Gradient descent learning algorithm overview: a general dynamical systems perspective.
Baldi, P
1995-01-01
Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.
Momentum-weighted conjugate gradient descent algorithm for gradient coil optimization.
Lu, Hanbing; Jesmanowicz, Andrzej; Li, Shi-Jiang; Hyde, James S
2004-01-01
MRI gradient coil design is a type of nonlinear constrained optimization. A practical problem in transverse gradient coil design using the conjugate gradient descent (CGD) method is that wire elements move at different rates along orthogonal directions (r, phi, z), and tend to cross, breaking the constraints. A momentum-weighted conjugate gradient descent (MW-CGD) method is presented to overcome this problem. This method takes advantage of the efficiency of the CGD method combined with momentum weighting, which is also an intrinsic property of the Levenberg-Marquardt algorithm, to adjust step sizes along the three orthogonal directions. A water-cooled, 12.8 cm inner diameter, three axis torque-balanced gradient coil for rat imaging was developed based on this method, with an efficiency of 2.13, 2.08, and 4.12 mT.m(-1).A(-1) along X, Y, and Z, respectively. Experimental data demonstrate that this method can improve efficiency by 40% and field uniformity by 27%. This method has also been applied to the design of a gradient coil for the human brain, employing remote current return paths. The benefits of this design include improved gradient field uniformity and efficiency, with a shorter length than gradient coil designs using coaxial return paths. Copyright 2003 Wiley-Liss, Inc.
Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals
Cho, Myung
2016-06-24
We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.
Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals
Cho, Myung; Cai, Jian-Feng; Liu, Suhui; Eldar, Yonina C.; Xu, Weiyu
2016-01-01
We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.
Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants
Parlos, A.G.; Atiya, Amir; Chong, K.T.
1991-01-01
A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process
Qiuyu Wang
2014-01-01
descent method at first finite number of steps and then by conjugate gradient method subsequently. Under some appropriate conditions, we show that the algorithm converges globally. Numerical experiments and comparisons by using some box-constrained problems from CUTEr library are reported. Numerical comparisons illustrate that the proposed method is promising and competitive with the well-known method—L-BFGS-B.
MODIFIED ARMIJO RULE ON GRADIENT DESCENT AND CONJUGATE GRADIENT
ZURAIDAH FITRIAH
2017-10-01
Full Text Available Armijo rule is an inexact line search method to determine step size in some descent method to solve unconstrained local optimization. Modified Armijo was introduced to increase the numerical performance of several descent algorithms that applying this method. The basic difference of Armijo and its modified are in existence of a parameter and estimating the parameter that is updated in every iteration. This article is comparing numerical solution and time of computation of gradient descent and conjugate gradient hybrid Gilbert-Nocedal (CGHGN that applying modified Armijo rule. From program implementation in Matlab 6, it's known that gradient descent was applying modified Armijo more effectively than CGHGN from one side: iteration needed to reach some norm of the gradient (input by the user. The amount of iteration was representing how long the step size of each algorithm in each iteration. In another side, time of computation has the same conclusion.
Huiru Zhao
2016-09-01
Full Text Available An important goal of China’s electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs and demanders (represented by DisCOs compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP and each rival’s bidding strategy as common knowledge (in dynamic game market models, and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models, which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n. The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant’s profit or social welfare
Error analysis of stochastic gradient descent ranking.
Chen, Hong; Tang, Yi; Li, Luoqing; Yuan, Yuan; Li, Xuelong; Tang, Yuanyan
2013-06-01
Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.
Applying Gradient Descent in Convolutional Neural Networks
Cui, Nan
2018-04-01
With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.
On Nonconvex Decentralized Gradient Descent
2016-08-01
and J. Bolte, On the convergence of the proximal algorithm for nonsmooth functions involving analytic features, Math . Program., 116: 5-16, 2009. [2] H...splitting, and regularized Gauss-Seidel methods, Math . Pro- gram., Ser. A, 137: 91-129, 2013. [3] P. Bianchi and J. Jakubowicz, Convergence of a multi-agent...subgradient method under random communication topologies , IEEE J. Sel. Top. Signal Process., 5:754-771, 2011. [11] A. Nedic and A. Ozdaglar, Distributed
Practical gradient-descent for memristive crossbars
Nair, Manu V; Dudek, Piotr
2015-01-01
This paper discusses implementations of gradientdescent based learning algorithms on memristive crossbar arrays. The Unregulated Step Descent (USD) is described as a practical algorithm for feed-forward on-line training of large crossbar arrays. It allows fast feed-forward fully parallel on-line hardware based learning, without requiring accurate models of the memristor behaviour and precise control of the programming pulses. The effect of device parameters, training parameters, and device va...
Gradient descent learning in and out of equilibrium
Caticha, Nestor; Araujo de Oliveira, Evaldo
2001-01-01
Relations between the off thermal equilibrium dynamical process of on-line learning and the thermally equilibrated off-line learning are studied for potential gradient descent learning. The approach of Opper to study on-line Bayesian algorithms is used for potential based or maximum likelihood learning. We look at the on-line learning algorithm that best approximates the off-line algorithm in the sense of least Kullback-Leibler information loss. The closest on-line algorithm works by updating the weights along the gradient of an effective potential, which is different from the parent off-line potential. A few examples are analyzed and the origin of the potential annealing is discussed
Several Guaranteed Descent Conjugate Gradient Methods for Unconstrained Optimization
San-Yang Liu
2014-01-01
Full Text Available This paper investigates a general form of guaranteed descent conjugate gradient methods which satisfies the descent condition gkTdk≤-1-1/4θkgk2 θk>1/4 and which is strongly convergent whenever the weak Wolfe line search is fulfilled. Moreover, we present several specific guaranteed descent conjugate gradient methods and give their numerical results for large-scale unconstrained optimization.
Gradient descent for robust kernel-based regression
Guo, Zheng-Chu; Hu, Ting; Shi, Lei
2018-06-01
In this paper, we study the gradient descent algorithm generated by a robust loss function over a reproducing kernel Hilbert space (RKHS). The loss function is defined by a windowing function G and a scale parameter σ, which can include a wide range of commonly used robust losses for regression. There is still a gap between theoretical analysis and optimization process of empirical risk minimization based on loss: the estimator needs to be global optimal in the theoretical analysis while the optimization method can not ensure the global optimality of its solutions. In this paper, we aim to fill this gap by developing a novel theoretical analysis on the performance of estimators generated by the gradient descent algorithm. We demonstrate that with an appropriately chosen scale parameter σ, the gradient update with early stopping rules can approximate the regression function. Our elegant error analysis can lead to convergence in the standard L 2 norm and the strong RKHS norm, both of which are optimal in the mini-max sense. We show that the scale parameter σ plays an important role in providing robustness as well as fast convergence. The numerical experiments implemented on synthetic examples and real data set also support our theoretical results.
Optimization algorithm based on densification and dynamic canonical descent
Bousson, K.; Correia, S. D.
2006-07-01
Stochastic methods have gained some popularity in global optimization in that most of them do not assume the cost functions to be differentiable. They have capabilities to avoid being trapped by local optima, and may converge even faster than gradient-based optimization methods on some problems. The present paper proposes an optimization method, which reduces the search space by means of densification curves, coupled with the dynamic canonical descent algorithm. The performances of the new method are shown on several known problems classically used for testing optimization algorithms, and proved to outperform competitive algorithms such as simulated annealing and genetic algorithms.
Robust Fully Distributed Minibatch Gradient Descent with Privacy Preservation
Gábor Danner
2018-01-01
Full Text Available Privacy and security are among the highest priorities in data mining approaches over data collected from mobile devices. Fully distributed machine learning is a promising direction in this context. However, it is a hard problem to design protocols that are efficient yet provide sufficient levels of privacy and security. In fully distributed environments, secure multiparty computation (MPC is often applied to solve these problems. However, in our dynamic and unreliable application domain, known MPC algorithms are not scalable or not robust enough. We propose a light-weight protocol to quickly and securely compute the sum query over a subset of participants assuming a semihonest adversary. During the computation the participants learn no individual values. We apply this protocol to efficiently calculate the sum of gradients as part of a fully distributed minibatch stochastic gradient descent algorithm. The protocol achieves scalability and robustness by exploiting the fact that in this application domain a “quick and dirty” sum computation is acceptable. We utilize the Paillier homomorphic cryptosystem as part of our solution combined with extreme lossy gradient compression to make the cost of the cryptographic algorithms affordable. We demonstrate both theoretically and experimentally, based on churn statistics from a real smartphone trace, that the protocol is indeed practically viable.
Block-Based Gradient Descent for Local Backlight Dimming and Flicker Reduction
Burini, Nino; Mantel, Claire; Nadernejad, Ehsan
2014-01-01
Local backlight dimming technology is a two-fold improvement for LED backlit LCD screens that allows to reduce power consumption and increase visual quality. This paper presents a fast version of an iterative backlight dimming algorithm based on gradient descent search. The speed is increased...
Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent
Ibnkahla Mohamed
2003-01-01
Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.
Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent
De Sa, Christopher; Feldman, Matthew; Ré, Christopher; Olukotun, Kunle
2018-01-01
Stochastic gradient descent (SGD) is one of the most popular numerical algorithms used in machine learning and other domains. Since this is likely to continue for the foreseeable future, it is important to study techniques that can make it run fast on parallel hardware. In this paper, we provide the first analysis of a technique called Buckwild! that uses both asynchronous execution and low-precision computation. We introduce the DMGC model, the first conceptualization of the parameter space that exists when implementing low-precision SGD, and show that it provides a way to both classify these algorithms and model their performance. We leverage this insight to propose and analyze techniques to improve the speed of low-precision SGD. First, we propose software optimizations that can increase throughput on existing CPUs by up to 11×. Second, we propose architectural changes, including a new cache technique we call an obstinate cache, that increase throughput beyond the limits of current-generation hardware. We also implement and analyze low-precision SGD on the FPGA, which is a promising alternative to the CPU for future SGD systems. PMID:29391770
Preconditioned stochastic gradient descent optimisation for monomodal image registration
Klein, S.; Staring, M.; Andersson, J.P.; Pluim, J.P.W.; Fichtinger, G.; Martel, A.; Peters, T.
2011-01-01
We present a stochastic optimisation method for intensity-based monomodal image registration. The method is based on a Robbins-Monro stochastic gradient descent method with adaptive step size estimation, and adds a preconditioning matrix. The derivation of the pre-conditioner is based on the
Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation
Zhou, XueFei
2018-04-01
With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.
Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm
Desmal, Abdulla; Bagci, Hakan
2017-01-01
steepest descent algorithm. The algorithm uses a projection operator to enforce the sparsity constraint by thresholding the solution at every iteration. Thresholding level and iteration step are selected carefully to increase the efficiency without
Reference-shaping adaptive control by using gradient descent optimizers.
Baris Baykant Alagoz
Full Text Available This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal for tracking of the controlled system output to a reference model output. The second process is the adaptation optimizer that performs for estimation of a time-varying adaptation gain, and it contributes to improvement of control signal generation. Numerical update equations derived for adaptation gain and control signal perform gradient descent optimization in order to decrease the model mismatch errors. To reduce noise sensitivity of the system, a dead zone rule is applied to the adaptation process. Simulation examples show the performance of the proposed Reference-Shaping Adaptive Control (RSAC method for several test scenarios. An experimental study demonstrates application of method for rotor control.
Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage
Vahab Akbarzadeh
2014-08-01
Full Text Available We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared.
Accelerating deep neural network training with inconsistent stochastic gradient descent.
Wang, Linnan; Yang, Yi; Min, Renqiang; Chakradhar, Srimat
2017-09-01
Stochastic Gradient Descent (SGD) updates Convolutional Neural Network (CNN) with a noisy gradient computed from a random batch, and each batch evenly updates the network once in an epoch. This model applies the same training effort to each batch, but it overlooks the fact that the gradient variance, induced by Sampling Bias and Intrinsic Image Difference, renders different training dynamics on batches. In this paper, we develop a new training strategy for SGD, referred to as Inconsistent Stochastic Gradient Descent (ISGD) to address this problem. The core concept of ISGD is the inconsistent training, which dynamically adjusts the training effort w.r.t the loss. ISGD models the training as a stochastic process that gradually reduces down the mean of batch's loss, and it utilizes a dynamic upper control limit to identify a large loss batch on the fly. ISGD stays on the identified batch to accelerate the training with additional gradient updates, and it also has a constraint to penalize drastic parameter changes. ISGD is straightforward, computationally efficient and without requiring auxiliary memories. A series of empirical evaluations on real world datasets and networks demonstrate the promising performance of inconsistent training. Copyright © 2017 Elsevier Ltd. All rights reserved.
Knox, C.E.; Vicroy, D.D.; Simmon, D.A.
1985-05-01
A simple, airborne, flight-management descent algorithm was developed and programmed into a small programmable calculator. The algorithm may be operated in either a time mode or speed mode. The time mode was designed to aid the pilot in planning and executing a fuel-conservative descent to arrive at a metering fix at a time designated by the air traffic control system. The speed model was designed for planning fuel-conservative descents when time is not a consideration. The descent path for both modes was calculated for a constant with considerations given for the descent Mach/airspeed schedule, gross weight, wind, wind gradient, and nonstandard temperature effects. Flight tests, using the algorithm on the programmable calculator, showed that the open-loop guidance could be useful to airline flight crews for planning and executing fuel-conservative descents.
Ionospheric forecasting model using fuzzy logic-based gradient descent method
D. Venkata Ratnam
2017-09-01
Full Text Available Space weather phenomena cause satellite to ground or satellite to aircraft transmission outages over the VHF to L-band frequency range, particularly in the low latitude region. Global Positioning System (GPS is primarily susceptible to this form of space weather. Faulty GPS signals are attributed to ionospheric error, which is a function of Total Electron Content (TEC. Importantly, precise forecasts of space weather conditions and appropriate hazard observant cautions required for ionospheric space weather observations are limited. In this paper, a fuzzy logic-based gradient descent method has been proposed to forecast the ionospheric TEC values. In this technique, membership functions have been tuned based on the gradient descent estimated values. The proposed algorithm has been tested with the TEC data of two geomagnetic storms in the low latitude station of KL University, Guntur, India (16.44°N, 80.62°E. It has been found that the gradient descent method performs well and the predicted TEC values are close to the original TEC measurements.
A conjugate gradient method with descent properties under strong Wolfe line search
Zull, N.; ‘Aini, N.; Shoid, S.; Ghani, N. H. A.; Mohamed, N. S.; Rivaie, M.; Mamat, M.
2017-09-01
The conjugate gradient (CG) method is one of the optimization methods that are often used in practical applications. The continuous and numerous studies conducted on the CG method have led to vast improvements in its convergence properties and efficiency. In this paper, a new CG method possessing the sufficient descent and global convergence properties is proposed. The efficiency of the new CG algorithm relative to the existing CG methods is evaluated by testing them all on a set of test functions using MATLAB. The tests are measured in terms of iteration numbers and CPU time under strong Wolfe line search. Overall, this new method performs efficiently and comparable to the other famous methods.
Dong Bing; Ren Deqing; Zhang Xi
2011-01-01
An adaptive optics (AO) system based on a stochastic parallel gradient descent (SPGD) algorithm is proposed to reduce the speckle noises in the optical system of a stellar coronagraph in order to further improve the contrast. The principle of the SPGD algorithm is described briefly and a metric suitable for point source imaging optimization is given. The feasibility and good performance of the SPGD algorithm is demonstrated by an experimental system featured with a 140-actuator deformable mirror and a Hartmann-Shark wavefront sensor. Then the SPGD based AO is applied to a liquid crystal array (LCA) based coronagraph to improve the contrast. The LCA can modulate the incoming light to generate a pupil apodization mask of any pattern. A circular stepped pattern is used in our preliminary experiment and the image contrast shows improvement from 10 -3 to 10 -4.5 at an angular distance of 2λ/D after being corrected by SPGD based AO.
Fractional-order gradient descent learning of BP neural networks with Caputo derivative.
Wang, Jian; Wen, Yanqing; Gou, Yida; Ye, Zhenyun; Chen, Hua
2017-05-01
Fractional calculus has been found to be a promising area of research for information processing and modeling of some physical systems. In this paper, we propose a fractional gradient descent method for the backpropagation (BP) training of neural networks. In particular, the Caputo derivative is employed to evaluate the fractional-order gradient of the error defined as the traditional quadratic energy function. The monotonicity and weak (strong) convergence of the proposed approach are proved in detail. Two simulations have been implemented to illustrate the performance of presented fractional-order BP algorithm on three small datasets and one large dataset. The numerical simulations effectively verify the theoretical observations of this paper as well. Copyright © 2017 Elsevier Ltd. All rights reserved.
Determination of accelerated factors in gradient descent iterations based on Taylor's series
Petrović Milena
2017-01-01
Full Text Available In this paper the efficiency of accelerated gradient descent methods regarding the way of determination of accelerated factor is considered. Due to the previous researches we assert that the use of Taylor's series of posed gradient descent iteration in calculation of accelerated parameter gives better final results than some other choices. We give a comparative analysis of efficiency of several methods with different approaches in obtaining accelerated parameter. According to the achieved results of numerical experiments we make a conclusion about the one of the most optimal way in defining accelerated parameter in accelerated gradient descent schemes.
Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent.
Guan, Naiyang; Tao, Dacheng; Luo, Zhigang; Yuan, Bo
2011-07-01
Nonnegative matrix factorization (NMF) has become a popular data-representation method and has been widely used in image processing and pattern-recognition problems. This is because the learned bases can be interpreted as a natural parts-based representation of data and this interpretation is consistent with the psychological intuition of combining parts to form a whole. For practical classification tasks, however, NMF ignores both the local geometry of data and the discriminative information of different classes. In addition, existing research results show that the learned basis is unnecessarily parts-based because there is neither explicit nor implicit constraint to ensure the representation parts-based. In this paper, we introduce the manifold regularization and the margin maximization to NMF and obtain the manifold regularized discriminative NMF (MD-NMF) to overcome the aforementioned problems. The multiplicative update rule (MUR) can be applied to optimizing MD-NMF, but it converges slowly. In this paper, we propose a fast gradient descent (FGD) to optimize MD-NMF. FGD contains a Newton method that searches the optimal step length, and thus, FGD converges much faster than MUR. In addition, FGD includes MUR as a special case and can be applied to optimizing NMF and its variants. For a problem with 165 samples in R(1600), FGD converges in 28 s, while MUR requires 282 s. We also apply FGD in a variant of MD-NMF and experimental results confirm its efficiency. Experimental results on several face image datasets suggest the effectiveness of MD-NMF.
Loizou, Nicolas
2017-12-27
In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent. We prove global nonassymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates (in L2 sense), and dual function values. We also show that the primal iterates converge at an accelerated linear rate in the L1 sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems.
Loizou, Nicolas; Richtarik, Peter
2017-01-01
In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent. We prove global nonassymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates (in L2 sense), and dual function values. We also show that the primal iterates converge at an accelerated linear rate in the L1 sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems.
Jia, Ningning; Lam, Edmund Y
2010-01-01
Inverse lithography technology (ILT) synthesizes photomasks by solving an inverse imaging problem through optimization of an appropriate functional. Much effort on ILT is dedicated to deriving superior masks at a nominal process condition. However, the lower k 1 factor causes the mask to be more sensitive to process variations. Robustness to major process variations, such as focus and dose variations, is desired. In this paper, we consider the focus variation as a stochastic variable, and treat the mask design as a machine learning problem. The stochastic gradient descent approach, which is a useful tool in machine learning, is adopted to train the mask design. Compared with previous work, simulation shows that the proposed algorithm is effective in producing robust masks
Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm
Desmal, Abdulla
2017-04-03
An efficient electromagnetic inversion scheme for imaging sparse 3-D domains is proposed. The scheme achieves its efficiency and accuracy by integrating two concepts. First, the nonlinear optimization problem is constrained using L₀ or L₁-norm of the solution as the penalty term to alleviate the ill-posedness of the inverse problem. The resulting Tikhonov minimization problem is solved using nonlinear Landweber iterations (NLW). Second, the efficiency of the NLW is significantly increased using a steepest descent algorithm. The algorithm uses a projection operator to enforce the sparsity constraint by thresholding the solution at every iteration. Thresholding level and iteration step are selected carefully to increase the efficiency without sacrificing the convergence of the algorithm. Numerical results demonstrate the efficiency and accuracy of the proposed imaging scheme in reconstructing sparse 3-D dielectric profiles.
Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer
Jing Yang
2018-03-01
Full Text Available This study proposes a modified convolutional neural network (CNN algorithm that is based on dropout and the stochastic gradient descent (SGD optimizer (MCNN-DS, after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1 For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2 Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3 For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%.
Andreea Koreanschi
2017-02-01
Full Text Available In this paper, an ‘in-house’ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm’s performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods, namely the artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated.
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.
Gonglin Yuan
Full Text Available Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1 βk ≥ 0 2 the search direction has the trust region property without the use of any line search method 3 the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.
Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou
2015-01-01
Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1) βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.
Non-homogeneous updates for the iterative coordinate descent algorithm
Yu, Zhou; Thibault, Jean-Baptiste; Bouman, Charles A.; Sauer, Ken D.; Hsieh, Jiang
2007-02-01
Statistical reconstruction methods show great promise for improving resolution, and reducing noise and artifacts in helical X-ray CT. In fact, statistical reconstruction seems to be particularly valuable in maintaining reconstructed image quality when the dosage is low and the noise is therefore high. However, high computational cost and long reconstruction times remain as a barrier to the use of statistical reconstruction in practical applications. Among the various iterative methods that have been studied for statistical reconstruction, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a novel method for further speeding the convergence of the ICD algorithm, and therefore reducing the overall reconstruction time for statistical reconstruction. The method, which we call nonhomogeneous iterative coordinate descent (NH-ICD) uses spatially non-homogeneous updates to speed convergence by focusing computation where it is most needed. Experimental results with real data indicate that the method speeds reconstruction by roughly a factor of two for typical 3D multi-slice geometries.
On Scalable Deep Learning and Parallelizing Gradient Descent
AUTHOR|(CDS)2129036; Möckel, Rico; Baranowski, Zbigniew; Canali, Luca
Speeding up gradient based methods has been a subject of interest over the past years with many practical applications, especially with respect to Deep Learning. Despite the fact that many optimizations have been done on a hardware level, the convergence rate of very large models remains problematic. Therefore, data parallel methods next to mini-batch parallelism have been suggested to further decrease the training time of parameterized models using gradient based methods. Nevertheless, asynchronous optimization was considered too unstable for practical purposes due to a lacking understanding of the underlying mechanisms. Recently, a theoretical contribution has been made which defines asynchronous optimization in terms of (implicit) momentum due to the presence of a queuing model of gradients based on past parameterizations. This thesis mainly builds upon this work to construct a better understanding why asynchronous optimization shows proportionally more divergent behavior when the number of parallel worker...
San-Yang Liu
2014-01-01
Full Text Available Two unified frameworks of some sufficient descent conjugate gradient methods are considered. Combined with the hyperplane projection method of Solodov and Svaiter, they are extended to solve convex constrained nonlinear monotone equations. Their global convergence is proven under some mild conditions. Numerical results illustrate that these methods are efficient and can be applied to solve large-scale nonsmooth equations.
Chein-Shan Liu
2012-04-01
Full Text Available It is well known that the numerical algorithms of the steepest descent method (SDM, and the conjugate gradient method (CGM are effective for solving well-posed linear systems. However, they are vulnerable to noisy disturbance for solving ill-posed linear systems. We propose the modifications of SDM and CGM, namely the modified steepest descent method (MSDM, and the modified conjugate gradient method (MCGM. The starting point is an invariant manifold defined in terms of a minimum functional and a fictitious time-like variable; however, in the final stage we can derive a purely iterative algorithm including an acceleration parameter. Through the Hopf bifurcation, this parameter indeed plays a major role to switch the situation of slow convergence to a new situation that the functional is stepwisely decreased very fast. Several numerical examples are examined and compared with exact solutions, revealing that the new algorithms of MSDM and MCGM have good computational efficiency and accuracy, even for the highly ill-conditioned linear equations system with a large noise being imposed on the given data.
Afan Galih Salman
2011-06-01
Full Text Available The artificial neural network (ANN technology in rainfall prediction can be done using the learning approach. The ANN prediction accuracy is measured by the determination coefficient (R2 and root mean square error (RMSE. This research implements Elman’s Recurrent ANN which is heuristically optimized based on el-nino southern oscilation (ENSO variables: wind, southern oscillation index (SOI, sea surface temperatur (SST dan outgoing long wave radiation (OLR to forecast regional monthly rainfall in Bongan Bali. The heuristic learning optimization done is basically a performance development of standard gradient descent learning algorithm into training algorithms: gradient descent momentum and adaptive learning rate. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 leap 74,6% while the second data group that is 50% training data and 50% testing data produce the maximum R2 leap 49,8%.
Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems
Scherrer, Chad; Halappanavar, Mahantesh; Tewari, Ambuj; Haglin, David J.
2012-07-03
We present a generic framework for parallel coordinate descent (CD) algorithms that has as special cases the original sequential algorithms of Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm of Bradley et al. We introduce two novel parallel algorithms that are also special cases---Thread-Greedy CD and Coloring-Based CD---and give performance measurements for an OpenMP implementation of these.
Higher-order force gradient symplectic algorithms
Chin, Siu A.; Kidwell, Donald W.
2000-12-01
We show that a recently discovered fourth order symplectic algorithm, which requires one evaluation of force gradient in addition to three evaluations of the force, when iterated to higher order, yielded algorithms that are far superior to similarly iterated higher order algorithms based on the standard Forest-Ruth algorithm. We gauge the accuracy of each algorithm by comparing the step-size independent error functions associated with energy conservation and the rotation of the Laplace-Runge-Lenz vector when solving a highly eccentric Kepler problem. For orders 6, 8, 10, and 12, the new algorithms are approximately a factor of 103, 104, 104, and 105 better.
Conjugate Gradient Algorithms For Manipulator Simulation
Fijany, Amir; Scheid, Robert E.
1991-01-01
Report discusses applicability of conjugate-gradient algorithms to computation of forward dynamics of robotic manipulators. Rapid computation of forward dynamics essential to teleoperation and other advanced robotic applications. Part of continuing effort to find algorithms meeting requirements for increased computational efficiency and speed. Method used for iterative solution of systems of linear equations.
Izumi, K. H.; Thompson, J. L.; Groce, J. L.; Schwab, R. W.
1986-01-01
The design requirements for a 4D path definition algorithm are described. These requirements were developed for the NASA ATOPS as an extension of the Local Flow Management/Profile Descent algorithm. They specify the processing flow, functional and data architectures, and system input requirements, and recommended the addition of a broad path revision (reinitialization) function capability. The document also summarizes algorithm design enhancements and the implementation status of the algorithm on an in-house PDP-11/70 computer. Finally, the requirements for the pilot-computer interfaces, the lateral path processor, and guidance and steering function are described.
Conjugate gradient algorithms using multiple recursions
Barth, T.; Manteuffel, T.
1996-12-31
Much is already known about when a conjugate gradient method can be implemented with short recursions for the direction vectors. The work done in 1984 by Faber and Manteuffel gave necessary and sufficient conditions on the iteration matrix A, in order for a conjugate gradient method to be implemented with a single recursion of a certain form. However, this form does not take into account all possible recursions. This became evident when Jagels and Reichel used an algorithm of Gragg for unitary matrices to demonstrate that the class of matrices for which a practical conjugate gradient algorithm exists can be extended to include unitary and shifted unitary matrices. The implementation uses short double recursions for the direction vectors. This motivates the study of multiple recursion algorithms.
Gradient algorithm applied to laboratory quantum control
Roslund, Jonathan; Rabitz, Herschel
2009-01-01
The exploration of a quantum control landscape, which is the physical observable as a function of the control variables, is fundamental for understanding the ability to perform observable optimization in the laboratory. For high control variable dimensions, trajectory-based methods provide a means for performing such systematic explorations by exploiting the measured gradient of the observable with respect to the control variables. This paper presents a practical, robust, easily implemented statistical method for obtaining the gradient on a general quantum control landscape in the presence of noise. In order to demonstrate the method's utility, the experimentally measured gradient is utilized as input in steepest-ascent trajectories on the landscapes of three model quantum control problems: spectrally filtered and integrated second harmonic generation as well as excitation of atomic rubidium. The gradient algorithm achieves efficiency gains of up to approximately three times that of the standard genetic algorithm and, as such, is a promising tool for meeting quantum control optimization goals as well as landscape analyses. The landscape trajectories directed by the gradient should aid in the continued investigation and understanding of controlled quantum phenomena.
An online supervised learning method based on gradient descent for spiking neurons.
Xu, Yan; Yang, Jing; Zhong, Shuiming
2017-09-01
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.
Tripathi, Ashish; McNulty, Ian; Shpyrko, Oleg G
2014-01-27
Ptychographic coherent x-ray diffractive imaging is a form of scanning microscopy that does not require optics to image a sample. A series of scanned coherent diffraction patterns recorded from multiple overlapping illuminated regions on the sample are inverted numerically to retrieve its image. The technique recovers the phase lost by detecting the diffraction patterns by using experimentally known constraints, in this case the measured diffraction intensities and the assumed scan positions on the sample. The spatial resolution of the recovered image of the sample is limited by the angular extent over which the diffraction patterns are recorded and how well these constraints are known. Here, we explore how reconstruction quality degrades with uncertainties in the scan positions. We show experimentally that large errors in the assumed scan positions on the sample can be numerically determined and corrected using conjugate gradient descent methods. We also explore in simulations the limits, based on the signal to noise of the diffraction patterns and amount of overlap between adjacent scan positions, of just how large these errors can be and still be rendered tractable by this method.
A unified framework of descent algorithms for nonlinear programs and variational inequalities
Patriksson, M.
1993-01-01
We present a framework of algorithms for the solution of continuous optimization and variational inequality problems. In the general algorithm, a search direction finding auxiliary problems is obtained by replacing the original cost function with an approximating monotone cost function. The proposed framework encompasses algorithm classes presented earlier by Cohen, Dafermos, Migdalas, and Tseng, and includes numerous descent and successive approximation type methods, such as Newton methods, Jacobi and Gauss-Siedel type decomposition methods for problems defined over Cartesian product sets, and proximal point methods, among others. The auxiliary problem of the general algorithm also induces equivalent optimization reformulation and descent methods for asymmetric variational inequalities. We study the convergence properties of the general algorithm when applied to unconstrained optimization, nondifferentiable optimization, constrained differentiable optimization, and variational inequalities; the emphasis of the convergence analyses is placed on basic convergence results, convergence using different line search strategies and truncated subproblem solutions, and convergence rate results. This analysis offer a unification of known results; moreover, it provides strengthenings of convergence results for many existing algorithms, and indicates possible improvements of their realizations. 482 refs
Groce, J. L.; Izumi, K. H.; Markham, C. H.; Schwab, R. W.; Thompson, J. L.
1986-01-01
The Local Flow Management/Profile Descent (LFM/PD) algorithm designed for the NASA Transport System Research Vehicle program is described. The algorithm provides fuel-efficient altitude and airspeed profiles consistent with ATC restrictions in a time-based metering environment over a fixed ground track. The model design constraints include accommodation of both published profile descent procedures and unpublished profile descents, incorporation of fuel efficiency as a flight profile criterion, operation within the performance capabilities of the Boeing 737-100 airplane with JT8D-7 engines, and conformity to standard air traffic navigation and control procedures. Holding and path stretching capabilities are included for long delay situations.
Fastest Rates for Stochastic Mirror Descent Methods
Hanzely, Filip
2018-03-20
Relative smoothness - a notion introduced by Birnbaum et al. (2011) and rediscovered by Bauschke et al. (2016) and Lu et al. (2016) - generalizes the standard notion of smoothness typically used in the analysis of gradient type methods. In this work we are taking ideas from well studied field of stochastic convex optimization and using them in order to obtain faster algorithms for minimizing relatively smooth functions. We propose and analyze two new algorithms: Relative Randomized Coordinate Descent (relRCD) and Relative Stochastic Gradient Descent (relSGD), both generalizing famous algorithms in the standard smooth setting. The methods we propose can be in fact seen as a particular instances of stochastic mirror descent algorithms. One of them, relRCD corresponds to the first stochastic variant of mirror descent algorithm with linear convergence rate.
Fastest Rates for Stochastic Mirror Descent Methods
Hanzely, Filip; Richtarik, Peter
2018-01-01
Relative smoothness - a notion introduced by Birnbaum et al. (2011) and rediscovered by Bauschke et al. (2016) and Lu et al. (2016) - generalizes the standard notion of smoothness typically used in the analysis of gradient type methods. In this work we are taking ideas from well studied field of stochastic convex optimization and using them in order to obtain faster algorithms for minimizing relatively smooth functions. We propose and analyze two new algorithms: Relative Randomized Coordinate Descent (relRCD) and Relative Stochastic Gradient Descent (relSGD), both generalizing famous algorithms in the standard smooth setting. The methods we propose can be in fact seen as a particular instances of stochastic mirror descent algorithms. One of them, relRCD corresponds to the first stochastic variant of mirror descent algorithm with linear convergence rate.
A coordinate descent MM algorithm for fast computation of sparse logistic PCA
Lee, Seokho
2013-06-01
Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding to be useful when the data dimension is high. We develop a computationally fast algorithm using a combination of coordinate descent and majorization-minimization (MM) auxiliary optimization. Our new algorithm decouples the joint estimation of multiple components into separate estimations and consists of closed-form elementwise updating formulas for each sparse principal component. The performance of the proposed algorithm is tested using simulation and high-dimensional real-world datasets. © 2013 Elsevier B.V. All rights reserved.
Qyyum, Muhammad Abdul; Long, Nguyen Van Duc; Minh, Le Quang; Lee, Moonyong
2018-01-01
Design optimization of the single mixed refrigerant (SMR) natural gas liquefaction (LNG) process involves highly non-linear interactions between decision variables, constraints, and the objective function. These non-linear interactions lead to an irreversibility, which deteriorates the energy efficiency of the LNG process. In this study, a simple and highly efficient hybrid modified coordinate descent (HMCD) algorithm was proposed to cope with the optimization of the natural gas liquefaction process. The single mixed refrigerant process was modeled in Aspen Hysys® and then connected to a Microsoft Visual Studio environment. The proposed optimization algorithm provided an improved result compared to the other existing methodologies to find the optimal condition of the complex mixed refrigerant natural gas liquefaction process. By applying the proposed optimization algorithm, the SMR process can be designed with the 0.2555 kW specific compression power which is equivalent to 44.3% energy saving as compared to the base case. Furthermore, in terms of coefficient of performance (COP), it can be enhanced up to 34.7% as compared to the base case. The proposed optimization algorithm provides a deep understanding of the optimization of the liquefaction process in both technical and numerical perspectives. In addition, the HMCD algorithm can be employed to any mixed refrigerant based liquefaction process in the natural gas industry.
New preconditioned conjugate gradient algorithms for nonlinear unconstrained optimization problems
Al-Bayati, A.; Al-Asadi, N.
1997-01-01
This paper presents two new predilection conjugate gradient algorithms for nonlinear unconstrained optimization problems and examines their computational performance. Computational experience shows that the new proposed algorithms generally imp lone the efficiency of Nazareth's [13] preconditioned conjugate gradient algorithm. (authors). 16 refs., 1 tab
Comparison of genetic algorithms with conjugate gradient methods
Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.
1972-01-01
Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.
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.
Bighnaraj Naik
2018-01-01
Full Text Available All the higher order ANNs (HONNs including functional link ANN (FLANN are sensitive to random initialization of weight and rely on the learning algorithms adopted. Although a selection of efficient learning algorithms for HONNs helps to improve the performance, on the other hand, initialization of weights with optimized weights rather than random weights also play important roles on its efficiency. In this paper, the problem solving approach of the teaching learning based optimization (TLBO along with learning ability of the gradient descent learning (GDL is used to obtain the optimal set of weight of FLANN learning model. TLBO does not require any specific parameters rather it requires only some of the common independent parameters like number of populations, number of iterations and stopping criteria, thereby eliminating the intricacy in selection of algorithmic parameters for adjusting the set of weights of FLANN model. The proposed TLBO-FLANN is implemented in MATLAB and compared with GA-FLANN, PSO-FLANN and HS-FLANN. The TLBO-FLANN is tested on various 5-fold cross validated benchmark data sets from UCI machine learning repository and analyzed under the null-hypothesis by using Friedman test, Holm’s procedure and post hoc ANOVA statistical analysis (Tukey test & Dunnett test.
Bakhtawar Baluch
2017-01-01
Full Text Available A new modified three-term conjugate gradient (CG method is shown for solving the large scale optimization problems. The idea relates to the famous Polak-Ribière-Polyak (PRP formula. As the numerator of PRP plays a vital role in numerical result and not having the jamming issue, PRP method is not globally convergent. So, for the new three-term CG method, the idea is to use the PRP numerator and combine it with any good CG formula’s denominator that performs well. The new modification of three-term CG method possesses the sufficient descent condition independent of any line search. The novelty is that by using the Wolfe Powell line search the new modification possesses global convergence properties with convex and nonconvex functions. Numerical computation with the Wolfe Powell line search by using the standard test function of optimization shows the efficiency and robustness of the new modification.
On the Strong Convergence of a Sufficient Descent Polak-Ribière-Polyak Conjugate Gradient Method
Min Sun
2014-01-01
Full Text Available Recently, Zhang et al. proposed a sufficient descent Polak-Ribière-Polyak (SDPRP conjugate gradient method for large-scale unconstrained optimization problems and proved its global convergence in the sense that lim infk→∞∥∇f(xk∥=0 when an Armijo-type line search is used. In this paper, motivated by the line searches proposed by Shi et al. and Zhang et al., we propose two new Armijo-type line searches and show that the SDPRP method has strong convergence in the sense that limk→∞∥∇f(xk∥=0 under the two new line searches. Numerical results are reported to show the efficiency of the SDPRP with the new Armijo-type line searches in practical computation.
Quasi Gradient Projection Algorithm for Sparse Reconstruction in Compressed Sensing
Xin Meng
2014-02-01
Full Text Available Compressed sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. The existing recovery algorithms based on the gradient projection can either need prior knowledge or recovery the signal poorly. In this paper, a new algorithm based on gradient projection is proposed, which is referred as Quasi Gradient Projection. The algorithm presented quasi gradient direction and two step sizes schemes along this direction. The algorithm doesn’t need any prior knowledge of the original signal. Simulation results demonstrate that the presented algorithm cans recovery the signal more correctly than GPSR which also don’t need prior knowledge. Meanwhile, the algorithm has a lower computation complexity.
A modified three-term PRP conjugate gradient algorithm for optimization models.
Wu, Yanlin
2017-01-01
The nonlinear conjugate gradient (CG) algorithm is a very effective method for optimization, especially for large-scale problems, because of its low memory requirement and simplicity. Zhang et al. (IMA J. Numer. Anal. 26:629-649, 2006) firstly propose a three-term CG algorithm based on the well known Polak-Ribière-Polyak (PRP) formula for unconstrained optimization, where their method has the sufficient descent property without any line search technique. They proved the global convergence of the Armijo line search but this fails for the Wolfe line search technique. Inspired by their method, we will make a further study and give a modified three-term PRP CG algorithm. The presented method possesses the following features: (1) The sufficient descent property also holds without any line search technique; (2) the trust region property of the search direction is automatically satisfied; (3) the steplengh is bounded from below; (4) the global convergence will be established under the Wolfe line search. Numerical results show that the new algorithm is more effective than that of the normal method.
Liu, Mingxue [Life Science and Engineering College, Southwest University of Science and Technology, Mianyang, 621010 (China); Key Laboratory of Solid Waste Treatment and Resource Recycle, Ministry of Education of China, Mianyang, 621010 (China); Dong, Faqin, E-mail: fqdong@swust.edu.cn [Key Laboratory of Solid Waste Treatment and Resource Recycle, Ministry of Education of China, Mianyang, 621010 (China); Zhang, Wei [Key Laboratory of Solid Waste Treatment and Resource Recycle, Ministry of Education of China, Mianyang, 621010 (China); Nie, Xiaoqin [Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Mianyang, 621010 (China); Sun, Shiyong [Key Laboratory of Solid Waste Treatment and Resource Recycle, Ministry of Education of China, Mianyang, 621010 (China); Wei, Hongfu; Luo, Lang; Xiang, Sha; Zhang, Gege [Life Science and Engineering College, Southwest University of Science and Technology, Mianyang, 621010 (China)
2016-08-15
Highlights: • A programmed gradient descent biosorption process was designed. • The adsorption and bioaccumulation quantity of strontium ions by yeast cell were measured. • The decrement of biosorbents after biosorption by ashing was analyzed. • A technological flow process of decrement solution for waste disposal was proposed. - Abstract: One of the waste disposal principles is decrement. The programmed gradient descent biosorption of strontium ions by Saccaromyces cerevisiae regarding bioremoval and ashing process for decrement were studied in present research. The results indicated that S. cerevisiae cells showed valid biosorption for strontium ions with greater than 90% bioremoval efficiency for high concentration strontium ions under batch culture conditions. The S. cerevisiae cells bioaccumulated approximately 10% of strontium ions in the cytoplasm besides adsorbing 90% strontium ions on cell wall. The programmed gradient descent biosorption presented good performance with a nearly 100% bioremoval ratio for low concentration strontium ions after 3 cycles. The ashing process resulted in a huge volume and weight reduction ratio as well as enrichment for strontium in the ash. XRD results showed that SrSO{sub 4} existed in ash. Simulated experiments proved that sulfate could adjust the precipitation of strontium ions. Finally, we proposed a technological flow process that combined the programmed gradient descent biosorption and ashing, which could yield great decrement and allow the supernatant to meet discharge standard. This technological flow process may be beneficial for nuclides and heavy metal disposal treatment in many fields.
Gradient Evolution-based Support Vector Machine Algorithm for Classification
Zulvia, Ferani E.; Kuo, R. J.
2018-03-01
This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.
Experiments with conjugate gradient algorithms for homotopy curve tracking
Irani, Kashmira M.; Ribbens, Calvin J.; Watson, Layne T.; Kamat, Manohar P.; Walker, Homer F.
1991-01-01
There are algorithms for finding zeros or fixed points of nonlinear systems of equations that are globally convergent for almost all starting points, i.e., with probability one. The essence of all such algorithms is the construction of an appropriate homotopy map and then tracking some smooth curve in the zero set of this homotopy map. HOMPACK is a mathematical software package implementing globally convergent homotopy algorithms with three different techniques for tracking a homotopy zero curve, and has separate routines for dense and sparse Jacobian matrices. The HOMPACK algorithms for sparse Jacobian matrices use a preconditioned conjugate gradient algorithm for the computation of the kernel of the homotopy Jacobian matrix, a required linear algebra step for homotopy curve tracking. Here, variants of the conjugate gradient algorithm are implemented in the context of homotopy curve tracking and compared with Craig's preconditioned conjugate gradient method used in HOMPACK. The test problems used include actual large scale, sparse structural mechanics problems.
Bhaya, Amit; Kaszkurewicz, Eugenius
2004-01-01
It is pointed out that the so called momentum method, much used in the neural network literature as an acceleration of the backpropagation method, is a stationary version of the conjugate gradient method. Connections with the continuous optimization method known as heavy ball with friction are also made. In both cases, adaptive (dynamic) choices of the so called learning rate and momentum parameters are obtained using a control Liapunov function analysis of the system.
Gradient Learning Algorithms for Ontology Computing
Gao, Wei; Zhu, Linli
2014-01-01
The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting. PMID:25530752
Gradient Learning Algorithms for Ontology Computing
Wei Gao
2014-01-01
Full Text Available The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting.
Conjugate-Gradient Algorithms For Dynamics Of Manipulators
Fijany, Amir; Scheid, Robert E.
1993-01-01
Algorithms for serial and parallel computation of forward dynamics of multiple-link robotic manipulators by conjugate-gradient method developed. Parallel algorithms have potential for speedup of computations on multiple linked, specialized processors implemented in very-large-scale integrated circuits. Such processors used to stimulate dynamics, possibly faster than in real time, for purposes of planning and control.
Parallel conjugate gradient algorithms for manipulator dynamic simulation
Fijany, Amir; Scheld, Robert E.
