Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.

PubMed

Sokoloski, Sacha

2017-09-01

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method.

PubMed

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.

Convergence Rates of Finite Difference Stochastic Approximation Algorithms

DTIC Science & Technology

2016-06-01

dfferences as gradient approximations. It is shown that the convergence of these algorithms can be accelerated by controlling the implementation of the...descent algorithm, under various updating schemes using finite dfferences as gradient approximations. It is shown that the convergence of these...the Kiefer-Wolfowitz algorithm and the mirror descent algorithm, under various updating schemes using finite differences as gradient approximations. It

Stochastic Spectral Descent for Discrete Graphical Models

DOE PAGES

Carlson, David; Hsieh, Ya-Ping; Collins, Edo; ...

2015-12-14

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 asmore » 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.« less

Steepest descent method implementation on unconstrained optimization problem using C++ program

NASA Astrophysics Data System (ADS)

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.

Recursive inverse kinematics for robot arms via Kalman filtering and Bryson-Frazier smoothing

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1987-01-01

This paper applies linear filtering and smoothing theory to solve recursively the inverse kinematics problem for serial multilink manipulators. This problem is to find a set of joint angles that achieve a prescribed tip position and/or orientation. A widely applicable numerical search solution is presented. The approach finds the minimum of a generalized distance between the desired and the actual manipulator tip position and/or orientation. Both a first-order steepest-descent gradient search and a second-order Newton-Raphson search are developed. The optimal relaxation factor required for the steepest descent method is computed recursively using an outward/inward procedure similar to those used typically for recursive inverse dynamics calculations. The second-order search requires evaluation of a gradient and an approximate Hessian. A Gauss-Markov approach is used to approximate the Hessian matrix in terms of products of first-order derivatives. This matrix is inverted recursively using a two-stage process of inward Kalman filtering followed by outward smoothing. This two-stage process is analogous to that recently developed by the author to solve by means of spatial filtering and smoothing the forward dynamics problem for serial manipulators.

Reconstructing the Surface Permittivity Distribution from Data Measured by the CONSERT Instrument aboard Rosetta: Method and Simulations

NASA Astrophysics Data System (ADS)

Plettemeier, D.; Statz, C.; Hegler, S.; Herique, A.; Kofman, W. W.

2014-12-01

One of the main scientific objectives of the Comet Nucleus Sounding Experiment by Radiowave Transmission (CONSERT) aboard Rosetta is to perform a dielectric characterization of comet 67P/Chuyurmov-Gerasimenko's nucleus by means of a bi-static sounding between the lander Philae launched onto the comet's surface and the orbiter Rosetta. For the sounding, the lander part of CONSERT will receive and process the radio signal emitted by the orbiter part of the instrument and transmit a signal to the orbiter to be received by CONSERT. CONSERT will also be operated as bi-static RADAR during the descent of the lander Philae onto the comet's surface. From data measured during the descent, we aim at reconstructing a surface permittivity map of the comet at the landing site and along the path below the descent trajectory. This surface permittivity map will give information on the bulk material right below and around the landing site and the surface roughness in areas covered by the instrument along the descent. The proposed method to estimate the surface permittivity distribution is based on a least-squares based inversion approach in frequency domain. The direct problem of simulating the wave-propagation between lander and orbiter at line-of-sight and the signal reflected on the comet's surface is modelled using a dielectric physical optics approximation. Restrictions on the measurement positions by the descent orbitography and limitations on the instrument dynamic range will be dealt with by application of a regularization technique where the surface permittivity distribution and the gradient with regard to the permittivity is projected in a domain defined by a viable model of the spatial material and roughness distribution. The least-squares optimization step of the reconstruction is performed in such domain on a reduced set of parameters yielding stable results. The viability of the proposed method is demonstrated by reconstruction results based on simulated data.

Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI

PubMed Central

Kamesh Iyer, Srikant; Tasdizen, Tolga; Likhite, Devavrat; DiBella, Edward

2016-01-01

Purpose: Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. Methods: The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. Results: Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. Conclusions: The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly. PMID:27036592

An annealed chaotic maximum neural network for bipartite subgraph problem.

PubMed

Wang, Jiahai; Tang, Zheng; Wang, Ronglong

2004-04-01

In this paper, based on maximum neural network, we propose a new parallel algorithm that can help the maximum neural network escape from local minima by including a transient chaotic neurodynamics for bipartite subgraph problem. The goal of the bipartite subgraph problem, which is an NP- complete problem, is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. Lee et al. presented a parallel algorithm using the maximum neural model (winner-take-all neuron model) for this NP- complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to a local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm. The simulation results show that our algorithm finds the optimum or near-optimum solution for the bipartite subgraph problem superior to that of the best existing parallel algorithms.

Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

Power Control and Optimization of Photovoltaic and Wind Energy Conversion Systems

NASA Astrophysics Data System (ADS)

Ghaffari, Azad

Power map and Maximum Power Point (MPP) of Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) highly depend on system dynamics and environmental parameters, e.g., solar irradiance, temperature, and wind speed. Power optimization algorithms for PV systems and WECS are collectively known as Maximum Power Point Tracking (MPPT) algorithm. Gradient-based Extremum Seeking (ES), as a non-model-based MPPT algorithm, governs the system to its peak point on the steepest descent curve regardless of changes of the system dynamics and variations of the environmental parameters. Since the power map shape defines the gradient vector, then a close estimate of the power map shape is needed to create user assignable transients in the MPPT algorithm. The Hessian gives a precise estimate of the power map in a neighborhood around the MPP. The estimate of the inverse of the Hessian in combination with the estimate of the gradient vector are the key parts to implement the Newton-based ES algorithm. Hence, we generate an estimate of the Hessian using our proposed perturbation matrix. Also, we introduce a dynamic estimator to calculate the inverse of the Hessian which is an essential part of our algorithm. We present various simulations and experiments on the micro-converter PV systems to verify the validity of our proposed algorithm. The ES scheme can also be used in combination with other control algorithms to achieve desired closed-loop performance. The WECS dynamics is slow which causes even slower response time for the MPPT based on the ES. Hence, we present a control scheme, extended from Field-Oriented Control (FOC), in combination with feedback linearization to reduce the convergence time of the closed-loop system. Furthermore, the nonlinear control prevents magnetic saturation of the stator of the Induction Generator (IG). The proposed control algorithm in combination with the ES guarantees the closed-loop system robustness with respect to high level parameter uncertainty in the IG dynamics. The simulation results verify the effectiveness of the proposed algorithm.

Stochastic parallel gradient descent based adaptive optics used for a high contrast imaging coronagraph

NASA Astrophysics Data System (ADS)

Dong, Bing; Ren, De-Qing; Zhang, Xi

2011-08-01

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.

A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations

NASA Astrophysics Data System (ADS)

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.

Nonuniformity correction for an infrared focal plane array based on diamond search block matching.

PubMed

Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian

2016-05-01

In scene-based nonuniformity correction algorithms, artificial ghosting and image blurring degrade the correction quality severely. In this paper, an improved algorithm based on the diamond search block matching algorithm and the adaptive learning rate is proposed. First, accurate transform pairs between two adjacent frames are estimated by the diamond search block matching algorithm. Then, based on the error between the corresponding transform pairs, the gradient descent algorithm is applied to update correction parameters. During the process of gradient descent, the local standard deviation and a threshold are utilized to control the learning rate to avoid the accumulation of matching error. Finally, the nonuniformity correction would be realized by a linear model with updated correction parameters. The performance of the proposed algorithm is thoroughly studied with four real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the nonuniformity with less ghosting artifacts in moving areas and can also overcome the problem of image blurring in static areas.

Preliminary Exploration of Adaptive State Predictor Based Human Operator Modeling

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.

2012-01-01

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.

Error analysis of stochastic gradient descent ranking.

PubMed

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.

Algorithm for Training a Recurrent Multilayer Perceptron

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Rais, Omar T.; Menon, Sunil K.; Atiya, Amir F.

2004-01-01

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

LASER APPLICATIONS AND OTHER TOPICS IN QUANTUM ELECTRONICS: Application of the stochastic parallel gradient descent algorithm for numerical simulation and analysis of the coherent summation of radiation from fibre amplifiers

NASA Astrophysics Data System (ADS)

Zhou, Pu; Wang, Xiaolin; Li, Xiao; Chen, Zilum; Xu, Xiaojun; Liu, Zejin

2009-10-01

Coherent summation of fibre laser beams, which can be scaled to a relatively large number of elements, is simulated by using the stochastic parallel gradient descent (SPGD) algorithm. The applicability of this algorithm for coherent summation is analysed and its optimisaton parameters and bandwidth limitations are studied.

Programmed gradient descent biosorption of strontium ions by Saccaromyces cerevisiae and ashing analysis: A decrement solution for nuclide and heavy metal disposal.

PubMed

Liu, Mingxue; Dong, Faqin; Zhang, Wei; Nie, Xiaoqin; Sun, Shiyong; Wei, Hongfu; Luo, Lang; Xiang, Sha; Zhang, Gege

2016-08-15

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 SrSO4 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. Copyright © 2016 Elsevier B.V. All rights reserved.

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE PAGES

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

2014-12-01

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Approximate N-Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System.

PubMed

Johnson, Marcus; Kamalapurkar, Rushikesh; Bhasin, Shubhendu; Dixon, Warren E

2015-08-01

An approximate online equilibrium solution is developed for an N -player nonzero-sum game subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic cost. A novel actor-critic-identifier structure is used, wherein a robust dynamic neural network is used to asymptotically identify the uncertain system with additive disturbances, and a set of critic and actor NNs are used to approximate the value functions and equilibrium policies, respectively. The weight update laws for the actor neural networks (NNs) are generated using a gradient-descent method, and the critic NNs are generated by least square regression, which are both based on the modified Bellman error that is independent of the system dynamics. A Lyapunov-based stability analysis shows that uniformly ultimately bounded tracking is achieved, and a convergence analysis demonstrates that the approximate control policies converge to a neighborhood of the optimal solutions. The actor, critic, and identifier structures are implemented in real time continuously and simultaneously. Simulations on two and three player games illustrate the performance of the developed method.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Dynamic Aberration Correction for Conformal Window of High-Speed Aircraft Using Optimized Model-Based Wavefront Sensorless Adaptive Optics.

PubMed

Dong, Bing; Li, Yan; Han, Xin-Li; Hu, Bin

2016-09-02

For high-speed aircraft, a conformal window is used to optimize the aerodynamic performance. However, the local shape of the conformal window leads to large amounts of dynamic aberrations varying with look angle. In this paper, deformable mirror (DM) and model-based wavefront sensorless adaptive optics (WSLAO) are used for dynamic aberration correction of an infrared remote sensor equipped with a conformal window and scanning mirror. In model-based WSLAO, aberration is captured using Lukosz mode, and we use the low spatial frequency content of the image spectral density as the metric function. Simulations show that aberrations induced by the conformal window are dominated by some low-order Lukosz modes. To optimize the dynamic correction, we can only correct dominant Lukosz modes and the image size can be minimized to reduce the time required to compute the metric function. In our experiment, a 37-channel DM is used to mimic the dynamic aberration of conformal window with scanning rate of 10 degrees per second. A 52-channel DM is used for correction. For a 128 × 128 image, the mean value of image sharpness during dynamic correction is 1.436 × 10(-5) in optimized correction and is 1.427 × 10(-5) in un-optimized correction. We also demonstrated that model-based WSLAO can achieve convergence two times faster than traditional stochastic parallel gradient descent (SPGD) method.

A multi-resolution approach for optimal mass transport

NASA Astrophysics Data System (ADS)

Dominitz, Ayelet; Angenent, Sigurd; Tannenbaum, Allen

2007-09-01

Optimal mass transport is an important technique with numerous applications in econometrics, fluid dynamics, automatic control, statistical physics, shape optimization, expert systems, and meteorology. Motivated by certain problems in image registration and medical image visualization, in this note, we describe a simple gradient descent methodology for computing the optimal L2 transport mapping which may be easily implemented using a multiresolution scheme. We also indicate how the optimal transport map may be computed on the sphere. A numerical example is presented illustrating our ideas.

Accelerating IMRT optimization by voxel sampling

NASA Astrophysics Data System (ADS)

Martin, Benjamin C.; Bortfeld, Thomas R.; Castañon, David A.

2007-12-01

This paper presents a new method for accelerating intensity-modulated radiation therapy (IMRT) optimization using voxel sampling. Rather than calculating the dose to the entire patient at each step in the optimization, the dose is only calculated for some randomly selected voxels. Those voxels are then used to calculate estimates of the objective and gradient which are used in a randomized version of a steepest descent algorithm. By selecting different voxels on each step, we are able to find an optimal solution to the full problem. We also present an algorithm to automatically choose the best sampling rate for each structure within the patient during the optimization. Seeking further improvements, we experimented with several other gradient-based optimization algorithms and found that the delta-bar-delta algorithm performs well despite the randomness. Overall, we were able to achieve approximately an order of magnitude speedup on our test case as compared to steepest descent.

An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units.

PubMed

Bukovsky, Ivo; Homma, Noriyasu

2017-09-01

Stability evaluation of a weight-update system of higher order neural units (HONUs) with polynomial aggregation of neural inputs (also known as classes of polynomial neural networks) for adaptation of both feedforward and recurrent HONUs by a gradient descent method is introduced. An essential core of the approach is based on the spectral radius of a weight-update system, and it allows stability monitoring and its maintenance at every adaptation step individually. Assuring the stability of the weight-update system (at every single adaptation step) naturally results in the adaptation stability of the whole neural architecture that adapts to the target data. As an aside, the used approach highlights the fact that the weight optimization of HONU is a linear problem, so the proposed approach can be generally extended to any neural architecture that is linear in its adaptable parameters.

Energy minimization in medical image analysis: Methodologies and applications.

PubMed

Zhao, Feng; Xie, Xianghua

2016-02-01

Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well. Copyright © 2015 John Wiley & Sons, Ltd.

A modified form of conjugate gradient method for unconstrained optimization problems

NASA Astrophysics Data System (ADS)

Ghani, Nur Hamizah Abdul; Rivaie, Mohd.; Mamat, Mustafa

2016-06-01

Conjugate gradient (CG) methods have been recognized as an interesting technique to solve optimization problems, due to the numerical efficiency, simplicity and low memory requirements. In this paper, we propose a new CG method based on the study of Rivaie et al. [7] (Comparative study of conjugate gradient coefficient for unconstrained Optimization, Aus. J. Bas. Appl. Sci. 5(2011) 947-951). Then, we show that our method satisfies sufficient descent condition and converges globally with exact line search. Numerical results show that our proposed method is efficient for given standard test problems, compare to other existing CG methods.

Analysis of Online Composite Mirror Descent Algorithm.

PubMed

Lei, Yunwen; Zhou, Ding-Xuan

2017-03-01

We study the convergence of the online composite mirror descent algorithm, which involves a mirror map to reflect the geometry of the data and a convex objective function consisting of a loss and a regularizer possibly inducing sparsity. Our error analysis provides convergence rates in terms of properties of the strongly convex differentiable mirror map and the objective function. For a class of objective functions with Hölder continuous gradients, the convergence rates of the excess (regularized) risk under polynomially decaying step sizes have the order [Formula: see text] after [Formula: see text] iterates. Our results improve the existing error analysis for the online composite mirror descent algorithm by avoiding averaging and removing boundedness assumptions, and they sharpen the existing convergence rates of the last iterate for online gradient descent without any boundedness assumptions. Our methodology mainly depends on a novel error decomposition in terms of an excess Bregman distance, refined analysis of self-bounding properties of the objective function, and the resulting one-step progress bounds.

Dynamic Aberration Correction for Conformal Window of High-Speed Aircraft Using Optimized Model-Based Wavefront Sensorless Adaptive Optics

PubMed Central

Dong, Bing; Li, Yan; Han, Xin-li; Hu, Bin

2016-01-01

For high-speed aircraft, a conformal window is used to optimize the aerodynamic performance. However, the local shape of the conformal window leads to large amounts of dynamic aberrations varying with look angle. In this paper, deformable mirror (DM) and model-based wavefront sensorless adaptive optics (WSLAO) are used for dynamic aberration correction of an infrared remote sensor equipped with a conformal window and scanning mirror. In model-based WSLAO, aberration is captured using Lukosz mode, and we use the low spatial frequency content of the image spectral density as the metric function. Simulations show that aberrations induced by the conformal window are dominated by some low-order Lukosz modes. To optimize the dynamic correction, we can only correct dominant Lukosz modes and the image size can be minimized to reduce the time required to compute the metric function. In our experiment, a 37-channel DM is used to mimic the dynamic aberration of conformal window with scanning rate of 10 degrees per second. A 52-channel DM is used for correction. For a 128 × 128 image, the mean value of image sharpness during dynamic correction is 1.436 × 10−5 in optimized correction and is 1.427 × 10−5 in un-optimized correction. We also demonstrated that model-based WSLAO can achieve convergence two times faster than traditional stochastic parallel gradient descent (SPGD) method. PMID:27598161

Dynamic fuzzy modeling of storm water infiltration in urban fractured aquifers

USGS Publications Warehouse

Hong, Y.-S.; Rosen, Michael R.; Reeves, R.R.

2002-01-01

In an urban fractured-rock aquifer in the Mt. Eden area of Auckland, New Zealand, disposal of storm water is via "soakholes" drilled directly into the top of the fractured basalt rock. The dynamic response of the groundwater level due to the storm water infiltration shows characteristics of a strongly time-varying system. A dynamic fuzzy modeling approach, which is based on multiple local models that are weighted using fuzzy membership functions, has been developed to identify and predict groundwater level fluctuations caused by storm water infiltration. The dynamic fuzzy model is initialized by the fuzzy clustering algorithm and optimized by the gradient-descent algorithm in order to effectively derive the multiple local models-each of which is associated with a locally valid model that represents the groundwater level state as a response to different intensities of rainfall events. The results have shown that even if the number of fuzzy local models derived is small, the fuzzy modeling approach developed provides good prediction results despite the highly time-varying nature of this urban fractured-rock aquifer system. Further, it allows interpretable representations of the dynamic behavior of the groundwater system due to storm water infiltration.

Multi-AUV Target Search Based on Bioinspired Neurodynamics Model in 3-D Underwater Environments.

PubMed

Cao, Xiang; Zhu, Daqi; Yang, Simon X

2016-11-01

Target search in 3-D underwater environments is a challenge in multiple autonomous underwater vehicles (multi-AUVs) exploration. This paper focuses on an effective strategy for multi-AUV target search in the 3-D underwater environments with obstacles. First, the Dempster-Shafer theory of evidence is applied to extract information of environment from the sonar data to build a grid map of the underwater environments. Second, a topologically organized bioinspired neurodynamics model based on the grid map is constructed to represent the dynamic environment. The target globally attracts the AUVs through the dynamic neural activity landscape of the model, while the obstacles locally push the AUVs away to avoid collision. Finally, the AUVs plan their search path to the targets autonomously by a steepest gradient descent rule. The proposed algorithm deals with various situations, such as static targets search, dynamic targets search, and one or several AUVs break down in the 3-D underwater environments with obstacles. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve search task of multiple targets with higher efficiency and adaptability compared with other algorithms.

14 CFR 23.69 - Enroute climb/descent.

Code of Federal Regulations, 2013 CFR

2013-01-01

... inoperative and its propeller in the minimum drag position; (2) The remaining engine(s) at not more than... climb/descent. (a) All engines operating. The steady gradient and rate of climb must be determined at... applicant with— (1) Not more than maximum continuous power on each engine; (2) The landing gear retracted...

14 CFR 23.69 - Enroute climb/descent.

Code of Federal Regulations, 2014 CFR

2014-01-01

... inoperative and its propeller in the minimum drag position; (2) The remaining engine(s) at not more than... climb/descent. (a) All engines operating. The steady gradient and rate of climb must be determined at... applicant with— (1) Not more than maximum continuous power on each engine; (2) The landing gear retracted...

14 CFR 23.69 - Enroute climb/descent.

Code of Federal Regulations, 2012 CFR

2012-01-01

... inoperative and its propeller in the minimum drag position; (2) The remaining engine(s) at not more than... climb/descent. (a) All engines operating. The steady gradient and rate of climb must be determined at... applicant with— (1) Not more than maximum continuous power on each engine; (2) The landing gear retracted...

14 CFR 23.69 - Enroute climb/descent.

Code of Federal Regulations, 2011 CFR

2011-01-01

... inoperative and its propeller in the minimum drag position; (2) The remaining engine(s) at not more than... climb/descent. (a) All engines operating. The steady gradient and rate of climb must be determined at... applicant with— (1) Not more than maximum continuous power on each engine; (2) The landing gear retracted...

Planning fuel-conservative descents in an airline environmental using a small programmable calculator: Algorithm development and flight test results

NASA Technical Reports Server (NTRS)

Knox, C. E.; Vicroy, D. D.; Simmon, D. A.

1985-01-01

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.

Planning fuel-conservative descents in an airline environmental using a small programmable calculator: algorithm development and flight test results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Knox, C.E.; Vicroy, D.D.; Simmon, D.A.

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, andmore » 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.« less

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Fractional-order gradient descent learning of BP neural networks with Caputo derivative.

PubMed

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.

Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation.

PubMed

Gao, Shan; Ye, Qixiang; Xing, Junliang; Kuijper, Arjan; Han, Zhenjun; Jiao, Jianbin; Ji, Xiangyang

2017-12-01

Tracking multiple persons is a challenging task when persons move in groups and occlude each other. Existing group-based methods have extensively investigated how to make group division more accurately in a tracking-by-detection framework; however, few of them quantify the group dynamics from the perspective of targets' spatial topology or consider the group in a dynamic view. Inspired by the sociological properties of pedestrians, we propose a novel socio-topology model with a topology-energy function to factor the group dynamics of moving persons and groups. In this model, minimizing the topology-energy-variance in a two-level energy form is expected to produce smooth topology transitions, stable group tracking, and accurate target association. To search for the strong minimum in energy variation, we design the discrete group-tracklet jump moves embedded in the gradient descent method, which ensures that the moves reduce the energy variation of group and trajectory alternately in the varying topology dimension. Experimental results on both RGB and RGB-D data sets show the superiority of our proposed model for multiple person tracking in crowd scenes.

Spatiotemporal topology and temporal sequence identification with an adaptive time-delay neural network

NASA Astrophysics Data System (ADS)

Lin, Daw-Tung; Ligomenides, Panos A.; Dayhoff, Judith E.

1993-08-01

Inspired from the time delays that occur in neurobiological signal transmission, we describe an adaptive time delay neural network (ATNN) which is a powerful dynamic learning technique for spatiotemporal pattern transformation and temporal sequence identification. The dynamic properties of this network are formulated through the adaptation of time-delays and synapse weights, which are adjusted on-line based on gradient descent rules according to the evolution of observed inputs and outputs. We have applied the ATNN to examples that possess spatiotemporal complexity, with temporal sequences that are completed by the network. The ATNN is able to be applied to pattern completion. Simulation results show that the ATNN learns the topology of a circular and figure eight trajectories within 500 on-line training iterations, and reproduces the trajectory dynamically with very high accuracy. The ATNN was also trained to model the Fourier series expansion of the sum of different odd harmonics. The resulting network provides more flexibility and efficiency than the TDNN and allows the network to seek optimal values for time-delays as well as optimal synapse weights.

Shape regularized active contour based on dynamic programming for anatomical structure segmentation

NASA Astrophysics Data System (ADS)

Yu, Tianli; Luo, Jiebo; Singhal, Amit; Ahuja, Narendra

2005-04-01

We present a method to incorporate nonlinear shape prior constraints into segmenting different anatomical structures in medical images. Kernel space density estimation (KSDE) is used to derive the nonlinear shape statistics and enable building a single model for a class of objects with nonlinearly varying shapes. The object contour is coerced by image-based energy into the correct shape sub-distribution (e.g., left or right lung), without the need for model selection. In contrast to an earlier algorithm that uses a local gradient-descent search (susceptible to local minima), we propose an algorithm that iterates between dynamic programming (DP) and shape regularization. DP is capable of finding an optimal contour in the search space that maximizes a cost function related to the difference between the interior and exterior of the object. To enforce the nonlinear shape prior, we propose two shape regularization methods, global and local regularization. Global regularization is applied after each DP search to move the entire shape vector in the shape space in a gradient descent fashion to the position of probable shapes learned from training. The regularized shape is used as the starting shape for the next iteration. Local regularization is accomplished through modifying the search space of the DP. The modified search space only allows a certain amount of deformation of the local shape from the starting shape. Both regularization methods ensure the consistency between the resulted shape with the training shapes, while still preserving DP"s ability to search over a large range and avoid local minima. Our algorithm was applied to two different segmentation tasks for radiographic images: lung field and clavicle segmentation. Both applications have shown that our method is effective and versatile in segmenting various anatomical structures under prior shape constraints; and it is robust to noise and local minima caused by clutter (e.g., blood vessels) and other similar structures (e.g., ribs). We believe that the proposed algorithm represents a major step in the paradigm shift to object segmentation under nonlinear shape constraints.

A new smoothing modified three-term conjugate gradient method for [Formula: see text]-norm minimization problem.

PubMed

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.

Acceleration of Convergence to Equilibrium in Markov Chains by Breaking Detailed Balance

NASA Astrophysics Data System (ADS)

Kaiser, Marcus; Jack, Robert L.; Zimmer, Johannes

2017-07-01

We analyse and interpret the effects of breaking detailed balance on the convergence to equilibrium of conservative interacting particle systems and their hydrodynamic scaling limits. For finite systems of interacting particles, we review existing results showing that irreversible processes converge faster to their steady state than reversible ones. We show how this behaviour appears in the hydrodynamic limit of such processes, as described by macroscopic fluctuation theory, and we provide a quantitative expression for the acceleration of convergence in this setting. We give a geometrical interpretation of this acceleration, in terms of currents that are antisymmetric under time-reversal and orthogonal to the free energy gradient, which act to drive the system away from states where (reversible) gradient-descent dynamics result in slow convergence to equilibrium.

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

PubMed

Miller, Vonda H; Jansen, Ben H

2008-12-01

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

Accurate modeling of switched reluctance machine based on hybrid trained WNN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shoujun, E-mail: sunnyway@nwpu.edu.cn; Ge, Lefei; Ma, Shaojie

2014-04-15

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, themore » nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.« less

Evaluation of pelvic descent disorders by dynamic contrast roentgenography.

PubMed

Takano, M; Hamada, A

2000-10-01

For precise diagnosis and rational treatment of the increasing number of patients with descent of intrapelvic organ(s) and anatomic plane(s), dynamic contrast roentgenography of multiple intrapelvic organs and planes is described. Sixty-six patients, consisting of 11 males, with a mean age (+/- standard deviation) of 65.6+/-14.2 years and with chief complaints of intrapelvic organ and perineal descent or defecation problems, were examined in this study. Dynamic contrast roentgenography was obtained by opacifying the ileum, urinary bladder, vagina, rectum, and the perineum. Films were taken at both squeeze and strain phases. On the films the lowest points of each organ and plane were plotted, and the distances from the standard line drawn at the upper surface of the sacrum were measured. The values were corrected to percentages according to the height of the sacrococcygeal bone of each patient. From these corrected values, organ or plane descents at strain and squeeze were diagnosed and graphically demonstrated as a descentgram in each patient. Among 17 cases with subjective symptoms of bladder descent, 9 cases (52.9 percent) showed roentgenographic descent. By the same token, among the cases with subjective feeling of descent of the vagina, uterus, peritoneum, perineum, rectum, and anus, roentgenographic descent was confirmed in 15 of 20 (75 percent), 7 of 9 (77.8 percent), 6 of 16 (37.5 percent), 33 of 33 (100 percent), 25 of 37 (67.6 percent), and 22 of 36 (61.6 percent), respectively. The descentgrams were divided into three patterns: anorectal descent type, female genital descent type, and total organ descent type. Dynamic contrast roentgenography and successive descentgraphy of multiple intrapelvic organs and planes are useful for objective diagnosis and rational treatment of patients with descent disorders of the intrapelvic organ(s) and plane(s).

An online supervised learning method based on gradient descent for spiking neurons.

PubMed

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.

Mars Smart Lander Simulations for Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

Striepe, S. A.; Way, D. W.; Balaram, J.

2002-01-01

Two primary simulations have been developed and are being updated for the Mars Smart Lander Entry, Descent, and Landing (EDL). The high fidelity engineering end-to-end EDL simulation that is based on NASA Langley's Program to Optimize Simulated Trajectories (POST) and the end-to-end real-time, hardware-in-the-loop simulation testbed, which is based on NASA JPL's (Jet Propulsion Laboratory) Dynamics Simulator for Entry, Descent and Surface landing (DSENDS). This paper presents the status of these Mars Smart Lander EDL end-to-end simulations at this time. Various models, capabilities, as well as validation and verification for these simulations are discussed.

Analysis of various descent trajectories for a hypersonic-cruise, cold-wall research airplane

NASA Technical Reports Server (NTRS)

Lawing, P. L.

1975-01-01

The probable descent operating conditions for a hypersonic air-breathing research airplane were examined. Descents selected were cruise angle of attack, high dynamic pressure, high lift coefficient, turns, and descents with drag brakes. The descents were parametrically exercised and compared from the standpoint of cold-wall (367 K) aircraft heat load. The descent parameters compared were total heat load, peak heating rate, time to landing, time to end of heat pulse, and range. Trends in total heat load as a function of cruise Mach number, cruise dynamic pressure, angle-of-attack limitation, pull-up g-load, heading angle, and drag-brake size are presented.

Hybrid DFP-CG method for solving unconstrained optimization problems

NASA Astrophysics Data System (ADS)

Osman, Wan Farah Hanan Wan; Asrul Hery Ibrahim, Mohd; Mamat, Mustafa

2017-09-01

The conjugate gradient (CG) method and quasi-Newton method are both well known method for solving unconstrained optimization method. In this paper, we proposed a new method by combining the search direction between conjugate gradient method and quasi-Newton method based on BFGS-CG method developed by Ibrahim et al. The Davidon-Fletcher-Powell (DFP) update formula is used as an approximation of Hessian for this new hybrid algorithm. Numerical result showed that the new algorithm perform well than the ordinary DFP method and proven to posses both sufficient descent and global convergence properties.

14 CFR 23.75 - Landing distance.

Code of Federal Regulations, 2010 CFR

2010-01-01

... to the 50 foot height and— (1) The steady approach must be at a gradient of descent not greater than... tests that a maximum steady approach gradient steeper than 5.2 percent, down to the 50-foot height, is safe. The gradient must be established as an operating limitation and the information necessary to...

Direct Temperature Measurements during Netlander Descent on Mars

NASA Astrophysics Data System (ADS)

Colombatti, G.; Angrilli, F.; Ferri, F.; Francesconi, A.; Fulchignoni, M.; Lion Stoppato, P. F.; Saggi, B.

1999-09-01

A new design for a platinum thermoresistance temperature sensor has been developed and tested in Earth's atmosphere and stratosphere. It will be one of the sensors equipping the scientific package ATMIS (Atmospheric and Meteorology Instrument System), which will be devoted to the measurement of the meteorological parameters during both the entry/descent phase and the surface phase, aboard the Netlanders. In particular vertical profiles of temperature, density and pressure will allow the resolution of vertical gradients to investigate the atmospheric structure and dynamics. In view of the future missions to Mars, Netlander represents a unique chance to increase significantly the climate record both in time and in space, doubling the current knowledge of the atmospheric parameters. Furthermore is the only opportunity to conduct direct measurement of temperature and pressure (outside the boundary layer of the airbags used for the landing). The temperature sensor proposed is a platinum thermoresistance, enhancement of HASI TEM (Cassini/Huygens Mission); a substantial improvement of the performances, i.e. a faster dynamic response, has been obtained. Two different prototypes of new design sensor have been built, laboratory test are proceeding and the second one has been already flown aboard a stratospheric balloon.

Adaptive filter design using recurrent cerebellar model articulation controller.

PubMed

Lin, Chih-Min; Chen, Li-Yang; Yeung, Daniel S

2010-07-01

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

Statistical Mechanics of Node-perturbation Learning with Noisy Baseline

NASA Astrophysics Data System (ADS)

Hara, Kazuyuki; Katahira, Kentaro; Okada, Masato

2017-02-01

Node-perturbation learning is a type of statistical gradient descent algorithm that can be applied to problems where the objective function is not explicitly formulated, including reinforcement learning. It estimates the gradient of an objective function by using the change in the object function in response to the perturbation. The value of the objective function for an unperturbed output is called a baseline. Cho et al. proposed node-perturbation learning with a noisy baseline. In this paper, we report on building the statistical mechanics of Cho's model and on deriving coupled differential equations of order parameters that depict learning dynamics. We also show how to derive the generalization error by solving the differential equations of order parameters. On the basis of the results, we show that Cho's results are also apply in general cases and show some general performances of Cho's model.

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

PubMed

Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

2016-02-01

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming.

PubMed

Oost, Elco; Koning, Gerhard; Sonka, Milan; Oemrawsingh, Pranobe V; Reiber, Johan H C; Lelieveldt, Boudewijn P F

2006-09-01

This paper describes a new approach to the automated segmentation of X-ray left ventricular (LV) angiograms, based on active appearance models (AAMs) and dynamic programming. A coupling of shape and texture information between the end-diastolic (ED) and end-systolic (ES) frame was achieved by constructing a multiview AAM. Over-constraining of the model was compensated for by employing dynamic programming, integrating both intensity and motion features in the cost function. Two applications are compared: a semi-automatic method with manual model initialization, and a fully automatic algorithm. The first proved to be highly robust and accurate, demonstrating high clinical relevance. Based on experiments involving 70 patient data sets, the algorithm's success rate was 100% for ED and 99% for ES, with average unsigned border positioning errors of 0.68 mm for ED and 1.45 mm for ES. Calculated volumes were accurate and unbiased. The fully automatic algorithm, with intrinsically less user interaction was less robust, but showed a high potential, mostly due to a controlled gradient descent in updating the model parameters. The success rate of the fully automatic method was 91% for ED and 83% for ES, with average unsigned border positioning errors of 0.79 mm for ED and 1.55 mm for ES.

Compensation of significant parametric uncertainties using sliding mode online learning

NASA Astrophysics Data System (ADS)

Schnetter, Philipp; Kruger, Thomas

An augmented nonlinear inverse dynamics (NID) flight control strategy using sliding mode online learning for a small unmanned aircraft system (UAS) is presented. Because parameter identification for this class of aircraft often is not valid throughout the complete flight envelope, aerodynamic parameters used for model based control strategies may show significant deviations. For the concept of feedback linearization this leads to inversion errors that in combination with the distinctive susceptibility of small UAS towards atmospheric turbulence pose a demanding control task for these systems. In this work an adaptive flight control strategy using feedforward neural networks for counteracting such nonlinear effects is augmented with the concept of sliding mode control (SMC). SMC-learning is derived from variable structure theory. It considers a neural network and its training as a control problem. It is shown that by the dynamic calculation of the learning rates, stability can be guaranteed and thus increase the robustness against external disturbances and system failures. With the resulting higher speed of convergence a wide range of simultaneously occurring disturbances can be compensated. The SMC-based flight controller is tested and compared to the standard gradient descent (GD) backpropagation algorithm under the influence of significant model uncertainties and system failures.

Individual predictions of eye-movements with dynamic scenes

NASA Astrophysics Data System (ADS)

Barth, Erhardt; Drewes, Jan; Martinetz, Thomas

2003-06-01

We present a model that predicts saccadic eye-movements and can be tuned to a particular human observer who is viewing a dynamic sequence of images. Our work is motivated by applications that involve gaze-contingent interactive displays on which information is displayed as a function of gaze direction. The approach therefore differs from standard approaches in two ways: (1) we deal with dynamic scenes, and (2) we provide means of adapting the model to a particular observer. As an indicator for the degree of saliency we evaluate the intrinsic dimension of the image sequence within a geometric approach implemented by using the structure tensor. Out of these candidate saliency-based locations, the currently attended location is selected according to a strategy found by supervised learning. The data are obtained with an eye-tracker and subjects who view video sequences. The selection algorithm receives candidate locations of current and past frames and a limited history of locations attended in the past. We use a linear mapping that is obtained by minimizing the quadratic difference between the predicted and the actually attended location by gradient descent. Being linear, the learned mapping can be quickly adapted to the individual observer.

A conjugate gradient method with descent properties under strong Wolfe line search

NASA Astrophysics Data System (ADS)

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.

A modified three-term PRP conjugate gradient algorithm for optimization models.

PubMed

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.

Reduced-gravity Testing of The Huygens Probe Ssp Tiltmeter and Hasi Accelerometer Sensors and Their Role In Reconstruction of The Probe Descent Dynamics

NASA Astrophysics Data System (ADS)

Ghafoor, N.; Zarnecki, J.

When the ESA Huygens Probe arrives at Titan in 2005, measurements taken during and after the descent through the atmosphere are likely to revolutionise our under- standing of SaturnSs most enigmatic moon. The accurate atmospheric profiling of Titan from these measurements will require knowledge of the probe descent trajectory and in some cases attitude history, whilst certain atmospheric information (e.g. wind speeds) may be inferred directly from the probe dynamics during descent. Two of the instruments identified as contributing valuable information for the reconstruction of the probeSs parachute descent dynamics are the Surface Science Package Tilt sensor (SSP-TIL) and the Huygens Atmospheric Structure Instrument servo accelerometer (HASI-ACC). This presentation provides an overview of these sensors and their static calibration before describing an investigation into their real-life dynamic performance under simulated Titan-gravity conditions via a low-cost parabolic flight opportunity. The combined use of SSP-TIL and HASI-ACC in characterising the aircraft dynam- ics is also demonstrated and some important challenges are highlighted. Results from some simple spin tests are also presented. Finally, having validated the performance of the sensors under simulated Titan conditions, estimates are made as to the output of SSP-TIL and HASI-ACC under a variety of probe dynamics, ranging from verti- cal descent with spin to a simple 3 degree-of-freedom parachute descent model with horizontal gusting. It is shown how careful consideration must be given to the instru- mentsS principles of operation in each case, and also the impact of the sampling rates and resolutions as selected for the Huygens mission. The presentation concludes with a discussion of ongoing work on more advanced descent modelling and surface dy- namics modelling, and also of a proposal for the testing of the sensors on a sea-surface.

Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai

2017-03-01

This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.

Aircraft Vortex Wake Descent and Decay under Real Atmospheric Effects

DOT National Transportation Integrated Search

1973-10-01

Aircraft vortex wake descent and decay in a real atmosphere is studied analytically. Factors relating to encounter hazard, wake generation, wake descent and stability, and atmospheric dynamics are considered. Operational equations for encounter hazar...

Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis

NASA Astrophysics Data System (ADS)

Jia, Ningning; Y Lam, Edmund

2010-04-01

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 k1 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.

Computational trigonometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gustafson, K.

1994-12-31

By means of the author`s earlier theory of antieigenvalues and antieigenvectors, a new computational approach to iterative methods is presented. This enables an explicit trigonometric understanding of iterative convergence and provides new insights into the sharpness of error bounds. Direct applications to Gradient descent, Conjugate gradient, GCR(k), Orthomin, CGN, GMRES, CGS, and other matrix iterative schemes will be given.

On the boundary conditions on a shock wave for hypersonic flow around a descent vehicle

NASA Astrophysics Data System (ADS)

Golomazov, M. M.; Ivankov, A. A.

2013-12-01

Stationary hypersonic flow around a descent vehicle is examined by considering equilibrium and nonequilibrium reactions. We study how physical-chemical processes and shock wave conditions for gas species influence the shock-layer structure. It is shown that conservation conditions of species on the shock wave cause high-temperature and concentration gradients in the shock layer when we calculate spacecraft deceleration trajectory in the atmosphere at 75 km altitude.

Dynamics of the Venera 13 and 14 descent modules in the parachute segment of descent

NASA Astrophysics Data System (ADS)

Vishniak, A. A.; Kariagin, V. P.; Kovtunenko, V. M.; Kotov, B. B.; Kuznetsov, V. V.; Lopatkin, A. I.; Perov, O. V.; Pichkhadze, K. M.; Rysev, O. V.

1983-05-01

The parachute system for the Venera 13 and 14 descent modules was designed to assure the prescribed duration of descent in the Venus cloud layer as well as the separation of heat-shield elements from the module. A mathematical model is developed which makes possible a numerical analysis of the dynamics of the module-parachute system with allowance for parachute inertia, atmospheric turbulence, the means by which the parachute is attachead to the module, and the elasticity and damping of the suspended system. A formula is derived for determining the period of oscillations of the module in the parachute segment of descent. A comparison of theoretical and experimental results shows that this formula can be used in the design calculations, especially at the early stage of module development.

Dynamic ground-effect measurements on the F-15 STOL and Maneuver Technology Demonstrator (S/MTD) configuration

NASA Technical Reports Server (NTRS)

Kemmerly, Guy T.

1990-01-01

A moving-model ground-effect testing method was used to study the influence of rate-of-descent on the aerodynamic characteristics for the F-15 STOL and Maneuver Technology Demonstrator (S/MTD) configuration for both the approach and roll-out phases of landing. The approach phase was modeled for three rates of descent, and the results were compared to the predictions from the F-15 S/MTD simulation data base (prediction based on data obtained in a wind tunnel with zero rate of descent). This comparison showed significant differences due both to the rate of descent in the moving-model test and to the presence of the ground boundary layer in the wind tunnel test. Relative to the simulation data base predictions, the moving-model test showed substantially less lift increase in ground effect, less nose-down pitching moment, and less increase in drag. These differences became more prominent at the larger thrust vector angles. Over the small range of rates of descent tested using the moving-model technique, the effect of rate of descent on longitudinal aerodynamics was relatively constant. The results of this investigation indicate no safety-of-flight problems with the lower jets vectored up to 80 deg on approach. The results also indicate that this configuration could employ a nozzle concept using lower reverser vector angles up to 110 deg on approach if a no-flare approach procedure were adopted and if inlet reingestion does not pose a problem.

14 CFR 23.75 - Landing distance.

Code of Federal Regulations, 2012 CFR

2012-01-01

... to the 50 foot height and— (1) The steady approach must be at a gradient of descent not greater than 5.2 percent (3 degrees) down to the 50-foot height. (2) In addition, an applicant may demonstrate by tests that a maximum steady approach gradient steeper than 5.2 percent, down to the 50-foot height, is...

14 CFR 23.75 - Landing distance.

Code of Federal Regulations, 2014 CFR

2014-01-01

... to the 50 foot height and— (1) The steady approach must be at a gradient of descent not greater than 5.2 percent (3 degrees) down to the 50-foot height. (2) In addition, an applicant may demonstrate by tests that a maximum steady approach gradient steeper than 5.2 percent, down to the 50-foot height, is...

14 CFR 23.75 - Landing distance.

Code of Federal Regulations, 2013 CFR

2013-01-01

... to the 50 foot height and— (1) The steady approach must be at a gradient of descent not greater than 5.2 percent (3 degrees) down to the 50-foot height. (2) In addition, an applicant may demonstrate by tests that a maximum steady approach gradient steeper than 5.2 percent, down to the 50-foot height, is...

Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent.

PubMed

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.

Ptychographic overlap constraint errors and the limits of their numerical recovery using conjugate gradient descent methods.

PubMed

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.

Coordinated Beamforming for MISO Interference Channel: Complexity Analysis and Efficient Algorithms

DTIC Science & Technology

2010-01-01

Algorithm The cyclic coordinate descent algorithm is also known as the nonlinear Gauss - Seidel iteration [32]. There are several studies of this type of...vkρ(vi−1). It can be shown that the above BB gradient projection direction is always a descent direction. The R-linear convergence of the BB method has...KKT solution ) of the inexact pricing algorithm for MISO interference channel. The latter is interesting since the convergence of the original pricing

Correlation Between Echodefecography and 3-Dimensional Vaginal Ultrasonography in the Detection of Perineal Descent in Women With Constipation Symptoms.

PubMed

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 control group without symptoms. Three-dimensional endovaginal ultrasonography is a reliable technique for assessment of perineal descent. Using this technique, excessive perineal descent can be defined as displacement of the anorectal junction >1 cm and/or its position below the symphysis pubis on Valsalva maneuver.

Limited-memory fast gradient descent method for graph regularized nonnegative matrix factorization.

PubMed

Guan, Naiyang; Wei, Lei; Luo, Zhigang; Tao, Dacheng

2013-01-01

Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix X[Symbol:see text]R(m x n) to the product of two lower-rank nonnegative factor matrices, i.e.,W[Symbol:see text]R(m x r) and H[Symbol:see text]R(r x n) (r < min {m,n}) and aims to preserve the local geometric structure of the dataset by minimizing squared Euclidean distance or Kullback-Leibler (KL) divergence between X and WH. The multiplicative update rule (MUR) is usually applied to optimize GNMF, but it suffers from the drawback of slow-convergence because it intrinsically advances one step along the rescaled negative gradient direction with a non-optimal step size. Recently, a multiple step-sizes fast gradient descent (MFGD) method has been proposed for optimizing NMF which accelerates MUR by searching the optimal step-size along the rescaled negative gradient direction with Newton's method. However, the computational cost of MFGD is high because 1) the high-dimensional Hessian matrix is dense and costs too much memory; and 2) the Hessian inverse operator and its multiplication with gradient cost too much time. To overcome these deficiencies of MFGD, we propose an efficient limited-memory FGD (L-FGD) method for optimizing GNMF. In particular, we apply the limited-memory BFGS (L-BFGS) method to directly approximate the multiplication of the inverse Hessian and the gradient for searching the optimal step size in MFGD. The preliminary results on real-world datasets show that L-FGD is more efficient than both MFGD and MUR. To evaluate the effectiveness of L-FGD, we validate its clustering performance for optimizing KL-divergence based GNMF on two popular face image datasets including ORL and PIE and two text corpora including Reuters and TDT2. The experimental results confirm the effectiveness of L-FGD by comparing it with the representative GNMF solvers.

