorithm is then employed to relocate selected nodes of nonconforming elements, followed by face-swapping a small fraction of them to eliminate tetrahedrons with high aspect ratios. The final conforming mesh is constructed by sub-tetrahedralizing remaining nonconforming elements, as well as tetrahedrons with hanging nodes. In addition to studying the convergence and analyzing element-wise errors in meshes generated using CISAMR, several example problems are presented to show the ability of this method for modeling 3D problems with intricate morphologies.
A parallel second-order adaptive mesh algorithm for incompressible flow in porous media.
Pau, George S H; Almgren, Ann S; Bell, John B; Lijewski, Michael J
2009-11-28
In this paper, we present a second-order accurate adaptive algorithm for solving multi-phase, incompressible flow in porous media. We assume a multi-phase form of Darcy's law with relative permeabilities given as a function of the phase saturation. The remaining equations express conservation of mass for the fluid constituents. In this setting, the total velocity, defined to be the sum of the phase velocities, is divergence free. The basic integration method is based on a total-velocity splitting approach in which we solve a second-order elliptic pressure equation to obtain a total velocity. This total velocity is then used to recast component conservation equations as nonlinear hyperbolic equations. Our approach to adaptive refinement uses a nested hierarchy of logically rectangular grids with simultaneous refinement of the grids in both space and time. The integration algorithm on the grid hierarchy is a recursive procedure in which coarse grids are advanced in time, fine grids are advanced multiple steps to reach the same time as the coarse grids and the data at different levels are then synchronized. The single-grid algorithm is described briefly, but the emphasis here is on the time-stepping procedure for the adaptive hierarchy. Numerical examples are presented to demonstrate the algorithm's accuracy and convergence properties and to illustrate the behaviour of the method.
Individual difference predictors of change in career adaptability over time
Zacher, Hannes
Career adaptability is a psychosocial construct that reflects individuals' resources for managing career tasks and challenges. This study investigated the effects of demographic characteristics and three sets of individual difference variables (Big Five personality traits, core self-evaluations, and
Adaptive algorithm for predicting increases in central loads of electrical energy systems
Energy Technology Data Exchange (ETDEWEB)
Arbachyauskene, N A; Pushinaytis, K V
1982-01-01
An adaptive algorithm for predicting increases in central loads of the electrical energy system is suggested for the task of evaluating the condition. The algorithm is based on the Kalman filter. In order to calculate the coefficient of intensification, the a priori assigned noise characteristics with low accuracy are used only in the beginning of the calculation. Further, the coefficient of intensification is calculated from the innovation sequence. This approach makes it possible to correct errors in the assignment of the statistical noise characteristics and to follow their changes. The algorithm is experimentally verified.
Hegde, Veena; Deekshit, Ravishankar; Satyanarayana, P. S.
2011-12-01
The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts or noise. Noise severely limits the utility of the recorded ECG and thus needs to be removed, for better clinical evaluation. In the present paper a new noise cancellation technique is proposed for removal of random noise like muscle artifact from ECG signal. A transform domain robust variable step size Griffiths' LMS algorithm (TVGLMS) is proposed for noise cancellation. For the TVGLMS, the robust variable step size has been achieved by using the Griffiths' gradient which uses cross-correlation between the desired signal contaminated with observation or random noise and the input. The algorithm is discrete cosine transform (DCT) based and uses symmetric property of the signal to represent the signal in frequency domain with lesser number of frequency coefficients when compared to that of discrete Fourier transform (DFT). The algorithm is implemented for adaptive line enhancer (ALE) filter which extracts the ECG signal in a noisy environment using LMS filter adaptation. The proposed algorithm is found to have better convergence error/misadjustment when compared to that of ordinary transform domain LMS (TLMS) algorithm, both in the presence of white/colored observation noise. The reduction in convergence error achieved by the new algorithm with desired signal decomposition is found to be lower than that obtained without decomposition. The experimental results indicate that the proposed method is better than traditional adaptive filter using LMS algorithm in the aspects of retaining geometrical characteristics of ECG signal.
Xu, Fan; Wang, Jiaxing; Zhu, Daiyin; Tu, Qi
2018-04-01
Speckle noise has always been a particularly tricky problem in improving the ranging capability and accuracy of Lidar system especially in harsh environment. Currently, effective speckle de-noising techniques are extremely scarce and should be further developed. In this study, a speckle noise reduction technique has been proposed based on independent component analysis (ICA). Since normally few changes happen in the shape of laser pulse itself, the authors employed the laser source as a reference pulse and executed the ICA decomposition to find the optimal matching position. In order to achieve the self-adaptability of algorithm, local Mean Square Error (MSE) has been defined as an appropriate criterion for investigating the iteration results. The obtained experimental results demonstrated that the self-adaptive pulse-matching ICA (PM-ICA) method could effectively decrease the speckle noise and recover the useful Lidar echo signal component with high quality. Especially, the proposed method achieves 4 dB more improvement of signal-to-noise ratio (SNR) than a traditional homomorphic wavelet method.
Breakthrough Therapies: Cystic Fibrosis (CF) Potentiators and Correctors
Solomon, George M.; Marshall, Susan G.; Ramsey, Bonnie W.; Rowe, Steven M.
2015-01-01
Cystic Fibrosis is caused by mutations in the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) gene resulting in abnormal protein function. Recent advances of targeted molecular therapies and high throughput screening have resulted in multiple drug therapies that target many important mutations in the CFTR protein. In this review, we provide the latest results and current progress of CFTR modulators for the treatment of cystic fibrosis, focusing on potentiators of CFTR channel gating and Phe508del processing correctors for the Phe508del CFTR mutation. Special emphasis is placed on the molecular basis underlying these new therapies and emerging results from the latest clinical trials. The future directions for augmenting the rescue of Phe508del with CFTR modulators is also emphasized. PMID:26097168
An Adaptive Large Neighborhood Search Algorithm for the Multi-mode RCPSP
DEFF Research Database (Denmark)
Muller, Laurent Flindt
We present an Adaptive Large Neighborhood Search algorithm for the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP). We incorporate techniques for deriving additional precedence relations and propose a new method, so-called mode-diminution, for removing modes during execution...
The (1+λ) evolutionary algorithm with self-adjusting mutation rate
DEFF Research Database (Denmark)
Doerr, Benjamin; Witt, Carsten; Gießen, Christian
2017-01-01
We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then upd......We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate...... is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the (1 + A) evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the (1 + A) EA finds the optimum in an expected optimization...... time (number of fitness evaluations) of O(nA/log A + n log n). This time is asymptotically smaller than the optimization time of the classic (1 + A) EA. Previous work shows that this performance is best-possible among all A-parallel mutation-based unbiased black-box algorithms. This result shows...
Adaptive antenna array algorithms and their impact on code division ...
African Journals Online (AJOL)
In this paper four each blind adaptive array algorithms are developed, and their performance under different test situations (e.g. A WGN (Additive White Gaussian Noise) channel, and multipath environment) is studied A MATLAB test bed is created to show their performance on these two test situations and an optimum one ...
Kerley, Dan; Smith, Malcolm; Dunn, Jennifer; Herriot, Glen; Véran, Jean-Pierre; Boyer, Corinne; Ellerbroek, Brent; Gilles, Luc; Wang, Lianqi
2016-08-01
The Narrow Field Infrared Adaptive Optics System (NFIRAOS) is the first light Adaptive Optics (AO) system for the Thirty Meter Telescope (TMT). A critical component of NFIRAOS is the Real-Time Controller (RTC) subsystem which provides real-time wavefront correction by processing wavefront information to compute Deformable Mirror (DM) and Tip/Tilt Stage (TTS) commands. The National Research Council of Canada - Herzberg (NRC-H), in conjunction with TMT, has developed a preliminary design for the NFIRAOS RTC. The preliminary architecture for the RTC is comprised of several Linux-based servers. These servers are assigned various roles including: the High-Order Processing (HOP) servers, the Wavefront Corrector Controller (WCC) server, the Telemetry Engineering Display (TED) server, the Persistent Telemetry Storage (PTS) server, and additional testing and spare servers. There are up to six HOP servers that accept high-order wavefront pixels, and perform parallelized pixel processing and wavefront reconstruction to produce wavefront corrector error vectors. The WCC server performs low-order mode processing, and synchronizes and aggregates the high-order wavefront corrector error vectors from the HOP servers to generate wavefront corrector commands. The Telemetry Engineering Display (TED) server is the RTC interface to TMT and other subsystems. The TED server receives all external commands and dispatches them to the rest of the RTC servers and is responsible for aggregating several offloading and telemetry values that are reported to other subsystems within NFIRAOS and TMT. The TED server also provides the engineering GUIs and real-time displays. The Persistent Telemetry Storage (PTS) server contains fault tolerant data storage that receives and stores telemetry data, including data for Point-Spread Function Reconstruction (PSFR).
Energy Technology Data Exchange (ETDEWEB)
Lober, R.R.; Tautges, T.J.; Vaughan, C.T.
1997-03-01
Paving is an automated mesh generation algorithm which produces all-quadrilateral elements. It can additionally generate these elements in varying sizes such that the resulting mesh adapts to a function distribution, such as an error function. While powerful, conventional paving is a very serial algorithm in its operation. Parallel paving is the extension of serial paving into parallel environments to perform the same meshing functions as conventional paving only on distributed, discretized models. This extension allows large, adaptive, parallel finite element simulations to take advantage of paving`s meshing capabilities for h-remap remeshing. A significantly modified version of the CUBIT mesh generation code has been developed to host the parallel paving algorithm and demonstrate its capabilities on both two dimensional and three dimensional surface geometries and compare the resulting parallel produced meshes to conventionally paved meshes for mesh quality and algorithm performance. Sandia`s {open_quotes}tiling{close_quotes} dynamic load balancing code has also been extended to work with the paving algorithm to retain parallel efficiency as subdomains undergo iterative mesh refinement.
Wang, Xingmei; Hao, Wenqian; Li, Qiming
2017-12-18
This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.
Brooks, Kevin R; Mond, Jonathan M; Stevenson, Richard J; Stephen, Ian D
2016-01-01
Body size misperception is common amongst the general public and is a core component of eating disorders and related conditions. While perennial media exposure to the "thin ideal" has been blamed for this misperception, relatively little research has examined visual adaptation as a potential mechanism. We examined the extent to which the bodies of "self" and "other" are processed by common or separate mechanisms in young women. Using a contingent adaptation paradigm, experiment 1 gave participants prolonged exposure to images both of the self and of another female that had been distorted in opposite directions (e.g., expanded other/contracted self), and assessed the aftereffects using test images both of the self and other. The directions of the resulting perceptual biases were contingent on the test stimulus, establishing at least some separation between the mechanisms encoding these body types. Experiment 2 used a cross adaptation paradigm to further investigate the extent to which these mechanisms are independent. Participants were adapted either to expanded or to contracted images of their own body or that of another female. While adaptation effects were largest when adapting and testing with the same body type, confirming the separation of mechanisms reported in experiment 1, substantial misperceptions were also demonstrated for cross adaptation conditions, demonstrating a degree of overlap in the encoding of self and other. In addition, the evidence of misperception of one's own body following exposure to "thin" and to "fat" others demonstrates the viability of visual adaptation as a model of body image disturbance both for those who underestimate and those who overestimate their own size.
A parallel adaptive finite difference algorithm for petroleum reservoir simulation
Energy Technology Data Exchange (ETDEWEB)
Hoang, Hai Minh
2005-07-01
Adaptive finite differential for problems arising in simulation of flow in porous medium applications are considered. Such methods have been proven useful for overcoming limitations of computational resources and improving the resolution of the numerical solutions to a wide range of problems. By local refinement of the computational mesh where it is needed to improve the accuracy of solutions, yields better solution resolution representing more efficient use of computational resources than is possible with traditional fixed-grid approaches. In this thesis, we propose a parallel adaptive cell-centered finite difference (PAFD) method for black-oil reservoir simulation models. This is an extension of the adaptive mesh refinement (AMR) methodology first developed by Berger and Oliger (1984) for the hyperbolic problem. Our algorithm is fully adaptive in time and space through the use of subcycling, in which finer grids are advanced at smaller time steps than the coarser ones. When coarse and fine grids reach the same advanced time level, they are synchronized to ensure that the global solution is conservative and satisfy the divergence constraint across all levels of refinement. The material in this thesis is subdivided in to three overall parts. First we explain the methodology and intricacies of AFD scheme. Then we extend a finite differential cell-centered approximation discretization to a multilevel hierarchy of refined grids, and finally we are employing the algorithm on parallel computer. The results in this work show that the approach presented is robust, and stable, thus demonstrating the increased solution accuracy due to local refinement and reduced computing resource consumption. (Author)
An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries
Vellev, Stoyan
2008-01-01
The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-determinis...
Gedefaw, Abel; Tilahun, Birkneh; Asefa, Anteneh
2015-01-01
This study was conducted to identify predictors of self-reported academic performance in undergraduate medical students at Hawassa University. An analytical cross-sectional study involving 592 undergraduate medical students was conducted in November 2012. The academic performance of the study subjects was measured by self-reported cumulative grade point average (GPA) using a self-administered questionnaire. Data were entered and analyzed using Statistical Package for the Social Sciences version 16 software. Pearson's bivariate correlations, multiple linear regression, and multiple logistic regression were used to identify predictors of academic performance. The self-reported academic performance of students had been decreasing as the academic years progressed, with the highest and lowest performance being in the premedicine (mean GPA 3.47) and clinical I (mean GPA 2.71) years, respectively. One hundred and fifty-eight (26.7%) of the participants had ever been delayed, 37 (6.2%) had ever re-sat for examination, and two (0.3%) had ever been warned due to academic failure. The overall variation in self-reported academic performance of the students was 32.8%. Participant age alone explained 21.9% of the variation. On the other hand, university entrance examination results, substance use at university, and medicine as first choice by students were identified as predictors of variation in self-reported academic performance, accounting for 6.9%, 2.7%, and academic performance was explained by the studied variables. Hence, efficacious mechanisms should be designed to combat the intervenable determinants of self-reported academic performance, like substance use and a low medical school entrance examination result. Further studies should also be undertaken to gain a better understanding of other unstudied determinants, like personality, learning style, cognitive ability, and the system used for academic evaluation.