1989-01-01
Parallel conjugate gradient algorithms for the computation of multibody dynamics are developed for the specialized case of a robot manipulator. For an n-dimensional positive-definite linear system, the Classical Conjugate Gradient (CCG) algorithms are guaranteed to converge in n iterations, each with a computation cost of O(n); this leads to a total computational cost of O(n sq) on a serial processor. A conjugate gradient algorithms is presented that provide greater efficiency using a preconditioner, which reduces the number of iterations required, and by exploiting parallelism, which reduces the cost of each iteration. Two Preconditioned Conjugate Gradient (PCG) algorithms are proposed which respectively use a diagonal and a tridiagonal matrix, composed of the diagonal and tridiagonal elements of the mass matrix, as preconditioners. Parallel algorithms are developed to compute the preconditioners and their inversions in O(log sub 2 n) steps using n processors. A parallel algorithm is also presented which, on the same architecture, achieves the computational time of O(log sub 2 n) for each iteration. Simulation results for a seven degree-of-freedom manipulator are presented. Variants of the proposed algorithms are also developed which can be efficiently implemented on the Robot Mathematics Processor (RMP).
Knox, C. E.; Cannon, D. G.
1980-01-01
A simple flight management descent algorithm designed to improve the accuracy of delivering an airplane in a fuel-conservative manner to a metering fix at a time designated by air traffic control was developed and flight tested. This algorithm provides a three dimensional path with terminal area time constraints (four dimensional) for an airplane to make an idle thrust, clean configured (landing gear up, flaps zero, and speed brakes retracted) descent to arrive at the metering fix at a predetermined time, altitude, and airspeed. The descent path was calculated for a constant Mach/airspeed schedule from linear approximations of airplane performance with considerations given for gross weight, wind, and nonstandard pressure and temperature effects. The flight management descent algorithm is described. The results of the flight tests flown with the Terminal Configured Vehicle airplane are presented.
Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.
Xu, Dongpo; Xia, Yili; Mandic, Danilo P
2016-02-01
The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.
Knox, C. E.; Cannon, D. G.
1979-01-01
A flight management algorithm designed to improve the accuracy of delivering the airplane fuel efficiently to a metering fix at a time designated by air traffic control is discussed. The algorithm provides a 3-D path with time control (4-D) for a test B 737 airplane to make an idle thrust, clean configured descent to arrive at the metering fix at a predetermined time, altitude, and airspeed. The descent path is calculated for a constant Mach/airspeed schedule from linear approximations of airplane performance with considerations given for gross weight, wind, and nonstandard pressure and temperature effects. The flight management descent algorithms and the results of the flight tests are discussed.
On the efficiency of a randomized mirror descent algorithm in online optimization problems
Gasnikov, A. V.; Nesterov, Yu. E.; Spokoiny, V. G.
2015-04-01
A randomized online version of the mirror descent method is proposed. It differs from the existing versions by the randomization method. Randomization is performed at the stage of the projection of a subgradient of the function being optimized onto the unit simplex rather than at the stage of the computation of a subgradient, which is common practice. As a result, a componentwise subgradient descent with a randomly chosen component is obtained, which admits an online interpretation. This observation, for example, has made it possible to uniformly interpret results on weighting expert decisions and propose the most efficient method for searching for an equilibrium in a zero-sum two-person matrix game with sparse matrix.
Implementing the conjugate gradient algorithm on multi-core systems
Wiggers, W.A.; Bakker, Vincent; Kokkeler, Andre B.J.; Smit, Gerardus Johannes Maria; Nurmi, J.; Takala, J.; Vainio, O.
2007-01-01
In linear solvers, like the conjugate gradient algorithm, sparse-matrix vector multiplication is an important kernel. Due to the sparseness of the matrices, the solver runs relatively slow. For digital optical tomography (DOT), a large set of linear equations have to be solved which currently takes
Christian Lester D. Gimeno
2017-11-01
Full Text Available –This research study focused on the development of a software that helps students design, write, validate and run their pseudocode in a semi Integrated Development Environment (IDE instead of manually writing it on a piece of paper.Specifically, the study aimed to develop lexical analyzer or lexer, syntax analyzer or parser using recursive descent parsing algorithm and an interpreter. The lexical analyzer reads pseudocodesource in a sequence of symbols or characters as lexemes.The lexemes are then analyzed by the lexer that matches a pattern for valid tokens and passes to the syntax analyzer or parser. The syntax analyzer or parser takes those valid tokens and builds meaningful commands using recursive descent parsing algorithm in a form of an abstract syntax tree. The generation of an abstract syntax tree is based on the specified grammar rule created by the researcher expressed in Extended Backus-Naur Form. The Interpreter takes the generated abstract syntax tree and starts the evaluation or interpretation to produce pseudocode output. The software was evaluated using white-box testing by several ICT professionals and black-box testing by several computer science students based on the International Organization for Standardization (ISO 9126 software quality standards. The overall results of the evaluation both for white-box and black-box were described as “Excellent in terms of functionality, reliability, usability, efficiency, maintainability and portability”.
Approximated Function Based Spectral Gradient Algorithm for Sparse Signal Recovery
Weifeng Wang
2014-02-01
Full Text Available Numerical algorithms for the l0-norm regularized non-smooth non-convex minimization problems have recently became a topic of great interest within signal processing, compressive sensing, statistics, and machine learning. Nevertheless, the l0-norm makes the problem combinatorial and generally computationally intractable. In this paper, we construct a new surrogate function to approximate l0-norm regularization, and subsequently make the discrete optimization problem continuous and smooth. Then we use the well-known spectral gradient algorithm to solve the resulting smooth optimization problem. Experiments are provided which illustrate this method is very promising.
Parallel preconditioned conjugate gradient algorithm applied to neutron diffusion problem
Majumdar, A.; Martin, W.R.
1992-01-01
Numerical solution of the neutron diffusion problem requires solving a linear system of equations such as Ax = b, where A is an n x n symmetric positive definite (SPD) matrix; x and b are vectors with n components. The preconditioned conjugate gradient (PCG) algorithm is an efficient iterative method for solving such a linear system of equations. In this paper, the authors describe the implementation of a parallel PCG algorithm on a shared memory machine (BBN TC2000) and on a distributed workstation (IBM RS6000) environment created by the parallel virtual machine parallelization software
Fourier domain preconditioned conjugate gradient algorithm for atmospheric tomography.
Yang, Qiang; Vogel, Curtis R; Ellerbroek, Brent L
2006-07-20
By 'atmospheric tomography' we mean the estimation of a layered atmospheric turbulence profile from measurements of the pupil-plane phase (or phase gradients) corresponding to several different guide star directions. We introduce what we believe to be a new Fourier domain preconditioned conjugate gradient (FD-PCG) algorithm for atmospheric tomography, and we compare its performance against an existing multigrid preconditioned conjugate gradient (MG-PCG) approach. Numerical results indicate that on conventional serial computers, FD-PCG is as accurate and robust as MG-PCG, but it is from one to two orders of magnitude faster for atmospheric tomography on 30 m class telescopes. Simulations are carried out for both natural guide stars and for a combination of finite-altitude laser guide stars and natural guide stars to resolve tip-tilt uncertainty.
A partitioned conjugate gradient algorithm for lattice Green functions
Bowler, K.C.; Kenway, R.D.; Pawley, G.S.; Wallace, D.J.
1984-01-01
Partitioning reduces by one the dimensionality of the lattice on which a propagator need be calculated using, for example, the conjugate gradient algorithm. Thus the quark propagator in lattice QCD may be determined by a computation on a single spatial hyperplane. For free fermions on a 16 3 x N lattice 2N-bit accuracy in the propagator is required to avoid rounding errors. (orig.)
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)
A coordinate descent MM algorithm for fast computation of sparse logistic PCA
Lee, Seokho; Huang, Jianhua Z.
2013-01-01
Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding
Modified cuckoo search: A new gradient free optimisation algorithm
Walton, S.; Hassan, O.; Morgan, K.; Brown, M.R.
2011-01-01
Highlights: → Modified cuckoo search (MCS) is a new gradient free optimisation algorithm. → MCS shows a high convergence rate, able to outperform other optimisers. → MCS is particularly strong at high dimension objective functions. → MCS performs well when applied to engineering problems. - Abstract: A new robust optimisation algorithm, which can be regarded as a modification of the recently developed cuckoo search, is presented. The modification involves the addition of information exchange between the top eggs, or the best solutions. Standard optimisation benchmarking functions are used to test the effects of these modifications and it is demonstrated that, in most cases, the modified cuckoo search performs as well as, or better than, the standard cuckoo search, a particle swarm optimiser, and a differential evolution strategy. In particular the modified cuckoo search shows a high convergence rate to the true global minimum even at high numbers of dimensions.
Conjugate descent formulation of backpropagation error in ...
nique of backpropagation was popularized in a paper by Rumelhart, et al. ... the training of a multilayer neural network using a gradient descent approach applied to a .... superior convergence of the conjugate descent method over a standard ...
A Penalization-Gradient Algorithm for Variational Inequalities
Abdellatif Moudafi
2011-01-01
Full Text Available This paper is concerned with the study of a penalization-gradient algorithm for solving variational inequalities, namely, find x̅∈C such that 〈Ax̅,y-x̅〉≥0 for all y∈C, where A:H→H is a single-valued operator, C is a closed convex set of a real Hilbert space H. Given Ψ:H→R ∪ {+∞} which acts as a penalization function with respect to the constraint x̅∈C, and a penalization parameter βk, we consider an algorithm which alternates a proximal step with respect to ∂Ψ and a gradient step with respect to A and reads as xk=(I+λkβk∂Ψ-1(xk-1-λkAxk-1. Under mild hypotheses, we obtain weak convergence for an inverse strongly monotone operator and strong convergence for a Lipschitz continuous and strongly monotone operator. Applications to hierarchical minimization and fixed-point problems are also given and the multivalued case is reached by replacing the multivalued operator by its Yosida approximate which is always Lipschitz continuous.
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).
Efficient conjugate gradient algorithms for computation of the manipulator forward dynamics
Fijany, Amir; Scheid, Robert E.
1989-01-01
The applicability of conjugate gradient algorithms for computation of the manipulator forward dynamics is investigated. The redundancies in the previously proposed conjugate gradient algorithm are analyzed. A new version is developed which, by avoiding these redundancies, achieves a significantly greater efficiency. A preconditioned conjugate gradient algorithm is also presented. A diagonal matrix whose elements are the diagonal elements of the inertia matrix is proposed as the preconditioner. In order to increase the computational efficiency, an algorithm is developed which exploits the synergism between the computation of the diagonal elements of the inertia matrix and that required by the conjugate gradient algorithm.
Dictionary descent in optimization
Temlyakov, Vladimir
2015-01-01
The problem of convex optimization is studied. Usually in convex optimization the minimization is over a d-dimensional domain. Very often the convergence rate of an optimization algorithm depends on the dimension d. The algorithms studied in this paper utilize dictionaries instead of a canonical basis used in the coordinate descent algorithms. We show how this approach allows us to reduce dimensionality of the problem. Also, we investigate which properties of a dictionary are beneficial for t...
Homaifar, Abdollah; Esterline, Albert; Kimiaghalam, Bahram
2005-01-01
The Hybrid Projected Gradient-Evolutionary Search Algorithm (HPGES) algorithm uses a specially designed evolutionary-based global search strategy to efficiently create candidate solutions in the solution space...
Two-Step Proximal Gradient Algorithm for Low-Rank Matrix Completion
Qiuyu Wang
2016-06-01
Full Text Available In this paper, we propose a two-step proximal gradient algorithm to solve nuclear norm regularized least squares for the purpose of recovering low-rank data matrix from sampling of its entries. Each iteration generated by the proposed algorithm is a combination of the latest three points, namely, the previous point, the current iterate, and its proximal gradient point. This algorithm preserves the computational simplicity of classical proximal gradient algorithm where a singular value decomposition in proximal operator is involved. Global convergence is followed directly in the literature. Numerical results are reported to show the efficiency of the algorithm.
A constrained conjugate gradient algorithm for computed tomography
Azevedo, S.G.; Goodman, D.M. [Lawrence Livermore National Lab., CA (United States)
1994-11-15
Image reconstruction from projections of x-ray, gamma-ray, protons and other penetrating radiation is a well-known problem in a variety of fields, and is commonly referred to as computed tomography (CT). Various analytical and series expansion methods of reconstruction and been used in the past to provide three-dimensional (3D) views of some interior quantity. The difficulties of these approaches lie in the cases where (a) the number of views attainable is limited, (b) the Poisson (or other) uncertainties are significant, (c) quantifiable knowledge of the object is available, but not implementable, or (d) other limitations of the data exist. We have adapted a novel nonlinear optimization procedure developed at LLNL to address limited-data image reconstruction problems. The technique, known as nonlinear least squares with general constraints or constrained conjugate gradients (CCG), has been successfully applied to a number of signal and image processing problems, and is now of great interest to the image reconstruction community. Previous applications of this algorithm to deconvolution problems and x-ray diffraction images for crystallography have shown the great promise.
The Physics of Compressive Sensing and the Gradient-Based Recovery Algorithms
Dai, Qi; Sha, Wei
2009-01-01
The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the different forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.
Algorithm for image retrieval based on edge gradient orientation statistical code.
Zeng, Jiexian; Zhao, Yonggang; Li, Weiye; Fu, Xiang
2014-01-01
Image edge gradient direction not only contains important information of the shape, but also has a simple, lower complexity characteristic. Considering that the edge gradient direction histograms and edge direction autocorrelogram do not have the rotation invariance, we put forward the image retrieval algorithm which is based on edge gradient orientation statistical code (hereinafter referred to as EGOSC) by sharing the application of the statistics method in the edge direction of the chain code in eight neighborhoods to the statistics of the edge gradient direction. Firstly, we construct the n-direction vector and make maximal summation restriction on EGOSC to make sure this algorithm is invariable for rotation effectively. Then, we use Euclidean distance of edge gradient direction entropy to measure shape similarity, so that this method is not sensitive to scaling, color, and illumination change. The experimental results and the algorithm analysis demonstrate that the algorithm can be used for content-based image retrieval and has good retrieval results.
A time domain phase-gradient based ISAR autofocus algorithm
Nel, W
2011-10-01
Full Text Available . Results on simulated and measured data show that the algorithm performs well. Unlike many other ISAR autofocus techniques, the algorithm does not make use of several computationally intensive iterations between the data and image domains as part...
Stochastic Spectral Descent for Discrete Graphical Models
Carlson, David; Hsieh, Ya-Ping; Collins, Edo; Carin, Lawrence; Cevher, Volkan
2015-01-01
Interest in deep probabilistic graphical models has in-creased in recent years, due to their state-of-the-art performance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a significant number of iterations to converge. Since the computational cost of gradient estimation is prohibitive even for modestly sized models, training becomes slow and practically usable models are kept small. In this paper we propose a new, largely tuning-free algorithm to address this problem. Our approach derives novel majorization bounds based on the Schatten- norm. Intriguingly, the minimizers of these bounds can be interpreted as gradient methods in a non-Euclidean space. We thus propose using a stochastic gradient method in non-Euclidean space. We both provide simple conditions under which our algorithm is guaranteed to converge, and demonstrate empirically that our algorithm leads to dramatically faster training and improved predictive ability compared to stochastic gradient descent for both directed and undirected graphical models.
TIGER: Development of Thermal Gradient Compensation Algorithms and Techniques
Hereford, James; Parker, Peter A.; Rhew, Ray D.
2004-01-01
In a wind tunnel facility, the direct measurement of forces and moments induced on the model are performed by a force measurement balance. The measurement balance is a precision-machined device that has strain gages at strategic locations to measure the strain (i.e., deformations) due to applied forces and moments. The strain gages convert the strain (and hence the applied force) to an electrical voltage that is measured by external instruments. To address the problem of thermal gradients on the force measurement balance NASA-LaRC has initiated a research program called TIGER - Thermally-Induced Gradients Effects Research. The ultimate goals of the TIGER program are to: (a) understand the physics of the thermally-induced strain and its subsequent impact on load measurements and (b) develop a robust thermal gradient compensation technique. This paper will discuss the impact of thermal gradients on force measurement balances, specific aspects of the TIGER program (the design of a special-purpose balance, data acquisition and data analysis challenges), and give an overall summary.
The Lanczos and Conjugate Gradient Algorithms in Finite Precision Arithmetic
Meurant, G.; Strakoš, Zdeněk
2006-01-01
Roč. 15, - (2006), s. 471-542 ISSN 0962-4929 R&D Projects: GA AV ČR 1ET400300415 Institutional research plan: CEZ:AV0Z10300504 Keywords : Lanczos method * conjugate gradient method * finite precision arithmetic * numerical stability * iterative methods Subject RIV: BA - General Mathematics
Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems
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.
Knox, C. E.
1984-01-01
A simple airborne flight management descent algorithm designed to define a flight profile subject to the constraints of using idle thrust, a clean airplane configuration (landing gear up, flaps zero, and speed brakes retracted), and fixed-time end conditions was developed and flight tested in the NASA TSRV B-737 research airplane. The research test flights, conducted in the Denver ARTCC automated time-based metering LFM/PD ATC environment, demonstrated that time guidance and control in the cockpit was acceptable to the pilots and ATC controllers and resulted in arrival of the airplane over the metering fix with standard deviations in airspeed error of 6.5 knots, in altitude error of 23.7 m (77.8 ft), and in arrival time accuracy of 12 sec. These accuracies indicated a good representation of airplane performance and wind modeling. Fuel savings will be obtained on a fleet-wide basis through a reduction of the time error dispersions at the metering fix and on a single-airplane basis by presenting the pilot with guidance for a fuel-efficient descent.
Ghosh, A
1988-08-01
Lanczos and conjugate gradient algorithms are important in computational linear algebra. In this paper, a parallel pipelined realization of these algorithms on a ring of optical linear algebra processors is described. The flow of data is designed to minimize the idle times of the optical multiprocessor and the redundancy of computations. The effects of optical round-off errors on the solutions obtained by the optical Lanczos and conjugate gradient algorithms are analyzed, and it is shown that optical preconditioning can improve the accuracy of these algorithms substantially. Algorithms for optical preconditioning and results of numerical experiments on solving linear systems of equations arising from partial differential equations are discussed. Since the Lanczos algorithm is used mostly with sparse matrices, a folded storage scheme to represent sparse matrices on spatial light modulators is also described.
Niu Lili; Qian Ming; Yu Wentao; Jin Qiaofeng; Ling Tao; Zheng Hairong; Wan Kun; Gao Shen
2010-01-01
This paper presents a new algorithm for ultrasonic particle image velocimetry (Echo PIV) for improving the flow velocity measurement accuracy and efficiency in regions with high velocity gradients. The conventional Echo PIV algorithm has been modified by incorporating a multiple iterative algorithm, sub-pixel method, filter and interpolation method, and spurious vector elimination algorithm. The new algorithms' performance is assessed by analyzing simulated images with known displacements, and ultrasonic B-mode images of in vitro laminar pipe flow, rotational flow and in vivo rat carotid arterial flow. Results of the simulated images show that the new algorithm produces much smaller bias from the known displacements. For laminar flow, the new algorithm results in 1.1% deviation from the analytically derived value, and 8.8% for the conventional algorithm. The vector quality evaluation for the rotational flow imaging shows that the new algorithm produces better velocity vectors. For in vivo rat carotid arterial flow imaging, the results from the new algorithm deviate 6.6% from the Doppler-measured peak velocities averagely compared to 15% of that from the conventional algorithm. The new Echo PIV algorithm is able to effectively improve the measurement accuracy in imaging flow fields with high velocity gradients.
Adaptive Regularization of Neural Networks Using Conjugate Gradient
Goutte, Cyril; Larsen, Jan
1998-01-01
Andersen et al. (1997) and Larsen et al. (1996, 1997) suggested a regularization scheme which iteratively adapts regularization parameters by minimizing validation error using simple gradient descent. In this contribution we present an improved algorithm based on the conjugate gradient technique........ Numerical experiments with feedforward neural networks successfully demonstrate improved generalization ability and lower computational cost...
An Improved Phase Gradient Autofocus Algorithm Used in Real-time Processing
Qing Ji-ming
2015-10-01
Full Text Available The Phase Gradient Autofocus (PGA algorithm can remove the high order phase error effectively, which is of great significance to get high resolution images in real-time processing. While PGA usually needs iteration, which necessitates long working hours. In addition, the performances of the algorithm are not stable in different scene applications. This severely constrains the application of PGA in real-time processing. Isolated scatter selection and windowing are two important algorithmic steps of Phase Gradient Autofocus Algorithm. Therefore, this paper presents an isolated scatter selection method based on sample mean and a windowing method based on pulse envelope. These two methods are highly adaptable to data, which would make the algorithm obtain better stability and need less iteration. The adaptability of the improved PGA is demonstrated with the experimental results of real radar data.
Practical mathematical optimization basic optimization theory and gradient-based algorithms
Snyman, Jan A
2018-01-01
This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and dir...
Large Airborne Full Tensor Gradient Data Inversion Based on a Non-Monotone Gradient Method
Sun, Yong; Meng, Zhaohai; Li, Fengting
2018-03-01
Following the development of gravity gradiometer instrument technology, the full tensor gravity (FTG) data can be acquired on airborne and marine platforms. Large-scale geophysical data can be obtained using these methods, making such data sets a number of the "big data" category. Therefore, a fast and effective inversion method is developed to solve the large-scale FTG data inversion problem. Many algorithms are available to accelerate the FTG data inversion, such as conjugate gradient method. However, the conventional conjugate gradient method takes a long time to complete data processing. Thus, a fast and effective iterative algorithm is necessary to improve the utilization of FTG data. Generally, inversion processing is formulated by incorporating regularizing constraints, followed by the introduction of a non-monotone gradient-descent method to accelerate the convergence rate of FTG data inversion. Compared with the conventional gradient method, the steepest descent gradient algorithm, and the conjugate gradient algorithm, there are clear advantages of the non-monotone iterative gradient-descent algorithm. Simulated and field FTG data were applied to show the application value of this new fast inversion method.
An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients
Anastasia S. Georgiou
2017-06-01
Full Text Available In recent work, we have illustrated the construction of an exploration geometry on free energy surfaces: the adaptive computer-assisted discovery of an approximate low-dimensional manifold on which the effective dynamics of the system evolves. Constructing such an exploration geometry involves geometry-biased sampling (through both appropriately-initialized unbiased molecular dynamics and through restraining potentials and, machine learning techniques to organize the intrinsic geometry of the data resulting from the sampling (in particular, diffusion maps, possibly enhanced through the appropriate Mahalanobis-type metric. In this contribution, we detail a method for exploring the conformational space of a stochastic gradient system whose effective free energy surface depends on a smaller number of degrees of freedom than the dimension of the phase space. Our approach comprises two steps. First, we study the local geometry of the free energy landscape using diffusion maps on samples computed through stochastic dynamics. This allows us to automatically identify the relevant coarse variables. Next, we use the information garnered in the previous step to construct a new set of initial conditions for subsequent trajectories. These initial conditions are computed so as to explore the accessible conformational space more efficiently than by continuing the previous, unbiased simulations. We showcase this method on a representative test system.
A gradient based algorithm to solve inverse plane bimodular problems of identification
Ran, Chunjiang; Yang, Haitian; Zhang, Guoqing
2018-02-01
This paper presents a gradient based algorithm to solve inverse plane bimodular problems of identifying constitutive parameters, including tensile/compressive moduli and tensile/compressive Poisson's ratios. For the forward bimodular problem, a FE tangent stiffness matrix is derived facilitating the implementation of gradient based algorithms, for the inverse bimodular problem of identification, a two-level sensitivity analysis based strategy is proposed. Numerical verification in term of accuracy and efficiency is provided, and the impacts of initial guess, number of measurement points, regional inhomogeneity, and noisy data on the identification are taken into accounts.
Data-driven gradient algorithm for high-precision quantum control
Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel
2018-04-01
In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.
Vecharynski, Eugene; Yang, Chao; Pask, John E.
2015-01-01
We present an iterative algorithm for computing an invariant subspace associated with the algebraically smallest eigenvalues of a large sparse or structured Hermitian matrix A. We are interested in the case in which the dimension of the invariant subspace is large (e.g., over several hundreds or thousands) even though it may still be small relative to the dimension of A. These problems arise from, for example, density functional theory (DFT) based electronic structure calculations for complex materials. The key feature of our algorithm is that it performs fewer Rayleigh–Ritz calculations compared to existing algorithms such as the locally optimal block preconditioned conjugate gradient or the Davidson algorithm. It is a block algorithm, and hence can take advantage of efficient BLAS3 operations and be implemented with multiple levels of concurrency. We discuss a number of practical issues that must be addressed in order to implement the algorithm efficiently on a high performance computer
Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.
1992-01-01
This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.
Wei, Yongjie; Ge, Baozhen; Wei, Yaolin
2009-03-20
In general, model-independent algorithms are sensitive to noise during laser particle size measurement. An improved conjugate gradient algorithm (ICGA) that can be used to invert particle size distribution (PSD) from diffraction data is presented. By use of the ICGA to invert simulated data with multiplicative or additive noise, we determined that additive noise is the main factor that induces distorted results. Thus the ICGA is amended by introduction of an iteration step-adjusting parameter and is used experimentally on simulated data and some samples. The experimental results show that the sensitivity of the ICGA to noise is reduced and the inverted results are in accord with the real PSD.
An algorithm for gradient-based dynamic optimization of UV ﬂash processes
Ritschel, Tobias Kasper Skovborg; Capolei, Andrea; Gaspar, Jozsef
2017-01-01
This paper presents a novel single-shooting algorithm for gradient-based solution of optimal control problems with vapor-liquid equilibrium constraints. Such optimal control problems are important in several engineering applications, for instance in control of distillation columns, in certain two...... softwareaswellastheperformanceofdiﬀerentcompilersinaLinuxoperatingsystem. Thesetestsindicatethatreal-timenonlinear model predictive control of UV ﬂash processes is computationally feasible....
Penalty Algorithm Based on Conjugate Gradient Method for Solving Portfolio Management Problem
Wang YaLin
2009-01-01
Full Text Available A new approach was proposed to reformulate the biobjectives optimization model of portfolio management into an unconstrained minimization problem, where the objective function is a piecewise quadratic polynomial. We presented some properties of such an objective function. Then, a class of penalty algorithms based on the well-known conjugate gradient methods was developed to find the solution of portfolio management problem. By implementing the proposed algorithm to solve the real problems from the stock market in China, it was shown that this algorithm is promising.
Burkitt, A.N.; Irving, A.C.
1988-01-01
Two of the methods that are widely used in lattice gauge theory calculations requiring inversion of the fermion matrix are the Lanczos and the conjugate gradient algorithms. Those algorithms are already known to be closely related. In fact for matrix inversion, in exact arithmetic, they give identical results at each iteration and are just alternative formulations of a single algorithm. This equivalence survives rounding errors. We give the identities between the coefficients of the two formulations, enabling many of the best features of them to be combined. (orig.)
Inversion of the fermion matrix and the equivalence of the conjugate gradient and Lanczos algorithms
Burkitt, A.N.; Irving, A.C.
1990-01-01
The Lanczos and conjugate gradient algorithms are widely used in lattice QCD calculations. The previously known close relationship between the two methods is explored and two commonly used implementations are shown to give identically the same results at each iteration, in exact arithmetic, for matrix inversion. The identities between the coefficients of the two algorithms are given, and many of the features of the two algorithms can now be combined. The effects of finite arithmetic are investigated and the particular Lanczos formulation is found to be most stable with respect to rounding errors. (orig.)
RES: Regularized Stochastic BFGS Algorithm
Mokhtari, Aryan; Ribeiro, Alejandro
2014-12-01
RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second order methods, on the other hand, is impracticable because computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both, the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian egeinvalues of the sample functions are sufficient to guarantee convergence to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.
Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.
Chang, Joshua; Paydarfar, David
2014-12-01
Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.
The q-G method : A q-version of the Steepest Descent method for global optimization.
Soterroni, Aline C; Galski, Roberto L; Scarabello, Marluce C; Ramos, Fernando M
2015-01-01
In this work, the q-Gradient (q-G) method, a q-version of the Steepest Descent method, is presented. The main idea behind the q-G method is the use of the negative of the q-gradient vector of the objective function as the search direction. The q-gradient vector, or simply the q-gradient, is a generalization of the classical gradient vector based on the concept of Jackson's derivative from the q-calculus. Its use provides the algorithm an effective mechanism for escaping from local minima. The q-G method reduces to the Steepest Descent method when the parameter q tends to 1. The algorithm has three free parameters and it is implemented so that the search process gradually shifts from global exploration in the beginning to local exploitation in the end. We evaluated the q-G method on 34 test functions, and compared its performance with 34 optimization algorithms, including derivative-free algorithms and the Steepest Descent method. Our results show that the q-G method is competitive and has a great potential for solving multimodal optimization problems.
Image defog algorithm based on open close filter and gradient domain recursive bilateral filter
Liu, Daqian; Liu, Wanjun; Zhao, Qingguo; Fei, Bowen
2017-11-01
To solve the problems of fuzzy details, color distortion, low brightness of the image obtained by the dark channel prior defog algorithm, an image defog algorithm based on open close filter and gradient domain recursive bilateral filter, referred to as OCRBF, was put forward. The algorithm named OCRBF firstly makes use of weighted quad tree to obtain more accurate the global atmospheric value, then exploits multiple-structure element morphological open and close filter towards the minimum channel map to obtain a rough scattering map by dark channel prior, makes use of variogram to correct the transmittance map,and uses gradient domain recursive bilateral filter for the smooth operation, finally gets recovery images by image degradation model, and makes contrast adjustment to get bright, clear and no fog image. A large number of experimental results show that the proposed defog method in this paper can be good to remove the fog , recover color and definition of the fog image containing close range image, image perspective, the image including the bright areas very well, compared with other image defog algorithms,obtain more clear and natural fog free images with details of higher visibility, what's more, the relationship between the time complexity of SIDA algorithm and the number of image pixels is a linear correlation.
Mariano-Goulart, D; Fourcade, M; Bernon, J L; Rossi, M; Zanca, M
2003-01-01
Thanks to an experimental study based on simulated and physical phantoms, the propagation of the stochastic noise in slices reconstructed using the conjugate gradient algorithm has been analysed versus iterations. After a first increase corresponding to the reconstruction of the signal, the noise stabilises before increasing linearly with iterations. The level of the plateau as well as the slope of the subsequent linear increase depends on the noise in the projection data.
A complete implementation of the conjugate gradient algorithm on a reconfigurable supercomputer
Dubois, David H.; Dubois, Andrew J.; Connor, Carolyn M.; Boorman, Thomas M.; Poole, Stephen W.
2008-01-01
The conjugate gradient is a prominent iterative method for solving systems of sparse linear equations. Large-scale scientific applications often utilize a conjugate gradient solver at their computational core. In this paper we present a field programmable gate array (FPGA) based implementation of a double precision, non-preconditioned, conjugate gradient solver for fmite-element or finite-difference methods. OUf work utilizes the SRC Computers, Inc. MAPStation hardware platform along with the 'Carte' software programming environment to ease the programming workload when working with the hybrid (CPUIFPGA) environment. The implementation is designed to handle large sparse matrices of up to order N x N where N <= 116,394, with up to 7 non-zero, 64-bit elements per sparse row. This implementation utilizes an optimized sparse matrix-vector multiply operation which is critical for obtaining high performance. Direct parallel implementations of loop unrolling and loop fusion are utilized to extract performance from the various vector/matrix operations. Rather than utilize the FPGA devices as function off-load accelerators, our implementation uses the FPGAs to implement the core conjugate gradient algorithm. Measured run-time performance data is presented comparing the FPGA implementation to a software-only version showing that the FPGA can outperform processors running up to 30x the clock rate. In conclusion we take a look at the new SRC-7 system and estimate the performance of this algorithm on that architecture.
A systematic approach to robust preconditioning for gradient-based inverse scattering algorithms
Nordebo, Sven; Fhager, Andreas; Persson, Mikael; Gustafsson, Mats
2008-01-01
This paper presents a systematic approach to robust preconditioning for gradient-based nonlinear inverse scattering algorithms. In particular, one- and two-dimensional inverse problems are considered where the permittivity and conductivity profiles are unknown and the input data consist of the scattered field over a certain bandwidth. A time-domain least-squares formulation is employed and the inversion algorithm is based on a conjugate gradient or quasi-Newton algorithm together with an FDTD-electromagnetic solver. A Fisher information analysis is used to estimate the Hessian of the error functional. A robust preconditioner is then obtained by incorporating a parameter scaling such that the scaled Fisher information has a unit diagonal. By improving the conditioning of the Hessian, the convergence rate of the conjugate gradient or quasi-Newton methods are improved. The preconditioner is robust in the sense that the scaling, i.e. the diagonal Fisher information, is virtually invariant to the numerical resolution and the discretization model that is employed. Numerical examples of image reconstruction are included to illustrate the efficiency of the proposed technique
Tsuruta, S; Misztal, I; Strandén, I
2001-05-01
Utility of the preconditioned conjugate gradient algorithm with a diagonal preconditioner for solving mixed-model equations in animal breeding applications was evaluated with 16 test problems. The problems included single- and multiple-trait analyses, with data on beef, dairy, and swine ranging from small examples to national data sets. Multiple-trait models considered low and high genetic correlations. Convergence was based on relative differences between left- and right-hand sides. The ordering of equations was fixed effects followed by random effects, with no special ordering within random effects. The preconditioned conjugate gradient program implemented with double precision converged for all models. However, when implemented in single precision, the preconditioned conjugate gradient algorithm did not converge for seven large models. The preconditioned conjugate gradient and successive overrelaxation algorithms were subsequently compared for 13 of the test problems. The preconditioned conjugate gradient algorithm was easy to implement with the iteration on data for general models. However, successive overrelaxation requires specific programming for each set of models. On average, the preconditioned conjugate gradient algorithm converged in three times fewer rounds of iteration than successive overrelaxation. With straightforward implementations, programs using the preconditioned conjugate gradient algorithm may be two or more times faster than those using successive overrelaxation. However, programs using the preconditioned conjugate gradient algorithm would use more memory than would comparable implementations using successive overrelaxation. Extensive optimization of either algorithm can influence rankings. The preconditioned conjugate gradient implemented with iteration on data, a diagonal preconditioner, and in double precision may be the algorithm of choice for solving mixed-model equations when sufficient memory is available and ease of implementation is
Fully 3D PET image reconstruction using a fourier preconditioned conjugate-gradient algorithm
Fessler, J.A.; Ficaro, E.P.
1996-01-01
Since the data sizes in fully 3D PET imaging are very large, iterative image reconstruction algorithms must converge in very few iterations to be useful. One can improve the convergence rate of the conjugate-gradient (CG) algorithm by incorporating preconditioning operators that approximate the inverse of the Hessian of the objective function. If the 3D cylindrical PET geometry were not truncated at the ends, then the Hessian of the penalized least-squares objective function would be approximately shift-invariant, i.e. G'G would be nearly block-circulant, where G is the system matrix. We propose a Fourier preconditioner based on this shift-invariant approximation to the Hessian. Results show that this preconditioner significantly accelerates the convergence of the CG algorithm with only a small increase in computation
Dynamic phasing of multichannel cw laser radiation by means of a stochastic gradient algorithm
Volkov, V A; Volkov, M V; Garanin, S G; Dolgopolov, Yu V; Kopalkin, A V; Kulikov, S M; Starikov, F A; Sukharev, S A; Tyutin, S V; Khokhlov, S V; Chaparin, D A [Russian Federal Nuclear Center ' All-Russian Research Institute of Experimental Physics' , Sarov, Nizhnii Novgorod region (Russian Federation)
2013-09-30
The phasing of a multichannel laser beam by means of an iterative stochastic parallel gradient (SPG) algorithm has been numerically and experimentally investigated. The operation of the SPG algorithm is simulated, the acceptable range of amplitudes of probe phase shifts is found, and the algorithm parameters at which the desired Strehl number can be obtained with a minimum number of iterations are determined. An experimental bench with phase modulators based on lithium niobate, which are controlled by a multichannel electronic unit with a real-time microcontroller, has been designed. Phasing of 16 cw laser beams at a system response bandwidth of 3.7 kHz and phase thermal distortions in a frequency band of about 10 Hz is experimentally demonstrated. The experimental data are in complete agreement with the calculation results. (control of laser radiation parameters)
Iris Location Algorithm Based on the CANNY Operator and Gradient Hough Transform
Zhong, L. H.; Meng, K.; Wang, Y.; Dai, Z. Q.; Li, S.
2017-12-01
In the iris recognition system, the accuracy of the localization of the inner and outer edges of the iris directly affects the performance of the recognition system, so iris localization has important research meaning. Our iris data contain eyelid, eyelashes, light spot and other noise, even the gray transformation of the images is not obvious, so the general methods of iris location are unable to realize the iris location. The method of the iris location based on Canny operator and gradient Hough transform is proposed. Firstly, the images are pre-processed; then, calculating the gradient information of images, the inner and outer edges of iris are coarse positioned using Canny operator; finally, according to the gradient Hough transform to realize precise localization of the inner and outer edge of iris. The experimental results show that our algorithm can achieve the localization of the inner and outer edges of the iris well, and the algorithm has strong anti-interference ability, can greatly reduce the location time and has higher accuracy and stability.
Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
Fang Ye
2017-02-01
Full Text Available In order to improve system performance such as throughput, heterogeneous network (HetNet has become an effective solution in Long Term Evolution-Advanced (LET-A. However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.
An optical flow algorithm based on gradient constancy assumption for PIV image processing
Zhong, Qianglong; Yang, Hua; Yin, Zhouping
2017-01-01
Particle image velocimetry (PIV) has matured as a flow measurement technique. It enables the description of the instantaneous velocity field of the flow by analyzing the particle motion obtained from digitally recorded images. Correlation based PIV evaluation technique is widely used because of its good accuracy and robustness. Although very successful, correlation PIV technique has some weakness which can be avoided by optical flow based PIV algorithms. At present, most of the optical flow methods applied to PIV are based on brightness constancy assumption. However, some factors of flow imaging technology and the nature property of the fluids make the brightness constancy assumption less appropriate in real PIV cases. In this paper, an implementation of a 2D optical flow algorithm (GCOF) based on gradient constancy assumption is introduced. The proposed GCOF assumes the edges of the illuminated PIV particles are constant during motion. It comprises two terms: a combined local-global gradient data term and a first-order divergence and vorticity smooth term. The approach can provide accurate dense motion fields. The approach are tested on synthetic images and on two experimental flows. The comparison of GCOF with other optical flow algorithms indicates the proposed method is more accurate especially in conditions of illumination variation. The comparison of GCOF with correlation PIV technique shows that the proposed GCOF has advantages on preserving small divergence and vorticity structures of the motion field and getting less outliers. As a consequence, the GCOF acquire a more accurate and better topological description of the turbulent flow. (paper)
Parametric estimation of the Duffing system by using a modified gradient algorithm
Aguilar-Ibanez, Carlos; Sanchez Herrera, Jorge; Garrido-Moctezuma, Ruben
2008-01-01
The Letter presents a strategy for recovering the unknown parameters of the Duffing oscillator using a measurable output signal. The suggested approach employs the construction of an integral parametrization of one auxiliary output. It is calculated by measuring the difference between the output and its respective delay output. First we estimate the auxiliary output, followed by the application of a modified gradient algorithm, then we adjust the gains of the proposed linear estimator, until this error converges to zero. The convergence of the proposed scheme is shown using Lyapunov method
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Application
Chambolle, Antonin; Ehrhardt, Matthias J.; Richtarik, Peter; Schö nlieb, Carola-Bibiane
2017-01-01
We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable. The analysis is carried out for general convex-concave saddle point problems and problems that are either partially smooth / strongly convex or fully smooth / strongly convex. We perform the analysis for arbitrary samplings of dual variables, and obtain known deterministic results as a special case. Several variants of our stochastic method significantly outperform the deterministic variant on a variety of imaging tasks.
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Application
Chambolle, Antonin
2017-06-15
We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable. The analysis is carried out for general convex-concave saddle point problems and problems that are either partially smooth / strongly convex or fully smooth / strongly convex. We perform the analysis for arbitrary samplings of dual variables, and obtain known deterministic results as a special case. Several variants of our stochastic method significantly outperform the deterministic variant on a variety of imaging tasks.
Microwave imaging of dielectric cylinder using level set method and conjugate gradient algorithm
Grayaa, K.; Bouzidi, A.; Aguili, T.
2011-01-01
In this paper, we propose a computational method for microwave imaging cylinder and dielectric object, based on combining level set technique and the conjugate gradient algorithm. By measuring the scattered field, we tried to retrieve the shape, localisation and the permittivity of the object. The forward problem is solved by the moment method, while the inverse problem is reformulate in an optimization one and is solved by the proposed scheme. It found that the proposed method is able to give good reconstruction quality in terms of the reconstructed shape and permittivity.
Stochastic quasi-gradient based optimization algorithms for dynamic reliability applications
Bourgeois, F.; Labeau, P.E.
2001-01-01
On one hand, PSA results are increasingly used in decision making, system management and optimization of system design. On the other hand, when severe accidental transients are considered, dynamic reliability appears appropriate to account for the complex interaction between the transitions between hardware configurations, the operator behavior and the dynamic evolution of the system. This paper presents an exploratory work in which the estimation of the system unreliability in a dynamic context is coupled with an optimization algorithm to determine the 'best' safety policy. Because some reliability parameters are likely to be distributed, the cost function to be minimized turns out to be a random variable. Stochastic programming techniques are therefore envisioned to determine an optimal strategy. Monte Carlo simulation is used at all stages of the computations, from the estimation of the system unreliability to that of the stochastic quasi-gradient. The optimization algorithm is illustrated on a HNO 3 supply system
Dynamic training algorithm for dynamic neural networks
Tan, Y.; Van Cauwenberghe, A.; Liu, Z.
1996-01-01
The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper
Shi, Junwei; Liu, Fei; Zhang, Guanglei; Luo, Jianwen; Bai, Jing
2014-04-01
Owing to the high degree of scattering of light through tissues, the ill-posedness of fluorescence molecular tomography (FMT) inverse problem causes relatively low spatial resolution in the reconstruction results. Unlike L2 regularization, L1 regularization can preserve the details and reduce the noise effectively. Reconstruction is obtained through a restarted L1 regularization-based nonlinear conjugate gradient (re-L1-NCG) algorithm, which has been proven to be able to increase the computational speed with low memory consumption. The algorithm consists of inner and outer iterations. In the inner iteration, L1-NCG is used to obtain the L1-regularized results. In the outer iteration, the restarted strategy is used to increase the convergence speed of L1-NCG. To demonstrate the performance of re-L1-NCG in terms of spatial resolution, simulation and physical phantom studies with fluorescent targets located with different edge-to-edge distances were carried out. The reconstruction results show that the re-L1-NCG algorithm has the ability to resolve targets with an edge-to-edge distance of 0.1 cm at a depth of 1.5 cm, which is a significant improvement for FMT.
Conjugate descent formulation of backpropagation error in ...
The feedforward neural network architecture uses backpropagation learning to determine optimal weights between dierent interconnected layers. This learning procedure uses a gradient descent technique applied to a sum-of-squares error function for the given input-output pattern. It employs an iterative procedure to ...
Chugh, Saryu; Arivu Selvan, K.; Nadesh, RK
2017-11-01
Numerous destructive things influence the working arrangement of human body as hypertension, smoking, obesity, inappropriate medication taking which causes many contrasting diseases as diabetes, thyroid, strokes and coronary diseases. The impermanence and horribleness of the environment situation is also the reason for the coronary disease. The structure of Apache start relies on the evolution which requires gathering of the data. To break down the significance of use programming focused on data structure the Apache stop ought to be utilized and it gives various central focuses as it is fast in light as it uses memory worked in preparing. Apache Spark continues running on dispersed environment and chops down the data in bunches giving a high profitability rate. Utilizing mining procedure as a part of the determination of coronary disease has been exhaustively examined indicating worthy levels of precision. Decision trees, Neural Network, Gradient Boosting Algorithm are the various apache spark proficiencies which help in collecting the information.