A three-term conjugate gradient method under the strong-Wolfe line search

NASA Astrophysics Data System (ADS)

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.

A modified conjugate gradient coefficient with inexact line search for unconstrained optimization

NASA Astrophysics Data System (ADS)

Aini, Nurul; Rivaie, Mohd; Mamat, Mustafa

2016-11-01

Conjugate gradient (CG) method is a line search algorithm mostly known for its wide application in solving unconstrained optimization problems. Its low memory requirements and global convergence properties makes it one of the most preferred method in real life application such as in engineering and business. In this paper, we present a new CG method based on AMR* and CD method for solving unconstrained optimization functions. The resulting algorithm is proven to have both the sufficient descent and global convergence properties under inexact line search. Numerical tests are conducted to assess the effectiveness of the new method in comparison to some previous CG methods. The results obtained indicate that our method is indeed superior.

Groundwater Productivity and Quality of The Quartzite Ridge of RÓdA~o and Their Vicinities (center of Portugal)

NASA Astrophysics Data System (ADS)

Duque, J.; Chambel, A.

When the ESA Huygens Probe arrives at Titan in 2005, measurements taken during and after the descent through the atmosphere are likely to revolutionise our under- standing of SaturnSs most enigmatic moon. The accurate atmospheric profiling of Titan from these measurements will require knowledge of the probe descent trajectory and in some cases attitude history, whilst certain atmospheric information (e.g. wind speeds) may be inferred directly from the probe dynamics during descent. Two of the instruments identified as contributing valuable information for the reconstruction of the probeSs parachute descent dynamics are the Surface Science Package Tilt sensor (SSP-TIL) and the Huygens Atmospheric Structure Instrument servo accelerometer (HASI-ACC). This presentation provides an overview of these sensors and their static calibration before describing an investigation into their real-life dynamic performance under simulated Titan-gravity conditions via a low-cost parabolic flight opportunity. The combined use of SSP-TIL and HASI-ACC in characterising the aircraft dynam- ics is also demonstrated and some important challenges are highlighted. Results from some simple spin tests are also presented. Finally, having validated the performance of the sensors under simulated Titan conditions, estimates are made as to the output of SSP-TIL and HASI-ACC under a variety of probe dynamics, ranging from verti- cal descent with spin to a simple 3 degree-of-freedom parachute descent model with horizontal gusting. It is shown how careful consideration must be given to the instru- mentsS principles of operation in each case, and also the impact of the sampling rates and resolutions as selected for the Huygens mission. The presentation concludes with a discussion of ongoing work on more advanced descent modelling and surface dy- namics modelling, and also of a proposal for the testing of the sensors on a sea-surface.

Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

PubMed

Mostafa, Hesham

2017-08-01

Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

Evaluation of vertical profiles to design continuous descent approach procedure

NASA Astrophysics Data System (ADS)

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 uncertainties in external factors. Analysis from operational feasibility perspective suggests that two key features of the performance based Flight Management System (FMS) i.e. required time of arrival (RTA) and geometric descent path would help in reduction of unpredictability associated with arrival time and vertical profile of aircraft guided by the FMS coupled with auto-pilot (AP) and auto-throttle (AT). The statistical analysis of the vertical profiles of CDA also suggests that for procedure design window type, 'AT or above' and 'AT or below' altitude and FPA constraints are more realistic and useful compared to obsolete 'AT' type altitude constraint.

SYNCHRONIZATION OF HETEROGENEOUS OSCILLATORS UNDER NETWORK MODIFICATIONS: PERTURBATION AND OPTIMIZATION OF THE SYNCHRONY ALIGNMENT FUNCTION

PubMed Central

Taylor, Dane; Skardal, Per Sebastian; Sun, Jie

2016-01-01

Synchronization is central to many complex systems in engineering physics (e.g., the power-grid, Josephson junction circuits, and electro-chemical oscillators) and biology (e.g., neuronal, circadian, and cardiac rhythms). Despite these widespread applications—for which proper functionality depends sensitively on the extent of synchronization—there remains a lack of understanding for how systems can best evolve and adapt to enhance or inhibit synchronization. We study how network modifications affect the synchronization properties of network-coupled dynamical systems that have heterogeneous node dynamics (e.g., phase oscillators with non-identical frequencies), which is often the case for real-world systems. Our approach relies on a synchrony alignment function (SAF) that quantifies the interplay between heterogeneity of the network and of the oscillators and provides an objective measure for a system’s ability to synchronize. We conduct a spectral perturbation analysis of the SAF for structural network modifications including the addition and removal of edges, which subsequently ranks the edges according to their importance to synchronization. Based on this analysis, we develop gradient-descent algorithms to efficiently solve optimization problems that aim to maximize phase synchronization via network modifications. We support these and other results with numerical experiments. PMID:27872501

Self-Organizing Hidden Markov Model Map (SOHMMM).

PubMed

Ferles, Christos; Stafylopatis, Andreas

2013-12-01

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent.

PubMed

Yao, Rui; Templeton, Alistair K; Liao, Yixiang; Turian, Julius V; Kiel, Krystyna D; Chu, James C H

2014-01-01

To validate an in-house optimization program that uses adaptive simulated annealing (ASA) and gradient descent (GD) algorithms and investigate features of physical dose and generalized equivalent uniform dose (gEUD)-based objective functions in high-dose-rate (HDR) brachytherapy for cervical cancer. Eight Syed/Neblett template-based cervical cancer HDR interstitial brachytherapy cases were used for this study. Brachytherapy treatment plans were first generated using inverse planning simulated annealing (IPSA). Using the same dwell positions designated in IPSA, plans were then optimized with both physical dose and gEUD-based objective functions, using both ASA and GD algorithms. Comparisons were made between plans both qualitatively and based on dose-volume parameters, evaluating each optimization method and objective function. A hybrid objective function was also designed and implemented in the in-house program. The ASA plans are higher on bladder V75% and D2cc (p=0.034) and lower on rectum V75% and D2cc (p=0.034) than the IPSA plans. The ASA and GD plans are not significantly different. The gEUD-based plans have higher homogeneity index (p=0.034), lower overdose index (p=0.005), and lower rectum gEUD and normal tissue complication probability (p=0.005) than the physical dose-based plans. The hybrid function can produce a plan with dosimetric parameters between the physical dose-based and gEUD-based plans. The optimized plans with the same objective value and dose-volume histogram could have different dose distributions. Our optimization program based on ASA and GD algorithms is flexible on objective functions, optimization parameters, and can generate optimized plans comparable with IPSA. Copyright © 2014 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography.

PubMed

Matthews, Thomas P; Wang, Kun; Li, Cuiping; Duric, Neb; Anastasio, Mark A

2017-05-01

Ultrasound computed tomography (USCT) holds great promise for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique has been combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This limitation can be overcome by replacing SGD with a structured optimization method, such as the regularized dual averaging method, that exploits knowledge of the composition of the cost function. In this paper, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing nonsmooth regularization penalties to be naturally incorporated. The wave equation is solved by the use of a time-domain method. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by the use of SGD.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.

PubMed

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.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models

PubMed Central

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)β k ≥ 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. PMID:26502409

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Y. M., E-mail: ymingy@gmail.com; Bednarz, B.; Svatos, M.

Purpose: The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and “4π” delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship withinmore » a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of “concurrent” Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner. Methods: The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively. Results: The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7–8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform. Conclusions: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.« less

Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

PubMed Central

Svatos, M.; Zankowski, C.; Bednarz, B.

2016-01-01

Purpose: The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and “4π” delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship within a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of “concurrent” Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner. Methods: The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively. Results: The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7–8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform. Conclusions: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead. PMID:27277051

Optimal control of switching time in switched stochastic systems with multi-switching times and different costs

NASA Astrophysics Data System (ADS)

Liu, Xiaomei; Li, Shengtao; Zhang, Kanjian

2017-08-01

In this paper, we solve an optimal control problem for a class of time-invariant switched stochastic systems with multi-switching times, where the objective is to minimise a cost functional with different costs defined on the states. In particular, we focus on problems in which a pre-specified sequence of active subsystems is given and the switching times are the only control variables. Based on the calculus of variation, we derive the gradient of the cost functional with respect to the switching times on an especially simple form, which can be directly used in gradient descent algorithms to locate the optimal switching instants. Finally, a numerical example is given, highlighting the validity of the proposed methodology.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

NASA Astrophysics Data System (ADS)

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.

Adaptive ISAR Imaging of Maneuvering Targets Based on a Modified Fourier Transform.

PubMed

Wang, Binbin; Xu, Shiyou; Wu, Wenzhen; Hu, Pengjiang; Chen, Zengping

2018-04-27

Focusing on the inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, this paper presents a new imaging method which works well when the target's maneuvering is not too severe. After translational motion compensation, we describe the equivalent rotation of maneuvering targets by two variables-the relative chirp rate of the linear frequency modulated (LFM) signal and the Doppler focus shift. The first variable indicates the target's motion status, and the second one represents the possible residual error of the translational motion compensation. With them, a modified Fourier transform matrix is constructed and then used for cross-range compression. Consequently, the imaging of maneuvering is converted into a two-dimensional parameter optimization problem in which a stable and clear ISAR image is guaranteed. A gradient descent optimization scheme is employed to obtain the accurate relative chirp rate and Doppler focus shift. Moreover, we designed an efficient and robust initialization process for the gradient descent method, thus, the well-focused ISAR images of maneuvering targets can be achieved adaptively. Human intervention is not needed, and it is quite convenient for practical ISAR imaging systems. Compared to precedent imaging methods, the new method achieves better imaging quality under reasonable computational cost. Simulation results are provided to validate the effectiveness and advantages of the proposed method.

Fiber-based coherent polarization beam combining with cascaded phase-locking and polarization-transforming controls

NASA Astrophysics Data System (ADS)

Yang, Yan; Geng, Chao; Li, Feng; Huang, Guan; Li, Xinyang

2018-05-01

In this paper, the fiber-based coherent polarization beam combining (CPBC) with cascaded phase-locking (PL) and polarization-transforming (PT) controls was proposed to combine imbalanced input beams where the number of the input beams is not binary, in which the PL control was performed using the piezoelectric-ring fiber-optic phase compensator, and the PT control was realized by the dynamic polarization controller, simultaneously. The principle of the proposed CPBC was introduced. The performance of the proposed CPBC was analyzed in comparison with the CPBC based on PL control and the CPBC based on PT control. The basic experiment of CPBC of three laser beams was carried out to validate the feasibility of the proposed CPBC, where cascaded controls of PL and PT were implemented based on stochastic parallel gradient descent algorithm. Simulation and experimental results show that the proposed CPBC incorporates the advantages of the two previous CPBC schemes and performs well in the closed loop. Moreover, the expansibility and the application of the proposed CPBC were validated by scaling the CPBC to combine seven laser beams. We believe that the proposed fiber-based CPBC with cascaded PL and PT controls has great potential in free space optical communications employing the multi-aperture receiver with asymmetric structure.

Experimental studies of the rotor flow downwash on the Stability of multi-rotor crafts in descent

NASA Astrophysics Data System (ADS)

Veismann, Marcel; Dougherty, Christopher; Gharib, Morteza

2017-11-01

All rotorcrafts, including helicopters and multicopters, have the inherent problem of entering rotor downwash during vertical descent. As a result, the craft is subject to highly unsteady flow, called vortex ring state (VRS), which leads to a loss of lift and reduced stability. To date, experimental efforts to investigate this phenomenon have been largely limited to analysis of a single, fixed rotor mounted in a horizontal wind tunnel. Our current work aims to understand the interaction of multiple rotors in vertical descent by mounting a multi-rotor craft in a low speed, vertical wind tunnel. Experiments were performed with a fixed and rotationally free mounting; the latter allowing us to better capture the dynamics of a free flying drone. The effect of rotor separation on stability, generated thrust, and rotor wake interaction was characterized using force gauge data and PIV analysis for various descent velocities. The results obtained help us better understand fluid-craft interactions of drones in vertical descent and identify possible sources of instability. The presented material is based upon work supported by the Center for Autonomous Systems and Technologies (CAST) at the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT).

Online learning in optical tomography: a stochastic approach

NASA Astrophysics Data System (ADS)

Chen, Ke; Li, Qin; Liu, Jian-Guo

2018-07-01

We study the inverse problem of radiative transfer equation (RTE) using stochastic gradient descent method (SGD) in this paper. Mathematically, optical tomography amounts to recovering the optical parameters in RTE using the incoming–outgoing pair of light intensity. We formulate it as a PDE-constraint optimization problem, where the mismatch of computed and measured outgoing data is minimized with same initial data and RTE constraint. The memory and computation cost it requires, however, is typically prohibitive, especially in high dimensional space. Smart iterative solvers that only use partial information in each step is called for thereafter. Stochastic gradient descent method is an online learning algorithm that randomly selects data for minimizing the mismatch. It requires minimum memory and computation, and advances fast, therefore perfectly serves the purpose. In this paper we formulate the problem, in both nonlinear and its linearized setting, apply SGD algorithm and analyze the convergence performance.

Generalization in Adaptation to Stable and Unstable Dynamics

PubMed Central

Kadiallah, Abdelhamid; Franklin, David W.; Burdet, Etienne

2012-01-01

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. PMID:23056191

An image morphing technique based on optimal mass preserving mapping.

PubMed

Zhu, Lei; Yang, Yan; Haker, Steven; Tannenbaum, Allen

2007-06-01

Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The L(2) mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods.

An Image Morphing Technique Based on Optimal Mass Preserving Mapping

PubMed Central

Zhu, Lei; Yang, Yan; Haker, Steven; Tannenbaum, Allen

2013-01-01

Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The L2 mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods. PMID:17547128

Application of Artificial Neural Networks in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate).

PubMed

Shahsavari, Shadab; Rezaie Shirmard, Leila; Amini, Mohsen; Abedin Dokoosh, Farid

2017-01-01

Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was optimized according to artificial neural networks (ANNs). Concentration of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), PVA and amount of Fingolimod is considered as the input value, and the particle size, polydispersity index, loading capacity, and entrapment efficacy as output data in experimental design study. In vitro release study was carried out for best formulation according to statistical analysis. ANNs are employed to generate the best model to determine the relationships between various values. In order to specify the model with the best accuracy and proficiency for the in vitro release, a multilayer percepteron with different training algorithm has been examined. Three training model formulations including Levenberg-Marquardt (LM), gradient descent, and Bayesian regularization were employed for training the ANN models. It is demonstrated that the predictive ability of each training algorithm is in the order of LM > gradient descent > Bayesian regularization. Also, optimum formulation was achieved by LM training function with 15 hidden layers and 20 neurons. The transfer function of the hidden layer for this formulation and the output layer were tansig and purlin, respectively. Also, the optimization process was developed by minimizing the error among the predicted and observed values of training algorithm (about 0.0341). Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

Intelligence system based classification approach for medical disease diagnosis

NASA Astrophysics Data System (ADS)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

The prediction of breast cancer in women who have no signs or symptoms of the disease as well as survivability after undergone certain surgery has been a challenging problem for medical researchers. The decision about presence or absence of diseases depends on the physician's intuition, experience and skill for comparing current indicators with previous one than on knowledge rich data hidden in a database. This measure is a very crucial and challenging task. The goal is to predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system. A framework describes methodology for designing and evaluation of classification performances of two discrete ANFIS systems of hybrid learning algorithms least square estimates with Modified Levenberg-Marquardt and Gradient descent algorithms that can be used by physicians to accelerate diagnosis process. The proposed method's performance was evaluated based on training and test datasets with mammographic mass and Haberman's survival Datasets obtained from benchmarked datasets of University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity is examined. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

Implementation of a Balance Operator in NCOM

DTIC Science & Technology

2016-04-07

the background temperature Tb and salinity Sb fields do), f is the Coriolis parameter, k is the vertical unit vector, ∇ is the horizontal gradient, p... effectively used as a natural metric in the space of cost function gradients. The associated geometry inhibits descent in the unbalanced directions...28) where f is the local Coriolis parameter, ∆yv is the local grid spacing in the y direction at a v point, and the overbars indicates horizontal

The lateral/directional stability characteristics of a four-propeller tilt-wing V/STOL model in low-speed steep descent. M.S. Thesis - Princeton Univ., N.J.

NASA Technical Reports Server (NTRS)

Dicarlo, D. J.

1971-01-01

Lateral-directional dynamic stability derivatives are presented for a O.1-scale model of the XC-142A tilt-wing transport. The tests involved various descending flight conditions achieved at constant speed and wing incidence by varying the vehicle angle of attack. The propeller blade angle and the speed were also changed in the steepest descent case. The experimental data were analyzed assuming that the dynamic motions of the vehicle may be described by linearized equations, with the lateral-directional characteristics of the full-scale aircraft also presented and discussed. Results from this experimental investigation indicated that the full-scale aircraft would have a stable lateral-directional motion in level flight, with the dynamic motion becoming less stable as the descent angle was increased.

Adaptive conversion of a high-order mode beam into a near-diffraction-limited beam.

PubMed

Zhao, Haichuan; Wang, Xiaolin; Ma, Haotong; Zhou, Pu; Ma, Yanxing; Xu, Xiaojun; Zhao, Yijun

2011-08-01

We present a new method for efficiently transforming a high-order mode beam into a nearly Gaussian beam with much higher beam quality. The method is based on modulation of phases of different lobes by stochastic parallel gradient descent algorithm and coherent addition after phase flattening. We demonstrate the method by transforming an LP11 mode into a nearly Gaussian beam. The experimental results reveal that the power in the diffraction-limited bucket in the far field is increased by more than a factor of 1.5.

A different approach to estimate nonlinear regression model using numerical methods

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Mokeshrayalu, G.; Balasiddamuni, P.

2017-11-01

This research paper concerns with the computational methods namely the Gauss-Newton method, Gradient algorithm methods (Newton-Raphson method, Steepest Descent or Steepest Ascent algorithm method, the Method of Scoring, the Method of Quadratic Hill-Climbing) based on numerical analysis to estimate parameters of nonlinear regression model in a very different way. Principles of matrix calculus have been used to discuss the Gradient-Algorithm methods. Yonathan Bard [1] discussed a comparison of gradient methods for the solution of nonlinear parameter estimation problems. However this article discusses an analytical approach to the gradient algorithm methods in a different way. This paper describes a new iterative technique namely Gauss-Newton method which differs from the iterative technique proposed by Gorden K. Smyth [2]. Hans Georg Bock et.al [10] proposed numerical methods for parameter estimation in DAE’s (Differential algebraic equation). Isabel Reis Dos Santos et al [11], Introduced weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel. For large-scale non smooth convex minimization the Hager and Zhang (HZ) conjugate gradient Method and the modified HZ (MHZ) method were presented by Gonglin Yuan et al [12].

A feasible DY conjugate gradient method for linear equality constraints

NASA Astrophysics Data System (ADS)

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.

Approximate solution of the p-median minimization problem

NASA Astrophysics Data System (ADS)

Il'ev, V. P.; Il'eva, S. D.; Navrotskaya, A. A.

2016-09-01

A version of the facility location problem (the well-known p-median minimization problem) and its generalization—the problem of minimizing a supermodular set function—is studied. These problems are NP-hard, and they are approximately solved by a gradient algorithm that is a discrete analog of the steepest descent algorithm. A priori bounds on the worst-case behavior of the gradient algorithm for the problems under consideration are obtained. As a consequence, a bound on the performance guarantee of the gradient algorithm for the p-median minimization problem in terms of the production and transportation cost matrix is obtained.

Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm.

PubMed

Liu, Xiaozheng; Yuan, Zhenming; Zhu, Junming; Xu, Dongrong

2013-12-07

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.

Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.

PubMed

Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou

2011-09-01

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

NASA Astrophysics Data System (ADS)

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

Live Speech Driven Head-and-Eye Motion Generators.

PubMed

Le, Binh H; Ma, Xiaohan; Deng, Zhigang

2012-11-01

This paper describes a fully automated framework to generate realistic head motion, eye gaze, and eyelid motion simultaneously based on live (or recorded) speech input. Its central idea is to learn separate yet interrelated statistical models for each component (head motion, gaze, or eyelid motion) from a prerecorded facial motion data set: 1) Gaussian Mixture Models and gradient descent optimization algorithm are employed to generate head motion from speech features; 2) Nonlinear Dynamic Canonical Correlation Analysis model is used to synthesize eye gaze from head motion and speech features, and 3) nonnegative linear regression is used to model voluntary eye lid motion and log-normal distribution is used to describe involuntary eye blinks. Several user studies are conducted to evaluate the effectiveness of the proposed speech-driven head and eye motion generator using the well-established paired comparison methodology. Our evaluation results clearly show that this approach can significantly outperform the state-of-the-art head and eye motion generation algorithms. In addition, a novel mocap+video hybrid data acquisition technique is introduced to record high-fidelity head movement, eye gaze, and eyelid motion simultaneously.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

PubMed Central

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889

Implementation of a Balance Operator in NCOM

DTIC Science & Technology

2016-04-07

the background temperature Tb and salinity Sb fields do), f is the Coriolis parameter, k is the vertical unit vector, ∇ is the horizontal gradient, p... effectively used as a natural metric in the space of cost function gradients. The associated geometry inhibits descent in the unbalanced directions and...28) where f is the local Coriolis parameter, ∆yv is the local grid spacing in the y direction at a v point, and the overbars indicates horizontal

A new family of Polak-Ribiere-Polyak conjugate gradient method with the strong-Wolfe line search

NASA Astrophysics Data System (ADS)

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.

Fast and Accurate Poisson Denoising With Trainable Nonlinear Diffusion.

PubMed

Feng, Wensen; Qiao, Peng; Chen, Yunjin; Wensen Feng; Peng Qiao; Yunjin Chen; Feng, Wensen; Chen, Yunjin; Qiao, Peng

2018-06-01

The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision, and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this paper we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly developed trainable nonlinear reaction diffusion (TNRD) model which has proven an extremely fast image restoration approach with performance surpassing recent state-of-the-arts. However, the straightforward direct gradient descent employed in the original TNRD-based denoising task is not applicable in this paper. To solve this problem, we resort to the proximal gradient descent method. We retrain the model parameters, including the linear filters and influence functions by taking into account the Poisson noise statistics, and end up with a well-trained nonlinear diffusion model specialized for Poisson denoising. The trained model provides strongly competitive results against state-of-the-art approaches, meanwhile bearing the properties of simple structure and high efficiency. Furthermore, our proposed model comes along with an additional advantage, that the diffusion process is well-suited for parallel computation on graphics processing units (GPUs). For images of size , our GPU implementation takes less than 0.1 s to produce state-of-the-art Poisson denoising performance.

ATMOS/ATLAS-3 Observations of Long-Lived Tracers and Descent in the Antarctic Vortex in November 1994

NASA Technical Reports Server (NTRS)

Abrams, M. C.; Manney, G. L.; Gunson, M. R.; Abbas, M. M.; Chang, A. Y.; Goldman, A.; Irion, F. W.; Michelsen, H. A.; Newchurch, M. J.; Rinsland, C. P.;

A new version of Stochastic-parallel-gradient-descent algorithm (SPGD) for phase correction of a distorted orbital angular momentum (OAM) beam

NASA Astrophysics Data System (ADS)

Jiao Ling, LIn; Xiaoli, Yin; Huan, Chang; Xiaozhou, Cui; Yi-Lin, Guo; Huan-Yu, Liao; Chun-YU, Gao; Guohua, Wu; Guang-Yao, Liu; Jin-KUn, Jiang; Qing-Hua, Tian

2018-02-01

Atmospheric turbulence limits the performance of orbital angular momentum-based free-space optical communication (FSO-OAM) system. In order to compensate phase distortion induced by atmospheric turbulence, wavefront sensorless adaptive optics (WSAO) has been proposed and studied in recent years. In this paper a new version of SPGD called MZ-SPGD, which combines the Z-SPGD based on the deformable mirror influence function and the M-SPGD based on the Zernike polynomials, is proposed. Numerical simulations show that the hybrid method decreases convergence times markedly but can achieve the same compensated effect compared to Z-SPGD and M-SPGD.

14 CFR 23.253 - High speed characteristics.

Code of Federal Regulations, 2013 CFR

2013-01-01

... and characteristics include gust upsets, inadvertent control movements, low stick force gradients in relation to control friction, passenger movement, leveling off from climb, and descent from Mach to... normal attitude and its speed reduced to VMO/MMO, without— (1) Exceptional piloting strength or skill; (2...

14 CFR 23.253 - High speed characteristics.

Code of Federal Regulations, 2014 CFR

2014-01-01

... and characteristics include gust upsets, inadvertent control movements, low stick force gradients in relation to control friction, passenger movement, leveling off from climb, and descent from Mach to... normal attitude and its speed reduced to VMO/MMO, without— (1) Exceptional piloting strength or skill; (2...

Using a Gradient Vector to Find Multiple Periodic Oscillations in Suspension Bridge Models

ERIC Educational Resources Information Center

Humphreys, L. D.; McKenna, P. J.

2005-01-01

This paper describes how the method of steepest descent can be used to find periodic solutions of differential equations. Applications to two suspension bridge models are discussed, and the method is used to find non-obvious large-amplitude solutions.

3D-Web-GIS RFID location sensing system for construction objects.

PubMed

Ko, Chien-Ho

2013-01-01

Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency.

Optimization of OT-MACH Filter Generation for Target Recognition

NASA Technical Reports Server (NTRS)

Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

3D-Web-GIS RFID Location Sensing System for Construction Objects

PubMed Central

2013-01-01

Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency. PMID:23864821

Cosmic Microwave Background Mapmaking with a Messenger Field

NASA Astrophysics Data System (ADS)

Huffenberger, Kevin M.; Næss, Sigurd K.

2018-01-01

We apply a messenger field method to solve the linear minimum-variance mapmaking equation in the context of Cosmic Microwave Background (CMB) observations. In simulations, the method produces sky maps that converge significantly faster than those from a conjugate gradient descent algorithm with a diagonal preconditioner, even though the computational cost per iteration is similar. The messenger method recovers large scales in the map better than conjugate gradient descent, and yields a lower overall χ2. In the single, pencil beam approximation, each iteration of the messenger mapmaking procedure produces an unbiased map, and the iterations become more optimal as they proceed. A variant of the method can handle differential data or perform deconvolution mapmaking. The messenger method requires no preconditioner, but a high-quality solution needs a cooling parameter to control the convergence. We study the convergence properties of this new method and discuss how the algorithm is feasible for the large data sets of current and future CMB experiments.

A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

NASA Astrophysics Data System (ADS)

Xu, Rui; Zhou, Miaolei

2018-04-01

Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks.

PubMed

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.

Development of gradient descent adaptive algorithms to remove common mode artifact for improvement of cardiovascular signal quality.

PubMed

Ciaccio, Edward J; Micheli-Tzanakou, Evangelia

2007-07-01

Common-mode noise degrades cardiovascular signal quality and diminishes measurement accuracy. Filtering to remove noise components in the frequency domain often distorts the signal. Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primary signal. Update of weight w was based upon the gradient term of the steepest descent equation: [see text], where the error epsilon is the difference between primary and weighted reference signals. nabla was estimated from Deltaepsilon(2) and Deltaw without using a variable Deltaw in the denominator which can cause instability. The Parallel Comparison (PC) algorithm computed Deltaepsilon(2) using fixed finite differences +/- Deltaw in parallel during each discrete time k. The ALOPEX algorithm computed Deltaepsilon(2)x Deltaw from time k to k + 1 to estimate nabla, with a random number added to account for Deltaepsilon(2) . Deltaw--> 0 near the optimal weighting. Using simulated data, both algorithms stably converged to the optimal weighting within 50-2000 discrete sample points k even with a SNR = 1:8 and weights which were initialized far from the optimal. Using a sharply pulsatile cardiac electrogram signal with added noise so that the SNR = 1:5, both algorithms exhibited stable convergence within 100 ms (100 sample points). Fourier spectral analysis revealed minimal distortion when comparing the signal without added noise to the ANC restored signal. ANC algorithms based upon difference calculations can rapidly and stably converge to the optimal weighting in simulated and real cardiovascular data. Signal quality is restored with minimal distortion, increasing the accuracy of biophysical measurement.

A General Method for Solving Systems of Non-Linear Equations

NASA Technical Reports Server (NTRS)

Nachtsheim, Philip R.; Deiss, Ron (Technical Monitor)

1995-01-01

The method of steepest descent is modified so that accelerated convergence is achieved near a root. It is assumed that the function of interest can be approximated near a root by a quadratic form. An eigenvector of the quadratic form is found by evaluating the function and its gradient at an arbitrary point and another suitably selected point. The terminal point of the eigenvector is chosen to lie on the line segment joining the two points. The terminal point found lies on an axis of the quadratic form. The selection of a suitable step size at this point leads directly to the root in the direction of steepest descent in a single step. Newton's root finding method not infrequently diverges if the starting point is far from the root. However, the current method in these regions merely reverts to the method of steepest descent with an adaptive step size. The current method's performance should match that of the Levenberg-Marquardt root finding method since they both share the ability to converge from a starting point far from the root and both exhibit quadratic convergence near a root. The Levenberg-Marquardt method requires storage for coefficients of linear equations. The current method which does not require the solution of linear equations requires more time for additional function and gradient evaluations. The classic trade off of time for space separates the two methods.

Vertical Descent and Landing Tests of a 0.13-Scale Model of the Convair XFY-1 Vertically Rising Airplane in Still Air, TED No. NACA DE 368

NASA Technical Reports Server (NTRS)

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.

Efficient two-dimensional compressive sensing in MIMO radar

NASA Astrophysics Data System (ADS)

Shahbazi, Nafiseh; Abbasfar, Aliazam; Jabbarian-Jahromi, Mohammad

2017-12-01

Compressive sensing (CS) has been a way to lower sampling rate leading to data reduction for processing in multiple-input multiple-output (MIMO) radar systems. In this paper, we further reduce the computational complexity of a pulse-Doppler collocated MIMO radar by introducing a two-dimensional (2D) compressive sensing. To do so, we first introduce a new 2D formulation for the compressed received signals and then we propose a new measurement matrix design for our 2D compressive sensing model that is based on minimizing the coherence of sensing matrix using gradient descent algorithm. The simulation results show that our proposed 2D measurement matrix design using gradient decent algorithm (2D-MMDGD) has much lower computational complexity compared to one-dimensional (1D) methods while having better performance in comparison with conventional methods such as Gaussian random measurement matrix.

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.

PubMed

Weidlich, Iwona E; Pevzner, Yuri; Miller, Benjamin T; Filippov, Igor V; Woodcock, H Lee; Brooks, Bernard R

2015-01-05

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms-Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity. © 2014 Wiley Periodicals, Inc.

Statistical Physics for Adaptive Distributed Control

NASA Technical Reports Server (NTRS)

Wolpert, David H.

2005-01-01

A viewgraph presentation on statistical physics for distributed adaptive control is shown. The topics include: 1) The Golden Rule; 2) Advantages; 3) Roadmap; 4) What is Distributed Control? 5) Review of Information Theory; 6) Iterative Distributed Control; 7) Minimizing L(q) Via Gradient Descent; and 8) Adaptive Distributed Control.

WS-BP: An efficient wolf search based back-propagation algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd; Rehman, M. Z.; Khan, Abdullah

2015-05-01

Wolf Search (WS) is a heuristic based optimization algorithm. Inspired by the preying and survival capabilities of the wolves, this algorithm is highly capable to search large spaces in the candidate solutions. This paper investigates the use of WS algorithm in combination with back-propagation neural network (BPNN) algorithm to overcome the local minima problem and to improve convergence in gradient descent. The performance of the proposed Wolf Search based Back-Propagation (WS-BP) algorithm is compared with Artificial Bee Colony Back-Propagation (ABC-BP), Bat Based Back-Propagation (Bat-BP), and conventional BPNN algorithms. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the WS-BP algorithm effectively avoids the local minima and converge to global minima.

Conjugate gradient filtering of instantaneous normal modes, saddles on the energy landscape, and diffusion in liquids.

PubMed

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.

An approach to multiobjective optimization of rotational therapy. II. Pareto optimal surfaces and linear combinations of modulated blocked arcs for a prostate geometry.

PubMed

Pardo-Montero, Juan; Fenwick, John D

2010-06-01

The purpose of this work is twofold: To further develop an approach to multiobjective optimization of rotational therapy treatments recently introduced by the authors [J. Pardo-Montero and J. D. Fenwick, "An approach to multiobjective optimization of rotational therapy," Med. Phys. 36, 3292-3303 (2009)], especially regarding its application to realistic geometries, and to study the quality (Pareto optimality) of plans obtained using such an approach by comparing them with Pareto optimal plans obtained through inverse planning. In the previous work of the authors, a methodology is proposed for constructing a large number of plans, with different compromises between the objectives involved, from a small number of geometrically based arcs, each arc prioritizing different objectives. Here, this method has been further developed and studied. Two different techniques for constructing these arcs are investigated, one based on image-reconstruction algorithms and the other based on more common gradient-descent algorithms. The difficulty of dealing with organs abutting the target, briefly reported in previous work of the authors, has been investigated using partial OAR unblocking. Optimality of the solutions has been investigated by comparison with a Pareto front obtained from inverse planning. A relative Euclidean distance has been used to measure the distance of these plans to the Pareto front, and dose volume histogram comparisons have been used to gauge the clinical impact of these distances. A prostate geometry has been used for the study. For geometries where a blocked OAR abuts the target, moderate OAR unblocking can substantially improve target dose distribution and minimize hot spots while not overly compromising dose sparing of the organ. Image-reconstruction type and gradient-descent blocked-arc computations generate similar results. The Pareto front for the prostate geometry, reconstructed using a large number of inverse plans, presents a hockey-stick shape comprising two regions: One where the dose to the target is close to prescription and trade-offs can be made between doses to the organs at risk and (small) changes in target dose, and one where very substantial rectal sparing is achieved at the cost of large target underdosage. Plans computed following the approach using a conformal arc and four blocked arcs generally lie close to the Pareto front, although distances of some plans from high gradient regions of the Pareto front can be greater. Only around 12% of plans lie a relative Euclidean distance of 0.15 or greater from the Pareto front. Using the alternative distance measure of Craft ["Calculating and controlling the error of discrete representations of Pareto surfaces in convex multi-criteria optimization," Phys. Medica (to be published)], around 2/5 of plans lie more than 0.05 from the front. Computation of blocked arcs is quite fast, the algorithms requiring 35%-80% of the running time per iteration needed for conventional inverse plan computation. The geometry-based arc approach to multicriteria optimization of rotational therapy allows solutions to be obtained that lie close to the Pareto front. Both the image-reconstruction type and gradient-descent algorithms produce similar modulated arcs, the latter one perhaps being preferred because it is more easily implementable in standard treatment planning systems. Moderate unblocking provides a good way of dealing with OARs which abut the PTV. Optimization of geometry-based arcs is faster than usual inverse optimization of treatment plans, making this approach more rapid than an inverse-based Pareto front reconstruction.

One Giant Leap for Categorizers: One Small Step for Categorization Theory

PubMed Central

Smith, J. David; Ell, Shawn W.

2015-01-01

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so. PMID:26332587

Efficient Online Learning Algorithms Based on LSTM Neural Networks.

PubMed

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.

Algorithms for Mathematical Programming with Emphasis on Bi-level Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldfarb, Donald; Iyengar, Garud

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.

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First,more » the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE PAGES

Zhou, Ping; Wang, Chenyu; Li, Mingjie; ...

2018-01-31

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

A rapid and robust gradient measurement technique using dynamic single-point imaging.

PubMed

Jang, Hyungseok; McMillan, Alan B

2017-09-01

We propose a new gradient measurement technique based on dynamic single-point imaging (SPI), which allows simple, rapid, and robust measurement of k-space trajectory. To enable gradient measurement, we utilize the variable field-of-view (FOV) property of dynamic SPI, which is dependent on gradient shape. First, one-dimensional (1D) dynamic SPI data are acquired from a targeted gradient axis, and then relative FOV scaling factors between 1D images or k-spaces at varying encoding times are found. These relative scaling factors are the relative k-space position that can be used for image reconstruction. The gradient measurement technique also can be used to estimate the gradient impulse response function for reproducible gradient estimation as a linear time invariant system. The proposed measurement technique was used to improve reconstructed image quality in 3D ultrashort echo, 2D spiral, and multi-echo bipolar gradient-echo imaging. In multi-echo bipolar gradient-echo imaging, measurement of the k-space trajectory allowed the use of a ramp-sampled trajectory for improved acquisition speed (approximately 30%) and more accurate quantitative fat and water separation in a phantom. The proposed dynamic SPI-based method allows fast k-space trajectory measurement with a simple implementation and no additional hardware for improved image quality. Magn Reson Med 78:950-962, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Crosswind Shear Gradient Affect on Wake Vortices

NASA Technical Reports Server (NTRS)

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.

Efficient spectral computation of the stationary states of rotating Bose-Einstein condensates by preconditioned nonlinear conjugate gradient methods

NASA Astrophysics Data System (ADS)

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.

Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators.

PubMed

Karayiannis, N B

2000-01-01

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.

Sicily 2002 Balloon Flight Campaign: A Test of the HASI Instrument

NASA Astrophysics Data System (ADS)

Bettanini, C.

A mock up of the probe descending in the Titan atmosphere for the Huygens Cassini Mission has been successfully launched with stratospheric balloon from Italian Space Agency Base "Luigi Broglio" in Sicily and recovered on May 30 th 2002. The probe has been lifted at 32 km altitude and then released to perform a 45 minutes descent decelerated by parachute, to simulate Huygens mission at Titan. Preliminary aerodynamics study of the probe has focused on the achievement of a descent velocity profile and a spin rate profile, satisfying the Huygens mission to Titan requirements. The descent velocity and spin rate have been calculated by solving a system of ODE describing the translational and rotational motion of the probe trough the earth atmosphere during parachute aided descent Results of these calculations have driven the choice of an appropriate angle of attack of the blades in the bottom of the probe and ballast weight during flight. The probe is hosting spares of HASI instruments, housekeeping sensors and other dedicated sensors, Beagle II UV Sensors and Huygens Tilt Sensor, for a total of 77 acquired sensor channels, sampled during ascent, drift and descent phase. Main goals are to verify sensor performance and perform a realistic functional test in dynamical and environmental conditions similar to those during the descent in Titan atmosphere and furthermore to investigate impact at ground to check the impact detection sequence of HASI accelerometer and HASI in the surface phase. An integrated data acquisition and instrument control system has been developed, based on PC architecture and soft -real-time application. Sensors channels have been sampled at the nominal HASI data rates, with a max rate of 1 kHz. Software has been developed for data acquisition, onboard storage and telemetry transmission satisfying all requests for real-time monitoring, diagnostic and redundancy.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Comparison of SIRT and SQS for Regularized Weighted Least Squares Image Reconstruction

PubMed Central

Gregor, Jens; Fessler, Jeffrey A.

2015-01-01

Tomographic image reconstruction is often formulated as a regularized weighted least squares (RWLS) problem optimized by iterative algorithms that are either inherently algebraic or derived from a statistical point of view. This paper compares a modified version of SIRT (Simultaneous Iterative Reconstruction Technique), which is of the former type, with a version of SQS (Separable Quadratic Surrogates), which is of the latter type. We show that the two algorithms minimize the same criterion function using similar forms of preconditioned gradient descent. We present near-optimal relaxation for both based on eigenvalue bounds and include a heuristic extension for use with ordered subsets. We provide empirical evidence that SIRT and SQS converge at the same rate for all intents and purposes. For context, we compare their performance with an implementation of preconditioned conjugate gradient. The illustrative application is X-ray CT of luggage for aviation security. PMID:26478906

A Comparative Study of Probability Collectives Based Multi-agent Systems and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Huang, Chien-Feng; Wolpert, David H.; Bieniawski, Stefan; Strauss, Charles E. M.

2005-01-01

We compare Genetic Algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach works in both continuous and discrete problems. It does not suffer from the resolution limitation of the finite bit length encoding of parameters into GA alleles. It also has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems. It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.

Adaptive State Predictor Based Human Operator Modeling on Longitudinal and Lateral Control

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.

2015-01-01

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to categorize these interactions of the pilot with an adaptive controller compensating during control surface failures. A general linear in-parameter model structure is used to represent a pilot. Three different estimation methods are explored. A gradient descent estimator (GDE), a least squares estimator with exponential forgetting (LSEEF), and a least squares estimator with bounded gain forgetting (LSEBGF) used the experiment data to predict pilot stick input. Previous results have found that the GDE and LSEEF methods are fairly accurate in predicting longitudinal stick input from commanded pitch. This paper discusses the accuracy of each of the three methods - GDE, LSEEF, and LSEBGF - to predict both pilot longitudinal and lateral stick input from the flight director's commanded pitch and bank attitudes.

A gradient system solution to Potts mean field equations and its electronic implementation.

PubMed

Urahama, K; Ueno, S

1993-03-01

A gradient system solution method is presented for solving Potts mean field equations for combinatorial optimization problems subject to winner-take-all constraints. In the proposed solution method the optimum solution is searched by using gradient descent differential equations whose trajectory is confined within the feasible solution space of optimization problems. This gradient system is proven theoretically to always produce a legal local optimum solution of combinatorial optimization problems. An elementary analog electronic circuit implementing the presented method is designed on the basis of current-mode subthreshold MOS technologies. The core constituent of the circuit is the winner-take-all circuit developed by Lazzaro et al. Correct functioning of the presented circuit is exemplified with simulations of the circuits implementing the scheme for solving the shortest path problems.

Simulating Descent and Landing of a Spacecraft

NASA Technical Reports Server (NTRS)

Balaram, J.; Jain, Abhinandan; Martin, Bryan; Lim, Christopher; Henriquez, David; McMahon, Elihu; Sohl, Garrett; Banerjee, Pranab; Steele, Robert; Bentley, Timothy

2005-01-01

The Dynamics Simulator for Entry, Descent, and Surface landing (DSENDS) software performs high-fidelity simulation of the Entry, Descent, and Landing (EDL) of a spacecraft into the atmosphere and onto the surface of a planet or a smaller body. DSENDS is an extension of the DShell and DARTS programs, which afford capabilities for mathematical modeling of the dynamics of a spacecraft as a whole and of its instruments, actuators, and other subsystems. DSENDS enables the modeling (including real-time simulation) of flight-train elements and all spacecraft responses during various phases of EDL. DSENDS provides high-fidelity models of the aerodynamics of entry bodies and parachutes plus supporting models of atmospheres. Terrain and real-time responses of terrain-imaging radar and lidar instruments can also be modeled. The program includes modules for simulation of guidance, navigation, hypersonic steering, and powered descent. Automated state-machine-driven model switching is used to represent spacecraft separations and reconfigurations. Models for computing landing contact and impact forces are expected to be added. DSENDS can be used as a stand-alone program or incorporated into a larger program that simulates operations in real time.

Performance comparison of a new hybrid conjugate gradient method under exact and inexact line searches

NASA Astrophysics Data System (ADS)

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.

A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Zhang, Jian; Li, Wei; Zhao, Dazhe; Huang, Min; Zaiane, Osmar

2017-03-01

The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ 2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ 2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ 2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ 2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ 2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optimized computational imaging methods for small-target sensing in lens-free holographic microscopy

NASA Astrophysics Data System (ADS)

Xiong, Zhen; Engle, Isaiah; Garan, Jacob; Melzer, Jeffrey E.; McLeod, Euan

2018-02-01

Lens-free holographic microscopy is a promising diagnostic approach because it is cost-effective, compact, and suitable for point-of-care applications, while providing high resolution together with an ultra-large field-of-view. It has been applied to biomedical sensing, where larger targets like eukaryotic cells, bacteria, or viruses can be directly imaged without labels, and smaller targets like proteins or DNA strands can be detected via scattering labels like micro- or nano-spheres. Automated image processing routines can count objects and infer target concentrations. In these sensing applications, sensitivity and specificity are critically affected by image resolution and signal-to-noise ratio (SNR). Pixel super-resolution approaches have been shown to boost resolution and SNR by synthesizing a high-resolution image from multiple, partially redundant, low-resolution images. However, there are several computational methods that can be used to synthesize the high-resolution image, and previously, it has been unclear which methods work best for the particular case of small-particle sensing. Here, we quantify the SNR achieved in small-particle sensing using regularized gradient-descent optimization method, where the regularization is based on cardinal-neighbor differences, Bayer-pattern noise reduction, or sparsity in the image. In particular, we find that gradient-descent with sparsity-based regularization works best for small-particle sensing. These computational approaches were evaluated on images acquired using a lens-free microscope that we assembled from an off-the-shelf LED array and color image sensor. Compared to other lens-free imaging systems, our hardware integration, calibration, and sample preparation are particularly simple. We believe our results will help to enable the best performance in lens-free holographic sensing.

Neural networks applications to control and computations

NASA Technical Reports Server (NTRS)

Luxemburg, Leon A.