A self-adapting and altitude-dependent regularization method for atmospheric profile retrievals
Directory of Open Access Journals (Sweden)
M. Ridolfi
2009-03-01
Full Text Available MIPAS is a Fourier transform spectrometer, operating onboard of the ENVISAT satellite since July 2002. The online retrieval algorithm produces geolocated profiles of temperature and of volume mixing ratios of six key atmospheric constituents: H_{2}O, O_{3}, HNO_{3}, CH_{4}, N_{2}O and NO_{2}. In the validation phase, oscillations beyond the error bars were observed in several profiles, particularly in CH_{4} and N_{2}O.
To tackle this problem, a Tikhonov regularization scheme has been implemented in the retrieval algorithm. The applied regularization is however rather weak in order to preserve the vertical resolution of the profiles.
In this paper we present a self-adapting and altitude-dependent regularization approach that detects whether the analyzed observations contain information about small-scale profile features, and determines the strength of the regularization accordingly. The objective of the method is to smooth out artificial oscillations as much as possible, while preserving the fine detail features of the profile when related information is detected in the observations.
The proposed method is checked for self consistency, its performance is tested on MIPAS observations and compared with that of some other regularization schemes available in the literature. In all the considered cases the proposed scheme achieves a good performance, thanks to its altitude dependence and to the constraints employed, which are specific of the inversion problem under consideration. The proposed method is generally applicable to iterative Gauss-Newton algorithms for the retrieval of vertical distribution profiles from atmospheric remote sounding measurements.
Energy Technology Data Exchange (ETDEWEB)
Donohoe, G.W.
1977-01-01
Sandia Laboratories' Digital Systems Division/1734, as part of its work on the Base and Installation Security Systems (BISS) program has been making use of adaptive digital filters to improve the signal-to-noise ratio of perimeter sensor signals. In particular, the Widrow-Hoff least-mean-squares algorithm has been used extensively. This non-recursive linear predictor has been successful in extracting aperiodic signals from periodic noise. The adaptive filter generates a predictor signal which is subtracted from the input signal to produce an error signal. The value of this error is fed back to the filter to improve the quality of the next prediction. Implementation of the Widrow adaptive filter using a Charge-Coupled Device tapped analog delay line, analog voltage multipliers and operational amplifiers is described. The resulting filter adapts to signals with frequency components as high as several megahertz.
National Research Council Canada - National Science Library
Lenahan, Jack; Nash, Michael P; Charles, Phil
2008-01-01
.... We present the following hypothesis: predictive deliberation management using self-adapting and self-modeling software will be required to provide mission planning adjustments after the start of a mission...
Algorithms and data structures for massively parallel generic adaptive finite element codes
Bangerth, Wolfgang
2011-12-01
Today\\'s largest supercomputers have 100,000s of processor cores and offer the potential to solve partial differential equations discretized by billions of unknowns. However, the complexity of scaling to such large machines and problem sizes has so far prevented the emergence of generic software libraries that support such computations, although these would lower the threshold of entry and enable many more applications to benefit from large-scale computing. We are concerned with providing this functionality for mesh-adaptive finite element computations. We assume the existence of an "oracle" that implements the generation and modification of an adaptive mesh distributed across many processors, and that responds to queries about its structure. Based on querying the oracle, we develop scalable algorithms and data structures for generic finite element methods. Specifically, we consider the parallel distribution of mesh data, global enumeration of degrees of freedom, constraints, and postprocessing. Our algorithms remove the bottlenecks that typically limit large-scale adaptive finite element analyses. We demonstrate scalability of complete finite element workflows on up to 16,384 processors. An implementation of the proposed algorithms, based on the open source software p4est as mesh oracle, is provided under an open source license through the widely used deal.II finite element software library. © 2011 ACM 0098-3500/2011/12-ART10 $10.00.
A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design
Directory of Open Access Journals (Sweden)
Liu Yan
2018-01-01
Full Text Available The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.
Properties of predictor based on relative neighborhood graph localized FIR filters
DEFF Research Database (Denmark)
Sørensen, John Aasted
1995-01-01
A time signal prediction algorithm based on relative neighborhood graph (RNG) localized FIR filters is defined. The RNG connects two nodes, of input space dimension D, if their lune does not contain any other node. The FIR filters associated with the nodes, are used for local approximation...... of the training vectors belonging to the lunes formed by the nodes. The predictor training is carried out by iteration through 3 stages: initialization of the RNG of the training signal by vector quantization, LS estimation of the FIR filters localized in the input space by RNG nodes and adaptation of the RNG...... nodes by equalizing the LS approximation error among the lunes formed by the nodes of the RNG. The training properties of the predictor is exemplified on a burst signal and characterized by the normalized mean square error (NMSE) and the mean valence of the RNG nodes through the adaptation...
An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient
Nobile, Fabio
2016-03-18
In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature), obtaining an enhanced version capable of using non-nested collocation points, and supporting quadrature and interpolation on unbounded sets. We also consider several profit indicators that are suitable to drive the adaptation process. We then use such algorithm to solve an important test case in Uncertainty Quantification problem, namely the Darcy equation with lognormal permeability random field, and compare the results with those obtained with the quasi-optimal sparse grids based on profit estimates, which we have proposed in our previous works (cf. e.g. Convergence of quasi-optimal sparse grids approximation of Hilbert-valued functions: application to random elliptic PDEs). To treat the case of rough permeability fields, in which a sparse grid approach may not be suitable, we propose to use the adaptive sparse grid quadrature as a control variate in a Monte Carlo simulation. Numerical results show that the adaptive sparse grids have performances similar to those of the quasi-optimal sparse grids and are very effective in the case of smooth permeability fields. Moreover, their use as control variate in a Monte Carlo simulation allows to tackle efficiently also problems with rough coefficients, significantly improving the performances of a standard Monte Carlo scheme.
Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application
Yang, Jian; Yang, Feng; Xi, Hong-Sheng; Guo, Wei; Sheng, Yanmin
2007-12-01
We propose a robust adaptive algorithm for generalized eigendecomposition problems that arise in modern signal processing applications. To that extent, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Starting from the proposed cost function and making use of an approximation of the Hessian matrix, a robust modified Newton algorithm is derived. A rigorous analysis of its convergence properties is presented by using stochastic approximation theory. We also apply this theory to solve the signal reception problem of multicarrier DS-CDMA to illustrate its practical application. The simulation results show that the proposed algorithm has fast convergence and excellent tracking capability, which are important in a practical time-varying communication environment.
Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application
Directory of Open Access Journals (Sweden)
Yang Jian
2007-01-01
Full Text Available We propose a robust adaptive algorithm for generalized eigendecomposition problems that arise in modern signal processing applications. To that extent, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Starting from the proposed cost function and making use of an approximation of the Hessian matrix, a robust modified Newton algorithm is derived. A rigorous analysis of its convergence properties is presented by using stochastic approximation theory. We also apply this theory to solve the signal reception problem of multicarrier DS-CDMA to illustrate its practical application. The simulation results show that the proposed algorithm has fast convergence and excellent tracking capability, which are important in a practical time-varying communication environment.
Predictors of self-rated health: a 12-month prospective study of IT and media workers.
Hasson, Dan; Arnetz, Bengt B; Theorell, Töres; Anderberg, Ulla Maria
2006-07-31
The aim of the present study was to determine health-related risk and salutogenic factors and to use these to construct prediction models for future self-rated health (SRH), i.e. find possible characteristics predicting individuals improving or worsening in SRH over time (0-12 months). A prospective study was conducted with measurements (physiological markers and self-ratings) at 0, 6 and 12 months, involving 303 employees (187 men and 116 women, age 23-64) from four information technology and two media companies. There were a multitude of statistically significant cross-sectional correlations (Spearman's Rho) between SRH and other self-ratings as well as physiological markers. Predictors of future SRH were baseline ratings of SRH, self-esteem and social support (logistic regression), and SRH, sleep quality and sense of coherence (linear regression). The results of the present study indicate that baseline SRH and other self-ratings are predictive of future SRH. It is cautiously implied that SRH, self-esteem, social support, sleep quality and sense of coherence might be predictors of future SRH and therefore possibly also of various future health outcomes.
Empirical study of self-configuring genetic programming algorithm performance and behaviour
International Nuclear Information System (INIS)
KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkin, E; KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkina, M
2015-01-01
The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems
Campbell, Stefan F.; Kaneshige, John T.
2010-01-01
Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).
Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data
Wong, Raymond K.; Mohammed, Sabah; Fiaidhi, Jinan; Sung, Yunsick
2017-01-01
Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method. PMID:28753613
Computing Fault-Containment Times of Self-Stabilizing Algorithms Using Lumped Markov Chains
Directory of Open Access Journals (Sweden)
Volker Turau
2018-05-01
Full Text Available The analysis of self-stabilizing algorithms is often limited to the worst case stabilization time starting from an arbitrary state, i.e., a state resulting from a sequence of faults. Considering the fact that these algorithms are intended to provide fault tolerance in the long run, this is not the most relevant metric. A common situation is that a running system is an a legitimate state when hit by a single fault. This event has a much higher probability than multiple concurrent faults. Therefore, the worst case time to recover from a single fault is more relevant than the recovery time from a large number of faults. This paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms based on Markov chains in combination with lumping. To illustrate the applicability of the techniques they are applied to a new self-stabilizing coloring algorithm.
Analysis of Online DBA Algorithm with Adaptive Sleep Cycle in WDM EPON
Pajčin, Bojan; Matavulj, Petar; Radivojević, Mirjana
2018-05-01
In order to manage Quality of Service (QoS) and energy efficiency in the optical access network, an online Dynamic Bandwidth Allocation (DBA) algorithm with adaptive sleep cycle is presented. This DBA algorithm has the ability to allocate an additional bandwidth to the end user within a single sleep cycle whose duration changes depending on the current buffers occupancy. The purpose of this DBA algorithm is to tune the duration of the sleep cycle depending on the network load in order to provide service to the end user without violating strict QoS requests in all network operating conditions.
Microscope self-calibration based on micro laser line imaging and soft computing algorithms
Apolinar Muñoz Rodríguez, J.
2018-06-01
A technique to perform microscope self-calibration via micro laser line and soft computing algorithms is presented. In this technique, the microscope vision parameters are computed by means of soft computing algorithms based on laser line projection. To implement the self-calibration, a microscope vision system is constructed by means of a CCD camera and a 38 μm laser line. From this arrangement, the microscope vision parameters are represented via Bezier approximation networks, which are accomplished through the laser line position. In this procedure, a genetic algorithm determines the microscope vision parameters by means of laser line imaging. Also, the approximation networks compute the three-dimensional vision by means of the laser line position. Additionally, the soft computing algorithms re-calibrate the vision parameters when the microscope vision system is modified during the vision task. The proposed self-calibration improves accuracy of the traditional microscope calibration, which is accomplished via external references to the microscope system. The capability of the self-calibration based on soft computing algorithms is determined by means of the calibration accuracy and the micro-scale measurement error. This contribution is corroborated by an evaluation based on the accuracy of the traditional microscope calibration.
Predictors of self-esteem in adolescents with a psychiatric referral.
Akdemir, Devrim; Çak, Tuna; Aslan, Cihan; Aydos, Büşra Sultan; Nalbant, Kevser; Çuhadaroğlu-Çetin, Füsun
2016-01-01
In the literature self-esteem is found to be lower in clinically referred adolescents compared to adolescents without any psychiatric disorder. The aim of this study is to examine self-esteem and associated socio-demographical and psychological factors in clinically referred adolescents in Turkey. Three hundred forty-nine adolescents aged between 12 and 18 years admitted to the Department of Child and Adolescent Psychiatry with a psychiatric complaint were enrolled. Rosenberg Self-Esteem Scale (RSES), Brief Symptom Inventory (BSI), Parenting Style Scale (PSS) and Sense of Identity Assessment Form (SIAF) were used for the evaluation. Self-esteem was lower in: girls, adolescents without siblings, living in non-nuclear families, with a past suicide attempt, and with history of a non-suicidal self-injurious behavior (NSSI). Self-esteem was negatively correlated with identity confusion on SIAF and positively correlated with acceptance/involvement on PSS. Significant predictors of self-esteem were gender, presence of a sibling, history of a NSSI and SIAF scores. Interactions between self-esteem and gender, psychiatric symptoms, parenting and identity development are complex in clinically referred adolescents. Further elucidation of the mechanisms through which these characteristics modify self-esteem will be necessary to guide families and clinicians to help adolescents to maintain high self-esteem levels.
Directory of Open Access Journals (Sweden)
Ignacio Santamaría
2008-04-01
Full Text Available This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity, such as the well-known Wiener and Hammerstein systems. In particular, we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem, we show how kernel canonical correlation analysis (KCCA emerges as the logical solution to this problem. We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems, we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm.