Method of T2 spectrum inversion with conjugate gradient algorithm from NMR data
Li Pengju; Shi Shangming; Song Yanjie
2010-01-01
Based on the optimization techniques, the T 2 spectrum inversion method of conjugate gradient that is easy to realize non-negativity constraint of T2 spectrum is proposed. The method transforms the linear mixed-determined problem of T2 spectrum inversion into the typical optimization problem of searching the minimum of objective function by building up the objective function according to the basic idea of geophysics modeling. The optimization problem above is solved with the conjugate gradient algorithm that has quick convergence rate and quadratic termination. The method has been applied to the inversion of noise free echo train generated from artificial spectrum, artificial echo train with signal-to-noise ratio (SNR)=25 and NMR experimental data of drilling core. The comparison between the inversion results of this paper and artificial spectrum or the result of software imported in NMR laboratory shows that the method can correctly invert T 2 spectrum from artificial NMR relaxation data even though SNR=25 and that inversion T 2 spectrum with good continuity and smoothness from core NMR experimental data accords perfectly with that of laboratory software imported, and moreover,the absolute error between the NMR porosity computed from T 2 spectrum and helium (He) porosity in laboratory is 0.65%. (authors)
Grebenkov, Denis S
2011-02-01
A new method for computing the signal attenuation due to restricted diffusion in a linear magnetic field gradient is proposed. A fast random walk (FRW) algorithm for simulating random trajectories of diffusing spin-bearing particles is combined with gradient encoding. As random moves of a FRW are continuously adapted to local geometrical length scales, the method is efficient for simulating pulsed-gradient spin-echo experiments in hierarchical or multiscale porous media such as concrete, sandstones, sedimentary rocks and, potentially, brain or lungs. Copyright © 2010 Elsevier Inc. All rights reserved.
Burt, Adam O.; Tinker, Michael L.
2014-01-01
In this paper, genetic algorithm based and gradient-based topology optimization is presented in application to a real hardware design problem. Preliminary design of a planetary lander mockup structure is accomplished using these methods that prove to provide major weight savings by addressing the structural efficiency during the design cycle. This paper presents two alternative formulations of the topology optimization problem. The first is the widely-used gradient-based implementation using commercially available algorithms. The second is formulated using genetic algorithms and internally developed capabilities. These two approaches are applied to a practical design problem for hardware that has been built, tested and proven to be functional. Both formulations converged on similar solutions and therefore were proven to be equally valid implementations of the process. This paper discusses both of these formulations at a high level.
Perez, L; Autrique, L; Gillet, M
2008-01-01
The aim of this paper is to investigate the thermal diffusivity identification of a multilayered material dedicated to fire protection. In a military framework, fire protection needs to meet specific requirements, and operational protective systems must be constantly improved in order to keep up with the development of new weapons. In the specific domain of passive fire protections, intumescent coatings can be an effective solution on the battlefield. Intumescent materials have the ability to swell up when they are heated, building a thick multi-layered coating which provides efficient thermal insulation to the underlying material. Due to the heat aggressions (fire or explosion) leading to the intumescent phenomena, high temperatures are considered and prevent from linearization of the mathematical model describing the system state evolution. Previous sensitivity analysis has shown that the thermal diffusivity of the multilayered intumescent coating is a key parameter in order to validate the predictive numerical tool and therefore for thermal protection optimisation. A conjugate gradient method is implemented in order to minimise the quadratic cost function related to the error between predicted temperature and measured temperature. This regularisation algorithm is well adapted for a large number of unknown parameters.
Embedding SAS approach into conjugate gradient algorithms for asymmetric 3D elasticity problems
Chen, Hsin-Chu; Warsi, N.A. [Clark Atlanta Univ., GA (United States); Sameh, A. [Univ. of Minnesota, Minneapolis, MN (United States)
1996-12-31
In this paper, we present two strategies to embed the SAS (symmetric-and-antisymmetric) scheme into conjugate gradient (CG) algorithms to make solving 3D elasticity problems, with or without global reflexive symmetry, more efficient. The SAS approach is physically a domain decomposition scheme that takes advantage of reflexive symmetry of discretized physical problems, and algebraically a matrix transformation method that exploits special reflexivity properties of the matrix resulting from discretization. In addition to offering large-grain parallelism, which is valuable in a multiprocessing environment, the SAS scheme also has the potential for reducing arithmetic operations in the numerical solution of a reasonably wide class of scientific and engineering problems. This approach can be applied directly to problems that have global reflexive symmetry, yielding smaller and independent subproblems to solve, or indirectly to problems with partial symmetry, resulting in loosely coupled subproblems. The decomposition is achieved by separating the reflexive subspace from the antireflexive one, possessed by a special class of matrices A, A {element_of} C{sup n x n} that satisfy the relation A = PAP where P is a reflection matrix (symmetric signed permutation matrix).
The 15th and last section of the CMS detector was lowered on Tuesday 22 January. The YE-1 endcap (1430 tonnes) began its 100-metre descent at 7 am and arrived gently on the floor of the experiment hall at 5.30 pm.
Vogel, Curtis R; Yang, Qiang
2006-08-21
We present two different implementations of the Fourier domain preconditioned conjugate gradient algorithm (FD-PCG) to efficiently solve the large structured linear systems that arise in optimal volume turbulence estimation, or tomography, for multi-conjugate adaptive optics (MCAO). We describe how to deal with several critical technical issues, including the cone coordinate transformation problem and sensor subaperture grid spacing. We also extend the FD-PCG approach to handle the deformable mirror fitting problem for MCAO.
Volkov, M V; Garanin, S G; Dolgopolov, Yu V; Kopalkin, A V; Kulikov, S M; Sinyavin, D N; Starikov, F A; Sukharev, S A; Tyutin, S V; Khokhlov, S V; Chaparin, D A [Russian Federal Nuclear Center ' All-Russian Research Institute of Experimental Physics' , Sarov, Nizhnii Novgorod region (Russian Federation)
2014-11-30
A seven-channel fibre laser system operated by the master oscillator – multichannel power amplifier scheme is the phase locked using a stochastic parallel gradient algorithm. The phase modulators on lithium niobate crystals are controlled by a multichannel electronic unit with the microcontroller processing signals in real time. The dynamic phase locking of the laser system with the bandwidth of 14 kHz is demonstrated, the time of phasing is 3 – 4 ms. (fibre and integrated-optical structures)
Zhongbo Sun
2014-01-01
Full Text Available Two modified three-term type conjugate gradient algorithms which satisfy both the descent condition and the Dai-Liao type conjugacy condition are presented for unconstrained optimization. The first algorithm is a modification of the Hager and Zhang type algorithm in such a way that the search direction is descent and satisfies Dai-Liao’s type conjugacy condition. The second simple three-term type conjugate gradient method can generate sufficient decent directions at every iteration; moreover, this property is independent of the steplength line search. Also, the algorithms could be considered as a modification of the MBFGS method, but with different zk. Under some mild conditions, the given methods are global convergence, which is independent of the Wolfe line search for general functions. The numerical experiments show that the proposed methods are very robust and efficient.
Wang Hua; Liu Feng; Crozier, Stuart; Xia Ling
2008-01-01
This paper presents a stabilized Bi-conjugate gradient algorithm (BiCGstab) that can significantly improve the performance of the impedance method, which has been widely applied to model low-frequency field induction phenomena in voxel phantoms. The improved impedance method offers remarkable computational advantages in terms of convergence performance and memory consumption over the conventional, successive over-relaxation (SOR)-based algorithm. The scheme has been validated against other numerical/analytical solutions on a lossy, multilayered sphere phantom excited by an ideal coil loop. To demonstrate the computational performance and application capability of the developed algorithm, the induced fields inside a human phantom due to a low-frequency hyperthermia device is evaluated. The simulation results show the numerical accuracy and superior performance of the method.
Wang, Hua; Liu, Feng; Xia, Ling; Crozier, Stuart
2008-11-21
This paper presents a stabilized Bi-conjugate gradient algorithm (BiCGstab) that can significantly improve the performance of the impedance method, which has been widely applied to model low-frequency field induction phenomena in voxel phantoms. The improved impedance method offers remarkable computational advantages in terms of convergence performance and memory consumption over the conventional, successive over-relaxation (SOR)-based algorithm. The scheme has been validated against other numerical/analytical solutions on a lossy, multilayered sphere phantom excited by an ideal coil loop. To demonstrate the computational performance and application capability of the developed algorithm, the induced fields inside a human phantom due to a low-frequency hyperthermia device is evaluated. The simulation results show the numerical accuracy and superior performance of the method.
The ﬁrst endcap disc of CMS being lowered slowly and carefully 100 m underground into the experimental cavern. The disc is one of 15 large pieces to make the grand descent. The uniquely shaped slice, 16 m high, about 50 cm thick weighs 400 tonnes. The two HF that were lowered earlier in November can also be seen in the foreground and background.
With precise coordination YE+1 was lowered into the cavern (9-Jan), soon joined by the ?rst barrel wheel YB+2 (19-Jan), then YB+1(4-Feb) and HB+ (13-Feb). The 1920 ton central barrel wheel YB0 rests brie?y on the pit-head cover in anticipation of a monumental descent (28-Feb) that will also trigger an intense campaign of installation of services and detectors underground.
Terrain reconstruction based on descent images for the Chang’e III landing area
Xu Xinchao
2015-10-01
Full Text Available A new method that combined image matching and shape from shading for terrain reconstruction was proposed to solve the lack of terrain in the landing area of Chang'e III. First, the reflection equation was established based on the Lommel– Seeliger reflection model. After edge extraction, the gradients of points on the edge were solved. The normal vectors of adjacent points were obtained using the smoothness constraint. Furthermore, the gradients of residual points in the image were determined through evolution. The inadequacy of the reflection equation was eliminated by considering the gradient as the constraint of the reflection equation. The normal vector of each point could be obtained by solving the reflection equation. The terrain without coordinate information was reconstructed by iterating the vector field. After using scaleinvariant feature transform to extract matching points in the descent images, the terrain was converted to a lander centroid coordinate system. Experiments were carried out with MATLAB-simulated images, laboratory images, and descent images of Chang'e III. Results show that the proposed method performs better than the classical SFS algorithm. The new method can provide reference for other deep space exploration activities.
Streuber, Gregg Mitchell
Environmental and economic factors motivate the pursuit of more fuel-efficient aircraft designs. Aerodynamic shape optimization is a powerful tool in this effort, but is hampered by the presence of multimodality in many design spaces. Gradient-based multistart optimization uses a sampling algorithm and multiple parallel optimizations to reliably apply fast gradient-based optimization to moderately multimodal problems. Ensuring that the sampled geometries remain physically realizable requires manually developing specialized linear constraints for each class of problem. Utilizing free-form deformation geometry control allows these linear constraints to be written in a geometry-independent fashion, greatly easing the process of applying the algorithm to new problems. This algorithm was used to assess the presence of multimodality when optimizing a wing in subsonic and transonic flows, under inviscid and viscous conditions, and a blended wing-body under transonic, viscous conditions. Multimodality was present in every wing case, while the blended wing-body was found to be generally unimodal.
Xiangrong Li
Full Text Available It is generally acknowledged that the conjugate gradient (CG method achieves global convergence--with at most a linear convergence rate--because CG formulas are generated by linear approximations of the objective functions. The quadratically convergent results are very limited. We introduce a new PRP method in which the restart strategy is also used. Moreover, the method we developed includes not only n-step quadratic convergence but also both the function value information and gradient value information. In this paper, we will show that the new PRP method (with either the Armijo line search or the Wolfe line search is both linearly and quadratically convergent. The numerical experiments demonstrate that the new PRP algorithm is competitive with the normal CG method.
Li, Xiangrong; Zhao, Xupei; Duan, Xiabin; Wang, Xiaoliang
2015-01-01
It is generally acknowledged that the conjugate gradient (CG) method achieves global convergence--with at most a linear convergence rate--because CG formulas are generated by linear approximations of the objective functions. The quadratically convergent results are very limited. We introduce a new PRP method in which the restart strategy is also used. Moreover, the method we developed includes not only n-step quadratic convergence but also both the function value information and gradient value information. In this paper, we will show that the new PRP method (with either the Armijo line search or the Wolfe line search) is both linearly and quadratically convergent. The numerical experiments demonstrate that the new PRP algorithm is competitive with the normal CG method.
The Modified HZ Conjugate Gradient Algorithm for Large-Scale Nonsmooth Optimization.
Yuan, Gonglin; Sheng, Zhou; Liu, Wenjie
2016-01-01
In this paper, the Hager and Zhang (HZ) conjugate gradient (CG) method and the modified HZ (MHZ) CG method are presented for large-scale nonsmooth convex minimization. Under some mild conditions, convergent results of the proposed methods are established. Numerical results show that the presented methods can be better efficiency for large-scale nonsmooth problems, and several problems are tested (with the maximum dimensions to 100,000 variables).
The Modified HZ Conjugate Gradient Algorithm for Large-Scale Nonsmooth Optimization.
Gonglin Yuan
Full Text Available In this paper, the Hager and Zhang (HZ conjugate gradient (CG method and the modified HZ (MHZ CG method are presented for large-scale nonsmooth convex minimization. Under some mild conditions, convergent results of the proposed methods are established. Numerical results show that the presented methods can be better efficiency for large-scale nonsmooth problems, and several problems are tested (with the maximum dimensions to 100,000 variables.
Van Kha, Tran; Van Vuong, Hoang; Thanh, Do Duc; Hung, Duong Quoc; Anh, Le Duc
2018-05-01
The maximum horizontal gradient method was first proposed by Blakely and Simpson (1986) for determining the boundaries between geological bodies with different densities. The method involves the comparison of a center point with its eight nearest neighbors in four directions within each 3 × 3 calculation grid. The horizontal location and magnitude of the maximum values are found by interpolating a second-order polynomial through the trio of points provided that the magnitude of the middle point is greater than its two nearest neighbors in one direction. In theoretical models of multiple sources, however, the above condition does not allow the maximum horizontal locations to be fully located, and it could be difficult to correlate the edges of complicated sources. In this paper, the authors propose an additional condition to identify more maximum horizontal locations within the calculation grid. This additional condition will improve the method algorithm for interpreting the boundaries of magnetic and/or gravity sources. The improved algorithm was tested on gravity models and applied to gravity data for the Phu Khanh basin on the continental shelf of the East Vietnam Sea. The results show that the additional locations of the maximum horizontal gradient could be helpful for connecting the edges of complicated source bodies.
González-Recio, O; Jiménez-Montero, J A; Alenda, R
2013-01-01
In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy
Sahbi Marrouchi
2014-01-01
Full Text Available Due to the continuous increase of the population and the perpetual progress of industry, the energy management presents nowadays a relevant topic that concerns researchers in electrical engineering. Indeed, in order to establish a good exploitation of the electrical grid, it is necessary to solve technical and economic problems. This can only be done through the resolution of the Unit Commitment Problem. Unit Commitment Problem allows optimizing the combination of the production units’ states and determining their production planning, in order to satisfy the expected consumption with minimal cost during a specified period which varies usually from 24 hours to one week. However, each production unit has some constraints that make this problem complex, combinatorial, and nonlinear. This paper presents a comparative study between a strategy based on hybrid gradient-genetic algorithm method and two strategies based on metaheuristic methods, fuzzy logic, and genetic algorithm, in order to predict the combinations and the unit commitment scheduling of each production unit in one side and to minimize the total production cost in the other side. To test the performance of the optimization proposed strategies, strategies have been applied to the IEEE electrical network 14 busses and the obtained results are very promising.
Regression Analysis of Top of Descent Location for Idle-thrust Descents
Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg
2013-01-01
In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.
Verhoeven, Ronald; Dalmau Codina, Ramon; Prats Menéndez, Xavier; de Gelder, Nico
2014-01-01
1 Abstract In this paper an initial implementation of a real - time aircraft trajectory optimization algorithm is presented . The aircraft trajectory for descent and approach is computed for minimum use of thrust and speed brake in support of a “green” continuous descent and approach flight operation, while complying with ATC time constraints for maintaining runway throughput and co...
Design of automation tools for management of descent traffic
Erzberger, Heinz; Nedell, William
1988-01-01
The design of an automated air traffic control system based on a hierarchy of advisory tools for controllers is described. Compatibility of the tools with the human controller, a key objective of the design, is achieved by a judicious selection of tasks to be automated and careful attention to the design of the controller system interface. The design comprises three interconnected subsystems referred to as the Traffic Management Advisor, the Descent Advisor, and the Final Approach Spacing Tool. Each of these subsystems provides a collection of tools for specific controller positions and tasks. This paper focuses primarily on the Descent Advisor which provides automation tools for managing descent traffic. The algorithms, automation modes, and graphical interfaces incorporated in the design are described. Information generated by the Descent Advisor tools is integrated into a plan view traffic display consisting of a high-resolution color monitor. Estimated arrival times of aircraft are presented graphically on a time line, which is also used interactively in combination with a mouse input device to select and schedule arrival times. Other graphical markers indicate the location of the fuel-optimum top-of-descent point and the predicted separation distances of aircraft at a designated time-control point. Computer generated advisories provide speed and descent clearances which the controller can issue to aircraft to help them arrive at the feeder gate at the scheduled times or with specified separation distances. Two types of horizontal guidance modes, selectable by the controller, provide markers for managing the horizontal flightpaths of aircraft under various conditions. The entire system consisting of descent advisor algorithm, a library of aircraft performance models, national airspace system data bases, and interactive display software has been implemented on a workstation made by Sun Microsystems, Inc. It is planned to use this configuration in operational
Kalkreuter, T.; Simma, H.
1995-07-01
The low-lying eigenvalues of a (sparse) hermitian matrix can be computed with controlled numerical errors by a conjugate gradient (CG) method. This CG algorithm is accelerated by alternating it with exact diagonalizations in the subspace spanned by the numerically computed eigenvectors. We study this combined algorithm in case of the Dirac operator with (dynamical) Wilson fermions in four-dimensional SU(2) gauge fields. The algorithm is numerically very stable and can be parallelized in an efficient way. On lattices of sizes 4 4 - 16 4 an acceleration of the pure CG method by a factor of 4 - 8 is found. (orig.)
Evolutionary algorithms applied to Landau-gauge fixing
Markham, J.F.
1998-01-01
Current algorithms used to put a lattice gauge configuration into Landau gauge either suffer from the problem of critical slowing-down or involve an additions computational expense to overcome it. Evolutionary Algorithms (EAs), which have been widely applied to other global optimisation problems, may be of use in gauge fixing. Also, being global, they should not suffer from critical slowing-down as do local gradient based algorithms. We apply EA'S and also a Steepest Descent (SD) based method to the problem of Landau Gauge Fixing and compare their performance. (authors)
Ghobadi, Kimia; Ghaffari, Hamid R; Aleman, Dionne M; Jaffray, David A; Ruschin, Mark
2012-06-01
The purpose of this work is to develop a framework to the inverse problem for radiosurgery treatment planning on the Gamma Knife(®) Perfexion™ (PFX) for intracranial targets. The approach taken in the present study consists of two parts. First, a hybrid grassfire and sphere-packing algorithm is used to obtain shot positions (isocenters) based on the geometry of the target to be treated. For the selected isocenters, a sector duration optimization (SDO) model is used to optimize the duration of radiation delivery from each collimator size from each individual source bank. The SDO model is solved using a projected gradient algorithm. This approach has been retrospectively tested on seven manually planned clinical cases (comprising 11 lesions) including acoustic neuromas and brain metastases. In terms of conformity and organ-at-risk (OAR) sparing, the quality of plans achieved with the inverse planning approach were, on average, improved compared to the manually generated plans. The mean difference in conformity index between inverse and forward plans was -0.12 (range: -0.27 to +0.03) and +0.08 (range: 0.00-0.17) for classic and Paddick definitions, respectively, favoring the inverse plans. The mean difference in volume receiving the prescribed dose (V(100)) between forward and inverse plans was 0.2% (range: -2.4% to +2.0%). After plan renormalization for equivalent coverage (i.e., V(100)), the mean difference in dose to 1 mm(3) of brainstem between forward and inverse plans was -0.24 Gy (range: -2.40 to +2.02 Gy) favoring the inverse plans. Beam-on time varied with the number of isocenters but for the most optimal plans was on average 33 min longer than manual plans (range: -17 to +91 min) when normalized to a calibration dose rate of 3.5 Gy/min. In terms of algorithm performance, the isocenter selection for all the presented plans was performed in less than 3 s, while the SDO was performed in an average of 215 min. PFX inverse planning can be performed using
Ghobadi, Kimia; Ghaffari, Hamid R.; Aleman, Dionne M.; Jaffray, David A.; Ruschin, Mark [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King' s College Road, Toronto, Ontario M5S 3G8 (Canada); Department of Radiation Oncology, University of Toronto, Radiation Medicine Program, Princess Margaret Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9 (Canada)
2012-06-15
Purpose: The purpose of this work is to develop a framework to the inverse problem for radiosurgery treatment planning on the Gamma Knife{sup Registered-Sign} Perfexion Trade-Mark-Sign (PFX) for intracranial targets. Methods: The approach taken in the present study consists of two parts. First, a hybrid grassfire and sphere-packing algorithm is used to obtain shot positions (isocenters) based on the geometry of the target to be treated. For the selected isocenters, a sector duration optimization (SDO) model is used to optimize the duration of radiation delivery from each collimator size from each individual source bank. The SDO model is solved using a projected gradient algorithm. This approach has been retrospectively tested on seven manually planned clinical cases (comprising 11 lesions) including acoustic neuromas and brain metastases. Results: In terms of conformity and organ-at-risk (OAR) sparing, the quality of plans achieved with the inverse planning approach were, on average, improved compared to the manually generated plans. The mean difference in conformity index between inverse and forward plans was -0.12 (range: -0.27 to +0.03) and +0.08 (range: 0.00-0.17) for classic and Paddick definitions, respectively, favoring the inverse plans. The mean difference in volume receiving the prescribed dose (V{sub 100}) between forward and inverse plans was 0.2% (range: -2.4% to +2.0%). After plan renormalization for equivalent coverage (i.e., V{sub 100}), the mean difference in dose to 1 mm{sup 3} of brainstem between forward and inverse plans was -0.24 Gy (range: -2.40 to +2.02 Gy) favoring the inverse plans. Beam-on time varied with the number of isocenters but for the most optimal plans was on average 33 min longer than manual plans (range: -17 to +91 min) when normalized to a calibration dose rate of 3.5 Gy/min. In terms of algorithm performance, the isocenter selection for all the presented plans was performed in less than 3 s, while the SDO was performed in an
Ghobadi, Kimia; Ghaffari, Hamid R.; Aleman, Dionne M.; Jaffray, David A.; Ruschin, Mark
2012-01-01
Purpose: The purpose of this work is to develop a framework to the inverse problem for radiosurgery treatment planning on the Gamma Knife ® Perfexion™ (PFX) for intracranial targets. Methods: The approach taken in the present study consists of two parts. First, a hybrid grassfire and sphere-packing algorithm is used to obtain shot positions (isocenters) based on the geometry of the target to be treated. For the selected isocenters, a sector duration optimization (SDO) model is used to optimize the duration of radiation delivery from each collimator size from each individual source bank. The SDO model is solved using a projected gradient algorithm. This approach has been retrospectively tested on seven manually planned clinical cases (comprising 11 lesions) including acoustic neuromas and brain metastases. Results: In terms of conformity and organ-at-risk (OAR) sparing, the quality of plans achieved with the inverse planning approach were, on average, improved compared to the manually generated plans. The mean difference in conformity index between inverse and forward plans was −0.12 (range: −0.27 to +0.03) and +0.08 (range: 0.00–0.17) for classic and Paddick definitions, respectively, favoring the inverse plans. The mean difference in volume receiving the prescribed dose (V 100 ) between forward and inverse plans was 0.2% (range: −2.4% to +2.0%). After plan renormalization for equivalent coverage (i.e., V 100 ), the mean difference in dose to 1 mm 3 of brainstem between forward and inverse plans was −0.24 Gy (range: −2.40 to +2.02 Gy) favoring the inverse plans. Beam-on time varied with the number of isocenters but for the most optimal plans was on average 33 min longer than manual plans (range: −17 to +91 min) when normalized to a calibration dose rate of 3.5 Gy/min. In terms of algorithm performance, the isocenter selection for all the presented plans was performed in less than 3 s, while the SDO was performed in an average of 215 min
Luteinizing hormone in testicular descent
Toppari, Jorma; Kaleva, Marko M; Virtanen, Helena E
2007-01-01
alone is not sufficient for normal testicular descent. The regulation of androgen production is influenced both by placental human chorionic gonadotropin (hCG) and pituitary luteinizing hormone (LH). There is evidence that the longer pregnancy continues, the more important role pituitary LH may have....... Insulin-like hormone-3 (INSL3) is suggested to be the main regulator of gubernacular development and therefore an apparent regulator of testicular descent. INSL3 production is also related to LH, and reduced INSL3 action is a possible cause for cryptorchidism. Cryptorchid boys have normal testosterone...
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
Wen-Xiang Wu
2014-01-01
Full Text Available The cost-based system optimum problem in networks with continuously distributed value of time is formulated as a path-based form, which cannot be solved by the Frank-Wolfe algorithm. In light of magnitude improvement in the availability of computer memory in recent years, path-based algorithms have been regarded as a viable approach for traffic assignment problems with reasonably large network sizes. We develop a path-based gradient projection algorithm for solving the cost-based system optimum model, based on Goldstein-Levitin-Polyak method which has been successfully applied to solve standard user equilibrium and system optimum problems. The Sioux Falls network tested is used to verify the effectiveness of the algorithm.
Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.
Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L
2017-10-01
The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.
Theseus' arid Peirithoos' descent into the underworld
Bremmer, Jan N.
2015-01-01
In my contribution I will first briefly discuss the earliest known literary descent, that by Enkidu, which almost certainly influenced the poet of the Odyssey in his depiction of Odysseus' descent. Then I will take a brief look at some descents in the Archaic Age, in particular the earliest
Liu, Xiaozheng; Yuan, Zhenming; Zhu, Junming; Xu, Dongrong
2013-01-01
The demons algorithm is a popular algorithm for non-rigid image registration because of its computational efficiency and simple implementation. The deformation forces of the classic demons algorithm were derived from image gradients by considering the deformation to decrease the intensity dissimilarity between images. However, the methods using the difference of image intensity for medical image registration are easily affected by image artifacts, such as image noise, non-uniform imaging and partial volume effects. The gradient magnitude image is constructed from the local information of an image, so the difference in a gradient magnitude image can be regarded as more reliable and robust for these artifacts. Then, registering medical images by considering the differences in both image intensity and gradient magnitude is a straightforward selection. In this paper, based on a diffeomorphic demons algorithm, we propose a chain-type diffeomorphic demons algorithm by combining the differences in both image intensity and gradient magnitude for medical image registration. Previous work had shown that the classic demons algorithm can be considered as an approximation of a second order gradient descent on the sum of the squared intensity differences. By optimizing the new dissimilarity criteria, we also present a set of new demons forces which were derived from the gradients of the image and gradient magnitude image. We show that, in controlled experiments, this advantage is confirmed, and yields a fast convergence. (paper)
Descent construction for GSpin groups
Hundley, Joseph
2016-01-01
In this paper the authors provide an extension of the theory of descent of Ginzburg-Rallis-Soudry to the context of essentially self-dual representations, that is, representations which are isomorphic to the twist of their own contragredient by some Hecke character. The authors' theory supplements the recent work of Asgari-Shahidi on the functorial lift from (split and quasisplit forms of) GSpin_{2n} to GL_{2n}.
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
Dynamic load balancing algorithm for molecular dynamics based on Voronoi cells domain decompositions
Fattebert, J.-L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Richards, D.F. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Glosli, J.N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2012-12-01
We present a new algorithm for automatic parallel load balancing in classical molecular dynamics. It assumes a spatial domain decomposition of particles into Voronoi cells. It is a gradient method which attempts to minimize a cost function by displacing Voronoi sites associated with each processor/sub-domain along steepest descent directions. Excellent load balance has been obtained for quasi-2D and 3D practical applications, with up to 440·10^{6} particles on 65,536 MPI tasks.
Conjugate descent formulation of backpropagation error in feedforward neural networks
NK Sharma
2009-06-01
Full Text Available The feedforward neural network architecture uses backpropagation learning to determine optimal weights between different interconnected layers. This learning procedure uses a gradient descent technique applied to a sum-of-squares error function for the given input-output pattern. It employs an iterative procedure to minimise the error function for a given set of patterns, by adjusting the weights of the network. The first derivates of the error with respect to the weights identify the local error surface in the descent direction. Hence the network exhibits a different local error surface for every different pattern presented to it, and weights are iteratively modified in order to minimise the current local error. The determination of an optimal weight vector is possible only when the total minimum error (mean of the minimum local errors for all patterns from the training set may be minimised. In this paper, we present a general mathematical formulation for the second derivative of the error function with respect to the weights (which represents a conjugate descent for arbitrary feedforward neural network topologies, and we use this derivative information to obtain the optimal weight vector. The local error is backpropagated among the units of hidden layers via the second order derivative of the error with respect to the weights of the hidden and output layers independently and also in combination. The new total minimum error point may be evaluated with the help of the current total minimum error and the current minimised local error. The weight modification processes is performed twice: once with respect to the present local error and once more with respect to the current total or mean error. We present some numerical evidence that our proposed method yields better network weights than those determined via a conventional gradient descent approach.
Phase gradient algorithm based on co-axis two-step phase-shifting interferometry and its application
Wang, Yawei; Zhu, Qiong; Xu, Yuanyuan; Xin, Zhiduo; Liu, Jingye
2017-12-01
A phase gradient method based on co-axis two-step phase-shifting interferometry, is used to reveal the detailed information of a specimen. In this method, the phase gradient distribution can only be obtained by calculating both the first-order derivative and the radial Hilbert transformation of the intensity difference between two phase-shifted interferograms. The feasibility and accuracy of this method were fully verified by the simulation results for a polystyrene sphere and a red blood cell. The empirical results demonstrated that phase gradient is sensitive to changes in the refractive index and morphology. Because phase retrieval and tedious phase unwrapping are not required, the calculation speed is faster. In addition, co-axis interferometry has high spatial resolution.
He, Xiaojun; Ma, Haotong; Luo, Chuanxin
2016-10-01
The optical multi-aperture imaging system is an effective way to magnify the aperture and increase the resolution of telescope optical system, the difficulty of which lies in detecting and correcting of co-phase error. This paper presents a method based on stochastic parallel gradient decent algorithm (SPGD) to correct the co-phase error. Compared with the current method, SPGD method can avoid detecting the co-phase error. This paper analyzed the influence of piston error and tilt error on image quality based on double-aperture imaging system, introduced the basic principle of SPGD algorithm, and discuss the influence of SPGD algorithm's key parameters (the gain coefficient and the disturbance amplitude) on error control performance. The results show that SPGD can efficiently correct the co-phase error. The convergence speed of the SPGD algorithm is improved with the increase of gain coefficient and disturbance amplitude, but the stability of the algorithm reduced. The adaptive gain coefficient can solve this problem appropriately. This paper's results can provide the theoretical reference for the co-phase error correction of the multi-aperture imaging system.
Steepest descent method implementation on unconstrained optimization problem using C++ program
Napitupulu, H.; Sukono; Mohd, I. Bin; Hidayat, Y.; Supian, S.
2018-03-01
Steepest Descent is known as the simplest gradient method. Recently, many researches are done to obtain the appropriate step size in order to reduce the objective function value progressively. In this paper, the properties of steepest descent method from literatures are reviewed together with advantages and disadvantages of each step size procedure. The development of steepest descent method due to its step size procedure is discussed. In order to test the performance of each step size, we run a steepest descent procedure in C++ program. We implemented it to unconstrained optimization test problem with two variables, then we compare the numerical results of each step size procedure. Based on the numerical experiment, we conclude the general computational features and weaknesses of each procedure in each case of problem.
Mühlherr, Bernhard; Weiss, Richard M
2015-01-01
Descent in Buildings begins with the resolution of a major open question about the local structure of Bruhat-Tits buildings. The authors then put their algebraic solution into a geometric context by developing a general fixed point theory for groups acting on buildings of arbitrary type, giving necessary and sufficient conditions for the residues fixed by a group to form a kind of subbuilding or "form" of the original building. At the center of this theory is the notion of a Tits index, a combinatorial version of the notion of an index in the relative theory of algebraic groups. These results
Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm
Jianyong Liu
2015-01-01
Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.
The performance of the backpropagation algorithm with varying slope of the activation function
Bai Yanping; Zhang Haixia; Hao Yilong
2009-01-01
Some adaptations are proposed to the basic BP algorithm in order to provide an efficient method to non-linear data learning and prediction. In this paper, an adopted BP algorithm with varying slope of activation function and different learning rates is put forward. The results of experiment indicated that this algorithm can get very good performance of training. We also test the prediction performance of our adopted BP algorithm on 16 instances. We compared the test results to the ones of the BP algorithm with gradient descent momentum and an adaptive learning rate. The results indicate this adopted BP algorithm gives best performance (100%) for test example, which conclude this adopted BP algorithm produces a smoothed reconstruction that learns better to new prediction function values than the BP algorithm improved with momentum.
preconditioning the modified conjugate gradient method
Admin
steepest descent method, the number of matrix-vector products per iteration .... modified CGM algorithm is used for large class of problems that is not ..... New Trends in the Mathematical and Computer Sciences with Applications to Real World.
OMER MAHMOUD
2007-08-01
Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.
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.
ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...
Chen Ming
2017-01-01
Full Text Available To solve the Flexible Job-shop Scheduling Problem (FJSP with different varieties and small batches, a modified meta-heuristic algorithm based on Genetic Algorithm (GA is proposed in which gene encoding is divided into process encoding and machine encoding, and according to the encoding mode, the machine gene fragment is connected with the process gene fragment and can be changed with the alteration of process genes. In order to get the global optimal solutions, the crossover and mutation operation of the process gene fragment and machine gene fragment are carried out respectively. In the initialization operation, the machines with shorter manufacturing time are more likely to be chosen to accelerate the convergence speed and then the tournament selection strategy is applied due to the minimum optimization objective. Meanwhile, a judgment condition of the crossover point quantity is introduced to speed up the population evolution and as an important interaction bridge between the current machine and alternative machines in the incidence matrix, a novel mutation operation of machine genes is proposed to achieve the replacement of manufacturing machines. The benchmark test shows the correctness of proposed algorithm and the case simulation proves the proposed algorithm has better performance compared with existing algorithms.
Güntürkün, Rüştü
2010-08-01
In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.
Random versus Deterministic Descent in RNA Energy Landscape Analysis
Luke Day
2016-01-01
Full Text Available Identifying sets of metastable conformations is a major research topic in RNA energy landscape analysis, and recently several methods have been proposed for finding local minima in landscapes spawned by RNA secondary structures. An important and time-critical component of such methods is steepest, or gradient, descent in attraction basins of local minima. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets where the size has an order of magnitude in the region of ~106. While the gain for each individual sample might be marginal, the overall run-time improvement can be significant. Moreover, for the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences, we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. For the large sample size we selected for descent procedures, the coverage of local minima is very high up to energy values of the region where the samples were randomly selected from the partial energy landscapes; that is, the difference to the total set of local minima is mainly due to the upper area of the energy landscapes.
Lukšan, Ladislav; Vlček, Jan
1998-01-01
Roč. 8, č. 3-4 (1998), s. 201-223 ISSN 1055-6788 R&D Projects: GA ČR GA201/96/0918 Keywords : nonlinear equations * Armijo-type descent methods * Newton-like methods * truncated methods * global convergence * nonsymmetric linear systems * conjugate gradient -type methods * residual smoothing * computational experiments Subject RIV: BB - Applied Statistics, Operational Research
Continuous Descent Operations using Energy Principles
De Jong, P.M.A.
2014-01-01
During today’s aircraft descents, Air Traf?c Control (ATC) commands aircraft to descend to specific altitudes and directions to maintain separation and spacing from other aircraft. When the aircraft is instructed to maintain an intermediate descent altitude, it requires engine thrust to maintain
Donovan H Parks
Full Text Available GenGIS is free and open source software designed to integrate biodiversity data with a digital map and information about geography and habitat. While originally developed with microbial community analyses and phylogeography in mind, GenGIS has been applied to a wide range of datasets. A key feature of GenGIS is the ability to test geographic axes that can correspond to routes of migration or gradients that influence community similarity. Here we introduce GenGIS version 2, which extends the linear gradient tests introduced in the first version to allow comprehensive testing of all possible linear geographic axes. GenGIS v2 also includes a new plugin framework that supports the development and use of graphically driven analysis packages: initial plugins include implementations of linear regression and the Mantel test, calculations of alpha-diversity (e.g., Shannon Index for all samples, and geographic visualizations of dissimilarity matrices. We have also implemented a recently published method for biomonitoring reference condition analysis (RCA, which compares observed species richness and diversity to predicted values to determine whether a given site has been impacted. The newest version of GenGIS supports vector data in addition to raster files. We demonstrate the new features of GenGIS by performing a full gradient analysis of an Australian kangaroo apple data set, by using plugins and embedded statistical commands to analyze human microbiome sample data, and by applying RCA to a set of samples from Atlantic Canada. GenGIS release versions, tutorials and documentation are freely available at http://kiwi.cs.dal.ca/GenGIS, and source code is available at https://github.com/beiko-lab/gengis.
Park, Gee Yong; Seong, Poong Hyun
1994-01-01
In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection
algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).
algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...
Sobolev gradients and differential equations
Neuberger, J W
2010-01-01
A Sobolev gradient of a real-valued functional on a Hilbert space is a gradient of that functional taken relative to an underlying Sobolev norm. This book shows how descent methods using such gradients allow a unified treatment of a wide variety of problems in differential equations. For discrete versions of partial differential equations, corresponding Sobolev gradients are seen to be vastly more efficient than ordinary gradients. In fact, descent methods with these gradients generally scale linearly with the number of grid points, in sharp contrast with the use of ordinary gradients. Aside from the first edition of this work, this is the only known account of Sobolev gradients in book form. Most of the applications in this book have emerged since the first edition was published some twelve years ago. What remains of the first edition has been extensively revised. There are a number of plots of results from calculations and a sample MatLab code is included for a simple problem. Those working through a fair p...
will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...
Hazard avoidance via descent images for safe landing
Yan, Ruicheng; Cao, Zhiguo; Zhu, Lei; Fang, Zhiwen
2013-10-01
In planetary or lunar landing missions, hazard avoidance is critical for landing safety. Therefore, it is very important to correctly detect hazards and effectively find a safe landing area during the last stage of descent. In this paper, we propose a passive sensing based HDA (hazard detection and avoidance) approach via descent images to lower the landing risk. In hazard detection stage, a statistical probability model on the basis of the hazard similarity is adopted to evaluate the image and detect hazardous areas, so that a binary hazard image can be generated. Afterwards, a safety coefficient, which jointly utilized the proportion of hazards in the local region and the inside hazard distribution, is proposed to find potential regions with less hazards in the binary hazard image. By using the safety coefficient in a coarse-to-fine procedure and combining it with the local ISD (intensity standard deviation) measure, the safe landing area is determined. The algorithm is evaluated and verified with many simulated descent downward looking images rendered from lunar orbital satellite images.
Bouchard, M
2001-01-01
In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.
Armstrong, Ian S; Hoffmann, Sandra A
2016-11-01
The interest in quantitative single photon emission computer tomography (SPECT) shows potential in a number of clinical applications and now several vendors are providing software and hardware solutions to allow 'SUV-SPECT' to mirror metrics used in PET imaging. This brief technical report assesses the accuracy of activity concentration measurements using a new algorithm 'xSPECT' from Siemens Healthcare. SPECT/CT data were acquired from a uniform cylinder with 5, 10, 15 and 20 s/projection and NEMA image quality phantom with 25 s/projection. The NEMA phantom had hot spheres filled with an 8 : 1 activity concentration relative to the background compartment. Reconstructions were performed using parameters defined by manufacturer presets available with the algorithm. The accuracy of activity concentration measurements was assessed. A dose calibrator-camera cross-calibration factor (CCF) was derived from the uniform phantom data. In uniform phantom images, a positive bias was observed, ranging from ∼6% in the lower count images to ∼4% in the higher-count images. On the basis of the higher-count data, a CCF of 0.96 was derived. As expected, considerable negative bias was measured in the NEMA spheres using region mean values whereas positive bias was measured in the four largest NEMA spheres. Nonmonotonically increasing recovery curves for the hot spheres suggested the presence of Gibbs edge enhancement from resolution modelling. Sufficiently accurate activity concentration measurements can easily be measured on images reconstructed with the xSPECT algorithm without a CCF. However, the use of a CCF is likely to improve accuracy further. A manual conversion of voxel values into SUV should be possible, provided that the patient weight, injected activity and time between injection and imaging are all known accurately.
Incoherent beam combining based on the momentum SPGD algorithm
Yang, Guoqing; Liu, Lisheng; Jiang, Zhenhua; Guo, Jin; Wang, Tingfeng
2018-05-01
Incoherent beam combining (ICBC) technology is one of the most promising ways to achieve high-energy, near-diffraction laser output. In this paper, the momentum method is proposed as a modification of the stochastic parallel gradient descent (SPGD) algorithm. The momentum method can improve the speed of convergence of the combining system efficiently. The analytical method is employed to interpret the principle of the momentum method. Furthermore, the proposed algorithm is testified through simulations as well as experiments. The results of the simulations and the experiments show that the proposed algorithm not only accelerates the speed of the iteration, but also keeps the stability of the combining process. Therefore the feasibility of the proposed algorithm in the beam combining system is testified.
Hybrid Steepest-Descent Methods for Triple Hierarchical Variational Inequalities
L. C. Ceng
2015-01-01
Full Text Available We introduce and analyze a relaxed iterative algorithm by combining Korpelevich’s extragradient method, hybrid steepest-descent method, and Mann’s iteration method. We prove that, under appropriate assumptions, the proposed algorithm converges strongly to a common element of the fixed point set of infinitely many nonexpansive mappings, the solution set of finitely many generalized mixed equilibrium problems (GMEPs, the solution set of finitely many variational inclusions, and the solution set of general system of variational inequalities (GSVI, which is just a unique solution of a triple hierarchical variational inequality (THVI in a real Hilbert space. In addition, we also consider the application of the proposed algorithm for solving a hierarchical variational inequality problem with constraints of finitely many GMEPs, finitely many variational inclusions, and the GSVI. The results obtained in this paper improve and extend the corresponding results announced by many others.
A guidance and control algorithm for scent tracking micro-robotic vehicle swarms
Dohner, J.L.
1998-03-01
Cooperative micro-robotic scent tracking vehicles are designed to collectively sniff out locations of high scent concentrations in unknown, geometrically complex environments. These vehicles are programmed with guidance and control algorithms that allow inter cooperation among vehicles. In this paper a cooperative guidance and control algorithm for scent tracking micro-robotic vehicles is presented. This algorithm is comprised of a sensory compensation sub-algorithm using point source cancellation, a guidance sub-algorithm using gradient descent tracking, and a control sub-algorithm using proportional feedback. The concepts of social rank and point source cancellation are new concepts introduced within. Simulation results for cooperative vehicles swarms are given. Limitations are discussed
A guidance and control algorithm for scent tracking micro-robotic vehicle swarms
Dohner, J.L. [Sandia National Labs., Albuquerque, NM (United States). Structural Dynamics Dept.
1998-03-01
Cooperative micro-robotic scent tracking vehicles are designed to collectively sniff out locations of high scent concentrations in unknown, geometrically complex environments. These vehicles are programmed with guidance and control algorithms that allow inter cooperation among vehicles. In this paper a cooperative guidance and control algorithm for scent tracking micro-robotic vehicles is presented. This algorithm is comprised of a sensory compensation sub-algorithm using point source cancellation, a guidance sub-algorithm using gradient descent tracking, and a control sub-algorithm using proportional feedback. The concepts of social rank and point source cancellation are new concepts introduced within. Simulation results for cooperative vehicles swarms are given. Limitations are discussed.
Dong Yun Kim; Poong Hyun Seong; .
1997-01-01
In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)
Propulsive Descent Technologies (PDT): Original Content Project
National Aeronautics and Space Administration — Future missions to Mars require landed mass that exceeds the capability of current entry, descent, and landing technology. New technology and techniques are...
Steepest descent approximations for accretive operator equations
Chidume, C.E.