1994-01-01

Several interrelated problems in the area of neural network computations are described. First an interpolation problem is considered, then a control problem is reduced to a problem of interpolation by a neural network via Lyapunov function approach, and finally a new, faster method of learning as compared with the gradient descent method, was introduced.

Real time on-chip sequential adaptive principal component analysis for data feature extraction and image compression

NASA Technical Reports Server (NTRS)

Duong, T. A.

2004-01-01

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principle Component Analysis (PCA) which will help optimize the hardware implementation in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation.

Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent

PubMed Central

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

Intelligent voltage control strategy for three-phase UPS inverters with output LC filter

NASA Astrophysics Data System (ADS)

Jung, J. W.; Leu, V. Q.; Dang, D. Q.; Do, T. D.; Mwasilu, F.; Choi, H. H.

2015-08-01

This paper presents a supervisory fuzzy neural network control (SFNNC) method for a three-phase inverter of uninterruptible power supplies (UPSs). The proposed voltage controller is comprised of a fuzzy neural network control (FNNC) term and a supervisory control term. The FNNC term is deliberately employed to estimate the uncertain terms, and the supervisory control term is designed based on the sliding mode technique to stabilise the system dynamic errors. To improve the learning capability, the FNNC term incorporates an online parameter training methodology, using the gradient descent method and Lyapunov stability theory. Besides, a linear load current observer that estimates the load currents is used to exclude the load current sensors. The proposed SFNN controller and the observer are robust to the filter inductance variations, and their stability analyses are described in detail. The experimental results obtained on a prototype UPS test bed with a TMS320F28335 DSP are presented to validate the feasibility of the proposed scheme. Verification results demonstrate that the proposed control strategy can achieve smaller steady-state error and lower total harmonic distortion when subjected to nonlinear or unbalanced loads compared to the conventional sliding mode control method.

Fast Optimization for Aircraft Descent and Approach Trajectory

NASA Technical Reports Server (NTRS)

Luchinsky, Dmitry G.; Schuet, Stefan; Brenton, J.; Timucin, Dogan; Smith, David; Kaneshige, John

2017-01-01

We address problem of on-line scheduling of the aircraft descent and approach trajectory. We formulate a general multiphase optimal control problem for optimization of the descent trajectory and review available methods of its solution. We develop a fast algorithm for solution of this problem using two key components: (i) fast inference of the dynamical and control variables of the descending trajectory from the low dimensional flight profile data and (ii) efficient local search for the resulting reduced dimensionality non-linear optimization problem. We compare the performance of the proposed algorithm with numerical solution obtained using optimal control toolbox General Pseudospectral Optimal Control Software. We present results of the solution of the scheduling problem for aircraft descent using novel fast algorithm and discuss its future applications.

Learning and optimization with cascaded VLSI neural network building-block chips

NASA Technical Reports Server (NTRS)

Duong, T.; Eberhardt, S. P.; Tran, M.; Daud, T.; Thakoor, A. P.

1992-01-01

To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oliver, R.; Soler, R.; Terradas, J.

Observations of active regions and limb prominences often show cold, dense blobs descending with an acceleration smaller than that of free fall. The dynamics of these condensations falling in the solar corona is investigated in this paper using a simple fully ionized plasma model. We find that the presence of a heavy condensation gives rise to a dynamical rearrangement of the coronal pressure that results in the formation of a large pressure gradient that opposes gravity. Eventually this pressure gradient becomes so large that the blob acceleration vanishes or even points upward. Then, the blob descent is characterized by anmore » initial acceleration phase followed by an essentially constant velocity phase. These two stages can be identified in published time-distance diagrams of coronal rain events. Both the duration of the first stage and the velocity attained by the blob increase for larger values of the ratio of blob to coronal density, for larger blob mass, and for smaller coronal temperature. Dense blobs are characterized by a detectable density growth (up to 60% in our calculations) and by a steepening of the density in their lower part, that could lead to the formation of a shock. They also emit sound waves that could be detected as small intensity changes with periods of the order of 100 s and lasting between a few and about 10 periods. Finally, the curvature of falling paths with large radii is only relevant when a very dense blob falls along inclined magnetic field lines.« less

Adjoint shape optimization for fluid-structure interaction of ducted flows

NASA Astrophysics Data System (ADS)

Heners, J. P.; Radtke, L.; Hinze, M.; Düster, A.

2018-03-01

Based on the coupled problem of time-dependent fluid-structure interaction, equations for an appropriate adjoint problem are derived by the consequent use of the formal Lagrange calculus. Solutions of both primal and adjoint equations are computed in a partitioned fashion and enable the formulation of a surface sensitivity. This sensitivity is used in the context of a steepest descent algorithm for the computation of the required gradient of an appropriate cost functional. The efficiency of the developed optimization approach is demonstrated by minimization of the pressure drop in a simple two-dimensional channel flow and in a three-dimensional ducted flow surrounded by a thin-walled structure.

Enhancement of the beam quality of non-uniform output slab laser amplifier with a 39-actuator rectangular piezoelectric deformable mirror.

PubMed

Yang, Ping; Ning, Yu; Lei, Xiang; Xu, Bing; Li, Xinyang; Dong, Lizhi; Yan, Hu; Liu, Wenjing; Jiang, Wenhan; Liu, Lei; Wang, Chao; Liang, Xingbo; Tang, Xiaojun

2010-03-29

We present a slab laser amplifier beam cleanup experimental system based on a 39-actuator rectangular piezoelectric deformable mirror. Rather than use a wave-front sensor to measure distortions in the wave-front and then apply a conjugation wave-front for compensating them, the system uses a Stochastic Parallel Gradient Descent algorithm to maximize the power contained within a far-field designated bucket. Experimental results demonstrate that at the output power of 335W, more than 30% energy concentrates in the 1x diffraction-limited area while the beam quality is enhanced greatly.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

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.

Railway obstacle detection algorithm using neural network

NASA Astrophysics Data System (ADS)

Yu, Mingyang; Yang, Peng; Wei, Sen

2018-05-01

Aiming at the difficulty of detection of obstacle in outdoor railway scene, a data-oriented method based on neural network to obtain image objects is proposed. First, we mark objects of images(such as people, trains, animals) acquired on the Internet. and then use the residual learning units to build Fast R-CNN framework. Then, the neural network is trained to get the target image characteristics by using stochastic gradient descent algorithm. Finally, a well-trained model is used to identify an outdoor railway image. if it includes trains and other objects, it will issue an alert. Experiments show that the correct rate of warning reached 94.85%.

Convergence of fractional adaptive systems using gradient approach.

PubMed

Gallegos, Javier A; Duarte-Mermoud, Manuel A

2017-07-01

Conditions for boundedness and convergence of the output error and the parameter error for various Caputo's fractional order adaptive schemes based on the steepest descent method are derived in this paper. To this aim, the concept of sufficiently exciting signals is introduced, characterized and related to the concept of persistently exciting signals used in the integer order case. An application is designed in adaptive indirect control of integer order systems using fractional equations to adjust parameters. This application is illustrated for a pole placement adaptive problem. Advantages of using fractional adjustment in control adaptive schemes are experimentally obtained. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Mini-batch optimized full waveform inversion with geological constrained gradient filtering

NASA Astrophysics Data System (ADS)

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.

GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications

NASA Astrophysics Data System (ADS)

Bhosale, Parag; Staring, Marius; Al-Ars, Zaid; Berendsen, Floris F.

2018-03-01

Currently, non-rigid image registration algorithms are too computationally intensive to use in time-critical applications. Existing implementations that focus on speed typically address this by either parallelization on GPU-hardware, or by introducing methodically novel techniques into CPU-oriented algorithms. Stochastic gradient descent (SGD) optimization and variations thereof have proven to drastically reduce the computational burden for CPU-based image registration, but have not been successfully applied in GPU hardware due to its stochastic nature. This paper proposes 1) NiftyRegSGD, a SGD optimization for the GPU-based image registration tool NiftyReg, 2) random chunk sampler, a new random sampling strategy that better utilizes the memory bandwidth of GPU hardware. Experiments have been performed on 3D lung CT data of 19 patients, which compared NiftyRegSGD (with and without random chunk sampler) with CPU-based elastix Fast Adaptive SGD (FASGD) and NiftyReg. The registration runtime was 21.5s, 4.4s and 2.8s for elastix-FASGD, NiftyRegSGD without, and NiftyRegSGD with random chunk sampling, respectively, while similar accuracy was obtained. Our method is publicly available at https://github.com/SuperElastix/NiftyRegSGD.

Medial-based deformable models in nonconvex shape-spaces for medical image segmentation.

PubMed

McIntosh, Chris; Hamarneh, Ghassan

2012-01-01

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

Dynamics of male pelvic floor muscle contraction observed with transperineal ultrasound imaging differ between voluntary and evoked coughs.

PubMed

Stafford, Ryan E; Mazzone, Stuart; Ashton-Miller, James A; Constantinou, Christos; Hodges, Paul W

2014-04-15

Coughing provokes stress urinary incontinence, and voluntary coughs are employed clinically to assess pelvic floor dysfunction. Understanding urethral dynamics during coughing in men is limited, and it is unclear whether voluntary coughs are an appropriate surrogate for spontaneous coughs. We aimed to investigate the dynamics of urethral motion in continent men during voluntary and evoked coughs. Thirteen men (28-42 years) with no history of urological disorders volunteered to participate. Transperineal ultrasound (US) images were recorded and synchronized with measures of intraabdominal pressure (IAP), airflow, and abdominal/chest wall electromyography during voluntary coughs and coughs evoked by inhalation of nebulized capsaicin. Temporal and spatial aspects of urethral movement induced by contraction of the striated urethral sphincter (SUS), levator ani (LA), and bulbocavernosus (BC) muscles and mechanical aspects of cough generation were investigated. Results showed coughing involved complex urethral dynamics. Urethral motion implied SUS and BC shortening and LA lengthening during preparatory and expulsion phases. Evoked coughs resulted in greater IAP, greater bladder base descent (LA lengthening), and greater midurethral displacement (SUS shortening). The preparatory inspiration cough phase was shorter during evoked coughs, as was the latency between onset of midurethral displacement and expulsion. Maximum midurethral displacement coincided with maximal bladder base descent during voluntary cough, but followed it during evoked cough. The data revealed complex interaction between muscles involved in continence in men. Spatial and temporal differences in urethral dynamics and cough mechanics between cough types suggest that voluntary coughing may not adequately assess capacity of the continence mechanism.

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

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.

Method and system for training dynamic nonlinear adaptive filters which have embedded memory

NASA Technical Reports Server (NTRS)

Rabinowitz, Matthew (Inventor)

2002-01-01

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

A piloted simulator evaluation of a ground-based 4-D descent advisor algorithm

NASA Technical Reports Server (NTRS)

Davis, Thomas J.; Green, Steven M.; Erzberger, Heinz

1990-01-01

A ground-based, four dimensional (4D) descent-advisor algorithm is under development at NASA-Ames. The algorithm combines detailed aerodynamic, propulsive, and atmospheric models with an efficient numerical integration scheme to generate 4D descent advisories. The ability is investigated of the 4D descent advisor algorithm to provide adequate control of arrival time for aircraft not equipped with on-board 4D guidance systems. A piloted simulation was conducted to determine the precision with which the descent advisor could predict the 4D trajectories of typical straight-in descents flown by airline pilots under different wind conditions. The effects of errors in the estimation of wind and initial aircraft weight were also studied. A description of the descent advisor as well as the result of the simulation studies are presented.

Capillary descent.

PubMed

Delannoy, Joachim; de Maleprade, Hélène; Clanet, Christophe; Quéré, David

2018-05-31

A superhydrophobic capillary tube immersed in water and brought in contact with the bath surface will be invaded by air, owing to its aerophilicity. We discuss this phenomenon where the ingredients of classical capillary rise are inverted, which leads to noticeable dynamical features. (1) The main regime of air invasion is linear in time, due to the viscous resistance of water. (2) Menisci in tubes with millimetre-size radii strongly oscillate before reaching their equilibrium depth, a consequence of inertia. On the whole, capillary descent provides a broad variety of dynamics where capillary effects, viscous friction and liquid inertia all play a role.

Assessing the ability to derive rates of polar middle-atmospheric descent using trace gas measurements from remote sensors

NASA Astrophysics Data System (ADS)

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.

A hybrid neural network model for noisy data regression.

PubMed

Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M

2004-04-01

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Huygens Atmospheric Structure Instrument (HASI) test by a stratospheric balloon experiment

NASA Astrophysics Data System (ADS)

Fulchignoni, M.; Gaborit, V.; Aboudam, A.; Angrilli, F.; Antonello, M.; Bastianello, S.; Bettanini, C.; Bianchini, G.; Colombatti, G.; Ferri, F.; Lion Stoppato, P.

2002-09-01

We developped a series of balloon experiments parachuting a 1:1 scale mock up of the Huygens probe from an altitude larger than 30 km in order to simulate at planetary scale the final part of the descent of the probe in the Titan atmosphere. The Earth atmosphere represents a natural laboratory where most of the physical parameters meet quite well the bulk condition of Titan's environment, with the exception of temperature. A first balloon experiment has been carried out in June 2001 and the results have been reported at the last DPS (V. Gaborit et al., BAAS 33, 38.03) The mock up of the probe descending in the Titan atmosphere for the Huygens Cassini Mission has been successfully launched with stratospheric balloon from Italian Space Agency Base "Luigi Broglio" in Sicily and recovered on May 30th 2002. The probe has been lifted at 32 km altitude and then released to perform a 45 minutes descent decelerated by parachute, to simulate Huygens mission at Titan. Preliminary aerodynamics study of the probe has focused on the achievement of a descent velocity profile and a spin rate profile, satisfying the Huygens mission to Titan requirements. The descent velocity and spin rate have been calculated by solving a system of ODE describing the translational and rotational motion of the probe trough the earth atmosphere during parachute aided descent Results of these calculations have driven the choice of an appropriate angle of attack of the blades in the bottom of the probe and ballast weight during flight. The probe is hosting spares of HASI sensors, housekeeping sensors and other dedicated sensors, Beagle II UV Sensors and Huygens SSP Tilt Sensor, for a total of 77 acquired sensor channels, sampled during ascent, drift and descent phase. Main goals are i) to verify sensor performance and perform a realistic functional test in dynamical and environmental conditions similar to those during the descent in Titan atmosphere; ii) to investigate impact at ground to check the impact detection sequence of HASI accelerometer and HASI in the surface phase; iii) to test the codes developped to perfor the descent trajectory reconstruction of the Huygens probe in the Titan atmosphere. An integrated data acquisition and instrument control system has been developed, based on PC architecture and soft-real-time application. Sensors channels have been sampled at the nominal HASI data rates, with a max rate of 1 kHz. Software has been developed for data acquisition, onboard storage and telemetry transmission satisfying all requests for real-time monitoring, diagnostic and redundancy.

Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs.

PubMed

Lin, F J; Wai, R J; Hong, C M

2001-01-01

A hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed, In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance.

Whole-body angular momentum during stair ascent and descent.

PubMed

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.

Product Distribution Theory for Control of Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Lee, Chia Fan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MAS's). First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint stare of the agents. Accordingly we can consider a team game in which the shared utility is a performance measure of the behavior of the MAS. For such a scenario the game is at equilibrium - the Lagrangian is optimized - when the joint distribution of the agents optimizes the system's expected performance. One common way to find that equilibrium is to have each agent run a reinforcement learning algorithm. Here we investigate the alternative of exploiting PD theory to run gradient descent on the Lagrangian. We present computer experiments validating some of the predictions of PD theory for how best to do that gradient descent. We also demonstrate how PD theory can improve performance even when we are not allowed to rerun the MAS from different initial conditions, a requirement implicit in some previous work.

Image counter-forensics based on feature injection

NASA Astrophysics Data System (ADS)

Iuliani, M.; Rossetto, S.; Bianchi, T.; De Rosa, Alessia; Piva, A.; Barni, M.

2014-02-01

Starting from the concept that many image forensic tools are based on the detection of some features revealing a particular aspect of the history of an image, in this work we model the counter-forensic attack as the injection of a specific fake feature pointing to the same history of an authentic reference image. We propose a general attack strategy that does not rely on a specific detector structure. Given a source image x and a target image y, the adversary processes x in the pixel domain producing an attacked image ~x, perceptually similar to x, whose feature f(~x) is as close as possible to f(y) computed on y. Our proposed counter-forensic attack consists in the constrained minimization of the feature distance Φ(z) =│ f(z) - f(y)│ through iterative methods based on gradient descent. To solve the intrinsic limit due to the numerical estimation of the gradient on large images, we propose the application of a feature decomposition process, that allows the problem to be reduced into many subproblems on the blocks the image is partitioned into. The proposed strategy has been tested by attacking three different features and its performance has been compared to state-of-the-art counter-forensic methods.

A new modified conjugate gradient coefficient for solving system of linear equations

NASA Astrophysics Data System (ADS)

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

Study of the dynamic of motion landing vehicles in the planet's atmosphere using inflatable braking device

NASA Astrophysics Data System (ADS)

Koryanov, Vsevolod; Harri, Ari-Matti; Kazakovtcev, Victor

At present paper analyzes the dynamics of movement of the landing vehicle (LV) with an inflatable braking device (IBD). During the movement in the planet's atmosphere with LV with IBD are significant aerodynamic loads, which can lead to a change in a non-rigid shape and appearance of the shell IBD current asymmetries LV with IBD. The presence arising in the manufacture of structural LV asymmetry results in a stabilized descent in the process of turning the LV with IBD various dynamic phenomena, such as the vibrational-rotational resonance, the resonance autorotation, altering the dynamics of angular motion of the LV. As a result of work carried out, among others, the following conclusions: 1. In the first step of descent of landing vehicle possible high angles of attack, however, the very small quantities of the velocity head. 2. In the second phase of descent arise spatial angles of attack, caused by small structural asymmetries of LV. These angles of attack, together with increasing magnitude of the velocity head cause these significant increase in lateral load. The increase in the transverse load leads to an increase in the asymmetry of the external form, which causes an additional increase in the spatial angle of attack. Depending on the magnitude of the transverse stiffness IBD or leads to a certain additional increase in the spatial angle of attack, or a possible buckling landing vehicle. 3. In the third (final) stage of the descent at subsonic speed landing vehicle with additional inflatable braking device does not influence the stiffness braking, changing the dynamics of angular motion slightly. This is due to the small size of the ram on the subsonic long trajectory and, accordingly, small deformation additional inflatable braking device. This research was supported by the European Commission Seventh Framework Programme FP7/2007-2013 under grant agreement n 263255 RITD.

Simultaneous digital super-resolution and nonuniformity correction for infrared imaging systems.

PubMed

Meza, Pablo; Machuca, Guillermo; Torres, Sergio; Martin, Cesar San; Vera, Esteban

2015-07-20

In this article, we present a novel algorithm to achieve simultaneous digital super-resolution and nonuniformity correction from a sequence of infrared images. We propose to use spatial regularization terms that exploit nonlocal means and the absence of spatial correlation between the scene and the nonuniformity noise sources. We derive an iterative optimization algorithm based on a gradient descent minimization strategy. Results from infrared image sequences corrupted with simulated and real fixed-pattern noise show a competitive performance compared with state-of-the-art methods. A qualitative analysis on the experimental results obtained with images from a variety of infrared cameras indicates that the proposed method provides super-resolution images with significantly less fixed-pattern noise.

CP decomposition approach to blind separation for DS-CDMA system using a new performance index

NASA Astrophysics Data System (ADS)

Rouijel, Awatif; Minaoui, Khalid; Comon, Pierre; Aboutajdine, Driss

2014-12-01

In this paper, we present a canonical polyadic (CP) tensor decomposition isolating the scaling matrix. This has two major implications: (i) the problem conditioning shows up explicitly and could be controlled through a constraint on the so-called coherences and (ii) a performance criterion concerning the factor matrices can be exactly calculated and is more realistic than performance metrics used in the literature. Two new algorithms optimizing the CP decomposition based on gradient descent are proposed. This decomposition is illustrated by an application to direct-sequence code division multiplexing access (DS-CDMA) systems; computer simulations are provided and demonstrate the good behavior of these algorithms, compared to others in the literature.

Object recognition in images via a factor graph model

NASA Astrophysics Data System (ADS)

He, Yong; Wang, Long; Wu, Zhaolin; Zhang, Haisu

2018-04-01

Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of- Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.

A Fast Deep Learning System Using GPU

DTIC Science & Technology

2014-06-01

hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and...widely used in data modeling until three decades later when efficient training algorithm for RBM is invented by Hinton [3] and the computing power is...be trained using most of optimization algorithms , such as BP, conjugate gradient descent (CGD) or Levenberg-Marquardt (LM). The advantage of this

Shape optimisation of an underwater Bernoulli gripper

NASA Astrophysics Data System (ADS)

Flint, Tim; Sellier, Mathieu

2015-11-01

In this work, we are interested in maximising the suction produced by an underwater Bernoulli gripper. Bernoulli grippers work by exploiting low pressure regions caused by the acceleration of a working fluid through a narrow channel, between the gripper and a surface, to provide a suction force. This mechanism allows for non-contact adhesion to various surfaces and may be used to hold a robot to the hull of a ship while it inspects welds for example. A Bernoulli type pressure analysis was used to model the system with a Darcy friction factor approximation to include the effects of frictional losses. The analysis involved a constrained optimisation in order to avoid cavitation within the mechanism which would result in decreased performance and damage to surfaces. A sensitivity based method and gradient descent approach was used to find the optimum shape of a discretised surface. The model's accuracy has been quantified against finite volume computational fluid dynamics simulation (ANSYS CFX) using the k- ω SST turbulence model. Preliminary results indicate significant improvement in suction force when compared to a simple geometry by retaining a pressure just above that at which cavitation would occur over as much surface area as possible. Doctoral candidate in the Mechanical Engineering Department of the University of Canterbury, New Zealand.

Performance of Optimized Actuator and Sensor Arrays in an Active Noise Control System

NASA Technical Reports Server (NTRS)

Palumbo, D. L.; Padula, S. L.; Lyle, K. H.; Cline, J. H.; Cabell, R. H.

1996-01-01

Experiments have been conducted in NASA Langley's Acoustics and Dynamics Laboratory to determine the effectiveness of optimized actuator/sensor architectures and controller algorithms for active control of harmonic interior noise. Tests were conducted in a large scale fuselage model - a composite cylinder which simulates a commuter class aircraft fuselage with three sections of trim panel and a floor. Using an optimization technique based on the component transfer functions, combinations of 4 out of 8 piezoceramic actuators and 8 out of 462 microphone locations were evaluated against predicted performance. A combinatorial optimization technique called tabu search was employed to select the optimum transducer arrays. Three test frequencies represent the cases of a strong acoustic and strong structural response, a weak acoustic and strong structural response and a strong acoustic and weak structural response. Noise reduction was obtained using a Time Averaged/Gradient Descent (TAGD) controller. Results indicate that the optimization technique successfully predicted best and worst case performance. An enhancement of the TAGD control algorithm was also evaluated. The principal components of the actuator/sensor transfer functions were used in the PC-TAGD controller. The principal components are shown to be independent of each other while providing control as effective as the standard TAGD.

A nonlocal strain gradient model for dynamic deformation of orthotropic viscoelastic graphene sheets under time harmonic thermal load

NASA Astrophysics Data System (ADS)

Radwan, Ahmed F.; Sobhy, Mohammed

2018-06-01

This work presents a nonlocal strain gradient theory for the dynamic deformation response of a single-layered graphene sheet (SLGS) on a viscoelastic foundation and subjected to a time harmonic thermal load for various boundary conditions. Material of graphene sheets is presumed to be orthotropic and viscoelastic. The viscoelastic foundation is modeled as Kelvin-Voigt's pattern. Based on the two-unknown plate theory, the motion equations are obtained from the dynamic version of the virtual work principle. The nonlocal strain gradient theory is established from Eringen nonlocal and strain gradient theories, therefore, it contains two material scale parameters, which are nonlocal parameter and gradient coefficient. These scale parameters have two different effects on the graphene sheets. The obtained deflection is compared with that predicted in the literature. Additional numerical examples are introduced to illustrate the influences of the two length scale coefficients and other parameters on the dynamic deformation of the viscoelastic graphene sheets.

Real-Time Adaptive Control of Flow-Induced Cavity Tones

NASA Technical Reports Server (NTRS)

Kegerise, Michael A.; Cabell, Randolph H.; Cattafesta, Louis N.

2004-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. The adaptive control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. The algorithm was also able t o maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Controller performance was evaluated with a measure of output disturbance rejection and an input sensitivity transfer function. The results suggest that disturbances entering the cavity flow are colocated with the control input at the cavity leading edge. In that case, only tonal components of the cavity wall-pressure fluctuations can be suppressed and arbitrary broadband pressure reduction is not possible. In the control-algorithm development, the cavity dynamics are treated as linear and time invariant (LTI) for a fixed Mach number. The experimental results lend support this treatment.

A Space Affine Matching Approach to fMRI Time Series Analysis.

PubMed

Chen, Liang; Zhang, Weishi; Liu, Hongbo; Feng, Shigang; Chen, C L Philip; Wang, Huili

2016-07-01

For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.

The Dropout Learning Algorithm

PubMed Central

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

Parachute dynamics and stability analysis. [using nonlinear differential equations of motion

NASA Technical Reports Server (NTRS)

Ibrahim, S. K.; Engdahl, R. A.

1974-01-01

The nonlinear differential equations of motion for a general parachute-riser-payload system are developed. The resulting math model is then applied for analyzing the descent dynamics and stability characteristics of both the drogue stabilization phase and the main descent phase of the space shuttle solid rocket booster (SRB) recovery system. The formulation of the problem is characterized by a minimum number of simplifying assumptions and full application of state-of-the-art parachute technology. The parachute suspension lines and the parachute risers can be modeled as elastic elements, and the whole system may be subjected to specified wind and gust profiles in order to assess their effects on the stability of the recovery system.

Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

NASA Technical Reports Server (NTRS)

Kopasakis, George

1997-01-01

Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

Radiographic visualization of magma dynamics in an erupting volcano.

PubMed

Tanaka, Hiroyuki K M; Kusagaya, Taro; Shinohara, Hiroshi

2014-03-10

Radiographic imaging of magma dynamics in a volcanic conduit provides detailed information about ascent and descent of magma, the magma flow rate, the conduit diameter and inflation and deflation of magma due to volatile expansion and release. Here we report the first radiographic observation of the ascent and descent of magma along a conduit utilizing atmospheric (cosmic ray) muons (muography) with dynamic radiographic imaging. Time sequential radiographic images show that the top of the magma column ascends right beneath the crater floor through which the eruption column was observed. In addition to the visualization of this magma inflation, we report a sequence of images that show magma descending. We further propose that the monitoring of temporal variations in the gas volume fraction of magma as well as its position in a conduit can be used to support existing eruption prediction procedures.

[Landscape pattern gradient dynamics and desakota features in rapid urbanization area: a case study in Panyu of Guangzhou].

PubMed

Yu, Long-Sheng; Fu, Yi-Fu; Yu, Huai-Yi; Li, Zhi-Qin

2011-01-01

In order to understand the landscape pattern gradient dynamics and desakota features in rapid urbanization area, this paper took the rapidly urbanizing Panyu District of Guangzhou City as a case, and analyzed its land use and land cover data, based on four Landsat TM images from 1990 to 2008. With the combination of gradient analysis and landscape pattern analysis, and by using the landscape indices in both class and landscape scales, the spatial dynamics and desakota feature of this rapidly urbanizing district were quantified. In the study district, there was a significant change in the landscape pattern, and a typical desakota feature presented along buffer gradient zones. Urban landscape increased and expanded annually, accompanied with serious fragmentation of agricultural landscape. The indices patch density, contagion, and landscape diversity, etc., changed regularly in the urbanization gradient, and the peak of landscape indices appeared in the gradient zone of 4-6 km away from the urban center. The landscape patterns at time series also reflected the differences among the dynamics in different gradient zones. The landscape pattern in desakota region was characterized by complex patch shape, high landscape diversity and fragmentation, and remarkable landscape dynamics. The peaks of landscape indices spread from the urban center to border areas, and desakota region was expanding gradually. The general trend of spatiotemporal dynamics in desakota region and its driving forces were discussed, which could be benefit to the regional land use policy-making and sustainable development planning.

Generation and precise control of dynamic biochemical gradients for cellular assays

NASA Astrophysics Data System (ADS)

Saka, Yasushi; MacPherson, Murray; Giuraniuc, Claudiu V.

2017-03-01

Spatial gradients of diffusible signalling molecules play crucial roles in controlling diverse cellular behaviour such as cell differentiation, tissue patterning and chemotaxis. In this paper, we report the design and testing of a microfluidic device for diffusion-based gradient generation for cellular assays. A unique channel design of the device eliminates cross-flow between the source and sink channels, thereby stabilizing gradients by passive diffusion. The platform also enables quick and flexible control of chemical concentration that makes highly dynamic gradients in diffusion chambers. A model with the first approximation of diffusion and surface adsorption of molecules recapitulates the experimentally observed gradients. Budding yeast cells cultured in a gradient of a chemical inducer expressed a reporter fluorescence protein in a concentration-dependent manner. This microfluidic platform serves as a versatile prototype applicable to a broad range of biomedical investigations.

Monte Carlo-based Reconstruction in Water Cherenkov Detectors using Chroma

NASA Astrophysics Data System (ADS)

Seibert, Stanley; Latorre, Anthony

2012-03-01

We demonstrate the feasibility of event reconstruction---including position, direction, energy and particle identification---in water Cherenkov detectors with a purely Monte Carlo-based method. Using a fast optical Monte Carlo package we have written, called Chroma, in combination with several variance reduction techniques, we can estimate the value of a likelihood function for an arbitrary event hypothesis. The likelihood can then be maximized over the parameter space of interest using a form of gradient descent designed for stochastic functions. Although slower than more traditional reconstruction algorithms, this completely Monte Carlo-based technique is universal and can be applied to a detector of any size or shape, which is a major advantage during the design phase of an experiment. As a specific example, we focus on reconstruction results from a simulation of the 200 kiloton water Cherenkov far detector option for LBNE.

Analysis of a New Variational Model to Restore Point-Like and Curve-Like Singularities in Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aubert, Gilles, E-mail: gaubert@unice.fr; Blanc-Feraud, Laure, E-mail: Laure.Blanc-Feraud@inria.fr; Graziani, Daniele, E-mail: Daniele.Graziani@inria.fr

2013-02-15

The paper is concerned with the analysis of a new variational model to restore point-like and curve-like singularities in biological images. To this aim we investigate the variational properties of a suitable energy which governs these pathologies. Finally in order to realize numerical experiments we minimize, in the discrete setting, a regularized version of this functional by fast descent gradient scheme.

On Nonconvex Decentralized Gradient Descent

DTIC Science & Technology

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

Applying Gradient Descent in Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

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.

Spacecraft-to-Earth Communications for Juno and Mars Science Laboratory Critical Events

NASA Technical Reports Server (NTRS)

Soriano, Melissa; Finley, Susan; Jongeling, Andre; Fort, David; Goodhart, Charles; Rogstad, David; Navarro, Robert

2012-01-01

Deep Space communications typically utilize closed loop receivers and Binary Phase Shift Keying (BPSK) or Quadrature Phase Shift Keying (QPSK). Critical spacecraft events include orbit insertion and entry, descent, and landing.---Low gain antennas--> low signal -to-noise-ratio.---High dynamics such as parachute deployment or spin --> Doppler shift. During critical events, open loop receivers and Multiple Frequency Shift Keying (MFSK) used. Entry, Descent, Landing (EDL) Data Analysis (EDA) system detects tones in real-time.

Investigation in Simulated Vertical Descent of the Characteristics of a Cargo-Dropping Device having Extensible Rotating Blades

NASA Technical Reports Server (NTRS)

Stone, Ralph W., Jr.; Hultz, Burton E.

1949-01-01

The characteristics of a cargo-dropping device having extensible rotating blades as load-carrying surfaces have been studied in simulated vertical descent in the Langley 20-foot free-spinning tunnel. The investigation included tests to determine the variation in vertical sinking speed with load. A study of the blade characteristics and of the test results indicated a method of dynamically balancing the blades to permit proper functioning of the device.

Speckle-metric-optimization-based adaptive optics for laser beam projection and coherent beam combining.

PubMed

Vorontsov, Mikhail; Weyrauch, Thomas; Lachinova, Svetlana; Gatz, Micah; Carhart, Gary

2012-07-15

Maximization of a projected laser beam's power density at a remotely located extended object (speckle target) can be achieved by using an adaptive optics (AO) technique based on sensing and optimization of the target-return speckle field's statistical characteristics, referred to here as speckle metrics (SM). SM AO was demonstrated in a target-in-the-loop coherent beam combining experiment using a bistatic laser beam projection system composed of a coherent fiber-array transmitter and a power-in-the-bucket receiver. SM sensing utilized a 50 MHz rate dithering of the projected beam that provided a stair-mode approximation of the outgoing combined beam's wavefront tip and tilt with subaperture piston phases. Fiber-integrated phase shifters were used for both the dithering and SM optimization with stochastic parallel gradient descent control.

Contrast Gradient-Based Blood Velocimetry With Computed Tomography: Theory, Simulations, and Proof of Principle in a Dynamic Flow Phantom.

PubMed

Korporaal, Johannes G; Benz, Matthias R; Schindera, Sebastian T; Flohr, Thomas G; Schmidt, Bernhard

2016-01-01

The aim of this study was to introduce a new theoretical framework describing the relationship between the blood velocity, computed tomography (CT) acquisition velocity, and iodine contrast enhancement in CT images, and give a proof of principle of contrast gradient-based blood velocimetry with CT. The time-averaged blood velocity (v(blood)) inside an artery along the axis of rotation (z axis) is described as the mathematical division of a temporal (Hounsfield unit/second) and spatial (Hounsfield unit/centimeter) iodine contrast gradient. From this new theoretical framework, multiple strategies for calculating the time-averaged blood velocity from existing clinical CT scan protocols are derived, and contrast gradient-based blood velocimetry was introduced as a new method that can calculate v(blood) directly from contrast agent gradients and the changes therein. Exemplarily, the behavior of this new method was simulated for image acquisition with an adaptive 4-dimensional spiral mode consisting of repeated spiral acquisitions with alternating scan direction. In a dynamic flow phantom with flow velocities between 5.1 and 21.2 cm/s, the same acquisition mode was used to validate the simulations and give a proof of principle of contrast gradient-based blood velocimetry in a straight cylinder of 2.5 cm diameter, representing the aorta. In general, scanning with the direction of blood flow results in decreased and scanning against the flow in increased temporal contrast agent gradients. Velocity quantification becomes better for low blood and high acquisition speeds because the deviation of the measured contrast agent gradient from the temporal gradient will increase. In the dynamic flow phantom, a modulation of the enhancement curve, and thus alternation of the contrast agent gradients, can be observed for the adaptive 4-dimensional spiral mode and is in agreement with the simulations. The measured flow velocities in the downslopes of the enhancement curves were in good agreement with the expected values, although the accuracy and precision worsened with increasing flow velocities. The new theoretical framework increases the understanding of the relationship between the blood velocity, CT acquisition velocity, and iodine contrast enhancement in CT images, and it interconnects existing blood velocimetry methods with research on transluminary attenuation gradients. With these new insights, novel strategies for CT blood velocimetry, such as the contrast gradient-based method presented in this article, may be developed.

Incoherent beam combining based on the momentum SPGD algorithm

NASA Astrophysics Data System (ADS)

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.

Aeroassisted orbital maneuvering using Lyapunov optimal feedback control

NASA Technical Reports Server (NTRS)

Grantham, Walter J.; Lee, Byoung-Soo

1987-01-01

A Liapunov optimal feedback controller incorporating a preferred direction of motion at each state of the system which is opposite to the gradient of a specified descent function is developed for aeroassisted orbital transfer from high-earth orbit to LEO. The performances of the Liapunov controller and a calculus-of-variations open-loop minimum-fuel controller, both of which are based on the 1962 U.S. Standard Atmosphere, are simulated using both the 1962 U.S. Standard Atmosphere and an atmosphere corresponding to the STS-6 Space Shuttle flight. In the STS-6 atmosphere, the calculus-of-variations open-loop controller fails to exit the atmosphere, while the Liapunov controller achieves the optimal minimum-fuel conditions, despite the + or - 40 percent fluctuations in the STS-6 atmosphere.

Quantitative characterization of turbidity by radiative transfer based reflectance imaging

PubMed Central

Tian, Peng; Chen, Cheng; Jin, Jiahong; Hong, Heng; Lu, Jun Q.; Hu, Xin-Hua

2018-01-01

A new and noncontact approach of multispectral reflectance imaging has been developed to inversely determine the absorption coefficient of μa, the scattering coefficient of μs and the anisotropy factor g of a turbid target from one measured reflectance image. The incident beam was profiled with a diffuse reflectance standard for deriving both measured and calculated reflectance images. A GPU implemented Monte Carlo code was developed to determine the parameters with a conjugate gradient descent algorithm and the existence of unique solutions was shown. We noninvasively determined embedded region thickness in heterogeneous targets and estimated in vivo optical parameters of nevi from 4 patients between 500 and 950nm for melanoma diagnosis to demonstrate the potentials of quantitative reflectance imaging. PMID:29760971

New hybrid conjugate gradient methods with the generalized Wolfe line search.

PubMed

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.

A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-01-01

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

Dynamic and Thermal Turbulent Time Scale Modelling for Homogeneous Shear Flows

NASA Technical Reports Server (NTRS)

Schwab, John R.; Lakshminarayana, Budugur

1994-01-01

A new turbulence model, based upon dynamic and thermal turbulent time scale transport equations, is developed and applied to homogeneous shear flows with constant velocity and temperature gradients. The new model comprises transport equations for k, the turbulent kinetic energy; tau, the dynamic time scale; k(sub theta), the fluctuating temperature variance; and tau(sub theta), the thermal time scale. It offers conceptually parallel modeling of the dynamic and thermal turbulence at the two equation level, and eliminates the customary prescription of an empirical turbulent Prandtl number, Pr(sub t), thus permitting a more generalized prediction capability for turbulent heat transfer in complex flows and geometries. The new model also incorporates constitutive relations, based upon invariant theory, that allow the effects of nonequilibrium to modify the primary coefficients for the turbulent shear stress and heat flux. Predictions of the new model, along with those from two other similar models, are compared with experimental data for decaying homogeneous dynamic and thermal turbulence, homogeneous turbulence with constant temperature gradient, and homogeneous turbulence with constant temperature gradient and constant velocity gradient. The new model offers improvement in agreement with the data for most cases considered in this work, although it was no better than the other models for several cases where all the models performed poorly.

Natural learning in NLDA networks.

PubMed

González, Ana; Dorronsoro, José R

2007-07-01

Non Linear Discriminant Analysis (NLDA) networks combine a standard Multilayer Perceptron (MLP) transfer function with the minimization of a Fisher analysis criterion. In this work we will define natural-like gradients for NLDA network training. Instead of a more principled approach, that would require the definition of an appropriate Riemannian structure on the NLDA weight space, we will follow a simpler procedure, based on the observation that the gradient of the NLDA criterion function J can be written as the expectation nablaJ(W)=E[Z(X,W)] of a certain random vector Z and defining then I=E[Z(X,W)Z(X,W)(t)] as the Fisher information matrix in this case. This definition of I formally coincides with that of the information matrix for the MLP or other square error functions; the NLDA J criterion, however, does not have this structure. Although very simple, the proposed approach shows much faster convergence than that of standard gradient descent, even when its costlier complexity is taken into account. While the faster convergence of natural MLP batch training can be also explained in terms of its relationship with the Gauss-Newton minimization method, this is not the case for NLDA training, as we will see analytically and numerically that the hessian and information matrices are different.

Disaggregation and separation dynamics of magnetic particles in a microfluidic flow under an alternating gradient magnetic field

NASA Astrophysics Data System (ADS)

Cao, Quanliang; Li, Zhenhao; Wang, Zhen; Qi, Fan; Han, Xiaotao

2018-05-01

How to prevent particle aggregation in the magnetic separation process is of great importance for high-purity separation, while it is a challenging issue in practice. In this work, we report a novel method to solve this problem for improving the selectivity of size-based separation by use of a gradient alternating magnetic field. The specially designed magnetic field is capable of dynamically adjusting the magnetic field direction without changing the direction of magnetic gradient force acting on the particles. Using direct numerical simulations, we show that particles within a certain center-to-center distance are inseparable under a gradient static magnetic field since they are easy aggregated and then start moving together. By contrast, it has been demonstrated that alternating repulsive and attractive interaction forces between particles can be generated to avoid the formation of aggregations when the alternating gradient magnetic field with a given alternating frequency is applied, enabling these particles to be continuously separated based on size-dependent properties. The proposed magnetic separation method and simulation results have the significance for fundamental understanding of particle dynamic behavior and improving the separation efficiency.

STS-1 operational flight profile. Volume 5: Descent, cycle 3. Appendix C: Monte Carlo dispersion analysis

NASA Technical Reports Server (NTRS)

1980-01-01

The results of three nonlinear the Monte Carlo dispersion analyses for the Space Transportation System 1 Flight (STS-1) Orbiter Descent Operational Flight Profile, Cycle 3 are presented. Fifty randomly selected simulation for the end of mission (EOM) descent, the abort once around (AOA) descent targeted line are steep target line, and the AOA descent targeted to the shallow target line are analyzed. These analyses compare the flight environment with system and operational constraints on the flight environment and in some cases use simplified system models as an aid in assessing the STS-1 descent flight profile. In addition, descent flight envelops are provided as a data base for use by system specialists to determine the flight readiness for STS-1. The results of these dispersion analyses supersede results of the dispersion analysis previously documented.

Dynamic simulation of knee-joint loading during gait using force-feedback control and surrogate contact modelling.

PubMed

Walter, Jonathan P; Pandy, Marcus G

2017-10-01

The aim of this study was to perform multi-body, muscle-driven, forward-dynamics simulations of human gait using a 6-degree-of-freedom (6-DOF) model of the knee in tandem with a surrogate model of articular contact and force control. A forward-dynamics simulation incorporating position, velocity and contact force-feedback control (FFC) was used to track full-body motion capture data recorded for multiple trials of level walking and stair descent performed by two individuals with instrumented knee implants. Tibiofemoral contact force errors for FFC were compared against those obtained from a standard computed muscle control algorithm (CMC) with a 6-DOF knee contact model (CMC6); CMC with a 1-DOF translating hinge-knee model (CMC1); and static optimization with a 1-DOF translating hinge-knee model (SO). Tibiofemoral joint loads predicted by FFC and CMC6 were comparable for level walking, however FFC produced more accurate results for stair descent. SO yielded reasonable predictions of joint contact loading for level walking but significant differences between model and experiment were observed for stair descent. CMC1 produced the least accurate predictions of tibiofemoral contact loads for both tasks. Our findings suggest that reliable estimates of knee-joint loading may be obtained by incorporating position, velocity and force-feedback control with a multi-DOF model of joint contact in a forward-dynamics simulation of gait. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Sicily 2002 balloon campaign: a test of the HASI instrument

NASA Astrophysics Data System (ADS)

Bettanini, C.; Fulchignoni, M.; Angrilli, F.; Lion Stoppato, P. F.; Antonello, M.; Bastianello, S.; Bianchini, G.; Colombatti, G.; Ferri, F.; Flamini, E.; Gaborit, V.; Aboudan, A.

2004-01-01

A mock-up of the probe descending in the Titan atmosphere as part of the Huygens Cassini Mission was successfully launched and recovered on 30th May 2002 after a stratospheric balloon launch from the Italian Space Agency Base "Luigi Broglio" in Trapani, Sicily. To simulate the Huygens mission at Titan, the probe was lifted to an altitude of 32 km and then released to perform a 45 min parachute decelerated descent. The probe was hosting spares of HASI instruments, housekeeping sensors and other dedicated sensors, Beagle II UV Sensors and Huygens Tilt Sensor, for a total of 76 acquired sensor channels and sampled during the ascent, drift and descent phase. An integrated data acquisition and instrument control system was developed, based on PC architecture and soft-real-time application. Sensors channels were sampled at the nominal HASI data rates, with a max rate of 1 kHz. Software was developed for data acquisition, onboard storage and telemetry transmission satisfying all requests for real-time monitoring, diagnostic and redundancy. The main goal of this flight was to verify sensor performance and perform a realistic functional test for HASI hardware in dynamical and environmental conditions similar to those of the Titan atmosphere as well as the impact detection sequence of HASI accelerometer and HASI during ground landing. Aerodynamic study of the probe has contributed in achieving descent velocity and spin rate profiles close to the ones envisioned for the Huygens Titan descent. Profiles have been calculated by solving a system of ODE describing the translational and rotational motion of the probe through the Earth's atmosphere during parachute aided descent. Results of these calculations have driven the choice of an appropriate angle of attack of the blades in the bottom of the probe and ballast weight during flight. Passive thermal control of the probe has also been designed and implemented in order to guarantee proper temperature ranges on critical components and instrument during all mission phases. Preliminary results of main instruments are also presented in this work.

A new approach to blind deconvolution of astronomical images

NASA Astrophysics Data System (ADS)

Vorontsov, S. V.; Jefferies, S. M.

