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Sample records for adaptive training algorithm

  1. The Training Effectiveness Algorithm.

    ERIC Educational Resources Information Center

    Cantor, Jeffrey A.

    1988-01-01

    Describes the Training Effectiveness Algorithm, a systematic procedure for identifying the cause of reported training problems which was developed for use in the U.S. Navy. A two-step review by subject matter experts is explained, and applications of the algorithm to other organizations and training systems are discussed. (Author/LRW)

  2. Adaptive continuous twisting algorithm

    NASA Astrophysics Data System (ADS)

    Moreno, Jaime A.; Negrete, Daniel Y.; Torres-González, Victor; Fridman, Leonid

    2016-09-01

    In this paper, an adaptive continuous twisting algorithm (ACTA) is presented. For double integrator, ACTA produces a continuous control signal ensuring finite time convergence of the states to zero. Moreover, the control signal generated by ACTA compensates the Lipschitz perturbation in finite time, i.e. its value converges to the opposite value of the perturbation. ACTA also keeps its convergence properties, even in the case that the upper bound of the derivative of the perturbation exists, but it is unknown.

  3. Fast autodidactic adaptive equalization algorithms

    NASA Astrophysics Data System (ADS)

    Hilal, Katia

    Autodidactic equalization by adaptive filtering is addressed in a mobile radio communication context. A general method, using an adaptive stochastic gradient Bussgang type algorithm, to deduce two low cost computation algorithms is given: one equivalent to the initial algorithm and the other having improved convergence properties thanks to a block criteria minimization. Two start algorithms are reworked: the Godard algorithm and the decision controlled algorithm. Using a normalization procedure, and block normalization, the performances are improved, and their common points are evaluated. These common points are used to propose an algorithm retaining the advantages of the two initial algorithms. This thus inherits the robustness of the Godard algorithm and the precision and phase correction of the decision control algorithm. The work is completed by a study of the stable states of Bussgang type algorithms and of the stability of the Godard algorithms, initial and normalized. The simulation of these algorithms, carried out in a mobile radio communications context, and under severe conditions on the propagation channel, gave a 75% reduction in the number of samples required for the processing in relation with the initial algorithms. The improvement of the residual error was of a much lower return. These performances are close to making possible the use of autodidactic equalization in the mobile radio system.

  4. Adaptive protection algorithm and system

    DOEpatents

    Hedrick, Paul [Pittsburgh, PA; Toms, Helen L [Irwin, PA; Miller, Roger M [Mars, PA

    2009-04-28

    An adaptive protection algorithm and system for protecting electrical distribution systems traces the flow of power through a distribution system, assigns a value (or rank) to each circuit breaker in the system and then determines the appropriate trip set points based on the assigned rank.

  5. Adaptive-feedback control algorithm.

    PubMed

    Huang, Debin

    2006-06-01

    This paper is motivated by giving the detailed proofs and some interesting remarks on the results the author obtained in a series of papers [Phys. Rev. Lett. 93, 214101 (2004); Phys. Rev. E 71, 037203 (2005); 69, 067201 (2004)], where an adaptive-feedback algorithm was proposed to effectively stabilize and synchronize chaotic systems. This note proves in detail the strictness of this algorithm from the viewpoint of mathematics, and gives some interesting remarks for its potential applications to chaos control & synchronization. In addition, a significant comment on synchronization-based parameter estimation is given, which shows some techniques proposed in literature less strict and ineffective in some cases.

  6. Climate Change Adaptation Training

    EPA Pesticide Factsheets

    A list of on-line training modules to help local government officials and those interested in water management issues better understand how the changing climate affects the services and resources they care about

  7. Cardiovascular adaptations to resistance training.

    PubMed

    Fleck, S J

    1988-10-01

    The cross-sectional and longitudinal data available indicate that the following conclusions are warranted concerning the effects of resistance training on the cardiovascular system. Resistance training causes increased absolute left ventricular wall thickness and left ventricular mass. These increases are not as evident when expressed relative to body surface area or lean body mass. There is little or no change in left ventricular internal dimensions, in absolute terms or relative to body surface area, resting heart rate, resting blood pressure, and diastolic function of the left ventricle due to resistance training. No change or slight positive effects are noted in systolic function of the left ventricle with training. During resistance training exercises, a large pressor response occurs and is attenuated in resistance trained athletes as compared to control subjects. Resistance training does result in adaptations of the cardiovascular system, with further research needed to elucidate its nature.