1993-03-01
A necessary and sufficient condition is established for the strong convergence of the steepest descent approximation to a solution of equations involving quasi-accretive operators defined on a uniformly smooth Banach space. (author). 49 refs
Pengpen, T; Soleimani, M
2015-06-13
Cone beam computed tomography (CBCT) is an imaging modality that has been used in image-guided radiation therapy (IGRT). For applications such as lung radiation therapy, CBCT images are greatly affected by the motion artefacts. This is mainly due to low temporal resolution of CBCT. Recently, a dual modality of electrical impedance tomography (EIT) and CBCT has been proposed, in which the high temporal resolution EIT imaging system provides motion data to a motion-compensated algebraic reconstruction technique (ART)-based CBCT reconstruction software. High computational time associated with ART and indeed other variations of ART make it less practical for real applications. This paper develops a motion-compensated conjugate gradient least-squares (CGLS) algorithm for CBCT. A motion-compensated CGLS offers several advantages over ART-based methods, including possibilities for explicit regularization, rapid convergence and parallel computations. This paper for the first time demonstrates motion-compensated CBCT reconstruction using CGLS and reconstruction results are shown in limited data CBCT considering only a quarter of the full dataset. The proposed algorithm is tested using simulated motion data in generic motion-compensated CBCT as well as measured EIT data in dual EIT-CBCT imaging. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
Ward, G.J.; Heckbert, P.S.; Technische Hogeschool Delft
1992-04-01
A new method for improving the accuracy of a diffuse interreflection calculation is introduced in a ray tracing context. The information from a hemispherical sampling of the luminous environment is interpreted in a new way to predict the change in irradiance as a function of position and surface orientation. The additional computation involved is modest and the benefit is substantial. An improved interpolation of irradiance resulting from the gradient calculation produces smoother, more accurate renderings. This result is achieved through better utilization of ray samples rather than additional samples or alternate sampling strategies. Thus, the technique is applicable to a variety of global illumination algorithms that use hemicubes or Monte Carlo sampling techniques
A comparison of three optimization algorithms for intensity modulated radiation therapy
Pflugfelder, D.; Wilkens, J.J.; Nill, S.; Oelfke, U.
2008-01-01
In intensity modulated treatment techniques, the modulation of each treatment field is obtained using an optimization algorithm. Multiple optimization algorithms have been proposed in the literature, e.g. steepest descent, conjugate gradient, quasi-Newton methods to name a few. The standard optimization algorithm in our in-house inverse planning tool KonRad is a quasi-Newton algorithm. Although this algorithm yields good results, it also has some drawbacks. Thus we implemented an improved optimization algorithm based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) routine. In this paper the improved optimization algorithm is described. To compare the two algorithms, several treatment plans are optimized using both algorithms. This included photon (IMRT) as well as proton (IMPT) intensity modulated therapy treatment plans. To present the results in a larger context the widely used conjugate gradient algorithm was also included into this comparison. On average, the improved optimization algorithm was six times faster to reach the same objective function value. However, it resulted not only in an acceleration of the optimization. Due to the faster convergence, the improved optimization algorithm usually terminates the optimization process at a lower objective function value. The average of the observed improvement in the objective function value was 37%. This improvement is clearly visible in the corresponding dose-volume-histograms. The benefit of the improved optimization algorithm is particularly pronounced in proton therapy plans. The conjugate gradient algorithm ranked in between the other two algorithms with an average speedup factor of two and an average improvement of the objective function value of 30%. (orig.)
The Dropout Learning Algorithm
Baldi, Pierre; Sadowski, Peter
2014-01-01
Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations. PMID:24771879
Entry, Descent, and Landing With Propulsive Deceleration
Palaszewski, Bryan
2012-01-01
The future exploration of the Solar System will require innovations in transportation and the use of entry, descent, and landing (EDL) systems at many planetary landing sites. The cost of space missions has always been prohibitive, and using the natural planetary and planet s moons atmospheres for entry, descent, and landing can reduce the cost, mass, and complexity of these missions. This paper will describe some of the EDL ideas for planetary entry and survey the overall technologies for EDL that may be attractive for future Solar System missions.
Seismic noise attenuation using an online subspace tracking algorithm
Zhou, Yatong; Li, Shuhua; Zhang, Dong; Chen, Yangkang
2018-02-01
We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.
Minimizing convex functions by continuous descent methods
Sergiu Aizicovici
2010-01-01
Full Text Available We study continuous descent methods for minimizing convex functions, defined on general Banach spaces, which are associated with an appropriate complete metric space of vector fields. We show that there exists an everywhere dense open set in this space of vector fields such that each of its elements generates strongly convergent trajectories.
Pixel-based OPC optimization based on conjugate gradients.
Ma, Xu; Arce, Gonzalo R
2011-01-31
Optical proximity correction (OPC) methods are resolution enhancement techniques (RET) used extensively in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the mask is divided into small pixels, each of which is modified during the optimization process. Two critical issues in PBOPC are the required computational complexity of the optimization process, and the manufacturability of the optimized mask. Most current OPC optimization methods apply the steepest descent (SD) algorithm to improve image fidelity augmented by regularization penalties to reduce the complexity of the mask. Although simple to implement, the SD algorithm converges slowly. The existing regularization penalties, however, fall short in meeting the mask rule check (MRC) requirements often used in semiconductor manufacturing. This paper focuses on developing OPC optimization algorithms based on the conjugate gradient (CG) method which exhibits much faster convergence than the SD algorithm. The imaging formation process is represented by the Fourier series expansion model which approximates the partially coherent system as a sum of coherent systems. In order to obtain more desirable manufacturability properties of the mask pattern, a MRC penalty is proposed to enlarge the linear size of the sub-resolution assistant features (SRAFs), as well as the distances between the SRAFs and the main body of the mask. Finally, a projection method is developed to further reduce the complexity of the optimized mask pattern.
Dynamically Predicting the Quality of Service: Batch, Online, and Hybrid Algorithms
Ya Chen
2017-01-01
Full Text Available This paper studies the problem of dynamically modeling the quality of web service. The philosophy of designing practical web service recommender systems is delivered in this paper. A general system architecture for such systems continuously collects the user-service invocation records and includes both an online training module and an offline training module for quality prediction. In addition, we introduce matrix factorization-based online and offline training algorithms based on the gradient descent algorithms and demonstrate the fitness of this online/offline algorithm framework to the proposed architecture. The superiority of the proposed model is confirmed by empirical studies on a real-life quality of web service data set and comparisons with existing web service recommendation algorithms.
NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM
ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi
2005-01-01
Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms.
De Sa, Christopher; Zhang, Ce; Olukotun, Kunle; Ré, Christopher
2015-12-01
Stochastic gradient descent (SGD) is a ubiquitous algorithm for a variety of machine learning problems. Researchers and industry have developed several techniques to optimize SGD's runtime performance, including asynchronous execution and reduced precision. Our main result is a martingale-based analysis that enables us to capture the rich noise models that may arise from such techniques. Specifically, we use our new analysis in three ways: (1) we derive convergence rates for the convex case (Hogwild!) with relaxed assumptions on the sparsity of the problem; (2) we analyze asynchronous SGD algorithms for non-convex matrix problems including matrix completion; and (3) we design and analyze an asynchronous SGD algorithm, called Buckwild!, that uses lower-precision arithmetic. We show experimentally that our algorithms run efficiently for a variety of problems on modern hardware.
Qu Li
2014-01-01
Full Text Available Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.
Zhang, Ye; Gong, Rongfang; Cheng, Xiaoliang; Gulliksson, Mårten
2018-06-01
This study considers the inverse source problem for elliptic partial differential equations with both Dirichlet and Neumann boundary data. The unknown source term is to be determined by additional boundary conditions. Unlike the existing methods found in the literature, which usually employ the first-order in time gradient-like system (such as the steepest descent methods) for numerically solving the regularized optimization problem with a fixed regularization parameter, we propose a novel method with a second-order in time dissipative gradient-like system and a dynamical selected regularization parameter. A damped symplectic scheme is proposed for the numerical solution. Theoretical analysis is given for both the continuous model and the numerical algorithm. Several numerical examples are provided to show the robustness of the proposed algorithm.
Evaluation of vertical profiles to design continuous descent approach procedure
Pradeep, Priyank
The current research focuses on predictability, variability and operational feasibility aspect of Continuous Descent Approach (CDA), which is among the key concepts of the Next Generation Air Transportation System (NextGen). The idle-thrust CDA is a fuel economical, noise and emission abatement procedure, but requires increased separation to accommodate for variability and uncertainties in vertical and speed profiles of arriving aircraft. Although a considerable amount of researches have been devoted to the estimation of potential benefits of the CDA, only few have attempted to explain the predictability, variability and operational feasibility aspect of CDA. The analytical equations derived using flight dynamics and Base of Aircraft and Data (BADA) Total Energy Model (TEM) in this research gives insight into dependency of vertical profile of CDA on various factors like wind speed and gradient, weight, aircraft type and configuration, thrust settings, atmospheric factors (deviation from ISA (DISA), pressure and density of the air) and descent speed profile. Application of the derived equations to idle-thrust CDA gives an insight into sensitivity of its vertical profile to multiple factors. This suggests fixed geometric flight path angle (FPA) CDA has higher degree of predictability and lesser variability at the cost of non-idle and low thrust engine settings. However, with optimized design this impact can be overall minimized. The CDA simulations were performed using Future ATM Concept Evaluation Tool (FACET) based on radar-track and aircraft type data (BADA) of the real air-traffic to some of the busiest airports in the USA (ATL, SFO and New York Metroplex (JFK, EWR and LGA)). The statistical analysis of the vertical profiles of CDA shows 1) mean geometric FPAs derived from various simulated vertical profiles are consistently shallower than 3° glideslope angle and 2) high level of variability in vertical profiles of idle-thrust CDA even in absence of
Ono, Shunsuke
2017-04-01
Minimizing L 0 gradient, the number of the non-zero gradients of an image, together with a quadratic data-fidelity to an input image has been recognized as a powerful edge-preserving filtering method. However, the L 0 gradient minimization has an inherent difficulty: a user-given parameter controlling the degree of flatness does not have a physical meaning since the parameter just balances the relative importance of the L 0 gradient term to the quadratic data-fidelity term. As a result, the setting of the parameter is a troublesome work in the L 0 gradient minimization. To circumvent the difficulty, we propose a new edge-preserving filtering method with a novel use of the L 0 gradient. Our method is formulated as the minimization of the quadratic data-fidelity subject to the hard constraint that the L 0 gradient is less than a user-given parameter α . This strategy is much more intuitive than the L 0 gradient minimization because the parameter α has a clear meaning: the L 0 gradient value of the output image itself, so that one can directly impose a desired degree of flatness by α . We also provide an efficient algorithm based on the so-called alternating direction method of multipliers for computing an approximate solution of the nonconvex problem, where we decompose it into two subproblems and derive closed-form solutions to them. The advantages of our method are demonstrated through extensive experiments.
Three-dimensional sparse electromagnetic imaging accelerated by projected steepest descent
Desmal, Abdulla
2016-11-02
An efficient and accurate scheme for solving the nonlinear electromagnetic inverse scattering problem on three-dimensional sparse investigation domains is proposed. The minimization problem is constructed in such a way that the data misfit between measurements and scattered fields (which are expressed as a nonlinear function of the contrast) is constrained by the contrast\\'s first norm. The resulting minimization problem is solved using nonlinear Landweber iterations accelerated using a steepest descent algorithm. A projection operator is applied at every iteration to enforce the sparsity constraint by thresholding the result of that iteration. Steepest descent algorithm ensures accelerated and convergent solution by utilizing larger iteration steps selected based on a necessary B-condition.
A Gradient Taguchi Method for Engineering Optimization
Hwang, Shun-Fa; Wu, Jen-Chih; He, Rong-Song
2017-10-01
To balance the robustness and the convergence speed of optimization, a novel hybrid algorithm consisting of Taguchi method and the steepest descent method is proposed in this work. Taguchi method using orthogonal arrays could quickly find the optimum combination of the levels of various factors, even when the number of level and/or factor is quite large. This algorithm is applied to the inverse determination of elastic constants of three composite plates by combining numerical method and vibration testing. For these problems, the proposed algorithm could find better elastic constants in less computation cost. Therefore, the proposed algorithm has nice robustness and fast convergence speed as compared to some hybrid genetic algorithms.
Nurses of African descent and career advancement.
Wesley, Yvonne; Dobal, May T
2009-01-01
The purpose of this article is to evaluate a leadership institute designed to promote career advancement and leadership in administration, education, and research among nurses of African descent. Government reports indicate that Black Americans receive lower quality health care than other racial groups even when insurance and income are equal. Moreover, the literature suggests that less than 10% of practicing professional nurses in America are Black-and of these, less than 1% are in senior executive leadership positions. However, the literature lacks detailed discussion of the effectiveness of leadership programs. This article provides an in-depth look at a leadership institute for Black nurses and outlines the impact of the program.
Noise annoyance caused by continuous descent approaches compared to regular descent procedures
White, K.; Arntzen, M.; Walker, F.; Waiyaki, F.M.; Meeter, M.; Bronkhorst, A.W.
2017-01-01
During Continuous Descent Approaches (CDAs) aircraft glide towards the runway resulting in reduced noise and fuel usage. Here, we investigated whether such landings cause less noise annoyance than a regular stepwise approach. Both landing types were compared in a controlled laboratory setting with a
Development and descent of the testis in relation to cryptorchidism
Virtanen, Helena E; Cortes, Dina; Rajpert-De Meyts, Ewa
2007-01-01
The testis descends in two phases. Animal studies suggest, that the transabdominal descent of the testis depends on the insulin-like hormone 3 (INSL3). Androgens are important in the inguinoscrotal testicular descent in animals and humans. In general, the cause of cryptorchidism is unknown...... and the aetiology is possibly multifactorial. Histological changes in cryptorchid testes demonstrate disturbed development. Conclusion: Since testicular descent is regulated by testis-derived hormones, cryptorchidism may reflect a functional defect of the testis....
The grand descent has begun for CMS
2006-01-01
Until recently, the CMS experimental cavern looked relatively empty; its detector was assembled entirely at ground level, to be lowered underground in 15 sections. On 2 November, the first hadronic forward calorimeter led the way with a grand descent. The first section of the CMS detector (centre of photo) arriving from the vertical shaft, viewed from the cavern floor. There is something unusual about the construction of the CMS detector. Instead of being built in the experimental cavern, like all the other detectors in the LHC experiments, it was constructed at ground level. This was to allow for easy access during the assembly of the detector and to minimise the size of the excavated cavern. The slightly nerve-wracking task of lowering it safely into the cavern in separate sections came after the complete detector was successfully tested with a magnetic field at ground level. In the early morning of 2 November, the first section of the CMS detector began its eagerly awaited descent into the underground ca...
A novel highly parallel algorithm for linearly unmixing hyperspectral images
Guerra, Raúl; López, Sebastián.; Callico, Gustavo M.; López, Jose F.; Sarmiento, Roberto
2014-10-01
Endmember extraction and abundances calculation represent critical steps within the process of linearly unmixing a given hyperspectral image because of two main reasons. The first one is due to the need of computing a set of accurate endmembers in order to further obtain confident abundance maps. The second one refers to the huge amount of operations involved in these time-consuming processes. This work proposes an algorithm to estimate the endmembers of a hyperspectral image under analysis and its abundances at the same time. The main advantage of this algorithm is its high parallelization degree and the mathematical simplicity of the operations implemented. This algorithm estimates the endmembers as virtual pixels. In particular, the proposed algorithm performs the descent gradient method to iteratively refine the endmembers and the abundances, reducing the mean square error, according with the linear unmixing model. Some mathematical restrictions must be added so the method converges in a unique and realistic solution. According with the algorithm nature, these restrictions can be easily implemented. The results obtained with synthetic images demonstrate the well behavior of the algorithm proposed. Moreover, the results obtained with the well-known Cuprite dataset also corroborate the benefits of our proposal.
Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent
Zunyi Tang
2013-01-01
Full Text Available Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.
Minimum-fuel turning climbout and descent guidance of transport jets
Neuman, F.; Kreindler, E.
1983-01-01
The complete flightpath optimization problem for minimum fuel consumption from takeoff to landing including the initial and final turns from and to the runway heading is solved. However, only the initial and final segments which contain the turns are treated, since the straight-line climbout, cruise, and descent problems have already been solved. The paths are derived by generating fields of extremals, using the necessary conditions of optimal control together with singular arcs and state constraints. Results show that the speed profiles for straight flight and turning flight are essentially identical except for the final horizontal accelerating or decelerating turns. The optimal turns require no abrupt maneuvers, and an approximation of the optimal turns could be easily integrated with present straight-line climb-cruise-descent fuel-optimization algorithms. Climbout at the optimal IAS rather than the 250-knot terminal-area speed limit would save 36 lb of fuel for the 727-100 aircraft.
Algorithms for the optimization of RBE-weighted dose in particle therapy.
Horcicka, M; Meyer, C; Buschbacher, A; Durante, M; Krämer, M
2013-01-21
We report on various algorithms used for the nonlinear optimization of RBE-weighted dose in particle therapy. Concerning the dose calculation carbon ions are considered and biological effects are calculated by the Local Effect Model. Taking biological effects fully into account requires iterative methods to solve the optimization problem. We implemented several additional algorithms into GSI's treatment planning system TRiP98, like the BFGS-algorithm and the method of conjugated gradients, in order to investigate their computational performance. We modified textbook iteration procedures to improve the convergence speed. The performance of the algorithms is presented by convergence in terms of iterations and computation time. We found that the Fletcher-Reeves variant of the method of conjugated gradients is the algorithm with the best computational performance. With this algorithm we could speed up computation times by a factor of 4 compared to the method of steepest descent, which was used before. With our new methods it is possible to optimize complex treatment plans in a few minutes leading to good dose distributions. At the end we discuss future goals concerning dose optimization issues in particle therapy which might benefit from fast optimization solvers.
A sampling algorithm for segregation analysis
Henshall John
2001-11-01
Full Text Available Abstract Methods for detecting Quantitative Trait Loci (QTL without markers have generally used iterative peeling algorithms for determining genotype probabilities. These algorithms have considerable shortcomings in complex pedigrees. A Monte Carlo Markov chain (MCMC method which samples the pedigree of the whole population jointly is described. Simultaneous sampling of the pedigree was achieved by sampling descent graphs using the Metropolis-Hastings algorithm. A descent graph describes the inheritance state of each allele and provides pedigrees guaranteed to be consistent with Mendelian sampling. Sampling descent graphs overcomes most, if not all, of the limitations incurred by iterative peeling algorithms. The algorithm was able to find the QTL in most of the simulated populations. However, when the QTL was not modeled or found then its effect was ascribed to the polygenic component. No QTL were detected when they were not simulated.
Block-conjugate-gradient method
McCarthy, J.F.
1989-01-01
It is shown that by using the block-conjugate-gradient method several, say s, columns of the inverse Kogut-Susskind fermion matrix can be found simultaneously, in less time than it would take to run the standard conjugate-gradient algorithm s times. The method improves in efficiency relative to the standard conjugate-gradient algorithm as the fermion mass is decreased and as the value of the coupling is pushed to its limit before the finite-size effects become important. Thus it is potentially useful for measuring propagators in large lattice-gauge-theory calculations of the particle spectrum
Enumerating set partitions according to the number of descents of ...
) according to the number of descents of size or more, where ≥ 1 is fixed. An explicit expression in terms of Stirling numbers of the second kind may be given for the total number of such descents in all the members of (,). We also ...
Testicular descent: INSL3, testosterone, genes and the intrauterine milieu
Bay, Katrine; Main, Katharina M; Toppari, Jorma
2011-01-01
Complete testicular descent is a sign of, and a prerequisite for, normal testicular function in adult life. The process of testis descent is dependent on gubernacular growth and reorganization, which is regulated by the Leydig cell hormones insulin-like peptide 3 (INSL3) and testosterone. Investi...
43 CFR 10.14 - Lineal descent and cultural affiliation.
2010-10-01
... Hawaiian organization and the human remains, funerary objects, sacred objects, or objects of cultural... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Lineal descent and cultural affiliation... GRAVES PROTECTION AND REPATRIATION REGULATIONS General § 10.14 Lineal descent and cultural affiliation...
Accelerated convergence of the steepest-descent method for magnetohydrodynamic equilibria
Handy, C.R.; Hirshman, S.P.
1984-06-01
Iterative schemes based on the method of steepest descent have recently been used to obtain magnetohydrodynamic (MHD) equilibria. Such schemes generate asymptotic geometric vector sequences whose convergence rate can be improved through the use of the epsilon-algorithm. The application of this nonlinear recursive technique to stiff systems is discussed. In principle, the epsilon-algorithm is capable of yielding quadratic convergence and therefore represents an attractive alternative to other quadratic convergence schemes requiring Jacobian matrix inversion. Because the damped MHD equations have eigenvalues with negative real parts (in the neighborhood of a stable equilibrium), the epsilon-algorithm will generally be stable. Concern for residual monotonic sequences leads to consideration of alternative methods for implementing the algorithm
Stochastic Spectral and Conjugate Descent Methods
Kovalev, Dmitry
2018-02-11
The state-of-the-art methods for solving optimization problems in big dimensions are variants of randomized coordinate descent (RCD). In this paper we introduce a fundamentally new type of acceleration strategy for RCD based on the augmentation of the set of coordinate directions by a few spectral or conjugate directions. As we increase the number of extra directions to be sampled from, the rate of the method improves, and interpolates between the linear rate of RCD and a linear rate independent of the condition number. We develop and analyze also inexact variants of these methods where the spectral and conjugate directions are allowed to be approximate only. We motivate the above development by proving several negative results which highlight the limitations of RCD with importance sampling.
Stochastic Spectral and Conjugate Descent Methods
Kovalev, Dmitry; Gorbunov, Eduard; Gasanov, Elnur; Richtarik, Peter
2018-01-01
The state-of-the-art methods for solving optimization problems in big dimensions are variants of randomized coordinate descent (RCD). In this paper we introduce a fundamentally new type of acceleration strategy for RCD based on the augmentation of the set of coordinate directions by a few spectral or conjugate directions. As we increase the number of extra directions to be sampled from, the rate of the method improves, and interpolates between the linear rate of RCD and a linear rate independent of the condition number. We develop and analyze also inexact variants of these methods where the spectral and conjugate directions are allowed to be approximate only. We motivate the above development by proving several negative results which highlight the limitations of RCD with importance sampling.
Planetary entry, descent, and landing technologies
Pichkhadze, K.; Vorontsov, V.; Polyakov, A.; Ivankov, A.; Taalas, P.; Pellinen, R.; Harri, A.-M.; Linkin, V.
2003-04-01
Martian meteorological lander (MML) is intended for landing on the Martian surface in order to monitor the atmosphere at landing point for one Martian year. MMLs shall become the basic elements of a global network of meteorological mini-landers, observing the dynamics of changes of the atmospheric parameters on the Red Planet. The MML main scientific tasks are as follows: (1) Study of vertical structure of the Martian atmosphere throughout the MML descent; (2) On-surface meteorological observations for one Martian year. One of the essential factors influencing the lander's design is its entry, descent, and landing (EDL) sequence. During Phase A of the MML development, five different options for the lander's design were carefully analyzed. All of these options ensure the accomplishment of the above-mentioned scientific tasks with high effectiveness. CONCEPT A (conventional approach): Two lander options (with a parachute system + airbag and an inflatable airbrake + airbag) were analyzed. They are similar in terms of fulfilling braking phases and completely analogous in landing by means of airbags. CONCEPT B (innovative approach): Three lander options were analyzed. The distinguishing feature is the presence of inflatable braking units (IBU) in their configurations. SELECTED OPTION (innovative approach): Incorporating a unique design approach and modern technologies, the selected option of the lander represents a combination of the options analyzed in the framework of Concept B study. Currently, the selected lander option undergoes systems testing (Phase D1). Several MMLs can be delivered to Mars in frameworks of various missions as primary or piggybacking payload: (1) USA-led "Mars Scout" (2007); (2) France-led "NetLander" (2007/2009); (3) Russia-led "Mars-Deimos-Phobos sample return" (2007); (4) Independent mission (currently under preliminary study); etc.
Estimating the degree of identity by descent in consanguineous couples.
Carr, Ian M; Markham, Sir Alexander F; Pena, Sérgio D J
2011-12-01
In some clinical and research settings, it is often necessary to identify the true level of "identity by descent" (IBD) between two individuals. However, as the individuals become more distantly related, it is increasingly difficult to accurately calculate this value. Consequently, we have developed a computer program that uses genome-wide SNP genotype data from related individuals to estimate the size and extent of IBD in their genomes. In addition, the software can compare a couple's IBD regions with either the autozygous regions of a relative affected by an autosomal recessive disease of unknown cause, or the IBD regions in the parents of the affected relative. It is then possible to calculate the probability of one of the couple's children suffering from the same disease. The software works by finding SNPs that exclude any possible IBD and then identifies regions that lack these SNPs, while exceeding a minimum size and number of SNPs. The accuracy of the algorithm was established by estimating the pairwise IBD between different members of a large pedigree with varying known coefficients of genetic relationship (CGR). © 2011 Wiley Periodicals, Inc.
Che-Ting Kuo
2015-02-01
Full Text Available This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS with a dynamic solution agent algorithm water flow like algorithm (WFA, for nonlinear system identification and blind source separation (BSS problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.
On the use of harmony search algorithm in the training of wavelet neural networks
Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline
2015-10-01
Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.
Preconditioning the modified conjugate gradient method ...
In this paper, the convergence analysis of the conventional conjugate Gradient method was reviewed. And the convergence analysis of the modified conjugate Gradient method was analysed with our extension on preconditioning the algorithm. Convergence of the algorithm is a function of the condition number of M-1A.
A. J. Hind
2011-01-01
Full Text Available Dimethyl sulphide (DMS is an important precursor of cloud condensation nuclei (CCN, particularly in the remote marine atmosphere. The SE Pacific is consistently covered with a persistent stratocumulus layer that increases the albedo over this large area. It is not certain whether the source of CCN to these clouds is natural and oceanic or anthropogenic and terrestrial. This unknown currently limits our ability to reliably model either the cloud behaviour or the oceanic heat budget of the region. In order to better constrain the marine source of CCN, it is necessary to have an improved understanding of the sea-air flux of DMS. Of the factors that govern the magnitude of this flux, the greatest unknown is the surface seawater DMS concentration. In the study area, there is a paucity of such data, although previous measurements suggest that the concentration can be substantially variable. In order to overcome such data scarcity, a number of climatologies and algorithms have been devised in the last decade to predict seawater DMS. Here we test some of these in the SE Pacific by comparing predictions with measurements of surface seawater made during the Vamos Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx in October and November of 2008. We conclude that none of the existing algorithms reproduce local variability in seawater DMS in this region very well. From these findings, we recommend the best algorithm choice for the SE Pacific and suggest lines of investigation for future work.
Arias, E.; Florez, E.; Pérez-Torres, J. F.
2017-06-01
A new algorithm for the determination of equilibrium structures suitable for metal nanoclusters is proposed. The algorithm performs a stochastic search of the minima associated with the nuclear potential energy function restricted to a sphere (similar to the Thomson problem), in order to guess configurations of the nuclear positions. Subsequently, the guessed configurations are further optimized driven by the total energy function using the conventional gradient descent method. This methodology is equivalent to using the valence shell electron pair repulsion model in guessing initial configurations in the traditional molecular quantum chemistry. The framework is illustrated in several clusters of increasing complexity: Cu7, Cu9, and Cu11 as benchmark systems, and Cu38 and Ni9 as novel systems. New equilibrium structures for Cu9, Cu11, Cu38, and Ni9 are reported.
in a Family of South Indian Descent
Muthiah Subramanian
2015-01-01
Full Text Available Inherited channelopathies are a heterogeneous group of disorders resulting from dysfunction of ion channels in cellular membranes. They may manifest as diseases affecting skeletal muscle contraction, the conduction system of the heart, nervous system function, and vision syndromes. We describe a family of South Indian descent with hypokalemic periodic paralysis in which four members also have idiopathic generalized epilepsy. Hypokalemic periodic paralysis is a genetically heterogeneous channelopathy that has been linked to mutations in genes encoding three ion channels CACNIAS, SCN4A, and KCNJ2 predominantly. Although data on specific gene in idiopathic generalized epilepsy is relatively scarce, mutations of voltage gated sodium channel subunit genes (CACNB4 and nonsense mutations in voltage gated calcium channels (CACNA1A have been linked to idiopathic generalized epilepsy in two families. We speculate that gene mutations altering the ability of the beta subunit to interact with the alpha subunit of the CaV1.1 channel and mutations in the pore-forming potassium channel subunit may be possible explanations for the combined manifestation of both diseases. Functional analysis of voltage gated calcium channel and other ion channels mutations may provide additional support and insight for the causal role of these mutations. The understanding of mutations in ion-channel genes will lead to improved diagnosis and treatment of such inherited channelopathies.
Trajectory Guidance for Mars Robotic Precursors: Aerocapture, Entry, Descent, and Landing
Sostaric, Ronald R.; Zumwalt, Carlie; Garcia-Llama, Eduardo; Powell, Richard; Shidner, Jeremy
2011-01-01
Future crewed missions to Mars require improvements in landed mass capability beyond that which is possible using state-of-the-art Mars Entry, Descent, and Landing (EDL) systems. Current systems are capable of an estimated maximum landed mass of 1-1.5 metric tons (MT), while human Mars studies require 20-40 MT. A set of technologies were investigated by the EDL Systems Analysis (SA) project to assess the performance of candidate EDL architectures. A single architecture was selected for the design of a robotic precursor mission, entitled Exploration Feed Forward (EFF), whose objective is to demonstrate these technologies. In particular, inflatable aerodynamic decelerators (IADs) and supersonic retro-propulsion (SRP) have been shown to have the greatest mass benefit and extensibility to future exploration missions. In order to evaluate these technologies and develop the mission, candidate guidance algorithms have been coded into the simulation for the purposes of studying system performance. These guidance algorithms include aerocapture, entry, and powered descent. The performance of the algorithms for each of these phases in the presence of dispersions has been assessed using a Monte Carlo technique.
Automation for Accommodating Fuel-Efficient Descents in Constrained Airspace
Coopenbarger, Richard A.
2010-01-01
Continuous descents at low engine power are desired to reduce fuel consumption, emissions and noise during arrival operations. The challenge is to allow airplanes to fly these types of efficient descents without interruption during busy traffic conditions. During busy conditions today, airplanes are commonly forced to fly inefficient, step-down descents as airtraffic controllers work to ensure separation and maximize throughput. NASA in collaboration with government and industry partners is developing new automation to help controllers accommodate continuous descents in the presence of complex traffic and airspace constraints. This automation relies on accurate trajectory predictions to compute strategic maneuver advisories. The talk will describe the concept behind this new automation and provide an overview of the simulations and flight testing used to develop and refine its underlying technology.
Kim, Dong Yun
1997-02-01
In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller
ExoMars entry, descent and landing science
Ferri, F.; Lewis, S. R.; Withers, P.; Aboudan, A.; Bettanini, C.; Colombatti, G.; Debei, S.; Golombek, M.; Harri, A. M.; Komatsu, G.; Leese, M. R.; Mäkinen, T.; Müller-Wodarg, I.; Ori, G. G.; Patel, M. R.
2011-01-01
The entry, descent and landing of ExoMars offer a rare (once-per-mission) opportunity to perform in situ investigation of the martian environment over a wide altitude range. Entry, Descent and Landing System (EDLS) measurements can provide essential data for atmospheric scientific investigations.\\ud \\ud We intend to perform atmospheric science measurements by exploiting data from EDLS engineering sensors and exploiting their readings beyond the expected engineering information.
Seasonal variability and descent of mid-latitude sporadic E layers at Arecibo
N. Christakis
2009-03-01
Full Text Available Sporadic E layers (Es follow regular daily patterns in variability and altitude descent, which are determined primarily by the vertical tidal wind shears in the lower thermosphere. In the present study a large set of sporadic E layer incoherent scatter radar (ISR measurements are analyzed. These were made at Arecibo (Geog. Lat. ~18° N; Magnetic Dip ~50° over many years with ISR runs lasting from several hours to several days, covering evenly all seasons. A new methodology is applied, in which both weak and strong layers are clearly traced by using the vertical electron density gradient as a function of altitude and time. Taking a time base equal to the 24-h local day, statistics were obtained on the seasonal behavior of the diurnal and semidiurnal tidal variability and altitude descent patterns of sporadic E at Arecibo. The diurnal tide, most likely the S(1,1 tide with a vertical wavelength around 25 km, controls fully the formation and descent of the metallic Es layers at low altitudes below 110 km. At higher altitudes, there are two prevailing layers formed presumably by vertical wind shears associated mainly with semidiurnal tides. These include: 1 a daytime layer starting at ~130 km around midday and descending down to 105 km by local midnight, and 2 a less frequent and weaker nighttime layer which starts prior to midnight at ~130 km, descending downwards at somewhat faster rate to reach 110 km by sunrise. The diurnal and semidiurnal-like pattern prevails, with some differences, in all seasons. The differences in occurrence, strength and descending speeds between the daytime and nighttime upper layers are not well understood from the present data alone and require further study.
A new modified conjugate gradient coefficient for solving system of linear equations
Hajar, N.; ‘Aini, N.; Shapiee, N.; Abidin, Z. Z.; Khadijah, W.; Rivaie, M.; Mamat, M.
2017-09-01
Conjugate gradient (CG) method is an evolution of computational method in solving unconstrained optimization problems. This approach is easy to implement due to its simplicity and has been proven to be effective in solving real-life application. Although this field has received copious amount of attentions in recent years, some of the new approaches of CG algorithm cannot surpass the efficiency of the previous versions. Therefore, in this paper, a new CG coefficient which retains the sufficient descent and global convergence properties of the original CG methods is proposed. This new CG is tested on a set of test functions under exact line search. Its performance is then compared to that of some of the well-known previous CG methods based on number of iterations and CPU time. The results show that the new CG algorithm has the best efficiency amongst all the methods tested. This paper also includes an application of the new CG algorithm for solving large system of linear equations
A variable neighborhood descent based heuristic to solve the capacitated location-routing problem
M. S. Jabal-Ameli
2011-01-01
Full Text Available Location-routing problem (LRP is established as a new research area in the context of location analysis. The primary concern of LRP is on locating facilities and routing of vehicles among established facilities and existing demand points. In this work, we address the capacitated LRP which arises in many practical applications within logistics and supply chain management. The objective is to minimize the overall system costs which include the fixed costs of opening depots and using vehicles at each depot site, and the variable costs associated with delivery activities. A novel heuristic is proposed which is based on variable neighborhood descent (VND algorithm to solve the resulted problem. The computational study indicates that the proposed VND based heuristic is highly competitive with the existing solution algorithms in terms of solution quality.
Optimization algorithms and applications
Arora, Rajesh Kumar
2015-01-01
Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc
Distributed Gradient Descent for Solving Optimal Power Flow in Radial Networks
Gan, Lingwen; Low, Steven H.
2016-01-01
Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control, One embodiment includes a node controller comprising a memory containing: a plurality of node operating parameters; and a plurality of node operating parameters describing operating parameters for a set of at least one node selected from the group consisting of at least one downstream node and at least one upstream node; wherein the processor is configured by the ...
A Numerical Approach to Optimal Coherent Quantum LQG Controller Design Using Gradient Descent
Sichani, Arash Kh.; Vladimirov, Igor G.; Petersen, Ian R.
2016-01-01
This paper is concerned with coherent quantum linear quadratic Gaussian (CQLQG) control. The problem is to find a stabilizing measurement-free quantum controller for a quantum plant so as to minimize a mean square cost for the fully quantum closed-loop system. The plant and controller are open quantum systems interconnected through bosonic quantum fields. In comparison with the observation-actuation structure of classical controllers, coherent quantum feedback is less invasive to the quantum ...
Gender difference in metacarpal descent of fifth metacarpal
Rafique, A.; Ali, H.; Ghani, S.
2006-01-01
To determine the difference in metacarpal descent of fifth metacarpal between men and women. Skyline of the 2nd and 3rd metacarpals were used as reference line, from which the descent of the 5th metacarpal head was measured. The position of 5th metacarpal head was documented as angle X. Metacarpal descent was defined as the difference between angle 'X' in relaxed and clenched fist position. The relaxed position was standardized by placing the forearm, wrist and palm on a shaped woodblock such that the wrist would be held in 25 - 30 degree in extension by a triangular spur, supported the 3rd metacarpal only. It was ensured that the movement of 4th and 5th metacarpals were not impaired. Analysis of variance was performed to compare the significance of means between genders at p < 0.05 level of significance. Metacarpal descent of the 5th metacarpal of both hands was significantly greater for women, with a mean of 7 degree as compared with a mean of 4 degree for the men. This decrease in angle 'X' was significant for the right 5th metacarpal relaxed and fist position and the fist position on the left. In contrast, women showed no significant differences between the various age groups for any of the variables tested.There was no relationship between metacarpal descent and hand dominance.Difference in metacarpal descent between men and women is significant and must be kept in mind when hand function is evaluated in both genders to assess the outcome of treatment and rehabilitation. (author)
Entry, Descent and Landing Systems Analysis Study: Phase 1 Report
DwyerCianciolo, Alicia M.; Davis, Jody L.; Komar, David R.; Munk, Michelle M.; Samareh, Jamshid A.; Powell, Richard W.; Shidner, Jeremy D.; Stanley, Douglas O.; Wilhite, Alan W.; Kinney, David J.;
2010-01-01
NASA senior management commissioned the Entry, Descent and Landing Systems Analysis (EDL-SA) Study in 2008 to identify and roadmap the Entry, Descent and Landing (EDL) technology investments that the agency needed to make in order to successfully land large payloads at Mars for both robotic and human-scale missions. This paper summarizes the motivation, approach and top-level results from Year 1 of the study, which focused on landing 10-50 mt on Mars, but also included a trade study of the best advanced parachute design for increasing the landed payloads within the EDL architecture of the Mars Science Laboratory (MSL) mission
Efficient spectral computation of the stationary states of rotating Bose-Einstein condensates by preconditioned nonlinear conjugate gradient methods
Antoine, Xavier; Levitt, Antoine; Tang, Qinglin
2017-08-01
We propose a preconditioned nonlinear conjugate gradient method coupled with a spectral spatial discretization scheme for computing the ground states (GS) of rotating Bose-Einstein condensates (BEC), modeled by the Gross-Pitaevskii Equation (GPE). We first start by reviewing the classical gradient flow (also known as imaginary time (IMT)) method which considers the problem from the PDE standpoint, leading to numerically solve a dissipative equation. Based on this IMT equation, we analyze the forward Euler (FE), Crank-Nicolson (CN) and the classical backward Euler (BE) schemes for linear problems and recognize classical power iterations, allowing us to derive convergence rates. By considering the alternative point of view of minimization problems, we propose the preconditioned steepest descent (PSD) and conjugate gradient (PCG) methods for the GS computation of the GPE. We investigate the choice of the preconditioner, which plays a key role in the acceleration of the convergence process. The performance of the new algorithms is tested in 1D, 2D and 3D. We conclude that the PCG method outperforms all the previous methods, most particularly for 2D and 3D fast rotating BECs, while being simple to implement.
Kim, Dong Yun; Seong, Poong Hyun
1996-01-01
In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller
Integrated Targeting and Guidance for Powered Planetary Descent
Azimov, Dilmurat M.; Bishop, Robert H.
2018-02-01
This paper presents an on-board guidance and targeting design that enables explicit state and thrust vector control and on-board targeting for planetary descent and landing. These capabilities are developed utilizing a new closed-form solution for the constant thrust arc of the braking phase of the powered descent trajectory. The key elements of proven targeting and guidance architectures, including braking and approach phase quartics, are employed. It is demonstrated that implementation of the proposed solution avoids numerical simulation iterations, thereby facilitating on-board execution of targeting procedures during the descent. It is shown that the shape of the braking phase constant thrust arc is highly dependent on initial mass and propulsion system parameters. The analytic solution process is explicit in terms of targeting and guidance parameters, while remaining generic with respect to planetary body and descent trajectory design. These features increase the feasibility of extending the proposed integrated targeting and guidance design to future cargo and robotic landing missions.
Screening for homozygosity by descent in families with autosomal
Home; Journals; Journal of Genetics; Volume 81; Issue 2 ... Perspectives Volume 81 Issue 2 August 2002 pp 59-63 ... disease locus in families with the recessive form of the disease, we used the approach of screening for homozygosity by descent in offspring of consanguineous and nonconsanguineous families with RP.
Neuromuscular function during stair descent in meniscectomized patients and controls
Thorlund, Jonas Bloch; Roos, Ewa M; Aagaard, Per
2011-01-01
The aim of this study was to identify differences in knee range of motion (ROM), movement speed, ground reaction forces (GRF) profile, neuromuscular activity, and muscle coactivation during the transition between stair descent and level walking in meniscectomized patients at high risk of knee...
Algorithms and Their Explanations
Benini, M.; Gobbo, F.; Beckmann, A.; Csuhaj-Varjú, E.; Meer, K.
2014-01-01
By analysing the explanation of the classical heapsort algorithm via the method of levels of abstraction mainly due to Floridi, we give a concrete and precise example of how to deal with algorithmic knowledge. To do so, we introduce a concept already implicit in the method, the ‘gradient of
Efficient Online Learning Algorithms Based on LSTM Neural Networks.
Ergen, Tolga; Kozat, Suleyman Serdar
2017-09-13
We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.
Dalmau Codina, Ramon; Prats Menéndez, Xavier
2017-01-01
Continuous descent operations with controlled times of arrival at one or several metering fixes could enable environmentally friendly procedures without compromising terminal airspace capacity. This paper focuses on controlled time of arrival updates once the descent has been already initiated, assessing the feasible time window (and associated fuel consumption) of continuous descent operations requiring neither thrust nor speed-brake usage along the whole descent (i.e. only elevator control ...
Shahram Mollaiy Berneti
2013-04-01
Full Text Available In this paper, a novel hybrid approach composed of adaptive neuro-fuzzy inference system (ANFIS and imperialist competitive algorithm is proposed. The imperialist competitive algorithm (ICA is used in this methodology to determine the most suitable initial membership functions of the ANFIS. The proposed model combines the global search ability of ICA with local search ability of gradient descent method. To illustrate the suitability and capability of the proposed model, this model is applied to predict oil flow rate of the wells utilizing data set of 31 wells in one of the northern Persian Gulf oil fields of Iran. The data set collected in a three month period for each well from Dec. 2002 to Nov. 2010. For the sake of performance evaluation, the results of the proposed model are compared with the conventional ANFIS model. The results show that the significant improvements are achievable using the proposed model in comparison with the results obtained by conventional ANFIS.
A feasible DY conjugate gradient method for linear equality constraints
LI, Can
2017-09-01
In this paper, we propose a feasible conjugate gradient method for solving linear equality constrained optimization problem. The method is an extension of the Dai-Yuan conjugate gradient method proposed by Dai and Yuan to linear equality constrained optimization problem. It can be applied to solve large linear equality constrained problem due to lower storage requirement. An attractive property of the method is that the generated direction is always feasible and descent direction. Under mild conditions, the global convergence of the proposed method with exact line search is established. Numerical experiments are also given which show the efficiency of the method.
Shao, Jiaxin; Rapacchi, Stanislas; Nguyen, Kim-Lien; Hu, Peng
2016-02-01
To develop an accurate and precise myocardial T1 mapping technique using an inversion recovery spoiled gradient echo readout at 3.0 Tesla (T). The modified Look-Locker inversion-recovery (MOLLI) sequence was modified to use fast low angle shot (FLASH) readout, incorporating a BLESSPC (Bloch Equation Simulation with Slice Profile Correction) T1 estimation algorithm, for accurate myocardial T1 mapping. The FLASH-MOLLI with BLESSPC fitting was compared with different approaches and sequences with regards to T1 estimation accuracy, precision and image artifact based on simulation, phantom studies, and in vivo studies of 10 healthy volunteers and three patients at 3.0 Tesla. The FLASH-MOLLI with BLESSPC fitting yields accurate T1 estimation (average error = -5.4 ± 15.1 ms, percentage error = -0.5% ± 1.2%) for T1 from 236-1852 ms and heart rate from 40-100 bpm in phantom studies. The FLASH-MOLLI sequence prevented off-resonance artifacts in all 10 healthy volunteers at 3.0T. In vivo, there was no significant difference between FLASH-MOLLI-derived myocardial T1 values and "ShMOLLI+IE" derived values (1458.9 ± 20.9 ms versus 1464.1 ± 6.8 ms, P = 0.50); However, the average precision by FLASH-MOLLI was significantly better than that generated by "ShMOLLI+IE" (1.84 ± 0.36% variance versus 3.57 ± 0.94%, P < 0.001). The FLASH-MOLLI with BLESSPC fitting yields accurate and precise T1 estimation, and eliminates banding artifacts associated with bSSFP at 3.0T. © 2015 Wiley Periodicals, Inc.
De Candia, T.r.; Lee, S.H.; Yang, J.; Browning, B.L.; Gejman, P. V.; Levinson, D. F.; Mowry, B. J.; Hewitt, J.K.; Goddard, M.E.; O'Donovan, M.C.; Purcell, S.M.; Posthuma, D.; Visscher, P. M.; Wray, N.R.; Keller, M. C.
2013-01-01
To investigate the extent to which the proportion of schizophrenia's additive genetic variation tagged by SNPs is shared by populations of European and African descent, we analyzed the largest combined African descent (AD [n = 2,142]) and European descent (ED [n = 4,990]) schizophrenia case-control
Full Gradient Solution to Adaptive Hybrid Control
Bean, Jacob; Schiller, Noah H.; Fuller, Chris
2017-01-01
This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.