2017-05-01

We readdress the strategy of finding approximate regularized solutions to the blind deconvolution problem, when both the object and the point-spread function (PSF) have finite support. Our approach consists in addressing fixed points of an iteration in which both the object x and the PSF y are approximated in an alternating manner, discarding the previous approximation for x when updating x (similarly for y), and considering the resultant fixed points as candidates for a sensible solution. Alternating approximations are performed by truncated iterative least-squares descents. The number of descents in the object- and in the PSF-space play a role of two regularization parameters. Selection of appropriate fixed points (which may not be unique) is performed by relaxing the regularization gradually, using the previous fixed point as an initial guess for finding the next one, which brings an approximation of better spatial resolution. We report the results of artificial experiments with noise-free data, targeted at examining the potential capability of the technique to deconvolve images of high complexity. We also show the results obtained with two sets of satellite images acquired using ground-based telescopes with and without adaptive optics compensation. The new approach brings much better results when compared with an alternating minimization technique based on positivity-constrained conjugate gradients, where the iterations stagnate when addressing data of high complexity. In the alternating-approximation step, we examine the performance of three different non-blind iterative deconvolution algorithms. The best results are provided by the non-negativity-constrained successive over-relaxation technique (+SOR) supplemented with an adaptive scheduling of the relaxation parameter. Results of comparable quality are obtained with steepest descents modified by imposing the non-negativity constraint, at the expense of higher numerical costs. The Richardson-Lucy (or expectation-maximization) algorithm fails to locate stable fixed points in our experiments, due apparently to inappropriate regularization properties.

Independent Assessment of the Backshell Pressure Field for Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2)

NASA Technical Reports Server (NTRS)

Prince, Jill L.; Shoenenberger, Mark

2017-01-01

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) project requested that the NASA Engineering and Safety Center (NESC) support a ballistic range test to measure backshell pressures on scale models of the Mars 2020 entry capsule. The MEDLI2 project needed the test to provide important dynamic pressure data to help select a backshell pressure port, quantify drag coefficient reconstruction uncertainties, and design the data acquisition hardware. This document contains the outcome of the NESC assessment.

Computer program development and user's manual for program PARACH. [to investigate parachute spent solid rocket booster during terminal descent

NASA Technical Reports Server (NTRS)

Murphree, H. I.

1979-01-01

A user's manual is provided for program PARACH, a FORTRAN digital computer program operational on the Univac 1108. A description of the program and operating instructions for it are included. Program PARACH is used to study the interaction dynamics of a parachute and its payload during terminal descent. Operating instructions, required input data, program options and limitations, and output data are described. Subroutines used in this program are also listed and explained.

Radiographic visualization of magma dynamics in an erupting volcano

PubMed Central

Tanaka, Hiroyuki K. M.; Kusagaya, Taro; Shinohara, Hiroshi

2014-01-01

Radiographic imaging of magma dynamics in a volcanic conduit provides detailed information about ascent and descent of magma, the magma flow rate, the conduit diameter and inflation and deflation of magma due to volatile expansion and release. Here we report the first radiographic observation of the ascent and descent of magma along a conduit utilizing atmospheric (cosmic ray) muons (muography) with dynamic radiographic imaging. Time sequential radiographic images show that the top of the magma column ascends right beneath the crater floor through which the eruption column was observed. In addition to the visualization of this magma inflation, we report a sequence of images that show magma descending. We further propose that the monitoring of temporal variations in the gas volume fraction of magma as well as its position in a conduit can be used to support existing eruption prediction procedures. PMID:24614612

A mesh gradient technique for numerical optimization

NASA Technical Reports Server (NTRS)

Willis, E. A., Jr.

1973-01-01

A class of successive-improvement optimization methods in which directions of descent are defined in the state space along each trial trajectory are considered. The given problem is first decomposed into two discrete levels by imposing mesh points. Level 1 consists of running optimal subarcs between each successive pair of mesh points. For normal systems, these optimal two-point boundary value problems can be solved by following a routine prescription if the mesh spacing is sufficiently close. A spacing criterion is given. Under appropriate conditions, the criterion value depends only on the coordinates of the mesh points, and its gradient with respect to those coordinates may be defined by interpreting the adjoint variables as partial derivatives of the criterion value function. In level 2, the gradient data is used to generate improvement steps or search directions in the state space which satisfy the boundary values and constraints of the given problem.

River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal

2012-06-01

Estimating sediment volume carried by a river is an important issue in water resources engineering. This paper compares the accuracy of three different soft computing methods, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP), in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data. The daily rainfall, streamflow and suspended sediment concentration data from Eel River near Dos Rios, at California, USA are used as a case study. The comparison results indicate that the GEP model performs better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. Levenberg-Marquardt, conjugate gradient and gradient descent training algorithms were used for the ANN models. Out of three algorithms, the Conjugate gradient algorithm was found to be better than the others.

On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms

NASA Astrophysics Data System (ADS)

Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2015-03-01

The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial

Learning Structured Classifiers with Dual Coordinate Ascent

DTIC Science & Technology

2010-06-01

stochastic gradient descent (SGD) [LeCun et al., 1998], and the margin infused relaxed algorithm (MIRA) [ Crammer et al., 2006]. This paper presents a...evaluate these methods on the Prague Dependency Treebank us- ing online large-margin learning tech- niques ( Crammer et al., 2003; McDonald et al., 2005...between two kinds of factors: hard constraint factors, which are used to rule out forbidden par- tial assignments by mapping them to zero potential values

A network of spiking neurons for computing sparse representations in an energy efficient way

PubMed Central

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B.

2013-01-01

Computing sparse redundant representations is an important problem both in applied mathematics and neuroscience. In many applications, this problem must be solved in an energy efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating via low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, such operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We compare the numerical performance of HDA with existing algorithms and show that in the asymptotic regime the representation error of HDA decays with time, t, as 1/t. We show that HDA is stable against time-varying noise, specifically, the representation error decays as 1/t for Gaussian white noise. PMID:22920853

A network of spiking neurons for computing sparse representations in an energy-efficient way.

PubMed

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B

2012-11-01

Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/√t for gaussian white noise.

Dynamic properties of polydisperse colloidal particles in the presence of thermal gradient studied by a modified Brownian dynamic model

NASA Astrophysics Data System (ADS)

Song, Dongxing; Jin, Hui; Jing, Dengwei; Wang, Xin

2018-03-01

Aggregation and migration of colloidal particles under the thermal gradient widely exists in nature and many industrial processes. In this study, dynamic properties of polydisperse colloidal particles in the presence of thermal gradient were studied by a modified Brownian dynamic model. Other than the traditional forces on colloidal particles, including Brownian force, hydrodynamic force, and electrostatic force from other particles, the electrostatic force from the asymmetric ionic diffusion layer under a thermal gradient has been considered and introduced into the Brownian dynamic model. The aggregation ratio of particles (R A), the balance time (t B) indicating the time threshold when {{R}A} becomes constant, the porosity ({{P}BA} ), fractal dimension (D f) and distributions of concentration (DISC) and aggregation (DISA) for the aggregated particles were discussed based on this model. The aggregated structures formed by polydisperse particles are less dense and the particles therein are loosely bonded. Also it showed a quite large compressibility as the increases of concentration and interparticle potential can significantly increase the fractal dimension. The thermal gradient can induce two competitive factors leading to a two-stage migration of particles. When t<{{t}B} , the unsynchronized aggregation is dominant and the particles slightly migrate along the thermal gradient. When t>{{t}B} , the thermophoresis becomes dominant thus the migrations of particles are against the thermal gradient. The effect of thermophoresis on the aggregate structures was found to be similar to the effect of increasing particle concentration. This study demonstrates how the thermal gradient affects the aggregation of monodisperse and polydisperse particles and can be a guide for the biomimetics and precise control of colloid system under the thermal gradient. Moreover, our model can be easily extended to other more complex colloidal systems considering shear, temperature fluctuation, surfactant, etc.

Smoothing of cost function leads to faster convergence of neural network learning

NASA Astrophysics Data System (ADS)

Xu, Li-Qun; Hall, Trevor J.

1994-03-01

One of the major problems in supervised learning of neural networks is the inevitable local minima inherent in the cost function f(W,D). This often makes classic gradient-descent-based learning algorithms that calculate the weight updates for each iteration according to (Delta) W(t) equals -(eta) (DOT)$DELwf(W,D) powerless. In this paper we describe a new strategy to solve this problem, which, adaptively, changes the learning rate and manipulates the gradient estimator simultaneously. The idea is to implicitly convert the local- minima-laden cost function f((DOT)) into a sequence of its smoothed versions {f(beta t)}Ttequals1, which, subject to the parameter (beta) t, bears less details at time t equals 1 and gradually more later on, the learning is actually performed on this sequence of functionals. The corresponding smoothed global minima obtained in this way, {Wt}Ttequals1, thus progressively approximate W-the desired global minimum. Experimental results on a nonconvex function minimization problem and a typical neural network learning task are given, analyses and discussions of some important issues are provided.

Broiler weight estimation based on machine vision and artificial neural network.

PubMed

Amraei, S; Abdanan Mehdizadeh, S; Salari, S

2017-04-01

1. Machine vision and artificial neural network (ANN) procedures were used to estimate live body weight of broiler chickens in 30 1-d-old broiler chickens reared for 42 d. 2. Imaging was performed two times daily. To localise chickens within the pen, an ellipse fitting algorithm was used and the chickens' head and tail removed using the Chan-Vese method. 3. The correlations between the body weight and 6 physical extracted features indicated that there were strong correlations between body weight and the 5 features including area, perimeter, convex area, major and minor axis length. 5. According to statistical analysis there was no significant difference between morning and afternoon data over 42 d. 6. In an attempt to improve the accuracy of live weight approximation different ANN techniques, including Bayesian regulation, Levenberg-Marquardt, Scaled conjugate gradient and gradient descent were used. Bayesian regulation with R 2 value of 0.98 was the best network for prediction of broiler weight. 7. The accuracy of the machine vision technique was examined and most errors were less than 50 g.

Assessment on EXPERT Descent and Landing System Aerodynamics

NASA Astrophysics Data System (ADS)

Wong, H.; Muylaert, J.; Northey, D.; Riley, D.

2009-01-01

EXPERT is a re-entry vehicle designed for validation of aero-thermodynamic models, numerical schemes in Computational Fluid Dynamics codes and test facilities for measuring flight data under an Earth re-entry environment. This paper addresses the design for the descent and landing sequence for EXPERT. It includes the descent sequence, the choice of drogue and main parachutes, and the parachute deployment condition, which can be supersonic or subsonic. The analysis is based mainly on an engineering tool, PASDA, together with some hand calculations for parachute sizing and design. The tool consists of a detailed 6-DoF simulation performed with the aerodynamics database of the vehicle, an empirical wakes model and the International Standard Atmosphere database. The aerodynamics database for the vehicle is generated by DNW experimental data and CFD codes within the framework of an ESA contract to CIRA. The analysis will be presented in terms of altitude, velocity, accelerations, angle-of- attack, pitch angle and angle of rigging line. Discussion on the advantages and disadvantages of each parachute deployment condition is included in addition to some comparison with the available data based on a Monte-Carlo method from a Russian company, FSUE NIIPS. Sensitivity on wind speed to the performance of EXPERT is shown to be strong. Supersonic deployment of drogue shows a better performance in stability at the expense of a larger G-load than those from the subsonic deployment of drogue. Further optimization on the parachute design is necessary in order to fulfill all the EXPERT specifications.

A spatiotemporally controllable chemical gradient generator via acoustically oscillating sharp-edge structures.

PubMed

Huang, Po-Hsun; Chan, Chung Yu; Li, Peng; Nama, Nitesh; Xie, Yuliang; Wei, Cheng-Hsin; Chen, Yuchao; Ahmed, Daniel; Huang, Tony Jun

2015-11-07

The ability to generate stable, spatiotemporally controllable concentration gradients is critical for resolving the dynamics of cellular response to a chemical microenvironment. Here we demonstrate an acoustofluidic gradient generator based on acoustically oscillating sharp-edge structures, which facilitates in a step-wise fashion the rapid mixing of fluids to generate tunable, dynamic chemical gradients. By controlling the driving voltage of a piezoelectric transducer, we demonstrated that the chemical gradient profiles can be conveniently altered (spatially controllable). By adjusting the actuation time of the piezoelectric transducer, moreover, we generated pulsatile chemical gradients (temporally controllable). With these two characteristics combined, we have developed a spatiotemporally controllable gradient generator. The applicability and biocompatibility of our acoustofluidic gradient generator are validated by demonstrating the migration of human dermal microvascular endothelial cells (HMVEC-d) in response to a generated vascular endothelial growth factor (VEGF) gradient, and by preserving the viability of HMVEC-d cells after long-term exposure to an acoustic field. Our device features advantages such as simple fabrication and operation, compact and biocompatible device, and generation of spatiotemporally tunable gradients.

Hybrid Active-Passive Systems for Control of Aircraft Interior Noise

NASA Technical Reports Server (NTRS)

Fuller, Chris R.

1999-01-01

Previous work has demonstrated the large potential for hybrid active-passive systems for attenuating interior noise in aircraft fuselages. The main advantage of an active-passive system is, by utilizing the natural dynamics of the actuator system, the control actuator power and weight is markedly reduced and stability/robustness is enhanced. Three different active-passive approaches were studied in the past year. The first technique utilizes multiple tunable vibration absorbers (ATVA) for reducing narrow band sound radiated from panels and transmitted through fuselage structures. The focus is on reducing interior noise due to propeller or turbo fan harmonic excitation. Two types of tunable vibration absorbers were investigated; a solid state system based upon a piezoelectric mechanical exciter and an electromechanical system based upon a Motran shaker. Both of these systems utilize a mass-spring dynamic effect to maximize tile output force near resonance of the shaker system and so can also be used as vibration absorbers. The dynamic properties of the absorbers (i.e. resonance frequency) were modified using a feedback signal from an accelerometer mounted on the active mass, passed through a compensator and fed into the drive component of the shaker system (piezoelectric element or voice coil respectively). The feedback loop consisted of a two coefficient FIR filter, implemented on a DSP, where the input is acceleration of tile ATVA mass and the output is a force acting in parallel with the stiffness of the absorber. By separating the feedback signal into real and imaginary components, the effective natural frequency and damping of the ATVA can be altered independently. This approach gave control of the resonance frequencies while also allowing the simultaneous removal of damping from the ATVA, thus increasing the ease of controllability and effectiveness. In order to obtain a "tuned" vibration absorber the chosen resonant frequency was set to the excitation frequency. In order to minimize sound radiation a gradient descent algorithm was developed which globally adapted the resonance frequencies of multiple ATVA's while minimizing a cost based upon the radiated sound power or sound energy obtained from an array of microphones.

Gradient Dynamics and Entropy Production Maximization

NASA Astrophysics Data System (ADS)

Janečka, Adam; Pavelka, Michal

2018-01-01

We compare two methods for modeling dissipative processes, namely gradient dynamics and entropy production maximization. Both methods require similar physical inputs-how energy (or entropy) is stored and how it is dissipated. Gradient dynamics describes irreversible evolution by means of dissipation potential and entropy, it automatically satisfies Onsager reciprocal relations as well as their nonlinear generalization (Maxwell-Onsager relations), and it has statistical interpretation. Entropy production maximization is based on knowledge of free energy (or another thermodynamic potential) and entropy production. It also leads to the linear Onsager reciprocal relations and it has proven successful in thermodynamics of complex materials. Both methods are thermodynamically sound as they ensure approach to equilibrium, and we compare them and discuss their advantages and shortcomings. In particular, conditions under which the two approaches coincide and are capable of providing the same constitutive relations are identified. Besides, a commonly used but not often mentioned step in the entropy production maximization is pinpointed and the condition of incompressibility is incorporated into gradient dynamics.

Pixel-based OPC optimization based on conjugate gradients.

PubMed

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.

Effects of aircraft and flight parameters on energy-efficient profile descents in time-based metered traffic

NASA Technical Reports Server (NTRS)

Dejarnette, F. R.

1984-01-01

Concepts to save fuel while preserving airport capacity by combining time based metering with profile descent procedures were developed. A computer algorithm is developed to provide the flight crew with the information needed to fly from an entry fix to a metering fix and arrive there at a predetermined time, altitude, and airspeed. The flight from the metering fix to an aim point near the airport was calculated. The flight path is divided into several descent and deceleration segments. Descents are performed at constant Mach numbers or calibrated airspeed, whereas decelerations occur at constant altitude. The time and distance associated with each segment are calculated from point mass equations of motion for a clean configuration with idle thrust. Wind and nonstandard atmospheric properties have a large effect on the flight path. It is found that uncertainty in the descent Mach number has a large effect on the predicted flight time. Of the possible combinations of Mach number and calibrated airspeed for a descent, only small changes were observed in the fuel consumed.

Material parameter estimation with terahertz time-domain spectroscopy.

PubMed

Dorney, T D; Baraniuk, R G; Mittleman, D M

2001-07-01

Imaging systems based on terahertz (THz) time-domain spectroscopy offer a range of unique modalities owing to the broad bandwidth, subpicosecond duration, and phase-sensitive detection of the THz pulses. Furthermore, the possibility exists for combining spectroscopic characterization or identification with imaging because the radiation is broadband in nature. To achieve this, we require novel methods for real-time analysis of THz waveforms. This paper describes a robust algorithm for extracting material parameters from measured THz waveforms. Our algorithm simultaneously obtains both the thickness and the complex refractive index of an unknown sample under certain conditions. In contrast, most spectroscopic transmission measurements require knowledge of the sample's thickness for an accurate determination of its optical parameters. Our approach relies on a model-based estimation, a gradient descent search, and the total variation measure. We explore the limits of this technique and compare the results with literature data for optical parameters of several different materials.

Wavefront sensorless adaptive optics ophthalmoscopy in the human eye

PubMed Central

Hofer, Heidi; Sredar, Nripun; Queener, Hope; Li, Chaohong; Porter, Jason

2011-01-01

Wavefront sensor noise and fidelity place a fundamental limit on achievable image quality in current adaptive optics ophthalmoscopes. Additionally, the wavefront sensor ‘beacon’ can interfere with visual experiments. We demonstrate real-time (25 Hz), wavefront sensorless adaptive optics imaging in the living human eye with image quality rivaling that of wavefront sensor based control in the same system. A stochastic parallel gradient descent algorithm directly optimized the mean intensity in retinal image frames acquired with a confocal adaptive optics scanning laser ophthalmoscope (AOSLO). When imaging through natural, undilated pupils, both control methods resulted in comparable mean image intensities. However, when imaging through dilated pupils, image intensity was generally higher following wavefront sensor-based control. Despite the typically reduced intensity, image contrast was higher, on average, with sensorless control. Wavefront sensorless control is a viable option for imaging the living human eye and future refinements of this technique may result in even greater optical gains. PMID:21934779

Active semi-supervised learning method with hybrid deep belief networks.

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Stable modeling based control methods using a new RBF network.

PubMed

Beyhan, Selami; Alci, Musa

2010-10-01

This paper presents a novel model with radial basis functions (RBFs), which is applied successively for online stable identification and control of nonlinear discrete-time systems. First, the proposed model is utilized for direct inverse modeling of the plant to generate the control input where it is assumed that inverse plant dynamics exist. Second, it is employed for system identification to generate a sliding-mode control input. Finally, the network is employed to tune PID (proportional + integrative + derivative) controller parameters automatically. The adaptive learning rate (ALR), which is employed in the gradient descent (GD) method, provides the global convergence of the modeling errors. Using the Lyapunov stability approach, the boundedness of the tracking errors and the system parameters are shown both theoretically and in real time. To show the superiority of the new model with RBFs, its tracking results are compared with the results of a conventional sigmoidal multi-layer perceptron (MLP) neural network and the new model with sigmoid activation functions. To see the real-time capability of the new model, the proposed network is employed for online identification and control of a cascaded parallel two-tank liquid-level system. Even though there exist large disturbances, the proposed model with RBFs generates a suitable control input to track the reference signal better than other methods in both simulations and real time. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

A Relation Between the Eikonal Equation Associated to a Potential Energy Surface and a Hyperbolic Wave Equation.

PubMed

Bofill, Josep Maria; Quapp, Wolfgang; Caballero, Marc

2012-12-11

The potential energy surface (PES) of a molecule can be decomposed into equipotential hypersurfaces. We show in this article that the hypersurfaces are the wave fronts of a certain hyperbolic partial differential equation, a wave equation. It is connected with the gradient lines, or the steepest descent, or the steepest ascent lines of the PES. The energy seen as a reaction coordinate plays the central role in this treatment.

Transmit Designs for the MIMO Broadcast Channel With Statistical CSI

NASA Astrophysics Data System (ADS)

Wu, Yongpeng; Jin, Shi; Gao, Xiqi; McKay, Matthew R.; Xiao, Chengshan

2014-09-01

We investigate the multiple-input multiple-output broadcast channel with statistical channel state information available at the transmitter. The so-called linear assignment operation is employed, and necessary conditions are derived for the optimal transmit design under general fading conditions. Based on this, we introduce an iterative algorithm to maximize the linear assignment weighted sum-rate by applying a gradient descent method. To reduce complexity, we derive an upper bound of the linear assignment achievable rate of each receiver, from which a simplified closed-form expression for a near-optimal linear assignment matrix is derived. This reveals an interesting construction analogous to that of dirty-paper coding. In light of this, a low complexity transmission scheme is provided. Numerical examples illustrate the significant performance of the proposed low complexity scheme.

Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms.

PubMed

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.

Eye Movement Patterns of the Elderly during Stair Descent:Effect of Illumination

NASA Astrophysics Data System (ADS)

Kasahara, Satoko; Okabe, Sonoko; Nakazato, Naoko; Ohno, Yuko

The relationship between the eye movement pattern during stair descent and illumination was studied in 4 elderly people in comparison with that in 5 young people. The illumination condition was light (85.0±30.9 lx) or dark (0.7±0.3 lx), and data of eye movements were obtained using an eye mark recorder. A flight of 15 steps was used for the experiment, and data on 3 steps in the middle, on which the descent movements were stabilized, were analyzed. The elderly subjects pointed their eyes mostly directly in front in the facial direction regardless of the illumination condition, but the young subjects tended to look down under the light condition. The young subjects are considered to have confirmed the safety of the front by peripheral vision, checked the stepping surface by central vision, and still maintained the upright position without leaning forward during stair descent. The elderly subjects, in contrast, always looked at the visual target by central vision even under the light condition and leaned forward. The range of eye movements was larger vertically than horizontally in both groups, and a characteristic eye movement pattern of repeating a vertical shuttle movement synchronous with descent of each step was observed. Under the dark condition, the young subjects widened the range of vertical eye movements and reduced duration of fixation. The elderly subjects showed no change in the range of eye movements but increased duration of fixation during stair descent. These differences in the eye movements are considered to be compensatory reactions to narrowing of the vertical visual field, reduced dark adaptation, and reduced dynamic visual acuity due to aging. These characteristics of eye movements of the elderly lead to an anteriorly leaned posture and lack of attention to the front during stair descent.

The Cassini/Huygens Doppler Wind Experiment: Results from the Titan Descent

NASA Technical Reports Server (NTRS)

Bird, M. K.; Dutta-Roy, R.; Allison, M.; Asmar, S. W.; Atkinson, D. H.; Edenhofer, P.; Plettemeier, D.; Tyler, G. L.

2005-01-01

The primary objective of the Doppler Wind Experiment (DWE), one of the six scientific investigations comprising the payload of the ESA Huygens Probe, is a determination of the wind velocity in Titan's atmosphere. Measurements of the Doppler shift of the S-band (2040 MHz) carrier signal to the Cassini Orbiter and to Earth were recorded during the Probe descent in order to deduce wind-induced motion of the Probe to an accuracy better than 1 m s-1. An experiment with the same scientific goal was performed with the Galileo Probe at Jupiter. Analogous to the Galileo experience, it was anticipated that the frequency of the Huygens radio signal could be measured on Earth to obtain an additional component of the horizontal winds. Specific secondary science objectives of DWE include measurements of: (a) Doppler fluctuations to determine the turbulence spectrum and possible wave activity in the Titan atmosphere; (b) Doppler and signal level modulation to monitor Probe descent dynamics (e.g., spinrate/spinphase, parachute swing); (c) Probe coordinates and orientation during descent and after impact on Titan.

Studies of the hormonal control of postnatal testicular descent in the rat.

PubMed

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.

An optimization-based framework for anisotropic simplex mesh adaptation

NASA Astrophysics Data System (ADS)

Yano, Masayuki; Darmofal, David L.

2012-09-01

We present a general framework for anisotropic h-adaptation of simplex meshes. Given a discretization and any element-wise, localizable error estimate, our adaptive method iterates toward a mesh that minimizes error for a given degrees of freedom. Utilizing mesh-metric duality, we consider a continuous optimization problem of the Riemannian metric tensor field that provides an anisotropic description of element sizes. First, our method performs a series of local solves to survey the behavior of the local error function. This information is then synthesized using an affine-invariant tensor manipulation framework to reconstruct an approximate gradient of the error function with respect to the metric tensor field. Finally, we perform gradient descent in the metric space to drive the mesh toward optimality. The method is first demonstrated to produce optimal anisotropic meshes minimizing the L2 projection error for a pair of canonical problems containing a singularity and a singular perturbation. The effectiveness of the framework is then demonstrated in the context of output-based adaptation for the advection-diffusion equation using a high-order discontinuous Galerkin discretization and the dual-weighted residual (DWR) error estimate. The method presented provides a unified framework for optimizing both the element size and anisotropy distribution using an a posteriori error estimate and enables efficient adaptation of anisotropic simplex meshes for high-order discretizations.

Fluid Dynamical Profiles and Constants of Motionfrom d-Branes

NASA Astrophysics Data System (ADS)

Jackiw, R.; Polychronakos, A. P.

Various fluid mechanical systems enjoy a hidden, higher-dimensional dynamical Poincaré symmetry, which arises owing to their descent from a Nambu-Goto action. Also, for the same reason, there are equivalence transformations between different models. These interconnections, summarized by the diagram below, are discussed in our paper.

Quaternion-valued echo state networks.

PubMed

Xia, Yili; Jahanchahi, Cyrus; Mandic, Danilo P

2015-04-01

Quaternion-valued echo state networks (QESNs) are introduced to cater for 3-D and 4-D processes, such as those observed in the context of renewable energy (3-D wind modeling) and human centered computing (3-D inertial body sensors). The introduction of QESNs is made possible by the recent emergence of quaternion nonlinear activation functions with local analytic properties, required by nonlinear gradient descent training algorithms. To make QENSs second-order optimal for the generality of quaternion signals (both circular and noncircular), we employ augmented quaternion statistics to introduce widely linear QESNs. To that end, the standard widely linear model is modified so as to suit the properties of dynamical reservoir, typically realized by recurrent neural networks. This allows for a full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances, and a rigorous account of second-order noncircularity (improperness), and the corresponding power mismatch and coupling between the data components. Simulations in the prediction setting on both benchmark circular and noncircular signals and on noncircular real-world 3-D body motion data support the analysis.

Approximate description of Stokes shifts in ICT fluorescence emission

NASA Astrophysics Data System (ADS)

Saielli, Giacomo; Braun, David; Polimeno, Antonino; Nordio, Pier Luigi

1996-07-01

The time-resolved emission spectrum of a dual fluorescent prototype system like DMABN is associated with an intramolecular adiabatic charge-transfer reaction and the simultaneous relaxation of the polarization coordinate describing the dynamic behaviour of the polar solvent. The dynamic Stokes shift of the frequency maximum of the long-wavelength emission band related to the charge-transfer (CT) state towards the red region is interpreted as a consequence of a kinetic pathway which deviates from steepest descent to the CT state, the rate-determining step being the solvent relaxation. The present stochastic treatment is based on the assumption that internal and solvent coordinates could be described separately, neglecting coupling elements in the case of slow solvent relaxation.

The Yearly Variation in Fall-Winter Arctic Winter Vortex Descent

NASA Technical Reports Server (NTRS)

Schoeberl, Mark R.; Newman, Paul A.

1999-01-01

Using the change in HALOE methane profiles from early September to late March, we have estimated the minimum amount of diabatic descent within the polar which takes place during Arctic winter. The year to year variations are a result in the year to year variations in stratospheric wave activity which (1) modify the temperature of the vortex and thus the cooling rate; (2) reduce the apparent descent by mixing high amounts of methane into the vortex. The peak descent amounts from HALOE methane vary from l0km -14km near the arrival altitude of 25 km. Using a diabatic trajectory calculation, we compare forward and backward trajectories over the course of the winter using UKMO assimilated stratospheric data. The forward calculation agrees fairly well with the observed descent. The backward calculation appears to be unable to produce the observed amount of descent, but this is only an apparent effect due to the density decrease in parcels with altitude. Finally we show the results for unmixed descent experiments - where the parcels are fixed in latitude and longitude and allowed to descend based on the local cooling rate. Unmixed descent is found to always exceed mixed descent, because when normal parcel motion is included, the path average cooling is always less than the cooling at a fixed polar point.

Control of Complex Dynamic Systems by Neural Networks

NASA Technical Reports Server (NTRS)

Spall, James C.; Cristion, John A.

1993-01-01

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

Planetary Wind Determination by Doppler Tracking of a Small Entry Probe Network

NASA Astrophysics Data System (ADS)

Atkinson, D. H.; Asmar, S.; Lazio, J.; Preston, R. A.

2017-12-01

To understand the origin and chemical/dynamical evolution of planetary atmospheres, measurements of atmospheric chemistries and processes including dynamics are needed. In situ measurements of planetary winds have been demonstrated on multiple occasions, including the Pioneer multiprobe and Venera missions to Venus, and the Galileo/Jupiter and Huygens/Titan probes. However, with the exception of Pioneer Venus, the retrieval of the zonal (east-west) wind profile has been limited to a single atmospheric slice. significantly improved understanding of the global dynamics requires sampling of multiple latitudes, times of day, and seasons. Simultaneous tracking of a small network of probes would enable measurements of spatially distributed winds providing a substantially improved characterization of a planet's global atmospheric circulation. Careful selection of descent locations would provide wind measurements at latitudes receiving different solar insolations, longitudes reflecting different times of day, and different seasons if both hemispheres are targeted. Doppler wind retrievals are limited by the stability of the probe and carrier spacecraft clocks, and must be equipped with an ultrastable oscillator, accelerometers for reconstructing the probe entry trajectory, and pressure / temperature sensors for determination of descent speed. A probe were equipped with both absolute and dynamic pressure sensors can measure planet center-relative and atmosphere-relative descent speeds, enabling the measurement of vertical winds from convection or atmospheric waves. Possible ambiguities arising from the assumption of no north-south winds could be removed if the probe were simultaneously tracked from the carrier spacecraft as well as from the Earth or a second spacecraft. The global circulation of an atmosphere comprising waves and flows that vary with location and depth is inherently tied to the thermal, chemical, and energy structure of the atmosphere. Wind measurements along a single vertical atmospheric slice cannot adequately represent the overall dynamical properties of the atmosphere. To more completely characterize the dynamical structure of a planetary atmosphere, it is proposed that future in situ planetary missions include a network of small probes dedicated to wind measurements.

Apollo lunar descent guidance

NASA Technical Reports Server (NTRS)

Klumpp, A. R.

1974-01-01

Apollo lunar-descent guidance transfers the Lunar Module from a near-circular orbit to touchdown, traversing a 17 deg central angle and a 15 km altitude in 11 min. A group of interactive programs in an onboard computer guide the descent, controlling altitude and the descent propulsion system throttle. A ground-based program pre-computes guidance targets. The concepts involved in this guidance are described. Explicit and implicit guidance are discussed, guidance equations are derived, and the earlier Apollo explicit equation is shown to be an inferior special case of the later implicit equation. Interactive guidance, by which the two-man crew selects a landing site in favorable terrain and directs the trajectory there, is discussed. Interactive terminal-descent guidance enables the crew to control the essentially vertical descent rate in order to land in minimum time with safe contact speed. The altitude maneuver routine uses concepts that make gimbal lock inherently impossible.

Individual-based models for adaptive diversification in high-dimensional phenotype spaces.

PubMed

Ispolatov, Iaroslav; Madhok, Vaibhav; Doebeli, Michael

2016-02-07

Most theories of evolutionary diversification are based on equilibrium assumptions: they are either based on optimality arguments involving static fitness landscapes, or they assume that populations first evolve to an equilibrium state before diversification occurs, as exemplified by the concept of evolutionary branching points in adaptive dynamics theory. Recent results indicate that adaptive dynamics may often not converge to equilibrium points and instead generate complicated trajectories if evolution takes place in high-dimensional phenotype spaces. Even though some analytical results on diversification in complex phenotype spaces are available, to study this problem in general we need to reconstruct individual-based models from the adaptive dynamics generating the non-equilibrium dynamics. Here we first provide a method to construct individual-based models such that they faithfully reproduce the given adaptive dynamics attractor without diversification. We then show that a propensity to diversify can be introduced by adding Gaussian competition terms that generate frequency dependence while still preserving the same adaptive dynamics. For sufficiently strong competition, the disruptive selection generated by frequency-dependence overcomes the directional evolution along the selection gradient and leads to diversification in phenotypic directions that are orthogonal to the selection gradient. Copyright © 2015 Elsevier Ltd. All rights reserved.

Controlling bridging and pinching with pixel-based mask for inverse lithography

NASA Astrophysics Data System (ADS)

Kobelkov, Sergey; Tritchkov, Alexander; Han, JiWan

2016-03-01

Inverse Lithography Technology (ILT) has become a viable computational lithography candidate in recent years as it can produce mask output that results in process latitude and CD control in the fab that is hard to match with conventional OPC/SRAF insertion approaches. An approach to solving the inverse lithography problem as a nonlinear, constrained minimization problem over a domain mask pixels was suggested in the paper by Y. Granik "Fast pixel-based mask optimization for inverse lithography" in 2006. The present paper extends this method to satisfy bridging and pinching constraints imposed on print contours. Namely, there are suggested objective functions expressing penalty for constraints violations, and their minimization with gradient descent methods is considered. This approach has been tested with an ILT-based Local Printability Enhancement (LPTM) tool in an automated flow to eliminate hotspots that can be present on the full chip after conventional SRAF placement/OPC and has been applied in 14nm, 10nm node production, single and multiple-patterning flows.

Small-Tip-Angle Spokes Pulse Design Using Interleaved Greedy and Local Optimization Methods

PubMed Central

Grissom, William A.; Khalighi, Mohammad-Mehdi; Sacolick, Laura I.; Rutt, Brian K.; Vogel, Mika W.

2013-01-01

Current spokes pulse design methods can be grouped into methods based either on sparse approximation or on iterative local (gradient descent-based) optimization of the transverse-plane spatial frequency locations visited by the spokes. These two classes of methods have complementary strengths and weaknesses: sparse approximation-based methods perform an efficient search over a large swath of candidate spatial frequency locations but most are incompatible with off-resonance compensation, multifrequency designs, and target phase relaxation, while local methods can accommodate off-resonance and target phase relaxation but are sensitive to initialization and suboptimal local cost function minima. This article introduces a method that interleaves local iterations, which optimize the radiofrequency pulses, target phase patterns, and spatial frequency locations, with a greedy method to choose new locations. Simulations and experiments at 3 and 7 T show that the method consistently produces single- and multifrequency spokes pulses with lower flip angle inhomogeneity compared to current methods. PMID:22392822

The Double Star Orbit Initial Value Problem

NASA Astrophysics Data System (ADS)

Hensley, Hagan

2018-04-01

Many precise algorithms exist to find a best-fit orbital solution for a double star system given a good enough initial value. Desmos is an online graphing calculator tool with extensive capabilities to support animations and defining functions. It can provide a useful visual means of analyzing double star data to arrive at a best guess approximation of the orbital solution. This is a necessary requirement before using a gradient-descent algorithm to find the best-fit orbital solution for a binary system.

Learning in Modular Systems

DTIC Science & Technology

2010-05-07

important for deep modular systems is that taking a series of small update steps and stopping before convergence, so called early stopping, is a form of regu...larization around the initial parameters of the system . For example, the stochastic gradient descent 5 1 u + 1 v = 1 6‖x2‖q = ‖x‖22q 22 Chapter 2...Aside from the overall speed of the classifier, no quantitative performance analysis was given, and the role played by the features in the larger system

Differential Frequency Hopping (DFH) Modulation for Underwater Acoustic Communications and Networking

DTIC Science & Technology

2009-10-09

trains the coefficients c of a finite impulse response (FIR) filter by gradient descent. The coefficients at iteration k + 1 are computed with the update... absorption . Figure 9 shows the reflection loss as a function of grazing angle for this bottom model. Note that below 30◦ this bottom model predicts...less than 1 dB loss per ray bounce. 11 Figure 9: Jackson bottom reflection loss for sand at 15 kHz Absorption Loss The absorption loss in the medium was

Pressure gradients fail to predict diffusio-osmosis

NASA Astrophysics Data System (ADS)

Liu, Yawei; Ganti, Raman; Frenkel, Daan

2018-05-01

We present numerical simulations of diffusio-osmotic flow, i.e. the fluid flow generated by a concentration gradient along a solid-fluid interface. In our study, we compare a number of distinct approaches that have been proposed for computing such flows and compare them with a reference calculation based on direct, non-equilibrium molecular dynamics simulations. As alternatives, we consider schemes that compute diffusio-osmotic flow from the gradient of the chemical potentials of the constituent species and from the gradient of the component of the pressure tensor parallel to the interface. We find that the approach based on treating chemical potential gradients as external forces acting on various species agrees with the direct simulations, thereby supporting the approach of Marbach et al (2017 J. Chem. Phys. 146 194701). In contrast, an approach based on computing the gradients of the microscopic pressure tensor does not reproduce the direct non-equilibrium results.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

PubMed

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

Simulation Results for Airborne Precision Spacing along Continuous Descent Arrivals

NASA Technical Reports Server (NTRS)

Barmore, Bryan E.; Abbott, Terence S.; Capron, William R.; Baxley, Brian T.

2008-01-01

This paper describes the results of a fast-time simulation experiment and a high-fidelity simulator validation with merging streams of aircraft flying Continuous Descent Arrivals through generic airspace to a runway at Dallas-Ft Worth. Aircraft made small speed adjustments based on an airborne-based spacing algorithm, so as to arrive at the threshold exactly at the assigned time interval behind their Traffic-To-Follow. The 40 aircraft were initialized at different altitudes and speeds on one of four different routes, and then merged at different points and altitudes while flying Continuous Descent Arrivals. This merging and spacing using flight deck equipment and procedures to augment or implement Air Traffic Management directives is called Flight Deck-based Merging and Spacing, an important subset of a larger Airborne Precision Spacing functionality. This research indicates that Flight Deck-based Merging and Spacing initiated while at cruise altitude and well prior to the Terminal Radar Approach Control entry can significantly contribute to the delivery of aircraft at a specified interval to the runway threshold with a high degree of accuracy and at a reduced pilot workload. Furthermore, previously documented work has shown that using a Continuous Descent Arrival instead of a traditional step-down descent can save fuel, reduce noise, and reduce emissions. Research into Flight Deck-based Merging and Spacing is a cooperative effort between government and industry partners.

LANDER program manual: A lunar ascent and descent simulation

NASA Technical Reports Server (NTRS)

1988-01-01

LANDER is a computer program used to predict the trajectory and flight performance of a spacecraft ascending or descending between a low lunar orbit of 15 to 500 nautical miles (nm) and the lunar surface. It is a three degree-of-freedom simulation which is used to analyze the translational motion of the vehicle during descent. Attitude dynamics and rotational motion are not considered. The program can be used to simulate either an ascent from the Moon or a descent to the Moon. For an ascent, the spacecraft is initialized at the lunar surface and accelerates vertically away from the ground at full thrust. When the local velocity becomes 30 ft/s, the vehicle turns downrange with a pitch-over maneuver and proceeds to fly a gravity turn until Main Engine Cutoff (MECO). The spacecraft then coasts until it reaches the requested holding orbit where it performs an orbital insertion burn. During a descent simulation, the lander begins in the holding orbit and performs a deorbit burn. It then coasts to pericynthion, where it reignites its engines and begins a gravity turn descent. When the local horizontal velocity becomes zero, the lander pitches up to a vertical orientation and begins to hover in search of a landing site. The lander hovers for a period of time specified by the user, and then lands.

Multibody Modeling and Simulation for the Mars Phoenix Lander Entry, Descent and Landing

NASA Technical Reports Server (NTRS)

Queen, Eric M.; Prince, Jill L.; Desai, Prasun N.

2008-01-01

A multi-body flight simulation for the Phoenix Mars Lander has been developed that includes high fidelity six degree-of-freedom rigid-body models for the parachute and lander system. The simulation provides attitude and rate history predictions of all bodies throughout the flight, as well as loads on each of the connecting lines. In so doing, a realistic behavior of the descending parachute/lander system dynamics can be simulated that allows assessment of the Phoenix descent performance and identification of potential sensitivities for landing. This simulation provides a complete end-to-end capability of modeling the entire entry, descent, and landing sequence for the mission. Time histories of the parachute and lander aerodynamic angles are presented. The response of the lander system to various wind models and wind shears is shown to be acceptable. Monte Carlo simulation results are also presented.

Further evaluation of waves and turbulence encountered by the Galileo Probe during descent in Jupiter's atmosphere

NASA Astrophysics Data System (ADS)

Seiff, Alvin; Kirk, Donn B.; Mihalov, John; Knight, Tony C. D.

Data from the Galileo Probe in Jupiter descent indicated descent velocity oscillations as large as ±5 m/s on a height scale of a few km, which suggested gravity waves in the atmosphere between 4 and 20 bars (Seiff et al., 1998), an important observation for atmospheric stability and dynamics. But we now find these velocity fluctuations to be inconsistent with simultaneous measurements of mean accelerations, which were relatively steady. This conflict is resolved in favor of the accelerometers. The velocity fluctuations can be explained from digital uncertainties in the slow rate of pressure rise. However, the accelerometers did record higher frequency perturbations of up to 0.1g. Attributed to turbulence, these imply turbulent velocities from 0.3 to 5 m/s at scales of 10 to 40 m. However, they were at least partly a result of unsteady parachute aerodynamics.

Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient.

PubMed

Bian, Liheng; Suo, Jinli; Chung, Jaebum; Ou, Xiaoze; Yang, Changhuei; Chen, Feng; Dai, Qionghai

2016-06-10

Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging. Mathematically, Fourier ptychographic (FP) reconstruction can be implemented as a phase retrieval optimization process, in which we only obtain low resolution intensity images corresponding to the sub-bands of the sample's high resolution (HR) spatial spectrum, and aim to retrieve the complex HR spectrum. In real setups, the measurements always suffer from various degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil location error, which would largely degrade the reconstruction. To efficiently address these degenerations, we propose a novel FP reconstruction method under a gradient descent optimization framework in this paper. The technique utilizes Poisson maximum likelihood for better signal modeling, and truncated Wirtinger gradient for effective error removal. Results on both simulated data and real data captured using our laser-illuminated FPM setup show that the proposed method outperforms other state-of-the-art algorithms. Also, we have released our source code for non-commercial use.

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

PubMed Central

Wang, Xiao-Jing

2016-01-01

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied. PMID:26928718

Sloshing of Cryogenic Helium Driven by Lateral Impulse/Gravity Gradient-Dominated/or g-Jitter-Dominated Accelerations and Orbital Dynamics

NASA Technical Reports Server (NTRS)

Hung, R. J.; Long, Y. T.; Zu, G. J.

1996-01-01

The coupling of slosh dynamics within a partially filled rotating dewar of superfluid helium 11 with spacecraft orbital dynamics is investigated in response to the environmental disturbances of (a) lateral impulses, (b) gravity gradients and (c) g-jitter forces. The purpose of this study is to investigate how the coupling of helium 11 fluid slosh dynamics driven by three cases of environmental force with spacecraft dynamics can affect the bubble deformations and their associated fluid and spacecraft mass centre fluctuations. The numerical computation of slosh dynamics is based on a rotational frame, while the spacecraft dynamics is associated with a non-rotational frame. Results show that the major contribution of orbital dynamics is driven by coupling with slosh dynamics. Neglecting the effect of slosh dynamics acting on the spacecraft may lead to the wrong results for the development of orbital and attitude control techniques.

A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments.

PubMed

Bravo, Rafael; Axelrod, David E

2013-11-18

Normal colon crypts consist of stem cells, proliferating cells, and differentiated cells. Abnormal rates of proliferation and differentiation can initiate colon cancer. We have measured the variation in the number of each of these cell types in multiple crypts in normal human biopsy specimens. This has provided the opportunity to produce a calibrated computational model that simulates cell dynamics in normal human crypts, and by changing model parameter values, to simulate the initiation and treatment of colon cancer. An agent-based model of stochastic cell dynamics in human colon crypts was developed in the multi-platform open-source application NetLogo. It was assumed that each cell's probability of proliferation and probability of death is determined by its position in two gradients along the crypt axis, a divide gradient and in a die gradient. A cell's type is not intrinsic, but rather is determined by its position in the divide gradient. Cell types are dynamic, plastic, and inter-convertible. Parameter values were determined for the shape of each of the gradients, and for a cell's response to the gradients. This was done by parameter sweeps that indicated the values that reproduced the measured number and variation of each cell type, and produced quasi-stationary stochastic dynamics. The behavior of the model was verified by its ability to reproduce the experimentally observed monocolonal conversion by neutral drift, the formation of adenomas resulting from mutations either at the top or bottom of the crypt, and by the robust ability of crypts to recover from perturbation by cytotoxic agents. One use of the virtual crypt model was demonstrated by evaluating different cancer chemotherapy and radiation scheduling protocols. A virtual crypt has been developed that simulates the quasi-stationary stochastic cell dynamics of normal human colon crypts. It is unique in that it has been calibrated with measurements of human biopsy specimens, and it can simulate the variation of cell types in addition to the average number of each cell type. The utility of the model was demonstrated with in silico experiments that evaluated cancer therapy protocols. The model is available for others to conduct additional experiments.