Energy Technology Data Exchange (ETDEWEB)
Courteau, R.; Bose, T. K. [Universite du Quebec a Trois-Rivieres, Hydrogen Research Institute, Trois-Rivieres, PQ (Canada)
2004-07-01
An algorithm for self-adaptive tuning of an internal combustion engine is proposed, based on a Kalman filter operating on a few selected metrics of the dynamic pressure curve. Piezoelectric transducers are devices to monitor dynamic cylinder pressure; spark plugs with embedded piezo elements are now available to provide diagnostic engine functions. Such transducers are also capable of providing signals to the engine controller to perform auto tuning, a function that is considered very useful particularly in vehicles using alternative fuels whose characteristics frequently show variations between fill-ups. 2 refs., 2 figs.
An adaptive left–right eigenvector evolution algorithm for vibration isolation control
International Nuclear Information System (INIS)
Wu, T Y
2009-01-01
The purpose of this research is to investigate the feasibility of utilizing an adaptive left and right eigenvector evolution (ALREE) algorithm for active vibration isolation. As depicted in the previous paper presented by Wu and Wang (2008 Smart Mater. Struct. 17 015048), the structural vibration behavior depends on both the disturbance rejection capability and mode shape distributions, which correspond to the left and right eigenvector distributions of the system, respectively. In this paper, a novel adaptive evolution algorithm is developed for finding the optimal combination of left–right eigenvectors of the vibration isolator, which is an improvement over the simultaneous left–right eigenvector assignment (SLREA) method proposed by Wu and Wang (2008 Smart Mater. Struct. 17 015048). The isolation performance index used in the proposed algorithm is defined by combining the orthogonality index of left eigenvectors and the modal energy ratio index of right eigenvectors. Through the proposed ALREE algorithm, both the left and right eigenvectors evolve such that the isolation performance index decreases, and therefore one can find the optimal combination of left–right eigenvectors of the closed-loop system for vibration isolation purposes. The optimal combination of left–right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active isolation control shows that the proposed method can be utilized to improve the vibration isolation performance compared with the previous approaches
Directory of Open Access Journals (Sweden)
Kevin R. Brooks
2016-07-01
Full Text Available Body size misperception is common amongst the general public and is a core component of eating disorders and related conditions. While perennial media exposure to the thin ideal has been blamed for this misperception, relatively little research has examined visual adaptation as a potential mechanism. We examined the extent to which the bodies of self and other are processed by common or separate mechanisms in young women. Using a contingent adaptation paradigm, experiment 1 gave participants prolonged exposure to images both of the self and of another female that had been distorted in opposite directions (e.g. expanded other/contracted self, and assessed the aftereffects using test images both of the self and other. The directions of the resulting perceptual biases were contingent on the test stimulus, establishing at least some separation between the mechanisms encoding these body types. Experiment 2 used a cross adaptation paradigm to further investigate the extent to which these mechanisms are independent. Participants were adapted either to expanded or to contracted images of their own body or that of another female. While adaptation effects were largest when adapting and testing with the same body type, confirming the separation of mechanisms reported in experiment 1, substantial misperceptions were also demonstrated for cross adaptation conditions, demonstrating a degree of overlap in the encoding of self and other. In addition, the evidence of misperception of one’s own body following exposure to thin and to fat others demonstrates the viability of visual adaptation as a model of body image disturbance both for those who underestimate and those who overestimate their own size.
Some improvements on adaptive genetic algorithms for reliability-related applications
Energy Technology Data Exchange (ETDEWEB)
Ye Zhisheng, E-mail: yez@nus.edu.s [Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119 260 (Singapore); Li Zhizhong [Department of Industrial Engineering, Tsinghua University, beijing 100084 (China); Xie Min [Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119 260 (Singapore)
2010-02-15
Adaptive genetic algorithms (GAs) have been shown to be able to improve GA performance in reliability-related optimization studies. However, there are different ways to implement adaptive GAs, some of which are even in conflict with each other. In this study, a simple parameter-adjusting method using mean and variance of each generation is introduced. This method is used to compare two of such conflicting adaptive GA methods: GAs with increasing mutation rate and decreasing crossover rate and GAs with decreasing mutation rate and increasing crossover rate. The illustrative examples indicate that adaptive GAs with decreasing mutation rate and increasing crossover rate finally yield better results. Furthermore, a population disturbance method is proposed to avoid local optimum solutions. This idea is similar to exotic migration to a tribal society. To solve the problem of large solution space, a variable roughening method is also embedded into GA. Two case studies are presented to demonstrate the effectiveness of the proposed method.
Some improvements on adaptive genetic algorithms for reliability-related applications
International Nuclear Information System (INIS)
Ye Zhisheng; Li Zhizhong; Xie Min
2010-01-01
Adaptive genetic algorithms (GAs) have been shown to be able to improve GA performance in reliability-related optimization studies. However, there are different ways to implement adaptive GAs, some of which are even in conflict with each other. In this study, a simple parameter-adjusting method using mean and variance of each generation is introduced. This method is used to compare two of such conflicting adaptive GA methods: GAs with increasing mutation rate and decreasing crossover rate and GAs with decreasing mutation rate and increasing crossover rate. The illustrative examples indicate that adaptive GAs with decreasing mutation rate and increasing crossover rate finally yield better results. Furthermore, a population disturbance method is proposed to avoid local optimum solutions. This idea is similar to exotic migration to a tribal society. To solve the problem of large solution space, a variable roughening method is also embedded into GA. Two case studies are presented to demonstrate the effectiveness of the proposed method.
Livneh, Hanoch; Wilson, Lisa M.
2003-01-01
Examines the relationships among four predictors (functional limitations, perceived visibility of condition, and two disability-associated affective responses-anxiety and depression), coping strategies, and two outcome measures of psychosocial adaptation to disability. Findings suggest that coping strategies add significantly to the variance in…
Right-left ambiguity resolution using field corrector readout in a large planar drift chamber
International Nuclear Information System (INIS)
Peyaud, B.; Rander, J.; Tarte, G.
1980-02-01
Induced signals on field corrector wires are used to resolve the right-left ambiguity in a large planar drift chamber. Efficient separation is obtained for +-3 cm drift cells, 4 meters long. Technical problems of the method, in particular the severe geometrical constraints, are discussed. Important features of the avalanche asymmetry can be inferred from the measurements
Cognitive predictors of adaptive functioning in children with symptomatic epilepsy.
Kerr, Elizabeth N; Fayed, Nora
2017-10-01
The current study sought to understand the contribution of the attention and working memory challenges experienced by children with active epilepsy without an intellectual disability to adaptive functioning (AF) while taking into account intellectual ability, co-occurring brain-based psychosocial diagnoses, and epilepsy-related variables. The relationship of attention and working memory with AF was examined in 76 children with active epilepsy with intellectual ability above the 2nd percentile recruited from a tertiary care center. AF was measured using the Scales of Independent Behavior-Revised (SIB-R) and compared with norm-referenced data. Standardized clinical assessments of attention span, sustained attention, as well as basic and more complex working memory were administered to children. Commonality analysis was used to investigate the importance of the variables with respect to the prediction of AF and to construct parsimonious models to elucidate the factors most important in explaining AF. Seventy-one percent of parents reported that their child experienced mild to severe difficulties with overall AF. Similar proportions of children displayed limitations in domain-specific areas of AF (Motor, Social/Communication, Person Living, and Community Living). The reduced models for Broad and domain-specific AF produced a maximum of seven predictor variables, with little loss in overall explained variance compared to the full models. Intellectual ability was a powerful predictor of Broad and domain-specific AF. Complex working memory was the only other cognitive predictor retained in each of the parsimonious models of AF. Sustained attention and complex working memory explained a large amount of the total variance in Motor AF. Children with a previously diagnosed comorbidity displayed lower Social/Communication, Personal Living, and Broad AF than those without a diagnosis. At least one epilepsy-related variable appeared in each of the reduced models, with age of
Adaptive disengagement buffers self-esteem from negative social feedback.
Leitner, Jordan B; Hehman, Eric; Deegan, Matthew P; Jones, James M
2014-11-01
The degree to which self-esteem hinges on feedback in a domain is known as a contingency of self-worth, or engagement. Although previous research has conceptualized engagement as stable, it would be advantageous for individuals to dynamically regulate engagement. The current research examined whether the tendency to disengage from negative feedback accounts for variability in self-esteem. We created the Adaptive Disengagement Scale (ADS) to capture individual differences in the tendency to disengage self-esteem from negative outcomes. Results demonstrated that the ADS is reliable and valid (Studies 1 and 2). Furthermore, in response to negative social feedback, higher scores on the ADS predicted greater state self-esteem (Study 3), and this relationship was mediated by disengagement (Study 4). These findings demonstrate that adaptive disengagement protects self-esteem from negative outcomes and that the ADS is a valid measure of individual differences in the implementation of this process. © 2014 by the Society for Personality and Social Psychology, Inc.
An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints
Directory of Open Access Journals (Sweden)
Jinmo Sung
2014-01-01
Full Text Available Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.
Directory of Open Access Journals (Sweden)
Gedefaw A
2015-04-01
Full Text Available Abel Gedefaw,1 Birkneh Tilahun,2 Anteneh Asefa3 1Department of Gynecology and Obstetrics, 2Department of Pediatrics and Child Health, 3School of Public and Environmental Health, College of Medicine and Health Sciences, Hawassa University, Hawassa, Ethiopia Background: This study was conducted to identify predictors of self-reported academic performance in undergraduate medical students at Hawassa University. Methods: An analytical cross-sectional study involving 592 undergraduate medical students was conducted in November 2012. The academic performance of the study subjects was measured by self-reported cumulative grade point average (GPA using a self-administered questionnaire. Data were entered and analyzed using Statistical Package for the Social Sciences version 16 software. Pearson's bivariate correlations, multiple linear regression, and multiple logistic regression were used to identify predictors of academic performance. Results: The self-reported academic performance of students had been decreasing as the academic years progressed, with the highest and lowest performance being in the premedicine (mean GPA 3.47 and clinical I (mean GPA 2.71 years, respectively. One hundred and fifty-eight (26.7% of the participants had ever been delayed, 37 (6.2% had ever re-sat for examination, and two (0.3% had ever been warned due to academic failure. The overall variation in self-reported academic performance of the students was 32.8%. Participant age alone explained 21.9% of the variation. On the other hand, university entrance examination results, substance use at university, and medicine as first choice by students were identified as predictors of variation in self-reported academic performance, accounting for 6.9%, 2.7%, and <1% of the variation, respectively. Students who had never used tobacco, alcohol, or khat after starting university were twice as likely to score a self-reported cumulative GPA above 3.0 (adjusted odds ratio 1.95, 95
Self-esteem and insight as predictors of symptom change in schizophrenia: a longitudinal study.
Erickson, Molly A; Lysaker, Paul H
2012-07-01
Though it is known that symptom profiles in schizophrenia change throughout the course of the illness, it is not yet clear which psychological antecedents predict these changes. The purpose of the present study was to explore "level of insight into mental illness" and "self-esteem" as predictors of positive symptom change in schizophrenia patients. Fifty-seven schizophrenia patients completed assessments of self-esteem, insight into mental illness, positive symptoms and paranoia once every four weeks for a total of eight individual testing sessions. Hierarchical linear regression analysis revealed that changes in self-esteem predicted future changes in paranoia as well as positive symptoms more broadly; decreases in self-esteem at any given time point were associated with an increase in persecutory beliefs and other positive symptoms at the following assessment. On the other hand, decreases in insight were not significantly associated with paranoia or positive symptoms, either as a stable trait of the mental illness or as a predictor of change over time. Taken together, these results suggest that change in self-esteem, but not insight, has a significant and unique association with positive symptoms of schizophrenia, and may be a valuable target for future treatment.
Adapting algorithms to massively parallel hardware
Sioulas, Panagiotis
2016-01-01
In the recent years, the trend in computing has shifted from delivering processors with faster clock speeds to increasing the number of cores per processor. This marks a paradigm shift towards parallel programming in which applications are programmed to exploit the power provided by multi-cores. Usually there is gain in terms of the time-to-solution and the memory footprint. Specifically, this trend has sparked an interest towards massively parallel systems that can provide a large number of processors, and possibly computing nodes, as in the GPUs and MPPAs (Massively Parallel Processor Arrays). In this project, the focus was on two distinct computing problems: k-d tree searches and track seeding cellular automata. The goal was to adapt the algorithms to parallel systems and evaluate their performance in different cases.
Directory of Open Access Journals (Sweden)
Sadika Ismail
2017-08-01
Full Text Available Orientation: Employers expect young graduates to have a well-rounded sense of self, to display a range of graduate employability capacities and to adapt to constant changes they are faced with in order to obtain and maintain employment. Research purpose: The goals of this study are (1 to investigate whether a significant relationship exists between graduate employability capacities, self-esteem and career adaptability, (2 to ascertain if a set of graduate employability capacities, when combined with self-esteem, has a significant relationship with a set of career adaptability capacities and (3 to identify the major variables that contribute to this relationship. Motivation for the study: The potential for career adaptability, graduate employability capacities and self-esteem of young adults promotes employability among graduates, thereby addressing and possibly reducing youth unemployment in South Africa. Research approach, design and method: A quantitative, cross-sectional research design approach was utilised in which descriptive statistics, Pearson product-moment correlations and canonical correlation analysis were employed to accomplish the objectives of this study. Respondents (N = 332 were enrolled at further education and training (FET colleges and were predominantly black (98.5% and female (62% students between the ages of 18 and 29. Main findings: The results displayed positive multivariate relationships between the variables and furthermore showed that graduate employability capacities contributed the most in terms of clarifying the respondents’ career adaptability as compared to their self-esteem. Practical and managerial implications: This study proposes that young adults’ career adaptability can be enhanced through the development of their self-esteem and particularly their graduate employability capacities, thus making them more employable. Contributions: Theoretically, this study proves useful because of the significant
Discrimination of human and nonhuman blood using Raman spectroscopy with self-reference algorithm
Bian, Haiyi; Wang, Peng; Wang, Jun; Yin, Huancai; Tian, Yubing; Bai, Pengli; Wu, Xiaodong; Wang, Ning; Tang, Yuguo; Gao, Jing
2017-09-01
We report a self-reference algorithm to discriminate human and nonhuman blood by calculating the ratios of identification Raman peaks to reference Raman peaks and choosing appropriate threshold values. The influence of using different reference peaks and identification peaks was analyzed in detail. The Raman peak at 1003 cm-1 was proved to be a stable reference peak to avoid the influencing factors, such as the incident laser intensity and the amount of sample. The Raman peak at 1341 cm-1 was found to be an efficient identification peak, which indicates that the difference between human and nonhuman blood results from the C-H bend in tryptophan. The comparison between self-reference algorithm and partial least square method was made. It was found that the self-reference algorithm not only obtained the discrimination results with the same accuracy, but also provided information on the difference of chemical composition. In addition, the performance of self-reference algorithm whose true positive rate is 100% is significant for customs inspection to avoid genetic disclosure and forensic science.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Energy Technology Data Exchange (ETDEWEB)
Xiu, Dongbin [Univ. of Utah, Salt Lake City, UT (United States)
2017-03-03
The focus of the project is the development of mathematical methods and high-performance computational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly efficient and scalable numerical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
An adaptive N-body algorithm of optimal order
Pruett, C D; Lacy, J M
2003-01-01
Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. T...