A three-term conjugate gradient method under the strong-Wolfe line search
Khadijah, Wan; Rivaie, Mohd; Mamat, Mustafa
2017-08-01
Recently, numerous studies have been concerned in conjugate gradient methods for solving large-scale unconstrained optimization method. In this paper, a three-term conjugate gradient method is proposed for unconstrained optimization which always satisfies sufficient descent direction and namely as Three-Term Rivaie-Mustafa-Ismail-Leong (TTRMIL). Under standard conditions, TTRMIL method is proved to be globally convergent under strong-Wolfe line search. Finally, numerical results are provided for the purpose of comparison.
An inverse source location algorithm for radiation portal monitor applications
Miller, Karen A.; Charlton, William S.
2010-01-01
Radiation portal monitors are being deployed at border crossings throughout the world to prevent the smuggling of nuclear and radiological materials; however, a tension exists between security and the free-flow of commerce. Delays at ports-of-entry have major economic implications, so it is imperative to minimize portal monitor screening time. We have developed an algorithm to locate a radioactive source using a distributed array of detectors, specifically for use at border crossings. To locate the source, we formulated an optimization problem where the objective function describes the least-squares difference between the actual and predicted detector measurements. The predicted measurements are calculated by solving the 3-D deterministic neutron transport equation given an estimated source position. The source position is updated using the steepest descent method, where the gradient of the objective function with respect to the source position is calculated using adjoint transport calculations. If the objective function is smaller than the convergence criterion, then the source position has been identified. This paper presents the derivation of the underlying equations in the algorithm as well as several computational test cases used to characterize its accuracy.
Scaling Sparse Matrices for Optimization Algorithms
Gajulapalli Ravindra S; Lasdon Leon S
2006-01-01
To iteratively solve large scale optimization problems in various contexts like planning, operations, design etc., we need to generate descent directions that are based on linear system solutions. Irrespective of the optimization algorithm or the solution method employed for the linear systems, ill conditioning introduced by problem characteristics or the algorithm or both need to be addressed. In [GL01] we used an intuitive heuristic approach in scaling linear systems that improved performan...
Environmental effects on hormonal regulation of testicular descent
Toppari, J; Virtanen, H E; Skakkebaek, N E
2006-01-01
cause some cases of undescended testis. Similarly, androgen insensitivity or androgen deficiency can cause cryptorchidism. Estrogens have been shown to down regulate INSL3 and thereby cause maldescent. Thus, a reduced androgen-estrogen ratio may disturb testicular descent. Environmental effects changing......Regulation of testicular descent is hormonally regulated, but the reasons for maldescent remain unknown in most cases. The main regulatory hormones are Leydig cell-derived testosterone and insulin-like factor 3 (INSL3). Luteinizing hormone (LH) stimulates the secretion of these hormones...... hypothesize that an exposure to a mixture of chemicals with anti-androgenic or estrogenic properties (either their own activity or their effect on androgen-estrogen ratio) may be involved in cryptorchidism....
Reversible cerebral vasoconstriction syndrome precipitated by airplane descent: Case report.
Hiraga, Akiyuki; Aotsuka, Yuya; Koide, Kyosuke; Kuwabara, Satoshi
2017-10-01
Background Reversible cerebral vasoconstriction syndrome (RCVS) is characterized by segmental vasospasm. Vasoactive agents and childbirth have been reported as precipitating factors for RCVS; however, RCVS induced by altitude change or air travel has rarely been reported. Case We present a case of a 74-year-old woman who presented with thunderclap headache during airplane descent. Magnetic resonance angiography demonstrated segmental vasoconstriction that improved 9 days after onset. Conclusion These findings indicate that airplane descent may be a trigger of RCVS. The time course of headache in the present case was similar to that of prolonged headache attributed to airplane travel, indicating that RCVS during air travel may have previously been overlooked and that some headache attributed to airplane travel cases may represent a milder form of RCVS.
RITD - Adapting Mars Entry, Descent and Landing System for Earth
Haukka, H.; Heilimo, J.; Harri, A.-M.; Aleksashkin, S.; Koryanov, V.; Arruego, I.; Schmidt, W.; Finchenko, V.; Martynov, M.; Ponomarenko, A.; Kazakovtsev, V.; Martin, S.
2015-10-01
We have developed an atmospheric re-entry and descent system concept based on inflatable hypersonic decelerator techniques that were originally developed for Mars. The ultimate goal of this EU-funded RITD-project (Re-entry: Inflatable Technology Development) was to assess the benefits of this technology when deploying small payloads from low Earth orbits to the surface of the Earth with modest costs. The principal goal was to assess and develop a preliminary EDLS design for the entire relevant range of aerodynamic regimes expected to be encountered in Earth's atmosphere during entry, descent and landing. Low Earth Orbit (LEO) and even Lunar applications envisaged include the use of the EDLS approach in returning payloads of 4-8 kg down to the surface.
Convergence results for a class of abstract continuous descent methods
Sergiu Aizicovici
2004-03-01
Full Text Available We study continuous descent methods for the minimization of Lipschitzian functions defined on a general Banach space. We establish convergence theorems for those methods which are generated by approximate solutions to evolution equations governed by regular vector fields. Since the complement of the set of regular vector fields is $sigma$-porous, we conclude that our results apply to most vector fields in the sense of Baire's categories.
Whole-body angular momentum during stair ascent and descent.
Silverman, Anne K; Neptune, Richard R; Sinitski, Emily H; Wilken, Jason M
2014-04-01
The generation of whole-body angular momentum is essential in many locomotor tasks and must be regulated in order to maintain dynamic balance. However, angular momentum has not been investigated during stair walking, which is an activity that presents a biomechanical challenge for balance-impaired populations. We investigated three-dimensional whole-body angular momentum during stair ascent and descent and compared it to level walking. Three-dimensional body-segment kinematic and ground reaction force (GRF) data were collected from 30 healthy subjects. Angular momentum was calculated using a 13-segment whole-body model. GRFs, external moment arms and net joint moments were used to interpret the angular momentum results. The range of frontal plane angular momentum was greater for stair ascent relative to level walking. In the transverse and sagittal planes, the range of angular momentum was smaller in stair ascent and descent relative to level walking. Significant differences were also found in the ground reaction forces, external moment arms and net joint moments. The sagittal plane angular momentum results suggest that individuals alter angular momentum to effectively counteract potential trips during stair ascent, and reduce the range of angular momentum to avoid falling forward during stair descent. Further, significant differences in joint moments suggest potential neuromuscular mechanisms that account for the differences in angular momentum between walking conditions. These results provide a baseline for comparison to impaired populations that have difficulty maintaining dynamic balance, particularly during stair ascent and descent. Copyright © 2014 Elsevier B.V. All rights reserved.
Additive preservers of the ascent, descent and related subsets
Mbekhta, M.; Müller, Vladimír; Oudghiri, M.
2014-01-01
Roč. 71, č. 1 (2014), s. 63-83 ISSN 0379-4024 R&D Projects: GA ČR GA201/09/0473; GA AV ČR IAA100190903 Institutional support: RVO:67985840 Keywords : additive preservers * ascent * descent Subject RIV: BA - General Mathematics Impact factor: 0.550, year: 2014 http://www.mathjournals.org/jot/2014-071-001/2014-071-001-004.html
Flight Management System Execution of Idle-Thrust Descents in Operations
Stell, Laurel L.
2011-01-01
To enable arriving aircraft to fly optimized descents computed by the flight management system (FMS) in congested airspace, ground automation must accurately predict descent trajectories. To support development of the trajectory predictor and its error models, commercial flights executed idle-thrust descents, and the recorded data includes the target speed profile and FMS intent trajectories. The FMS computes the intended descent path assuming idle thrust after top of descent (TOD), and any intervention by the controllers that alters the FMS execution of the descent is recorded so that such flights are discarded from the analysis. The horizontal flight path, cruise and meter fix altitudes, and actual TOD location are extracted from the radar data. Using more than 60 descents in Boeing 777 aircraft, the actual speeds are compared to the intended descent speed profile. In addition, three aspects of the accuracy of the FMS intent trajectory are analyzed: the meter fix crossing time, the TOD location, and the altitude at the meter fix. The actual TOD location is within 5 nmi of the intent location for over 95% of the descents. Roughly 90% of the time, the airspeed is within 0.01 of the target Mach number and within 10 KCAS of the target descent CAS, but the meter fix crossing time is only within 50 sec of the time computed by the FMS. Overall, the aircraft seem to be executing the descents as intended by the designers of the onboard automation.
Airborne Management of Traffic Conflicts in Descent With Arrival Constraints
Doble, Nathan A.; Barhydt, Richard; Krishnamurthy, Karthik
2005-01-01
NASA is studying far-term air traffic management concepts that may increase operational efficiency through a redistribution of decisionmaking authority among airborne and ground-based elements of the air transportation system. One component of this research, En Route Free Maneuvering, allows trained pilots of equipped autonomous aircraft to assume responsibility for traffic separation. Ground-based air traffic controllers would continue to separate traffic unequipped for autonomous operations and would issue flow management constraints to all aircraft. To evaluate En Route Free Maneuvering operations, a human-in-the-loop experiment was jointly conducted by the NASA Ames and Langley Research Centers. In this experiment, test subject pilots used desktop flight simulators to resolve conflicts in cruise and descent, and to adhere to air traffic flow constraints issued by test subject controllers. Simulators at NASA Langley were equipped with a prototype Autonomous Operations Planner (AOP) flight deck toolset to assist pilots with conflict management and constraint compliance tasks. Results from the experiment are presented, focusing specifically on operations during the initial descent into the terminal area. Airborne conflict resolution performance in descent, conformance to traffic flow management constraints, and the effects of conflicting traffic on constraint conformance are all presented. Subjective data from subject pilots are also presented, showing perceived levels of workload, safety, and acceptability of autonomous arrival operations. Finally, potential AOP functionality enhancements are discussed along with suggestions to improve arrival procedures.
Inflammatory bowel disease in children of middle eastern descent.
Naidoo, Christina Mai Ying; Leach, Steven T; Day, Andrew S; Lemberg, Daniel A
2014-01-01
Increasing rates of inflammatory bowel disease (IBD) are now seen in populations where it was once uncommon. The pattern of IBD in children of Middle Eastern descent in Australia has never been reported. This study aimed to investigate the burden of IBD in children of Middle Eastern descent at the Sydney Children's Hospital, Randwick (SCHR). The SCHR IBD database was used to identify patients of self-reported Middle Eastern ethnicity diagnosed between 1987 and 2011. Demographic, diagnosis, and management data was collected for all Middle Eastern children and an age and gender matched non-Middle Eastern IBD control group. Twenty-four patients of Middle Eastern descent were identified. Middle Eastern Crohn's disease patients had higher disease activity at diagnosis, higher use of thiopurines, and less restricted colonic disease than controls. Although there were limitations with this dataset, we estimated a higher prevalence of IBD in Middle Eastern children and they had a different disease phenotype and behavior compared to the control group, with less disease restricted to the colon and likely a more active disease course.
Testicular descent: INSL3, testosterone, genes and the intrauterine milieu.
Bay, Katrine; Main, Katharina M; Toppari, Jorma; Skakkebæk, Niels E
2011-04-01
Complete testicular descent is a sign of, and a prerequisite for, normal testicular function in adult life. The process of testis descent is dependent on gubernacular growth and reorganization, which is regulated by the Leydig cell hormones insulin-like peptide 3 (INSL3) and testosterone. Investigation of the role of INSL3 and its receptor, relaxin-family peptide receptor 2 (RXFP2), has contributed substantially to our understanding of the hormonal control of testicular descent. Cryptorchidism is a common congenital malformation, which is seen in 2-9% of newborn boys, and confers an increased risk of infertility and testicular cancer in adulthood. Although some cases of isolated cryptorchidism in humans can be ascribed to known genetic defects, such as mutations in INSL3 or RXFP2, the cause of cryptorchidism remains unknown in most patients. Several animal and human studies are currently underway to test the hypothesis that in utero factors, including environmental and maternal lifestyle factors, may be involved in the etiology of cryptorchidism. Overall, the etiology of isolated cryptorchidism seems to be complex and multifactorial, involving both genetic and nongenetic components.
Minimizing inner product data dependencies in conjugate gradient iteration
Vanrosendale, J.
1983-01-01
The amount of concurrency available in conjugate gradient iteration is limited by the summations required in the inner product computations. The inner product of two vectors of length N requires time c log(N), if N or more processors are available. This paper describes an algebraic restructuring of the conjugate gradient algorithm which minimizes data dependencies due to inner product calculations. After an initial start up, the new algorithm can perform a conjugate gradient iteration in time c*log(log(N)).
Global Convergence of a Spectral Conjugate Gradient Method for Unconstrained Optimization
Jinkui Liu
2012-01-01
Full Text Available A new nonlinear spectral conjugate descent method for solving unconstrained optimization problems is proposed on the basis of the CD method and the spectral conjugate gradient method. For any line search, the new method satisfies the sufficient descent condition gkTdk<−∥gk∥2. Moreover, we prove that the new method is globally convergent under the strong Wolfe line search. The numerical results show that the new method is more effective for the given test problems from the CUTE test problem library (Bongartz et al., 1995 in contrast to the famous CD method, FR method, and PRP method.
Entry, Descent, and Landing for Human Mars Missions
Munk, Michelle M.; DwyerCianciolo, Alicia M.
2012-01-01
One of the most challenging aspects of a human mission to Mars is landing safely on the Martian surface. Mars has such low atmospheric density that decelerating large masses (tens of metric tons) requires methods that have not yet been demonstrated, and are not yet planned in future Mars missions. To identify the most promising options for Mars entry, descent, and landing, and to plan development of the needed technologies, NASA's Human Architecture Team (HAT) has refined candidate methods for emplacing needed elements of the human Mars exploration architecture (such as ascent vehicles and habitats) on the Mars surface. This paper explains the detailed, optimized simulations that have been developed to define the mass needed at Mars arrival to accomplish the entry, descent, and landing functions. Based on previous work, technology options for hypersonic deceleration include rigid, mid-L/D (lift-to-drag ratio) aeroshells, and inflatable aerodynamic decelerators (IADs). The hypersonic IADs, or HIADs, are about 20% less massive than the rigid vehicles, but both have their technology development challenges. For the supersonic regime, supersonic retropropulsion (SRP) is an attractive option, since a propulsive stage must be carried for terminal descent and can be ignited at higher speeds. The use of SRP eliminates the need for an additional deceleration system, but SRP is at a low Technology Readiness Level (TRL) in that the interacting plumes are not well-characterized, and their effect on vehicle stability has not been studied, to date. These architecture-level assessments have been used to define the key performance parameters and a technology development strategy for achieving the challenging mission of landing large payloads on Mars.
A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization
Zhijun Luo
2014-01-01
Full Text Available A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.
Fast Algorithms for Earth Mover Distance Based on Optimal Transport and L1 Regularization II
2016-09-01
gradient ascent in the dual variable Φ and a gradient descent in the primal variable m. In our updates for (6), we use simple exact formulae. Since the...0. References [1] Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré. Gradient flows: in metric spaces and in the space of probability measures...continuity and summability of transport densities: simpler proofs and new estimates. Calculus of Variations and Partial Differential Equations, 36 (3
Distributed Coordinate Descent Method for Learning with Big Data
Richtárik, Peter; Takáč, Martin
2013-01-01
In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent method for solving loss minimization problems with big data. We initially partition the coordinates (features) and assign each partition to a different node of a cluster. At every iteration, each node picks a random subset of the coordinates from those it owns, independently from the other computers, and in parallel computes and applies updates to the selected coordinates based on a simple closed-form formula. We give bound...
Predictive Modeling for NASA Entry, Descent and Landing Missions
Wright, Michael
2016-01-01
Entry, Descent and Landing (EDL) Modeling and Simulation (MS) is an enabling capability for complex NASA entry missions such as MSL and Orion. MS is used in every mission phase to define mission concepts, select appropriate architectures, design EDL systems, quantify margin and risk, ensure correct system operation, and analyze data returned from the entry. In an environment where it is impossible to fully test EDL concepts on the ground prior to use, accurate MS capability is required to extrapolate ground test results to expected flight performance.
A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.
Araújo, Ricardo de A
2012-04-01
Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature. Copyright © 2011 Elsevier Ltd. All rights reserved.
Crosswind Shear Gradient Affect on Wake Vortices
Proctor, Fred H.; Ahmad, Nashat N.
2011-01-01
Parametric simulations with a Large Eddy Simulation (LES) model are used to explore the influence of crosswind shear on aircraft wake vortices. Previous studies based on field measurements, laboratory experiments, as well as LES, have shown that the vertical gradient of crosswind shear, i.e. the second vertical derivative of the environmental crosswind, can influence wake vortex transport. The presence of nonlinear vertical shear of the crosswind velocity can reduce the descent rate, causing a wake vortex pair to tilt and change in its lateral separation. The LES parametric studies confirm that the vertical gradient of crosswind shear does influence vortex trajectories. The parametric results also show that vortex decay from the effects of shear are complex since the crosswind shear, along with the vertical gradient of crosswind shear, can affect whether the lateral separation between wake vortices is increased or decreased. If the separation is decreased, the vortex linking time is decreased, and a more rapid decay of wake vortex circulation occurs. If the separation is increased, the time to link is increased, and at least one of the vortices of the vortex pair may have a longer life time than in the case without shear. In some cases, the wake vortices may never link.
Shape tracking with occlusions via coarse-to-fine region-based sobolev descent
Yang, Yanchao
2015-05-01
We present a method to track the shape of an object from video. The method uses a joint shape and appearance model of the object, which is propagated to match shape and radiance in subsequent frames, determining object shape. Self-occlusions and dis-occlusions of the object from camera and object motion pose difficulties to joint shape and appearance models in tracking. They are unable to adapt to new shape and appearance information, leading to inaccurate shape detection. In this work, we model self-occlusions and dis-occlusions in a joint shape and appearance tracking framework. Self-occlusions and the warp to propagate the model are coupled, thus we formulate a joint optimization problem. We derive a coarse-to-fine optimization method, advantageous in tracking, that initially perturbs the model by coarse perturbations before transitioning to finer-scale perturbations seamlessly. This coarse-to-fine behavior is automatically induced by gradient descent on a novel infinite-dimensional Riemannian manifold that we introduce. The manifold consists of planar parameterized regions, and the metric that we introduce is a novel Sobolev metric. Experiments on video exhibiting occlusions/dis-occlusions, complex radiance and background show that occlusion/dis-occlusion modeling leads to superior shape accuracy. © 2014 IEEE.
Stereotypes of women of Asian descent in midwifery: some evidence.
Bowler, I M
1993-03-01
The subject of this paper is part of a larger study which investigated the delivery of maternity care to women of South Asian descent in Britain (Bowler, 1990). An ethnographic approach was used and the main method of data collection was non-participant observation in antenatal clinics, labour and postnatal wards in a teaching hospital maternity unit. These observations were supported by data from interviews with midwives. It was found that the midwives commonly use stereotypes of women in order to help them to provide care. These stereotypes are particularly likely to be used in situations where the midwife has difficulty (through pressure of time or other circumstances) in getting to know an individual woman. The stereotype of women of Asian descent contained four main themes: communication problems; failure to comply with care and service abuse; making a fuss about nothing; a lack of normal maternal instinct. Reasons for stereotyping are explored. Effects on service provision in the areas of family planning and breast feeding are highlighted.
Steepest descent moment method for three-dimensional magnetohydrodynamic equilibria
Hirshman, S.P.; Whitson, J.C.
1983-11-01
An energy principle is used to obtain the solution of the magnetohydrodynamic (MHD) equilibrium equation J Vector x B Vector - del p = 0 for nested magnetic flux surfaces that are expressed in the inverse coordinate representation x Vector = x Vector(rho, theta, zeta). Here, theta and zeta are poloidal and toroidal flux coordinate angles, respectively, and p = p(rho) labels a magnetic surface. Ordinary differential equations in rho are obtained for the Fourier amplitudes (moments) in the doubly periodic spectral decomposition of x Vector. A steepest descent iteration is developed for efficiently solving these nonlinear, coupled moment equations. The existence of a positive-definite energy functional guarantees the monotonic convergence of this iteration toward an equilibrium solution (in the absence of magnetic island formation). A renormalization parameter lambda is introduced to ensure the rapid convergence of the Fourier series for x Vector, while simultaneously satisfying the MHD requirement that magnetic field lines are straight in flux coordinates. A descent iteration is also developed for determining the self-consistent value for lambda
Arachnid aloft: directed aerial descent in neotropical canopy spiders.
Yanoviak, Stephen P; Munk, Yonatan; Dudley, Robert
2015-09-06
The behaviour of directed aerial descent has been described for numerous taxa of wingless hexapods as they fall from the tropical rainforest canopy, but is not known in other terrestrial arthropods. Here, we describe similar controlled aerial behaviours for large arboreal spiders in the genus Selenops (Selenopidae). We dropped 59 such spiders from either canopy platforms or tree crowns in Panama and Peru; the majority (93%) directed their aerial trajectories towards and then landed upon nearby tree trunks. Following initial dorsoventral righting when necessary, falling spiders oriented themselves and then translated head-first towards targets; directional changes were correlated with bilaterally asymmetric motions of the anterolaterally extended forelegs. Aerial performance (i.e. the glide index) decreased with increasing body mass and wing loading, but not with projected surface area of the spider. Along with the occurrence of directed aerial descent in ants, jumping bristletails, and other wingless hexapods, this discovery of targeted gliding in selenopid spiders further indicates strong selective pressures against uncontrolled falls into the understory for arboreal taxa. © 2015 The Author(s).
Mars Exploration Rover Terminal Descent Mission Modeling and Simulation
Raiszadeh, Behzad; Queen, Eric M.
2004-01-01
Because of NASA's added reliance on simulation for successful interplanetary missions, the MER mission has developed a detailed EDL trajectory modeling and simulation. This paper summarizes how the MER EDL sequence of events are modeled, verification of the methods used, and the inputs. This simulation is built upon a multibody parachute trajectory simulation tool that has been developed in POST I1 that accurately simulates the trajectory of multiple vehicles in flight with interacting forces. In this model the parachute and the suspended bodies are treated as 6 Degree-of-Freedom (6 DOF) bodies. The terminal descent phase of the mission consists of several Entry, Descent, Landing (EDL) events, such as parachute deployment, heatshield separation, deployment of the lander from the backshell, deployment of the airbags, RAD firings, TIRS firings, etc. For an accurate, reliable simulation these events need to be modeled seamlessly and robustly so that the simulations will remain numerically stable during Monte-Carlo simulations. This paper also summarizes how the events have been modeled, the numerical issues, and modeling challenges.
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
Richtarik, Peter; Taká č, Martin
2017-01-01
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several equivalent interpretations, allowing for researchers from various communities to leverage their domain specific insights. In particular, our reformulation can be equivalently seen as a stochastic optimization problem, stochastic linear system, stochastic fixed point problem and a probabilistic intersection problem. We prove sufficient, and necessary and sufficient conditions for the reformulation to be exact. Further, we propose and analyze three stochastic algorithms for solving the reformulated problem---basic, parallel and accelerated methods---with global linear convergence rates. The rates can be interpreted as condition numbers of a matrix which depends on the system matrix and on the reformulation parameters. This gives rise to a new phenomenon which we call stochastic preconditioning, and which refers to the problem of finding parameters (matrix and distribution) leading to a sufficiently small condition number. Our basic method can be equivalently interpreted as stochastic gradient descent, stochastic Newton method, stochastic proximal point method, stochastic fixed point method, and stochastic projection method, with fixed stepsize (relaxation parameter), applied to the reformulations.
Innovative applications of genetic algorithms to problems in accelerator physics
Alicia Hofler
2013-01-01
Full Text Available The genetic algorithm (GA is a powerful technique that implements the principles nature uses in biological evolution to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing Continuous Electron Beam Accelerator Facility nuclear physics machine, the proposed Medium-energy Electron-Ion Collider at Jefferson Lab, and a radio frequency gun-based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, include a newly devised enhancement which leads to improved convergence to the optimum, and make recommendations for future GA developments and accelerator applications.
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
Richtarik, Peter
2017-06-04
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several equivalent interpretations, allowing for researchers from various communities to leverage their domain specific insights. In particular, our reformulation can be equivalently seen as a stochastic optimization problem, stochastic linear system, stochastic fixed point problem and a probabilistic intersection problem. We prove sufficient, and necessary and sufficient conditions for the reformulation to be exact. Further, we propose and analyze three stochastic algorithms for solving the reformulated problem---basic, parallel and accelerated methods---with global linear convergence rates. The rates can be interpreted as condition numbers of a matrix which depends on the system matrix and on the reformulation parameters. This gives rise to a new phenomenon which we call stochastic preconditioning, and which refers to the problem of finding parameters (matrix and distribution) leading to a sufficiently small condition number. Our basic method can be equivalently interpreted as stochastic gradient descent, stochastic Newton method, stochastic proximal point method, stochastic fixed point method, and stochastic projection method, with fixed stepsize (relaxation parameter), applied to the reformulations.
Frequency-domain full-waveform inversion with non-linear descent directions
Geng, Yu; Pan, Wenyong; Innanen, Kristopher A.
2018-05-01
Full-waveform inversion (FWI) is a highly non-linear inverse problem, normally solved iteratively, with each iteration involving an update constructed through linear operations on the residuals. Incorporating a flexible degree of non-linearity within each update may have important consequences for convergence rates, determination of low model wavenumbers and discrimination of parameters. We examine one approach for doing so, wherein higher order scattering terms are included within the sensitivity kernel during the construction of the descent direction, adjusting it away from that of the standard Gauss-Newton approach. These scattering terms are naturally admitted when we construct the sensitivity kernel by varying not the current but the to-be-updated model at each iteration. Linear and/or non-linear inverse scattering methodologies allow these additional sensitivity contributions to be computed from the current data residuals within any given update. We show that in the presence of pre-critical reflection data, the error in a second-order non-linear update to a background of s0 is, in our scheme, proportional to at most (Δs/s0)3 in the actual parameter jump Δs causing the reflection. In contrast, the error in a standard Gauss-Newton FWI update is proportional to (Δs/s0)2. For numerical implementation of more complex cases, we introduce a non-linear frequency-domain scheme, with an inner and an outer loop. A perturbation is determined from the data residuals within the inner loop, and a descent direction based on the resulting non-linear sensitivity kernel is computed in the outer loop. We examine the response of this non-linear FWI using acoustic single-parameter synthetics derived from the Marmousi model. The inverted results vary depending on data frequency ranges and initial models, but we conclude that the non-linear FWI has the capability to generate high-resolution model estimates in both shallow and deep regions, and to converge rapidly, relative to a
Zhang, Xiao-bo; Tan, Jun; Song, Peng; Li, Jin-shan; Xia, Dong-ming; Liu, Zhao-lun
2017-01-01
The gradient preconditioning approach based on seismic wave energy can effectively avoid the huge storage consumption in the gradient preconditioning algorithms based on Hessian matrices in time-domain full waveform inversion (FWI), but the accuracy
Sequential unconstrained minimization algorithms for constrained optimization
Byrne, Charles
2008-01-01
The problem of minimizing a function f(x):R J → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G k (x)=f(x)+g k (x), to obtain x k . The auxiliary functions g k (x):D subset of R J → R + are nonnegative on the set D, each x k is assumed to lie within D, and the objective is to minimize the continuous function f:R J → R over x in the set C = D-bar, the closure of D. We assume that such minimizers exist, and denote one such by x-circumflex. We assume that the functions g k (x) satisfy the inequalities 0≤g k (x)≤G k-1 (x)-G k-1 (x k-1 ), for k = 2, 3, .... Using this assumption, we show that the sequence {(x k )} is decreasing and converges to f(x-circumflex). If the restriction of f(x) to D has bounded level sets, which happens if x-circumflex is unique and f(x) is closed, proper and convex, then the sequence {x k } is bounded, and f(x*)=f(x-circumflex), for any cluster point x*. Therefore, if x-circumflex is unique, x* = x-circumflex and {x k } → x-circumflex. When x-circumflex is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton–Raphson method. The proof techniques used for SUMMA can be extended to obtain related results
The Role of la Familia for Women of Mexican Descent Who Are Leaders in Higher Education
Elizondo, Sandra Gray
2012-01-01
The purpose of this qualitative case study was to describe the role of "la familia" for women of Mexican descent as it relates to their development as leaders and their leadership in academia. Purposeful sampling was utilized to reach the goal of 18 participants who were female academic leaders of Mexican descent teaching full time in…
Ascent, descent, nullity, defect, and related notions for linear relations in linear spaces
Sandovici, Adrian; de Snoo, Henk; Winkler, Henrik
2007-01-01
For a linear relation in a linear space the concepts of ascent, descent, nullity, and defect are introduced and studied. It is shown that the results of A.E. Taylor and M.A. Kaashoek concerning the relationship between ascent, descent, nullity, and defect for the case of linear operators remain
Counseling and Psychotherapy with Clients of Middle Eastern Descent: A Qualitative Inquiry
Boghosian, Sara
2011-01-01
It is becoming increasingly important for clinical and counseling psychologists to have multicultural competence skills for treating an increasingly diverse client population. The psychology literature related to culturally competent treatment with persons of Middle Eastern descent is currently limited. In this study, qualitative methodology was utilized to explore the mental health attitudes and psychotherapy experiences of clients of Middle Eastern descent. Participants described culturally...
Ethnic Identity and Acculturative Stress as Mediators of Depression in Students of Asian Descent
Lantrip, Crystal; Mazzetti, Francesco; Grasso, Joseph; Gill, Sara; Miller, Janna; Haner, Morgynn; Rude, Stephanie; Awad, Germine
2015-01-01
This study underscored the importance of addressing the well-being of college students of Asian descent, because these students had higher rates of depression and lower positive feelings about their ethnic group compared with students of European descent, as measured by the Affirmation subscale of the Ethnic Identity Scale. Affirmation mediated…
Adapting Mars Entry, Descent and Landing System for Earth
Heilimo, J.; Harri, A.-M.; Aleksashkin, S.; Koryanov, V.; Guerrero, H.; Schmidt, W.; Haukka, H.; Finchenko, V.; Martynov, M.; Ostresko, B.; Ponomarenko, A.; Kazakovtsev, V.; Arruego, I.; Martin, S.; Siili, T.
2013-09-01
In 2001 - 2011 an inflatable Entry, Descent and Landing System (EDLS) for Martian atmosphere was developed by FMI and the MetNet team. This MetNet Mars Lander EDLS is used in both the initial deceleration during atmospheric entry and in the final deceleration before the semi-hard impact of the penetrator to Martian surface. The EDLS design is ingenious and its applicability to Earth's atmosphere is studied in the on-going project. In particular, the behavior of the system in the critical transonic aerodynamic (from hypersonic to subsonic) regime will be investigated. This project targets to analyze and test the transonic behavior of this compact and light weight payload entry system to Earth's atmosphere [1]. Scaling and adaptation for terrestrial atmospheric conditions, instead of a completely new design, is a favorable approach for providing a new re-entry vehicle for terrestrial space applications.
Human Mars Entry, Descent, and Landing Architecture Study Overview
Cianciolo, Alicia D.; Polsgrove, Tara T.
2016-01-01
The Entry, Descent, and Landing (EDL) Architecture Study is a multi-NASA center activity to analyze candidate EDL systems as they apply to human Mars landing in the context of the Evolvable Mars Campaign. The study, led by the Space Technology Mission Directorate (STMD), is performed in conjunction with the NASA's Science Mission Directorate and the Human Architecture Team, sponsored by NASA's Human Exploration and Operations Mission Directorate. The primary objective is to prioritize future STMD EDL technology investments by (1) generating Phase A-level designs for selected concepts to deliver 20 t human class payloads, (2) developing a parameterized mass model for each concept capable of examining payloads between 5 and 40 t, and (3) evaluating integrated system performance using trajectory simulations. This paper summarizes the initial study results.
Xu, Qiaofeng; Sawatzky, Alex; Anastasio, Mark A.; Yang, Deshan; Tan, Jun
2016-01-01
Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated
Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A.
2016-01-01
Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated
Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm.
Stefanoiu, Anca; Weinmann, Andreas; Storath, Martin; Navab, Nassir; Baust, Maximilian
2016-05-11
This paper presents a method for the simultaneous segmentation and regularization of a series of shapes from a corresponding sequence of images. Such series arise as time series of 2D images when considering video data, or as stacks of 2D images obtained by slicewise tomographic reconstruction. We first derive a model where the regularization of the shape signal is achieved by a total variation prior on the shape manifold. The method employs a modified Kendall shape space to facilitate explicit computations together with the concept of Sobolev gradients. For the proposed model, we derive an efficient and computationally accessible splitting scheme. Using a generalized forward-backward approach, our algorithm treats the total variation atoms of the splitting via proximal mappings, whereas the data terms are dealt with by gradient descent. The potential of the proposed method is demonstrated on various application examples dealing with 3D data. We explain how to extend the proposed combined approach to shape fields which, for instance, arise in the context of 3D+t imaging modalities, and show an application in this setup as well.
M. Peng
2017-07-01
Full Text Available The multi-source DEMs generated using the images acquired in the descent and landing phase and after landing contain supplementary information, and this makes it possible and beneficial to produce a higher-quality DEM through fusing the multi-scale DEMs. The proposed fusion method consists of three steps. First, source DEMs are split into small DEM patches, then the DEM patches are classified into a few groups by local density peaks clustering. Next, the grouped DEM patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP algorithm is used to achieve sparse representation. We use the real DEMs generated from Chang’e-3 descent images and navigation camera (Navcam stereo images to validate the proposed method. Through the experiments, we have reconstructed a seamless DEM with the highest resolution and the largest spatial coverage among the input data. The experimental results demonstrated the feasibility of the proposed method.
Ground reaction forces and frictional demands during stair descent: effects of age and illumination.
Christina, Kathryn A; Cavanagh, Peter R
2002-04-01
Stair descent is an inherently risky and demanding task that older adults often encounter in everyday life. It is believed that slip between the foot or shoe sole and the stair surface may play a role in stair related falls, however, there are no reports on slip resistance requirements for stair descent. The aim of this study was to determine the required coefficient of friction (RCOF) necessary for safe stair descent in 12 young and 12 older adults, under varied illuminance conditions. The RCOF during stair descent was found to be comparable in magnitude and time to that for overground walking, and thus, with adequate footwear and dry stair surfaces, friction does not appear to be a major determinant of stair safety. Illuminance level had little effect on the dependent variables quantified in this study. However, the older participants demonstrated safer strategies than the young during stair descent, as reflected by differences in the ground reaction forces and lower RCOF.
On efficient randomized algorithms for finding the PageRank vector
Gasnikov, A. V.; Dmitriev, D. Yu.
2015-03-01
Two randomized methods are considered for finding the PageRank vector; in other words, the solution of the system p T = p T P with a stochastic n × n matrix P, where n ˜ 107-109, is sought (in the class of probability distributions) with accuracy ɛ: ɛ ≫ n -1. Thus, the possibility of brute-force multiplication of P by the column is ruled out in the case of dense objects. The first method is based on the idea of Markov chain Monte Carlo algorithms. This approach is efficient when the iterative process p {/t+1 T} = p {/t T} P quickly reaches a steady state. Additionally, it takes into account another specific feature of P, namely, the nonzero off-diagonal elements of P are equal in rows (this property is used to organize a random walk over the graph with the matrix P). Based on modern concentration-of-measure inequalities, new bounds for the running time of this method are presented that take into account the specific features of P. In the second method, the search for a ranking vector is reduced to finding the equilibrium in the antagonistic matrix game where S n (1) is a unit simplex in ℝ n and I is the identity matrix. The arising problem is solved by applying a slightly modified Grigoriadis-Khachiyan algorithm (1995). This technique, like the Nazin-Polyak method (2009), is a randomized version of Nemirovski's mirror descent method. The difference is that randomization in the Grigoriadis-Khachiyan algorithm is used when the gradient is projected onto the simplex rather than when the stochastic gradient is computed. For sparse matrices P, the method proposed yields noticeably better results.
Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing
Ono, Masahiro; Kuwata, Yoshiaki
2013-01-01
A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.
Two-way regularization for MEG source reconstruction via multilevel coordinate descent
Siva Tian, Tian
2013-12-01
Magnetoencephalography (MEG) source reconstruction refers to the inverse problem of recovering the neural activity from the MEG time course measurements. A spatiotemporal two-way regularization (TWR) method was recently proposed by Tian et al. to solve this inverse problem and was shown to outperform several one-way regularization methods and spatiotemporal methods. This TWR method is a two-stage procedure that first obtains a raw estimate of the source signals and then refines the raw estimate to ensure spatial focality and temporal smoothness using spatiotemporal regularized matrix decomposition. Although proven to be effective, the performance of two-stage TWR depends on the quality of the raw estimate. In this paper we directly solve the MEG source reconstruction problem using a multivariate penalized regression where the number of variables is much larger than the number of cases. A special feature of this regression is that the regression coefficient matrix has a spatiotemporal two-way structure that naturally invites a two-way penalty. Making use of this structure, we develop a computationally efficient multilevel coordinate descent algorithm to implement the method. This new one-stage TWR method has shown its superiority to the two-stage TWR method in three simulation studies with different levels of complexity and a real-world MEG data analysis. © 2013 Wiley Periodicals, Inc., A Wiley Company.
Magnetotelluric inversion via reverse time migration algorithm of seismic data
Ha, Taeyoung; Shin, Changsoo
2007-01-01
We propose a new algorithm for two-dimensional magnetotelluric (MT) inversion. Our algorithm is an MT inversion based on the steepest descent method, borrowed from the backpropagation technique of seismic inversion or reverse time migration, introduced in the middle 1980s by Lailly and Tarantola. The steepest descent direction can be calculated efficiently by using the symmetry of numerical Green's function derived from a mixed finite element method proposed by Nedelec for Maxwell's equation, without calculating the Jacobian matrix explicitly. We construct three different objective functions by taking the logarithm of the complex apparent resistivity as introduced in the recent waveform inversion algorithm by Shin and Min. These objective functions can be naturally separated into amplitude inversion, phase inversion and simultaneous inversion. We demonstrate our algorithm by showing three inversion results for synthetic data
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.
A Spectral Conjugate Gradient Method for Unconstrained Optimization
Birgin, E. G.; Martinez, J. M.
2001-01-01
A family of scaled conjugate gradient algorithms for large-scale unconstrained minimization is defined. The Perry, the Polak-Ribiere and the Fletcher-Reeves formulae are compared using a spectral scaling derived from Raydan's spectral gradient optimization method. The best combination of formula, scaling and initial choice of step-length is compared against well known algorithms using a classical set of problems. An additional comparison involving an ill-conditioned estimation problem in Optics is presented
Haplotype assembly in polyploid genomes and identical by descent shared tracts.
Aguiar, Derek; Istrail, Sorin
2013-07-01
Genome-wide haplotype reconstruction from sequence data, or haplotype assembly, is at the center of major challenges in molecular biology and life sciences. For complex eukaryotic organisms like humans, the genome is vast and the population samples are growing so rapidly that algorithms processing high-throughput sequencing data must scale favorably in terms of both accuracy and computational efficiency. Furthermore, current models and methodologies for haplotype assembly (i) do not consider individuals sharing haplotypes jointly, which reduces the size and accuracy of assembled haplotypes, and (ii) are unable to model genomes having more than two sets of homologous chromosomes (polyploidy). Polyploid organisms are increasingly becoming the target of many research groups interested in the genomics of disease, phylogenetics, botany and evolution but there is an absence of theory and methods for polyploid haplotype reconstruction. In this work, we present a number of results, extensions and generalizations of compass graphs and our HapCompass framework. We prove the theoretical complexity of two haplotype assembly optimizations, thereby motivating the use of heuristics. Furthermore, we present graph theory-based algorithms for the problem of haplotype assembly using our previously developed HapCompass framework for (i) novel implementations of haplotype assembly optimizations (minimum error correction), (ii) assembly of a pair of individuals sharing a haplotype tract identical by descent and (iii) assembly of polyploid genomes. We evaluate our methods on 1000 Genomes Project, Pacific Biosciences and simulated sequence data. HapCompass is available for download at http://www.brown.edu/Research/Istrail_Lab/. Supplementary data are available at Bioinformatics online.
Aliberti, Sandra; Mezêncio, Bruno; Amadio, Alberto Carlos; Serrão, Julio Cerca; Mochizuki, Luis
2018-05-23
Knee pain during stair managing is a common complaint among individuals with PFP and can negatively affect their activities of daily living. Gait modification programs can be used to decrease patellofemoral pain. Immediate effects of a stair descent distal gait modification session that intended to emphasize forefoot landing during stair descent are described in this study. To analyze the immediate effects of a distal gait modification session on lower extremity movements and intensity of pain in women with patellofemoral pain during stair descent. Nonrandomized controlled trial. Sixteen women with patellofemoral pain were allocated into two groups: (1) Gait Modification Group (n = 8); and 2) Control Group (n = 8). The intensity of pain (visual analog scale) and kinematics of knee, ankle, and forefoot (multi-segmental foot model) during stair descent were assessed before and after the intervention. After the gait modification session, there was an increase of forefoot eversion and ankle plantarflexion as well as a decrease of knee flexion. An immediate decrease in patellofemoral pain intensity during stair descent was also observed. The distal gait modification session changed the lower extremity kinetic chain strategy of movement, increasing foot and ankle movement contribution and decreasing knee contribution to the task. An immediate decrease in patellofemoral pain intensity during stair descent was also observed. To emphasize forefoot landing may be a useful intervention to immediately relieve pain in patients with patellofemoral pain during stair descent. Clinical studies are needed to verify the gait modification session effects in medium and long terms.
Mahnke, Martina; Uprichard, Emma
2014-01-01
Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...
An Optimized Method for Terrain Reconstruction Based on Descent Images
Xu Xinchao
2016-02-01
Full Text Available An optimization method is proposed to perform high-accuracy terrain reconstruction of the landing area of Chang’e III. First, feature matching is conducted using geometric model constraints. Then, the initial terrain is obtained and the initial normal vector of each point is solved on the basis of the initial terrain. By changing the vector around the initial normal vector in small steps a set of new vectors is obtained. By combining these vectors with the direction of light and camera, the functions are set up on the basis of a surface reflection model. Then, a series of gray values is derived by solving the equations. The new optimized vector is recorded when the obtained gray value is closest to the corresponding pixel. Finally, the optimized terrain is obtained after iteration of the vector field. Experiments were conducted using the laboratory images and descent images of Chang’e III. The results showed that the performance of the proposed method was better than that of the classical feature matching method. It can provide a reference for terrain reconstruction of the landing area in subsequent moon exploration missions.
Foot and Ankle Kinematics During Descent From Varying Step Heights.
Gerstle, Emily E; O'Connor, Kristian; Keenan, Kevin G; Cobb, Stephen C
2017-12-01
In the general population, one-third of incidences during step negotiation occur during the transition to level walking. Furthermore, falls during curb negotiation are a common cause of injury in older adults. Distal foot kinematics may be an important factor in determining injury risk associated with transition step negotiation. The purpose of this study was to identify foot and ankle kinematics of uninjured individuals during descent from varying step heights. A 7-segment foot model was used to quantify kinematics as participants walked on a level walkway, stepped down a single step (heights: 5 cm, 15 cm, 25 cm), and continued walking. As step height increased, landing strategy transitioned from the rearfoot to the forefoot, and the rearfoot, lateral and medial midfoot, and medial forefoot became more plantar flexed. During weight acceptance, sagittal plane range of motion of the rearfoot, lateral midfoot, and medial and lateral forefoot increased as step height increased. The changes in landing strategy and distal foot function suggest a less stable ankle position at initial contact and increased demand on the distal foot at initial contact and through the weight acceptance phase of transition step negotiation as step height increases.
Race, language, and mental evolution in Darwin's descent of man.
Alter, Stephen G
2007-01-01
Charles Darwin was notoriously ambiguous in his remarks about the relationship between human evolution and biological race. He stressed the original unity of the races, yet he also helped to popularize the notion of a racial hierarchy filling the gaps between the highest anthropoids and civilized Europeans. A focus on Darwin's explanation of how humans initially evolved, however, shows that he mainly stressed not hierarchy but a version of humanity's original mental unity. In his book The Descent of Man, Darwin emphasized a substantial degree of mental development (including the incipient use of language) in the early, monogenetic phase of human evolution. This development, he argued, necessarily came before primeval man's numerical increase, geographic dispersion, and racial diversification, because only thus could one explain how that group was able to spread at the expense of rival ape-like populations. This scenario stood opposed to a new evolutionary polygenism formulated in the wake of Darwin's Origin of Species by his ostensible supporters Alfred Russel Wallace and Ernst Haeckel. Darwin judged this outlook inadequate to the task of explaining humanity's emergence. (c) 2007 Wiley Periodicals, Inc.