Refractive index-based detection of gradient elution liquid chromatography using chip-integrated microring resonator arrays.

PubMed

Wade, James H; Bailey, Ryan C

2014-01-07

Refractive index-based sensors offer attractive characteristics as nondestructive and universal detectors for liquid chromatographic separations, but a small dynamic range and sensitivity to minor thermal perturbations limit the utility of commercial RI detectors for many potential applications, especially those requiring the use of gradient elutions. As such, RI detectors find use almost exclusively in sample abundant, isocratic separations when interfaced with high-performance liquid chromatography. Silicon photonic microring resonators are refractive index-sensitive optical devices that feature good sensitivity and tremendous dynamic range. The large dynamic range of microring resonators allows the sensors to function across a wide spectrum of refractive indices, such as that encountered when moving from an aqueous to organic mobile phase during a gradient elution, a key analytical advantage not supported in commercial RI detectors. Microrings are easily configured into sensor arrays, and chip-integrated control microrings enable real-time corrections of thermal drift. Thermal controls allow for analyses at any temperature and, in the absence of rigorous temperature control, obviates extended detector equilibration wait times. Herein, proof of concept isocratic and gradient elution separations were performed using well-characterized model analytes (e.g., caffeine, ibuprofen) in both neat buffer and more complex sample matrices. These experiments demonstrate the ability of microring arrays to perform isocratic and gradient elutions under ambient conditions, avoiding two major limitations of commercial RI-based detectors and maintaining comparable bulk RI sensitivity. Further benefit may be realized in the future through selective surface functionalization to impart degrees of postcolumn (bio)molecular specificity at the detection phase of a separation. The chip-based and microscale nature of microring resonators also make it an attractive potential detection technology that could be integrated within lab-on-a-chip and microfluidic separation devices.

Algorithm based on the Thomson problem for determination of equilibrium structures of metal nanoclusters

NASA Astrophysics Data System (ADS)

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.

A dual estimate method for aeromagnetic compensation

NASA Astrophysics Data System (ADS)

Ma, Ming; Zhou, Zhijian; Cheng, Defu

2017-11-01

Scalar aeromagnetic surveys have played a vital role in prospecting. However, before analysis of the surveys’ aeromagnetic data is possible, the aircraft’s magnetic interference should be removed. The extensively adopted linear model for aeromagnetic compensation is computationally efficient but faces an underfitting problem. On the other hand, the neural model proposed by Williams is more powerful at fitting but always suffers from an overfitting problem. This paper starts off with an analysis of these two models and then proposes a dual estimate method to combine them together to improve accuracy. This method is based on an unscented Kalman filter, but a gradient descent method is implemented over the iteration so that the parameters of the linear model are adjustable during flight. The noise caused by the neural model’s overfitting problem is suppressed by introducing an observation noise.

Algorithm based on the Thomson problem for determination of equilibrium structures of metal nanoclusters.

PubMed

Arias, E; Florez, E; Pérez-Torres, J F

2017-06-28

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: Cu 7 , Cu 9 , and Cu 11 as benchmark systems, and Cu 38 and Ni 9 as novel systems. New equilibrium structures for Cu 9 , Cu 11 , Cu 38 , and Ni 9 are reported.

Air-Traffic Controllers Evaluate The Descent Advisor

NASA Technical Reports Server (NTRS)

Tobias, Leonard; Volckers, Uwe; Erzberger, Heinz

1992-01-01

Report describes study of Descent Advisor algorithm: software automation aid intended to assist air-traffic controllers in spacing traffic and meeting specified times or arrival. Based partly on mathematical models of weather conditions and performances of aircraft, it generates suggested clearances, including top-of-descent points and speed-profile data to attain objectives. Study focused on operational characteristics with specific attention to how it can be used for prediction, spacing, and metering.

Elucidating the evolutionary conserved DNA-binding specificities of WRKY transcription factors by molecular dynamics and in vitro binding assays

PubMed Central

Brand, Luise H.; Fischer, Nina M.; Harter, Klaus; Kohlbacher, Oliver; Wanke, Dierk

2013-01-01

WRKY transcription factors constitute a large protein family in plants that is involved in the regulation of developmental processes and responses to biotic or abiotic stimuli. The question arises how stimulus-specific responses are mediated given that the highly conserved WRKY DNA-binding domain (DBD) exclusively recognizes the ‘TTGACY’ W-box consensus. We speculated that the W-box consensus might be more degenerate and yet undetected differences in the W-box consensus of WRKYs of different evolutionary descent exist. The phylogenetic analysis of WRKY DBDs suggests that they evolved from an ancestral group IIc-like WRKY early in the eukaryote lineage. A direct descent of group IIc WRKYs supports a monophyletic origin of all other group II and III WRKYs from group I by loss of an N-terminal DBD. Group I WRKYs are of paraphyletic descent and evolved multiple times independently. By homology modeling, molecular dynamics simulations and in vitro DNA–protein interaction-enzyme-linked immunosorbent assay with AtWRKY50 (IIc), AtWRKY33 (I) and AtWRKY11 (IId) DBDs, we revealed differences in DNA-binding specificities. Our data imply that other components are essentially required besides the W-box-specific binding to DNA to facilitate a stimulus-specific WRKY function. PMID:23975197

Preliminary test results of a flight management algorithm for fuel conservative descents in a time based metered traffic environment. [flight tests of an algorithm to minimize fuel consumption of aircraft based on flight time

NASA Technical Reports Server (NTRS)

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.

A dynamic regularized gradient model of the subgrid-scale stress tensor for large-eddy simulation

NASA Astrophysics Data System (ADS)

Vollant, A.; Balarac, G.; Corre, C.

2016-02-01

Large-eddy simulation (LES) solves only the large scales part of turbulent flows by using a scales separation based on a filtering operation. The solution of the filtered Navier-Stokes equations requires then to model the subgrid-scale (SGS) stress tensor to take into account the effect of scales smaller than the filter size. In this work, a new model is proposed for the SGS stress model. The model formulation is based on a regularization procedure of the gradient model to correct its unstable behavior. The model is developed based on a priori tests to improve the accuracy of the modeling for both structural and functional performances, i.e., the model ability to locally approximate the SGS unknown term and to reproduce enough global SGS dissipation, respectively. LES is then performed for a posteriori validation. This work is an extension to the SGS stress tensor of the regularization procedure proposed by Balarac et al. ["A dynamic regularized gradient model of the subgrid-scale scalar flux for large eddy simulations," Phys. Fluids 25(7), 075107 (2013)] to model the SGS scalar flux. A set of dynamic regularized gradient (DRG) models is thus made available for both the momentum and the scalar equations. The second objective of this work is to compare this new set of DRG models with direct numerical simulations (DNS), filtered DNS in the case of classic flows simulated with a pseudo-spectral solver and with the standard set of models based on the dynamic Smagorinsky model. Various flow configurations are considered: decaying homogeneous isotropic turbulence, turbulent plane jet, and turbulent channel flows. These tests demonstrate the stable behavior provided by the regularization procedure, along with substantial improvement for velocity and scalar statistics predictions.

Measuring small compartment dimensions by probing diffusion dynamics via Non-uniform Oscillating-Gradient Spin-Echo (NOGSE) NMR.

PubMed

Shemesh, Noam; Alvarez, Gonzalo A; Frydman, Lucio

2013-12-01

Noninvasive measurements of microstructure in materials, cells, and in biological tissues, constitute a unique capability of gradient-assisted NMR. Diffusion-diffraction MR approaches pioneered by Callaghan demonstrated this ability; Oscillating-Gradient Spin-Echo (OGSE) methodologies tackle the demanding gradient amplitudes required for observing diffraction patterns by utilizing constant-frequency oscillating gradient pairs that probe the diffusion spectrum, D(ω). Here we present a new class of diffusion MR experiments, termed Non-uniform Oscillating-Gradient Spin-Echo (NOGSE), which dynamically probe multiple frequencies of the diffusion spectral density at once, thus affording direct microstructural information on the compartment's dimension. The NOGSE methodology applies N constant-amplitude gradient oscillations; N-1 of these oscillations are spaced by a characteristic time x, followed by a single gradient oscillation characterized by a time y, such that the diffusion dynamics is probed while keeping (N-1)x+y≡TNOGSE constant. These constant-time, fixed-gradient-amplitude, multi-frequency attributes render NOGSE particularly useful for probing small compartment dimensions with relatively weak gradients - alleviating difficulties associated with probing D(ω) frequency-by-frequency or with varying relaxation weightings, as in other diffusion-monitoring experiments. Analytical descriptions of the NOGSE signal are given, and the sequence's ability to extract small compartment sizes with a sensitivity towards length to the sixth power, is demonstrated using a microstructural phantom. Excellent agreement between theory and experiments was evidenced even upon applying weak gradient amplitudes. An MR imaging version of NOGSE was also implemented in ex vivo pig spinal cords and mouse brains, affording maps based on compartment sizes. The effects of size distributions on NOGSE are also briefly analyzed. Copyright © 2013 Elsevier Inc. All rights reserved.

Piecewise convexity of artificial neural networks.

PubMed

Rister, Blaine; Rubin, Daniel L

2017-10-01

Although artificial neural networks have shown great promise in applications including computer vision and speech recognition, there remains considerable practical and theoretical difficulty in optimizing their parameters. The seemingly unreasonable success of gradient descent methods in minimizing these non-convex functions remains poorly understood. In this work we offer some theoretical guarantees for networks with piecewise affine activation functions, which have in recent years become the norm. We prove three main results. First, that the network is piecewise convex as a function of the input data. Second, that the network, considered as a function of the parameters in a single layer, all others held constant, is again piecewise convex. Third, that the network as a function of all its parameters is piecewise multi-convex, a generalization of biconvexity. From here we characterize the local minima and stationary points of the training objective, showing that they minimize the objective on certain subsets of the parameter space. We then analyze the performance of two optimization algorithms on multi-convex problems: gradient descent, and a method which repeatedly solves a number of convex sub-problems. We prove necessary convergence conditions for the first algorithm and both necessary and sufficient conditions for the second, after introducing regularization to the objective. Finally, we remark on the remaining difficulty of the global optimization problem. Under the squared error objective, we show that by varying the training data, a single rectifier neuron admits local minima arbitrarily far apart, both in objective value and parameter space. Copyright © 2017 Elsevier Ltd. All rights reserved.

Lunar Surface Access Module Descent Engine Turbopump Technology: Detailed Design

NASA Technical Reports Server (NTRS)

Alvarez, Erika; Forbes, John C.; Thornton, Randall J.

2010-01-01

The need for a high specific impulse LOX/LH2 pump-fed lunar lander engine has been established by NASA for the new lunar exploration architecture. Studies indicate that a 4-engine cluster in the thrust range of 9,000-lbf each is a candidate configuration for the main propulsion of the manned lunar lander vehicle. The lander descent engine will be required to perform multiple burns including the powered descent onto the lunar surface. In order to achieve the wide range of thrust required, the engines must be capable of throttling approximately 10:1. Working under internal research and development funding, NASA Marshall Space Flight Center (MSFC) has been conducting the development of a 9,000-lbf LOX/LH2 lunar lander descent engine technology testbed. This paper highlights the detailed design and analysis efforts to develop the lander engine Fuel Turbopump (FTP) whose operating speeds range from 30,000-rpm to 100,000-rpm. The capability of the FTP to operate across this wide range of speeds imposes several structural and dynamic challenges, and the small size of the FTP creates scaling and manufacturing challenges that are also addressed in this paper.

Lunar Surface Access Module Descent Engine Turbopump Technology: Detailed Design

NASA Technical Reports Server (NTRS)

Alarez, Erika; Thornton, Randall J.; Forbes, John C.

2008-01-01

The need for a high specific impulse LOX/LH2 pump-fed lunar lander engine has been established by NASA for the new lunar exploration architecture. Studies indicate that a 4-engine cluster in the thrust range of 9,000-lbf each is a candidate configuration for the main propulsion of the manned lunar lander vehicle. The lander descent engine will be required to perform minor mid-course corrections, a Lunar Orbit Insertion (LOI) burn, a de-orbit burn, and the powered descent onto the lunar surface. In order to achieve the wide range of thrust required, the engines must be capable of throttling approximately 10:1. Working under internal research and development funding, NASA Marshall Space Flight Center (MSFC) has been conducting the development of a 9,000-lbf LOX/LH2 lunar lander descent engine testbed. This paper highlights the detailed design and analysis efforts to develop the lander engine Fuel Turbopump (FTP) whose operating speeds range from 30,000-rpm to 100,000-rpm. The capability of the FTP to operate across this wide range of speeds imposes several structural and dynamic challenges, and the small size of the FTP creates scaling and manufacturing challenges that are also addressed in this paper.

Dynamic neural network models of the premotoneuronal circuitry controlling wrist movements in primates.

PubMed

Maier, M A; Shupe, L E; Fetz, E E

2005-10-01

Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations. The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.

Dynamic microscale temperature gradient in a gold nanorod solution measured by diffraction-limited nanothermometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Chengmingyue; Gan, Xiaosong; Li, Xiangping

2015-09-21

We quantify the dynamic microscale temperature gradient in a gold nanorod solution using quantum-dot-based microscopic fluorescence nanothermometry. By incorporating CdSe quantum dots into the solution as a nanothermometer, precise temperature mapping with diffraction-limited spatial resolution and sub-degree temperature resolution is achieved. The acquired data on heat generation and dissipation show an excellent agreement with theoretical simulations. This work reveals an effective approach for noninvasive temperature regulation with localized nanoheaters in microfluidic environment.

Trouble with diffusion: Reassessing hillslope erosion laws with a particle-based model

NASA Astrophysics Data System (ADS)

Tucker, Gregory E.; Bradley, D. Nathan

2010-03-01

Many geomorphic systems involve a broad distribution of grain motion length scales, ranging from a few particle diameters to the length of an entire hillslope or stream. Studies of analogous physical systems have revealed that such broad motion distributions can have a significant impact on macroscale dynamics and can violate the assumptions behind standard, local gradient flux laws. Here, a simple particle-based model of sediment transport on a hillslope is used to study the relationship between grain motion statistics and macroscopic landform evolution. Surface grains are dislodged by random disturbance events with probabilities and distances that depend on local microtopography. Despite its simplicity, the particle model reproduces a surprisingly broad range of slope forms, including asymmetric degrading scarps and cinder cone profiles. At low slope angles the dynamics are diffusion like, with a short-range, thin-tailed hop length distribution, a parabolic, convex upward equilibrium slope form, and a linear relationship between transport rate and gradient. As slope angle steepens, the characteristic grain motion length scale begins to approach the length of the slope, leading to planar equilibrium forms that show a strongly nonlinear correlation between transport rate and gradient. These high-probability, long-distance motions violate the locality assumption embedded in many common gradient-based geomorphic transport laws. The example of a degrading scarp illustrates the potential for grain motion dynamics to vary in space and time as topography evolves. This characteristic renders models based on independent, stationary statistics inapplicable. An accompanying analytical framework based on treating grain motion as a survival process is briefly outlined.

Rapid Generation of Optimal Asteroid Powered Descent Trajectories Via Convex Optimization

NASA Technical Reports Server (NTRS)

Pinson, Robin; Lu, Ping

2015-01-01

This paper investigates a convex optimization based method that can rapidly generate the fuel optimal asteroid powered descent trajectory. The ultimate goal is to autonomously design the optimal powered descent trajectory on-board the spacecraft immediately prior to the descent burn. Compared to a planetary powered landing problem, the major difficulty is the complex gravity field near the surface of an asteroid that cannot be approximated by a constant gravity field. This paper uses relaxation techniques and a successive solution process that seeks the solution to the original nonlinear, nonconvex problem through the solutions to a sequence of convex optimal control problems.

Bracing of the Reconstructed and Osteoarthritic Knee during High Dynamic Load Tasks.

PubMed

Hart, Harvi F; Crossley, Kay M; Collins, Natalie J; Ackland, David C

2017-06-01

Lateral compartment osteoarthritis accompanied by abnormal knee biomechanics is frequently reported in individuals with knee osteoarthritis after anterior cruciate ligament reconstruction (ACLR). The aim of this study was to evaluate changes in knee biomechanics produced by an adjusted and unadjusted varus knee brace during high dynamic loading activities in individuals with lateral knee osteoarthritis after ACLR and valgus malalignment. Nineteen participants who had undergone ACLR 5 to 20 yr previously and had symptomatic and radiographic lateral knee osteoarthritis with valgus malalignment were assessed. Quantitative motion analysis experiments were conducted during hopping, stair ascent, and descent under three test conditions: (i) no brace, (ii) unadjusted brace with sagittal plane support and neutral frontal plane alignment, and (iii) adjusted brace with sagittal plane support and varus realignment (valgus to neutral). Sagittal, frontal, and transverse plane knee kinematics, external joint moment, and angular impulse data were calculated. Relative to an unbraced knee, braced conditions significantly increased knee flexion and adduction angles during hopping (P = 0.003 and P = 0.005; respectively), stair ascent (P = 0.003 and P < 0.001, respectively), and descent (P = 0.009 and P < 0.001, respectively). In addition, the brace conditions increased knee flexion (P < 0.001) and adduction (P = 0.001) angular impulses and knee stiffness (P < 0.001) during hopping, as well as increased knee adduction moments during stair ascent (P = 0.008) and flexion moments during stair descent (P = 0.006). There were no significant differences between the adjusted and the unadjusted brace conditions (P > 0.05). A knee brace, with or without varus alignment, can modulate knee kinematics and external joint moments during hopping, stairs ascent, and descent in individuals with predominant lateral knee osteoarthritis after ACLR. Longer-term use of a brace may have implications in slowing osteoarthritis progression.

Excitonic enhancement of nonradiative energy transfer to bulk silicon with the hybridization of cascaded quantum dots

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yeltik, Aydan; Guzelturk, Burak; Akhavan, Shahab

2013-12-23

We report enhanced sensitization of silicon through nonradiative energy transfer (NRET) of the excitons in an energy-gradient structure composed of a cascaded bilayer of green- and red-emitting CdTe quantum dots (QDs) on bulk silicon. Here NRET dynamics were systematically investigated comparatively for the cascaded energy-gradient and mono-dispersed QD structures at room temperature. We show experimentally that NRET from the QD layer into silicon is enhanced by 40% in the case of an energy-gradient cascaded structure as compared to the mono-dispersed structures, which is in agreement with the theoretical analysis based on the excited state population-depopulation dynamics of the QDs.

Dynamic response of porous functionally graded material nanobeams subjected to moving nanoparticle based on nonlocal strain gradient theory

NASA Astrophysics Data System (ADS)

Barati, Mohammad Reza

2017-11-01

Up to now, nonlocal strain gradient theory (NSGT) is broadly applied to examine free vibration, static bending and buckling of nanobeams. This theory captures nonlocal stress field effects together with the microstructure-dependent strain gradient effects. In this study, forced vibrations of NSGT nanobeams on elastic substrate subjected to moving loads are examined. The nanobeam is made of functionally graded material (FGM) with even and uneven porosity distributions inside the material structure. The graded material properties with porosities are described by a modified power-law model. Dynamic deflection of the nanobeam is obtained via Galerkin and inverse Laplace transform methods. The importance of nonlocal parameter, strain gradient parameter, moving load velocity, porosity volume fraction, type of porosity distribution and elastic foundation on forced vibration behavior of nanobeams are discussed.

Occurrence of Sporadic -E layer during the Low Solar Activity over the Anomaly Crest Region Bhopal, India

NASA Astrophysics Data System (ADS)

Bhawre, Purushottam

2016-07-01

Ionospheric anomaly crest regions are most challenging for scientific community to understand its mechanism and investigation, for this purpose we are investigating some inospheric result for this region. The study is based on the ionogram data recorded by IPS-71 Digital Ionosonde installed over anomaly crust region Bhopal (Geo.Lat.23.2° N, Geo. Long77.4° E, Dip latitude18.4°) over a four year period from January 2007 to December 2010, covering the ending phase of 23rd Solar Cycle and starting phase of 24th solar cycle. This particular period is felt to be very suitable for examining the sunspot number and it encompasses periods of low solar activities. Quarterly ionograms are analyzed for 24 hours during these study years and have been carefully examined to note down the presence of sporadic- E. We also note down the space weather activities along with the study. The studies are divided in mainly four parts with space and geomagnetic activities during these periods. The occurrence probability of this layer is highest in summer solstice, moderate during equinox and low during winter solstice. Remarkable occurrence peaks appear from June to July in summer and from December to January in winter. The layer occurrence showed a double peak variation with distinct layer groups, in the morning (0200 LT) and the other during evening (1800 LT).The morning layer descent was associated with layer density increase indicating the strengthening of the layer while it decreased during the evening layer descent. The result indicates the presence of semi-diurnal tide over the location while the higher descent velocities could be due to the modulation of the ionization by gravity waves along with the tides. The irregularities associated with the gradient-drift instability disappear during the counter electrojet and the current flow is reversed in westward.

Frequency-domain full-waveform inversion with non-linear descent directions

NASA Astrophysics Data System (ADS)

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 benchmark FWI approach involving the standard gradient.

Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.

PubMed

Ferrari, Ulisse

2016-08-01

Maximum entropy models provide the least constrained probability distributions that reproduce statistical properties of experimental datasets. In this work we characterize the learning dynamics that maximizes the log-likelihood in the case of large but finite datasets. We first show how the steepest descent dynamics is not optimal as it is slowed down by the inhomogeneous curvature of the model parameters' space. We then provide a way for rectifying this space which relies only on dataset properties and does not require large computational efforts. We conclude by solving the long-time limit of the parameters' dynamics including the randomness generated by the systematic use of Gibbs sampling. In this stochastic framework, rather than converging to a fixed point, the dynamics reaches a stationary distribution, which for the rectified dynamics reproduces the posterior distribution of the parameters. We sum up all these insights in a "rectified" data-driven algorithm that is fast and by sampling from the parameters' posterior avoids both under- and overfitting along all the directions of the parameters' space. Through the learning of pairwise Ising models from the recording of a large population of retina neurons, we show how our algorithm outperforms the steepest descent method.

POST2 End-To-End Descent and Landing Simulation for the Autonomous Landing and Hazard Avoidance Technology Project

NASA Technical Reports Server (NTRS)

Fisher, Jody l.; Striepe, Scott A.

2007-01-01

The Program to Optimize Simulated Trajectories II (POST2) is used as a basis for an end-to-end descent and landing trajectory simulation that is essential in determining the design and performance capability of lunar descent and landing system models and lunar environment models for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project. This POST2-based ALHAT simulation provides descent and landing simulation capability by integrating lunar environment and lander system models (including terrain, sensor, guidance, navigation, and control models), along with the data necessary to design and operate a landing system for robotic, human, and cargo lunar-landing success. This paper presents the current and planned development and model validation of the POST2-based end-to-end trajectory simulation used for the testing, performance and evaluation of ALHAT project system and models.

Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks.

PubMed

Wang, Wenjun; Chen, Xue; Jiao, Pengfei; Jin, Di

2017-12-05

Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.

Towards exaggerated emphysema stereotypes

NASA Astrophysics Data System (ADS)

Chen, C.; Sørensen, L.; Lauze, F.; Igel, C.; Loog, M.; Feragen, A.; de Bruijne, M.; Nielsen, M.

2012-03-01

Classification is widely used in the context of medical image analysis and in order to illustrate the mechanism of a classifier, we introduce the notion of an exaggerated image stereotype based on training data and trained classifier. The stereotype of some image class of interest should emphasize/exaggerate the characteristic patterns in an image class and visualize the information the employed classifier relies on. This is useful for gaining insight into the classification and serves for comparison with the biological models of disease. In this work, we build exaggerated image stereotypes by optimizing an objective function which consists of a discriminative term based on the classification accuracy, and a generative term based on the class distributions. A gradient descent method based on iterated conditional modes (ICM) is employed for optimization. We use this idea with Fisher's linear discriminant rule and assume a multivariate normal distribution for samples within a class. The proposed framework is applied to computed tomography (CT) images of lung tissue with emphysema. The synthesized stereotypes illustrate the exaggerated patterns of lung tissue with emphysema, which is underpinned by three different quantitative evaluation methods.

Thermal decay of a metastable state: Influence of rescattering on the quasistationary dynamical rate

NASA Astrophysics Data System (ADS)

Chushnyakova, M. V.; Gontchar, I. I.

2018-03-01

We study the effect of backscattering of the Brownian particles as they escape out of a metastable state overcoming the potential barrier. For this aim, we model this process numerically using the Langevin equations. This modeling is performed for the wide range of the friction constant covering both the energy and spatial diffusion regimes. It is shown how the influence of the descent stage on the quasistationary decay rate gradually disappears as the friction constant decreases. It is found that, in the energy diffusion regime, the rescattering absents and the descent stage does not influence the decay rate. As the value of friction increases, the descent alters the value of the rate by more than 50% for different values of thermal energy and different shapes of the potential. To study the influence of the backscattering on the decay rate, four potentials have been considered which coincide near the potential well and the barrier but differ beyond the barrier. It is shown that the potential for which the well and the barrier are described by two smoothly joined parabolas ("the parabolic potential") plays a role of a dividing range for the mutual layout of the quasistationary dynamical rate and the widely used in the literature Kramers rate. Namely, for the potentials with steeper tails, the Kramers rate RKM underestimates the true quasistationary dynamical rate RD, whereas for the less steep tails the opposite holds (inversion of RD/RKM ). It is demonstrated that the mutual layout of the values of RD for different potentials is explained by the rescattering of the particles from the potential tail.

Empirical relation between induced velocity, thrust, and rate of descent of a helicopter rotor as determined by wind-tunnel tests on four model rotors

NASA Technical Reports Server (NTRS)

Castles, Walter, Jr.; Gray, Robin B.

1951-01-01

The empirical relation between the induced velocity, thrust, and rate of vertical descent of a helicopter rotor was calculated from wind tunnel force tests on four model rotors by the application of blade-element theory to the measured values of the thrust, torque, blade angle, and equivalent free-stream rate of descent. The model tests covered the useful range of C(sub t)/sigma(sub e) (where C(sub t) is the thrust coefficient and sigma(sub e) is the effective solidity) and the range of vertical descent from hovering to descent velocities slightly greater than those for autorotation. The three bladed models, each of which had an effective solidity of 0.05 and NACA 0015 blade airfoil sections, were as follows: (1) constant-chord, untwisted blades of 3-ft radius; (2) untwisted blades of 3-ft radius having a 3/1 taper; (3) constant-chord blades of 3-ft radius having a linear twist of 12 degrees (washout) from axis of rotation to tip; and (4) constant-chord, untwisted blades of 2-ft radius. Because of the incorporation of a correction for blade dynamic twist and the use of a method of measuring the approximate equivalent free-stream velocity, it is believed that the data obtained from this program are more applicable to free-flight calculations than the data from previous model tests.

Empirical Relation Between Induced Velocity, Thrust, and Rate of Descent of a Helicopter Rotor as Determined by Wind-tunnel Tests on Four Model Rotors

NASA Technical Reports Server (NTRS)

Castles, Walter, Jr; Gray, Robin B

1951-01-01

The empirical relation between the induced velocity, thrust, and rate of vertical descent of a helicopter rotor was calculated from wind tunnel force tests on four model rotors by the application of blade-element theory to the measured values of the thrust, torque, blade angle, and equivalent free-stream rate of descent. The model tests covered the useful range of C(sub t)/sigma(sub e) (where C(sub t) is the thrust coefficient and sigma(sub e) is the effective solidity) and the range of vertical descent from hovering to descent velocities slightly greater than those for autorotation. The three bladed models, each of which had an effective solidity of 0.05 and NACA 0015 blade airfoil sections, were as follows: (1) constant-chord, untwisted blades of 3-ft radius; (2) untwisted blades of 3-ft radius having a 3/1 taper; (3) constant-chord blades of 3-ft radius having a linear twist of 12 degrees (washout) from axis of rotation to tip; and (4) constant-chord, untwisted blades of 2-ft radius. Because of the incorporation of a correction for blade dynamic twist and the use of a method of measuring the approximate equivalent free-stream velocity, it is believed that the data obtained from this program are more applicable to free-flight calculations than the data from previous model tests.

Toward the Darwinian transition: Switching between distributed and speciated states in a simple model of early life.

PubMed

Arnoldt, Hinrich; Strogatz, Steven H; Timme, Marc

2015-01-01

It has been hypothesized that in the era just before the last universal common ancestor emerged, life on earth was fundamentally collective. Ancient life forms shared their genetic material freely through massive horizontal gene transfer (HGT). At a certain point, however, life made a transition to the modern era of individuality and vertical descent. Here we present a minimal model for stochastic processes potentially contributing to this hypothesized "Darwinian transition." The model suggests that HGT-dominated dynamics may have been intermittently interrupted by selection-driven processes during which genotypes became fitter and decreased their inclination toward HGT. Stochastic switching in the population dynamics with three-point (hypernetwork) interactions may have destabilized the HGT-dominated collective state and essentially contributed to the emergence of vertical descent and the first well-defined species in early evolution. A systematic nonlinear analysis of the stochastic model dynamics covering key features of evolutionary processes (such as selection, mutation, drift and HGT) supports this view. Our findings thus suggest a viable direction out of early collective evolution, potentially enabling the start of individuality and vertical Darwinian evolution.

Flight Test of 31.2 Diameter Modified Ringsail Parachute Deployed at Mach 1.39, Dynamic Pressure 11 Pounds per Square Foot

NASA Technical Reports Server (NTRS)

Preisser, John S.; Eckstrom, Clinton V.; Murrow, Harold N.

1967-01-01

A 31.2-foot (9.51 meter) nominal diameter (reference area 764 ft(exp 2) (71.0 m(exp 2)) ringsail parachute modified to provide 15-percent geometric porosity was flight tested while attached to a 201-pound mass (91.2 kilogram) instrumented payload as part of the rocket launch portion of the NASA Planetary Entry Parachute Program (PEPP). The parachute deployment was initiated by the firing of a mortar at a Mach number of 1.39 and a dynamic pressure of 11.0 lb/ft(exp 2) (527 newtons/m(exp 2)) at an altitude of 122,500 feet (37.3 kilometers). The parachute deployed to suspension-line stretch (snatch force) in 0.35 second, and 0.12 second later the drag force increase associated with parachute inflation began. The parachute inflated in 0.24 second to the full-open condition for a total elapsed opening time of 0.71 second. The maximum opening load of 3970 pounds (17,700 newtons) came at the time the parachute was just fully opened. During the deceleration period, the parachute exhibited an average drag coefficient of 0.52 and oscillations of the parachute canopy were less than 5 degrees. During the steady-state terminal descent portion of the test period, the average effective drag coefficient (based on vertical descent velocity) was 0.52.

Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

NASA Technical Reports Server (NTRS)

Leong, Harrison Monfook

1988-01-01

General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

Coherent beam combining of collimated fiber array based on target-in-the-loop technique

NASA Astrophysics Data System (ADS)

Li, Xinyang; Geng, Chao; Zhang, Xiaojun; Rao, Changhui

2011-11-01

Coherent beam combining (CBC) of fiber array is a promising way to generate high power and high quality laser beams. Target-in-the-loop (TIL) technique might be an effective way to ensure atmosphere propagation compensation without wavefront sensors. In this paper, we present very recent research work about CBC of collimated fiber array using TIL technique at the Key Lab on Adaptive Optics (KLAO), CAS. A novel Adaptive Fiber Optics Collimator (AFOC) composed of phase-locking module and tip/tilt control module was developed. CBC experimental setup of three-element fiber array was established. Feedback control is realized using stochastic parallel gradient descent (SPGD) algorithm. The CBC based on TIL with piston and tip/tilt correction simultaneously is demonstrated. And the beam pointing to locate or sweep position of combined spot on target was achieved through TIL technique too. The goal of our work is achieve multi-element CBC for long-distance transmission in atmosphere.

Blocking, descent and gravity waves: Observations and modelling of a MAP northerly föhn event

NASA Astrophysics Data System (ADS)

Jiang, Qingfang; Doyle, James D.; Smith, Ronald B.

2005-01-01

A northerly föhn event observed during the special observational period of the Mesoscale Alpine Programme is investigated based on observational analysis and numerical modelling. The focus of this study includes three dynamical processes associated with mountain perturbations and their interactions, namely, windward flow blocking, descent and warming on the lee side, and mountain waves. Observations indicate the presence of a deep weak-flow layer underneath a stable layer, associated with Alpine-scale blocking. Satellite imagery reveals a föhninduced cloud-free area to the south of the Alps, which is consistent with flow descent diagnosed from radiosondes and constant-volume balloons. Moderate-amplitude stationary waves were observed by research aircraft over the major Alpine peaks. Satellite images and balloon data indicate the presence of stationary trapped-wave patterns located to the north of the Alpine massif.Satisfactory agreement is found between observations and a real-data COAMPS simulation nested to 1 km resolution. COAMPS indicates the presence of trapped waves associated with a sharp decrease of Scorer parameter above a stable layer in the mid-troposphere. Underneath the stable layer, moist low-level flow is blocked to the north of the Alps. The warm air in the stable layer descends in the lee and recovers its altitude over a relatively short horizontal distance through a hydraulic jump.Blocking reduces the effective mountain and hence significantly reduces mountain drag. A simple empirical formula for estimation of the effective mountain height, he, is derived based on numerical simulations. The formula states he/hc = (h/hc), where h is the real mountain height and hc is the critical mountain height to have flow stagnation.

Camera Image Transformation and Registration for Safe Spacecraft Landing and Hazard Avoidance

NASA Technical Reports Server (NTRS)

Jones, Brandon M.

2005-01-01

Inherent geographical hazards of Martian terrain may impede a safe landing for science exploration spacecraft. Surface visualization software for hazard detection and avoidance may accordingly be applied in vehicles such as the Mars Exploration Rover (MER) to induce an autonomous and intelligent descent upon entering the planetary atmosphere. The focus of this project is to develop an image transformation algorithm for coordinate system matching between consecutive frames of terrain imagery taken throughout descent. The methodology involves integrating computer vision and graphics techniques, including affine transformation and projective geometry of an object, with the intrinsic parameters governing spacecraft dynamic motion and camera calibration.

Local flow management/profile descent algorithm. Fuel-efficient, time-controlled profiles for the NASA TSRV airplane

NASA Technical Reports Server (NTRS)

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.

Development and test results of a flight management algorithm for fuel conservative descents in a time-based metered traffic environment

NASA Technical Reports Server (NTRS)

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.

Mars MetNet Precursor Mission Status

NASA Astrophysics Data System (ADS)

Harri, Ari-Matti; Aleksashkin, Sergey; Guerrero, Héctor; Schmidt, Walter; Genzer, Maria; Vazquez, Luis; Haukka, Harri

2013-04-01

A new kind of planetary exploration mission for Mars is being developed in collaboration between the Finnish Meteorological Institute (FMI), Lavochkin Association (LA), Space Research Institute (IKI) and Institutio Nacional de Tecnica Aerospacial (INTA). The Mars MetNet mission is based on a new semi-hard landing vehicle called MetNet Lander (MNL), using an inflatable entry and descent system instead of rigid heat shields and parachutes as earlier semi-hard landing devices have used. This way the ratio of the payload mass to the overall mass is optimized. The landing impact will burrow the payload container into the Martian soil providing a more favorable thermal environment for the electronics and a suitable orientation of the telescopic boom with external sensors and the radio link antenna. It is planned to deploy several tens of MNLs on the Martian surface operating at least partly at the same time to allow meteorological network science. For the precursor mission (MMPM) intended to verify the landing concept and key technology during a real Mars mission all qualification activities are completed and the payload and system flight model components are being manufactured. The descent processes dynamic properties are monitored by a special 3-axis accelerometer combined with a 3-axis gyrometer. The data will be sent via auxiliary beacon antenna throughout the descent phase starting shortly after separation from the spacecraft. Details of the current MMPM system and payload configuration and their performance parameters will be shown.

Field evaluation of descent advisor trajectory prediction accuracy

DOT National Transportation Integrated Search

1996-07-01

The Descent Advisor (DA) automation tool has undergone a series of field tests : at the Denver Air Route Traffic Control Center to study the feasibility of : DA-based clearances and procedures. The latest evaluation, conducted in the : fall of 1995, ...

Alignment dynamics of diffusive scalar gradient in a two-dimensional model flow

NASA Astrophysics Data System (ADS)

Gonzalez, M.

2018-04-01

The Lagrangian two-dimensional approach of scalar gradient kinematics is revisited accounting for molecular diffusion. Numerical simulations are performed in an analytic, parameterized model flow, which enables considering different regimes of scalar gradient dynamics. Attention is especially focused on the influence of molecular diffusion on Lagrangian statistical orientations and on the dynamics of scalar gradient alignment.

Investigation of Stable Atmospheric Stratification Effect on the Dynamics of Descending Vortex Pairs

DOT National Transportation Integrated Search

1979-02-01

The physics of vortex flows in stratified fluids is studied with the objective of determining the influence of stable stratification on the descent of aircraft vortex pairs. Vortex rings descending into linear and discontinuous density stratification...

The Huygens Atmospheric Structure Instrument (HASI): Expected Results at Titan and Performance Verification in Terrestrial Atmosphere

NASA Technical Reports Server (NTRS)

Ferri, F.; Fulchignoni, M.; Colombatti, G.; Stoppato, P. F. Lion; Zarnecki, J. C.; Harri, A. M.; Schwingenschuh, K.; Hamelin, M.; Flamini, E.; Bianchini, G.;

Applications of thermal-gradients method for the optimization of α-amylase crystallization conditions based on dynamic and static light scattering data

NASA Astrophysics Data System (ADS)

Delboni, L. F.; Iulek, J.; Burger, R.; da Silva, A. C. R.; Moreno, A.

2002-02-01

The expression, purification, crystallization, and characterization by X-ray diffraction of α-amylase are described here. Dynamic and static light scattering methods with a temperature controller was used to optimize the crystallization conditions of α-amylase from Bacillus stearothermophilus an important enzyme in many fields of industrial activity. After applying thermal gradients for growing crystals, X-ray cryo-crystallographic methods were employed for the data collection. Crystals grown by these thermal-gradients diffracted up to a maximum resolution of 3.8 Å, which allowed the determination of the unit cell constants as follows: a=61.7 Å, b=86.7 Å, c=92.2 Å and space group C222 (or C222 1).

Adaptive beam shaping for improving the power coupling of a two-Cassegrain-telescope

NASA Astrophysics Data System (ADS)

Ma, Haotong; Hu, Haojun; Xie, Wenke; Zhao, Haichuan; Xu, Xiaojun; Chen, Jinbao

2013-08-01

We demonstrate the adaptive beam shaping for improving the power coupling of a two-Cassegrain-telescope based on the stochastic parallel gradient descent (SPGD) algorithm and dual phase only liquid crystal spatial light modulators (LC-SLMs). Adaptive pre-compensation the wavefront of projected laser beam at the transmitter telescope is chosen to improve the power coupling efficiency. One phase only LC-SLM adaptively optimizes phase distribution of the projected laser beam and the other generates turbulence phase screen. The intensity distributions of the dark hollow beam after passing through the turbulent atmosphere with and without adaptive beam shaping are analyzed in detail. The influence of propagation distance and aperture size of the Cassegrain-telescope on coupling efficiency are investigated theoretically and experimentally. These studies show that the power coupling can be significantly improved by adaptive beam shaping. The technique can be used in optical communication, deep space optical communication and relay mirror.

Impact of a variational objective analysis scheme on a regional area numerical model: The Italian Air Force Weather Service experience

NASA Astrophysics Data System (ADS)

Bonavita, M.; Torrisi, L.

2005-03-01

A new data assimilation system has been designed and implemented at the National Center for Aeronautic Meteorology and Climatology of the Italian Air Force (CNMCA) in order to improve its operational numerical weather prediction capabilities and provide more accurate guidance to operational forecasters. The system, which is undergoing testing before operational use, is based on an “observation space” version of the 3D-VAR method for the objective analysis component, and on the High Resolution Regional Model (HRM) of the Deutscher Wetterdienst (DWD) for the prognostic component. Notable features of the system include a completely parallel (MPI+OMP) implementation of the solution of analysis equations by a preconditioned conjugate gradient descent method; correlation functions in spherical geometry with thermal wind constraint between mass and wind field; derivation of the objective analysis parameters from a statistical analysis of the innovation increments.

Gradient-based multiconfiguration Shepard interpolation for generating potential energy surfaces for polyatomic reactions.

PubMed

Tishchenko, Oksana; Truhlar, Donald G

2010-02-28

This paper describes and illustrates a way to construct multidimensional representations of reactive potential energy surfaces (PESs) by a multiconfiguration Shepard interpolation (MCSI) method based only on gradient information, that is, without using any Hessian information from electronic structure calculations. MCSI, which is called multiconfiguration molecular mechanics (MCMM) in previous articles, is a semiautomated method designed for constructing full-dimensional PESs for subsequent dynamics calculations (classical trajectories, full quantum dynamics, or variational transition state theory with multidimensional tunneling). The MCSI method is based on Shepard interpolation of Taylor series expansions of the coupling term of a 2 x 2 electronically diabatic Hamiltonian matrix with the diagonal elements representing nonreactive analytical PESs for reactants and products. In contrast to the previously developed method, these expansions are truncated in the present version at the first order, and, therefore, no input of electronic structure Hessians is required. The accuracy of the interpolated energies is evaluated for two test reactions, namely, the reaction OH+H(2)-->H(2)O+H and the hydrogen atom abstraction from a model of alpha-tocopherol by methyl radical. The latter reaction involves 38 atoms and a 108-dimensional PES. The mean unsigned errors averaged over a wide range of representative nuclear configurations (corresponding to an energy range of 19.5 kcal/mol in the former case and 32 kcal/mol in the latter) are found to be within 1 kcal/mol for both reactions, based on 13 gradients in one case and 11 in the other. The gradient-based MCMM method can be applied for efficient representations of multidimensional PESs in cases where analytical electronic structure Hessians are too expensive or unavailable, and it provides new opportunities to employ high-level electronic structure calculations for dynamics at an affordable cost.

Design of automation tools for management of descent traffic

NASA Technical Reports Server (NTRS)

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 evaluations at an en route center.

Truncated Conjugate Gradient: An Optimal Strategy for the Analytical Evaluation of the Many-Body Polarization Energy and Forces in Molecular Simulations.

PubMed

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 algorithm. The T(P)CG-1(ω) level provides less accurate solutions for inhomogeneous systems, but its applicability to well-conditioned problems such as water is remarkable, with only two matrix-vector product evaluations.

Truncated Conjugate Gradient: An Optimal Strategy for the Analytical Evaluation of the Many-Body Polarization Energy and Forces in Molecular Simulations

PubMed Central

2016-01-01

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 algorithm. The T(P)CG-1(ω) level provides less accurate solutions for inhomogeneous systems, but its applicability to well-conditioned problems such as water is remarkable, with only two matrix-vector product evaluations. PMID:28068773

Modified artificial fish school algorithm for free space optical communication with sensor-less adaptive optics system

NASA Astrophysics Data System (ADS)

Cao, Jingtai; Zhao, Xiaohui; Li, Zhaokun; Liu, Wei; Gu, Haijun

2017-11-01

The performance of free space optical (FSO) communication system is limited by atmospheric turbulent extremely. Adaptive optics (AO) is the significant method to overcome the atmosphere disturbance. Especially, for the strong scintillation effect, the sensor-less AO system plays a major role for compensation. In this paper, a modified artificial fish school (MAFS) algorithm is proposed to compensate the aberrations in the sensor-less AO system. Both the static and dynamic aberrations compensations are analyzed and the performance of FSO communication before and after aberrations compensations is compared. In addition, MAFS algorithm is compared with artificial fish school (AFS) algorithm, stochastic parallel gradient descent (SPGD) algorithm and simulated annealing (SA) algorithm. It is shown that the MAFS algorithm has a higher convergence speed than SPGD algorithm and SA algorithm, and reaches the better convergence value than AFS algorithm, SPGD algorithm and SA algorithm. The sensor-less AO system with MAFS algorithm effectively increases the coupling efficiency at the receiving terminal with fewer numbers of iterations. In conclusion, the MAFS algorithm has great significance for sensor-less AO system to compensate atmospheric turbulence in FSO communication system.

Multi-Mission Simulation and Visualization for Real-Time Telemetry Display, Playback and EDL Event Reconstruction

NASA Technical Reports Server (NTRS)

Pomerantz, M. I.; Lim, C.; Myint, S.; Woodward, G.; Balaram, J.; Kuo, C.

2012-01-01

he Jet Propulsion Laboratory's Entry, Descent and Landing (EDL) Reconstruction Task has developed a software system that provides mission operations personnel and analysts with a real time telemetry-based live display, playback and post-EDL reconstruction capability that leverages the existing high-fidelity, physics-based simulation framework and modern game engine-derived 3D visualization system developed in the JPL Dynamics and Real Time Simulation (DARTS) Lab. Developed as a multi-mission solution, the EDL Telemetry Visualization (ETV) system has been used for a variety of projects including NASA's Mars Science Laboratory (MSL), NASA'S Low Density Supersonic Decelerator (LDSD) and JPL's MoonRise Lunar sample return proposal.