International Nuclear Information System (INIS)
Tiilikainen, J; Tilli, J-M; Bosund, V; Mattila, M; Hakkarainen, T; Airaksinen, V-M; Lipsanen, H
2007-01-01
Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present
Black hole algorithm for determining model parameter in self-potential data
Sungkono; Warnana, Dwa Desa
2018-01-01
Analysis of self-potential (SP) data is increasingly popular in geophysical method due to its relevance in many cases. However, the inversion of SP data is often highly nonlinear. Consequently, local search algorithms commonly based on gradient approaches have often failed to find the global optimum solution in nonlinear problems. Black hole algorithm (BHA) was proposed as a solution to such problems. As the name suggests, the algorithm was constructed based on the black hole phenomena. This paper investigates the application of BHA to solve inversions of field and synthetic self-potential (SP) data. The inversion results show that BHA accurately determines model parameters and model uncertainty. This indicates that BHA is highly potential as an innovative approach for SP data inversion.
Energy Technology Data Exchange (ETDEWEB)
Fontana, W.
1990-12-13
In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.
Optimization algorithms intended for self-tuning feedwater heater model
International Nuclear Information System (INIS)
Czop, P; Barszcz, T; Bednarz, J
2013-01-01
This work presents a self-tuning feedwater heater model. This work continues the work on first-principle gray-box methodology applied to diagnostics and condition assessment of power plant components. The objective of this work is to review and benchmark the optimization algorithms regarding the time required to achieve the best model fit to operational power plant data. The paper recommends the most effective algorithm to be used in the model adjustment process.
Self-organized spectrum chunk selection algorithm for Local Area LTE-Advanced
DEFF Research Database (Denmark)
Kumar, Sanjay; Wang, Yuanye; Marchetti, Nicola
2010-01-01
This paper presents a self organized spectrum chunk selection algorithm in order to minimize the mutual intercell interference among Home Node Bs (HeNBs), aiming to improve the system throughput performance compared to the existing frequency reuse one scheme. The proposed algorithm is useful...
The impact of culture on adaptive versus maladaptive self-reflection.
Grossmann, Igor; Kross, Ethan
2010-08-01
Although recent findings indicate that people can reflect either adaptively or maladaptively over negative experiences, extant research has not examined how culture influences this process. We compared the self-reflective practices of Russians (members of an interdependent culture characterized by a tendency to brood) and Americans (members of an independent culture in which self-reflection has been studied extensively). We predicted that self-reflection would be associated with less-detrimental outcomes among Russians because they self-distance more when analyzing their feelings than Americans do. Findings from two studies supported these predictions. In Study 1, self-reflection was associated with fewer depressive symptoms among Russians than among Americans. In Study 2, Russians displayed less distress and a more adaptive pattern of construals than Americans after reflecting over a recent negative event. In addition, they self-distanced more than Americans while analyzing their feelings, and self-distancing mediated the cultural differences in self-reflection. These findings demonstrate how culture shapes the way people reflect over negative experiences.
Predictors of self-rated health: a 12-month prospective study of IT and media workers
Directory of Open Access Journals (Sweden)
Arnetz Bengt B
2006-07-01
Full Text Available Abstract Objective The aim of the present study was to determine health-related risk and salutogenic factors and to use these to construct prediction models for future self-rated health (SRH, i.e. find possible characteristics predicting individuals improving or worsening in SRH over time (0–12 months. Methods A prospective study was conducted with measurements (physiological markers and self-ratings at 0, 6 and 12 months, involving 303 employees (187 men and 116 women, age 23–64 from four information technology and two media companies. Results There were a multitude of statistically significant cross-sectional correlations (Spearman's Rho between SRH and other self-ratings as well as physiological markers. Predictors of future SRH were baseline ratings of SRH, self-esteem and social support (logistic regression, and SRH, sleep quality and sense of coherence (linear regression. Conclusion The results of the present study indicate that baseline SRH and other self-ratings are predictive of future SRH. It is cautiously implied that SRH, self-esteem, social support, sleep quality and sense of coherence might be predictors of future SRH and therefore possibly also of various future health outcomes.
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.
Experimental Evaluation of a Braille-Reading-Inspired Finger Motion Adaptive Algorithm.
Ulusoy, Melda; Sipahi, Rifat
2016-01-01
Braille reading is a complex process involving intricate finger-motion patterns and finger-rubbing actions across Braille letters for the stimulation of appropriate nerves. Although Braille reading is performed by smoothly moving the finger from left-to-right, research shows that even fluent reading requires right-to-left movements of the finger, known as "reversal". Reversals are crucial as they not only enhance stimulation of nerves for correctly reading the letters, but they also show one to re-read the letters that were missed in the first pass. Moreover, it is known that reversals can be performed as often as in every sentence and can start at any location in a sentence. Here, we report experimental results on the feasibility of an algorithm that can render a machine to automatically adapt to reversal gestures of one's finger. Through Braille-reading-analogous tasks, the algorithm is tested with thirty sighted subjects that volunteered in the study. We find that the finger motion adaptive algorithm (FMAA) is useful in achieving cooperation between human finger and the machine. In the presence of FMAA, subjects' performance metrics associated with the tasks have significantly improved as supported by statistical analysis. In light of these encouraging results, preliminary experiments are carried out with five blind subjects with the aim to put the algorithm to test. Results obtained from carefully designed experiments showed that subjects' Braille reading accuracy in the presence of FMAA was more favorable then when FMAA was turned off. Utilization of FMAA in future generation Braille reading devices thus holds strong promise.
Predictors of dropout from internet-based self-help cognitive behavioral therapy for insomnia.
Yeung, Wing-Fai; Chung, Ka-Fai; Ho, Fiona Yan-Yee; Ho, Lai-Ming
2015-10-01
Dropout from self-help cognitive-behavioral therapy for insomnia (CBT-I) potentially diminishes therapeutic effect and poses clinical concern. We analyzed the characteristics of subjects who did not complete a 6-week internet-based CBT-I program. Receiver operator characteristics (ROC) analysis was used to identify potential variables and cutoff for predicting dropout among 207 participants with self-report insomnia 3 or more nights per week for at least 3 months randomly assigned to self-help CBT-I with telephone support (n = 103) and self-help CBT-I (n = 104). Seventy-two participants (34.4%) did not complete all 6 sessions, while 42 of the 72 (56.9%) dropped out prior to the fourth session. Significant predictors of non-completion are total sleep time (TST) ≥ 6.82 h, Hospital Anxiety and Depression Scale depression score ≥ 9 and Insomnia Severity Index score dropout. Longer TST and less severe insomnia predict dropout in this study of self-help CBT-I, in contrast to shorter TST as a predictor in 2 studies of face-to-face CBT-I, while greater severity of depression predicts dropout in both this study and a study of face-to-face CBT-I. Strategies for minimizing dropout from internet-based CBT-I are discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.
Algorithm improvement for phase control of subharmonic buncher
International Nuclear Information System (INIS)
Zhang Junqiang; Yu Luyang; Yin Chongxian; Zhao Minghua; Zhong Shaopeng
2011-01-01
To realize digital phase control of subharmonic buncher,a low level radio frequency control system using down converter, IQ modulator and demodulator techniques, and commercial PXI system, was developed on the platform of LabVIEW. A single-neuron adaptive PID (proportional-integral-derivative) control algorithm with ability of self learning was adopted, satisfying the requirements of phase stability. By comparison with the traditional PID algorithm in field testing, the new algorithm has good stability, fast response and strong anti-interference ability. (authors)
A globally convergent MC algorithm with an adaptive learning rate.
Peng, Dezhong; Yi, Zhang; Xiang, Yong; Zhang, Haixian
2012-02-01
This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.
Angular-contact ball-bearing internal load estimation algorithm using runtime adaptive relaxation
Medina, H.; Mutu, R.
2017-07-01
An algorithm to estimate internal loads for single-row angular contact ball bearings due to externally applied thrust loads and high-operating speeds is presented. A new runtime adaptive relaxation procedure and blending function is proposed which ensures algorithm stability whilst also reducing the number of iterations needed to reach convergence, leading to an average reduction in computation time in excess of approximately 80%. The model is validated based on a 218 angular contact bearing and shows excellent agreement compared to published results.
Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.
Liu, Li; Lin, Weikai; Jin, Mingwu
2015-01-01
In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning. In TV minimization, the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain, while the step size of algebraic reconstruction technique (ART) in POCS is determined based on the data noise level. In addition, projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs. The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data. Our methods with automatic parameter tuning achieve similar, if not better, reconstruction performance compared to a representative two-stage algorithm. Copyright © 2014 Elsevier Ltd. All rights reserved.
An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication
Wang, Pangwei; Wang, Yunpeng; Yu, Guizhen; Tang, Tieqiao
2014-05-01
For the Cooperative Adaptive Cruise Control (CACC) Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.
An automated algorithm for photoreceptors counting in adaptive optics retinal images
Liu, Xu; Zhang, Yudong; Yun, Dai
2012-10-01
Eyes are important organs of humans that detect light and form spatial and color vision. Knowing the exact number of cones in retinal image has great importance in helping us understand the mechanism of eyes' function and the pathology of some eye disease. In order to analyze data in real time and process large-scale data, an automated algorithm is designed to label cone photoreceptors in adaptive optics (AO) retinal images. Images acquired by the flood-illuminated AO system are taken to test the efficiency of this algorithm. We labeled these images both automatically and manually, and compared the results of the two methods. A 94.1% to 96.5% agreement rate between the two methods is achieved in this experiment, which demonstrated the reliability and efficiency of the algorithm.
Directory of Open Access Journals (Sweden)
Jie-Sheng Wang
2015-06-01
Full Text Available In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO algorithm combined with the least squares method (LMS to optimize the adaptive network-based fuzzy inference system (ANFIS model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.
Directory of Open Access Journals (Sweden)
S. Jamali
2008-10-01
Full Text Available This paper presents an algorithm for adaptive determination of the dead timeduring transient arcing faults and blocking automatic reclosing during permanent faults onoverhead transmission lines. The discrimination between transient and permanent faults ismade by the zero sequence voltage measured at the relay point. If the fault is recognised asan arcing one, then the third harmonic of the zero sequence voltage is used to evaluate theextinction time of the secondary arc and to initiate reclosing signal. The significantadvantage of this algorithm is that it uses an adaptive threshold level and therefore itsperformance is independent of fault location, line parameters and the system operatingconditions. The proposed algorithm has been successfully tested under a variety of faultlocations and load angles on a 400KV overhead line using Electro-Magnetic TransientProgram (EMTP. The test results validate the algorithm ability in determining thesecondary arc extinction time during transient faults as well as blocking unsuccessfulautomatic reclosing during permanent faults.
Li, Shuo; Jin, Weiqi; Li, Li; Li, Yiyang
2018-05-01
Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.
Multimodal Estimation of Distribution Algorithms.
Yang, Qiang; Chen, Wei-Neng; Li, Yun; Chen, C L Philip; Xu, Xiang-Min; Zhang, Jun
2016-02-15
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima.
An implicit adaptation algorithm for a linear model reference control system
Mabius, L.; Kaufman, H.
1975-01-01
This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.
Accuracy of vaginal symptom self-diagnosis algorithms for deployed military women.
Ryan-Wenger, Nancy A; Neal, Jeremy L; Jones, Ashley S; Lowe, Nancy K
2010-01-01
Deployed military women have an increased risk for development of vaginitis due to extreme temperatures, primitive sanitation, hygiene and laundry facilities, and unavailable or unacceptable healthcare resources. The Women in the Military Self-Diagnosis (WMSD) and treatment kit was developed as a field-expedient solution to this problem. The primary study aims were to evaluate the accuracy of women's self-diagnosis of vaginal symptoms and eight diagnostic algorithms and to predict potential self-medication omission and commission error rates. Participants included 546 active duty, deployable Army (43.3%) and Navy (53.6%) women with vaginal symptoms who sought healthcare at troop medical clinics on base.In the clinic lavatory, women conducted a self-diagnosis using a sterile cotton swab to obtain vaginal fluid, a FemExam card to measure positive or negative pH and amines, and the investigator-developed WMSD Decision-Making Guide. Potential self-diagnoses were "bacterial infection" (bacterial vaginosis [BV] and/or trichomonas vaginitis [TV]), "yeast infection" (candida vaginitis [CV]), "no infection/normal," or "unclear." The Affirm VPIII laboratory reference standard was used to detect clinically significant amounts of vaginal fluid DNA for organisms associated with BV, TV, and CV. Women's self-diagnostic accuracy was 56% for BV/TV and 69.2% for CV. False-positives would have led to a self-medication commission error rate of 20.3% for BV/TV and 8% for CV. Potential self-medication omission error rates due to false-negatives were 23.7% for BV/TV and 24.8% for CV. The positive predictive value of diagnostic algorithms ranged from 0% to 78.1% for BV/TV and 41.7% for CV. The algorithms were based on clinical diagnostic standards. The nonspecific nature of vaginal symptoms, mixed infections, and a faulty device intended to measure vaginal pH and amines explain why none of the algorithms reached the goal of 95% accuracy. The next prototype of the WMSD kit will not include
Path Planning Algorithms for the Adaptive Sensor Fleet
Stoneking, Eric; Hosler, Jeff
2005-01-01
The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.