Physics-based Entry, Descent and Landing Risk Model
Gee, Ken; Huynh, Loc C.; Manning, Ted
2014-01-01
A physics-based risk model was developed to assess the risk associated with thermal protection system failures during the entry, descent and landing phase of a manned spacecraft mission. In the model, entry trajectories were computed using a three-degree-of-freedom trajectory tool, the aerothermodynamic heating environment was computed using an engineering-level computational tool and the thermal response of the TPS material was modeled using a one-dimensional thermal response tool. The model was capable of modeling the effect of micrometeoroid and orbital debris impact damage on the TPS thermal response. A Monte Carlo analysis was used to determine the effects of uncertainties in the vehicle state at Entry Interface, aerothermodynamic heating and material properties on the performance of the TPS design. The failure criterion was set as a temperature limit at the bondline between the TPS and the underlying structure. Both direct computation and response surface approaches were used to compute the risk. The model was applied to a generic manned space capsule design. The effect of material property uncertainty and MMOD damage on risk of failure were analyzed. A comparison of the direct computation and response surface approach was undertaken.
The glucokinase mutation p.T206P is common among MODY patients of Jewish Ashkenazi descent.
Gozlan, Yael; Tenenbaum, Ariel; Shalitin, Shlomit; Lebenthal, Yael; Oron, Tal; Cohen, Ohad; Phillip, Moshe; Gat-Yablonski, Galia
2012-09-01
Maturity-onset diabetes of the young (MODY) is characterized by an autosomal dominant mode of inheritance; a primary defect in insulin secretion with non-ketotic hyperglycemia, age of onset under 25 yr; and lack of autoantibodies. Heterozygous mutations in glucokinase (GCK) are associated with mild fasting hyperglycemia and gestational diabetes mellitus while homozygous or compound heterozygous GCK mutations result in permanent neonatal diabetes mellitus. Given that both the Israeli-Arabic and the various Israeli-Jewish communities tend to maintain ethnic seclusion, we speculated that it would be possible to identify a relatively narrow spectrum of mutations in the Israeli population. To characterize the genetic basis of GCK-MODY in the different ethnic groups of the Israeli population. Patients with clinically identified GCK-MODY and their first degree family members. Molecular analysis of GCK was performed on genomic DNA using polymerase chain reaction, denaturing gradient gel electrophoresis (DGGE), and sequencing. Bioinformatic model was preformed using the NEST program. Mutations in GCK were identified in 25 families and were all family-specific, except c.616A>C. p.T206P. This mutation was identified in six unrelated families, all patients from a Jewish-Ashkenazi descent, thus indicating an ethno-genetic correlation. A simple, fast, and relatively cheap DGGE/restriction-digestion assay was developed. The high incidence of the mutant allele in GCK-MODY patients of Jewish-Ashkenazi descent suggests a founder effect. We propose that clinically identified GCK-MODY patients of Jewish-Ashkenazi origin be first tested for this mutation. © 2011 John Wiley & Sons A/S.
Algorithms for Mathematical Programming with Emphasis on Bi-level Models
Goldfarb, Donald [Columbia University; Iyengar, Garud [Columbia University
2014-05-22
The research supported by this grant was focused primarily on first-order methods for solving large scale and structured convex optimization problems and convex relaxations of nonconvex problems. These include optimal gradient methods, operator and variable splitting methods, alternating direction augmented Lagrangian methods, and block coordinate descent methods.
Zhifeng Dai
2014-01-01
Full Text Available Combining the Rosen gradient projection method with the two-term Polak-Ribière-Polyak (PRP conjugate gradient method, we propose a two-term Polak-Ribière-Polyak (PRP conjugate gradient projection method for solving linear equality constraints optimization problems. The proposed method possesses some attractive properties: (1 search direction generated by the proposed method is a feasible descent direction; consequently the generated iterates are feasible points; (2 the sequences of function are decreasing. Under some mild conditions, we show that it is globally convergent with Armijio-type line search. Preliminary numerical results show that the proposed method is promising.
Ghani, N. H. A.; Mohamed, N. S.; Zull, N.; Shoid, S.; Rivaie, M.; Mamat, M.
2017-09-01
Conjugate gradient (CG) method is one of iterative techniques prominently used in solving unconstrained optimization problems due to its simplicity, low memory storage, and good convergence analysis. This paper presents a new hybrid conjugate gradient method, named NRM1 method. The method is analyzed under the exact and inexact line searches in given conditions. Theoretically, proofs show that the NRM1 method satisfies the sufficient descent condition with both line searches. The computational result indicates that NRM1 method is capable in solving the standard unconstrained optimization problems used. On the other hand, the NRM1 method performs better under inexact line search compared with exact line search.
Chowdhary, J; Keyes, T
2002-02-01
Instantaneous normal modes (INM's) are calculated during a conjugate-gradient (CG) descent of the potential energy landscape, starting from an equilibrium configuration of a liquid or crystal. A small number (approximately equal to 4) of CG steps removes all the Im-omega modes in the crystal and leaves the liquid with diffusive Im-omega which accurately represent the self-diffusion constant D. Conjugate gradient filtering appears to be a promising method, applicable to any system, of obtaining diffusive modes and facilitating INM theory of D. The relation of the CG-step dependent INM quantities to the landscape and its saddles is discussed.
National Aeronautics and Space Administration — The landing of the Mars Science Laboratory represents the upper limit of current Entry, Descent, and Landing (EDL) capabilities for Mars exploration. The succession...
Intelligent Data Understanding for Architecture Analysis of Entry, Descent, and Landing
National Aeronautics and Space Administration — Because Entry, Descent and Landing (EDL) system validations are limited in Earth environments, these technologies rely heavily on models and analysis tools to...
Computing several eigenpairs of Hermitian problems by conjugate gradient iterations
Ovtchinnikov, E.E.
2008-01-01
The paper is concerned with algorithms for computing several extreme eigenpairs of Hermitian problems based on the conjugate gradient method. We analyse computational strategies employed by various algorithms of this kind reported in the literature and identify their limitations. Our criticism is illustrated by numerical tests on a set of problems from electronic structure calculations and acoustics
Gradient-type methods in inverse parabolic problems
Kabanikhin, Sergey; Penenko, Aleksey
2008-01-01
This article is devoted to gradient-based methods for inverse parabolic problems. In the first part, we present a priori convergence theorems based on the conditional stability estimates for linear inverse problems. These theorems are applied to backwards parabolic problem and sideways parabolic problem. The convergence conditions obtained coincide with sourcewise representability in the self-adjoint backwards parabolic case but they differ in the sideways case. In the second part, a variational approach is formulated for a coefficient identification problem. Using adjoint equations, a formal gradient of an objective functional is constructed. A numerical test illustrates the performance of conjugate gradient algorithm with the formal gradient.
Machado-Joseph Disease in Pedigrees of Azorean descent is Linked to Chromosome 14
George-Hyslop, P. St; Rogaeva, E.; Huterer, J.; Tsuda, T.; Santos, J.; Haines, J. L.; Schlumpf, K.; Rogaev, E. I.; Liang, Y.; McLachlan, D. R. Crapper; Kennedy, J.; Weissenbach, J.; Billingsley, G. D.; Cox, D. W.; Lang, A. E.
1994-01-01
A locus for Machado-Joseph disease (MJD) has recently been mapped to a 30-cM region of chromosome 14q in five pedigrees of Japanese descent. MJD is a clinically pleomorphic neurodegenerative disease that was originally described in subjects of Azorean descent. In light of the nonallelic heterogeneity in other inherited spinocere-bellar ataxias, we were interested to determine if the MJD phenotype in Japanese and Azorean pedigrees arose from mutations at the same locus. We provide evidence tha...
Salman, Afan Galih
2011-01-01
Penggunaan teknologi di bidang Artificial Intellegence khususnya teknologi Jaringan Syaraf Tiruan (JST) dalampendugaan curah hujan dapat dilakukan dengan metoda pendekatan pembelajaran. Berdasarkan kemampuanbelajar yang dimilikinya, maka JST dapat dilatih untuk mempelajari dan menganalisa pola data masa lalu danberusaha mencari suatu formula atau fungsi yang akan menghubungkan pola data masa lalu dengan keluaranyang diinginkan pada saat ini. Keakuratan hasil prediksi JST diukur berdasarkan ko...
Mars 2020 Entry, Descent and Landing Instrumentation (MEDLI2)
Bose, Deepak; Wright, Henry; White, Todd; Schoenenberger, Mark; Santos, Jose; Karlgaard, Chris; Kuhl, Chris; Oishi, TOmo; Trombetta, Dominic
2016-01-01
This paper will introduce Mars Entry Descent and Landing Instrumentation (MEDLI2) on NASA's Mars2020 mission. Mars2020 is a flagship NASA mission with science and technology objectives to help answer questions about possibility of life on Mars as well as to demonstrate technologies for future human expedition. Mars2020 is scheduled for launch in 2020. MEDLI2 is a suite of instruments embedded in the heatshield and backshell thermal protection systems of Mars2020 entry vehicle. The objectives of MEDLI2 are to gather critical aerodynamics, aerothermodynamics and TPS performance data during EDL phase of the mission. MEDLI2 builds up the success of MEDLI flight instrumentation on Mars Science Laboratory mission in 2012. MEDLI instrumentation suite measured surface pressure and TPS temperature on the heatshield during MSL entry into Mars. MEDLI data has since been used for unprecedented reconstruction of aerodynamic drag, vehicle attitude, in-situ atmospheric density, aerothermal heating, transition to turbulence, in-depth TPS performance and TPS ablation. [1,2] In addition to validating predictive models, MEDLI data has highlighted extra margin available in the MSL forebody TPS, which can potentially be used to reduce vehicle parasitic mass. MEDLI2 expands the scope of instrumentation by focusing on quantities of interest not addressed in MEDLI suite. The type the sensors are expanded and their layout on the TPS modified to meet these new objectives. The paper will provide key motivation and governing requirements that drive the choice and the implementation of the new sensor suite. The implementation considerations of sensor selection, qualification, and demonstration of minimal risk to the host mission will be described. The additional challenges associated with mechanical accommodation, electrical impact, data storage and retrieval for MEDLI2 system, which extends sensors to backshell will also be described.
Studies of the hormonal control of postnatal testicular descent in the rat.
Spencer, J R; Vaughan, E D; Imperato-McGinley, J
1993-03-01
Dihydrotestosterone is believed to control the transinguinal phase of testicular descent based on hormonal manipulation studies performed in postnatal rats. In the present study, these hormonal manipulation experiments were repeated, and the results were compared with those obtained using the antiandrogens flutamide and cyproterone acetate. 17 beta-estradiol completely blocked testicular descent, but testosterone and dihydrotestosterone were equally effective in reversing this inhibition. Neither flutamide nor cyproterone acetate prevented testicular descent in postnatal rats despite marked peripheral antiandrogenic action. Further analysis of the data revealed a correlation between testicular size and descent. Androgen receptor blockade did not produce a marked reduction in testicular size and consequently did not prevent testicular descent, whereas estradiol alone caused marked testicular atrophy and testicular maldescent. Reduction of the estradiol dosage or concomitant administration of androgens or human chorionic gonadotropin resulted in both increased testicular size and degree of descent. These data suggest that growth of the neonatal rat testis may contribute to its passage into the scrotum.
Conflation of Short Identity-by-Descent Segments Bias Their Inferred Length Distribution
Charleston W. K. Chiang
2016-05-01
Full Text Available Identity-by-descent (IBD is a fundamental concept in genetics with many applications. In a common definition, two haplotypes are said to share an IBD segment if that segment is inherited from a recent shared common ancestor without intervening recombination. Segments several cM long can be efficiently detected by a number of algorithms using high-density SNP array data from a population sample, and there are currently efforts to detect shorter segments from sequencing. Here, we study a problem of identifiability: because existing approaches detect IBD based on contiguous segments of identity-by-state, inferred long segments of IBD may arise from the conflation of smaller, nearby IBD segments. We quantified this effect using coalescent simulations, finding that significant proportions of inferred segments 1–2 cM long are results of conflations of two or more shorter segments, each at least 0.2 cM or longer, under demographic scenarios typical for modern humans for all programs tested. The impact of such conflation is much smaller for longer (> 2 cM segments. This biases the inferred IBD segment length distribution, and so can affect downstream inferences that depend on the assumption that each segment of IBD derives from a single common ancestor. As an example, we present and analyze an estimator of the de novo mutation rate using IBD segments, and demonstrate that unmodeled conflation leads to underestimates of the ages of the common ancestors on these segments, and hence a significant overestimate of the mutation rate. Understanding the conflation effect in detail will make its correction in future methods more tractable.
Stereo vision with distance and gradient recognition
Kim, Soo-Hyun; Kang, Suk-Bum; Yang, Tae-Kyu
2007-12-01
Robot vision technology is needed for the stable walking, object recognition and the movement to the target spot. By some sensors which use infrared rays and ultrasonic, robot can overcome the urgent state or dangerous time. But stereo vision of three dimensional space would make robot have powerful artificial intelligence. In this paper we consider about the stereo vision for stable and correct movement of a biped robot. When a robot confront with an inclination plane or steps, particular algorithms are needed to go on without failure. This study developed the recognition algorithm of distance and gradient of environment by stereo matching process.
Travelling gradient thermocouple calibration
Broomfield, G.H.
1975-01-01
A short discussion of the origins of the thermocouple EMF is used to re-introduce the idea that the Peltier and Thompson effects are indistinguishable from one another. Thermocouples may be viewed as devices which generate an EMF at junctions or as integrators of EMF's developed in thermal gradients. The thermal gradient view is considered the more appropriate, because of its better accord with theory and behaviour, the correct approach to calibration, and investigation of service effects is immediately obvious. Inhomogeneities arise in thermocouples during manufacture and in service. The results of travelling gradient measurements are used to show that such effects are revealed with a resolution which depends on the length of the gradient although they may be masked during simple immersion calibration. Proposed tests on thermocouples irradiated in a nuclear reactor are discussed
Quaternion Gradient and Hessian
Xu, Dongpo; Mandic, Danilo P.
2014-01-01
The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel gen...
Chen, G.S.
1997-01-01
We apply and compare the preconditioned generalized conjugate gradient methods to solve the linear system equation that arises in the two-dimensional neutron and photon transport equation in this paper. Several subroutines are developed on the basis of preconditioned generalized conjugate gradient methods for time-independent, two-dimensional neutron and photon transport equation in the transport theory. These generalized conjugate gradient methods are used. TFQMR (transpose free quasi-minimal residual algorithm), CGS (conjuage gradient square algorithm), Bi-CGSTAB (bi-conjugate gradient stabilized algorithm) and QMRCGSTAB (quasi-minimal residual variant of bi-conjugate gradient stabilized algorithm). These sub-routines are connected to computer program DORT. Several problems are tested on a personal computer with Intel Pentium CPU. (author)
De Götzen , Amalia; Mion , Luca; Tache , Olivier
2007-01-01
International audience; We call sound algorithms the categories of algorithms that deal with digital sound signal. Sound algorithms appeared in the very infancy of computer. Sound algorithms present strong specificities that are the consequence of two dual considerations: the properties of the digital sound signal itself and its uses, and the properties of auditory perception.
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Identification of chaotic systems by neural network with hybrid learning algorithm
Pan, S.-T.; Lai, C.-C.
2008-01-01
Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods
Modified conjugate gradient method for diagonalizing large matrices.
Jie, Quanlin; Liu, Dunhuan
2003-11-01
We present an iterative method to diagonalize large matrices. The basic idea is the same as the conjugate gradient (CG) method, i.e, minimizing the Rayleigh quotient via its gradient and avoiding reintroducing errors to the directions of previous gradients. Each iteration step is to find lowest eigenvector of the matrix in a subspace spanned by the current trial vector and the corresponding gradient of the Rayleigh quotient, as well as some previous trial vectors. The gradient, together with the previous trial vectors, play a similar role as the conjugate gradient of the original CG algorithm. Our numeric tests indicate that this method converges significantly faster than the original CG method. And the computational cost of one iteration step is about the same as the original CG method. It is suitable for first principle calculations.
Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim
2014-01-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using
Gradient Alloy for Optical Packaging
National Aeronautics and Space Administration — Advances in additive manufacturing, such as Laser Engineered Net Shaping (LENS), enables the fabrication of compositionally gradient microstructures, i.e. gradient...
Aerodynamics of Reentry Vehicle Clipper at Descent Phase
Semenov, Yu. P.; Reshetin, A. G.; Dyadkin, A. A.; Petrov, N. K.; Simakova, T. V.; Tokarev, V. A.
2005-02-01
From Gagarin spacecraft to reusable orbiter Buran, RSC Energia has traveled a long way in the search for the most optimal and, which is no less important, the most reliable spacecraft for manned space flight. During the forty years of space exploration, in cooperation with a broad base of subcontractors, a number of problems have been solved which assure a safe long stay in space. Vostok and Voskhod spacecraft were replaced with Soyuz supporting a crew of three. During missions to a space station, it provides crew rescue capability in case of a space station emergency at all times (the spacecraft life is 200 days).The latest modification of Soyuz spacecraft -Soyuz TMA -in contrast to its predecessors, allows to become a space flight participant to a person of virtually any anthropometric parameters with a mass of 50 to 95 kg capable of withstanding up to 6 g load during descent. At present, Soyuz TMA spacecraft are the state-of-the-art, reliable and only means of the ISS crew delivery, in-flight support and return. Introduced on the basis of many years of experience in operation of manned spacecraft were not only the principles of deep redundancy of on-board systems and equipment, but, to assure the main task of the spacecraft -the crew return to Earth -the principles of functional redundancy. That is, vital operations can be performed by different systems based on different physical principles. The emergency escape system that was developed is the only one in the world that provides crew rescue in case of LV failure at any phase in its flight. Several generations of space stations that have been developed have broadened, virtually beyond all limits, capabilities of man in space. The docking system developed at RSC Energia allowed not only to dock spacecraft in space, but also to construct in orbit various complex space systems. These include large space stations, and may include in the future the in-orbit construction of systems for the exploration of the Moon and
Novel maximum-margin training algorithms for supervised neural networks.
Ludwig, Oswaldo; Nunes, Urbano
2010-06-01
This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by
Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana
2016-01-01
With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.
Canonical trivialization of gravitational gradients
Niedermaier, Max
2017-01-01
A one-parameter family of canonical transformations is constructed that reduces the Hamiltonian form of the Einstein–Hilbert action to its strong coupling limit where dynamical spatial gradients are absent. The parameter can alternatively be viewed as the overall scale of the spatial metric or as a fractional inverse power of Newton’s constant. The generating function of the canonical transformation is constructed iteratively as a powerseries in the parameter to all orders. The algorithm draws on Lie–Deprit transformation theory and defines a ‘trivialization map’ with several bonus properties: (i) Trivialization of the Hamiltonian constraint implies that of the action while the diffeomorphism constraint is automatically co-transformed. (ii) Only a set of ordinary differential equations needs to be solved to drive the iteration via a homological equation where no gauge fixing is required. (iii) In contrast to (the classical limit of) a Lagrangian trivialization map the algorithm also produces series solutions of the field equations. (iv) In the strong coupling theory temporal gauge variations are abelian, nevertheless the map intertwines with the respective gauge symmetries on the action, the field equations, and their solutions. (paper)
Canonical trivialization of gravitational gradients
Niedermaier, Max
2017-06-01
A one-parameter family of canonical transformations is constructed that reduces the Hamiltonian form of the Einstein-Hilbert action to its strong coupling limit where dynamical spatial gradients are absent. The parameter can alternatively be viewed as the overall scale of the spatial metric or as a fractional inverse power of Newton’s constant. The generating function of the canonical transformation is constructed iteratively as a powerseries in the parameter to all orders. The algorithm draws on Lie-Deprit transformation theory and defines a ‘trivialization map’ with several bonus properties: (i) Trivialization of the Hamiltonian constraint implies that of the action while the diffeomorphism constraint is automatically co-transformed. (ii) Only a set of ordinary differential equations needs to be solved to drive the iteration via a homological equation where no gauge fixing is required. (iii) In contrast to (the classical limit of) a Lagrangian trivialization map the algorithm also produces series solutions of the field equations. (iv) In the strong coupling theory temporal gauge variations are abelian, nevertheless the map intertwines with the respective gauge symmetries on the action, the field equations, and their solutions.
High Gradient Accelerator Research
Temkin, Richard
2016-01-01
The goal of the MIT program of research on high gradient acceleration is the development of advanced acceleration concepts that lead to a practical and affordable next generation linear collider at the TeV energy level. Other applications, which are more near-term, include accelerators for materials processing; medicine; defense; mining; security; and inspection. The specific goals of the MIT program are: • Pioneering theoretical research on advanced structures for high gradient acceleration, including photonic structures and metamaterial structures; evaluation of the wakefields in these advanced structures • Experimental research to demonstrate the properties of advanced structures both in low-power microwave cold test and high-power, high-gradient test at megawatt power levels • Experimental research on microwave breakdown at high gradient including studies of breakdown phenomena induced by RF electric fields and RF magnetic fields; development of new diagnostics of the breakdown process • Theoretical research on the physics and engineering features of RF vacuum breakdown • Maintaining and improving the Haimson / MIT 17 GHz accelerator, the highest frequency operational accelerator in the world, a unique facility for accelerator research • Providing the Haimson / MIT 17 GHz accelerator facility as a facility for outside users • Active participation in the US DOE program of High Gradient Collaboration, including joint work with SLAC and with Los Alamos National Laboratory; participation of MIT students in research at the national laboratories • Training the next generation of Ph. D. students in the field of accelerator physics.
Joux, Antoine
2009-01-01
Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic
The Mars Science Laboratory Entry, Descent, and Landing Flight Software
Gostelow, Kim P.
2013-01-01
This paper describes the design, development, and testing of the EDL program from the perspective of the software engineer. We briefly cover the overall MSL flight software organization, and then the organization of EDL itself. We discuss the timeline, the structure of the GNC code (but not the algorithms as they are covered elsewhere in this conference) and the command and telemetry interfaces. Finally, we cover testing and the influence that testability had on the EDL flight software design.
Analysis of foot clearance in firefighters during ascent and descent of stairs.
Kesler, Richard M; Horn, Gavin P; Rosengren, Karl S; Hsiao-Wecksler, Elizabeth T
2016-01-01
Slips, trips, and falls are a leading cause of injury to firefighters with many injuries occurring while traversing stairs, possibly exaggerated by acute fatigue from firefighting activities and/or asymmetric load carriage. This study examined the effects that fatigue, induced by simulated firefighting activities, and hose load carriage have on foot clearance while traversing stairs. Landing and passing foot clearances for each stair during ascent and descent of a short staircase were investigated. Clearances decreased significantly (p < 0.05) post-exercise for nine of 12 ascent parameters and increased for two of eight descent parameters. Load carriage resulted in significantly decreased (p < 0.05) clearance over three ascent parameters, and one increase during descent. Decreased clearances during ascent caused by fatigue or load carriage may result in an increased trip risk. Increased clearances during descent may suggest use of a compensation strategy to ensure stair clearance or an increased risk of over-stepping during descent. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Timothy R. Rebbeck
2013-01-01
Full Text Available Prostate cancer (CaP is the leading cancer among men of African descent in the USA, Caribbean, and Sub-Saharan Africa (SSA. The estimated number of CaP deaths in SSA during 2008 was more than five times that among African Americans and is expected to double in Africa by 2030. We summarize publicly available CaP data and collected data from the men of African descent and Carcinoma of the Prostate (MADCaP Consortium and the African Caribbean Cancer Consortium (AC3 to evaluate CaP incidence and mortality in men of African descent worldwide. CaP incidence and mortality are highest in men of African descent in the USA and the Caribbean. Tumor stage and grade were highest in SSA. We report a higher proportion of T1 stage prostate tumors in countries with greater percent gross domestic product spent on health care and physicians per 100,000 persons. We also observed that regions with a higher proportion of advanced tumors reported lower mortality rates. This finding suggests that CaP is underdiagnosed and/or underreported in SSA men. Nonetheless, CaP incidence and mortality represent a significant public health problem in men of African descent around the world.
Giovannini, Massimo
2015-01-01
Cosmological singularities are often discussed by means of a gradient expansion that can also describe, during a quasi-de Sitter phase, the progressive suppression of curvature inhomogeneities. While the inflationary event horizon is being formed the two mentioned regimes coexist and a uniform expansion can be conceived and applied to the evolution of spatial gradients across the protoinflationary boundary. It is argued that conventional arguments addressing the preinflationary initial conditions are necessary but generally not sufficient to guarantee a homogeneous onset of the conventional inflationary stage.
High gradient superconducting quadrupoles
Lundy, R.A.; Brown, B.C.; Carson, J.A.; Fisk, H.E.; Hanft, R.H.; Mantsch, P.M.; McInturff, A.D.; Remsbottom, R.H.
1987-07-01
Prototype superconducting quadrupoles with a 5 cm aperture and gradient of 16 kG/cm have been built and tested as candidate magnets for the final focus at SLC. The magnets are made from NbTi Tevatron style cable with 10 inner and 14 outer turns per quadrant. Quench performance and multipole data are presented. Design and data for a low current, high gradient quadrupole, similar in cross section but wound with a cable consisting of five insulated conductors are also discussed
TRW - Lunar Descent Engine. Chapter 6, Appendix H
Elverum, Gerard W.
2009-01-01
it came to Apollo 13, we went back into the record, and said, "Hey, we have pushed this system around up there on Apollo 5, and we have also restarted this tandem configuration." The requirements on Apollo 13 were to put it back into play. The spacecraft was out of free return to the earth at the time of the accident. It would not have come back. NASA said, "Okay, we ll use the descent engine to put the spacecraft in a free trajectory; it will go around the moon and be on free trajectory back to Earth." Then, as it came around the far side of the moon, the guys found out that they had an oxygen problem. As you remember, things were getting pretty bad in there. They said, "We ve got to get it back as fast as we can. Is it okay if we re-fire the engine? Now, we re in a free trajectory, so we want to put as much delta-v (or change in velocity) in as we can. Can we re-fire right now?" We said, "Yes, the data says it has been this period of time." We could re-fire the engine, run the rest of the duty cycle up as far as we needed while preserving enough fluids to make the final correction as the spacecraft got near Earth, and restart the engine. It was pretty fortuitous that we could give them those answers.
A new family of Polak-Ribiere-Polyak conjugate gradient method with the strong-Wolfe line search
Ghani, Nur Hamizah Abdul; Mamat, Mustafa; Rivaie, Mohd
2017-08-01
Conjugate gradient (CG) method is an important technique in unconstrained optimization, due to its effectiveness and low memory requirements. The focus of this paper is to introduce a new CG method for solving large scale unconstrained optimization. Theoretical proofs show that the new method fulfills sufficient descent condition if strong Wolfe-Powell inexact line search is used. Besides, computational results show that our proposed method outperforms to other existing CG methods.
Hougardy, Stefan
2016-01-01
Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.
Convective descent simulations of drilling discharges on Georges and Sable Island banks
Andrade, Y.; Loder, J.W.
1997-01-01
Factors affecting the fate of drilling mud discharges on Georges and Sable Island Banks were examined. The Koh and Chang jet discharge model was used to simulate the convective descent of a jet discharge of relatively dense materials at sites representing different hydrographic and depth regimes with a range of mud densities, and different discharge configurations, ocean currents and seasonal stratifications. The study revealed the dependence of the depth of descent and properties of the discharge plume on the discharge configuration and oceanographic conditions. The factors that affected the depth of descent were mud density, depth of release, initial downward volume flux of the discharge, current strength and ocean stratification. 22 refs., 3 tabs., 59 figs
Videodefaecography combined with measurement of the anorectal angle and of perineal descent
Skomorowska, E.; Henrichsen, S.; Christiansen, J.; Hegedues, V.; Glostrup Sygehus, Copenhagen
1987-01-01
Cineradiographic defaecography combined with measurement of the anorectal angle and descent of the pelvic floor is proposed. The method used in 73 women gave valuable information in 48 patients who complained of anal incompetence, rectal tenesmus, and chronic constipation. In these patients, high and low rectal intussusception, rectocele, and pathologic movement of the pelvic floor were detected. Some of these phenomena could only be diagnosed by the radiologic method here described. Quantitations of the anorectal angle and descent of the pelvic floor placed the group with constipation halfway between normal individuals and those with anal incompetence. The value of this finding is discussed. Recent improvements in anorectal surgery often make videodefaecography decisive for the choice of the optimal operative method. Therefore, videodefaecography together with measurement of the anorectal angle and pelvic floor descent is recommended whenever anorectal surgery for correction of functional disturbances is contemplated. (orig.)
Entry, Descent, and Landing Performance for a Mid-Lift-to-Drag Ratio Vehicle at Mars
Johnson, Breanna J.; Braden, Ellen M.; Sostaric, Ronald R.; Cerimele, Christopher J.; Lu, Ping
2018-01-01
In an effort to mature the design of the Mid-Lift-to-Drag ratio Rigid Vehicle (MRV) candidate of the NASA Evolvable Mars Campaign (EMC) architecture study, end-to-end six-degree-of-freedom (6DOF) simulations are needed to ensure a successful entry, descent, and landing (EDL) design. The EMC study is assessing different vehicle and mission architectures to determine which candidate would be best to deliver a 20 metric ton payload to the surface of Mars. Due to the large mass payload and the relatively low atmospheric density of Mars, all candidates of the EMC study propose to use Supersonic Retro-Propulsion (SRP) throughout the descent and landing phase, as opposed to parachutes, in order to decelerate to a subsonic touchdown. This paper presents a 6DOF entry-to-landing performance and controllability study with sensitivities to dispersions, particularly in the powered descent and landing phases.
Tel, G.
We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of
Evolutionary analyses of non-genealogical bonds produced by introgressive descent.
Bapteste, Eric; Lopez, Philippe; Bouchard, Frédéric; Baquero, Fernando; McInerney, James O; Burian, Richard M
2012-11-06
All evolutionary biologists are familiar with evolutionary units that evolve by vertical descent in a tree-like fashion in single lineages. However, many other kinds of processes contribute to evolutionary diversity. In vertical descent, the genetic material of a particular evolutionary unit is propagated by replication inside its own lineage. In what we call introgressive descent, the genetic material of a particular evolutionary unit propagates into different host structures and is replicated within these host structures. Thus, introgressive descent generates a variety of evolutionary units and leaves recognizable patterns in resemblance networks. We characterize six kinds of evolutionary units, of which five involve mosaic lineages generated by introgressive descent. To facilitate detection of these units in resemblance networks, we introduce terminology based on two notions, P3s (subgraphs of three nodes: A, B, and C) and mosaic P3s, and suggest an apparatus for systematic detection of introgressive descent. Mosaic P3s correspond to a distinct type of evolutionary bond that is orthogonal to the bonds of kinship and genealogy usually examined by evolutionary biologists. We argue that recognition of these evolutionary bonds stimulates radical rethinking of key questions in evolutionary biology (e.g., the relations among evolutionary players in very early phases of evolutionary history, the origin and emergence of novelties, and the production of new lineages). This line of research will expand the study of biological complexity beyond the usual genealogical bonds, revealing additional sources of biodiversity. It provides an important step to a more realistic pluralist treatment of evolutionary complexity.
Pizzoferrato, Anne-Cécile; Fauconnier, Arnaud; Bader, Georges; de Tayrac, Renaud; Fort, Julie; Fritel, Xavier
2016-07-01
Obstetric trauma during childbirth is considered a major risk factor for postpartum urinary incontinence (UI), particularly stress urinary incontinence. Our aim was to investigate the relation between postpartum UI, mode of delivery, and urethral descent, and to define a group of women who are particularly at risk of postnatal UI. A total of 186 women were included their first pregnancy. Validated questionnaires about urinary symptoms during pregnancy, 2 and 12 months after delivery, were administered. Urethral descent was assessed clinically and by ultrasound at inclusion. Multivariate logistic regression analysis was used to determine the risk factors for UI during pregnancy, at 2 months and 1 year after first delivery. The prevalence of UI was 38.6, 46.5, 35.6, and 34.4 % at inclusion, late pregnancy, 2 months postpartum, and 1 year postpartum respectively. No significant association was found between UI at late pregnancy and urethral descent assessed clinically or by ultrasound. The only risk factor for UI at 2 months postpartum was UI at inclusion (OR 6.27 [95 % CI 2.70-14.6]). The risk factors for UI at 1 year postpartum were UI at inclusion (6.14 [2.22-16.9]), body mass index (BMI), and urethral descent at inclusion, assessed clinically (7.21 [2.20-23.7]) or by ultrasound. The mode of delivery was not associated with urethral descent. Prenatal urethral descent and UI during pregnancy are risk factors for UI at 1 year postpartum. These results indicate that postnatal UI is more strongly influenced by susceptibility factors existing before first delivery than by the mode of delivery.
Gaze, Eric C.
2005-01-01
We introduce a cooperative learning, group lab for a Calculus III course to facilitate comprehension of the gradient vector and directional derivative concepts. The lab is a hands-on experience allowing students to manipulate a tangent plane and empirically measure the effect of partial derivatives on the direction of optimal ascent. (Contains 7…
Conditionally-uniform Feasible Grid Search Algorithm
Dziubinski, Matt P.
We present and evaluate a numerical optimization method (together with an algorithm for choosing the starting values) pertinent to the constrained optimization problem arising in the estimation of the GARCH models with inequality constraints, in particular the Simplied Component GARCH Model...... (SCGARCH), together with algorithms for the objective function and analytical gradient computation for SCGARCH....
Corrugated thimble tube for controlling control rod descent in nuclear reactor
Luetzow, H.J.
1981-01-01
A thimble tube construction is described which will provide a controlled descent for a control rod while minimizing the reaction forces which must be absorbed by the thimble tube and reducing the possibility that a foreign particle could interfere with the free descent of a control rod. A thimble tube is formed with helically-corrugate internal walls which cooperate with a control rod contained in the tube in an emergency situation to provide a progressively-increasing hydraulic restraining force as each adjacent corrugation is encountered
Entry, Descent and Landing Systems Analysis: Exploration Class Simulation Overview and Results
DwyerCianciolo, Alicia M.; Davis, Jody L.; Shidner, Jeremy D.; Powell, Richard W.
2010-01-01
NASA senior management commissioned the Entry, Descent and Landing Systems Analysis (EDL-SA) Study in 2008 to identify and roadmap the Entry, Descent and Landing (EDL) technology investments that the agency needed to make in order to successfully land large payloads at Mars for both robotic and exploration or human-scale missions. The year one exploration class mission activity considered technologies capable of delivering a 40-mt payload. This paper provides an overview of the exploration class mission study, including technologies considered, models developed and initial simulation results from the EDL-SA year one effort.
Shang, Shang; Bai, Jing; Song, Xiaolei; Wang, Hongkai; Lau, Jaclyn
2007-01-01
Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient-based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.
A new LMS algorithm for analysis of atrial fibrillation signals
Ciaccio Edward J; Biviano Angelo B; Whang William; Garan Hasan
2012-01-01
Abstract Background A biomedical signal can be defined by its extrinsic features (x-axis and y-axis shift and scale) and intrinsic features (shape after normalization of extrinsic features). In this study, an LMS algorithm utilizing the method of differential steepest descent is developed, and is tested by normalization of extrinsic features in complex fractionated atrial electrograms (CFAE). Method Equations for normalization of x-axis and y-axis shift and scale are first derived. The algori...
Full cyclic coordinate descent: solving the protein loop closure problem in Cα space
Hamelryck Thomas
2005-06-01
Full Text Available Abstract Background Various forms of the so-called loop closure problem are crucial to protein structure prediction methods. Given an N- and a C-terminal end, the problem consists of finding a suitable segment of a certain length that bridges the ends seamlessly. In homology modelling, the problem arises in predicting loop regions. In de novo protein structure prediction, the problem is encountered when implementing local moves for Markov Chain Monte Carlo simulations. Most loop closure algorithms keep the bond angles fixed or semi-fixed, and only vary the dihedral angles. This is appropriate for a full-atom protein backbone, since the bond angles can be considered as fixed, while the (φ, ψ dihedral angles are variable. However, many de novo structure prediction methods use protein models that only consist of Cα atoms, or otherwise do not make use of all backbone atoms. These methods require a method that alters both bond and dihedral angles, since the pseudo bond angle between three consecutive Cα atoms also varies considerably. Results Here we present a method that solves the loop closure problem for Cα only protein models. We developed a variant of Cyclic Coordinate Descent (CCD, an inverse kinematics method from the field of robotics, which was recently applied to the loop closure problem. Since the method alters both bond and dihedral angles, which is equivalent to applying a full rotation matrix, we call our method Full CCD (FCDD. FCCD replaces CCD's vector-based optimization of a rotation around an axis with a singular value decomposition-based optimization of a general rotation matrix. The method is easy to implement and numerically stable. Conclusion We tested the method's performance on sets of random protein Cα segments between 5 and 30 amino acids long, and a number of loops of length 4, 8 and 12. FCCD is fast, has a high success rate and readily generates conformations close to those of real loops. The presence of constraints
Bigravity from gradient expansion
Yamashita, Yasuho; Tanaka, Takahiro
2016-01-01
We discuss how the ghost-free bigravity coupled with a single scalar field can be derived from a braneworld setup. We consider DGP two-brane model without radion stabilization. The bulk configuration is solved for given boundary metrics, and it is substituted back into the action to obtain the effective four-dimensional action. In order to obtain the ghost-free bigravity, we consider the gradient expansion in which the brane separation is supposed to be sufficiently small so that two boundary metrics are almost identical. The obtained effective theory is shown to be ghost free as expected, however, the interaction between two gravitons takes the Fierz-Pauli form at the leading order of the gradient expansion, even though we do not use the approximation of linear perturbation. We also find that the radion remains as a scalar field in the four-dimensional effective theory, but its coupling to the metrics is non-trivial.
2010-03-31
nonimaging design capabilities to incorporate 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 12-04-2011 13. SUPPLEMENTARY NOTES The views, opinions...Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS Imaging Optics, Nonimaging Optics, Gradient Index Optics, Camera, Concentrator...imaging and nonimaging design capabilities to incorporate manufacturable GRIN lenses can provide imaging lens systems that are compact and
Gradient Optimization for Analytic conTrols - GOAT
Assémat, Elie; Machnes, Shai; Tannor, David; Wilhelm-Mauch, Frank
Quantum optimal control becomes a necessary step in a number of studies in the quantum realm. Recent experimental advances showed that superconducting qubits can be controlled with an impressive accuracy. However, most of the standard optimal control algorithms are not designed to manage such high accuracy. To tackle this issue, a novel quantum optimal control algorithm have been introduced: the Gradient Optimization for Analytic conTrols (GOAT). It avoids the piecewise constant approximation of the control pulse used by standard algorithms. This allows an efficient implementation of very high accuracy optimization. It also includes a novel method to compute the gradient that provides many advantages, e.g. the absence of backpropagation or the natural route to optimize the robustness of the control pulses. This talk will present the GOAT algorithm and a few applications to transmons systems.
A Wind Tunnel Study on the Mars Pathfinder (MPF) Lander Descent Pressure Sensor
Soriano, J. Francisco; Coquilla, Rachael V.; Wilson, Gregory R.; Seiff, Alvin; Rivell, Tomas
2001-01-01
The primary focus of this study was to determine the accuracy of the Mars Pathfinder lander local pressure readings in accordance with the actual ambient atmospheric pressures of Mars during parachute descent. In order to obtain good measurements, the plane of the lander pressure sensor opening should ideally be situated so that it is parallel to the freestream. However, due to two unfavorable conditions, the sensor was positioned in locations where correction factors are required. One of these disadvantages is due to the fact that the parachute attachment point rotated the lander's center of gravity forcing the location of the pressure sensor opening to be off tangent to the freestream. The second and most troublesome factor was that the lander descends with slight oscillations that could vary the amplitude of the sensor readings. In order to accurately map the correction factors required at each sensor position, an experiment simulating the lander descent was conducted in the Martian Surface Wind Tunnel at NASA Ames Research Center. Using a 115 scale model at Earth ambient pressures, the test settings provided the necessary Reynolds number conditions in which the actual lander was possibly subjected to during the descent. In the analysis and results of this experiment, the readings from the lander sensor were converted to the form of pressure coefficients. With a contour map of pressure coefficients at each lander oscillatory position, this report will provide a guideline to determine the correction factors required for the Mars Pathfinder lander descent pressure sensor readings.
Murray, Ashley; Ellis, Monica U.; Castellanos, Ted; Gaul, Zaneta; Sutton, Madeline Y.; Sneed, Carl D.
2014-01-01
We examined approaches used by African-American mothers and mothers of Latino descent for informal sex-related discussions with their children to inform sexually transmitted infection (STI)/HIV intervention development efforts. We recruited mothers (of children aged 12-15) from youth service agencies and a university in southern California.…
Bissoonauth, Anu
2018-01-01
This paper investigated linguistic practices and choices of Australian children of Indian descent, an under-researched group, who are studying Hindi in primary education. Data was collected using a questionnaire and semi-structured interviews with sixty participants across 3 primary schools in the Sydney area. The findings revealed, as expected,…
Parents and Siblings As Early Resources for Young Children's Learning in Mexican-Descent Families.
Perez-Granados, Deanne R.; Callanan, Maureen A.
1997-01-01
Interviews with parents from 50 Mexican-descent families revealed that parents encouraged their preschool children to ask questions about science and causal relationships; older and younger siblings learned different skills from one another; and children learned through observation and imitation. Discusses issues of "match" between home…
Long-Time Asymptotics for the Korteweg-de Vries Equation via Nonlinear Steepest Descent
Grunert, Katrin; Teschl, Gerald
2009-01-01
We apply the method of nonlinear steepest descent to compute the long-time asymptotics of the Korteweg-de Vries equation for decaying initial data in the soliton and similarity region. This paper can be viewed as an expository introduction to this method
Iterative algorithms for large sparse linear systems on parallel computers
Adams, L. M.
1982-01-01
Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.
Tapping the Brake for Entry, Descent, and Landing
Gnoffo, Peter A.; Thompson, Kyle; Korzun, Ashley
2016-01-01
A matrix of simulations of hypersonic flow over blunt entry vehicles with steady and pulsing retropropulsion jets is presented. Retropropulsion in the supersonic domain is primarily designed to reduce vehicle velocity directly with thrust. Retropropulsion in the hypersonic domain may enable significant pressure recovery through unsteady, oblique shocks while providing a buffer of reactant gases with relatively low total temperature. Improved pressure recovery, a function of Mach number squared and oblique shock angle, could potentially serve to increase aerodynamic drag in this domain. Pulsing jets are studied to include an additional degree of freedom to search for resonances in an already unsteady flow domain with an objective to maximize the time-averaged drag coefficient. In this paradigm, small jets with minimal footprints of the nozzle exit on the vehicle forebody may be capable of delivering the requisite perturbations to the flow. Simulations are executed assuming inviscid, symmetric flow of a perfect gas to enable a rapid assessment of the parameter space (nozzle geometry, plenum conditions, jet pulse frequency). The pulsed-jet configuration produces moderately larger drag than the constant jet configuration but smaller drag than the jet-off case in this preliminary examination of a single design point. The fundamentals of a new algorithm for this challenging application with time dependent, interacting discontinuities using the feature detection capabilities of Walsh functions are introduced.
Impact of race on the professional lives of physicians of African descent.
Nunez-Smith, Marcella; Curry, Leslie A; Bigby, JudyAnn; Berg, David; Krumholz, Harlan M; Bradley, Elizabeth H
2007-01-02
Increasing the racial and ethnic diversity of the physician workforce is a national priority. However, insight into the professional experiences of minority physicians is limited. This knowledge is fundamental to developing effective strategies to recruit, retain, and support a diverse physician workforce. To characterize how physicians of African descent experience race in the workplace. Qualitative study based on in-person and in-depth racially concordant interviews using a standard discussion guide. The 6 New England states in the United States. 25 practicing physicians of African descent representing a diverse range of primary practice settings, specialties, and ages. Professional experiences of physicians of African descent. 1) Awareness of race permeates the experience of physicians of African descent in the health care workplace; 2) race-related experiences shape interpersonal interactions and define the institutional climate; 3) responses to perceived racism at work vary along a spectrum from minimization to confrontation; 4) the health care workplace is often silent on issues of race; and 5) collective race-related experiences can result in "racial fatigue," with personal and professional consequences for physicians. The study was restricted to New England and may not reflect the experiences of physicians in other geographic regions. The findings are meant to be hypothesis-generating and require additional follow-up studies. The issue of race remains a pervasive influence in the work lives of physicians of African descent. Without sufficient attention to the specific ways in which race shapes physicians' work experiences, health care organizations are unlikely to create environments that successfully foster and sustain a diverse physician workforce.