Adaptive learning and control for MIMO system based on adaptive dynamic programming.

PubMed

Fu, Jian; He, Haibo; Zhou, Xinmin

2011-07-01

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.

Seismic noise attenuation using an online subspace tracking algorithm

NASA Astrophysics Data System (ADS)

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.

Adaptive distance metric learning for diffusion tensor image segmentation.

PubMed

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

PubMed Central

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

Stable adaptive PI control for permanent magnet synchronous motor drive based on improved JITL technique.

PubMed

Zheng, Shiqi; Tang, Xiaoqi; Song, Bao; Lu, Shaowu; Ye, Bosheng

2013-07-01

In this paper, a stable adaptive PI control strategy based on the improved just-in-time learning (IJITL) technique is proposed for permanent magnet synchronous motor (PMSM) drive. Firstly, the traditional JITL technique is improved. The new IJITL technique has less computational burden and is more suitable for online identification of the PMSM drive system which is highly real-time compared to traditional JITL. In this way, the PMSM drive system is identified by IJITL technique, which provides information to an adaptive PI controller. Secondly, the adaptive PI controller is designed in discrete time domain which is composed of a PI controller and a supervisory controller. The PI controller is capable of automatically online tuning the control gains based on the gradient descent method and the supervisory controller is developed to eliminate the effect of the approximation error introduced by the PI controller upon the system stability in the Lyapunov sense. Finally, experimental results on the PMSM drive system show accurate identification and favorable tracking performance. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Waveform inversion with source encoding for breast sound speed reconstruction in ultrasound computed tomography.

PubMed

Wang, Kun; Matthews, Thomas; Anis, Fatima; Li, Cuiping; Duric, Neb; Anastasio, Mark A

2015-03-01

Ultrasound computed tomography (USCT) holds great promise for improving the detection and management of breast cancer. Because they are based on the acoustic wave equation, waveform inversion-based reconstruction methods can produce images that possess improved spatial resolution properties over those produced by ray-based methods. However, waveform inversion methods are computationally demanding and have not been applied widely in USCT breast imaging. In this work, source encoding concepts are employed to develop an accelerated USCT reconstruction method that circumvents the large computational burden of conventional waveform inversion methods. This method, referred to as the waveform inversion with source encoding (WISE) method, encodes the measurement data using a random encoding vector and determines an estimate of the sound speed distribution by solving a stochastic optimization problem by use of a stochastic gradient descent algorithm. Both computer simulation and experimental phantom studies are conducted to demonstrate the use of the WISE method. The results suggest that the WISE method maintains the high spatial resolution of waveform inversion methods while significantly reducing the computational burden.

Separating figure from ground with a parallel network.

PubMed

Kienker, P K; Sejnowski, T J; Hinton, G E; Schumacher, L E

1986-01-01

The differentiation of figure from ground plays an important role in the perceptual organization of visual stimuli. The rapidity with which we can discriminate the inside from the outside of a figure suggests that at least this step in the process may be performed in visual cortex by a large number of neurons in several different areas working together in parallel. We have attempted to simulate this collective computation by designing a network of simple processing units that receives two types of information: bottom-up input from the image containing the outlines of a figure, which may be incomplete, and a top-down attentional input that biases one part of the image to be the inside of the figure. No presegmentation of the image was assumed. Two methods for performing the computation were explored: gradient descent, which seeks locally optimal states, and simulated annealing, which attempts to find globally optimal states by introducing noise into the computation. For complete outlines, gradient descent was faster, but the range of input parameters leading to successful performance was very narrow. In contrast, simulated annealing was more robust: it worked over a wider range of attention parameters and a wider range of outlines, including incomplete ones. Our network model is too simplified to serve as a model of human performance, but it does demonstrate that one global property of outlines can be computed through local interactions in a parallel network. Some features of the model, such as the role of noise in escaping from nonglobal optima, may generalize to more realistic models.

Ordering pathway of block copolymers under dynamic thermal gradients studied by in situ GISAXS

DOE PAGES

Samant, Saumil; Strzalka, Joseph; Yager, Kevin G.; ...

2016-10-31

Dynamic thermal gradient-based processes for directed self-assembly of block copolymer (BCP) thin films such as cold zone annealing (CZA) have demonstrated much potential for rapidly fabricating highly ordered patterns of BCP domains with facile orientation control. As a demonstration, hexagonally packed predominantly vertical cylindrical morphology, technologically relevant for applications such as membranes and lithography, was achieved in 1 μm thick cylinder-forming PS-b-PMMA (cBCP) films by applying sharp thermal gradients (CZA-Sharp) at optimum sample sweep rates. A thorough understanding of the molecular level mechanisms and pathways of the BCP ordering that occur during this CZA-S process is presented, useful to fullymore » exploit the potential of CZA-S for large-scale BCP-based device fabrication. To that end, we developed a customized CZA-S assembly to probe the dynamic structure evolution and ordering of the PS-b-PMMA cBCP film in situ as it undergoes the CZA-S process using the grazing incidence small-angle X-ray scattering (GISAXS) technique. Four distinct regimes of BCP ordering were observed within the gradient that include microphase separation from an “as cast” unordered state (Regime I), evolution of vertical cylinders under a thermally imposed strain gradient (Regime II), reorientation of a fraction of cylinders due to preferential substrate interactions (Regime III), and finally grain-coarsening on the cooling edge (Regime IV). The ordering pathway in the different regimes is further described within the framework of an energy landscape. A novel aspect of this study is the identification of a grain-coarsening regime on the cooling edge of the gradient, previously obscure in zone annealing studies of BCPs. Furthermore, such insights into the development of highly ordered BCP nanostructures under template-free thermal gradient fields can potentially have important ramifications in the field of BCP-directed self-assembly and self-assembling polymer systems more broadly.« less

Evaluating the accuracy performance of Lucas-Kanade algorithm in the circumstance of PIV application

NASA Astrophysics Data System (ADS)

Pan, Chong; Xue, Dong; Xu, Yang; Wang, JinJun; Wei, RunJie

2015-10-01

Lucas-Kanade (LK) algorithm, usually used in optical flow filed, has recently received increasing attention from PIV community due to its advanced calculation efficiency by GPU acceleration. Although applications of this algorithm are continuously emerging, a systematic performance evaluation is still lacking. This forms the primary aim of the present work. Three warping schemes in the family of LK algorithm: forward/inverse/symmetric warping, are evaluated in a prototype flow of a hierarchy of multiple two-dimensional vortices. Second-order Newton descent is also considered here. The accuracy & efficiency of all these LK variants are investigated under a large domain of various influential parameters. It is found that the constant displacement constraint, which is a necessary building block for GPU acceleration, is the most critical issue in affecting LK algorithm's accuracy, which can be somehow ameliorated by using second-order Newton descent. Moreover, symmetric warping outbids the other two warping schemes in accuracy level, robustness to noise, convergence speed and tolerance to displacement gradient, and might be the first choice when applying LK algorithm to PIV measurement.

Geodesic regression on orientation distribution functions with its application to an aging study.

PubMed

Du, Jia; Goh, Alvina; Kushnarev, Sergey; Qiu, Anqi

2014-02-15

In this paper, we treat orientation distribution functions (ODFs) derived from high angular resolution diffusion imaging (HARDI) as elements of a Riemannian manifold and present a method for geodesic regression on this manifold. In order to find the optimal regression model, we pose this as a least-squares problem involving the sum-of-squared geodesic distances between observed ODFs and their model fitted data. We derive the appropriate gradient terms and employ gradient descent to find the minimizer of this least-squares optimization problem. In addition, we show how to perform statistical testing for determining the significance of the relationship between the manifold-valued regressors and the real-valued regressands. Experiments on both synthetic and real human data are presented. In particular, we examine aging effects on HARDI via geodesic regression of ODFs in normal adults aged 22 years old and above. © 2013 Elsevier Inc. All rights reserved.

Atmospheric observations for STS-1 landing

NASA Technical Reports Server (NTRS)

Turner, R. E.; Arnold, J. E.; Wilson, G. S.

1981-01-01

A summary of synoptic weather conditions existing over the western United States is given for the time of shuttle descent into Edwards Air Force Base, California. The techniques and methods used to furnish synoptic atmospheric data at the surface and aloft for flight verification of the STS-1 orbiter during its descent into Edwards Air Force Base are specified. Examples of the upper level data set are given.

Lagrangian Transport Calculations Using UARS Data. Part 2; Ozone

NASA Technical Reports Server (NTRS)

Manney, Gloria L.; Zurek, R. W.; Froidevaux, L.; Waters, J. W.; ONeill, A.; Swinbank, R.

1995-01-01

Trajectory calculations are used to examine ozone transport in the polar winter stratosphere during periods of the Upper Atmosphere Research Satellite (UARS) observations. The value of these calculations for determining mass transport was demonstrated previously using UARS observations of long-lived tracers, In the middle stratosphere, the overall ozone behavior observed by the Microwave Limb Sounder in the polar vortex is reproduced by this purely dynamical model. Calculations show the evolution of ozone in the lower stratosphere during early winter to be dominated by dynamics in December 1992 in the Arctic. Calculations for June 1992 in the Antarctic show evidence of chemical ozone destruction and indicate that approx. 50% of the chemical destruction may be masked by dynamical effects, mainly diabatic descent, which bring higher ozone into the lower-stratospheric vortex. Estimating differences between calculated and observed fields suggests that dynamical changes masked approx. 20% - 35% of chemical ozone loss during late February and early March 1993 in the Arctic. In the Antarctic late winter, in late August and early September 1992, below approx. 520 K, the evolution of vortex-averaged ozone is entirely dominated by chemical effects; above this level, however, chemical ozone depletion can be partially or completely masked by dynamical effects. Our calculations for 1992 showed that chemical loss was nearly completely compensated by increases due to diabatic descent at 655 K.

Learning Maximal Entropy Models from finite size datasets: a fast Data-Driven algorithm allows to sample from the posterior distribution

NASA Astrophysics Data System (ADS)

Ferrari, Ulisse

A maximal entropy model provides the least constrained probability distribution that reproduces experimental averages of an observables set. In this work we characterize the learning dynamics that maximizes the log-likelihood in the case of large but finite datasets. We first show how the steepest descent dynamics is not optimal as it is slowed down by the inhomogeneous curvature of the model parameters space. We then provide a way for rectifying this space which relies only on dataset properties and does not require large computational efforts. We conclude by solving the long-time limit of the parameters dynamics including the randomness generated by the systematic use of Gibbs sampling. In this stochastic framework, rather than converging to a fixed point, the dynamics reaches a stationary distribution, which for the rectified dynamics reproduces the posterior distribution of the parameters. We sum up all these insights in a ``rectified'' Data-Driven algorithm that is fast and by sampling from the parameters posterior avoids both under- and over-fitting along all the directions of the parameters space. Through the learning of pairwise Ising models from the recording of a large population of retina neurons, we show how our algorithm outperforms the steepest descent method. This research was supported by a Grant from the Human Brain Project (HBP CLAP).

Reach-scale characterization of large woody debris in a low-gradient, Midwestern U.S.A. river system

NASA Astrophysics Data System (ADS)

Martin, Derek J.; Pavlowsky, Robert T.; Harden, Carol P.

2016-06-01

Addition of large woody debris (LWD) to rivers has increasingly become a popular stream restoration strategy, particularly in river systems of the Midwestern United States. However, our knowledge of LWD dynamics is mostly limited to high gradient montane river systems, or coastal river systems. The LWD-related management of low-gradient, Midwestern river systems is thus largely based on higher gradient analogs of LWD dynamics. This research characterizes fluvial wood loads and investigates the relationships between fluvial wood, channel morphology, and sediment deposition in a relatively low-gradient, semiconfined, alluvial river. The LWD and channel morphology were surveyed at nine reaches along the Big River in southeastern Missouri to investigate those relationships in comparison to other regions. Wood loads in the Big River are low (3-114 m3/100 m) relative to those of higher gradient river systems of the Pacific Northwest, but high relative to lower-gradient river systems of the Eastern United States. Wood characteristics such as size and orientation suggest that the dominant LWD recruitment mechanism in the Big River is bank erosion. Also, ratios of wood geometry to channel geometry show that the Big River maintains a relatively high wood transport capacity for most of its length. Although LWD creates sites for sediment storage, the overall impact on reach-scale sediment storage in the Big River is low (< 4.2% of total in-channel storage). However, wood loads, and thus opportunities for sediment storage, have the potential to grow in the future as Midwestern riparian forests mature. This study represents the first of its kind within this particular type of river system and within this region and thus serves as a basis for understanding fluvial wood dynamics in low-gradient river systems of the Midwestern United States.

A coronagraph based on two spatial light modulators for active amplitude apodizing and phase corrections

NASA Astrophysics Data System (ADS)

Dou, Jiangpei; Ren, Deqing; Zhang, Xi; Zhu, Yongtian; Zhao, Gang; Wu, Zhen; Chen, Rui; Liu, Chengchao; Yang, Feng; Yang, Chao

2014-08-01

Almost all high-contrast imaging coronagraphs proposed until now are based on passive coronagraph optical components. Recently, Ren and Zhu proposed for the first time a coronagraph that integrates a liquid crystal array (LCA) for the active pupil apodizing and a deformable mirror (DM) for the phase corrections. Here, for demonstration purpose, we present the initial test result of a coronagraphic system that is based on two liquid crystal spatial light modulators (SLM). In the system, one SLM is served as active pupil apodizing and amplitude correction to suppress the diffraction light; another SLM is used to correct the speckle noise that is caused by the wave-front distortions. In this way, both amplitude and phase error can be actively and efficiently compensated. In the test, we use the stochastic parallel gradient descent (SPGD) algorithm to control two SLMs, which is based on the point spread function (PSF) sensing and evaluation and optimized for a maximum contrast in the discovery area. Finally, it has demonstrated a contrast of 10-6 at an inner working angular distance of ~6.2 λ/D, which is a promising technique to be used for the direct imaging of young exoplanets on ground-based telescopes.

Dynamic Maternal Gradients Control Timing and Shift-Rates for Drosophila Gap Gene Expression

PubMed Central

Verd, Berta; Crombach, Anton

2017-01-01

Pattern formation during development is a highly dynamic process. In spite of this, few experimental and modelling approaches take into account the explicit time-dependence of the rules governing regulatory systems. We address this problem by studying dynamic morphogen interpretation by the gap gene network in Drosophila melanogaster. Gap genes are involved in segment determination during early embryogenesis. They are activated by maternal morphogen gradients encoded by bicoid (bcd) and caudal (cad). These gradients decay at the same time-scale as the establishment of the antero-posterior gap gene pattern. We use a reverse-engineering approach, based on data-driven regulatory models called gene circuits, to isolate and characterise the explicitly time-dependent effects of changing morphogen concentrations on gap gene regulation. To achieve this, we simulate the system in the presence and absence of dynamic gradient decay. Comparison between these simulations reveals that maternal morphogen decay controls the timing and limits the rate of gap gene expression. In the anterior of the embyro, it affects peak expression and leads to the establishment of smooth spatial boundaries between gap domains. In the posterior of the embryo, it causes a progressive slow-down in the rate of gap domain shifts, which is necessary to correctly position domain boundaries and to stabilise the spatial gap gene expression pattern. We use a newly developed method for the analysis of transient dynamics in non-autonomous (time-variable) systems to understand the regulatory causes of these effects. By providing a rigorous mechanistic explanation for the role of maternal gradient decay in gap gene regulation, our study demonstrates that such analyses are feasible and reveal important aspects of dynamic gene regulation which would have been missed by a traditional steady-state approach. More generally, it highlights the importance of transient dynamics for understanding complex regulatory processes in development. PMID:28158178

Dynamic Maternal Gradients Control Timing and Shift-Rates for Drosophila Gap Gene Expression.

PubMed

Verd, Berta; Crombach, Anton; Jaeger, Johannes

2017-02-01

Pattern formation during development is a highly dynamic process. In spite of this, few experimental and modelling approaches take into account the explicit time-dependence of the rules governing regulatory systems. We address this problem by studying dynamic morphogen interpretation by the gap gene network in Drosophila melanogaster. Gap genes are involved in segment determination during early embryogenesis. They are activated by maternal morphogen gradients encoded by bicoid (bcd) and caudal (cad). These gradients decay at the same time-scale as the establishment of the antero-posterior gap gene pattern. We use a reverse-engineering approach, based on data-driven regulatory models called gene circuits, to isolate and characterise the explicitly time-dependent effects of changing morphogen concentrations on gap gene regulation. To achieve this, we simulate the system in the presence and absence of dynamic gradient decay. Comparison between these simulations reveals that maternal morphogen decay controls the timing and limits the rate of gap gene expression. In the anterior of the embyro, it affects peak expression and leads to the establishment of smooth spatial boundaries between gap domains. In the posterior of the embryo, it causes a progressive slow-down in the rate of gap domain shifts, which is necessary to correctly position domain boundaries and to stabilise the spatial gap gene expression pattern. We use a newly developed method for the analysis of transient dynamics in non-autonomous (time-variable) systems to understand the regulatory causes of these effects. By providing a rigorous mechanistic explanation for the role of maternal gradient decay in gap gene regulation, our study demonstrates that such analyses are feasible and reveal important aspects of dynamic gene regulation which would have been missed by a traditional steady-state approach. More generally, it highlights the importance of transient dynamics for understanding complex regulatory processes in development.

Color-gradient lattice Boltzmann model for simulating droplet motion with contact-angle hysteresis.

PubMed

Ba, Yan; Liu, Haihu; Sun, Jinju; Zheng, Rongye

2013-10-01

Lattice Boltzmann method (LBM) is an effective tool for simulating the contact-line motion due to the nature of its microscopic dynamics. In contact-line motion, contact-angle hysteresis is an inherent phenomenon, but it is neglected in most existing color-gradient based LBMs. In this paper, a color-gradient based multiphase LBM is developed to simulate the contact-line motion, particularly with the hysteresis of contact angle involved. In this model, the perturbation operator based on the continuum surface force concept is introduced to model the interfacial tension, and the recoloring operator proposed by Latva-Kokko and Rothman is used to produce phase segregation and resolve the lattice pinning problem. At the solid surface, the color-conserving wetting boundary condition [Hollis et al., IMA J. Appl. Math. 76, 726 (2011)] is applied to improve the accuracy of simulations and suppress spurious currents at the contact line. In particular, we present a numerical algorithm to allow for the effect of the contact-angle hysteresis, in which an iterative procedure is used to determine the dynamic contact angle. Numerical simulations are conducted to verify the developed model, including the droplet partial wetting process and droplet dynamical behavior in a simple shear flow. The obtained results are compared with theoretical solutions and experimental data, indicating that the model is able to predict the equilibrium droplet shape as well as the dynamic process of partial wetting and thus permits accurate prediction of contact-line motion with the consideration of contact-angle hysteresis.

Assessment of a model of forest dynamics under contrasting climate and disturbance regimes in the Pacific Northwest [FORCLIM

USGS Publications Warehouse

Busing, Richard T.; Solomon, Allen M.

2005-01-01

An individual-based model of forest dynamics (FORCLIM) was tested for its ability to simulate forest composition and structure in the Pacific Northwest region of North America. Simulation results across gradients of climate and disturbance were compared to forest survey data from several vegetation zones in western Oregon. Modelled patterns of tree species composition, total basal area and stand height across climate gradients matched those in the forest survey data. However, the density of small stems (<50 cm DBH) was underestimated by the model. Thus actual size-class structure and other density-based parameters of stand structure were not simulated with high accuracy. The addition of partial-stand disturbances at moderate frequencies (<0.01 yr-1) often improved agreement between simulated and actual results. Strengths and weaknesses of the FORCLIM model in simulating forest dynamics and structure in the Pacific Northwest are discussed.

Motion Driven by Strain Gradient Fields

PubMed Central

Wang, Chao; Chen, Shaohua

2015-01-01

A new driving mechanism for direction-controlled motion of nano-scale objects is proposed, based on a model of stretching a graphene strip linked to a rigid base with linear springs of identical stiffness. We find that the potential energy difference induced by the strain gradient field in the graphene strip substrate can generate sufficient force to overcome the static and kinetic friction forces between the nano-flake and the strip substrate, resulting in the nanoscale flake motion in the direction of gradient reduction. The dynamics of the nano-flake can be manipulated by tuning the stiffness of linear springs, stretching velocity and the flake size. This fundamental law of directional motion induced by strain gradient could be very useful for promising designs of nanoscale manipulation, transportation and smart surfaces. PMID:26323603

On the modeling of breath-by-breath oxygen uptake kinetics at the onset of high-intensity exercises: simulated annealing vs. GRG2 method.

PubMed

Bernard, Olivier; Alata, Olivier; Francaux, Marc

2006-03-01

Modeling in the time domain, the non-steady-state O2 uptake on-kinetics of high-intensity exercises with empirical models is commonly performed with gradient-descent-based methods. However, these procedures may impair the confidence of the parameter estimation when the modeling functions are not continuously differentiable and when the estimation corresponds to an ill-posed problem. To cope with these problems, an implementation of simulated annealing (SA) methods was compared with the GRG2 algorithm (a gradient-descent method known for its robustness). Forty simulated Vo2 on-responses were generated to mimic the real time course for transitions from light- to high-intensity exercises, with a signal-to-noise ratio equal to 20 dB. They were modeled twice with a discontinuous double-exponential function using both estimation methods. GRG2 significantly biased two estimated kinetic parameters of the first exponential (the time delay td1 and the time constant tau1) and impaired the precision (i.e., standard deviation) of the baseline A0, td1, and tau1 compared with SA. SA significantly improved the precision of the three parameters of the second exponential (the asymptotic increment A2, the time delay td2, and the time constant tau2). Nevertheless, td2 was significantly biased by both procedures, and the large confidence intervals of the whole second component parameters limit their interpretation. To compare both algorithms on experimental data, 26 subjects each performed two transitions from 80 W to 80% maximal O2 uptake on a cycle ergometer and O2 uptake was measured breath by breath. More than 88% of the kinetic parameter estimations done with the SA algorithm produced the lowest residual sum of squares between the experimental data points and the model. Repeatability coefficients were better with GRG2 for A1 although better with SA for A2 and tau2. Our results demonstrate that the implementation of SA improves significantly the estimation of most of these kinetic parameters, but a large inaccuracy remains in estimating the parameter values of the second exponential.

Critical Spacecraft-to-Earth Communications for Mars Exploration Rover (MER) entry, descent and landing

NASA Technical Reports Server (NTRS)

Hurd, William J.; Estabrook, Polly; Racho, Caroline S.; Satorius, Edgar H.

2002-01-01

For planetary lander missions, the most challenging phase of the spacecraft to ground communications is during the entry, descent, and landing (EDL). As each 2003 Mars Exploration Rover (MER) enters the Martian atmosphere, it slows dramatically. The extreme acceleration and jerk cause extreme Doppler dynamics on the X-band signal received on Earth. When the vehicle slows sufficiently, the parachute is deployed, causing almost a step in deceleration. After parachute deployment, the lander is lowered beneath the parachute on a bridle. The swinging motion of the lander imparts high Doppler dynamics on the signal and causes the received signal strength to vary widely, due to changing antenna pointing angles. All this time, the vehicle transmits important health and status information that is especially critical if the landing is not successful. Even using the largest Deep Space Network antennas, the weak signal and high dynamics render it impossible to conduct reliable phase coherent communications. Therefore, a specialized form of frequency-shift-keying will be used. This paper describes the EDL scenario, the signal conditions, the methods used to detect and frequency-track the carrier and to detect the data modulation, and the resulting performance estimates.

Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.

PubMed

Lu, Jun; McFarland, Dennis J; Wolpaw, Jonathan R

2013-02-01

Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.

Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.

2013-02-01

Objective. Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an ‘adaptive Laplacian (ALAP) filter’, can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.

A model to describe the surface gradient-nanograin formation and property of friction stir processed laser Co-Cr-Ni-Mo alloy

NASA Astrophysics Data System (ADS)

Li, Ruidi; Yuan, Tiechui; Qiu, Zili

2014-07-01

A gradient-nanograin surface layer of Co-base alloy was prepared by friction stir processing (FSP) of laser-clad coating in this work. However, it is lack of a quantitatively function relationship between grain refinement and FSP conditions. Based on this, an analytic model is derived for the correlations between carbide size, hardness and rotary speed, layer depth during in-situ FSP of laser-clad Co-Cr-Ni-Mo alloy. The model is based on the principle of typical plastic flow in friction welding and dynamic recrystallization. The FSP experiment for modification of laser-clad Co-based alloy was conducted and its gradient nanograin and hardness were characterized. It shows that the model is consistent with experimental results.

Kurtosis Approach for Nonlinear Blind Source Separation

NASA Technical Reports Server (NTRS)

Duong, Vu A.; Stubbemd, Allen R.

2005-01-01

In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation.

On Vehicle Placement to Intercept Moving Targets (Preprint)

DTIC Science & Technology

2010-03-09

which is feasible only if X1 −X2 = 0 and Y1 − Y2 = 0. We now present the main result for this section. Theorem 3.4 (Minimizing expected cost) From an...Vandenberghe (2004)) leads the vehicle to the unique global minimizer of Cexp. Let V ⊂ [0,W ], and choose φ(x) such that φ(x) = 0,∀x ∈ [0,W ] \\ V. Then, Theorem ...R>0, and following gradient descent with V as the region of integration, the vehicle remains inside [0,W ] × R>0 at all subsequent times. 3 Theorem

Product Distribution Theory and Semi-Coordinate Transformations

NASA Technical Reports Server (NTRS)

Airiau, Stephane; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for doing distributed adaptive control of a multiagent system (MAS). We introduce the technique of "coordinate transformations" in PD theory gradient descent. These transformations selectively couple a few agents with each other into "meta-agents". Intuitively, this can be viewed as a generalization of forming binding contracts between those agents. Doing this sacrifices a bit of the distributed nature of the MAS, in that there must now be communication from multiple agents in determining what joint-move is finally implemented However, as we demonstrate in computer experiments, these transformations improve the performance of the MAS.

Deep kernel learning method for SAR image target recognition

NASA Astrophysics Data System (ADS)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

The study of micro-inextensible piezoelectric cantilever plate

NASA Astrophysics Data System (ADS)

Chen, L. H.; Xu, J. W.; Zhang, W.

2018-06-01

In this paper, a micro-inextensible piezoelectric cantilever plate is analyzed and its nonlinear dynamic behaviour is studied. The nonlinear oscillation differential equation is established by using Hamilton’s principle with the application of strain gradient theory to consider the size effect, and inextensible theory to consider the large deformation and rotation effect of cantilever plate. Based on MATLAB software, using the Runge-Kuta method, we can obtain the response of the nonlinear oscillation differential equation. The influences of the strain gradient length scale parameter and voltage on the dynamic response of micro piezoelectric cantilever plate are investigated separately. The results confirmed an increase of the stiffness of the system by using the strain gradient theory and the amplitude of the vibration is reduced. The vibration of the system can be controlled by applying an active voltage. The effect of external excitation frequency on nonlinear dynamic behaviour is considered by using Poincare surface of section and diagrams of waveforms, phase and bifurcation.

Flight Test of a 30-Foot Nominal Diameter Cross Parachute Deployed at a Mach Number of 1.57 and a Dynamic Pressure of 9.7 Pounds per Square Foot

NASA Technical Reports Server (NTRS)

Eckstrom, Clinton V.; Preisser, John S.

1968-01-01

A 30-foot (9.1-meter) nominal-diameter cross-type parachute with a cloth area (reference area) of 709 square feet (65.9 square meters) was flight tested in the rocket-launched portion of the NASA Planetary Entry Parachute Program (PEPP). The test parachute was ejected from an instrumented payload by means of a mortar when the system was at a Mach number of 1.57 and a dynamic pressure of 9.7 psf. The parachute deployed to suspension-line stretch in 0.44 second with a resulting snatch-force loading of 1100 pounds (4900 newtons), Canopy inflation began at 0.58 second and a first full inflation was achieved at approximately 0.77 second. The maximum opening load occurred at 0.81 second and was 4255 pounds (18,930 newtons). Thereafter, the test item exhibited a canopy-shape instability in that the four panel arms experienced fluctuations, a "scissoring" type of motion predominating throughout the test period. Calculated values of axial-force coefficient during the deceleration portion of the test varied between 0.35 and 1.05, with an average value of 0.69. During descent, canopy-shape variations had reduced to small amplitudes and resultant pitch-yaw angles of the payload with respect to the local vertical averaged less than 10 degrees. The effective drag coefficient, based on the vertical components of velocity and acceleration during system descent, was 0.78.

Ultrafast MR imaging of the pelvic floor.

PubMed

Unterweger, M; Marincek, B; Gottstein-Aalame, N; Debatin, J F; Seifert, B; Ochsenbein-Imhof, N; Perucchini, D; Kubik-Huch, R A

2001-04-01

The aim of this study was to compare pelvic floor anatomy and laxity at rest and on straining (Valsalva's maneuver) using dynamic ultrafast MR imaging in women who were continent versus those with stress incontinence differing in obstetric history. Thirty continent women were divided into three equal groups (nulliparous, previous cesarean delivery, previous vaginal delivery) and compared with 10 women with stress-incontinence with a history of at least one vaginal delivery. MR imaging of the pelvic floor at rest and on maximal strain was performed, using axial T2-weighted fast spin-echo images followed by sagittal ultrafast T2-weighted single-shot fast spin-echo sequences. Mean population age (age range, 22-45 years; mean +/- SD, 36 +/- 5.4 years), was similar in the four groups, as was parity in the three parous groups. Mean distances between the bladder floor and pubococcygeal line at rest did not differ between the four groups. On straining, bladder floor descent was 1.1 +/- 0.9, 1.0 +/- 1.1, and 1.9 +/- 0.9 cm in continent nulliparous, cesarean delivery, and vaginal delivery women, respectively, versus 3.2 +/- 1.0 cm in incontinent women (p = 0.0005). Cervical descent was greater in incontinent versus nulliparous women (p = 0.0019). Bladder floor descent was greater in the continent vaginal delivery group than in continent cesarean delivery control patients (p = 0.04). In patients with stress incontinence, symptoms did not correlate with amplitude of descent. The right levator muscle was thinner overall than the left, regardless of frequency direction (p = 0.001). Ultrafast MR imaging using the T2-weighted single-shot fast spin-echo sequence allows dynamic evaluation of the pelvic compartments at maximal strain with no need for contrast medium. Pelvic floor laxity and supporting fascia abnormalities were most common in patients with stress incontinence followed by continent women with a history of vaginal delivery. The results are therefore compatible with the hypothesis of vaginal delivery as a contributory factor to stress incontinence in older parous women.

A novel technique for real-time estimation of edge pedestal density gradients via reflectometer time delay data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, L., E-mail: zeng@fusion.gat.com; Doyle, E. J.; Rhodes, T. L.

2016-11-15

A new model-based technique for fast estimation of the pedestal electron density gradient has been developed. The technique uses ordinary mode polarization profile reflectometer time delay data and does not require direct profile inversion. Because of its simple data processing, the technique can be readily implemented via a Field-Programmable Gate Array, so as to provide a real-time density gradient estimate, suitable for use in plasma control systems such as envisioned for ITER, and possibly for DIII-D and Experimental Advanced Superconducting Tokamak. The method is based on a simple edge plasma model with a linear pedestal density gradient and low scrape-off-layermore » density. By measuring reflectometer time delays for three adjacent frequencies, the pedestal density gradient can be estimated analytically via the new approach. Using existing DIII-D profile reflectometer data, the estimated density gradients obtained from the new technique are found to be in good agreement with the actual density gradients for a number of dynamic DIII-D plasma conditions.« less

A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.

PubMed

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.

Kinetic comparison of older men and women during walk-to-stair descent transition.

PubMed

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 (p<0.05; -0.82 Nm/kg, 0.89 Nm/kg, 3.83 W/kg, and 1.78 W/kg, respectively). The lead leg knee eccentric power was also lower in women (p<0.05). This decreased ability to exert knee control during stair descent transition may predispose women to a higher risk of fall. Copyright © 2014 Elsevier B.V. All rights reserved.

Design and simulation of a descent controller for strategic four-dimensional aircraft navigation. M.S. Thesis

NASA Technical Reports Server (NTRS)

Lax, F. M.

1975-01-01

A time-controlled navigation system applicable to the descent phase of flight for airline transport aircraft was developed and simulated. The design incorporates the linear discrete-time sampled-data version of the linearized continuous-time system describing the aircraft's aerodynamics. Using optimal linear quadratic control techniques, an optimal deterministic control regulator which is implementable on an airborne computer is designed. The navigation controller assists the pilot in complying with assigned times of arrival along a four-dimensional flight path in the presence of wind disturbances. The strategic air traffic control concept is also described, followed by the design of a strategic control descent path. A strategy for determining possible times of arrival at specified waypoints along the descent path and for generating the corresponding route-time profiles that are within the performance capabilities of the aircraft is presented. Using a mathematical model of the Boeing 707-320B aircraft along with a Boeing 707 cockpit simulator interfaced with an Adage AGT-30 digital computer, a real-time simulation of the complete aircraft aerodynamics was achieved. The strategic four-dimensional navigation controller for longitudinal dynamics was tested on the nonlinear aircraft model in the presence of 15, 30, and 45 knot head-winds. The results indicate that the controller preserved the desired accuracy and precision of a time-controlled aircraft navigation system.

Optimization of rotational arc station parameter optimized radiation therapy.

PubMed

Dong, P; Ungun, B; Boyd, S; Xing, L

2016-09-01

To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was reduced by 8% and 6%, respectively. For the brain case, the doses to the eyes, chiasm, and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the head and neck case. The dosimetric quality and delivery efficiency presented here indicate that SPORT is an intriguing alternative treatment modality. With the widespread adoption of digital linac, SPORT should lead to improved patient care in the future.

Optimization of rotational arc station parameter optimized radiation therapy

PubMed Central

Dong, P.; Ungun, B.; Boyd, S.; Xing, L.

2016-01-01

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was reduced by 8% and 6%, respectively. For the brain case, the doses to the eyes, chiasm, and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the head and neck case. Conclusions: The dosimetric quality and delivery efficiency presented here indicate that SPORT is an intriguing alternative treatment modality. With the widespread adoption of digital linac, SPORT should lead to improved patient care in the future. PMID:27587028

Optimization of rotational arc station parameter optimized radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, P.; Ungun, B.

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trappedmore » in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was reduced by 8% and 6%, respectively. For the brain case, the doses to the eyes, chiasm, and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the head and neck case. Conclusions: The dosimetric quality and delivery efficiency presented here indicate that SPORT is an intriguing alternative treatment modality. With the widespread adoption of digital linac, SPORT should lead to improved patient care in the future.« less

Vibration of initially stressed carbon nanotubes under magneto-thermal environment for nanoparticle delivery via higher-order nonlocal strain gradient theory

NASA Astrophysics Data System (ADS)

Farajpour, M. R.; Shahidi, A. R.; Tabataba'i-Nasab, F.; Farajpour, A.

2018-06-01

In this paper, the forced vibration of a single-walled carbon nanotube (SWCNT) under a moving nanoparticle is investigated based on the higher-order nonlocal strain gradient theory. The SWCNT is subjected to thermo-mechanical stresses and an external longitudinal magnetic field. The influences of higher-order stress gradients in conjunction with the strain gradient nonlocality are taken into account. Using Hamilton's principle and Maxwell's equations, the higher-order differential equations of motion are derived. An analytical solution is obtained for the dynamic deflection of SWCNTs using the Galerkin method. Furthermore, the governing differential equation is solved numerically using the precise integration method. The results of the two solution procedures are compared and an excellent agreement is found between them. Finally, the influences of various scale parameters, the velocity of the moving nanoparticle, the initial axial stress, the temperature change and longitudinal magnetic field on the dynamic response of SWCNTs are investigated.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Erosional dynamics, flexural isostasy, and long-lived escarpments: A numerical modeling study

NASA Technical Reports Server (NTRS)

Tucker, Gregory E.; Slingerland, Rudy L.

1994-01-01

Erosional escarpments common features of high-elevation rifted continets. Fission track data suffest that these escarpments form by base level lowering and/or marginal uplift during rifting, followed by lateral retreat of an erosion front across tens to hundreds of kioometers. Previous modeling studies have shown that this characteristic pattern of denudation can have a profound impact upon marginal isostatic uplift and the evolution of offshore sedimentary basins. Yet at present there is only a rudimentary understanding of the geomorphic mechanisms capable of driving such prolonged escarpment retreat. In this study we present a nonlinear, two-dimensional landscape evolution model tha tis used to asses the necessary and sufficient conditions for long-term retreat of a rift-generated escarpment. The model represents topography as a grid of cells, with drainage networkds evolving as water flows across the grid in the direction of steepest descent. The model accounts for sediment production by weathering, fluvial sediment transport, bedrock channel erosion, and hillslope sediment transport by diffusive mechanisms and by mass failure. Numerical experiments presented explore the effects of different combinations of erosion processes and of dynamic coupling between denudation and flexural isostatic uplift. Model results suggest that the necessary and sufficient conditions for long-term escarpment retreat are (1) incising bedrock channels in which the erosion rate increases with increasing drainage area, so that the channels steepen and propagate headward; (2) a low rate of sediment production relative to sediment transport efficiency, which promotes relief-generating processes over diffusive ones; (3) high continental elevation, which allows greater freedom for fluvial dissection; and (4) any process, including flexural isostatic uplift, that helps to maintain a drainage divide near an escarpment crest. Flexural isostatic uplift also facilitates escarpment, thereby increasing channel gradients and accelerating erosion which in turn generates additional isostatic uplift. Of all the above conditions, high continental elevation is common ot most rift margin escarpments and may ultimately be the most important factor.

Optimizing wavefront-guided corrections for highly aberrated eyes in the presence of registration uncertainty

PubMed Central

Shi, Yue; Queener, Hope M.; Marsack, Jason D.; Ravikumar, Ayeswarya; Bedell, Harold E.; Applegate, Raymond A.

2013-01-01

Dynamic registration uncertainty of a wavefront-guided correction with respect to underlying wavefront error (WFE) inevitably decreases retinal image quality. A partial correction may improve average retinal image quality and visual acuity in the presence of registration uncertainties. The purpose of this paper is to (a) develop an algorithm to optimize wavefront-guided correction that improves visual acuity given registration uncertainty and (b) test the hypothesis that these corrections provide improved visual performance in the presence of these uncertainties as compared to a full-magnitude correction or a correction by Guirao, Cox, and Williams (2002). A stochastic parallel gradient descent (SPGD) algorithm was used to optimize the partial-magnitude correction for three keratoconic eyes based on measured scleral contact lens movement. Given its high correlation with logMAR acuity, the retinal image quality metric log visual Strehl was used as a predictor of visual acuity. Predicted values of visual acuity with the optimized corrections were validated by regressing measured acuity loss against predicted loss. Measured loss was obtained from normal subjects viewing acuity charts that were degraded by the residual aberrations generated by the movement of the full-magnitude correction, the correction by Guirao, and optimized SPGD correction. Partial-magnitude corrections optimized with an SPGD algorithm provide at least one line improvement of average visual acuity over the full magnitude and the correction by Guirao given the registration uncertainty. This study demonstrates that it is possible to improve the average visual acuity by optimizing wavefront-guided correction in the presence of registration uncertainty. PMID:23757512

TRAGEN: Computer program to simulate an aircraft steered to follow a specified verticle profile. User's guide

NASA Technical Reports Server (NTRS)

1983-01-01

The longitudinal dynamics of a medium range twin-jet or tri-jet transport aircraft are simulated. For the climbing trajectory, the thrust is constrained to maximum value, and for descent, the thrust is set at idle. For cruise, the aircraft is held in the trim condition. For climb or descent, the aircraft is steered to follow either (a) a fixed profile which is input to the program or (b) a profile computed at the beginning of that segment of the run. For climb, the aircraft is steered to maintain the given airspeed as a function of altitude. For descent, the aircraft is steered to maintain the given altitude as a function of range-to-go. In both cases, the control variable is angle-of-attack. The given output trajectory is presented and compared with the input trajectory. Step climb is treated just as climb. For cruise, the Breguet equations are used to compute the fuel burned to achieve a given range and to connect given initial and final values of altitude and Mach number.

One Dimension Analytical Model of Normal Ballistic Impact on Ceramic/Metal Gradient Armor

NASA Astrophysics Data System (ADS)

Liu, Lisheng; Zhang, Qingjie; Zhai, Pengcheng; Cao, Dongfeng

2008-02-01

An analytical model of normal ballistic impact on the ceramic/metal gradient armor, which is based on modified Alekseevskii-Tate equations, has been developed. The process of gradient armour impacted by the long rod can be divided into four stages in this model. First stage is projectile's mass erosion or flowing phase, mushrooming phase and rigid phase; second one is the formation of comminuted ceramic conoid; third one is the penetration of gradient layer and last one is the penetration of metal back-up plate. The equations of third stage have been advanced by assuming the behavior of gradient layer as rigid-plastic and considering the effect of strain rate on the dynamic yield strength.

One Dimension Analytical Model of Normal Ballistic Impact on Ceramic/Metal Gradient Armor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu Lisheng; Zhang Qingjie; Zhai Pengcheng

2008-02-15

An analytical model of normal ballistic impact on the ceramic/metal gradient armor, which is based on modified Alekseevskii-Tate equations, has been developed. The process of gradient armour impacted by the long rod can be divided into four stages in this model. First stage is projectile's mass erosion or flowing phase, mushrooming phase and rigid phase; second one is the formation of comminuted ceramic conoid; third one is the penetration of gradient layer and last one is the penetration of metal back-up plate. The equations of third stage have been advanced by assuming the behavior of gradient layer as rigid-plastic andmore » considering the effect of strain rate on the dynamic yield strength.« less

Design keys for paper-based concentration gradient generators.

PubMed

Schaumburg, Federico; Urteaga, Raúl; Kler, Pablo A; Berli, Claudio L A

2018-08-03

The generation of concentration gradients is an essential operation for several analytical processes implemented on microfluidic paper-based analytical devices. The dynamic gradient formation is based on the transverse dispersion of chemical species across co-flowing streams. In paper channels, this transverse flux of molecules is dominated by mechanical dispersion, which is substantially different than molecular diffusion, which is the mechanism acting in conventional microchannels. Therefore, the design of gradient generators on paper requires strategies different from those used in traditional microfluidics. This work considers the foundations of transverse dispersion in porous substrates to investigate the optimal design of microfluidic paper-based concentration gradient generators (μPGGs) by computer simulations. A set of novel and versatile μPGGs were designed in the format of numerical prototypes, and virtual experiments were run to explore the ranges of operation and the overall performance of such devices. Then physical prototypes were fabricated and experimentally tested in our lab. Finally, some basic rules for the design of optimized μPGGs are proposed. Apart from improving the efficiency of mixers, diluters and μPGGs, the results of this investigation are relevant to attain highly controlled concentration fields on paper-based devices. Copyright © 2018 Elsevier B.V. All rights reserved.

Entry Descent and Landing Workshop Proceedings. Volume 1; Mars2020 Entry, Descent, and Landing Instrumentation (MEDLI2): Project Overview

NASA Technical Reports Server (NTRS)

Bose, Deepak; Wright, Henry

2015-01-01

Aerothermal & TPS: a) Determine Forebody Aerothermal Heating. b) Determine In-depth TPS Temperature. c) Determine Backshell Aerothermal Environment. Aerodynamics and Atmosphere: a) Reconstruct Atmospheric Density, Winds, and Wind-Relative Attitude. b) Determine Hypersonic & Supersonic Aerodynamics Forces. c) Base Pressure Contribution to Drag.

Flight Data Entry, Descent, and Landing (EDL) Repository

NASA Technical Reports Server (NTRS)

Martinez, Elmain M.; Winterhalter, Daniel

2012-01-01

Dr. Daniel Winterhalter, NASA Engineering and Safety Center Chief Engineer at the Jet Propulsion Laboratory, requested the NASA Engineering and Safety Center sponsor a 3-year effort to collect entry, descent, and landing material and to establish a NASA-wide archive to serve the material. The principle focus of this task was to identify entry, descent, and landing repository material that was at risk of being permanently lost due to damage, decay, and undocumented storage. To provide NASA-wide access to this material, a web-based digital archive was created. This document contains the outcome of the effort.

Test results of flight guidance for fuel conservative descents in a time-based metered air traffic environment. [terminal configured vehicle

NASA Technical Reports Server (NTRS)

Knox, C. E.; Person, L. H., Jr.

1981-01-01

The NASA developed, implemented, and flight tested a flight management 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. This algorithm provides a 3D path with time control (4D) for the TCV B-737 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 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 are described and flight test results are presented.

A novel three-dimensional dynamic anorectal ultrasonography technique for the assessment of perineal descent, compared with defaecography.