An adaptive algorithm for simulation of stochastic reaction-diffusion processes
International Nuclear Information System (INIS)
Ferm, Lars; Hellander, Andreas; Loetstedt, Per
2010-01-01
We propose an adaptive hybrid method suitable for stochastic simulation of diffusion dominated reaction-diffusion processes. For such systems, simulation of the diffusion requires the predominant part of the computing time. In order to reduce the computational work, the diffusion in parts of the domain is treated macroscopically, in other parts with the tau-leap method and in the remaining parts with Gillespie's stochastic simulation algorithm (SSA) as implemented in the next subvolume method (NSM). The chemical reactions are handled by SSA everywhere in the computational domain. A trajectory of the process is advanced in time by an operator splitting technique and the timesteps are chosen adaptively. The spatial adaptation is based on estimates of the errors in the tau-leap method and the macroscopic diffusion. The accuracy and efficiency of the method are demonstrated in examples from molecular biology where the domain is discretized by unstructured meshes.
Lewandowski, Gary W; Ackerman, Robert A
2006-08-01
The present authors investigated whether an individual's motivations that are related to need fulfillment and self-expansion within a romantic relationship can predict self-reported susceptibility to infidelity. A sample of 109 college students (50 men, 59 women) who were in dating relationships completed questionnaires that assessed 5 types of variables of need fulfillment (i.e., intimacy, companionship, sex, security, and emotional involvement), 3 types of self-expansion variables (i.e., self-expansion, inclusion of the other in the self, and potential for self-expansion), and susceptibility to infidelity. As the present authors predicted, both sets of predictors (need fulfillment and self-expansion) significantly contributed to the variance in susceptibility to infidelity. The present findings indicated the possibility that, when a relationship is not able to fulfill needs or provide ample self-expansion for an individual, his or her susceptibility to infidelity increases.
Directory of Open Access Journals (Sweden)
Kalaivani Annadurai
2017-01-01
Full Text Available Background: Inappropriate self medication is one of the leading causes of growing antibiotic resistance in developing nations which poses a major public health threat worldwide and assessment of self medication practices is essential for better understanding of the problem. Aim and Objectives: To find out the predictors of self medication use among the residents of Nellikuppam village, Kancheepuram District, Tamil Nadu. To assess the self medication practices among the residents of Nellikuppam village, Kancheepuram District, Tamil Nadu. Material and Methods: This was a descriptive cross sectional study conducted among 335 adult households with six months recall period in Nellikuppam village of Tamil Nadu during May to October, 2014 using a pretested semi-structured questionnaire. Results: Prevalence of self medication among adult rural population was 53.43% and only half of the study population opined that it was harmful. Pharmacists (72.06% were the major source of drug information on self medication. Paracetomol (84.91% was the commonest drug used for self medication. Major predictors were perception of illness as minor ailment and unavailability of doctors in their locality. Nearly half of the current self medication users (47.49% were in the idea of practicing self medication in the future. Conclusion: This study results implies the need for proper enforcement of legal measures towards the restriction of over the counter medicine and creating awareness among general population on adverse reaction of self medication.
Self-adapting denoising, alignment and reconstruction in electron tomography in materials science
Energy Technology Data Exchange (ETDEWEB)
Printemps, Tony, E-mail: tony.printemps@cea.fr [Université Grenoble Alpes, F-38000 Grenoble (France); CEA, LETI, MINATEC Campus, F-38054 Grenoble (France); Mula, Guido [Dipartimento di Fisica, Università di Cagliari, Cittadella Universitaria, S.P. 8km 0.700, 09042 Monserrato (Italy); Sette, Daniele; Bleuet, Pierre; Delaye, Vincent; Bernier, Nicolas; Grenier, Adeline; Audoit, Guillaume; Gambacorti, Narciso; Hervé, Lionel [Université Grenoble Alpes, F-38000 Grenoble (France); CEA, LETI, MINATEC Campus, F-38054 Grenoble (France)
2016-01-15
An automatic procedure for electron tomography is presented. This procedure is adapted for specimens that can be fashioned into a needle-shaped sample and has been evaluated on inorganic samples. It consists of self-adapting denoising, automatic and accurate alignment including detection and correction of tilt axis, and 3D reconstruction. We propose the exploitation of a large amount of information of an electron tomography acquisition to achieve robust and automatic mixed Poisson–Gaussian noise parameter estimation and denoising using undecimated wavelet transforms. The alignment is made by mixing three techniques, namely (i) cross-correlations between neighboring projections, (ii) common line algorithm to get a precise shift correction in the direction of the tilt axis and (iii) intermediate reconstructions to precisely determine the tilt axis and shift correction in the direction perpendicular to that axis. Mixing alignment techniques turns out to be very efficient and fast. Significant improvements are highlighted in both simulations and real data reconstructions of porous silicon in high angle annular dark field mode and agglomerated silver nanoparticles in incoherent bright field mode. 3D reconstructions obtained with minimal user-intervention present fewer artefacts and less noise, which permits easier and more reliable segmentation and quantitative analysis. After careful sample preparation and data acquisition, the denoising procedure, alignment and reconstruction can be achieved within an hour for a 3D volume of about a hundred million voxels, which is a step toward a more routine use of electron tomography. - Highlights: • Goal: perform a reliable and user-independent 3D electron tomography reconstruction. • Proposed method: self-adapting denoising and alignment prior to 3D reconstruction. • Noise estimation and denoising are performed using wavelet transform. • Tilt axis determination is done automatically as well as projection alignment.
Directory of Open Access Journals (Sweden)
Jingbo Zhang
2018-01-01
Full Text Available In the field of cognitive radio spectrum sensing, the adaptive silence period management mechanism (ASPM has improved the problem of the low time-resource utilization rate of the traditional silence period management mechanism (TSPM. However, in the case of the low signal-to-noise ratio (SNR, the ASPM algorithm will increase the probability of missed detection for the primary user (PU. Focusing on this problem, this paper proposes an improved adaptive silence period management (IA-SPM algorithm which can adaptively adjust the sensing parameters of the current period in combination with the feedback information from the data communication with the sensing results of the previous period. The feedback information in the channel is achieved with frequency resources rather than time resources in order to adapt to the parameter change in the time-varying channel. The Monte Carlo simulation results show that the detection probability of the IA-SPM is 10–15% higher than that of the ASPM under low SNR conditions.
Dynamic game balancing implementation using adaptive algorithm in mobile-based Safari Indonesia game
Yuniarti, Anny; Nata Wardanie, Novita; Kuswardayan, Imam
2018-03-01
In developing a game there is one method that should be applied to maintain the interest of players, namely dynamic game balancing. Dynamic game balancing is a process to match a player’s playing style with the behaviour, attributes, and game environment. This study applies dynamic game balancing using adaptive algorithm in scrolling shooter game type called Safari Indonesia which developed using Unity. The game of this type is portrayed by a fighter aircraft character trying to defend itself from insistent enemy attacks. This classic game is chosen to implement adaptive algorithms because it has quite complex attributes to be developed using dynamic game balancing. Tests conducted by distributing questionnaires to a number of players indicate that this method managed to reduce frustration and increase the pleasure factor in playing.
A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.
Li, Yuhong; Gong, Guanghong; Li, Ni
2018-01-01
In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.
A Parallel Adaptive Particle Swarm Optimization Algorithm for Economic/Environmental Power Dispatch
Directory of Open Access Journals (Sweden)
Jinchao Li
2012-01-01
Full Text Available A parallel adaptive particle swarm optimization algorithm (PAPSO is proposed for economic/environmental power dispatch, which can overcome the premature characteristic, the slow-speed convergence in the late evolutionary phase, and lacking good direction in particles’ evolutionary process. A search population is randomly divided into several subpopulations. Then for each subpopulation, the optimal solution is searched synchronously using the proposed method, and thus parallel computing is realized. To avoid converging to a local optimum, a crossover operator is introduced to exchange the information among the subpopulations and the diversity of population is sustained simultaneously. Simulation results show that the proposed algorithm can effectively solve the economic/environmental operation problem of hydropower generating units. Performance comparisons show that the solution from the proposed method is better than those from the conventional particle swarm algorithm and other optimization algorithms.
Demirchyan, Anahit; Petrosyan, Varduhi; Thompson, Michael E
2012-11-14
Self-rated health is a widely used health outcome measure that strongly correlates with physical and mental health status and predicts mortality. This study identified the set of predictors of fair/poor self-rated health in adult female and male populations of Armenia during a period of long-lasting socio-economic transition to a market economy. Differences in self-rated health were analyzed along three dimensions: socioeconomic, behavioral/attitudinal, and psychosocial. The study utilized data from a 2006 nationwide household health survey that used a multi-stage probability proportional to size cluster sampling with a combination of interviewer-administered and self-administered surveys. Both female and male representatives of a household aged 18 and over completed the self-administered survey. Multivariate odds ratios (OR) for fair/poor self-rated health were calculated for different sets of variables and logistic regression models fitted separately for women and men to identify the determinants of fair/poor self-rated health. Overall, 2310 women and 462 men participated in the survey. The rate of fair/poor self-rated health was 61.8% among women and 59.7% among men. For women, the set of independent predictors of fair/poor self-rated health included age, unemployment, poverty, low affordability of healthcare, depression, and weak social support. For men, the set included age, lower education, depression, weak social support, and drinking alcohol less than once a week. For both genders, depression and weak social support demonstrated the strongest independent association with fair/poor self-rated health. The prevalence of fair/poor self-rated health was similar among men and women in this study, but the sets of independent predictors of perceived health differed somewhat, possibly, reflecting lifestyle differences between men and women in Armenia. Nevertheless, psychosocial variables were the strongest predictors of fair/poor self-rated health for both genders
Directory of Open Access Journals (Sweden)
Demirchyan Anahit
2012-11-01
Full Text Available Abstract Introduction Self-rated health is a widely used health outcome measure that strongly correlates with physical and mental health status and predicts mortality. This study identified the set of predictors of fair/poor self-rated health in adult female and male populations of Armenia during a period of long-lasting socio-economic transition to a market economy. Methods Differences in self-rated health were analyzed along three dimensions: socioeconomic, behavioral/attitudinal, and psychosocial. The study utilized data from a 2006 nationwide household health survey that used a multi-stage probability proportional to size cluster sampling with a combination of interviewer-administered and self-administered surveys. Both female and male representatives of a household aged 18 and over completed the self-administered survey. Multivariate odds ratios (OR for fair/poor self-rated health were calculated for different sets of variables and logistic regression models fitted separately for women and men to identify the determinants of fair/poor self-rated health. Results Overall, 2310 women and 462 men participated in the survey. The rate of fair/poor self-rated health was 61.8% among women and 59.7% among men. For women, the set of independent predictors of fair/poor self-rated health included age, unemployment, poverty, low affordability of healthcare, depression, and weak social support. For men, the set included age, lower education, depression, weak social support, and drinking alcohol less than once a week. For both genders, depression and weak social support demonstrated the strongest independent association with fair/poor self-rated health. Conclusions The prevalence of fair/poor self-rated health was similar among men and women in this study, but the sets of independent predictors of perceived health differed somewhat, possibly, reflecting lifestyle differences between men and women in Armenia. Nevertheless, psychosocial variables were the
Towards Self-adaptation for Dependable Service-Oriented Systems
Cardellini, Valeria; Casalicchio, Emiliano; Grassi, Vincenzo; Lo Presti, Francesco; Mirandola, Raffaela
Increasingly complex information systems operating in dynamic environments ask for management policies able to deal intelligently and autonomously with problems and tasks. An attempt to deal with these aspects can be found in the Service-Oriented Architecture (SOA) paradigm that foresees the creation of business applications from independently developed services, where services and applications build up complex dependencies. Therefore the dependability of SOA systems strongly depends on their ability to self-manage and adapt themselves to cope with changes in the operating conditions and to meet the required dependability with a minimum of resources. In this paper we propose a model-based approach to the realization of self-adaptable SOA systems, aimed at the fulfillment of dependability requirements. Specifically, we provide a methodology driving the system adaptation and we discuss the architectural issues related to its implementation. To bring this approach to fruition, we developed a prototype tool and we show the results that can be achieved with a simple example.
Algorithmic Mechanism Design of Evolutionary Computation.
Pei, Yan
2015-01-01
We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.
Self-Efficacy versus Perceived Enjoyment as Predictors of Physical Activity Behavior
Lewis, Beth A.; Williams, David M.; Frayeh, Amanda L.; Marcus, Bess H.