Hong, Yoon No Gregory; Shin, Choongsoo S
2015-12-01
Falls on stairs often result in severe injury and occur twice as frequently in women. However, gender differences in kinetics and kinematics during stair descent are unknown. Thus, this study aimed to determine whether gender differences of knee and ankle biomechanics exist in the sagittal plane during the stair-to-ground descending transition. It was hypothesized that 1) women would reveal higher ground-toe-trochanter angle and lower ground-toe length during stair-to-ground descent transition than men; and 2) women would reveal lower peak knee extension moment during stair-to-ground descent transition than men. Fifteen men and fifteen women were recruited and performed a stair descent activity. Kinetic and kinematic data were obtained using a force plate and motion capture system. The women performed the stair descent with a lower peak knee extension moment and a peak knee power at the early weight acceptance phase. The women also revealed a higher ground-toe-trochanter angle and a lower ground-toe length, which indicated a more forward position of the lower extremity relative to the toe contact point at both the initial contact and at the time of peak kinematic and kinetic events. This study found that knee and ankle kinematics and kinetics differed significantly between the genders due to differences in ground-toe-trochanter angle. Women have a different stair descending strategy that reduces the demand of the lower extremity muscle function, but this strategy seems to increase the risk of falls. Copyright © 2015 Elsevier Ltd. All rights reserved.
Suyan Tian
Full Text Available The existence of fundamental differences between lung adenocarcinoma (AC and squamous cell carcinoma (SCC in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.
Du, Shouqiang; Chen, Miao
2018-01-01
We consider a kind of nonsmooth optimization problems with [Formula: see text]-norm minimization, which has many applications in compressed sensing, signal reconstruction, and the related engineering problems. Using smoothing approximate techniques, this kind of nonsmooth optimization problem can be transformed into a general unconstrained optimization problem, which can be solved by the proposed smoothing modified three-term conjugate gradient method. The smoothing modified three-term conjugate gradient method is based on Polak-Ribière-Polyak conjugate gradient method. For the Polak-Ribière-Polyak conjugate gradient method has good numerical properties, the proposed method possesses the sufficient descent property without any line searches, and it is also proved to be globally convergent. Finally, the numerical experiments show the efficiency of the proposed method.
Mapping the Conjugate Gradient Algorithm onto High Performance Heterogeneous Computers
2014-05-01
Solution of sparse indefinite systems of linear equations. Society for Industrial and Applied Mathematis 12(4), 617 –629. Parker, M. ( 2009 ). Taking advantage...44 vii 11 LIST OF SYMBOLS, ABBREVIATIONS, AND NOMENCLATURE API Application Programming Interface ASIC Application Specific Integrated Circuit...FPGA designer, 1 16 2 thus, final implementations were nearly always performed using fixed-point or integer arithmetic (Parker 2009 ). With the recent
New hybrid conjugate gradient methods with the generalized Wolfe line search.
Xu, Xiao; Kong, Fan-Yu
2016-01-01
The conjugate gradient method was an efficient technique for solving the unconstrained optimization problem. In this paper, we made a linear combination with parameters β k of the DY method and the HS method, and putted forward the hybrid method of DY and HS. We also proposed the hybrid of FR and PRP by the same mean. Additionally, to present the two hybrid methods, we promoted the Wolfe line search respectively to compute the step size α k of the two hybrid methods. With the new Wolfe line search, the two hybrid methods had descent property and global convergence property of the two hybrid methods that can also be proved.
The evolving approach to the evaluation of low-gradient aortic stenosis.
Cutting, William B; Bavry, Anthony A
2018-04-07
Severe aortic stenosis (AS) is typically identified by a low valve area (≤1.0 cm 2 ) and high mean gradient (≥40 mm Hg). A subset of patients are found to have a less than severe mean gradient (gradient AS (stage D2) or normal ejection fraction with low-gradient AS (stage D3). Determining the true severity of disease within these categories has proved difficult. In this review we illustrate both traditional and novel techniques that can be used for further valvular assessment. We also propose a simple algorithm that can be used to evaluate low-gradient AS. Published by Elsevier Inc.
Seismic noise attenuation using an online subspace tracking algorithm
Zhou, Yatong; Li, Shuhua; Zhang, D.; Chen, Yangkang
2018-01-01
We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient
A very fast implementation of 2D iterative reconstruction algorithms
Toft, Peter Aundal; Jensen, Peter James
1996-01-01
that iterative reconstruction algorithms can be implemented and run almost as fast as direct reconstruction algorithms. The method has been implemented in a software package that is available for free, providing reconstruction algorithms using ART, EM, and the Least Squares Conjugate Gradient Method...
Developing operation algorithms for vision subsystems in autonomous mobile robots
Shikhman, M. V.; Shidlovskiy, S. V.
2018-05-01
The paper analyzes algorithms for selecting keypoints on the image for the subsequent automatic detection of people and obstacles. The algorithm is based on the histogram of oriented gradients and the support vector method. The combination of these methods allows successful selection of dynamic and static objects. The algorithm can be applied in various autonomous mobile robots.
Accelerated gradient methods for constrained image deblurring
Bonettini, S; Zanella, R; Zanni, L; Bertero, M
2008-01-01
In this paper we propose a special gradient projection method for the image deblurring problem, in the framework of the maximum likelihood approach. We present the method in a very general form and we give convergence results under standard assumptions. Then we consider the deblurring problem and the generality of the proposed algorithm allows us to add a energy conservation constraint to the maximum likelihood problem. In order to improve the convergence rate, we devise appropriate scaling strategies and steplength updating rules, especially designed for this application. The effectiveness of the method is evaluated by means of a computational study on astronomical images corrupted by Poisson noise. Comparisons with standard methods for image restoration, such as the expectation maximization algorithm, are also reported.
Eigen-Gradients for Traffic Sign Recognition
Sheila Esmeralda Gonzalez-Reyna
2013-01-01
Full Text Available Traffic sign detection and recognition systems include a variety of applications like autonomous driving, road sign inventory, and driver support systems. Machine learning algorithms provide useful tools for traffic sign identification tasks. However, classification algorithms depend on the preprocessing stage to obtain high accuracy rates. This paper proposes a road sign characterization method based on oriented gradient maps and the Karhunen-Loeve transform in order to improve classification performance. Dimensionality reduction may be important for portable applications on resource constrained devices like FPGAs; therefore, our approach focuses on achieving a good classification accuracy by using a reduced amount of attributes compared to some state-of-the-art methods. The proposed method was tested using German Traffic Sign Recognition Benchmark, reaching a dimensionality reduction of 99.3% and a classification accuracy of 95.9% with a Multi-Layer Perceptron.
A fast, preconditioned conjugate gradient Toeplitz solver
Pan, Victor; Schrieber, Robert
1989-01-01
A simple factorization is given of an arbitrary hermitian, positive definite matrix in which the factors are well-conditioned, hermitian, and positive definite. In fact, given knowledge of the extreme eigenvalues of the original matrix A, an optimal improvement can be achieved, making the condition numbers of each of the two factors equal to the square root of the condition number of A. This technique is to applied to the solution of hermitian, positive definite Toeplitz systems. Large linear systems with hermitian, positive definite Toeplitz matrices arise in some signal processing applications. A stable fast algorithm is given for solving these systems that is based on the preconditioned conjugate gradient method. The algorithm exploits Toeplitz structure to reduce the cost of an iteration to O(n log n) by applying the fast Fourier Transform to compute matrix-vector products. Matrix factorization is used as a preconditioner.
Bayesian posterior sampling via stochastic gradient Fisher scoring
Ahn, S.; Korattikara, A.; Welling, M.; Langford, J.; Pineau, J.
2012-01-01
In this paper we address the following question: "Can we approximately sample from a Bayesian posterior distribution if we are only allowed to touch a small mini-batch of data-items for every sample we generate?". An algorithm based on the Langevin equation with stochastic gradients (SGLD) was
Wetting of flat gradient surfaces.
Bormashenko, Edward
2018-04-01
Gradient, chemically modified, flat surfaces enable directed transport of droplets. Calculation of apparent contact angles inherent for gradient surfaces is challenging even for atomically flat ones. Wetting of gradient, flat solid surfaces is treated within the variational approach, under which the contact line is free to move along the substrate. Transversality conditions of the variational problem give rise to the generalized Young equation valid for gradient solid surfaces. The apparent (equilibrium) contact angle of a droplet, placed on a gradient surface depends on the radius of the contact line and the values of derivatives of interfacial tensions. The linear approximation of the problem is considered. It is demonstrated that the contact angle hysteresis is inevitable on gradient surfaces. Electrowetting of gradient surfaces is discussed. Copyright © 2018 Elsevier Inc. All rights reserved.
Chen, Y.-M.; Koniges, A. E.; Anderson, D. V.
1989-10-01
The biconjugate gradient method (BCG) provides an attractive alternative to the usual conjugate gradient algorithms for the solution of sparse systems of linear equations with nonsymmetric and indefinite matrix operators. A preconditioned algorithm is given, whose form resembles the incomplete L-U conjugate gradient scheme (ILUCG2) previously presented. Although the BCG scheme requires the storage of two additional vectors, it converges in a significantly lesser number of iterations (often half), while the number of calculations per iteration remains essentially the same.
Aviat, Félix; Levitt, Antoine; Stamm, Benjamin; Maday, Yvon; Ren, Pengyu; Ponder, Jay W; Lagardère, Louis; Piquemal, Jean-Philip
2017-01-10
We introduce a new class of methods, denoted as Truncated Conjugate Gradient(TCG), to solve the many-body polarization energy and its associated forces in molecular simulations (i.e. molecular dynamics (MD) and Monte Carlo). The method consists in a fixed number of Conjugate Gradient (CG) iterations. TCG approaches provide a scalable solution to the polarization problem at a user-chosen cost and a corresponding optimal accuracy. The optimality of the CG-method guarantees that the number of the required matrix-vector products are reduced to a minimum compared to other iterative methods. This family of methods is non-empirical, fully adaptive, and provides analytical gradients, avoiding therefore any energy drift in MD as compared to popular iterative solvers. Besides speed, one great advantage of this class of approximate methods is that their accuracy is systematically improvable. Indeed, as the CG-method is a Krylov subspace method, the associated error is monotonically reduced at each iteration. On top of that, two improvements can be proposed at virtually no cost: (i) the use of preconditioners can be employed, which leads to the Truncated Preconditioned Conjugate Gradient (TPCG); (ii) since the residual of the final step of the CG-method is available, one additional Picard fixed point iteration ("peek"), equivalent to one step of Jacobi Over Relaxation (JOR) with relaxation parameter ω, can be made at almost no cost. This method is denoted by TCG-n(ω). Black-box adaptive methods to find good choices of ω are provided and discussed. Results show that TPCG-3(ω) is converged to high accuracy (a few kcal/mol) for various types of systems including proteins and highly charged systems at the fixed cost of four matrix-vector products: three CG iterations plus the initial CG descent direction. Alternatively, T(P)CG-2(ω) provides robust results at a reduced cost (three matrix-vector products) and offers new perspectives for long polarizable MD as a production
Conjugate gradient coupled with multigrid for an indefinite problem
Gozani, J.; Nachshon, A.; Turkel, E.
1984-01-01
An iterative algorithm for the Helmholtz equation is presented. This scheme was based on the preconditioned conjugate gradient method for the normal equations. The preconditioning is one cycle of a multigrid method for the discrete Laplacian. The smoothing algorithm is red-black Gauss-Seidel and is constructed so it is a symmetric operator. The total number of iterations needed by the algorithm is independent of h. By varying the number of grids, the number of iterations depends only weakly on k when k(3)h(2) is constant. Comparisons with a SSOR preconditioner are presented.
A new simple iterative reconstruction algorithm for SPECT transmission measurement
Hwang, D.S.; Zeng, G.L.
2005-01-01
This paper proposes a new iterative reconstruction algorithm for transmission tomography and compares this algorithm with several other methods. The new algorithm is simple and resembles the emission ML-EM algorithm in form. Due to its simplicity, it is easy to implement and fast to compute a new update at each iteration. The algorithm also always guarantees non-negative solutions. Evaluations are performed using simulation studies and real phantom data. Comparisons with other algorithms such as convex, gradient, and logMLEM show that the proposed algorithm is as good as others and performs better in some cases
Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.
Zhao, Shengkui; Jones, Douglas L; Khoo, Suiyang; Man, Zhihong
2014-09-01
A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.
Sadegh, Payman
1997-01-01
This paper deals with a projection algorithm for stochastic approximation using simultaneous perturbation gradient approximation for optimization under inequality constraints where no direct gradient of the loss function is available and the inequality constraints are given as explicit functions...... of the optimization parameters. It is shown that, under application of the projection algorithm, the parameter iterate converges almost surely to a Kuhn-Tucker point, The procedure is illustrated by a numerical example, (C) 1997 Elsevier Science Ltd....
Dwyer Cianciolo, Alicia M. (Editor)
2011-01-01
NASA senior management commissioned the Entry, Descent and Landing Systems Analysis (EDL-SA) Study in 2008 to identify and roadmap the Entry, Descent and Landing (EDL) technology investments that the agency needed to successfully land large payloads at Mars for both robotic and human-scale missions. Year 1 of the study focused on technologies required for Exploration-class missions to land payloads of 10 to 50 mt. Inflatable decelerators, rigid aeroshell and supersonic retro-propulsion emerged as the top candidate technologies. In Year 2 of the study, low TRL technologies identified in Year 1, inflatables aeroshells and supersonic retropropulsion, were combined to create a demonstration precursor robotic mission. This part of the EDL-SA Year 2 effort, called Exploration Feed Forward (EFF), took much of the systems analysis simulation and component model development from Year 1 to the next level of detail.
Dwyer Ciancolo, Alicia M.; Davis, Jody L.; Engelund, Walter C.; Komar, D. R.; Queen, Eric M.; Samareh, Jamshid A.; Way, David W.; Zang, Thomas A.; Murch, Jeff G.; Krizan, Shawn A.;
2011-01-01
NASA senior management commissioned the Entry, Descent and Landing Systems Analysis (EDL-SA) Study in 2008 to identify and roadmap the Entry, Descent and Landing (EDL) technology investments that the agency needed to successfully land large payloads at Mars for both robotic and human-scale missions. Year 1 of the study focused on technologies required for Exploration-class missions to land payloads of 10 to 50 t. Inflatable decelerators, rigid aeroshell and supersonic retro-propulsion emerged as the top candidate technologies. In Year 2 of the study, low TRL technologies identified in Year 1, inflatables aeroshells and supersonic retropropulsion, were combined to create a demonstration precursor robotic mission. This part of the EDL-SA Year 2 effort, called Exploration Feed Forward (EFF), took much of the systems analysis simulation and component model development from Year 1 to the next level of detail.
Automated gravity gradient tensor inversion for underwater object detection
Wu, Lin; Tian, Jinwen
2010-01-01
Underwater abnormal object detection is a current need for the navigation security of autonomous underwater vehicles (AUVs). In this paper, an automated gravity gradient tensor inversion algorithm is proposed for the purpose of passive underwater object detection. Full-tensor gravity gradient anomalies induced by an object in the partial area can be measured with the technique of gravity gradiometry on an AUV. Then the automated algorithm utilizes the anomalies, using the inverse method to estimate the mass and barycentre location of the arbitrary-shaped object. A few tests on simple synthetic models will be illustrated, in order to evaluate the feasibility and accuracy of the new algorithm. Moreover, the method is applied to a complicated model of an abnormal object with gradiometer and AUV noise, and interference from a neighbouring illusive smaller object. In all cases tested, the estimated mass and barycentre location parameters are found to be in good agreement with the actual values
Boiroux, Dimitri; Juhl, Rune; Madsen, Henrik
2016-01-01
This paper addresses maximum likelihood parameter estimation of continuous-time nonlinear systems with discrete-time measurements. We derive an efficient algorithm for the computation of the log-likelihood function and its gradient, which can be used in gradient-based optimization algorithms....... This algorithm uses UD decomposition of symmetric matrices and the array algorithm for covariance update and gradient computation. We test our algorithm on the Lotka-Volterra equations. Compared to the maximum likelihood estimation based on finite difference gradient computation, we get a significant speedup...
Modelling the descent of nitric oxide during the elevated stratopause event of January 2013
Orsolini, Yvan J.; Limpasuvan, Varavut; Pérot, Kristell; Espy, Patrick; Hibbins, Robert; Lossow, Stefan; Raaholt Larsson, Katarina; Murtagh, Donal
2017-03-01
Using simulations with a whole-atmosphere chemistry-climate model nudged by meteorological analyses, global satellite observations of nitrogen oxide (NO) and water vapour by the Sub-Millimetre Radiometer instrument (SMR), of temperature by the Microwave Limb Sounder (MLS), as well as local radar observations, this study examines the recent major stratospheric sudden warming accompanied by an elevated stratopause event (ESE) that occurred in January 2013. We examine dynamical processes during the ESE, including the role of planetary wave, gravity wave and tidal forcing on the initiation of the descent in the mesosphere-lower thermosphere (MLT) and its continuation throughout the mesosphere and stratosphere, as well as the impact of model eddy diffusion. We analyse the transport of NO and find the model underestimates the large descent of NO compared to SMR observations. We demonstrate that the discrepancy arises abruptly in the MLT region at a time when the resolved wave forcing and the planetary wave activity increase, just before the elevated stratopause reforms. The discrepancy persists despite doubling the model eddy diffusion. While the simulations reproduce an enhancement of the semi-diurnal tide following the onset of the 2013 SSW, corroborating new meteor radar observations at high northern latitudes over Trondheim (63.4°N), the modelled tidal contribution to the forcing of the mean meridional circulation and to the descent is a small portion of the resolved wave forcing, and lags it by about ten days.
Machado-Joseph disease in pedigrees of Azorean descent is linked to chromosome 14.
St George-Hyslop, P; Rogaeva, E; Huterer, J; Tsuda, T; Santos, J; Haines, J L; Schlumpf, K; Rogaev, E I; Liang, Y; McLachlan, D R
1994-07-01
A locus for Machado-Joseph disease (MJD) has recently been mapped to a 30-cM region of chromosome 14q in five pedigrees of Japanese descent. MJD is a clinically pleomorphic neurodegenerative disease that was originally described in subjects of Azorean descent. In light of the nonallelic heterogeneity in other inherited spinocerebellar ataxias, we were interested to determine if the MJD phenotype in Japanese and Azorean pedigrees arose from mutations at the same locus. We provide evidence that MJD in five pedigrees of Azorean descent is also linked to chromosome 14q in an 18-cM region between the markers D14S67 and AACT (multipoint lod score +7.00 near D14S81). We also report molecular evidence for homozygosity at the MJD locus in an MJD-affected subject with severe, early-onset symptoms. These observations confirm the initial report of linkage of MJD to chromosome 14; suggest that MJD in Japanese and Azorean subjects may represent allelic or identical mutations at the same locus; and provide one possible explanation (MJD gene dosage) for the observed phenotypic heterogeneity in this disease.
Vilar, L.C.Q.; Sasaki, C.A.G.; Sorella, S.P.
1996-09-01
The supersymmetric descent equations in superspace are discussed by means of the introduction of two operators ζ α , ζ -α which allow to decompose the supersymmetric covariant derivatives D α , D -α as BRS commutators. (author). 27 refs., 4 tabs
Gradient Boosting Machines, A Tutorial
Alexey eNatekin
2013-12-01
Full Text Available Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods. A theoretical information is complemented with many descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. A set of practical examples of gradient boosting applications are presented and comprehensively analyzed.
Murad-Regadas, Sthela M; Pinheiro Regadas, Francisco Sergio; Rodrigues, Lusmar V; da Silva Vilarinho, Adjra; Buchen, Guilherme; Borges, Livia Olinda; Veras, Lara B; da Cruz, Mariana Murad
2016-12-01
Defecography is an established method of evaluating dynamic anorectal dysfunction, but conventional defecography does not allow for visualization of anatomic structures. The purpose of this study was to describe the use of dynamic 3-dimensional endovaginal ultrasonography for evaluating perineal descent in comparison with echodefecography (3-dimensional anorectal ultrasonography) and to study the relationship between perineal descent and symptoms and anatomic/functional abnormalities of the pelvic floor. This was a prospective study. The study was conducted at a large university tertiary care hospital. Consecutive female patients were eligible if they had pelvic floor dysfunction, obstructed defecation symptoms, and a score >6 on the Cleveland Clinic Florida Constipation Scale. Each patient underwent both echodefecography and dynamic 3-dimensional endovaginal ultrasonography to evaluate posterior pelvic floor dysfunction. Normal perineal descent was defined on echodefecography as puborectalis muscle displacement ≤2.5 cm; excessive perineal descent was defined as displacement >2.5 cm. Of 61 women, 29 (48%) had normal perineal descent; 32 (52%) had excessive perineal descent. Endovaginal ultrasonography identified 27 of the 29 patients in the normal group as having anorectal junction displacement ≤1 cm (mean = 0.6 cm; range, 0.1-1.0 cm) and a mean anorectal junction position of 0.6 cm (range, 0-2.3 cm) above the symphysis pubis during the Valsalva maneuver and correctly identified 30 of the 32 patients in the excessive perineal descent group. The κ statistic showed almost perfect agreement (κ = 0.86) between the 2 methods for categorization into the normal and excessive perineal descent groups. Perineal descent was not related to fecal or urinary incontinence or anatomic and functional factors (sphincter defects, pubovisceral muscle defects, levator hiatus area, grade II or III rectocele, intussusception, or anismus). The study did not include a
Gradient waveform synthesis for magnetic propulsion using MRI gradient coils
Han, B H; Lee, S Y; Park, S
2008-01-01
Navigating an untethered micro device in a living subject is of great interest for both diagnostic and therapeutic applications. Magnetic propulsion of an untethered device carrying a magnetic core in it is one of the promising methods to navigate the device. MRI gradients coils are thought to be suitable for navigating the device since they are capable of magnetic propulsion in any direction while providing magnetic resonance images. For precise navigation of the device, especially in the peripheral region of the gradient coils, the concomitant gradient fields, as well as the linear gradient fields in the main magnetic field direction, should be considered in driving the gradient coils. For simple gradient coil configurations, the Maxwell coil in the z-direction and the Golay coil in the x- and y-directions, we have calculated the magnetic force fields, which are not necessarily the same as the conventional linear gradient fields of MRI. Using the calculated magnetic force fields, we have synthesized gradient waveforms to navigate the device along a desired path
A study of block algorithms for fermion matrix inversion
Henty, D.
1990-01-01
We compare the convergence properties of Lanczos and Conjugate Gradient algorithms applied to the calculation of columns of the inverse fermion matrix for Kogut-Susskind and Wilson fermions in lattice QCD. When several columns of the inverse are required simultaneously, a block version of the Lanczos algorithm is most efficient at small mass, being over 5 times faster than the single algorithms. The block algorithm is also less susceptible to critical slowing down. (orig.)
Regularized image denoising based on spectral gradient optimization
Lukić, Tibor; Lindblad, Joakim; Sladoje, Nataša
2011-01-01
Image restoration methods, such as denoising, deblurring, inpainting, etc, are often based on the minimization of an appropriately defined energy function. We consider energy functions for image denoising which combine a quadratic data-fidelity term and a regularization term, where the properties of the latter are determined by a used potential function. Many potential functions are suggested for different purposes in the literature. We compare the denoising performance achieved by ten different potential functions. Several methods for efficient minimization of regularized energy functions exist. Most are only applicable to particular choices of potential functions, however. To enable a comparison of all the observed potential functions, we propose to minimize the objective function using a spectral gradient approach; spectral gradient methods put very weak restrictions on the used potential function. We present and evaluate the performance of one spectral conjugate gradient and one cyclic spectral gradient algorithm, and conclude from experiments that both are well suited for the task. We compare the performance with three total variation-based state-of-the-art methods for image denoising. From the empirical evaluation, we conclude that denoising using the Huber potential (for images degraded by higher levels of noise; signal-to-noise ratio below 10 dB) and the Geman and McClure potential (for less noisy images), in combination with the spectral conjugate gradient minimization algorithm, shows the overall best performance
Spectral edge: gradient-preserving spectral mapping for image fusion.
Connah, David; Drew, Mark S; Finlayson, Graham D
2015-12-01
This paper describes a novel approach to image fusion for color display. Our goal is to generate an output image whose gradient matches that of the input as closely as possible. We achieve this using a constrained contrast mapping paradigm in the gradient domain, where the structure tensor of a high-dimensional gradient representation is mapped exactly to that of a low-dimensional gradient field which is then reintegrated to form an output. Constraints on output colors are provided by an initial RGB rendering. Initially, we motivate our solution with a simple "ansatz" (educated guess) for projecting higher-D contrast onto color gradients, which we expand to a more rigorous theorem to incorporate color constraints. The solution to these constrained optimizations is closed-form, allowing for simple and hence fast and efficient algorithms. The approach can map any N-D image data to any M-D output and can be used in a variety of applications using the same basic algorithm. In this paper, we focus on the problem of mapping N-D inputs to 3D color outputs. We present results in five applications: hyperspectral remote sensing, fusion of color and near-infrared or clear-filter images, multilighting imaging, dark flash, and color visualization of magnetic resonance imaging diffusion-tensor imaging.
Creutz, M.
1987-11-01
A large variety of Monte Carlo algorithms are being used for lattice gauge simulations. For purely bosonic theories, present approaches are generally adequate; nevertheless, overrelaxation techniques promise savings by a factor of about three in computer time. For fermionic fields the situation is more difficult and less clear. Algorithms which involve an extrapolation to a vanishing step size are all quite closely related. Methods which do not require such an approximation tend to require computer time which grows as the square of the volume of the system. Recent developments combining global accept/reject stages with Langevin or microcanonical updatings promise to reduce this growth to V/sup 4/3/
Hu, T C
2002-01-01
Newly enlarged, updated second edition of a valuable text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. 153 black-and-white illus. 23 tables.Newly enlarged, updated second edition of a valuable, widely used text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discussed are binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. New to this edition: Chapter 9
Mini-batch optimized full waveform inversion with geological constrained gradient filtering
Yang, Hui; Jia, Junxiong; Wu, Bangyu; Gao, Jinghuai
2018-05-01
High computation cost and generating solutions without geological sense have hindered the wide application of Full Waveform Inversion (FWI). Source encoding technique is a way to dramatically reduce the cost of FWI but subject to fix-spread acquisition setup requirement and slow convergence for the suppression of cross-talk. Traditionally, gradient regularization or preconditioning is applied to mitigate the ill-posedness. An isotropic smoothing filter applied on gradients generally gives non-geological inversion results, and could also introduce artifacts. In this work, we propose to address both the efficiency and ill-posedness of FWI by a geological constrained mini-batch gradient optimization method. The mini-batch gradient descent optimization is adopted to reduce the computation time by choosing a subset of entire shots for each iteration. By jointly applying the structure-oriented smoothing to the mini-batch gradient, the inversion converges faster and gives results with more geological meaning. Stylized Marmousi model is used to show the performance of the proposed method on realistic synthetic model.
Denaturing gradient gel electrophoresis
Kocherginskaya, S.A.; Cann, I.K.O.; Mackie, R.I.
2005-01-01
It is worthwhile considering that only some 30 species make up the bulk of the bacterial population in human faeces at any one time based on the classical cultivation-based approach. The situation in the rumen is similar. Thus, it is practical to focus on specific groups of interest within the complex community. These may be the predominant or the most active species, specific physiological groups or readily identifiable (genetic) clusters of phylogenetically related organisms. Several 16S rDNA fingerprinting techniques can be invaluable for selecting and monitoring sequences or phylogenetic groups of interest and are described below. Over the past few decades, considerable attention was focussed on the identification of pure cultures of microbes on the basis of genetic polymorphisms of DNA encoding rRNA such as ribotyping, amplified fragment length polymorphism and randomly amplified polymorphic DNA. However, many of these methods require prior cultivation and are less suitable for use in analysis of complex mixed populations although important in describing cultivated microbial diversity in molecular terms. Much less attention was given to molecular characterization of complex communities. In particular, research into diversity and community structure over time has been revolutionized by the advent of molecular fingerprinting techniques for complex communities. Denaturing or temperature gradient gel electrophoresis (DGGE/TGGE) methods have been successfully applied to the analysis of human, pig, cattle, dog and rodent intestinal populations
Ion temperature gradient instability
1989-01-01
Anomalous ion thermal conductivity remains an open physics issue for the present generation of high temperature Tokamaks. It is generally believed to be due to Ion Temperature Gradient Instability (η i mode). However, it has been difficult, if not impossible to identify this instability and study the anomalous transport due to it, directly. Therefore the production and identification of the mode is pursued in the simpler and experimentally convenient configuration of the Columbia Linear Machine (CLM). CLM is a steady state machine which already has all the appropriate parameters, except η i . This parameter is being increased to the appropriate value of the order of 1 by 'feathering' a tungsten screen located between the plasma source and the experimental cell to flatten the density profile and appropriate redesign of heating antennas to steepen the ion temperature profile. Once the instability is produced and identified, a thorough study of the characteristics of the mode can be done via a wide range of variation of all the critical parameters: η i , parallel wavelength, etc
General approach for solving the density gradient theory in the interfacial tension calculations
Liang, Xiaodong; Michelsen, Michael Locht
2017-01-01
Within the framework of the density gradient theory, the interfacial tension can be calculated by finding the density profiles that minimize an integral of two terms over the system of infinite width. It is found that the two integrands exhibit a constant difference along the interface for a finite...... property evaluations compared to other methods. The performance of the algorithm with recommended parameters is analyzed for various systems, and the efficiency is further compared with the geometric-mean density gradient theory, which only needs to solve nonlinear algebraic equations. The results show...... that the algorithm is only 5-10 times less efficient than solving the geometric-mean density gradient theory....
Characterization of gradient control systems
Cortés, Jorge; van der Schaft, Arjan; Crouch, Peter E.
2005-01-01
Given a general nonlinear affine control system with outputs and a torsion-free affine connection defined on its state space, we investigate the gradient realization problem: we give necessary and sufficient conditions under which the control system can be written as a gradient control system
Characterization of Gradient Control Systems
Cortés, Jorge; Schaft, Arjan van der; Crouch, Peter E.
2005-01-01
Given a general nonlinear affine control system with outputs and a torsion-free affine connection defined on its state space, we investigate the gradient realization problem: we give necessary and sufficient conditions under which the control system can be written as a gradient control system
Electric field gradients in metals
Schatz, G.
1979-01-01
A review of the recent works on electric field gradient in metals is given. The main emphasis is put on the temperature dependence of the electric field gradient in nonmagnetic metals. Some methods of investigation of this effect using nuclear probes are described. One of them is nuclear accoustic resonance method. (S.B.)
Anna Bourmistrova
2011-02-01
Full Text Available The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS vehicle, though it is also applicable to two-wheel-steering (TWS vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while the dynamic center of rotation is the output of dynamic equations of motion of the vehicle using steering angle and velocity measurements as inputs. We use kinematic condition of steering to set the steering angles in such a way that the kinematic center of rotation of the vehicle sits at a desired point. At low speeds the ideal and actual paths of the vehicle are very close. With increase of forward speed the road and tire characteristics, along with the motion dynamics of the vehicle cause the vehicle to turn about time-varying points. By adjusting the steering angles, our algorithm controls the dynamic turning center of the vehicle so that it coincides with the road curvature center, hence keeping the vehicle on a given road autonomously. The position and orientation errors are used as feedback signals in a closed loop control to adjust the steering angles. The application of the presented autodriver algorithm demonstrates reliable performance under different driving conditions.
The geomagnetic field gradient tensor
Kotsiaros, Stavros; Olsen, Nils
2012-01-01
We develop the general mathematical basis for space magnetic gradiometry in spherical coordinates. The magnetic gradient tensor is a second rank tensor consisting of 3 × 3 = 9 spatial derivatives. Since the geomagnetic field vector B is always solenoidal (∇ · B = 0) there are only eight independent...... tensor elements. Furthermore, in current free regions the magnetic gradient tensor becomes symmetric, further reducing the number of independent elements to five. In that case B is a Laplacian potential field and the gradient tensor can be expressed in series of spherical harmonics. We present properties...... of the magnetic gradient tensor and provide explicit expressions of its elements in terms of spherical harmonics. Finally we discuss the benefit of using gradient measurements for exploring the Earth’s magnetic field from space, in particular the advantage of the various tensor elements for a better determination...
Comparison of gradient methods for gain tuning of a PD controller applied on a quadrotor system
Kim, Jinho; Wilkerson, Stephen A.; Gadsden, S. Andrew
2016-05-01
Many mechanical and electrical systems have utilized the proportional-integral-derivative (PID) control strategy. The concept of PID control is a classical approach but it is easy to implement and yields a very good tracking performance. Unmanned aerial vehicles (UAVs) are currently experiencing a significant growth in popularity. Due to the advantages of PID controllers, UAVs are implementing PID controllers for improved stability and performance. An important consideration for the system is the selection of PID gain values in order to achieve a safe flight and successful mission. There are a number of different algorithms that can be used for real-time tuning of gains. This paper presents two algorithms for gain tuning, and are based on the method of steepest descent and Newton's minimization of an objective function. This paper compares the results of applying these two gain tuning algorithms in conjunction with a PD controller on a quadrotor system.
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....
NON–DESCENT VAGINAL HYSTERECTOMY FOR BENIGN GYNAECOLOGICAL DISEASE – A PROSPECTIVE STUDY
Thulasi
2016-04-01
Full Text Available OBJECTIVE To assess safety and feasibility of non-descent vaginal hysterectomy for benign gynaecological disease. METHODS A prospective study was conducted at the Department of Obstetrics and Gynaecology of P K Das Institute of Medical Sciences from January 2013 to December 2013. An effort was made to perform hysterectomies vaginally in women with benign or premalignant conditions in the absence of prolapse. A suspected adnexal pathology, endometriosis, immobility of uterus, uterus size more than 16 weeks was excluded from the study. Vaginal hysterectomy was done in usual manner. In bigger size uterus, morcellation techniques like bisection, debulking, coring, myomectomy, or combination of these were used to remove the uterus. Data regarding age, parity, uterine size, estimated blood loss, length of operation, intraoperative and postoperative complications and hospital stay were recorded. RESULTS A total of 100 cases were selected for non-descent vaginal hysterectomy. Among them, 97 cases successfully underwent nondescent vaginal hysterectomy. Majority of the patients (55% were in age group 40-45 yrs. Four patients were nulligravida and eight patients had previous LSCS. Uterine size was ≤ 12 weeks in 84 cases and > 12-16 weeks in 16 cases. Commonest indication was leiomyoma of uterus (43%. Mean duration of surgery was 70±20.5 minutes. Mean blood loss was 150±65 mL. Reasons for failure to perform NDVH was difficulty in opening pouch of Douglas in two cases because of adhesions and in one case there was difficulty in reaching the fundal myoma which prevented the uterine descent. Intra–operatively, one case had bladder injury (1% that had previous 2 LSCS. Postoperatively, complications were minimal which included postoperative fever (11%, UTI (8% and vaginal cuff infection was (4%. Mean hospital stay was 3.5 days. CONCLUSION Vaginal hysterectomy is safe, feasible in most of the women requiring hysterectomy for benign conditions with less
Entry, Descent, and Landing Communications for the 2011 Mars Science Laboratory
Abilleira, Fernando; Shidner, Jeremy D.
2012-01-01
The Mars Science Laboratory (MSL), established as the most advanced rover to land on the surface of Mars to date, launched on November 26th, 2011 and arrived to the Martian Gale Crater during the night of August 5th, 2012 (PDT). MSL will investigate whether the landing region was ever suitable to support carbon-based life, and examine rocks, soil, and the atmosphere with a sophisticated suite of tools. This paper addresses the flight system requirement by which the vehicle transmitted indications of the following events using both X-band tones and UHF telemetry to allow identification of probable root causes should a mission anomaly have occurred: Heat-Rejection System (HRS) venting, completion of the cruise stage separation, turn to entry attitude, atmospheric deceleration, bank angle reversal commanded, parachute deployment, heatshield separation, radar ground acquisition, powered descent initiation, rover separation from the descent stage, and rover release. During Entry, Descent, and Landing (EDL), the flight system transmitted a UHF telemetry stream adequate to determine the state of the spacecraft (including the presence of faults) at 8 kbps initiating from cruise stage separation through at least one minute after positive indication of rover release on the surface of Mars. The flight system also transmitted X-band semaphore tones from Entry to Landing plus one minute although since MSL was occulted, as predicted, by Mars as seen from the Earth, Direct-To-Earth (DTE) communications were interrupted at approximately is approx. 5 min after Entry ( approximately 130 prior to Landing). The primary data return paths were through the Deep Space Network (DSN) for DTE and the existing Mars network of orbiting assets for UHF, which included the Mars Reconnaissance Orbiter (MRO), Mars Odyssey (ODY), and Mars Express (MEX) elements. These orbiters recorded the telemetry data stream and returned it back to Earth via the DSN. The paper also discusses the total power
Implementing the Mars Science Laboratory Terminal Descent Sensor Field Test Campaign
Montgomery, James F.; Bodie, James H.; Brown, Joseph D.; Chen, Allen; Chen, Curtis W.; Essmiller, John C.; Fisher, Charles D.; Goldberg, Hannah R.; Lee, Steven W.; Shaffer, Scott J.
2012-01-01
The Mars Science Laboratory (MSL) will deliver a 900 kg rover to the surface of Mars in August 2012. MSL will utilize a new pulse-Doppler landing radar, the Terminal Descent Sensor (TDS). The TDS employs six narrow-beam antennas to provide unprecedented slant range and velocity performance at Mars to enable soft touchdown of the MSL rover using a unique sky crane Entry, De-scent, and Landing (EDL) technique. Prior to use on MSL, the TDS was put through a rigorous verification and validation (V&V) process. A key element of this V&V was operating the TDS over a series of field tests, using flight-like profiles expected during the descent and landing of MSL over Mars-like terrain on Earth. Limits of TDS performance were characterized with additional testing meant to stress operational modes outside of the expected EDL flight profiles. The flight envelope over which the TDS must operate on Mars encompasses such a large range of altitudes and velocities that a variety of venues were neces-sary to cover the test space. These venues included an F/A-18 high performance aircraft, a Eurocopter AS350 AStar helicopter and 100-meter tall Echo Towers at the China Lake Naval Air Warfare Center. Testing was carried out over a five year period from July 2006 to June 2011. TDS performance was shown, in gen-eral, to be excellent over all venues. This paper describes the planning, design, and implementation of the field test campaign plus results and lessons learned.
A Cockpit Comfort Level of the Descent Capsule-Shaped Vehicles
V. E. Minenko
2017-01-01
Full Text Available The article formulates the task of selecting a design-layout pattern for a transport spacecraft, in terms of reaching a proper comfort level for the crew to have appropriate functioning. Using the example of a domestic spacecraft and an American one, it has been shown that the type of launch vehicle, the launch specifics, the operational overloads, and the overall mass restrictions have a dramatic impact on the choice of the design-layout pattern of the spacecraft. The free volume of the pressure cockpit per each member of the crew is considered as the main characteristic to show a level of the spacecraft comfort. Using the average statistical data on the layout density of different equipment, the article estimates the possible increase of this characteristic for the cutting-edge descent vehicles. Using the example of the descent vehicles of Soyuz and Apollo class, the article shows a dependence of the raising weight of a descent vehicle on the free volume of its pressure cockpit. Attention is drawn to the fact that the limit of increasing free space of the spacecraft compartments to achieve maximum comfort should correspond to a set of functions that the crew performs in the compartments considered. Otherwise, the increase in the spacecraft mass will prove to be unjustified. The results stated in the article can be useful to developers of manned spacecraft, as well as to teachers and students. In the long term it is worthwhile adding the article material with the mass and volume indicators, as well as with the estimate results of the comfort level of modern manned spacecrafts being under design in Russia and USA, such as PTK NP (“Federation”, “Orion”, “Dragon V2”.
Combining Step Gradients and Linear Gradients in Density.
Kumar, Ashok A; Walz, Jenna A; Gonidec, Mathieu; Mace, Charles R; Whitesides, George M
2015-06-16
Combining aqueous multiphase systems (AMPS) and magnetic levitation (MagLev) provides a method to produce hybrid gradients in apparent density. AMPS—solutions of different polymers, salts, or surfactants that spontaneously separate into immiscible but predominantly aqueous phases—offer thermodynamically stable steps in density that can be tuned by the concentration of solutes. MagLev—the levitation of diamagnetic objects in a paramagnetic fluid within a magnetic field gradient—can be arranged to provide a near-linear gradient in effective density where the height of a levitating object above the surface of the magnet corresponds to its density; the strength of the gradient in effective density can be tuned by the choice of paramagnetic salt and its concentrations and by the strength and gradient in the magnetic field. Including paramagnetic salts (e.g., MnSO4 or MnCl2) in AMPS, and placing them in a magnetic field gradient, enables their use as media for MagLev. The potential to create large steps in density with AMPS allows separations of objects across a range of densities. The gradients produced by MagLev provide resolution over a continuous range of densities. By combining these approaches, mixtures of objects with large differences in density can be separated and analyzed simultaneously. Using MagLev to add an effective gradient in density also enables tuning the range of densities captured at an interface of an AMPS by simply changing the position of the container in the magnetic field. Further, by creating AMPS in which phases have different concentrations of paramagnetic ions, the phases can provide different resolutions in density. These results suggest that combining steps in density with gradients in density can enable new classes of separations based on density.
New generalized conjugate gradient methods for the non-quadratic model in unconstrained optimization
Al-Bayati, A.
2001-01-01
This paper present two new conjugate gradient algorithms which use the non-quadratic model in unconstrained optimization. The first is a new generalized self-scaling variable metric algorithm based on the sloboda generalized conjugate gradient method which is invariant to a nonlinear scaling of a stricity convex quadratic function; the second is an interleaving between the generalized sloboda method and the first algorithm; all these algorithm use exact line searches. Numerical comparisons over twenty test functions show that the interleaving algorithm is best overall and requires only about half the function evaluations of the Sloboda method: interleaving algorithms are likely to be preferred when the dimensionality of the problem is increased. (author). 29 refs., 1 tab
Cohomological descent theory for a morphism of stacks and for equivariant derived categories
Elagin, Alexei D
2011-01-01
In the paper, we find necessary and sufficient conditions under which, if X→S is a morphism of algebraic varieties (or, in a more general case, of stacks), the derived category of S can be recovered by using the tools of descent theory from the derived category of X. We show that for an action of a linearly reductive algebraic group G on a scheme X this result implies the equivalence of the derived category of G-equivariant sheaves on X and the category of objects in the derived category of sheaves on X with a given action of G on each object. Bibliography: 18 titles.
Bathke, C.
1978-03-01
A description is presented of a general algorithm for locating the extremum of a multi-dimensional constrained function. The algorithm employs a series of techniques dominated by random shrinkage, steepest descent, and adaptive creeping. A discussion follows of the algorithm's application to a ''real world'' problem, namely the optimization of the price of electricity, P/sub eh/, from a hybrid fusion-fission reactor. Upon the basis of comparisons with other optimization schemes of a survey nature, the algorithm is concluded to yield a good approximation to the location of a function's optimum
Spatial gradient tuning in metamaterials
Driscoll, Tom; Goldflam, Michael; Jokerst, Nan; Basov, Dimitri; Smith, David
2011-03-01
Gradient Index (GRIN) metamaterials have been used to create devices inspired by, but often surpassing the potential of, conventional GRIN optics. The unit-cell nature of metamaterials presents the opportunity to exert much greater control over spatial gradients than is possible in natural materials. This is true not only during the design phase but also offers the potential for real-time reconfiguration of the metamaterial gradient. This ability fits nicely into the picture of transformation-optics, in which spatial gradients can enable an impressive suite of innovative devices. We discuss methods to exert control over metamaterial response, focusing on our recent demonstrations using Vanadium Dioxide. We give special attention to role of memristance and mem-capacitance observed in Vanadium Dioxide, which simplify the demands of stimuli and addressing, as well as intersecting metamaterials with the field of memory-materials.