PubMed

Murad-Regadas, S M; dos Santos, D; Soares, G; Regadas, F S P; Rodrigues, L V; Buchen, G; Kenmoti, V T; Surimã, W S; Fernandes, G O da S

2012-06-01

The purpose of the study was to describe a novel three-dimensional dynamic anorectal ultrasonography technique (dynamic 3-DAUS) for assessment of perineal descent (PD) and establishment of normal range values, comparing it with defaecography. Secondarily, the study compares the ability of the two techniques to identify various pelvic floor dysfunctions. A prospective study was undertaken in 29 women (mean age 43 years) with obstructed defecation disorder. All patients underwent defaecography and dynamic 3-DAUS and the results were compared. Lee kappa coefficients (K) were used. On defaecography, PD > 3 cm was detected in 12 patients. On dynamic 3-DAUS, 10 of these patients had PD > 2.5 cm. Seventeen had normal PD on defaecography and PD ≤ 2.5 cm on dynamic 3-DAUS (K 0.85). Normal relaxation was observed in 10 patients and anismus in 14 with both techniques (K 0.65). Both techniques identified five patients without rectocele, two with grade I rectocele (K 0.89 and 1.00, respectively) and 10 with grade II and nine with grade III (K 0.72 and 0.77, respectively). Rectal intussusception was identified in six patients on defaecography. These were confirmed on dynamic 3-DAUS in addition to the identification of another seven cases indicating moderate agreement (K 0.46). Enterocele/sigmoidocele grade III was identified in one patient with both techniques, indicating substantial agreement (K 0.65). Dynamic 3-DAUS was shown to be a reliable technique for the assessment of PD and pelvic floor dysfunctions, identifying all disorders and confirming findings from defaecography. © 2011 The Authors. Colorectal Disease © 2011 The Association of Coloproctology of Great Britain and Ireland.

A Symmetric Time-Varying Cluster Rate of Descent Model

NASA Technical Reports Server (NTRS)

Ray, Eric S.

2015-01-01

A model of the time-varying rate of descent of the Orion vehicle was developed based on the observed correlation between canopy projected area and drag coefficient. This initial version of the model assumes cluster symmetry and only varies the vertical component of velocity. The cluster fly-out angle is modeled as a series of sine waves based on flight test data. The projected area of each canopy is synchronized with the primary fly-out angle mode. The sudden loss of projected area during canopy collisions is modeled at minimum fly-out angles, leading to brief increases in rate of descent. The cluster geometry is converted to drag coefficient using empirically derived constants. A more complete model is under development, which computes the aerodynamic response of each canopy to its local incidence angle.

The glucokinase mutation p.T206P is common among MODY patients of Jewish Ashkenazi descent.

PubMed

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.

Final STS-35 Columbia descent BET products and results for LaRC OEX investigations

NASA Technical Reports Server (NTRS)

Oakes, Kevin F.; Findlay, John T.; Jasinski, Rachel A.; Wood, James S.

1991-01-01

Final STS-35 'Columbia' descent Best Estimate Trajectory (BET) products have been developed for Langley Research Center (LaRC) Orbiter Experiments (OEX) investigations. Included are the reconstructed inertial trajectory profile; the Extended BET, which combines the inertial data and, in this instance, the National Weather Service atmospheric information obtained via Johnson Space Center; and the Aerodynamic BET. The inertial BET utilized Inertial Measurement Unit 1 (IMU1) dynamic measurements for deterministic propagation during the ENTREE estimation process. The final estimate was based on the considerable ground based C-band tracking coverage available as well as Tracking Data and Relay Satellite System (TDRSS) Doppler data, a unique use of the latter for endo-atmospheric flight determinations. The actual estimate required simultaneous solutions for the spacecraft position and velocity, spacecraft attitude, and six IMU parameters - three gyro biases and three accelerometer scale factor correction terms. The anchor epoch for this analysis was 19,200 Greenwich Mean Time (GMT) seconds which corresponds to an initial Shuttle altitude of approximately 513 kft. The atmospheric data incorporated were evaluated based on Shuttle derived considerations as well as comparisons with other models. The AEROBET was developed based on the Extended BET, the measured spacecraft configuration information, final mass properties, and the final Orbiter preoperation databook. The latter was updated based on aerodynamic consensus incrementals derived by the latest published FAD. The rectified predictions were compared versus the flight computed values and the resultant differences were correlated versus ensemble results for twenty-two previous STS entry flights.

Rekindled vision of hypersonic travel

NASA Technical Reports Server (NTRS)

Colladay, Raymond S.

1987-01-01

NASA has joined with the DOD to conduct the National Aerospace Plane (NASP) program, whose experimental test vehicle will be designated the X-30. NASP will study the X-30's takeoff from a runway under its own power, acceleration to high Mach number on the basis of airbreathing propulsion, emergence into LEO, reentry into the earth atmosphere, and descent to a powered horizontal landing. NASP will thereby generate technology base data for three distinct types of aircraft: upper-atmosphere hypersonic-cruise aircraft, LEO space transports, and military transatmospheric vehicles. The current concept-validation phase of NASP focuses on airbreathing propulsion, lightweight/high-strength heat-resistant materials, and computational fluid dynamics.

African and Non-African Admixture Components in African Americans and An African Caribbean Population

PubMed Central

Murray, Tanda; Beaty, Terri H.; Mathias, Rasika A.; Rafaels, Nicholas; Grant, Audrey Virginia; Faruque, Mezbah U.; Watson, Harold R.; Ruczinski, Ingo; Dunston, Georgia M.; Barnes, Kathleen C.

2013-01-01

Admixture is a potential source of confounding in genetic association studies, so it becomes important to detect and estimate admixture in a sample of unrelated individuals. Populations of African descent in the US and the Caribbean share similar historical backgrounds but the distributions of African admixture may differ. We selected 416 ancestry informative markers (AIMs) to estimate and compare admixture proportions using STRUCTURE in 906 unrelated African Americans (AAs) and 294 Barbadians (ACs) from a study of asthma. This analysis showed AAs on average were 72.5% African, 19.6% European and 8% Asian, while ACs were 77.4% African, 15.9% European, and 6.7% Asian which were significantly different. A principal components analysis based on these AIMs yielded one primary eigenvector that explained 54.04% of the variation and captured a gradient from West African to European admixture. This principal component was highly correlated with African vs. European ancestry as estimated by STRUCTURE (r2 = 0.992, r2 = 0.912, respectively). To investigate other African contributions to African American and Barbadian admixture, we performed PCA on ~14,000 (14k) genome-wide SNPs in AAs, ACs, Yorubans, Luhya and Maasai African groups, and estimated genetic distances (FST). We found AAs and ACs were closest genetically (FST = 0.008), and both were closer to the Yorubans than the other East African populations. In our sample of individuals of African descent, ~400 well-defined AIMs were just as good for detecting substructure as ~14,000 random SNPs drawn from a genome-wide panel of markers. PMID:20717976

Human-in-the-loop Bayesian optimization of wearable device parameters

PubMed Central

Malcolm, Philippe; Speeckaert, Jozefien; Siviy, Christoper J.; Walsh, Conor J.; Kuindersma, Scott

2017-01-01

The increasing capabilities of exoskeletons and powered prosthetics for walking assistance have paved the way for more sophisticated and individualized control strategies. In response to this opportunity, recent work on human-in-the-loop optimization has considered the problem of automatically tuning control parameters based on realtime physiological measurements. However, the common use of metabolic cost as a performance metric creates significant experimental challenges due to its long measurement times and low signal-to-noise ratio. We evaluate the use of Bayesian optimization—a family of sample-efficient, noise-tolerant, and global optimization methods—for quickly identifying near-optimal control parameters. To manage experimental complexity and provide comparisons against related work, we consider the task of minimizing metabolic cost by optimizing walking step frequencies in unaided human subjects. Compared to an existing approach based on gradient descent, Bayesian optimization identified a near-optimal step frequency with a faster time to convergence (12 minutes, p < 0.01), smaller inter-subject variability in convergence time (± 2 minutes, p < 0.01), and lower overall energy expenditure (p < 0.01). PMID:28926613

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

NASA Astrophysics Data System (ADS)

Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi

2015-12-01

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inherent smoothness of intensity patterns for intensity modulated radiation therapy generated by simultaneous projection algorithms

NASA Astrophysics Data System (ADS)

Xiao, Ying; Michalski, Darek; Censor, Yair; Galvin, James M.

2004-07-01

The efficient delivery of intensity modulated radiation therapy (IMRT) depends on finding optimized beam intensity patterns that produce dose distributions, which meet given constraints for the tumour as well as any critical organs to be spared. Many optimization algorithms that are used for beamlet-based inverse planning are susceptible to large variations of neighbouring intensities. Accurately delivering an intensity pattern with a large number of extrema can prove impossible given the mechanical limitations of standard multileaf collimator (MLC) delivery systems. In this study, we apply Cimmino's simultaneous projection algorithm to the beamlet-based inverse planning problem, modelled mathematically as a system of linear inequalities. We show that using this method allows us to arrive at a smoother intensity pattern. Including nonlinear terms in the simultaneous projection algorithm to deal with dose-volume histogram (DVH) constraints does not compromise this property from our experimental observation. The smoothness properties are compared with those from other optimization algorithms which include simulated annealing and the gradient descent method. The simultaneous property of these algorithms is ideally suited to parallel computing technologies.

Multigrid optimal mass transport for image registration and morphing

NASA Astrophysics Data System (ADS)

Rehman, Tauseef ur; Tannenbaum, Allen

2007-02-01

In this paper we present a computationally efficient Optimal Mass Transport algorithm. This method is based on the Monge-Kantorovich theory and is used for computing elastic registration and warping maps in image registration and morphing applications. This is a parameter free method which utilizes all of the grayscale data in an image pair in a symmetric fashion. No landmarks need to be specified for correspondence. In our work, we demonstrate significant improvement in computation time when our algorithm is applied as compared to the originally proposed method by Haker et al [1]. The original algorithm was based on a gradient descent method for removing the curl from an initial mass preserving map regarded as 2D vector field. This involves inverting the Laplacian in each iteration which is now computed using full multigrid technique resulting in an improvement in computational time by a factor of two. Greater improvement is achieved by decimating the curl in a multi-resolutional framework. The algorithm was applied to 2D short axis cardiac MRI images and brain MRI images for testing and comparison.

Multi-focus image fusion with the all convolutional neural network

NASA Astrophysics Data System (ADS)

Du, Chao-ben; Gao, She-sheng

2018-01-01

A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network (CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN (ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

Tracer-Based Determination of Vortex Descent in the 1999-2000 Arctic Winter

NASA Technical Reports Server (NTRS)

Greenblatt, Jeffery B.; Jost, Hans-Juerg; Loewenstein, Max; Podolske, James R.; Hurst, Dale F.; Elkins, James W.; Schauffler, Sue M.; Atlas, Elliot L.; Herman, Robert L.; Webster, Christopher R.

2001-01-01

A detailed analysis of available in situ and remotely sensed N2O and CH4 data measured in the 1999-2000 winter Arctic vortex has been performed in order to quantify the temporal evolution of vortex descent. Differences in potential temperature (theta) among balloon and aircraft vertical profiles (an average of 19-23 K on a given N2O or CH4 isopleth) indicated significant vortex inhomogeneity in late fall as compared with late winter profiles. A composite fall vortex profile was constructed for November 26, 1999, whose error bars encompassed the observed variability. High-latitude, extravortex profiles measured in different years and seasons revealed substantial variability in N2O and CH4 on theta surfaces, but all were clearly distinguishable from the first vortex profiles measured in late fall 1999. From these extravortex-vortex differences, we inferred descent prior to November 26: 397+/-15 K (1sigma) at 30 ppbv N2O and 640 ppbv CH4, and 28+/-13 K above 200 ppbv N2O and 1280 ppbv CH4. Changes in theta were determined on five N2O and CH4 isopleths from November 26 through March 12, and descent rates were calculated on each N2O isopleth for several time intervals. The maximum descent rates were seen between November 26 and January 27: 0.82+/-0.20 K/day averaged over 50-250 ppbv N2O. By late winter (February 26-March 12), the average rate had decreased to 0.10+/-0.25 K/day. Descent rates also decreased with increasing N2O; the winter average (November 26-March 5) descent rate varied from 0.75+/-0.10 K/day at 50 ppbv to 0.40+/-0.11 K/day at 250 ppbv. Comparison of these results with observations and models of descent in prior years showed very good overall agreement. Two models of the 1999-2000 vortex descent, SLIMCAT and REPROBUS, despite theta offsets with respect to observed profiles of up to 20 K on most tracer isopleths, produced descent rates that agreed very favorably with the inferred rates from observation.

Gradient Plasticity Model and its Implementation into MARMOT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barker, Erin I.; Li, Dongsheng; Zbib, Hussein M.

2013-08-01

The influence of strain gradient on deformation behavior of nuclear structural materials, such as boby centered cubic (bcc) iron alloys has been investigated. We have developed and implemented a dislocation based strain gradient crystal plasticity material model. A mesoscale crystal plasticity model for inelastic deformation of metallic material, bcc steel, has been developed and implemented numerically. Continuum Dislocation Dynamics (CDD) with a novel constitutive law based on dislocation density evolution mechanisms was developed to investigate the deformation behaviors of single crystals, as well as polycrystalline materials by coupling CDD and crystal plasticity (CP). The dislocation density evolution law in thismore » model is mechanism-based, with parameters measured from experiments or simulated with lower-length scale models, not an empirical law with parameters back-fitted from the flow curves.« less

Theoretical analysis of degradation mechanisms in the formation of morphogen gradients

NASA Astrophysics Data System (ADS)

Bozorgui, Behnaz; Teimouri, Hamid; Kolomeisky, Anatoly B.

2015-07-01

Fundamental biological processes of development of tissues and organs in multicellular organisms are governed by various signaling molecules, which are called morphogens. It is known that spatial and temporal variations in the concentration profiles of signaling molecules, which are frequently referred as morphogen gradients, lead to a cell differentiation via activating specific genes in a concentration-dependent manner. It is widely accepted that the establishment of the morphogen gradients involves multiple biochemical reactions and diffusion processes. One of the critical elements in the formation of morphogen gradients is a degradation of signaling molecules. We develop a new theoretical approach that provides a comprehensive description of the degradation mechanisms. It is based on the idea that the degradation works as an effective potential that drives the signaling molecules away from the source region. Utilizing the method of first-passage processes, the dynamics of the formation of morphogen gradients for various degradation mechanisms is explicitly evaluated. It is found that linear degradation processes lead to a dynamic behavior specified by times to form the morphogen gradients that depend linearly on the distance from the source. This is because the effective potential due to the degradation is quite strong. At the same time, nonlinear degradation mechanisms yield a quadratic scaling in the morphogen gradients formation times since the effective potentials are much weaker. Physical-chemical explanations of these phenomena are presented.

Testing the limits of gradient sensing

PubMed Central

Lakhani, Vinal

2017-01-01

The ability to detect a chemical gradient is fundamental to many cellular processes. In multicellular organisms gradient sensing plays an important role in many physiological processes such as wound healing and development. Unicellular organisms use gradient sensing to move (chemotaxis) or grow (chemotropism) towards a favorable environment. Some cells are capable of detecting extremely shallow gradients, even in the presence of significant molecular-level noise. For example, yeast have been reported to detect pheromone gradients as shallow as 0.1 nM/μm. Noise reduction mechanisms, such as time-averaging and the internalization of pheromone molecules, have been proposed to explain how yeast cells filter fluctuations and detect shallow gradients. Here, we use a Particle-Based Reaction-Diffusion model of ligand-receptor dynamics to test the effectiveness of these mechanisms and to determine the limits of gradient sensing. In particular, we develop novel simulation methods for establishing chemical gradients that not only allow us to study gradient sensing under steady-state conditions, but also take into account transient effects as the gradient forms. Based on reported measurements of reaction rates, our results indicate neither time-averaging nor receptor endocytosis significantly improves the cell’s accuracy in detecting gradients over time scales associated with the initiation of polarized growth. Additionally, our results demonstrate the physical barrier of the cell membrane sharpens chemical gradients across the cell. While our studies are motivated by the mating response of yeast, we believe our results and simulation methods will find applications in many different contexts. PMID:28207738

Pixel-By Estimation of Scene Motion in Video

NASA Astrophysics Data System (ADS)

Tashlinskii, A. G.; Smirnov, P. V.; Tsaryov, M. G.

2017-05-01

The paper considers the effectiveness of motion estimation in video using pixel-by-pixel recurrent algorithms. The algorithms use stochastic gradient decent to find inter-frame shifts of all pixels of a frame. These vectors form shift vectors' field. As estimated parameters of the vectors the paper studies their projections and polar parameters. It considers two methods for estimating shift vectors' field. The first method uses stochastic gradient descent algorithm to sequentially process all nodes of the image row-by-row. It processes each row bidirectionally i.e. from the left to the right and from the right to the left. Subsequent joint processing of the results allows compensating inertia of the recursive estimation. The second method uses correlation between rows to increase processing efficiency. It processes rows one after the other with the change in direction after each row and uses obtained values to form resulting estimate. The paper studies two criteria of its formation: gradient estimation minimum and correlation coefficient maximum. The paper gives examples of experimental results of pixel-by-pixel estimation for a video with a moving object and estimation of a moving object trajectory using shift vectors' field.

High-resolution Anorectal Manometry for Identifying Defecatory Disorders and Rectal Structural Abnormalities in Women.

PubMed

Prichard, David O; Lee, Taehee; Parthasarathy, Gopanandan; Fletcher, Joel G; Zinsmeister, Alan R; Bharucha, Adil E

2017-03-01

Contrary to conventional wisdom, the rectoanal gradient during evacuation is negative in many healthy people, undermining the utility of anorectal high-resolution manometry (HRM) for diagnosing defecatory disorders. We aimed to compare HRM and magnetic resonance imaging (MRI) for assessing rectal evacuation and structural abnormalities. We performed a retrospective analysis of 118 patients (all female; 51 with constipation, 48 with fecal incontinence, and 19 with rectal prolapse; age, 53 ± 1 years) assessed by HRM, the rectal balloon expulsion test (BET), and MRI at Mayo Clinic, Rochester, Minnesota, from February 2011 through March 2013. Thirty healthy asymptomatic women (age, 37 ± 2 years) served as controls. We used principal components analysis of HRM variables to identify rectoanal pressure patterns associated with rectal prolapse and phenotypes of patients with prolapse. Compared with patients with normal findings from the rectal BET, patients with an abnormal BET had lower median rectal pressure (36 vs 22 mm Hg, P = .002), a more negative median rectoanal gradient (-6 vs -29 mm Hg, P = .006) during evacuation, and a lower proportion of evacuation on the basis of MRI analysis (median of 40% vs 80%, P < .0001). A score derived from rectal pressure and anorectal descent during evacuation and a patulous anal canal was associated (P = .005) with large rectoceles (3 cm or larger). A principal component (PC) logistic model discriminated between patients with and without prolapse with 96% accuracy. Among patients with prolapse, there were 2 phenotypes, which were characterized by high (PC1) or low (PC2) anal pressures at rest and squeeze along with higher rectal and anal pressures (PC1) or a higher rectoanal gradient during evacuation (PC2). In a retrospective analysis of patients assessed by HRM, measurements of rectal evacuation by anorectal HRM, BET, and MRI were correlated. HRM alone and together with anorectal descent during evacuation may identify rectal prolapse and large rectoceles, respectively, and also identify unique phenotypes of rectal prolapse. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Stability and nuclear dynamics of the Bicoid morphogen gradient

PubMed Central

Gregor, Thomas; Wieschaus, Eric F.; McGregor, Alistair P.; Bialek, William; Tank, David W.

2008-01-01

Patterning in multicellular organisms results from spatial gradients in morphogen concentration, but the dynamics of these gradients remains largely unexplored. We characterize, through in vivo optical imaging, the development and stability of the Bicoid morphogen gradient in Drosophila embryos that express a Bicoid-eGFP fusion protein. The gradient is established rapidly (~1 hour after fertilization) with nuclear Bicoid concentration rising and falling during mitosis. Interphase levels result from a rapid equilibrium between Bicoid uptake and removal. Initial interphase concentration in nuclei in successive cycles is constant (±10%), demonstrating a form of gradient stability, but subsequently decays by approximately 30%. Both direct photobleaching measurements and indirect estimates of Bicoid-eGFP diffusion constants (D ≤ 1 μm2/s), provide a consistent picture of Bicoid transport on short (~min) time scales, but challenge traditional models of long range gradient formation. A new model is presented emphasizing the possible role of nuclear dynamics in shaping and scaling the gradient. PMID:17632061

Effects of aircraft and flight parameters on energy-efficient profile descents in time-based metered traffic

NASA Technical Reports Server (NTRS)

Dejarnette, F. R.

1984-01-01

Attention is given to a computer algorithm yielding the data required for a flight crew to navigate from an entry fix, about 100 nm from an airport, to a metering fix, and arrive there at a predetermined time, altitude, and airspeed. The flight path is divided into several descent and deceleration segments. Results for the case of a B-737 airliner indicate that wind and nonstandard atmospheric properties have a significant effect on the flight path and must be taken into account. While a range of combinations of Mach number and calibrated airspeed is possible for the descent segments leading to the metering fix, only small changes in the fuel consumed were observed for this range of combinations. A combination that is based on scheduling flexibility therefore seems preferable.

A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons

PubMed Central

Vavoulis, Dimitrios V.; Straub, Volko A.; Aston, John A. D.; Feng, Jianfeng

2012-01-01

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models. PMID:22396632

Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

NASA Astrophysics Data System (ADS)

Zheng, Qin; Yang, Zubin; Sha, Jianxin; Yan, Jun

2017-02-01

In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

Motion-based threat detection using microrods: experiments and numerical simulations.

PubMed

Ezhilan, Barath; Gao, Wei; Pei, Allen; Rozen, Isaac; Dong, Renfeng; Jurado-Sanchez, Beatriz; Wang, Joseph; Saintillan, David

2015-05-07

Motion-based chemical sensing using microscale particles has attracted considerable recent attention. In this paper, we report on new experiments and Brownian dynamics simulations that cast light on the dynamics of both passive and active microrods (gold wires and gold-platinum micromotors) in a silver ion gradient. We demonstrate that such microrods can be used for threat detection in the form of a silver ion source, allowing for the determination of both the location of the source and concentration of silver. This threat detection strategy relies on the diffusiophoretic motion of both passive and active microrods in the ionic gradient and on the speed acceleration of the Au-Pt micromotors in the presence of silver ions. A Langevin model describing the microrod dynamics and accounting for all of these effects is presented, and key model parameters are extracted from the experimental data, thereby providing a reliable estimate for the full spatiotemporal distribution of the silver ions in the vicinity of the source.

A Smoothed Eclipse Model for Solar Electric Propulsion Trajectory Optimization

NASA Technical Reports Server (NTRS)

Aziz, Jonathan D.; Scheeres, Daniel J.; Parker, Jeffrey S.; Englander, Jacob A.

2017-01-01

Solar electric propulsion (SEP) is the dominant design option for employing low-thrust propulsion on a space mission. Spacecraft solar arrays power the SEP system but are subject to blackout periods during solar eclipse conditions. Discontinuity in power available to the spacecraft must be accounted for in trajectory optimization, but gradient-based methods require a differentiable power model. This work presents a power model that smooths the eclipse transition from total eclipse to total sunlight with a logistic function. Example trajectories are computed with differential dynamic programming, a second-order gradient-based method.

Trajectory Design Considerations for Small Body Touch-and-Go

NASA Technical Reports Server (NTRS)

Wallace, Mark; Broschart, Stephen; Bonfiglio, Eugene; Bhaskharan, Shyam; Cangahuala, Alberto

2011-01-01

Outline: (1) Trajectory Description (2) Design Drivers: (2a) Dynamics (2b) Environment (2c) Spacecraft and Ground and System Capabilities (2d) Mission Objectives (3) Design Choices (4) Historical Precedents (5) Case Studies. What is Touch-and-Go (TAG)? (1) Descent to the surface (2) Brief contact (3) Ascends to a safe distance

GREAT: a gradient-based color-sampling scheme for Retinex.

PubMed

Lecca, Michela; Rizzi, Alessandro; Serapioni, Raul Paolo

2017-04-01

Modeling the local color spatial distribution is a crucial step for the algorithms of the Milano Retinex family. Here we present GREAT, a novel, noise-free Milano Retinex implementation based on an image-aware spatial color sampling. For each channel of a color input image, GREAT computes a 2D set of edges whose magnitude exceeds a pre-defined threshold. Then GREAT re-scales the channel intensity of each image pixel, called target, by the average of the intensities of the selected edges weighted by a function of their positions, gradient magnitudes, and intensities relative to the target. In this way, GREAT enhances the input image, adjusting its brightness, contrast and dynamic range. The use of the edges as pixels relevant to color filtering is justified by the importance that edges play in human color sensation. The name GREAT comes from the expression "Gradient RElevAnce for ReTinex," which refers to the threshold-based definition of a gradient relevance map for edge selection and thus for image color filtering.

Breast ultrasound computed tomography using waveform inversion with source encoding

NASA Astrophysics Data System (ADS)

Wang, Kun; Matthews, Thomas; Anis, Fatima; Li, Cuiping; Duric, Neb; Anastasio, Mark A.

2015-03-01

Ultrasound computed tomography (USCT) holds great promise for improving the detection and management of breast cancer. Because they are based on the acoustic wave equation, waveform inversion-based reconstruction methods can produce images that possess improved spatial resolution properties over those produced by ray-based methods. However, waveform inversion methods are computationally demanding and have not been applied widely in USCT breast imaging. In this work, source encoding concepts are employed to develop an accelerated USCT reconstruction method that circumvents the large computational burden of conventional waveform inversion methods. This method, referred to as the waveform inversion with source encoding (WISE) method, encodes the measurement data using a random encoding vector and determines an estimate of the speed-of-sound distribution by solving a stochastic optimization problem by use of a stochastic gradient descent algorithm. Computer-simulation studies are conducted to demonstrate the use of the WISE method. Using a single graphics processing unit card, each iteration can be completed within 25 seconds for a 128 × 128 mm2 reconstruction region. The results suggest that the WISE method maintains the high spatial resolution of waveform inversion methods while significantly reducing the computational burden.

A differential memristive synapse circuit for on-line learning in neuromorphic computing systems

NASA Astrophysics Data System (ADS)

Nair, Manu V.; Muller, Lorenz K.; Indiveri, Giacomo

2017-12-01

Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network’s throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper, we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two benchmark classification tasks.

Classification of breast cancer cytological specimen using convolutional neural network

NASA Astrophysics Data System (ADS)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI.

PubMed

Hu, Changwei; Qu, Xiaobo; Guo, Di; Bao, Lijun; Chen, Zhong

2011-09-01

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ℓ(2) data fidelity term and ℓ(1) sparsity regularization term. Rather than manually setting the regularization parameter for the ℓ(1) term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression. Copyright © 2011 Elsevier Inc. All rights reserved.

Socialisation interculturelle et dynamiques identitaires chez les jeunes adultes issus de l'immigration maghrébine en France

NASA Astrophysics Data System (ADS)

Qribi, Adelhak; Courtinat, Amélie; Prêteur, Yves

2010-12-01

Intercultural socialisation and identity dynamics among young adults of Maghrebi immigrant descent - This study analyses different identity orientations expressed by young people of North African immigrant descent in France in relation to their conditions of socialisation. Identity is considered in terms of cultural identification, self-esteem and the activeness or passiveness of the relationship with reality. Socialisation is studied from the perspective of the interrelationship between family education and school experience. A survey was conducted by questionnaire among 120 young people aged between 18 and 25, of both sexes, from a working-class background. A multivariate analysis (Descending Hierarchical Classification) determined a structure of four groups in terms of differentiation or assimilation and conformity or individuation. It was found that strategies balancing ontological and pragmatic concerns appear to play a role in the dynamics of a subject's self-affirmation and autonomy. The attachment to a certain religious tradition in these cases seems to be compatible with modernity and is a factor of psychological balance and social integration.

Evolutionary analyses of non-genealogical bonds produced by introgressive descent.

PubMed

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.

Forces Generated by Vastus Lateralis and Vastus Medialis Decrease with Increasing Stair Descent Speed.

PubMed

Caruthers, Elena J; Oxendale, Kassandra K; Lewis, Jacqueline M; Chaudhari, Ajit M W; Schmitt, Laura C; Best, Thomas M; Siston, Robert A

2018-04-01

Stair descent (SD) is a common, difficult task for populations who are elderly or have orthopaedic pathologies. Joint torques of young, healthy populations during SD increase at the hip and ankle with increasing speed but not at the knee, contrasting torque patterns during gait. To better understand the sources of the knee torque pattern, we used dynamic simulations to estimate knee muscle forces and how they modulate center of mass (COM) acceleration across SD speeds (slow, self-selected, and fast) in young, healthy adults. The vastus lateralis and vastus medialis forces decreased from slow to self-selected speeds as the individual lowered to the next step. Since the vasti are primary contributors to vertical support during SD, they produced lower forces at faster speeds due to the lower need for vertical COM support observed at faster speeds. In contrast, the semimembranosus and rectus femoris forces increased across successive speeds, allowing the semimembranosus to increase acceleration downward and forward and the rectus femoris to provide more vertical support and resistance to forward progression as SD speed increased. These results demonstrate the utility of dynamic simulations to extend beyond traditional inverse dynamics analyses to gain further insight into muscle mechanisms during tasks like SD.

Coupled dynamics of translation and collapse of acoustically driven microbubbles.

PubMed

Reddy, Anil J; Szeri, Andrew J

2002-10-01

Pressure gradients drive the motion of microbubbles relative to liquids in which they are suspended. Examples include the hydrostatic pressure due to a gravitational field, and the pressure gradients in a sound field, useful for acoustic levitation. In this paper, the equations describing the coupled dynamics of radial oscillation and translation of a microbubble are given. The formulation is based on a recently derived expression for the hydrodynamic force on a bubble of changing size in an incompressible liquid [J. Magnaudet and D. Legendre, Phys. Fluids 10, 550-556 (1998)]. The complex interaction between radial and translation dynamics is best understood by examination of the added momentum associated with the liquid motion caused by the moving bubble. Translation is maximized when the bubble collapses violently. The new theory for coupled collapse and translation dynamics is compared to past experiments and to previous theories for decoupled translation dynamics. Special attention is paid to bubbles of relevance in biomedical applications.

The lateral variation of P n velocity gradient under Eurasia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiaoning

We report that mantle lid P wave velocity gradient, or P n velocity gradient, reflects the depth and lateral variations of thermal and rheological state of the uppermost mantle. Mapping the P n velocity gradient and its lateral variation helps us gain insight into the temperature, composition, and dynamics of the uppermost mantle. In addition, because P n velocity gradient has profound influence on P n propagation behavior, an accurate mapping of P n velocity gradient also improves the modeling and prediction of P n travel times and amplitudes. In this study, I used measured P n travel times tomore » derive path-specific P n velocity gradients. I then inverted these velocity gradients for two-dimensional (2-D) P n velocity-gradient models for Eurasia based on the assumption that a path-specific Pn velocity gradient is the mean of laterally varying P n velocity gradients along the P n path. Result from a Monte Carlo simulation indicates that the assumption is appropriate. The 2-D velocity-gradient models show that most of Eurasia has positive velocity gradients. High velocity gradients exist mainly in tectonically active regions. Most tectonically stable regions show low and more uniform velocity gradients. In conclusion, strong velocity-gradient variations occur largely along convergent plate boundaries, particularly under overriding plates.« less

The lateral variation of P n velocity gradient under Eurasia

DOE PAGES

Yang, Xiaoning

2017-05-03

We report that mantle lid P wave velocity gradient, or P n velocity gradient, reflects the depth and lateral variations of thermal and rheological state of the uppermost mantle. Mapping the P n velocity gradient and its lateral variation helps us gain insight into the temperature, composition, and dynamics of the uppermost mantle. In addition, because P n velocity gradient has profound influence on P n propagation behavior, an accurate mapping of P n velocity gradient also improves the modeling and prediction of P n travel times and amplitudes. In this study, I used measured P n travel times tomore » derive path-specific P n velocity gradients. I then inverted these velocity gradients for two-dimensional (2-D) P n velocity-gradient models for Eurasia based on the assumption that a path-specific Pn velocity gradient is the mean of laterally varying P n velocity gradients along the P n path. Result from a Monte Carlo simulation indicates that the assumption is appropriate. The 2-D velocity-gradient models show that most of Eurasia has positive velocity gradients. High velocity gradients exist mainly in tectonically active regions. Most tectonically stable regions show low and more uniform velocity gradients. In conclusion, strong velocity-gradient variations occur largely along convergent plate boundaries, particularly under overriding plates.« less

Northern Hemisphere Nitrous Oxide Morphology during the 1989 AASE and the 1991-1992 AASE 2 Campaigns

NASA Technical Reports Server (NTRS)

Podolske, James R.; Loewenstein, Max; Weaver, Alex; Strahan, Susan; Chan, K. Roland

1993-01-01

Nitrous oxide vertical profiles and latitudinal distributions for the 1989 AASE and 1992 AASE II northern polar winters are developed from the ATLAS N2O dataset, using both potential temperature and pressure as vertical coordinates. Morphologies show strong descent occurring poleward of the polar jet. The AASE II morphology shows a mid latitude 'surf zone,' characterized by strong horizontal mixing, and a horizontal gradient south of 30 deg N due to the sub-tropical jet. These features are similar to those produced by two-dimensional photochemical models which include coupling between transport, radiation, and chemistry.

Northern hemisphere nitrous oxide morphology during the 1989 AASE and the 1991-1992 AASE 2 campaigns

NASA Technical Reports Server (NTRS)

Podolske, James R.; Loewenstein, Max; Weaver, Alex; Strahan, Susan E.; Chan, K. Roland

1993-01-01

Nitrous oxide vertical profiles and latitudinal distributions for the 1989 Airborne Antarctic Ozone Experiment (AASE) and 1992 AASE 2 northern polar winters are developed from the ATLAS N2O dataset, using both potential temperature and pressure as vertical coordinates. Morphologies show strong descent occuring poleward of the polar jet. The AASE 2 morphology shows a mid latitude 'surf zone', characterized by strong horizontal mixing, and a horizontal gradient south of 30 deg N due to the sub-tropical jet. These features are similar to those produced by two-dimensional photochemical models which include coupling between transport, radiation, and chemistry.

Output Feedback Stabilization for a Class of Multi-Variable Bilinear Stochastic Systems with Stochastic Coupling Attenuation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Qichun; Zhou, Jinglin; Wang, Hong

In this paper, stochastic coupling attenuation is investigated for a class of multi-variable bilinear stochastic systems and a novel output feedback m-block backstepping controller with linear estimator is designed, where gradient descent optimization is used to tune the design parameters of the controller. It has been shown that the trajectories of the closed-loop stochastic systems are bounded in probability sense and the stochastic coupling of the system outputs can be effectively attenuated by the proposed control algorithm. Moreover, the stability of the stochastic systems is analyzed and the effectiveness of the proposed method has been demonstrated using a simulated example.

A Gradient Taguchi Method for Engineering Optimization

NASA Astrophysics Data System (ADS)

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.

Kurtosis Approach Nonlinear Blind Source Separation

NASA Technical Reports Server (NTRS)

Duong, Vu A.; Stubbemd, Allen R.

2005-01-01

In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation Keywords: Independent Component Analysis, Kurtosis, Higher order statistics.

Atmospheric tides on Venus. III - The planetary boundary layer

NASA Technical Reports Server (NTRS)

Dobrovolskis, A. R.

1983-01-01

Diurnal solar heating of Venus' surface produces variable temperatures, winds, and pressure gradients within a shallow layer at the bottom of the atmosphere. The corresponding asymmetric mass distribution experiences a tidal torque tending to maintain Venus' slow retrograde rotation. It is shown that including viscosity in the boundary layer does not materially affect the balance of torques. On the other hand, friction between the air and ground can reduce the predicted wind speeds from about 5 to about 1 m/sec in the lower atmosphere, more consistent with the observations from Venus landers and descent probes. Implications for aeolian activity on Venus' surface and for future missions are discussed.

Quantum generalisation of feedforward neural networks

NASA Astrophysics Data System (ADS)

Wan, Kwok Ho; Dahlsten, Oscar; Kristjánsson, Hlér; Gardner, Robert; Kim, M. S.

2017-09-01

We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e., unitary (the classical networks we generalise are called feedforward, and have step-function activation functions). The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.

A hybrid Gerchberg-Saxton-like algorithm for DOE and CGH calculation

NASA Astrophysics Data System (ADS)

Wang, Haichao; Yue, Weirui; Song, Qiang; Liu, Jingdan; Situ, Guohai

2017-02-01

The Gerchberg-Saxton (GS) algorithm is widely used in various disciplines of modern sciences and technologies where phase retrieval is required. However, this legendary algorithm most likely stagnates after a few iterations. Many efforts have been taken to improve this situation. Here we propose to introduce the strategy of gradient descent and weighting technique to the GS algorithm, and demonstrate it using two examples: design of a diffractive optical element (DOE) to achieve off-axis illumination in lithographic tools, and design of a computer generated hologram (CGH) for holographic display. Both numerical simulation and optical experiments are carried out for demonstration.

Modelling the descent of nitric oxide during the elevated stratopause event of January 2013

NASA Astrophysics Data System (ADS)

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.

The effects of Navy ship ladder descent on the knee internal joint reaction forces

NASA Astrophysics Data System (ADS)

Coulter, Jonathan D.; Weinhandl, Joshua T.; Bawab, Sebastian Y.; Ringleb, Stacie I.

2017-02-01

Military populations may be at risk for developing knee osteoarthritis and other knee problems when descending a Navy ship ladder, which differs from traditional stairs due to non-overlapping treads, a larger rise and a steeper inclination angle. The purpose of this study was to develop a forward dynamic model of the descent of a Navy ship ladder to determine how this motion affects the internal knee reaction forces and how altering the hamstring/quadriceps ratio affects the internal joint reaction forces in the knee. Kinematic and kinetic data were collected from three male sailors descending a replica of a Navy ship ladder and were used as input into a model constructed in OpenSim. The peak resultant joint reaction force was 6.6 × BW, which was greater than values reported in the literature in traditional stairs. Peak compressive and anterior joint reaction forces, 4.05 × BW and 5.46 × BW, respectively, were greater than reported values for a squat, a motion similar to descending a ship ladder. The average peak vertical and anterior internal joint reaction force at the knee were 4.05 × BW and 5.46 × BW, respectively. The resultant joint reaction forces calculated from the ladder descent were greater than stair descent and squatting. Little effects were found in the joint reaction forces after adjusting the quadriceps to hamstring muscle strength ratios, possibly because these ratios might change the distribution of the contact forces across the joint, not the resultant forces.

Neural network explanation using inversion.

PubMed

Saad, Emad W; Wunsch, Donald C

2007-01-01

An important drawback of many artificial neural networks (ANN) is their lack of explanation capability [Andrews, R., Diederich, J., & Tickle, A. B. (1996). A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8, 373-389]. This paper starts with a survey of algorithms which attempt to explain the ANN output. We then present HYPINV, a new explanation algorithm which relies on network inversion; i.e. calculating the ANN input which produces a desired output. HYPINV is a pedagogical algorithm, that extracts rules, in the form of hyperplanes. It is able to generate rules with arbitrarily desired fidelity, maintaining a fidelity-complexity tradeoff. To our knowledge, HYPINV is the only pedagogical rule extraction method, which extracts hyperplane rules from continuous or binary attribute neural networks. Different network inversion techniques, involving gradient descent as well as an evolutionary algorithm, are presented. An information theoretic treatment of rule extraction is presented. HYPINV is applied to example synthetic problems, to a real aerospace problem, and compared with similar algorithms using benchmark problems.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

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.

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

Peak-Seeking Control For Reduced Fuel Consumption: Flight-Test Results For The Full-Scale Advanced Systems Testbed FA-18 Airplane

NASA Technical Reports Server (NTRS)

Brown, Nelson

2013-01-01

A peak-seeking control algorithm for real-time trim optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control algorithm is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an F/A-18 airplane are used for optimization of fuel flow. Results from six research flights are presented herein. The optimization algorithm found a trim configuration that required approximately 3 percent less fuel flow than the baseline trim at the same flight condition. This presentation also focuses on the design of the flight experiment and the practical challenges of conducting the experiment.

Iterative free-energy optimization for recurrent neural networks (INFERNO).

PubMed

Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias

2017-01-01

The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

Smart-Divert Powered Descent Guidance to Avoid the Backshell Landing Dispersion Ellipse

NASA Technical Reports Server (NTRS)

Carson, John M.; Acikmese, Behcet

2013-01-01

A smart-divert capability has been added into the Powered Descent Guidance (PDG) software originally developed for Mars pinpoint and precision landing. The smart-divert algorithm accounts for the landing dispersions of the entry backshell, which separates from the lander vehicle at the end of the parachute descent phase and prior to powered descent. The smart-divert PDG algorithm utilizes the onboard fuel and vehicle thrust vectoring to mitigate landing error in an intelligent way: ensuring that the lander touches down with minimum- fuel usage at the minimum distance from the desired landing location that also avoids impact by the descending backshell. The smart-divert PDG software implements a computationally efficient, convex formulation of the powered-descent guidance problem to provide pinpoint or precision-landing guidance solutions that are fuel-optimal and satisfy physical thrust bound and pointing constraints, as well as position and speed constraints. The initial smart-divert implementation enforced a lateral-divert corridor parallel to the ground velocity vector; this was based on guidance requirements for MSL (Mars Science Laboratory) landings. This initial method was overly conservative since the divert corridor was infinite in the down-range direction despite the backshell landing inside a calculable dispersion ellipse. Basing the divert constraint instead on a local tangent to the backshell dispersion ellipse in the direction of the desired landing site provides a far less conservative constraint. The resulting enhanced smart-divert PDG algorithm avoids impact with the descending backshell and has reduced conservatism.

Gaussian process inference for estimating pharmacokinetic parameters of dynamic contrast-enhanced MR images.

PubMed

Wang, Shijun; Liu, Peter; Turkbey, Baris; Choyke, Peter; Pinto, Peter; Summers, Ronald M

2012-01-01

In this paper, we propose a new pharmacokinetic model for parameter estimation of dynamic contrast-enhanced (DCE) MRI by using Gaussian process inference. Our model is based on the Tofts dual-compartment model for the description of tracer kinetics and the observed time series from DCE-MRI is treated as a Gaussian stochastic process. The parameter estimation is done through a maximum likelihood approach and we propose a variant of the coordinate descent method to solve this likelihood maximization problem. The new model was shown to outperform a baseline method on simulated data. Parametric maps generated on prostate DCE data with the new model also provided better enhancement of tumors, lower intensity on false positives, and better boundary delineation when compared with the baseline method. New statistical parameter maps from the process model were also found to be informative, particularly when paired with the PK parameter maps.

Flight Test of a 30-Foot Nominal-Diameter Disk-Gap-Band Parachute Deployed at Mach 1.56 and Dynamic Pressure of 11.4 Pounds per Square Foot

NASA Technical Reports Server (NTRS)

Eckstrom, Clinton V.; Preisser, John S.

1967-01-01

A 30-foot (9.1 meter) nominal-diameter disk-gap-band parachute (reference area 707 sq ft (65.7 m(exp 2)) was flight tested with a 200-pound (90.7 kg) instrumented payload as part of the NASA Planetary Entry Parachute Program. A deployment mortar ejected the test parachute when the payload was at a Mach number of 1.56 and a dynamic pressure of 11.4 lb/sq ft (546 newtons per m 2 ) at an altitude of 127,500 feet (38.86 km). The parachute reached suspension line stretch in 0.37 second resulting in a snatch force loading of 1270 pounds (5650 N). Canopy inflation began 0.10 second after line stretch. A delay in the opening process occurred and was apparently due to a momentary interference of the glass-fiber shroud used in packing the parachute bag in the mortar. Continuous canopy inflation began 0.73 second after initiation of deployment and 0.21 second later full inflation was attained for a total elapsed time from mortar fire of 0.94 second. The maximum opening load of 3915 pounds (17,400 newtons) occurred at the time the canopy was first fully opened. The parachute exhibited an average drag coefficient of 0.52 during the deceleration period and pitch-yaw oscillations of the canopy were less than 5 degrees. During the steady-state descent portion of the test period, the average effective drag coefficient was about 0.47 (based on vertical descent velocity and total system weight).

Bridging the Ethnic Divide: Student and School Characteristics in African American, Asian-Descent, Latino, and White Adolescents' Cross-Ethnic Friend Nominations

ERIC Educational Resources Information Center

Hamm, Jill V.; Bradford Brown, B.; Heck, Daniel J.

2005-01-01

Based on the revised social contact theory, correlates of cross-ethnic friend nomination among 580 African American, 948 Asian-descent, 860 Latino, and 3986 White adolescents were examined. Socioeconomic and academic disparities between ethnic groups differentiated cross-ethnic friend nomination between schools for all groups but African…

Multigrid one shot methods for optimal control problems: Infinite dimensional control

NASA Technical Reports Server (NTRS)

Arian, Eyal; Taasan, Shlomo

1994-01-01

The multigrid one shot method for optimal control problems, governed by elliptic systems, is introduced for the infinite dimensional control space. ln this case, the control variable is a function whose discrete representation involves_an increasing number of variables with grid refinement. The minimization algorithm uses Lagrange multipliers to calculate sensitivity gradients. A preconditioned gradient descent algorithm is accelerated by a set of coarse grids. It optimizes for different scales in the representation of the control variable on different discretization levels. An analysis which reduces the problem to the boundary is introduced. It is used to approximate the two level asymptotic convergence rate, to determine the amplitude of the minimization steps, and the choice of a high pass filter to be used when necessary. The effectiveness of the method is demonstrated on a series of test problems. The new method enables the solutions of optimal control problems at the same cost of solving the corresponding analysis problems just a few times.