2015-01-01
Objective Self-efficacy and physical activity (PA) enjoyment are related to PA behavior, but it is unclear which is more important and how they interrelate. The purpose of this study was to examine how these two constructs interrelate to influence PA behavior. Design Participants were low active adults (n=448) participating in a RCT examining the effect of a PA promotion intervention. Participants completed physical activity, enjoyment, and self-efficacy measures at baseline, six, and 12 months. Results Self-efficacy and enjoyment at both baseline and six months predicted PA at 12 months. However, enjoyment was a stronger predictor than self-efficacy in that self-efficacy no longer predicted PA behavior when included alongside enjoyment. In follow-up mediation analyses, enjoyment at six months did not mediate the effect of baseline self-efficacy on 12-month PA; however, six-month self-efficacy mediated the effect of baseline enjoyment on 12-month PA. Conclusion Our results indicate that interventions should perhaps initially focus on increasing enjoyment of physical activity. Greater PA enjoyment appears to influence individuals’ self-reported ability to engage in regular PA (i.e., higher self-efficacy ratings). Additional research is needed to better understand the interrelationships between self-efficacy and enjoyment and how these constructs affect PA. PMID:26541890
An effective algorithm for approximating adaptive behavior in seasonal environments
DEFF Research Database (Denmark)
Sainmont, Julie; Andersen, Ken Haste; Thygesen, Uffe Høgsbro
2015-01-01
Behavior affects most aspects of ecological processes and rates, and yet modeling frameworks which efficiently predict and incorporate behavioral responses into ecosystem models remain elusive. Behavioral algorithms based on life-time optimization, adaptive dynamics or game theory are unsuited...... for large global models because of their high computational demand. We compare an easily integrated, computationally efficient behavioral algorithm known as Gilliam's rule against the solution from a life-history optimization. The approximation takes into account only the current conditions to optimize...... behavior; the so-called "myopic approximation", "short sighted", or "static optimization". We explore the performance of the myopic approximation with diel vertical migration (DVM) as an example of a daily routine, a behavior with seasonal dependence that trades off predation risk with foraging...
Terahertz adaptive optics with a deformable mirror.
Brossard, Mathilde; Sauvage, Jean-François; Perrin, Mathias; Abraham, Emmanuel
2018-04-01
We report on the wavefront correction of a terahertz (THz) beam using adaptive optics, which requires both a wavefront sensor that is able to sense the optical aberrations, as well as a wavefront corrector. The wavefront sensor relies on a direct 2D electro-optic imaging system composed of a ZnTe crystal and a CMOS camera. By measuring the phase variation of the THz electric field in the crystal, we were able to minimize the geometrical aberrations of the beam, thanks to the action of a deformable mirror. This phase control will open the route to THz adaptive optics in order to optimize the THz beam quality for both practical and fundamental applications.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
Directory of Open Access Journals (Sweden)
Zhiwei Ye
2015-01-01
Full Text Available Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
Beamspace Adaptive Beamforming for Hydrodynamic Towed Array Self-Noise Cancellation
National Research Council Canada - National Science Library
Premus, Vincent
2001-01-01
... against signal self-nulling associated with steering vector mismatch. Particular attention is paid to the definition of white noise gain as the metric that reflects the level of mainlobe adaptive nulling for an adaptive beamformer...
Beamspace Adaptive Beamforming for Hydrodynamic Towed Array Self-Noise Cancellation
National Research Council Canada - National Science Library
Premus, Vincent
2000-01-01
... against signal self-nulling associated with steering vector mismatch. Particular attention is paid to the definition of white noise gain as the metric that reflects the level of mainlobe adaptive nulling for an adaptive beamformer...
International Nuclear Information System (INIS)
Lee, S.; Lee, J.; Song, S.; Yoon, J.; Lee, B.
2015-01-01
Highlights: • This paper presents an outline of the new project of the 154 kV SFCL in Korea. • And then we review some protection problems for the application of 154 kV SFCLs. • This paper proposes a new adaptive protection algorithm for 154 kV SFCLs. • The developed algorithm is tested in a simple distance relay system. - Abstract: In general, SFCLs can have a negative impact on the protective coordination in power transmission system because of the variable impedance of SFCLs. It is very important to solve the protection problems of the power system for the successful application of SFCLs to real power transmission system. This paper reviews some protection problems which can be caused by the application of 154 kV SFCLs to power transmission systems in South Korea. And then we propose an adaptive protection algorithm to solve the problems. The adaptive protection algorithm uses the real time information of the SFCL system operation.
Self-adapted thermocouple-diagnostic complex
International Nuclear Information System (INIS)
Alekseev, S.V.; Grankovskij, K.Eh.; Olejnikov, P.P.; Prijmak, S.V.; Shikalov, V.F.
2003-01-01
A self-adapted thermocouple-diagnostic complex (STDC) for obtaining the reliable data on the coolant temperature in the reactors of NPP is described. The STDC in based on the thermal pulse monitoring of a thermocouple in the measuring channel of a reactor. Measurement method and STDC composition are substantiated. It is shown that introduction of the developed STDC ensures realization of precise and reliable temperature monitoring in the reactors of all types [ru
Shu, Tongxin; Xia, Min; Chen, Jiahong; Silva, Clarence de
2017-11-05
Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.
Directory of Open Access Journals (Sweden)
Tongxin Shu
2017-11-01
Full Text Available Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA, while achieving around the same Normalized Mean Error (NME, DDASA is superior in saving 5.31% more battery energy.
A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation
Directory of Open Access Journals (Sweden)
Cinthia Peraza
2016-10-01
Full Text Available In this paper, a new fuzzy harmony search algorithm (FHS for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR and pitch adjustment (PArate parameters that improve the convergence rate of traditional harmony search algorithm (HS. The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.
Energy Technology Data Exchange (ETDEWEB)
Doose, C.; Kim, S.H.
1997-08-01
Local feedback for the APS storage ring uses local bumps to control the position and angle of the positron beam through each x-ray source point. Induced eddy currents in the aluminum vacuum chamber dominate the AC characteristics of the corrector magnetic fields. Small differences in the geometries at each magnet location change the eddy current effects and result in bump closure errors which must be reduced in order to minimize the coupling between each of the many local loops and the global control loop. By a combination of flux-damping coils, flux-shielding copper sheets, and a set of steel laminations for end-flux clamping, the differences of the eddy current effects between two corrector magnets were reduced from 0.18 Gm/A to 0.035 Gm/A in the frequency span of 0.1-100 Hz.
Predictors of Self-care among the Elderly with Diabetes Type 2: Using Social Cognitive Theory.
Borhaninejad, Vahidreza; Iranpour, Abedin; Shati, Mohsen; Tahami, Ahmad Naghibzadeh; Yousefzadeh, Gholamrezan; Fadayevatan, Reza
Diabetes is one of the most common chronic diseases among the elderly and is also a very serious health problem. Adopting theory-based self-care behaviors is an effective means in managing such diseases. This study aimed to determine the predictors of diabetes self-care in the elderly in Kerman based on a social cognitive theory. In this cross-sectional study, 384 elderly diabetic patients who had referred to health screening centers in Kerman were chosen via cluster sampling. To collect information about self-care and its predictors, Toobert Glasgow's diabetes self-efficacy scale as well as a questionnaire was used which was based on social cognitive theory constructs. The validity and reliability of the questionnaire was confirmed. The data were analyzed using Pearson correlation and linear regression analysis in SPSS software 17. Among the subjects, 67.37% (252) had poor self-care ability; 29.14% (109) had average ability, and 3.40% (13) enjoyed a proper level of self- care ability. There was a significant relationship between the constructs of the social cognitive theory (knowledge, self- efficacy, social support, outcome expectations, outcome expectancy and self-regulation) and the self-care score. Furthermore, the mentioned constructs could predict 0.47% of the variance of the self-care behaviors. self-care behaviors in this study were poor. Therefore, it is necessary to develop an educational intervention based on cognitive theory constructs with the goal of properly managing diabetes in the elderly patients. Copyright © 2016 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Improving the Stability of the LMF Adaptive Algorithm Using the Median Filteer
DEFF Research Database (Denmark)
Bysted, Tommy Kristensen; Rusu, Corneliu
1998-01-01
environments and secondly it enables the use of larger step-size of the adaptive algorithm especially when the signals are corrupted by noise. The disadvantages are a small raise in the computational complexity and slower convergence than the LMF. Two examples are given which illustrates the behavior...
Direct numerical simulation of bubbles with adaptive mesh refinement with distributed algorithms
International Nuclear Information System (INIS)
Talpaert, Arthur
2017-01-01
This PhD work presents the implementation of the simulation of two-phase flows in conditions of water-cooled nuclear reactors, at the scale of individual bubbles. To achieve that, we study several models for Thermal-Hydraulic flows and we focus on a technique for the capture of the thin interface between liquid and vapour phases. We thus review some possible techniques for adaptive Mesh Refinement (AMR) and provide algorithmic and computational tools adapted to patch-based AMR, which aim is to locally improve the precision in regions of interest. More precisely, we introduce a patch-covering algorithm designed with balanced parallel computing in mind. This approach lets us finely capture changes located at the interface, as we show for advection test cases as well as for models with hyperbolic-elliptic coupling. The computations we present also include the simulation of the incompressible Navier-Stokes system, which models the shape changes of the interface between two non-miscible fluids. (author) [fr
Factors Affecting Self-Esteem Among Juveniles from Youth Educational Centers
Directory of Open Access Journals (Sweden)
Karol Konaszewski
2017-07-01
Full Text Available The article is an analysis of the results of the studies conducted among juveniles (boys and girls in the case of whom the family court applied the educational means of placing them in the Youth Educational Centre. The aim of the study was to find out the correlations between self-esteem, personality traits and the environmental determinants (support factors and risk factors among juveniles (boys and girls. The total of 481 juveniles staying in Youth Educational Centers participated in the study. Applied research tools: The Rosenberg Self Esteem Scale (SES, in the Polish adaptation by I. Dzwonkowska, M. Łaguna and K. Lachowicz-Tabaczek, NEO-FFI by P.T. Costa and R.R. McCrae was used to diagnose personality traits included in a popular five-factor model (it has been adapted into Polish by B. Zawadzki, J. Strelau, P. Szczepaniak, and M. Śliwińska and a questionnaire concerning support factors and risk factors was constructed to measure environmental determinants. The analysis model showed that the significant predictors of self-esteem were neuroticism, extraversion, conscientiousness and negative relations at school. In girls group the significant predictors of self-esteem were neuroticism, conscientiousness, family support and negative relations at school, while in boys group the significant predictors of self-esteem were neuroticism, extraversion and negative relations at family.
Directory of Open Access Journals (Sweden)
Si-wei Liu
2016-12-01
Conclusion: The level of self-differentiation of undergraduate nursing studentsaffects their professional adaptability. Nursing educators should consider the characteristics of self-differentiation of undergraduate nursing students in developing measures to improve their professional adaptability.
Hanglberger, Dominik; Merz, Joachim
2015-01-01
Empirical analyses using cross-sectional and panel data found significantly higher levels of job satisfaction for the self-employed than for employees. We argue that by neglecting anticipation and adaptation effects estimates in previous studies might be misleading. To test this, we specify models accounting for anticipation and adaptation to self-employment and general job changes. In contrast to recent literature we find no specific long-term effect of self-employment on job satisfaction. A...
International Nuclear Information System (INIS)
Xu, Meng; Droguett, Enrique López; Lins, Isis Didier; Chagas Moura, Márcio das
2017-01-01
The q-Weibull model is based on the Tsallis non-extensive entropy and is able to model various behaviors of the hazard rate function, including bathtub curves, by using a single set of parameters. Despite its flexibility, the q-Weibull has not been widely used in reliability applications partly because of the complicated parameters estimation. In this work, the parameters of the q-Weibull are estimated by the maximum likelihood (ML) method. Due to the intricate system of nonlinear equations, derivative-based optimization methods may fail to converge. Thus, the heuristic optimization method of artificial bee colony (ABC) is used instead. To deal with the slow convergence of ABC, it is proposed an adaptive hybrid ABC (AHABC) algorithm that dynamically combines Nelder-Mead simplex search method with ABC for the ML estimation of the q-Weibull parameters. Interval estimates for the q-Weibull parameters, including confidence intervals based on the ML asymptotic theory and on bootstrap methods, are also developed. The AHABC is validated via numerical experiments involving the q-Weibull ML for reliability applications and results show that it produces faster and more accurate convergence when compared to ABC and similar approaches. The estimation procedure is applied to real reliability failure data characterized by a bathtub-shaped hazard rate. - Highlights: • Development of an Adaptive Hybrid ABC (AHABC) algorithm for q-Weibull distribution. • AHABC combines local Nelder-Mead simplex method with ABC to enhance local search. • AHABC efficiently finds the optimal solution for the q-Weibull ML problem. • AHABC outperforms ABC and self-adaptive hybrid ABC in accuracy and convergence speed. • Useful model for reliability data with non-monotonic hazard rate.
Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining
2017-12-01
We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.
Nouwen, Arie; Ford, Teri; Balan, Andreea Teodora; Twisk, Jos; Ruggiero, Laurie; White, David
2011-11-01
This prospective study examined relationships between constructs from social-cognitive theory (Bandura, 1986) and self-determination theory (Deci & Ryan, 1985; Deci & Ryan, 1991) and the diabetes outcomes of dietary self-care and diabetes control. Longitudinal data were collected from 237 people newly diagnosed with Type 2 diabetes who filled in questionnaires on dietary self-care, and motivational factors derived from social-cognitive theory and self-determination theory. Blood samples were taken to assess diabetes control (HbA1c). Repeated measurements were taken every 3-4 months for a total of five time points over 18 months. Predictor measures included autonomy support, autonomous and controlled motivation, amotivation, dietary self-efficacy, positive and negative outcome expectancies for dietary self-care and self-evaluation. Age, sex, BMI, and diabetes knowledge were included as control measures. Using Generalized Estimating Equations (GEE) analyses two models were tested: a standard model reflecting longitudinal associations between absolute values of predicted and outcome variables; and a change model examining motivational predictors of changes over time in diabetes outcomes of dietary self-care and diabetes control (HbA1c). Dietary self-care was longitudinally associated with self-efficacy, self-evaluation (the strongest predictor) autonomy support and autonomous motivation, but not with controlled motivation or outcome expectancies. Changes in dietary self-care were predicted by changes in self-efficacy, self-evaluation, and controlled motivation but not by changes in autonomous motivation or autonomy support. Negative outcome expectancies regarding diet were longitudinally associated with HbA1c, and changes in negative outcome expectancies predicted changes in HbA1c. However, there were indications that dietary self-care predicted changes in HbA1c. The results indicate that autonomy support, self-efficacy and, in particular, self-evaluation are key
Rinn, Anne N.; Miner, Kathi; Taylor, Aaron B.