  8. Feedback in Videogame-Based Adaptive Training

    ERIC Educational Resources Information Center

    Rivera, Iris Daliz

    2010-01-01

    The field of training has been changing rapidly due to advances in technology such as videogame-based adaptive training. Videogame-based adaptive training has provided flexibility and adaptability for training in cost-effective ways. Although this method of training may have many benefits for the trainee, current research has not kept up to pace…

  9. Adaptive skin detection based on online training

    NASA Astrophysics Data System (ADS)

    Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang

    2007-11-01

    Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

  10. Cardiovascular Adaptations to Exercise Training.

    PubMed

    Hellsten, Ylva; Nyberg, Michael

    2015-12-15

    Aerobic exercise training leads to cardiovascular changes that markedly increase aerobic power and lead to improved endurance performance. The functionally most important adaptation is the improvement in maximal cardiac output which is the result of an enlargement in cardiac dimension, improved contractility, and an increase in blood volume, allowing for greater filling of the ventricles and a consequent larger stroke volume. In parallel with the greater maximal cardiac output, the perfusion capacity of the muscle is increased, permitting for greater oxygen delivery. To accommodate the higher aerobic demands and perfusion levels, arteries, arterioles, and capillaries adapt in structure and number. The diameters of the larger conduit and resistance arteries are increased minimizing resistance to flow as the cardiac output is distributed in the body and the wall thickness of the conduit and resistance arteries is reduced, a factor contributing to increased arterial compliance. Endurance training may also induce alterations in the vasodilator capacity, although such adaptations are more pronounced in individuals with reduced vascular function. The microvascular net increases in size within the muscle allowing for an improved capacity for oxygen extraction by the muscle through a greater area for diffusion, a shorter diffusion distance, and a longer mean transit time for the erythrocyte to pass through the smallest blood vessels. The present article addresses the effect of endurance training on systemic and peripheral cardiovascular adaptations with a focus on humans, but also covers animal data.

  11. IIR algorithms for adaptive line enhancement

    SciTech Connect

    David, R.A.; Stearns, S.D.; Elliott, G.R.; Etter, D.M.

    1983-01-01

    We introduce a simple IIR structure for the adaptive line enhancer. Two algorithms based on gradient-search techniques are presented for adapting the structure. Results from experiments which utilized real data as well as computer simulations are provided.

  12. Improved LMS algorithm for adaptive beamforming

    NASA Technical Reports Server (NTRS)

    Godara, Lal C.

    1990-01-01

    Two adaptive algorithms which make use of all the available samples to estimate the required gradient are proposed and studied. The first algorithm is referred to as the recursive LMS (least mean squares) and is applicable to a general array. The second algorithm is referred to as the improved LMS algorithm and exploits the Toeplitz structure of the ACM (array correlation matrix); it can be used only for an equispaced linear array.

  13. QPSO-based adaptive DNA computing algorithm.

    PubMed

    Karakose, Mehmet; Cigdem, Ugur

    2013-01-01

    DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm.

  14. Nutrition and training adaptations in aquatic sports.

    PubMed

    Mujika, Iñigo; Stellingwerff, Trent; Tipton, Kevin

    2014-08-01

    The adaptive response to training is determined by the combination of the intensity, volume, and frequency of the training. Various periodized approaches to training are used by aquatic sports athletes to achieve performance peaks. Nutritional support to optimize training adaptations should take periodization into consideration; that is, nutrition should also be periodized to optimally support training and facilitate adaptations. Moreover, other aspects of training (e.g., overload training, tapering and detraining) should be considered when making nutrition recommendations for aquatic athletes. There is evidence, albeit not in aquatic sports, that restricting carbohydrate availability may enhance some training adaptations. More research needs to be performed, particularly in aquatic sports, to determine the optimal strategy for periodizing carbohydrate intake to optimize adaptations. Protein nutrition is an important consideration for optimal training adaptations. Factors other than the total amount of daily protein intake should be considered. For instance, the type of protein, timing and pattern of protein intake and the amount of protein ingested at any one time influence the metabolic response to protein ingestion. Body mass and composition are important for aquatic sport athletes in relation to power-to-mass and for aesthetic reasons. Protein may be particularly important for athletes desiring to maintain muscle while losing body mass. Nutritional supplements, such as b-alanine and sodium bicarbonate, may have particular usefulness for aquatic athletes' training adaptation.

  15. Adaptive Technologies for Training and Education

    ERIC Educational Resources Information Center

    Durlach, Paula J., Ed; Lesgold, Alan M., Ed.

    2012-01-01

    This edited volume provides an overview of the latest advancements in adaptive training technology. Intelligent tutoring has been deployed for well-defined and relatively static educational domains such as algebra and geometry. However, this adaptive approach to computer-based training has yet to come into wider usage for domains that are less…

  16. Adaptive link selection algorithms for distributed estimation

    NASA Astrophysics Data System (ADS)

    Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent

    2015-12-01

    This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.