Modeling chemical gradients in sediments under losing and gaining flow conditions: The GRADIENT code
Boano, Fulvio; De Falco, Natalie; Arnon, Shai
2018-02-01
Interfaces between sediments and water bodies often represent biochemical hotspots for nutrient reactions and are characterized by steep concentration gradients of different reactive solutes. Vertical profiles of these concentrations are routinely collected to obtain information on nutrient dynamics, and simple codes have been developed to analyze these profiles and determine the magnitude and distribution of reaction rates within sediments. However, existing publicly available codes do not consider the potential contribution of water flow in the sediments to nutrient transport, and their applications to field sites with significant water-borne nutrient fluxes may lead to large errors in the estimated reaction rates. To fill this gap, the present work presents GRADIENT, a novel algorithm to evaluate distributions of reaction rates from observed concentration profiles. GRADIENT is a Matlab code that extends a previously published framework to include the role of nutrient advection, and provides robust estimates of reaction rates in sediments with significant water flow. This work discusses the theoretical basis of the method and shows its performance by comparing the results to a series of synthetic data and to laboratory experiments. The results clearly show that in systems with losing or gaining fluxes, the inclusion of such fluxes is critical for estimating local and overall reaction rates in sediments.
Clavel, Marie-Annick; Magne, Julien; Pibarot, Philippe
2016-09-07
An important proportion of patients with aortic stenosis (AS) have a 'low-gradient' AS, i.e. a small aortic valve area (AVA gradient (gradient discrepancy raises uncertainty about the actual stenosis severity and thus about the indication for aortic valve replacement (AVR) if the patient has symptoms and/or left ventricular (LV) systolic dysfunction. The most frequent cause of low-gradient (LG) AS is the presence of a low LV outflow state, which may occur with reduced left ventricular ejection fraction (LVEF), i.e. classical low-flow, low-gradient (LF-LG), or preserved LVEF, i.e. paradoxical LF-LG. Furthermore, a substantial proportion of patients with AS may have a normal-flow, low-gradient (NF-LG) AS: i.e. a small AVA-low-gradient combination but with a normal flow. One of the most important clinical challenges in these three categories of patients with LG AS (classical LF-LG, paradoxical LF-LG, and NF-LG) is to differentiate a true-severe AS that generally benefits from AVR vs. a pseudo-severe AS that should be managed conservatively. A low-dose dobutamine stress echocardiography may be used for this purpose in patients with classical LF-LG AS, whereas aortic valve calcium scoring by multi-detector computed tomography is the preferred modality in those with paradoxical LF-LG or NF-LG AS. Although patients with LF-LG severe AS have worse outcomes than those with high-gradient AS following AVR, they nonetheless display an important survival benefit with this intervention. Some studies suggest that transcatheter AVR may be superior to surgical AVR in patients with LF-LG AS. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.
Graded/Gradient Porous Biomaterials
Xigeng Miao
2009-12-01
Full Text Available Biomaterials include bioceramics, biometals, biopolymers and biocomposites and they play important roles in the replacement and regeneration of human tissues. However, dense bioceramics and dense biometals pose the problem of stress shielding due to their high Young’s moduli compared to those of bones. On the other hand, porous biomaterials exhibit the potential of bone ingrowth, which will depend on porous parameters such as pore size, pore interconnectivity, and porosity. Unfortunately, a highly porous biomaterial results in poor mechanical properties. To optimise the mechanical and the biological properties, porous biomaterials with graded/gradient porosity, pores size, and/or composition have been developed. Graded/gradient porous biomaterials have many advantages over graded/gradient dense biomaterials and uniform or homogenous porous biomaterials. The internal pore surfaces of graded/gradient porous biomaterials can be modified with organic, inorganic, or biological coatings and the internal pores themselves can also be filled with biocompatible and biodegradable materials or living cells. However, graded/gradient porous biomaterials are generally more difficult to fabricate than uniform or homogenous porous biomaterials. With the development of cost-effective processing techniques, graded/gradient porous biomaterials can find wide applications in bone defect filling, implant fixation, bone replacement, drug delivery, and tissue engineering.
Dose gradient curve: A new tool for evaluating dose gradient.
Sung, KiHoon; Choi, Young Eun
2018-01-01
Stereotactic radiotherapy, which delivers an ablative high radiation dose to a target volume for maximum local tumor control, requires a rapid dose fall-off outside the target volume to prevent extensive damage to nearby normal tissue. Currently, there is no tool to comprehensively evaluate the dose gradient near the target volume. We propose the dose gradient curve (DGC) as a new tool to evaluate the quality of a treatment plan with respect to the dose fall-off characteristics. The average distance between two isodose surfaces was represented by the dose gradient index (DGI) estimated by a simple equation using the volume and surface area of isodose levels. The surface area was calculated by mesh generation and surface triangulation. The DGC was defined as a plot of the DGI of each dose interval as a function of the dose. Two types of DGCs, differential and cumulative, were generated. The performance of the DGC was evaluated using stereotactic radiosurgery plans for virtual targets. Over the range of dose distributions, the dose gradient of each dose interval was well-characterized by the DGC in an easily understandable graph format. Significant changes in the DGC were observed reflecting the differences in planning situations and various prescription doses. The DGC is a rational method for visualizing the dose gradient as the average distance between two isodose surfaces; the shorter the distance, the steeper the dose gradient. By combining the DGC with the dose-volume histogram (DVH) in a single plot, the DGC can be utilized to evaluate not only the dose gradient but also the target coverage in routine clinical practice.
MILC staggered conjugate gradient performance on Intel KNL
DeTar, Carleton; Doerfler, Douglas; Gottlieb, Steven; Jha, Ashish; Kalamkar, Dhiraj; Li, Ruizi; Toussaint, Doug
2016-01-01
We review our work done to optimize the staggered conjugate gradient (CG) algorithm in the MILC code for use with the Intel Knights Landing (KNL) architecture. KNL is the second gener- ation Intel Xeon Phi processor. It is capable of massive thread parallelism, data parallelism, and high on-board memory bandwidth and is being adopted in supercomputing centers for scientific research. The CG solver consumes the majority of time in production running, so we have spent most of our effort on it. ...
An Airborne Conflict Resolution Approach Using a Genetic Algorithm
Mondoloni, Stephane; Conway, Sheila
2001-01-01
An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.
Casanova, Henri; Robert, Yves
2008-01-01
""…The authors of the present book, who have extensive credentials in both research and instruction in the area of parallelism, present a sound, principled treatment of parallel algorithms. … This book is very well written and extremely well designed from an instructional point of view. … The authors have created an instructive and fascinating text. The book will serve researchers as well as instructors who need a solid, readable text for a course on parallelism in computing. Indeed, for anyone who wants an understandable text from which to acquire a current, rigorous, and broad vi
Gustavson, Fred G.; Reid, John K.; Wasniewski, Jerzy
2007-01-01
We present subroutines for the Cholesky factorization of a positive-definite symmetric matrix and for solving corresponding sets of linear equations. They exploit cache memory by using the block hybrid format proposed by the authors in a companion article. The matrix is packed into n(n + 1)/2 real...... variables, and the speed is usually better than that of the LAPACK algorithm that uses full storage (n2 variables). Included are subroutines for rearranging a matrix whose upper or lower-triangular part is packed by columns to this format and for the inverse rearrangement. Also included is a kernel...
Gaebler, John A.; Tolson, Robert H.
2010-01-01
In the study of entry, descent, and landing, Monte Carlo sampling methods are often employed to study the uncertainty in the designed trajectory. The large number of uncertain inputs and outputs, coupled with complicated non-linear models, can make interpretation of the results difficult. Three methods that provide statistical insights are applied to an entry, descent, and landing simulation. The advantages and disadvantages of each method are discussed in terms of the insights gained versus the computational cost. The first method investigated was failure domain bounding which aims to reduce the computational cost of assessing the failure probability. Next a variance-based sensitivity analysis was studied for the ability to identify which input variable uncertainty has the greatest impact on the uncertainty of an output. Finally, probabilistic sensitivity analysis is used to calculate certain sensitivities at a reduced computational cost. These methods produce valuable information that identifies critical mission parameters and needs for new technology, but generally at a significant computational cost.
Kinematic analyses during stair descent in young women with patellofemoral pain.
Grenholm, Anton; Stensdotter, Ann-Katrin; Häger-Ross, Charlotte
2009-01-01
Compensatory movement strategies may develop in response to pain to avoid stress on the affected area. Patellofemoral pain is characterised by intermittent periods of pain and the present study addresses whether long-term pain leads to compensatory movement strategies that remain even when the pain is absent. Lower extremity kinematics in three dimensions was studied in stair descent in 17 women with patellofemoral and in 17 matched controls. A two-dimensional geometric model was constructed to normalise kinematic data for subjects with varying anthropometrics when negotiating stairs of fixed proportions. There were minor differences in movement patterns between groups. Knee joint angular velocity in the stance leg at foot contact was lower and the movement trajectory tended to be jerkier in the patellofemoral group. The two-dimensional model showed greater plantar flexion in the swing leg in preparation for foot placement in the patellofemoral group. The results indicate that an altered stair descent strategy in the patellofemoral group may remain also in the absence of pain. The biomechanical interpretation presumes that the strategy is aimed to reduce knee joint loading by less knee joint moment and lower impact force.
Effects of environmental changes in a stair climbing intervention: generalization to stair descent.
Webb, Oliver J; Eves, Frank F
2007-01-01
Visual improvements have been shown to encourage stair use in worksites independently of written prompts. This study examined whether visual modifications alone can influence behavior in a shopping mall. Climbing one flight of stairs, however, will not confer health benefits. Therefore, this study also assessed whether exposure to the intervention encouraged subsequent stair use. Interrupted time-series design. Escalators flanked by a staircase on either side. Ascending and descending pedestrians (N = 81,948). Following baseline monitoring, a colorful design was introduced on the stair risers of one staircase (the target staircase). A health promotion message was superimposed later on top. The intervention was visible only to ascending pedestrians. Thus, any rise in descending stair use would indicate increased intention to use stairs, which endured after initial exposure to the intervention. Observers inconspicuously coded pedestrians' means of ascent/descent and demographic characteristics. The design alone had no meaningful impact. Addition of the message, however, increased stair climbing at the target and nontarget staircases by 190% and 52%, respectively. The message also produced a modest increase in stair descent at the target (25%) and nontarget (9%) staircases. In public venues, a message component is critical to the success of interventions. In addition, it appears that exposure to an intervention can encourage pedestrians to use stairs on a subsequent occasion.
The effect of the descent technique and truck cabin layout on the landing impact forces.
Patenaude, S; Marchand, D; Samperi, S; Bélanger, M
2001-12-01
The majority of injuries to truckers are caused by falls during the descent from the cab of the truck. Several studies have shown that the techniques used to descend from the truck and the layout of the truck's cabin are the principal cause of injury. The goal of the present study was to measure the effects of the descent techniques used by the trucker and the layout of the truck's cabin on the impact forces absorbed by the lower limbs and the back. Kinematic data, obtained with the aid of a video camera, were combined with the force platform data to allow for calculation of the lower limb and L5-S1 torques as well as L5-S1 compressive forces. The trucker descended from two different conventional tractor cabin layouts. Each trucker descended from cabin using either "facing the truck" (FT) or "back to the truck" (BT) techniques. The results demonstrate that the BT technique produces greater ground impact forces than the FT technique, particularly when the truck does not have a handrail. The BT technique also causes an increase in the compressive forces exerted on the back. In conclusion, the use of the FT technique along with the aids (i.e., handrails and all the steps) help lower the landing impact forces as well as the lumbosacral compressive forces.
Kinetic comparison of older men and women during walk-to-stair descent transition.
Singhal, Kunal; Kim, Jemin; Casebolt, Jeffrey; Lee, Sangwoo; Han, Ki Hoon; Kwon, Young-Hoo
2014-09-01
Stair walking is one of the most challenging tasks for older adults, with women reporting higher incidence of falls. The purpose of this study was to investigate the gender differences in kinetics during stair descent transition. Twenty-eight participants (12 male and 16 female; 68.5 and 69.0 years of mean age, respectively) performed stair descent from level walking in a step-over-step manner at a self-selected speed over a custom-made three-step staircase with embedded force plates. Kinematic and force data were combined using inverse dynamics to generate kinetic data for gender comparison. The top and the first step on the staircase were chosen for analysis. Women showed a higher trail leg peak hip abductor moment (-1.0 Nm/kg), lower trail leg peak knee extensor moment and eccentric power (0.74 Nm/kg and 3.15 W/kg), and lower peak concentric power at trail leg ankle joint (1.29 W/kg) as compared to men (ppredispose women to a higher risk of fall. Copyright © 2014 Elsevier B.V. All rights reserved.
Effects of the Phoenix Lander descent thruster plume on the Martian surface
Plemmons, D. H.; Mehta, M.; Clark, B. C.; Kounaves, S. P.; Peach, L. L.; Renno, N. O.; Tamppari, L.; Young, S. M. M.
2008-08-01
The exhaust plume of Phoenix's hydrazine monopropellant pulsed descent thrusters will impact the surface of Mars during its descent and landing phase in the northern polar region. Experimental and computational studies have been performed to characterize the chemical compounds in the thruster exhausts. No undecomposed hydrazine is observed above the instrument detection limit of 0.2%. Forty-five percent ammonia is measured in the exhaust at steady state. Water vapor is observed at a level of 0.25%, consistent with fuel purity analysis results. Moreover, the dynamic interactions of the thruster plumes with the ground have been studied. Large pressure overshoots are produced at the ground during the ramp-up and ramp-down phases of the duty cycle of Phoenix's pulsed engines. These pressure overshoots are superimposed on the 10 Hz quasi-steady ground pressure perturbations with amplitude of about 5 kPa (at touchdown altitude) and have a maximum amplitude of about 20-40 kPa. A theoretical explanation for the physics that causes these pressure perturbations is briefly described in this article. The potential for soil erosion and uplifting at the landing site is also discussed. The objectives of the research described in this article are to provide empirical and theoretical data for the Phoenix Science Team to mitigate any potential problem. The data will also be used to ensure proper interpretation of the results from on-board scientific instrumentation when Martian soil samples are analyzed.
A Direct Search Algorithm for Global Optimization
Enrique Baeyens
2016-06-01
Full Text Available A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.
Ryan, Niall J.; Kinnison, Douglas E.; Garcia, Rolando R.; Hoffmann, Christoph G.; Palm, Mathias; Raffalski, Uwe; Notholt, Justus
2018-02-01
We investigate the reliability of using trace gas measurements from remote sensing instruments to infer polar atmospheric descent rates during winter within 46-86 km altitude. Using output from the Specified Dynamics Whole Atmosphere Community Climate Model (SD-WACCM) between 2008 and 2014, tendencies of carbon monoxide (CO) volume mixing ratios (VMRs) are used to assess a common assumption of dominant vertical advection of tracers during polar winter. The results show that dynamical processes other than vertical advection are not negligible, meaning that the transport rates derived from trace gas measurements do not represent the mean descent of the atmosphere. The relative importance of vertical advection is lessened, and exceeded by other processes, during periods directly before and after a sudden stratospheric warming, mainly due to an increase in eddy transport. It was also found that CO chemistry cannot be ignored in the mesosphere due to the night-time layer of OH at approximately 80 km altitude. CO VMR profiles from the Kiruna Microwave Radiometer and the Microwave Limb Sounder were compared to SD-WACCM output, and show good agreement on daily and seasonal timescales. SD-WACCM CO profiles are combined with the CO tendencies to estimate errors involved in calculating the mean descent of the atmosphere from remote sensing measurements. The results indicate errors on the same scale as the calculated descent rates, and that the method is prone to a misinterpretation of the direction of air motion. The true rate of atmospheric descent is seen to be masked by processes, other than vertical advection, that affect CO. We suggest an alternative definition of the rate calculated using remote sensing measurements: not as the mean descent of the atmosphere, but as an effective rate of vertical transport for the trace gas under observation.
de Graft Aikins Ama
2009-08-01
Full Text Available Abstract Background Most European countries are ethnically and culturally diverse. Globally, cardiovascular disease (CVD is the leading cause of death. The major risk factors for CVD have been well established. This picture holds true for all regions of the world and in different ethnic groups. However, the prevalence of CVD and related risk factors vary among ethnic groups. Methods This article provides a review of current understanding of the epidemiology of vascular disease, principally coronary heart disease (CHD, stroke and related risk factors among populations of Sub-Sahara African descent (henceforth, African descent in comparison with the European populations in Europe. Results Compared with European populations, populations of African descent have an increased risk of stroke, whereas CHD is less common. They also have higher rates of hypertension and diabetes than European populations. Obesity is highly prevalent, but smoking rate is lower among African descent women. Older people of African descent have more favourable lipid profile and dietary habits than their European counterparts. Alcohol consumption is less common among populations of African descent. The rate of physical activity differs between European countries. Dutch African-Suriname men and women are less physically active than the White-Dutch whereas British African women are more physically active than women in the general population. Literature on psychosocial stress shows inconsistent results. Conclusion Hypertension and diabetes are highly prevalent among African populations, which may explain their high rate of stroke in Europe. The relatively low rate of CHD may be explained by the low rates of other risk factors including a more favourable lipid profile and the low prevalence of smoking. The risk factors are changing, and on the whole, getting worse especially among African women. Cohort studies and clinical trials are therefore needed among these groups to
Bernal, Javier; Torres-Jimenez, Jose
2015-01-01
SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.
Simulations of Flame Acceleration and DDT in Mixture Composition Gradients
Zheng, Weilin; Kaplan, Carolyn; Houim, Ryan; Oran, Elaine
2017-11-01
Unsteady, multidimensional, fully compressible numerical simulations of methane-air in an obstructed channel with spatial gradients in equivalence ratios have been carried to determine the effects of the gradients on flame acceleration and transition to detonation. Results for gradients perpendicular to the propagation direction were considered here. A calibrated, optimized chemical-diffusive model that reproduces correct flame and detonation properties for methane-air over a range of equivalence ratios was derived from a combination of a genetic algorithm with a Nelder-Mead optimization scheme. Inhomogeneous mixtures of methane-air resulted in slower flame acceleration and longer distance to DDT. Detonations were more likely to decouple into a flame and a shock under sharper concentration gradients. Detailed analyses of temperature and equivalence ratio illustrated that vertical gradients can greatly affect the formation of hot spots that initiate detonation by changing the strength of leading shock wave and local equivalence ratio near the base of obstacles. This work is supported by the Alpha Foundation (Grant No. AFC215-20).
Ferri, F.; Karatekin, O.; Aboudan, A.; VanHove, B.; Colombatti, C.; Bettanini, C.; Debei, S.; Lewis, S.; Forget, F.
2017-09-01
On the 19th October 2016, Schiaparelli, the Entry Demonstrator Module (EDM) of the ESA ExoMars Program entered into the martian atmosphere. Although it did not complete a safe landing on Mars, it transmitted data throughout its descent to the surface, until the loss of signal at 1 minute before the expected touch-down on Mars' surface. From the flight data, we reconstruct the actual dynamics of the vehicle during its descent towards Mars's surface and retrieve the atmospheric profile, in terms of density, pressure and temperature, along its trajectory for atmospheric investigations.
Hargreaves, Brian
2012-01-01
Gradient echo sequences are widely used in magnetic resonance imaging (MRI) for numerous applications ranging from angiography to perfusion to functional MRI. Compared with spin-echo techniques, the very short repetition times of gradient-echo methods enable very rapid 2D and 3D imaging, but also lead to complicated “steady states.” Signal and contrast behavior can be described graphically and mathematically, and depends strongly on the type of spoiling: fully balanced (no spoiling), gradient spoiling, or RF-spoiling. These spoiling options trade off between high signal and pure T1 contrast while the flip angle also affects image contrast in all cases, both of which can be demonstrated theoretically and in image examples. As with spin-echo sequences, magnetization preparation can be added to gradient-echo sequences to alter image contrast. Gradient echo sequences are widely used for numerous applications such as 3D perfusion imaging, functional MRI, cardiac imaging and MR angiography. PMID:23097185
The influence of ALN-Al gradient material gradient index on ballistic performance
Wang Youcong; Liu Qiwen; Li Yao; Shen Qiang
2013-01-01
Ballistic performance of the gradient material is superior to laminated material, and gradient materials have different gradient types. Using ls-dyna to simulate the ballistic performance of ALN-AL gradient target plates which contain three gradient index (b = 1, b = 0.5, b = 2). Through Hopkinson bar numerical simulation to the target plate materials, we obtained the reflection stress wave and transmission stress wave state of gradient material to get the best gradient index. The internal stress state of gradient material is simulated by amplification processing of the target plate model. When the gradient index b is equal to 1, the gradient target plate is best of all.
Phan, J C; Bush, T M; Donald, F; Ward, M
1999-01-01
The prevalence rate and disease manifestations of systemic lupus erythematosus (SLE) have been noted to vary among different ethnic groups. There has been no description in the English literature of SLE in the Vietnamese population. This is the first report, which details the clinical and laboratory features as well as an estimation of the prevalence of SLE in patients with a Vietnamese ancestry living in the United States. We performed a retrospective chart review of clinical and laboratory features of patients of Vietnamese descent with SLE. The case finding was performed by a review of the rheumatology clinic records at two large teaching hospitals in Santa Clara County searching for patients with SLE with a Vietnamese surname. In addition, we recruited patients by contacting all of the rheumatologists practicing in the county. Twenty-three patients of Vietnamese descent were identified with SLE in Santa Clara County. The estimated prevalence of SLE in the patients of Vietnamese descent was 42 cases per 100 000 persons. Eighty-seven per cent of the cases were born in Vietnam. The clinical and laboratory features of SLE were similar to prior published reviews except for a relatively high prevalence of anti-RNP antibody (54%). The patients with anti-RNP antibody exhibited features of overlap syndrome. There was a high rate of exposure to tuberculosis (TB). Fifty-eight per cent of patients had a positive purified protein derivative (PPD) skin test and 27% of patients had a history of clinical TB. Forty-four per cent of patients had evidence of hepatitis B exposure. The prevalence of SLE in the Vietnamese population in Santa Clara County is similar to that of other Asian populations. There was a relatively high prevalence of anti-RNP antibody in our patient group which was associated with overlap features. As expected in an immigrant population from Southeast Asia, there was a high rate of prior exposure to tuberculosis and hepatitis B. Clinicians should diligently
Jans, S.M.; Daemers, D.O.A.; Vos, R.; Lagro-Janssen, A.L.M.
2009-01-01
OBJECTIVES: to investigate the prevalence of anaemia in pregnancy according to the cut-off points used in the national clinical guideline 'Anaemia in Primary Care Midwifery Practice', and to investigate a possible difference in prevalence between pregnant women of Northern European descent compared
Jans, S. M. P. J.; Daemers, D. O. A.; de Vos, R.; Lagro-Jansen, A. L. M.
2009-01-01
to investigate the prevalence of anaemia in pregnancy according to the cut-off points used in the national clinical guideline 'Anaemia in Primary Care Midwifery Practice', and to investigate a possible difference in prevalence between pregnant women of Northern European descent compared with women
Jans, S.M.P.J.; Daemers, D.O.A.; Vos, R. de; Lagro-Janssen, A.L.M.
2009-01-01
Objectives - to investigate the prevalence of anaemia in pregnancy according to the cut-off points used in the national clinical guideline ‘Anaemia in Primary Care Midwifery Practice’, and to investigate a possible difference in prevalence between pregnant women of Northern European descent compared
Testosterone secretion during gubernacular development and testicular descent in the dog.
Baumans, V; Dieleman, S J; Wouterse, H S; van Tol, L; Dijkstra, G; Wensing, C J
1985-01-01
Serum testosterone concentrations ranged from 0.24 to 1.45 nmol/l between Day 53 post coitum (p.c.) until Day 40 post partum (p.p.) and did not show variations that could be correlated with the process of testicular descent. The intratesticular androgen appeared to be mainly testosterone, its concentration being about 5000-fold higher than that in serum whereas 5 alpha-dihydrotestosterone could not be demonstrated. The intratesticular testosterone concentration at the initiation of gubernacular regression (Day 0) was apparently, but not significantly, higher than at Day 49 p.c. and at Day 40 p.p. The ability of the neonatal canine testis to synthesize testosterone was indicated by increased serum testosterone concentrations after hCG stimulation.
Quantification of Plume-Soil Interaction and Excavation Due to the Sky Crane Descent Stage
Vizcaino, Jeffrey; Mehta, Manish
2015-01-01
The quantification of the particulate erosion that occurs as a result of a rocket exhaust plume impinging on soil during extraterrestrial landings is critical for future robotic and human lander mission design. The aerodynamic environment that results from the reflected plumes results in dust lifting, site alteration and saltation, all of which create a potentially erosive and contaminant heavy environment for the lander vehicle and any surrounding structures. The Mars Science Lab (MSL), weighing nearly one metric ton, required higher levels of thrust from its retro propulsive systems and an entirely new descent system to minimize these effects. In this work we seek to quantify plume soil interaction and its resultant soil erosion caused by the MSL's Sky Crane descent stage engines by performing three dimensional digital terrain and elevation mapping of the Curiosity rover's landing site. Analysis of plume soil interaction altitude and time was performed by detailed examination of the Mars Descent Imager (MARDI) still frames and reconstructed inertial measurement unit (IMU) sensor data. Results show initial plume soil interaction from the Sky Crane's eight engines began at ground elevations greater than 60 meters and more than 25 seconds before the rovers' touchdown event. During this time, viscous shear erosion (VSE) was dominant typically resulting in dusting of the surface with flow propagating nearly parallel to the surface. As the vehicle descended and decreased to four powered engines plume-plume and plume soil interaction increased the overall erosion rate at the surface. Visibility was greatly reduced at a height of roughly 20 meters above the surface and fell to zero ground visibility shortly after. The deployment phase of the Sky Crane descent stage hovering at nearly six meters above the surface showed the greatest amount of erosion with several large particles of soil being kicked up, recirculated, and impacting the bottom of the rover chassis. Image
Rosas, Lisa G.; Guendelman, Sylvia; Harley, Kim; Fernald, Lia C. H.; Neufeld, Lynnette; Mejia, Fabiola
2010-01-01
The prevalence of childhood obesity is high among young children of Mexican origin in the United States, however, the determinants are poorly understood. We conducted a binational study with a sample from California (CA) and Mexico (MX), to identify and compare the most important factors associated with overweight and obesity among children of Mexican descent. Significantly more children were classified as overweight or obese in CA compared to MX (53.3 vs. 14.9%, P obese mother was significantly associated with being overweight or obese. In MX, male gender, high socioeconomic status and very low food insecurity were associated with being overweight or obese. These data offer hypotheses for how migration may influence the high prevalence of overweight among the Mexican children in California. PMID:20217234
Cheatwood, F. McNeil; Bose, Deepak; Karlgaard, Christopher D.; Kuhl, Christopher A.; Santos, Jose A.; Wright, Michael J.
2014-01-01
The Mars Science Laboratory (MSL) entry vehicle (EV) successfully entered the Mars atmosphere and landed the Curiosity rover safely on the surface of the planet in Gale crater on August 6, 2012. MSL carried the MSL Entry, Descent, and Landing (EDL) Instrumentation (MEDLI). MEDLI delivered the first in-depth understanding of the Mars entry environments and the response of the entry vehicle to those environments. MEDLI was comprised of three major subsystems: the Mars Entry Atmospheric Data System (MEADS), the MEDLI Integrated Sensor Plugs (MISP), and the Sensor Support Electronics (SSE). Ultimately, the entire MEDLI sensor suite consisting of both MEADS and MISP provided measurements that were used for trajectory reconstruction and engineering validation of aerodynamic, atmospheric, and thermal protection system (TPS) models in addition to Earth-based systems testing procedures. This report contains in-depth hardware descriptions, performance evaluation, and data information of the three MEDLI subsystems.
Natural selection and the distribution of identity-by-descent in the human genome
Albrechtsen, Anders; Moltke, Ida; Nielsen, Rasmus
2010-01-01
There has recently been considerable interest in detecting natural selection in the human genome. Selection will usually tend to increase identity-by-descent (IBD) among individuals in a population, and many methods for detecting recent and ongoing positive selection indirectly take advantage...... of this. In this article we show that excess IBD sharing is a general property of natural selection and we show that this fact makes it possible to detect several types of selection including a type that is otherwise difficult to detect: selection acting on standing genetic variation. Motivated by this......, we use a recently developed method for identifying IBD sharing among individuals from genome-wide data to scan populations from the new HapMap phase 3 project for regions with excess IBD sharing in order to identify regions in the human genome that have been under strong, very recent selection...
Roumeliotis, Chris; Grinblat, Jonathan; Reeves, Glenn
2013-01-01
Second Chance (SECC) was a bare bones version of Mars Science Laboratory's (MSL) Entry Descent & Landing (EDL) flight software that ran on Curiosity's backup computer, which could have taken over swiftly in the event of a reset of Curiosity's prime computer, in order to land her safely on Mars. Without SECC, a reset of Curiosity's prime computer would have lead to catastrophic mission failure. Even though a reset of the prime computer never occurred, SECC had the important responsibility as EDL's guardian angel, and this responsibility would not have seen such success without unparalleled systems engineering. This paper will focus on the systems engineering behind SECC: Covering a brief overview of SECC's design, the intense schedule to use SECC as a backup system, the verification and validation of the system's "Do No Harm" mandate, the system's overall functional performance, and finally, its use on the fateful day of August 5th, 2012.
Genetic drift. Descent, lineage, and pedigree of the Trojans in Homer's Iliad.
Bazopoulou-Kyrkanidou, Euterpe
2007-12-15
Homer's Iliad, is an epic poem that describes the last 70 days of the Trojan War, which was waged against the city of Troy by the Achaeans. Here, the descent, lineage, and the pedigree of the Trojans are presented. In the Illiad, they are said to have originated from Zeus. Beginning with him, the Trojan pedigree comprised 17 men in 8 generations with Dardanus, founder of Dardania in the second generation; Tros, King of the Trojans in the fourth generation; and the two heroes Hector and Aeneas in the eighth generation. In the seventh generation, Priam, as King of the Trojans, had a huge family, including 50 sons: 19 children with his wife Hecabe, other sons with many different wives, and some daughters as well. Hector, the first born, became leader of the Trojans. Hector's brother, Paris, in abducting Helen of Sparta, the wife of King Menelaus, caused the Trojan War to break out. (c) 2007 Wiley-Liss, Inc.
Frohlich, J; Godolphin, W J; Reeve, C E; Evelyn, K A
1978-01-01
A 16-year-old male (S.F.) and his 21-year-old sister (D.H.) from a large family of Italian and Swedish descent had virtually identical lipoprotein pattern and complete absence of LCAT activity. Both had typical corneal opacities and mild anemia with target cells. S.F., but not D.H., presented with proteinuria, which has increased over three years of follow-up. His kidney biopsy revealed lipid deposits in the glomerular basement membrane. Ten relatives in 4 generations had normal LCAT activity and/or lipoprotein pattern. The patients and their relatives had haptoglobin type 2. Factors that might influence the different clinical presentation in our patients (previous renal disease, diet, abnormal lipoproteins), prognosis, and treatment (diet, enzyme replacement, cholestyramine) are discussed.
A Multidisciplinary Tool for Systems Analysis of Planetary Entry, Descent, and Landing (SAPE)
Samareh, Jamshid A.
2009-01-01
SAPE is a Python-based multidisciplinary analysis tool for systems analysis of planetary entry, descent, and landing (EDL) for Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Titan. The purpose of SAPE is to provide a variable-fidelity capability for conceptual and preliminary analysis within the same framework. SAPE includes the following analysis modules: geometry, trajectory, aerodynamics, aerothermal, thermal protection system, and structural sizing. SAPE uses the Python language-a platform-independent open-source software for integration and for the user interface. The development has relied heavily on the object-oriented programming capabilities that are available in Python. Modules are provided to interface with commercial and government off-the-shelf software components (e.g., thermal protection systems and finite-element analysis). SAPE runs on Microsoft Windows and Apple Mac OS X and has been partially tested on Linux.
Nonnegative least-squares image deblurring: improved gradient projection approaches
Benvenuto, F.; Zanella, R.; Zanni, L.; Bertero, M.
2010-02-01
The least-squares approach to image deblurring leads to an ill-posed problem. The addition of the nonnegativity constraint, when appropriate, does not provide regularization, even if, as far as we know, a thorough investigation of the ill-posedness of the resulting constrained least-squares problem has still to be done. Iterative methods, converging to nonnegative least-squares solutions, have been proposed. Some of them have the 'semi-convergence' property, i.e. early stopping of the iteration provides 'regularized' solutions. In this paper we consider two of these methods: the projected Landweber (PL) method and the iterative image space reconstruction algorithm (ISRA). Even if they work well in many instances, they are not frequently used in practice because, in general, they require a large number of iterations before providing a sensible solution. Therefore, the main purpose of this paper is to refresh these methods by increasing their efficiency. Starting from the remark that PL and ISRA require only the computation of the gradient of the functional, we propose the application to these algorithms of special acceleration techniques that have been recently developed in the area of the gradient methods. In particular, we propose the application of efficient step-length selection rules and line-search strategies. Moreover, remarking that ISRA is a scaled gradient algorithm, we evaluate its behaviour in comparison with a recent scaled gradient projection (SGP) method for image deblurring. Numerical experiments demonstrate that the accelerated methods still exhibit the semi-convergence property, with a considerable gain both in the number of iterations and in the computational time; in particular, SGP appears definitely the most efficient one.
Gradient-based methods for production optimization of oil reservoirs
Suwartadi, Eka
2012-07-01
Production optimization for water flooding in the secondary phase of oil recovery is the main topic in this thesis. The emphasis has been on numerical optimization algorithms, tested on case examples using simple hypothetical oil reservoirs. Gradientbased optimization, which utilizes adjoint-based gradient computation, is used to solve the optimization problems. The first contribution of this thesis is to address output constraint problems. These kinds of constraints are natural in production optimization. Limiting total water production and water cut at producer wells are examples of such constraints. To maintain the feasibility of an optimization solution, a Lagrangian barrier method is proposed to handle the output constraints. This method incorporates the output constraints into the objective function, thus avoiding additional computations for the constraints gradient (Jacobian) which may be detrimental to the efficiency of the adjoint method. The second contribution is the study of the use of second-order adjoint-gradient information for production optimization. In order to speedup convergence rate in the optimization, one usually uses quasi-Newton approaches such as BFGS and SR1 methods. These methods compute an approximation of the inverse of the Hessian matrix given the first-order gradient from the adjoint method. The methods may not give significant speedup if the Hessian is ill-conditioned. We have developed and implemented the Hessian matrix computation using the adjoint method. Due to high computational cost of the Newton method itself, we instead compute the Hessian-timesvector product which is used in a conjugate gradient algorithm. Finally, the last contribution of this thesis is on surrogate optimization for water flooding in the presence of the output constraints. Two kinds of model order reduction techniques are applied to build surrogate models. These are proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM
Malin, Michal C.; Ravine, Michael A.; Caplinger, Michael A.; Tony Ghaemi, F.; Schaffner, Jacob A.; Maki, Justin N.; Bell, James F.; Cameron, James F.; Dietrich, William E.; Edgett, Kenneth S.; Edwards, Laurence J.; Garvin, James B.; Hallet, Bernard; Herkenhoff, Kenneth E.; Heydari, Ezat; Kah, Linda C.; Lemmon, Mark T.; Minitti, Michelle E.; Olson, Timothy S.; Parker, Timothy J.; Rowland, Scott K.; Schieber, Juergen; Sletten, Ron; Sullivan, Robert J.; Sumner, Dawn Y.; Aileen Yingst, R.; Duston, Brian M.; McNair, Sean; Jensen, Elsa H.
2017-08-01
The Mars Science Laboratory Mast camera and Descent Imager investigations were designed, built, and operated by Malin Space Science Systems of San Diego, CA. They share common electronics and focal plane designs but have different optics. There are two Mastcams of dissimilar focal length. The Mastcam-34 has an f/8, 34 mm focal length lens, and the M-100 an f/10, 100 mm focal length lens. The M-34 field of view is about 20° × 15° with an instantaneous field of view (IFOV) of 218 μrad; the M-100 field of view (FOV) is 6.8° × 5.1° with an IFOV of 74 μrad. The M-34 can focus from 0.5 m to infinity, and the M-100 from 1.6 m to infinity. All three cameras can acquire color images through a Bayer color filter array, and the Mastcams can also acquire images through seven science filters. Images are ≤1600 pixels wide by 1200 pixels tall. The Mastcams, mounted on the 2 m tall Remote Sensing Mast, have a 360° azimuth and 180° elevation field of regard. Mars Descent Imager is fixed-mounted to the bottom left front side of the rover at 66 cm above the surface. Its fixed focus lens is in focus from 2 m to infinity, but out of focus at 66 cm. The f/3 lens has a FOV of 70° by 52° across and along the direction of motion, with an IFOV of 0.76 mrad. All cameras can acquire video at 4 frames/second for full frames or 720p HD at 6 fps. Images can be processed using lossy Joint Photographic Experts Group and predictive lossless compression.
The effects of the diurnal atmospheric variability on entry, descent and landing on Mars
Marčeta D.
2014-01-01
Full Text Available Landing on Mars is extremely challenging task due to the fact that the Martian atmosphere is the most hostile environment in the Solar system to perform the entry, descent and landing (EDL process, because it is thick enough to create substantial heating of the entry vehicle but not thick enough to reduce its velocity to the one necessary for safe landing. Beside this, the atmosphere is very dynamic mainly due to high eccentricity of the Martian orbit, obliquity of the orbital to the equatorial plane and close alignment of the winter solstice and the orbital perihelion. Although seasonal variations of atmospheric parameters are significantly larger than the diurnal, it is very important to analyze diurnal cycles as they can significantly change vertical and horizontal atmospheric profiles in very short time intervals. This can present a serious threat to missions which have very precise timings and specific requirements such as the requirement for the daytime landing to enable ground images acquisition during the descent and landing phase. A 3-degrees-of-freedom trajectory integration routine was combined with the Mars Global Reference Atmospheric Model (Mars-GRAM to identify the dependence of the EDL profiles on the diurnal cycles of atmospheric parameters throughout the Martian year. The obtained results show that the influence of the diurnal cycles is the largest at the equator and decreases relatively symmetrically towards the poles with a slightly stronger influence in the northern hemisphere. Also, there is a significant influence of the orbital position of Mars on the effect of diurnal atmospheric variations which causes that, around the orbital perihelion and winter solstice, there is some kind of inversion of the dependance of optimal entry timing on latitude of the landing site comparing to the rest of the Martian year. [Projekat Ministarstva nauke Republike Srbije, br. 176002
Descent imager/spectral radiometer (DISR) instrument aboard the Huygens probe of Titan
Tomasko, Martin G.; Doose, Lyn R.; Smith, Peter H.; Fellows, C.; Rizk, B.; See, C.; Bushroe, M.; McFarlane, E.; Wegryn, E.; Frans, E.; Clark, R.; Prout, M.; Clapp, S.
1996-10-01
The Huygen's probe of the atmosphere of Saturn's moon Titan includes one optical instrument sensitive to the wavelengths of solar radiation. The goals of this investigation fall into four broad areas: 1) the measurement of the profile of solar heating to support an improved understanding of the thermal balance of Titan and the role of the greenhouse effect in maintaining Titan's temperature structure; 2) the measurement of the size, vertical distribution, and optical properties of the aerosol and cloud particles in Titan's atmosphere to support studies of the origin, chemistry, life cycles, and role in the radiation balance of Titan played by these particles; 3) the composition of the atmosphere, particularly the vertical profile of the mixing ratio of methane, a condensable constituent in Titan's atmosphere; and 4) the physical state, composition, topography, and physical processes at work in determining the nature of the surface of Titan and its interaction with Titan's atmosphere. In order to accomplish these objectives, the Descent Imager/Spectral Radiometer (DISR) instrument makes extensive use of fiber optics to bring the light from several different sets of foreoptics to a silicon CCD detector, to a pair of InGaAs linear array detectors, and to three silicon photometers. Together these detectors permit DISR to make panoramic images of the clouds and surface of Titan, to measure the spectrum of upward and downward streaming sunlight from 350 to 1700 nm at a resolving power of about 200, to measure the reflection spectrum of >= 3000 locations on the surface, to measure the brightness and polarization of the solar aureole between 4 and 30 degrees from the sun at 500 and 935 nm, to separate the direct and diffuse downward solar flux at each wavelength measured, and to measure the continuous reflection spectrum of the ground between 850 and 1600 nm using an onboard lamp in the last 100 m of the descent.
Atmospheric Risk Assessment for the Mars Science Laboratory Entry, Descent, and Landing System
Chen, Allen; Vasavada, Ashwin; Cianciolo, Alicia; Barnes, Jeff; Tyler, Dan; Hinson, David; Lewis, Stephen
2010-01-01
In 2012, the Mars Science Laboratory (MSL) mission will pioneer the next generation of robotic Entry, Descent, and Landing (EDL) systems, by delivering the largest and most capable rover to date to the surface of Mars. As with previous Mars landers, atmospheric conditions during entry, descent, and landing directly impact the performance of MSL's EDL system. While the vehicle's novel guided entry system allows it to "fly out" a range of atmospheric uncertainties, its trajectory through the atmosphere creates a variety of atmospheric sensitivities not present on previous Mars entry systems and landers. Given the mission's stringent landing capability requirements, understanding the atmosphere state and spacecraft sensitivities takes on heightened importance. MSL's guided entry trajectory differs significantly from recent Mars landers and includes events that generate different atmospheric sensitivities than past missions. The existence of these sensitivities and general advancement in the state of Mars atmospheric knowledge has led the MSL team to employ new atmosphere modeling techniques in addition to past practices. A joint EDL engineering and Mars atmosphere science and modeling team has been created to identify the key system sensitivities, gather available atmospheric data sets, develop relevant atmosphere models, and formulate methods to integrate atmosphere information into EDL performance assessments. The team consists of EDL engineers, project science staff, and Mars atmospheric scientists from a variety of institutions. This paper provides an overview of the system performance sensitivities that have driven the atmosphere modeling approach, discusses the atmosphere data sets and models employed by the team as a result of the identified sensitivities, and introduces the tools used to translate atmospheric knowledge into quantitative EDL performance assessments.
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.
Smith, Charlee C., Jr.; Lovell, Powell M., Jr.
1954-01-01
An investigation is being conducted to determine the dynamic stability and control characteristics of a 0.13-scale flying model of Convair XFY-1 vertically rising airplane. This paper presents the results of flight and force tests to determine the stability and control characteristics of the model in vertical descent and landings in still air. The tests indicated that landings, including vertical descent from altitudes representing up to 400 feet for the full-scale airplane and at rates of descent up to 15 or 20 feet per second (full scale), can be performed satisfactorily. Sustained vertical descent in still air probably will be more difficult to perform because of large random trim changes that become greater as the descent velocity is increased. A slight steady head wind or cross wind might be sufficient to eliminate the random trim changes.
Chen, G.S.; Yang, D.Y.
1998-01-01
We apply and compare the preconditioned generalized conjugate gradient methods to solve the linear system equation that arises in the two-dimensional neutron and photon transport equation in this paper. Several subroutines are developed on the basis of preconditioned generalized conjugate gradient methods for time-independent, two-dimensional neutron and photon transport equation in the transport theory. These generalized conjugate gradient methods are used: TFQMR (transpose free quasi-minimal residual algorithm) CGS (conjugate gradient square algorithm), Bi-CGSTAB (bi-conjugate gradient stabilized algorithm) and QMRCGSTAB (quasi-minimal residual variant of bi-conjugate gradient stabilized algorithm). These subroutines are connected to computer program DORT. Several problems are tested on a personal computer with Intel Pentium CPU. The reasons to choose the generalized conjugate gradient methods are that the methods have better residual (equivalent to error) control procedures in the computation and have better convergent rate. The pointwise incomplete LU factorization ILU, modified pointwise incomplete LU factorization MILU, block incomplete factorization BILU and modified blockwise incomplete LU factorization MBILU are the preconditioning techniques used in the several testing problems. In Bi-CGSTAB, CGS, TFQMR and QMRCGSTAB method, we find that either CGS or Bi-CGSTAB method combined with preconditioner MBILU is the most efficient algorithm in these methods in the several testing problems. The numerical solution of flux by preconditioned CGS and Bi-CGSTAB methods has the same result as those from Cray computer, obtained by either the point successive relaxation method or the line successive relaxation method combined with Gaussian elimination
Kamassah, Sharon
2010-01-01
This research study focused on the factors that enable women of South Asian and African descent to succeed as leaders in the college system. The findings were derived from online questionnaires and in-depth interviews of 16 racialized women from two Greater Toronto Area (GTA) colleges. Many factors and recommendations were shared. Some of the…