2013-01-01
The purpose of the current study was to examine four family context variables (socioeconomic status, mother's level of education, father's level of education, and perceived family social support) as predictors of math self-concept among undergraduate STEM majors to better understand the gender differential in math self-concept. Participants…
Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao
2016-06-01
An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.
A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System
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S. M. Odeh
2015-01-01
Full Text Available This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC and Genetic Algorithms (GAs and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC, and up to 31% in the comparison with a traditional logic controller, FLC.
A Novel adaptative Discrete Cuckoo Search Algorithm for parameter optimization in computer vision
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loubna benchikhi
2017-10-01
Full Text Available Computer vision applications require choosing operators and their parameters, in order to provide the best outcomes. Often, the users quarry on expert knowledge and must experiment many combinations to find manually the best one. As performance, time and accuracy are important, it is necessary to automate parameter optimization at least for crucial operators. In this paper, a novel approach based on an adaptive discrete cuckoo search algorithm (ADCS is proposed. It automates the process of algorithms’ setting and provides optimal parameters for vision applications. This work reconsiders a discretization problem to adapt the cuckoo search algorithm and presents the procedure of parameter optimization. Some experiments on real examples and comparisons to other metaheuristic-based approaches: particle swarm optimization (PSO, reinforcement learning (RL and ant colony optimization (ACO show the efficiency of this novel method.
DEFF Research Database (Denmark)
Phung, Thien Kieu Thi; Siersma, Volkert; Vogel, Asmus
2018-01-01
OBJECTIVE: Self-assessment of health is a strong and independent predictor of mortality for cognitively intact people. Because the ability of patients with dementia to rate their own health is questionable, caregiver-rated health for patients may serve as a proxy. The authors aimed to validate...... and compare self- and caregiver-rated health for patients with dementia as independent predictors of patient mortality. METHODS: This was a post-hoc analysis of data from The Danish Alzheimer's Disease Intervention Study, a randomized controlled trial of psychosocial intervention for 330 patients with mild...... dementia and their caregivers with a 36-month follow-up. Patients and caregivers rated patients' health on the Euro Quality of Life Visual Analog Scale (EQ-VAS) from 0 (worst) to 100 (best). The ability of self- and caregiver-rated health for the patient to predict patient mortality was analyzed as hazard...
Saravanakumar, G; Nayak, C G
2007-01-01
A modification of Smith predictor for controlling the higher order processes with integral action ad long dead-time is proposed in this paper. The controller used in this Smith predictor is an Integral-Proportional Derivative controller, where the Integrator is in the forward path and the Proportional and Derivative control are in the feedback, acting on the feedback signal. The main objective of this paper is to design a Dead Time Compensator(DTC), which has minimum tuning parameters, simple controller tuning, robust performance of tuning formulae and to obtain a critically damped system which is as fast as possible in its setpoint and load disturbance rejection performance. The controller in this paper is tuned by an adaptive method. This paper also presents a survey of various dead time compensators and their performance analysis.
ADART: an adaptive algebraic reconstruction algorithm for discrete tomography.
Maestre-Deusto, F Javier; Scavello, Giovanni; Pizarro, Joaquín; Galindo, Pedro L
2011-08-01
In this paper we suggest an algorithm based on the Discrete Algebraic Reconstruction Technique (DART) which is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography. Adaptive DART (ADART) goes a step further than DART on the reduction of the number of unknowns of the associated linear system achieving a significant reduction in the pixel error rate of reconstructed objects. The proposed methodology automatically adapts the border definition criterion at each iteration, resulting in a reduction of the number of pixels belonging to the border, and consequently of the number of unknowns in the general algebraic reconstruction linear system to be solved, being this reduction specially important at the final stage of the iterative process. Experimental results show that reconstruction errors are considerably reduced using ADART when compared to original DART, both in clean and noisy environments.
Statistical behaviour of adaptive multilevel splitting algorithms in simple models
International Nuclear Information System (INIS)
Rolland, Joran; Simonnet, Eric
2015-01-01
Adaptive multilevel splitting algorithms have been introduced rather recently for estimating tail distributions in a fast and efficient way. In particular, they can be used for computing the so-called reactive trajectories corresponding to direct transitions from one metastable state to another. The algorithm is based on successive selection–mutation steps performed on the system in a controlled way. It has two intrinsic parameters, the number of particles/trajectories and the reaction coordinate used for discriminating good or bad trajectories. We investigate first the convergence in law of the algorithm as a function of the timestep for several simple stochastic models. Second, we consider the average duration of reactive trajectories for which no theoretical predictions exist. The most important aspect of this work concerns some systems with two degrees of freedom. They are studied in detail as a function of the reaction coordinate in the asymptotic regime where the number of trajectories goes to infinity. We show that during phase transitions, the statistics of the algorithm deviate significatively from known theoretical results when using non-optimal reaction coordinates. In this case, the variance of the algorithm is peaking at the transition and the convergence of the algorithm can be much slower than the usual expected central limit behaviour. The duration of trajectories is affected as well. Moreover, reactive trajectories do not correspond to the most probable ones. Such behaviour disappears when using the optimal reaction coordinate called committor as predicted by the theory. We finally investigate a three-state Markov chain which reproduces this phenomenon and show logarithmic convergence of the trajectory durations
Directory of Open Access Journals (Sweden)
Duka Adrian-Vasile
2011-12-01
Full Text Available This paper examines the development of a genetic adaptive fuzzy control system for the Inverted Pendulum. The inverted pendulum is a classical problem in Control Engineering, used for testing different control algorithms. The goal is to balance the inverted pendulum in the upright position by controlling the horizontal force applied to its cart. Because it is unstable and has a complicated nonlinear dynamics, the inverted pendulum is a good testbed for the development of nonconventional advanced control techniques. Fuzzy logic technique has been successfully applied to control this type of system, however most of the time the design of the fuzzy controller is done in an ad-hoc manner, and choosing certain parameters (controller gains, membership functions proves difficult. This paper examines the implementation of an adaptive control method based on genetic algorithms (GA, which can be used on-line to produce the adaptation of the fuzzy controller’s gains in order to achieve the stabilization of the pendulum. The performances of the proposed control algorithms are evaluated and shown by means of digital simulation.
Torkaman, Mahya; Miri, Sakineh; Farokhzadian, Jamileh
2018-02-12
Background Reduction of the adaptation and self-esteem can be the consequence of opium addiction and imprisonment. Drug use causes inappropriate behaviors in women, which are quite different from those in men. Social deviations, prostitution, high-risk sexual behaviors, abortion, divorce and imprisonment followed by loss of self-esteem are the consequences of women's addiction. The present study was conducted to assess the relationship between adaptation and self-esteem in addicted female prisoners. Methods In this descriptive analytical study, 130 addicted female prisoners were selected from a prison in the south east of Iran using census sampling. The data were collected by a demographic questionnaire, the Rosenberg's self-esteem scale and the bell adjustment inventory (BAI). Results According to the results, women's adaptation fell into the 'very unsatisfactory' range. The highest mean was related to the emotional dimension, while the lowest mean was in terms of the health dimension. In total, 96.4% of the participating women had low adaptation. The mean total self-esteem fell into the low range; in fact, 84.6% of the women had a low self-esteem. The results showed no significant relationships between adaptation and self-esteem in these women; however, self-esteem was significantly and inversely related to health and emotional adaptation. Conclusion The findings showed that the majority of the women had unsatisfactory adaptation as well as poor self-esteem. No significant relationships were observed between adaptation and self-esteem in the addicted female prisoners.
A comparison of two adaptive algorithms for the control of active engine mounts
Hillis, A. J.; Harrison, A. J. L.; Stoten, D. P.
2005-08-01
This paper describes work conducted in order to control automotive active engine mounts, consisting of a conventional passive mount and an internal electromagnetic actuator. Active engine mounts seek to cancel the oscillatory forces generated by the rotation of out-of-balance masses within the engine. The actuator generates a force dependent on a control signal from an algorithm implemented with a real-time DSP. The filtered-x least-mean-square (FXLMS) adaptive filter is used as a benchmark for comparison with a new implementation of the error-driven minimal controller synthesis (Er-MCSI) adaptive controller. Both algorithms are applied to an active mount fitted to a saloon car equipped with a four-cylinder turbo-diesel engine, and have no a priori knowledge of the system dynamics. The steady-state and transient performance of the two algorithms are compared and the relative merits of the two approaches are discussed. The Er-MCSI strategy offers significant computational advantages as it requires no cancellation path modelling. The Er-MCSI controller is found to perform in a fashion similar to the FXLMS filter—typically reducing chassis vibration by 50-90% under normal driving conditions.
An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.
Kim, Jinkwon; Min, Se Dong; Lee, Myoungho
2011-06-27
Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.
An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects
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Min Se Dong
2011-06-01
Full Text Available Abstract Background Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. Methods In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. Results A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. Conclusions The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.
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Emer Bernal
2017-01-01
Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.
An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU
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Hailong Xu
2016-03-01
Full Text Available Nowadays, software-defined radio (SDR has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP and Space-Frequency Adaptive Processing (SFAP are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications.
Joo, Young Ju; Lim, Kyu Yon; Kim, Jiyeon
2013-01-01
This study investigates the predictors of learner satisfaction, achievement and persistence in an online university located in South Korea. The specific predictors were learners' locus of control, self-efficacy, and task value, and the mediating effects of learner satisfaction and achievement were also tested. Structural equation modeling (SEM)…
THE DEVELOPMENT OF SELF STRUCTURES AND ACTIVE COPING
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J. Knežević
2016-03-01
Full Text Available In addition to cope with usual stressful circumstances at work, nowadays, it is important to examine what kind of mental capacities of medical staff are adaptive in respect of a new type of stress – job insecurity. Special focus is put upon self structures as personality determinants and the role they have in coping.. The aim of the study was to determine the role of the self structures in active coping with job insecurity. It was supposed that the increasing integration of self structures leads to increasing use of active coping strategies. Perceived job insecurity was measured by The job insecurity perception scale (Knežević and Majstorović, 2013. The Ego Functioning Questionnaire (Majstorović, Legault and Green-Demers, 2008 was used to evaluate types of ego-functioning; coping were assessed by the Cybernetic coping scale (Edwards and Baglioni, 1993. In order to test the hypothesis the multivariate regression analysis was developed with self-regulation as predictor and active coping strategy as a criterion. A significant model F(3, 306 = 26,73, p < 0,001, was obtained with all the predictors selected as significant. The prediction directions were as expected - Integrated and Ego-investing self were positive predictors (β = 0,35, p < 0,001, and β = 0,16, p < 0,01, respectively, while the impersonal self singled out as a negative predictor (β = –0,13, p < 0,05. The results have shown that the development of self structures is valid predictor for the active coping of medical staff when facing with job insecurity.
Oliveira, Cátia; Laja, Pedro; Carvalho, Joana; Quinta Gomes, Ana; Vilarinho, Sandra; Janssen, Erick; Nobre, Pedro J
2014-11-01
Both emotions and cognitions seem to play a role in determining sexual arousal. However, no studies to date have tested the effects of self-reported thoughts on subjective sexual arousal and genital response using psychophysiological methods. The aim of the present study was to evaluate the role of self-reported thoughts and affect during exposure to erotic material in predicting subjective and genital responses in sexually healthy men. Twenty-seven men were presented with two explicit films, and genital responses, subjective sexual arousal, self-reported thoughts, and positive and negative affect were assessed. Men's genital responses, subjective sexual arousal, affective responses, and self-reported thoughts during exposure to sexual stimulus were measured. Regression analyses revealed that genital responses were predicted by self-reported thoughts (explaining 20% of the variance) but not by affect during exposure to erotic films. On the other hand, subjective sexual arousal was significantly predicted by both positive and negative affect (explaining 18% of the variance) and self-reported thoughts (explaining 37% of the variance). Follow-up analyses using the single predictors showed that "sexual arousal thoughts" were the only significant predictor of subjective response (β = 0.64; P < 0.01) and that "distracting/disengaging thoughts" were the best predictor of genital response (β = -0.51; P < 0.05). The findings of this study suggest that both affect and sexual arousal thoughts play an important role in men's subjective sexual response, whereas genital response seems to be better predicted by distracting thoughts. © 2014 International Society for Sexual Medicine.
Energy Technology Data Exchange (ETDEWEB)
Vrugt, Jasper A [Los Alamos National Laboratory; Hyman, James M [Los Alamos National Laboratory; Robinson, Bruce A [Los Alamos National Laboratory; Higdon, Dave [Los Alamos National Laboratory; Ter Braak, Cajo J F [NETHERLANDS; Diks, Cees G H [UNIV OF AMSTERDAM
2008-01-01
Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.
Lobato, Fran Sérgio
2017-01-01
This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification
Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.
MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.