Measurement of lung expansion with computed tomography and comparison with quantitative histology.

PubMed

Coxson, H O; Mayo, J R; Behzad, H; Moore, B J; Verburgt, L M; Staples, C A; Paré, P D; Hogg, J C

1995-11-01

The total and regional lung volumes were estimated from computed tomography (CT), and the pleural pressure gradient was determined by using the milliliters of gas per gram of tissue estimated from the X-ray attenuation values and the pressure-volume curve of the lung. The data show that CT accurately estimated the volume of the resected lobe but overestimated its weight by 24 +/- 19%. The volume of gas per gram of tissue was less in the gravity-dependent regions due to a pleural pressure gradient of 0.24 +/- 0.08 cmH2O/cm of descent in the thorax. The proportion of tissue to air obtained with CT was similar to that obtained by quantitative histology. We conclude that the CT scan can be used to estimate total and regional lung volumes and that measurements of the proportions of tissue and air within the thorax by CT can be used in conjunction with quantitative histology to evaluate lung structure.

The categorization of African descent populations in Europe and the USA: should lexicons of recommended terminology be evidence-based?

PubMed

Aspinall, P J

2008-01-01

This review attempts to evaluate a proposed lexicon for African-descent populations from the viewpoint of saliency amongst those described and wider official and scientific usage, focusing on Britain and the USA. It is argued that it is unsatisfactory to privilege the term 'African American' over 'Black' for African-descent populations in the USA as the evidence base shows that both labels compete as self-designations on co-equal terms, while 'Black' is the prevalent term in scientific writing. Moreover, 'African American' is not an inclusive term for the African-descent population and it is not known how prevalent and enduring the term will prove to be. With respect to Britain, the census terms of 'Black African' and 'Black Caribbean' are well established, the increasing popularity of 'Black British' also being recognized in census labels. Given the increasing interest in the relationship between ethnic identity and health, there are arguments for documenting the diversity of terminology amongst different user constituencies in country-specific settings. The approach of synthetic glossaries of consensual terms may, through the need for economy and parsimony in the use of terminology, contribute to an unsatisfactory paring of that diversity.

HEPPA-II model-measurement intercomparison project: EPP indirect effects during the dynamically perturbed NH winter 2008-2009

NASA Astrophysics Data System (ADS)

Funke, Bernd; Ball, William; Bender, Stefan; Gardini, Angela; Harvey, V. Lynn; Lambert, Alyn; López-Puertas, Manuel; Marsh, Daniel R.; Meraner, Katharina; Nieder, Holger; Päivärinta, Sanna-Mari; Pérot, Kristell; Randall, Cora E.; Reddmann, Thomas; Rozanov, Eugene; Schmidt, Hauke; Seppälä, Annika; Sinnhuber, Miriam; Sukhodolov, Timofei; Stiller, Gabriele P.; Tsvetkova, Natalia D.; Verronen, Pekka T.; Versick, Stefan; von Clarmann, Thomas; Walker, Kaley A.; Yushkov, Vladimir

2017-03-01

We compare simulations from three high-top (with upper lid above 120 km) and five medium-top (with upper lid around 80 km) atmospheric models with observations of odd nitrogen (NOx = NO + NO2), temperature, and carbon monoxide from seven satellite instruments (ACE-FTS on SciSat, GOMOS, MIPAS, and SCIAMACHY on Envisat, MLS on Aura, SABER on TIMED, and SMR on Odin) during the Northern Hemisphere (NH) polar winter 2008/2009. The models included in the comparison are the 3-D chemistry transport model 3dCTM, the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model, FinROSE, the Hamburg Model of the Neutral and Ionized Atmosphere (HAMMONIA), the Karlsruhe Simulation Model of the Middle Atmosphere (KASIMA), the modelling tools for SOlar Climate Ozone Links studies (SOCOL and CAO-SOCOL), and the Whole Atmosphere Community Climate Model (WACCM4). The comparison focuses on the energetic particle precipitation (EPP) indirect effect, that is, the polar winter descent of NOx largely produced by EPP in the mesosphere and lower thermosphere. A particular emphasis is given to the impact of the sudden stratospheric warming (SSW) in January 2009 and the subsequent elevated stratopause (ES) event associated with enhanced descent of mesospheric air. The chemistry climate model simulations have been nudged toward reanalysis data in the troposphere and stratosphere while being unconstrained above. An odd nitrogen upper boundary condition obtained from MIPAS observations has further been applied to medium-top models. Most models provide a good representation of the mesospheric tracer descent in general, and the EPP indirect effect in particular, during the unperturbed (pre-SSW) period of the NH winter 2008/2009. The observed NOx descent into the lower mesosphere and stratosphere is generally reproduced within 20 %. Larger discrepancies of a few model simulations could be traced back either to the impact of the models' gravity wave drag scheme on the polar wintertime meridional circulation or to a combination of prescribed NOx mixing ratio at the uppermost model layer and low vertical resolution. In March-April, after the ES event, however, modelled mesospheric and stratospheric NOx distributions deviate significantly from the observations. The too-fast and early downward propagation of the NOx tongue, encountered in most simulations, coincides with a temperature high bias in the lower mesosphere (0.2-0.05 hPa), likely caused by an overestimation of descent velocities. In contrast, upper-mesospheric temperatures (at 0.05-0.001 hPa) are generally underestimated by the high-top models after the onset of the ES event, being indicative for too-slow descent and hence too-low NOx fluxes. As a consequence, the magnitude of the simulated NOx tongue is generally underestimated by these models. Descending NOx amounts simulated with medium-top models are on average closer to the observations but show a large spread of up to several hundred percent. This is primarily attributed to the different vertical model domains in which the NOx upper boundary condition is applied. In general, the intercomparison demonstrates the ability of state-of-the-art atmospheric models to reproduce the EPP indirect effect in dynamically and geomagnetically quiescent NH winter conditions. The encountered differences between observed and simulated NOx, CO, and temperature distributions during the perturbed phase of the 2009 NH winter, however, emphasize the need for model improvements in the dynamical representation of elevated stratopause events in order to allow for a better description of the EPP indirect effect under these particular conditions.

High dynamic range image acquisition based on multiplex cameras

NASA Astrophysics Data System (ADS)

Zeng, Hairui; Sun, Huayan; Zhang, Tinghua

2018-03-01

High dynamic image is an important technology of photoelectric information acquisition, providing higher dynamic range and more image details, and it can better reflect the real environment, light and color information. Currently, the method of high dynamic range image synthesis based on different exposure image sequences cannot adapt to the dynamic scene. It fails to overcome the effects of moving targets, resulting in the phenomenon of ghost. Therefore, a new high dynamic range image acquisition method based on multiplex cameras system was proposed. Firstly, different exposure images sequences were captured with the camera array, using the method of derivative optical flow based on color gradient to get the deviation between images, and aligned the images. Then, the high dynamic range image fusion weighting function was established by combination of inverse camera response function and deviation between images, and was applied to generated a high dynamic range image. The experiments show that the proposed method can effectively obtain high dynamic images in dynamic scene, and achieves good results.

Development of Tokamak Transport Solvers for Stiff Confinement Systems

NASA Astrophysics Data System (ADS)

St. John, H. E.; Lao, L. L.; Murakami, M.; Park, J. M.

2006-10-01

Leading transport models such as GLF23 [1] and MM95 [2] describe turbulent plasma energy, momentum and particle flows. In order to accommodate existing transport codes and associated solution methods effective diffusivities have to be derived from these turbulent flow models. This can cause significant problems in predicting unique solutions. We have developed a parallel transport code solver, GCNMP, that can accommodate both flow based and diffusivity based confinement models by solving the discretized nonlinear equations using modern Newton, trust region, steepest descent and homotopy methods. We present our latest development efforts, including multiple dynamic grids, application of two-level parallel schemes, and operator splitting techniques that allow us to combine flow based and diffusivity based models in tokamk simulations. 6pt [1] R.E. Waltz, et al., Phys. Plasmas 4, 7 (1997). [2] G. Bateman, et al., Phys. Plasmas 5, 1793 (1998).

Intracellular dynamics during directional sensing of chemotactic cells

NASA Astrophysics Data System (ADS)

Amselem, Gabriel; Bodenschatz, Eberhard; Beta, Carsten

2007-03-01

We use an experimental approach based on the photo-chemical release of signaling molecules in microfluidic environments to expose chemotactic cells to well controlled chemoattractant stimuli. We apply this technique to study intracellular translocation of fluorescently labeled PH-domain proteins in the social ameba Dictyostelium discoideum. Single chemotactic Dictyostelium cells are exposed to localized, well defined gradients in the chemoattractant cAMP and their translocation response is quantified as a function of the external gradient.

Predictability of Top of Descent Location for Operational Idle-Thrust Descents

NASA Technical Reports Server (NTRS)

Stell, Laurel L.

2010-01-01

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 uncertainty models, commercial flights executed idle-thrust descents at a specified descent speed, and the recorded data included the specified descent speed profile, aircraft weight, and the winds entered into the FMS as well as the radar data. The FMS computed the intended descent path assuming idle thrust after top of descent (TOD), and the controllers and pilots then endeavored to allow the FMS to fly the descent to the meter fix with minimal human intervention. The horizontal flight path, cruise and meter fix altitudes, and actual TOD location were extracted from the radar data. Using approximately 70 descents each in Boeing 757 and Airbus 319/320 aircraft, multiple regression estimated TOD location as a linear function of the available predictive factors. The cruise and meter fix altitudes, descent speed, and wind clearly improve goodness of fit. The aircraft weight improves fit for the Airbus descents but not for the B757. Except for a few statistical outliers, the residuals have absolute value less than 5 nmi. Thus, these predictive factors adequately explain the TOD location, which indicates the data do not include excessive noise.

A Dictionary Learning Approach with Overlap for the Low Dose Computed Tomography Reconstruction and Its Vectorial Application to Differential Phase Tomography

PubMed Central

Mirone, Alessandro; Brun, Emmanuel; Coan, Paola

2014-01-01

X-ray based Phase-Contrast Imaging (PCI) techniques have been demonstrated to enhance the visualization of soft tissues in comparison to conventional imaging methods. Nevertheless the delivered dose as reported in the literature of biomedical PCI applications often equals or exceeds the limits prescribed in clinical diagnostics. The optimization of new computed tomography strategies which include the development and implementation of advanced image reconstruction procedures is thus a key aspect. In this scenario, we implemented a dictionary learning method with a new form of convex functional. This functional contains in addition to the usual sparsity inducing and fidelity terms, a new term which forces similarity between overlapping patches in the superimposed regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing norm of the patch basis functions coefficients, and a coefficient multiplying the norm of the differences between patches in the overlapping regions. The solution is found by applying the iterative proximal gradient descent method with FISTA acceleration. The gradient is computed by calculating projection of the solution and its error backprojection at each iterative step. We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic data for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the European Synchrotron Radiation Facility tomography reconstruction code PyHST, has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography. PMID:25531987

A dictionary learning approach with overlap for the low dose computed tomography reconstruction and its vectorial application to differential phase tomography.

PubMed

Mirone, Alessandro; Brun, Emmanuel; Coan, Paola

2014-01-01

X-ray based Phase-Contrast Imaging (PCI) techniques have been demonstrated to enhance the visualization of soft tissues in comparison to conventional imaging methods. Nevertheless the delivered dose as reported in the literature of biomedical PCI applications often equals or exceeds the limits prescribed in clinical diagnostics. The optimization of new computed tomography strategies which include the development and implementation of advanced image reconstruction procedures is thus a key aspect. In this scenario, we implemented a dictionary learning method with a new form of convex functional. This functional contains in addition to the usual sparsity inducing and fidelity terms, a new term which forces similarity between overlapping patches in the superimposed regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing L1 norm of the patch basis functions coefficients, and a coefficient multiplying the L2 norm of the differences between patches in the overlapping regions. The solution is found by applying the iterative proximal gradient descent method with FISTA acceleration. The gradient is computed by calculating projection of the solution and its error backprojection at each iterative step. We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic data for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the European Synchrotron Radiation Facility tomography reconstruction code PyHST, has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography.

Alterations in knee kinematics after partial medial meniscectomy are activity dependent.

PubMed

Edd, Shannon N; Netravali, Nathan A; Favre, Julien; Giori, Nicholas J; Andriacchi, Thomas P

2015-06-01

Alterations in knee kinematics after partial meniscectomy have been linked to the increased risk of osteoarthritis in this population. Understanding differences in kinematics during static versus dynamic activities of increased demand can provide important information regarding the possible underlying mechanisms of these alterations. Differences in the following 2 kinematics measures will increase with activity demand: (1) the offset toward external tibial rotation for the meniscectomized limb compared with the contralateral limb during stance and (2) the difference in knee flexion angle at initial foot contact between the meniscectomized and contralateral limbs. Controlled laboratory study. This study compared side-to-side differences in knee flexion and rotation angles during static and dynamic activities. Thirteen patients (2 female) were tested in a motion capture laboratory at 6 ± 2 months after unilateral, arthroscopic, partial medial meniscectomy during a static reference pose and during 3 dynamic activities: walking, stair ascent, and stair descent. The meniscectomized limb demonstrated more external tibial rotation compared with the contralateral limb during dynamic activities, and there was a trend that this offset increased with activity demand (repeated-measures analysis of variance [ANOVA] for activity, P = .07; mean limb difference: static pose, -0.1° ± 3.3°, P = .5; walking, 1.2° ± 3.8°, P = .1; stair ascent, 2.0° ± 3.2°, P = .02; stair descent, 3.0° ± 3.5°, P = .005). Similarly, the meniscectomized knee was more flexed at initial contact than the contralateral limb during dynamic activities (repeated-measures ANOVA for activity P = .006; mean limb difference: reference pose, 1.0° ± 2.5°, P = .09; walking, 2.0° ± 3.9°, P = .05; stair ascent, 5.9° ± 5.3°, P = .009; stair descent, 3.5° ± 4.0°, P = .004). These results suggest both a structural element and a potential muscular element for the differences in kinematics after partial medial meniscectomy and highlight the importance of challenging the knee with activities of increased demands to detect differences in kinematics from the contralateral limb. With further investigation, these findings could help guide clinical rehabilitation of patients with torn meniscus tissue, especially in the context of the patients' increased risk of joint degeneration. © 2015 The Author(s).

Tracer-based Determination of Vortex Descent in the 1999/2000 Arctic Winter

NASA Technical Reports Server (NTRS)

Greenblatt, Jeffrey B.; Jost, Hans-Juerg; Loewenstein, Max; Podolske, James R.; Hurst, Dale F.; Elkins, James W.; Schauffler, Sue M.; Atlas, Elliot L.; Herman, Robert L.; Webster, Chrisotopher R.

2002-01-01

A detailed analysis of available in situ and remotely sensed N2O and CH4 data measured in the 1999/2000 winter Arctic vortex has been performed in order to quantify the temporal evolution of vortex descent. Differences in potential temperature (theta) among balloon and aircraft vertical profiles (an average of 19-23 K on a given N2O or CH4 isopleth) indicated significant vortex inhomogeneity in late fall as compared with late winter profiles. A composite fall vortex profile was constructed for 26 November 1999, whose error bars encompassed the observed variability. High-latitude extravortex profiles measured in different years and seasons revealed substantial variability in N2O and CH4 on theta surfaces, but all were clearly distinguishable from the first vortex profiles measured in late fall 1999. From these extravortex-vortex differences we inferred descent prior to 26 November: as much as 397 plus or minus 15 K (lsigma) at 30 ppbv N2O and 640 ppbv CH4, and falling to 28 plus or minus 13 K above 200 ppbv N2O and 1280 ppbv CH4. Changes in theta were determined on five N2O and CH4 isopleths from 26 November through 12 March, and descent rates were calculated on each N2O isopleth for several time intervals. The maximum descent rates were seen between 26 November and 27 January: 0.82 plus or minus 0.20 K/day averaged over 50- 250 ppbv N2O. By late winter (26 February to 12 March), the average rate had decreased to 0.10 plus or minus 0.25 K/day. Descent rates also decreased with increasing N2O; the winter average (26 November to 5 March) descent rate varied from 0.75 plus or minus 0.10 K/day at 50 ppbv to 0.40 plus or minus 0.11 K/day at 250 ppbv. Comparison of these results with observations and models of descent in prior years showed very good overall agreement. Two models of the 1999/2000 vortex descent, SLIMCAT and REPROBUS, despite theta offsets with respect to observed profiles of up to 20 K on most tracer isopleths, produced descent rates that agreed very favorably with the inferred rates from observation.

Controlling chimeras

NASA Astrophysics Data System (ADS)

Bick, Christian; Martens, Erik A.

2015-03-01

Coupled phase oscillators model a variety of dynamical phenomena in nature and technological applications. Non-local coupling gives rise to chimera states which are characterized by a distinct part of phase-synchronized oscillators while the remaining ones move incoherently. Here, we apply the idea of control to chimera states: using gradient dynamics to exploit drift of a chimera, it will attain any desired target position. Through control, chimera states become functionally relevant; for example, the controlled position of localized synchrony may encode information and perform computations. Since functional aspects are crucial in (neuro-)biology and technology, the localized synchronization of a chimera state becomes accessible to develop novel applications. Based on gradient dynamics, our control strategy applies to any suitable observable and can be generalized to arbitrary dimensions. Thus, the applicability of chimera control goes beyond chimera states in non-locally coupled systems.

Static and Dynamic Model Update of an Inflatable/Rigidizable Torus Structure

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, mercedes C.

2006-01-01

The present work addresses the development of an experimental and computational procedure for validating finite element models. A torus structure, part of an inflatable/rigidizable Hexapod, is used to demonstrate the approach. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with optimization is used to modify key model parameters. Static test results are used to update stiffness parameters and dynamic test results are used to update the mass distribution. Updated parameters are computed using gradient and non-gradient based optimization algorithms. Results show significant improvements in model predictions after parameters are updated. Lessons learned in the areas of test procedures, modeling approaches, and uncertainties quantification are presented.

Protein gradients in single cells induced by their coupling to "morphogen"-like diffusion

NASA Astrophysics Data System (ADS)

Nandi, Saroj Kumar; Safran, Sam A.

2018-05-01

One of the many ways cells transmit information within their volume is through steady spatial gradients of different proteins. However, the mechanism through which proteins without any sources or sinks form such single-cell gradients is not yet fully understood. One of the models for such gradient formation, based on differential diffusion, is limited to proteins with large ratios of their diffusion constants or to specific protein-large molecule interactions. We introduce a novel mechanism for gradient formation via the coupling of the proteins within a single cell with a molecule, that we call a "pronogen," whose action is similar to that of morphogens in multi-cell assemblies; the pronogen is produced with a fixed flux at one side of the cell. This coupling results in an effectively non-linear diffusion degradation model for the pronogen dynamics within the cell, which leads to a steady-state gradient of the protein concentration. We use stability analysis to show that these gradients are linearly stable with respect to perturbations.

Attitude Issues on the Huygens Probe: Balloon Dropped Mock up Role in Determining Reconstruction Strategies During Descent in Lower Atmosphere

NASA Technical Reports Server (NTRS)

Bettanini, C.; Angrilli, F.

2005-01-01

As part of the collaboration with Italian Space Agency on HASI instrument for Huygens mission, University of Padova has been conducting since 2001 scientific activity on Stratospheric Balloon Launches from the Trapani base in Sicily. The most recent boomerang flight in July 2003 has successfully flown a mock up of the Huygens probe hosting spares of flight scientific units and extra housekeeping and scientific sensors on a parachuted descent from 33 kilometre altitude. This work presents the studies conducted on attitude reconstruction of the probe, as well as the utilisation of iterative extended Kalman filtering in investigating vanes induced spin rate and in providing a baseline for the performance evaluation of Huygens accelerometers operations. Finally some possible contributions on the reconstruction of the lower part of Titan descent for Huygens probe are suggested based on the confrontation of sensor data for 2003 flight.

Edge remap for solids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamm, James R.; Love, Edward; Robinson, Allen C.

We review the edge element formulation for describing the kinematics of hyperelastic solids. This approach is used to frame the problem of remapping the inverse deformation gradient for Arbitrary Lagrangian-Eulerian (ALE) simulations of solid dynamics. For hyperelastic materials, the stress state is completely determined by the deformation gradient, so remapping this quantity effectively updates the stress state of the material. A method, inspired by the constrained transport remap in electromagnetics, is reviewed, according to which the zero-curl constraint on the inverse deformation gradient is implicitly satisfied. Open issues related to the accuracy of this approach are identified. An optimization-based approachmore » is implemented to enforce positivity of the determinant of the deformation gradient. The efficacy of this approach is illustrated with numerical examples.« less

Riparian hydraulic gradient and stream-groundwater exchange dynamics in steep headwater valleys

Treesearch

T.J. Voltz; M.N. Gooseff; A.S. Ward; K. Singha; M. Fitzgerald; T. Wagener

2013-01-01

Patterns of riparian hydraulic gradients and stream-groundwater exchange in headwater catchments provide the hydrologic context for important ecological processes. Although the controls are relatively well understood, their dynamics during periods of hydrologic change is not. We investigate riparian hydraulic gradients over three different time scales in two steep,...

Helicopter flight dynamics simulation with a time-accurate free-vortex wake model

NASA Astrophysics Data System (ADS)

Ribera, Maria

This dissertation describes the implementation and validation of a coupled rotor-fuselage simulation model with a time-accurate free-vortex wake model capable of capturing the response to maneuvers of arbitrary amplitude. The resulting model has been used to analyze different flight conditions, including both steady and transient maneuvers. The flight dynamics model is based on a system of coupled nonlinear rotor-fuselage differential equations in first-order, state-space form. The rotor model includes flexible blades, with coupled flap-lag-torsion dynamics and swept tips; the rigid body dynamics are modeled with the non-linear Euler equations. The free wake models the rotor flow field by tracking the vortices released at the blade tips. Their behavior is described by the equations of vorticity transport, which is approximated using finite differences, and solved using a time-accurate numerical scheme. The flight dynamics model can be solved as a system of non-linear algebraic trim equations to determine the steady state solution, or integrated in time in response to pilot-applied controls. This study also implements new approaches to reduce the prohibitive computational costs associated with such complex models without losing accuracy. The mathematical model was validated for trim conditions in level flight, turns, climbs and descents. The results obtained correlate well with flight test data, both in level flight as well as turning and climbing and descending flight. The swept tip model was also found to improve the trim predictions, particularly at high speed. The behavior of the rigid body and the rotor blade dynamics were also studied and related to the aerodynamic load distributions obtained with the free wake induced velocities. The model was also validated in a lateral maneuver from hover. The results show improvements in the on-axis prediction, and indicate a possible relation between the off-axis prediction and the lack of rotor-body interaction aerodynamics. The swept blade model improves both the on-axis and off-axis response. An axial descent though the vortex ring state was simulated. As theǒrtex ring" goes through the rotor, the unsteady loads produce large attitude changes, unsteady flapping, fluctuating thrust and an increase in power required. A roll reversal maneuver was found useful in understanding the cross-couplings effects found in rotorcraft, specifically the effect of the aerodynamic loading on the rotor orientation and the off-axis response.

Optimal Control-Based Adaptive NN Design for a Class of Nonlinear Discrete-Time Block-Triangular Systems.

PubMed

Liu, Yan-Jun; Tong, Shaocheng

2016-11-01

In this paper, we propose an optimal control scheme-based adaptive neural network design for a class of unknown nonlinear discrete-time systems. The controlled systems are in a block-triangular multi-input-multi-output pure-feedback structure, i.e., there are both state and input couplings and nonaffine functions to be included in every equation of each subsystem. The design objective is to provide a control scheme, which not only guarantees the stability of the systems, but also achieves optimal control performance. The main contribution of this paper is that it is for the first time to achieve the optimal performance for such a class of systems. Owing to the interactions among subsystems, making an optimal control signal is a difficult task. The design ideas are that: 1) the systems are transformed into an output predictor form; 2) for the output predictor, the ideal control signal and the strategic utility function can be approximated by using an action network and a critic network, respectively; and 3) an optimal control signal is constructed with the weight update rules to be designed based on a gradient descent method. The stability of the systems can be proved based on the difference Lyapunov method. Finally, a numerical simulation is given to illustrate the performance of the proposed scheme.

Hazard avoidance via descent images for safe landing

NASA Astrophysics Data System (ADS)

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.

European population substructure is associated with mucocutaneous manifestations and autoantibody production in systemic lupus erythematosus

PubMed Central

Chung, Sharon A.; Tian, Chao; Taylor, Kimberly E.; Lee, Annette T.; Ortmann, Ward A.; Hom, Geoffrey; Graham, Robert R.; Nititham, Joanne; Kelly, Jennifer A.; Morrisey, Jean; Wu, Hui; Yin, Hong; Alarcón-Riquelme, Marta E.; Tsao, Betty P.; Harley, John B.; Gaffney, Patrick M.; Moser, Kathy L.; Manzi, Susan; Petri, Michelle; Gregersen, Peter K.; Langefeld, Carl D.; Behrens, Timothy W.; Seldin, Michael F.; Criswell, Lindsey A.

2009-01-01

Objective To determine whether genetic substructure in European-derived populations is associated with specific manifestations of systemic lupus erythematosus (SLE), including mucocutaneous phenotypes, autoantibody production, and renal disease. Methods SLE patients of European descent (n=1754) from 8 case collections were genotyped for over 1,400 ancestry informative markers that define a north/south gradient of European substructure. Based on these genetic markers, we used the STRUCTURE program to characterize each SLE patient in terms of percent northern (vs. southern) European ancestry. Non-parametric methods, including tests of trend, were used to identify associations between northern European ancestry and specific SLE manifestations. Results In multivariate analyses, increasing levels of northern European ancestry were significantly associated with photosensitivity (ptrend=0.0021, OR for highest quartile of northern European ancestry compared to lowest quartile 1.64, 95% CI 1.13–2.35) and discoid rash (ptrend=0.014, ORhigh-low 1.93, 95% CI 0.98–3.83). In contrast, northern European ancestry was protective for anticardiolipin (ptrend=1.6 × 10−4, ORhigh-low 0.46, 95% CI 0.30–0.69) and anti-dsDNA (ptrend=0.017, ORhigh-low 0.67, 95% CI 0.46–0.96) autoantibody production. Conclusions This study demonstrates that specific SLE manifestations vary according to northern vs. southern European ancestry. Thus, genetic ancestry may contribute to the clinical heterogeneity and variation in disease outcomes among SLE patients of European descent. Moreover, these results suggest that genetic studies of SLE subphenotypes will need to carefully address issues of population substructure due to genetic ancestry. PMID:19644962

Effects of urban land-use change in East China on the East Asian summer monsoon based on the CAM5.1 model

NASA Astrophysics Data System (ADS)

Ma, Hongyun; Jiang, Zhihong; Song, Jie; Dai, Aiguo; Yang, Xiuqun; Huo, Fei

2016-05-01

The effects of urban land-use change in East China on the East Asian summer monsoon (EASM) are investigated using a Community Atmosphere Model Version 5.1. The results show that the urban land-use change in East China causes spatially-varying changes in surface net radiation and heat fluxes, atmospheric circulation, and water budgets. It results in significant surface warming (cooling) and precipitation decrease (increase) in a large region north (south) of 30°N. Urban expansion agglomerated in (29°-41°N, 110°-122°E) alters the surface energy budget and warms the surface, resulting in strengthened southwesterly airflow south of 25°N and increased convergence below the mid-troposphere between 20° and 30°N. A concomitant northward downdraft associated with the increased convection generates an anomalous high pressure north of 30°N. Meanwhile, the downdraft not only produces adiabatic warming but also inhibits the dynamic condition for precipitation formation. The anomalous high pressure formed in North China prevents the southwesterly airflow from advancing northward, leading to increase the convergence and precipitation in South China. These changes reduce the meridional temperature gradient in the mid-lower troposphere and weaken the westerly airflow near 30°N. In addition, horizontal transport of vorticity north of 35°N weakens significantly, which leads to an anomalous barotropic structure of anticyclonic there. As a result, the anomalous anticyclonic circulation and descent north of 30°N are strengthened. At the same time, the anomalous cyclonic circulation and ascent south of 30°N are enhanced. These process induced by the thermal state changes due to urbanization weakens the EASM.

Lattice-Boltzmann-based simulations of diffusiophoresis of colloids and cells

NASA Astrophysics Data System (ADS)

Kreft Pearce, Jennifer; Castigliego, Joshua

Increasing environmental degradation due to plastic pollutants requires innovative solutions that facilitate the extraction of pollutants without harming local biota. We present results from a lattice-Boltzmann-base Brownian Dynamics simulation on diffusiophoresis and the separation of particles within the system. A gradient in viscosity that simulates a concentration gradient in a dissolved polymer allows us to separate various types of particles based on their deformability. As seen in previous experiments, simulated particles that have a higher deformability react differently to the polymer matrix than those with a lower deformability. Therefore, the particles can be separated from each other. The system described above was simulated with various concentration gradients as well as various Soret coefficients in order to optimize the separation of the particles. This simulation, in particular, was intended to model an oceanic system where the particles of interest were motile and nonmotile plankton and microplastics. The separation of plankton from the microplastics was achieved.

Gradient-based interpolation method for division-of-focal-plane polarimeters.

PubMed

Gao, Shengkui; Gruev, Viktor

2013-01-14

Recent advancements in nanotechnology and nanofabrication have allowed for the emergence of the division-of-focal-plane (DoFP) polarization imaging sensors. These sensors capture polarization properties of the optical field at every imaging frame. However, the DoFP polarization imaging sensors suffer from large registration error as well as reduced spatial-resolution output. These drawbacks can be improved by applying proper image interpolation methods for the reconstruction of the polarization results. In this paper, we present a new gradient-based interpolation method for DoFP polarimeters. The performance of the proposed interpolation method is evaluated against several previously published interpolation methods by using visual examples and root mean square error (RMSE) comparison. We found that the proposed gradient-based interpolation method can achieve better visual results while maintaining a lower RMSE than other interpolation methods under various dynamic ranges of a scene ranging from dim to bright conditions.

Navigating in murky waters: How multiracial Black individuals cope with racism.

PubMed

Snyder, Cyndy R

2016-01-01

Multiracial people are often lauded as evidence of the waning significance of race and racism in the United States. In reality, the experiences of multiracial people illuminate the ways that racism still exists and efforts to classify people based on assumed racial characteristics for the purposes of inclusion and exclusion are alive and well. Multiracial individuals experience racism from multiple sources and in various forms, which has the potential to negatively impact their development and well-being. Thus, scholars and practitioners must better understand how the growing population of multiracial individuals learns to cope with such racism. The central aim of this qualitative interview study was to shed light on the ways in which multiracial individuals of African descent in the United States cope with and respond to racism. Findings are organized around 5 broad conceptual themes for coping with and addressing racism: avoidance and internalization, anger and violence, education and advocacy, seeking culture and community, and chameleon identities. Findings of this study speak to the dynamic nature of strategies used to cope with racism and hold implications for practices and programs designed to support positive racial identity development among multiracial individuals of African descent. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Optimum Strategies for Selecting Descent Flight-Path Angles

NASA Technical Reports Server (NTRS)

Wu, Minghong G. (Inventor); Green, Steven M. (Inventor)

2016-01-01

An information processing system and method for adaptively selecting an aircraft descent flight path for an aircraft, are provided. The system receives flight adaptation parameters, including aircraft flight descent time period, aircraft flight descent airspace region, and aircraft flight descent flyability constraints. The system queries a plurality of flight data sources and retrieves flight information including any of winds and temperatures aloft data, airspace/navigation constraints, airspace traffic demand, and airspace arrival delay model. The system calculates a set of candidate descent profiles, each defined by at least one of a flight path angle and a descent rate, and each including an aggregated total fuel consumption value for the aircraft following a calculated trajectory, and a flyability constraints metric for the calculated trajectory. The system selects a best candidate descent profile having the least fuel consumption value while the fly ability constraints metric remains within aircraft flight descent flyability constraints.

The 3D Recognition, Generation, Fusion, Update and Refinement (RG4) Concept

NASA Technical Reports Server (NTRS)

Maluf, David A.; Cheeseman, Peter; Smelyanskyi, Vadim N.; Kuehnel, Frank; Morris, Robin D.; Norvig, Peter (Technical Monitor)

2001-01-01

This paper describes an active (real time) recognition strategy whereby information is inferred iteratively across several viewpoints in descent imagery. We will show how we use inverse theory within the context of parametric model generation, namely height and spectral reflection functions, to generate model assertions. Using this strategy in an active context implies that, from every viewpoint, the proposed system must refine its hypotheses taking into account the image and the effect of uncertainties as well. The proposed system employs probabilistic solutions to the problem of iteratively merging information (images) from several viewpoints. This involves feeding the posterior distribution from all previous images as a prior for the next view. Novel approaches will be developed to accelerate the inversion search using novel statistic implementations and reducing the model complexity using foveated vision. Foveated vision refers to imagery where the resolution varies across the image. In this paper, we allow the model to be foveated where the highest resolution region is called the foveation region. Typically, the images will have dynamic control of the location of the foveation region. For descent imagery in the Entry, Descent, and Landing (EDL) process, it is possible to have more than one foveation region. This research initiative is directed towards descent imagery in connection with NASA's EDL applications. Three-Dimensional Model Recognition, Generation, Fusion, Update, and Refinement (RGFUR or RG4) for height and the spectral reflection characteristics are in focus for various reasons, one of which is the prospect that their interpretation will provide for real time active vision for automated EDL.

Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scherrer, Chad; Halappanavar, Mahantesh; Tewari, Ambuj

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.

Making Sense of Women of African Descent's Place in the Politics of (Urban) Space through the Vehicle of Popular Education.

ERIC Educational Resources Information Center

Amoo-Adare, Epifania

This paper is a brief account and argument for using Built Environment Education Workshops (BEEWs) as a data collection method. The research is based on women of African descent and the connections among their social practices, the spaces that generate them and are generated by them, and the language they use to mediate and/or negotiate those…

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Simulating Food Web Dynamics along a Gradient: Quantifying Human Influence

PubMed Central

Jordán, Ferenc; Gjata, Nerta; Mei, Shu; Yule, Catherine M.

2012-01-01

Realistically parameterized and dynamically simulated food-webs are useful tool to explore the importance of the functional diversity of ecosystems, and in particular relations between the dynamics of species and the whole community. We present a stochastic dynamical food web simulation for the Kelian River (Borneo). The food web was constructed for six different locations, arrayed along a gradient of increasing human perturbation (mostly resulting from gold mining activities) along the river. Along the river, the relative importance of grazers, filterers and shredders decreases with increasing disturbance downstream, while predators become more dominant in governing eco-dynamics. Human activity led to increased turbidity and sedimentation which adversely impacts primary productivity. Since the main difference between the study sites was not the composition of the food webs (structure is quite similar) but the strengths of interactions and the abundance of the trophic groups, a dynamical simulation approach seemed to be useful to better explain human influence. In the pristine river (study site 1), when comparing a structural version of our model with the dynamical model we found that structurally central groups such as omnivores and carnivores were not the most important ones dynamically. Instead, primary consumers such as invertebrate grazers and shredders generated a greater dynamical response. Based on the dynamically most important groups, bottom-up control is replaced by the predominant top-down control regime as distance downstream and human disturbance increased. An important finding, potentially explaining the poor structure to dynamics relationship, is that indirect effects are at least as important as direct ones during the simulations. We suggest that our approach and this simulation framework could serve systems-based conservation efforts. Quantitative indicators on the relative importance of trophic groups and the mechanistic modeling of eco-dynamics could greatly contribute to understanding various aspects of functional diversity. PMID:22768346

Region Segmentation in the Frequency Domain Applied to Upper Airway Real-Time Magnetic Resonance Images

PubMed Central

Narayanan, Shrikanth

2009-01-01

We describe a method for unsupervised region segmentation of an image using its spatial frequency domain representation. The algorithm was designed to process large sequences of real-time magnetic resonance (MR) images containing the 2-D midsagittal view of a human vocal tract airway. The segmentation algorithm uses an anatomically informed object model, whose fit to the observed image data is hierarchically optimized using a gradient descent procedure. The goal of the algorithm is to automatically extract the time-varying vocal tract outline and the position of the articulators to facilitate the study of the shaping of the vocal tract during speech production. PMID:19244005

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms.

Visualization of an endogenous retinoic acid gradient across embryonic development.

PubMed

Shimozono, Satoshi; Iimura, Tadahiro; Kitaguchi, Tetsuya; Higashijima, Shin-Ichi; Miyawaki, Atsushi

2013-04-18

In vertebrate development, the body plan is determined by primordial morphogen gradients that suffuse the embryo. Retinoic acid (RA) is an important morphogen involved in patterning the anterior-posterior axis of structures, including the hindbrain and paraxial mesoderm. RA diffuses over long distances, and its activity is spatially restricted by synthesizing and degrading enzymes. However, gradients of endogenous morphogens in live embryos have not been directly observed; indeed, their existence, distribution and requirement for correct patterning remain controversial. Here we report a family of genetically encoded indicators for RA that we have termed GEPRAs (genetically encoded probes for RA). Using the principle of fluorescence resonance energy transfer we engineered the ligand-binding domains of RA receptors to incorporate cyan-emitting and yellow-emitting fluorescent proteins as fluorescence resonance energy transfer donor and acceptor, respectively, for the reliable detection of ambient free RA. We created three GEPRAs with different affinities for RA, enabling the quantitative measurement of physiological RA concentrations. Live imaging of zebrafish embryos at the gastrula and somitogenesis stages revealed a linear concentration gradient of endogenous RA in a two-tailed source-sink arrangement across the embryo. Modelling of the observed linear RA gradient suggests that the rate of RA diffusion exceeds the spatiotemporal dynamics of embryogenesis, resulting in stability to perturbation. Furthermore, we used GEPRAs in combination with genetic and pharmacological perturbations to resolve competing hypotheses on the structure of the RA gradient during hindbrain formation and somitogenesis. Live imaging of endogenous concentration gradients across embryonic development will allow the precise assignment of molecular mechanisms to developmental dynamics and will accelerate the application of approaches based on morphogen gradients to tissue engineering and regenerative medicine.

Gradient elution behavior of proteins in hydrophobic interaction chromatography with U-shaped retention factor curves.

PubMed

Creasy, Arch; Lomino, Joseph; Barker, Gregory; Khetan, Anurag; Carta, Giorgio

2018-04-27

Protein retention in hydrophobic interaction chromatography is described by the solvophobic theory as a function of the kosmostropic salt concentration. In general, an increase in salt concentration drives protein partitioning to the hydrophobic surface while a decrease reduces it. In some cases, however, protein retention also increases at low salt concentrations resulting in a U-shaped retention factor curve. During gradient elution the salt concentration is gradually decreased from a high value thereby reducing the retention factor and increasing the protein chromatographic velocity. For these conditions, a steep gradient can overtake the protein in the column, causing it to rebind. Two dynamic models, one based on the local equilibrium theory and the other based on the linear driving force approximation, are presented. We show that the normalized gradient slope determines whether the protein elutes in the gradient, partially elutes, or is trapped in the column. Experimental results are presented for two different monoclonal antibodies and for lysozyme on Capto Phenyl (High Sub) resin. One of the mAbs and lysozyme exhibit U-shaped retention factor curves and for each, we determine the critical gradient slope beyond which 100% recovery is no longer possible. Elution with a reverse gradient is also demonstrated at low salt concentrations for these proteins. Understanding this behavior has implications in the design of gradient elution since the gradient slope impacts protein recovery. Copyright © 2018 Elsevier B.V. All rights reserved.

Infrared and visible image fusion based on total variation and augmented Lagrangian.

PubMed

Guo, Hanqi; Ma, Yong; Mei, Xiaoguang; Ma, Jiayi

2017-11-01

This paper proposes a new algorithm for infrared and visible image fusion based on gradient transfer that achieves fusion by preserving the intensity of the infrared image and then transferring gradients in the corresponding visible one to the result. The gradient transfer suffers from the problems of low dynamic range and detail loss because it ignores the intensity from the visible image. The new algorithm solves these problems by providing additive intensity from the visible image to balance the intensity between the infrared image and the visible one. It formulates the fusion task as an l 1 -l 1 -TV minimization problem and then employs variable splitting and augmented Lagrangian to convert the unconstrained problem to a constrained one that can be solved in the framework of alternating the multiplier direction method. Experiments demonstrate that the new algorithm achieves better fusion results with a high computation efficiency in both qualitative and quantitative tests than gradient transfer and most state-of-the-art methods.

Direct-to-Earth Communications with Mars Science Laboratory During Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

Soriano, Melissa; Finley, Susan; Fort, David; Schratz, Brian; Ilott, Peter; Mukai, Ryan; Estabrook, Polly; Oudrhiri, Kamal; Kahan, Daniel; Satorius, Edgar

2013-01-01

Mars Science Laboratory (MSL) undergoes extreme heating and acceleration during Entry, Descent, and Landing (EDL) on Mars. Unknown dynamics lead to large Doppler shifts, making communication challenging. During EDL, a special form of Multiple Frequency Shift Keying (MFSK) communication is used for Direct-To-Earth (DTE) communication. The X-band signal is received by the Deep Space Network (DSN) at the Canberra Deep Space Communication complex, then down-converted, digitized, and recorded by open-loop Radio Science Receivers (RSR), and decoded in real-time by the EDL Data Analysis (EDA) System. The EDA uses lock states with configurable Fast Fourier Transforms to acquire and track the signal. RSR configuration and channel allocation is shown. Testing prior to EDL is discussed including software simulations, test bed runs with MSL flight hardware, and the in-flight end-to-end test. EDA configuration parameters and signal dynamics during pre-entry, entry, and parachute deployment are analyzed. RSR and EDA performance during MSL EDL is evaluated, including performance using a single 70-meter DSN antenna and an array of two 34-meter DSN antennas as a back up to the 70-meter antenna.

Methods to estimate effective population size using pedigree data: Examples in dog, sheep, cattle and horse

PubMed Central

2013-01-01

Background Effective population sizes of 140 populations (including 60 dog breeds, 40 sheep breeds, 20 cattle breeds and 20 horse breeds) were computed using pedigree information and six different computation methods. Simple demographical information (number of breeding males and females), variance of progeny size, or evolution of identity by descent probabilities based on coancestry or inbreeding were used as well as identity by descent rate between two successive generations or individual identity by descent rate. Results Depending on breed and method, effective population sizes ranged from 15 to 133 056, computation method and interaction between computation method and species showing a significant effect on effective population size (P < 0.0001). On average, methods based on number of breeding males and females and variance of progeny size produced larger values (4425 and 356, respectively), than those based on identity by descent probabilities (average values between 93 and 203). Since breeding practices and genetic substructure within dog breeds increased inbreeding, methods taking into account the evolution of inbreeding produced lower effective population sizes than those taking into account evolution of coancestry. The correlation level between the simplest method (number of breeding males and females, requiring no genealogical information) and the most sophisticated one ranged from 0.44 to 0.60 according to species. Conclusions When choosing a method to compute effective population size, particular attention should be paid to the species and the specific genetic structure of the population studied. PMID:23281913

Vectorial mask optimization methods for robust optical lithography

NASA Astrophysics Data System (ADS)

Ma, Xu; Li, Yanqiu; Guo, Xuejia; Dong, Lisong; Arce, Gonzalo R.

2012-10-01

Continuous shrinkage of critical dimension in an integrated circuit impels the development of resolution enhancement techniques for low k1 lithography. Recently, several pixelated optical proximity correction (OPC) and phase-shifting mask (PSM) approaches were developed under scalar imaging models to account for the process variations. However, the lithography systems with larger-NA (NA>0.6) are predominant for current technology nodes, rendering the scalar models inadequate to describe the vector nature of the electromagnetic field that propagates through the optical lithography system. In addition, OPC and PSM algorithms based on scalar models can compensate for wavefront aberrations, but are incapable of mitigating polarization aberrations in practical lithography systems, which can only be dealt with under the vector model. To this end, we focus on developing robust pixelated gradient-based OPC and PSM optimization algorithms aimed at canceling defocus, dose variation, wavefront and polarization aberrations under a vector model. First, an integrative and analytic vector imaging model is applied to formulate the optimization problem, where the effects of process variations are explicitly incorporated in the optimization framework. A steepest descent algorithm is then used to iteratively optimize the mask patterns. Simulations show that the proposed algorithms can effectively improve the process windows of the optical lithography systems.

Model-Free Optimal Tracking Control via Critic-Only Q-Learning.

PubMed

Luo, Biao; Liu, Derong; Huang, Tingwen; Wang, Ding

2016-10-01

Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine nonlinear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solving the tracking Hamilton-Jacobi-Bellman equation. First, the Q-learning algorithm is proposed based on the augmented system, and its convergence is established. Using only one neural network for approximating the Q-function, the CoQL method is developed to implement the Q-learning algorithm. Furthermore, the convergence of the CoQL method is proved with the consideration of neural network approximation error. With the convergent Q-function obtained from the CoQL method, the adaptive optimal tracking control is designed based on the gradient descent scheme. Finally, the effectiveness of the developed CoQL method is demonstrated through simulation studies. The developed CoQL method learns with off-policy data and implements with a critic-only structure, thus it is easy to realize and overcome the inadequate exploration problem.

Network traffic anomaly prediction using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

2017-03-01

As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.