Harmony Search Based Parameter Ensemble Adaptation for Differential Evolution
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Rammohan Mallipeddi
2013-01-01
Full Text Available In differential evolution (DE algorithm, depending on the characteristics of the problem at hand and the available computational resources, different strategies combined with a different set of parameters may be effective. In addition, a single, well-tuned combination of strategies and parameters may not guarantee optimal performance because different strategies combined with different parameter settings can be appropriate during different stages of the evolution. Therefore, various adaptive/self-adaptive techniques have been proposed to adapt the DE strategies and parameters during the course of evolution. In this paper, we propose a new parameter adaptation technique for DE based on ensemble approach and harmony search algorithm (HS. In the proposed method, an ensemble of parameters is randomly sampled which form the initial harmony memory. The parameter ensemble evolves during the course of the optimization process by HS algorithm. Each parameter combination in the harmony memory is evaluated by testing them on the DE population. The performance of the proposed adaptation method is evaluated using two recently proposed strategies (DE/current-to-pbest/bin and DE/current-to-gr_best/bin as basic DE frameworks. Numerical results demonstrate the effectiveness of the proposed adaptation technique compared to the state-of-the-art DE based algorithms on a set of challenging test problems (CEC 2005.
Adaptive local backlight dimming algorithm based on local histogram and image characteristics
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Burini, Nino; Korhonen, Jari
2013-01-01
-off between power consumption and image quality preservation than the other algorithms representing the state of the art among feature based backlight algorithms. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.......Liquid Crystal Display (LCDs) with Light Emitting Diode (LED) backlight is a very popular display technology, used for instance in television sets, monitors and mobile phones. This paper presents a new backlight dimming algorithm that exploits the characteristics of the target image......, such as the local histograms and the average pixel intensity of each backlight segment, to reduce the power consumption of the backlight and enhance image quality. The local histogram of the pixels within each backlight segment is calculated and, based on this average, an adaptive quantile value is extracted...
Higher order methods for burnup calculations with Bateman solutions
International Nuclear Information System (INIS)
Isotalo, A.E.; Aarnio, P.A.
2011-01-01
Highlights: → Average microscopic reaction rates need to be estimated at each step. → Traditional predictor-corrector methods use zeroth and first order predictions. → Increasing predictor order greatly improves results. → Increasing corrector order does not improve results. - Abstract: A group of methods for burnup calculations solves the changes in material compositions by evaluating an explicit solution to the Bateman equations with constant microscopic reaction rates. This requires predicting representative averages for the one-group cross-sections and flux during each step, which is usually done using zeroth and first order predictions for their time development in a predictor-corrector calculation. In this paper we present the results of using linear, rather than constant, extrapolation on the predictor and quadratic, rather than linear, interpolation on the corrector. Both of these are done by using data from the previous step, and thus do not affect the stepwise running time. The methods were tested by implementing them into the reactor physics code Serpent and comparing the results from four test cases to accurate reference results obtained with very short steps. Linear extrapolation greatly improved results for thermal spectra and should be preferred over the constant one currently used in all Bateman solution based burnup calculations. The effects of using quadratic interpolation on the corrector were, on the other hand, predominantly negative, although not enough so to conclusively decide between the linear and quadratic variants.
A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms.
Caldas, Rafael; Mundt, Marion; Potthast, Wolfgang; Buarque de Lima Neto, Fernando; Markert, Bernd
2017-09-01
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects. Copyright © 2017 Elsevier B.V. All rights reserved.
Logical NAND and NOR Operations Using Algorithmic Self-assembly of DNA Molecules
Wang, Yanfeng; Cui, Guangzhao; Zhang, Xuncai; Zheng, Yan
DNA self-assembly is the most advanced and versatile system that has been experimentally demonstrated for programmable construction of patterned systems on the molecular scale. It has been demonstrated that the simple binary arithmetic and logical operations can be computed by the process of self assembly of DNA tiles. Here we report a one-dimensional algorithmic self-assembly of DNA triple-crossover molecules that can be used to execute five steps of a logical NAND and NOR operations on a string of binary bits. To achieve this, abstract tiles were translated into DNA tiles based on triple-crossover motifs. Serving as input for the computation, long single stranded DNA molecules were used to nucleate growth of tiles into algorithmic crystals. Our method shows that engineered DNA self-assembly can be treated as a bottom-up design techniques, and can be capable of designing DNA computer organization and architecture.
Luque Salas, Bárbara; Yáñez Rodríguez, Virginia; Tabernero Urbieta, Carmen; Cuadrado, Esther
2017-02-01
This research aims to understand the role of coping strategies and self-efficacy expectations as predictors of life satisfaction in a sample of parents of boys and girls diagnosed with autistic spectrum disorder. A total of 129 parents (64 men and 65 women) answered a questionnaire on life-satisfaction, coping strategies and self-efficacy scales. Using a regression model, results show that the age of the child is associated with a lower level of satisfaction in parents. The results show that self-efficacy is the variable that best explains the level of satisfaction in mothers, while the use of problem solving explains a higher level of satisfaction in fathers. Men and women show similar levels of life satisfaction; however significant differences were found in coping strategies where women demonstrated higher expressing emotions and social support strategies than men. The development of functional coping strategies and of a high level of self-efficacy represents a key tool for adapting to caring for children with autism. Our results indicated the necessity of early intervention with parents to promote coping strategies, self-efficacy and high level of life satisfaction.
Development of a multiple HTS current lead assembly for corrector magnets application
International Nuclear Information System (INIS)
Wu, J.L.; Dederer, J.T.; Singh, S.K.
1994-01-01
Vapor-cooled current leads used for transmitting power to superconducting power equipment such as the corrector magnets in the SSC spools can introduce a significant heat leak into the cryostat which results in cryogen boil-off. Replenishing the boil-off or refrigerating and liquefying the vapors associated with the cooling of these leads may constitute a significant portion of the operating cost and/or the capital investment of the power equipment. Theoretical studies and experiments have demonstrated that the heat leak introduced by a current lead can be significantly reduced by using ceramic high temperature superconductor (HTSC) as part of the conductor in the current leads. A HTSC reduces heat leak in a current lead by being superconducting in the temperature range below its critical temperature and by having a low temperature thermal conductivity which is generally orders of magnitude lower than the copper alloys commonly used as the current lead conductors. This combination reduces Joule heating and heat conduction, resulting in lower heat leak to the cryostat. To demonstrate the advantages and large scale application of this technology, Westinghouse Science ampersand Technology Center has continued its efforts in High Temperature Superconducting (HTS) current lead development. The efforts include qualification testing and selection of commercial sources of HTSC for current leads and the successful development of a 12 x 100 A multiple HTS current lead assembly prototype for SSC Corrector Element Power Lead application. The efforts on the design, fabrication and testing of the multiple HTS lead assembly is reported below
Trans gene regulation in adaptive evolution: a genetic algorithm model.
Behera, N; Nanjundiah, V
1997-09-21
This is a continuation of earlier studies on the evolution of infinite populations of haploid genotypes within a genetic algorithm framework. We had previously explored the evolutionary consequences of the existence of indeterminate-"plastic"-loci, where a plastic locus had a finite probability in each generation of functioning (being switched "on") or not functioning (being switched "off"). The relative probabilities of the two outcomes were assigned on a stochastic basis. The present paper examines what happens when the transition probabilities are biased by the presence of regulatory genes. We find that under certain conditions regulatory genes can improve the adaptation of the population and speed up the rate of evolution (on occasion at the cost of lowering the degree of adaptation). Also, the existence of regulatory loci potentiates selection in favour of plasticity. There is a synergistic effect of regulatory genes on plastic alleles: the frequency of such alleles increases when regulatory loci are present. Thus, phenotypic selection alone can be a potentiating factor in a favour of better adaptation. Copyright 1997 Academic Press Limited.
Gallo, Maria F; Steiner, Markus J; Hobbs, Marcia M; Weaver, Mark A; Hoke, Theresa Hatzell; Van Damme, Kathleen; Jamieson, Denise J; Macaluso, Maurizio
2010-12-01
Research on the determinants of condom use and condom non-use generally has relied on self-reported data with questionable validity. We identified predictors of recent, unprotected sex among 331 female sex workers in Madagascar using two outcome measures: self-reports of unprotected sex within the past 48 h and detection of prostate-specific antigen (PSA), a biological marker of recent semen exposure. Multivariable logistic regression revealed that self-reported unprotected sex was associated with three factors: younger age, having a sipa (emotional partner) in the prior seven days, and no current use of hormonal contraception. The sole factor related to having PSA detected was prevalent chlamydial infection (adjusted odds ratio, 4.5; 95% confidence interval, 2.0-10.1). Differences in predictors identified suggest that determinants of unprotected sex, based on self-reported behaviors, might not correlate well with risk of semen exposure. Caution must be taken when interpreting self-reported sexual behavior measures or when adjusting for them in analyses evaluating interventions for the prevention of HIV/STIs.
Directory of Open Access Journals (Sweden)
Huan Zhou
2017-09-01
Full Text Available Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.
Modifications of the branch-and-bound algorithm for application in constrained adaptive testing
Veldkamp, Bernard P.
2000-01-01
A mathematical programming approach is presented for computer adaptive testing (CAT) with many constraints on the item and test attributes. Because mathematical programming problems have to be solved while the examinee waits for the next item, a fast implementation of the Branch-and-Bound algorithm
Hunter, H. E.
1972-01-01
The Avco Data Analysis and Prediction Techniques (ADAPT) were employed to determine laws capable of detecting failures in a heat plant up to three days in advance of the occurrence of the failure. The projected performance of algorithms yielded a detection probability of 90% with false alarm rates of the order of 1 per year for a sample rate of 1 per day with each detection, followed by 3 hourly samplings. This performance was verified on 173 independent test cases. The program also demonstrated diagnostic algorithms and the ability to predict the time of failure to approximately plus or minus 8 hours up to three days in advance of the failure. The ADAPT programs produce simple algorithms which have a unique possibility of a relatively low cost updating procedure. The algorithms were implemented on general purpose computers at Kennedy Space Flight Center and tested against current data.
An adaptive N-body algorithm of optimal order
International Nuclear Information System (INIS)
Pruett, C. David.; Rudmin, Joseph W.; Lacy, Justin M.
2003-01-01
Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. Test cases for both benign and chaotic N-body systems reveal that optimal order is dynamic. That is, in addition to dependency upon N and the desired accuracy, optimal order depends upon the configuration of the bodies at any instant
Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.
2014-01-01
The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.
Directory of Open Access Journals (Sweden)
F. F. Ngwane
2015-01-01
Full Text Available We propose a block hybrid trigonometrically fitted (BHT method, whose coefficients are functions of the frequency and the step-size for directly solving general second-order initial value problems (IVPs, including systems arising from the semidiscretization of hyperbolic Partial Differential Equations (PDEs, such as the Telegraph equation. The BHT is formulated from eight discrete hybrid formulas which are provided by a continuous two-step hybrid trigonometrically fitted method with two off-grid points. The BHT is implemented in a block-by-block fashion; in this way, the method does not suffer from the disadvantages of requiring starting values and predictors which are inherent in predictor-corrector methods. The stability property of the BHT is discussed and the performance of the method is demonstrated on some numerical examples to show accuracy and efficiency advantages.
A Study of Self-efficacy in Patients with Coronary Artery Disease and Its Predictors
Ezzat Paryad; Touba Hosseinzade; Ehsan Kazemnejad; Shahla Asiri
2013-01-01
Background and Objectives: Enhancement of empowerment in patients with coronary artery disease has a major role in the promotion of their health behaviors. Promotion of self-efficacy is a one of the ways for improving this skill, which has a significant impact on improving patients’ condition and on preventing complications and readmission. The objective of this study was to determine the predictors of general, exercise, and diet behavior self-efficacies in coronary artery disease patients.Me...
International Nuclear Information System (INIS)
Zhou Shumin; Sun Yamin; Tang Bin
2007-01-01
In order to enhance the time synchronization quality of the distributed system, a time synchronization algorithm of distributed system based on server time-revise and workstation self-adjust is proposed. The time-revise cycle and self-adjust process is introduced in the paper. The algorithm reduces network flow effectively and enhances the quality of clock-synchronization. (authors)
Zhu, Jiemin; Chan, Wai Chi Sally; Zhou, Xiuzhu; Ye, Benlan; He, Hong-Gu
2014-06-01
to examine breast feeding self-efficacy and identify its predictors among expectant Chinese mothers in the antenatal period. a cross-sectional descriptive questionnaire survey was conducted in the antenatal clinics of three university hospitals in China between September and December 2011. expectant mothers planning to breast feed, and who were at least 18 years of age, expecting a single, healthy, full-term baby, and competent in Mandarin (n=201). a socio-demographic data sheet, the Chinese version of the Breastfeeding Self-Efficacy Scale, and the Perceived Social Support Scale. the expectant Chinese mothers reported moderate levels of breast feeding self-efficacy. Expectant mothers who had had previous experience in breast feeding, who had watched other mothers breast feed their infants, or who had made the decision to breast feed earlier reported higher breast feeding self-efficacy. Expectant mothers' perceived social support, perceived attitude of significant others, including husband, mothers, and friends, towards breast feeding are correlated with breast feeding self-efficacy. The best-fit regression analysis revealed five variables that explained 34% of the variance in breast feeding self-efficacy in the antenatal period: perceived social support, previous experience of breast feeding, previous experience of watching others breast feed, timing of maternal decision to breast feed, and perceived husband's attitude towards breast feeding. this study highlighted the importance of improving Chinese mothers' breast feeding self-efficacy by considering the main predictors found in this study. health care professionals could develop strategies to promote breast feeding self-efficacy, such as providing opportunities for expectant mothers to learn from others' successful experience, adopt a family-centred approach in the provision of breast feeding education, provide breast feeding education at the beginning of pregnancy or even earlier, and rally comprehensive social