  17. Training Modalities to Increase Sensorimotor Adaptability

    NASA Technical Reports Server (NTRS)

    Bloomberg, J. J.; Mulavara, A. P.; Peters, B. T.; Brady, R.; Audas, C.; Cohen, H. S.

    2009-01-01

    During the acute phase of adaptation to novel gravitational environments, sensorimotor disturbances have the potential to disrupt the ability of astronauts to perform required mission tasks. The goal of our current series of studies is develop a sensorimotor adaptability (SA) training program designed to facilitate recovery of functional capabilities when astronauts transition to different gravitational environments. The project has conducted a series of studies investigating the efficacy of treadmill training combined with a variety of sensory challenges (incongruent visual input, support surface instability) designed to increase adaptability. SA training using a treadmill combined with exposure to altered visual input was effective in producing increased adaptability in a more complex over-ground ambulatory task on an obstacle course. This confirms that for a complex task like walking, treadmill training contains enough of the critical features of overground walking to be an effective training modality. SA training can be optimized by using a periodized training schedule. Test sessions that each contain short-duration exposures to multiple perturbation stimuli allows subjects to acquire a greater ability to rapidly reorganize appropriate response strategies when encountering a novel sensory environment. Using a treadmill mounted on top of a six degree-of-freedom motion base platform we investigated locomotor training responses produced by subjects introduced to a dynamic walking surface combined with alterations in visual flow. Subjects who received this training had improved locomotor performance and faster reaction times when exposed to the novel sensory stimuli compared to control subjects. Results also demonstrate that individual sensory biases (i.e. increased visual dependency) can predict adaptive responses to novel sensory environments suggesting that individual training prescription can be developed to enhance adaptability. These data indicate that SA

  18. Optimal Hops-Based Adaptive Clustering Algorithm

    NASA Astrophysics Data System (ADS)

    Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong

    This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.

  19. Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

    PubMed Central

    2014-01-01

    Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971

  20. Adaptive cuckoo search algorithm for unconstrained optimization.

    PubMed

    Ong, Pauline

    2014-01-01

    Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

  1. A simple algorithm for averaging spike trains.

    PubMed

    Julienne, Hannah; Houghton, Conor

    2013-02-25

    Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.

  2. Adaptive Algorithms for HF Antenna Arrays.

    DTIC Science & Technology

    1987-07-01

    SUBJECT TERMS (Contnue on reverse dfnoceaq and identiy by bkICk numnber) FIELD GROUP SUB-GROUP HP Adaptive Arrays HrF Comunications Systems 4 HP...Although their heavy computational load renders them impractical *1 for many applications, the advancements in cheap, fast digital hardware have...or digital form. For many applications, the LMS algorithm represents a good trade off between speed of convergence* and implementational The speed of

  3. A parallel adaptive mesh refinement algorithm

    NASA Technical Reports Server (NTRS)

    Quirk, James J.; Hanebutte, Ulf R.

    1993-01-01

    Over recent years, Adaptive Mesh Refinement (AMR) algorithms which dynamically match the local resolution of the computational grid to the numerical solution being sought have emerged as powerful tools for solving problems that contain disparate length and time scales. In particular, several workers have demonstrated the effectiveness of employing an adaptive, block-structured hierarchical grid system for simulations of complex shock wave phenomena. Unfortunately, from the parallel algorithm developer's viewpoint, this class of scheme is quite involved; these schemes cannot be distilled down to a small kernel upon which various parallelizing strategies may be tested. However, because of their block-structured nature such schemes are inherently parallel, so all is not lost. In this paper we describe the method by which Quirk's AMR algorithm has been parallelized. This method is built upon just a few simple message passing routines and so it may be implemented across a broad class of MIMD machines. Moreover, the method of parallelization is such that the original serial code is left virtually intact, and so we are left with just a single product to support. The importance of this fact should not be underestimated given the size and complexity of the original algorithm.

  4. Training Adaptive Decision-Making.

    SciTech Connect

    Abbott, Robert G.; Forsythe, James C.

    2014-10-01

    Adaptive Thinking has been defined here as the capacity to recognize when a course of action that may have previously been effective is no longer effective and there is need to adjust strategy. Research was undertaken with human test subjects to identify the factors that contribute to adaptive thinking. It was discovered that those most effective in settings that call for adaptive thinking tend to possess a superior capacity to quickly and effectively generate possible courses of action, as measured using the Category Generation test. Software developed for this research has been applied to develop capabilities enabling analysts to identify crucial factors that are predictive of outcomes in fore-on-force simulation exercises.

  5. Adaptive Routing Algorithm for Priority Flows in a Network

    DTIC Science & Technology

    2012-06-14

    ADAPTIVE ROUTING ALGORITHM FOR PRIORITY FLOWS IN A NETWORK THESIS Timothy J. Carbino, Captain...ADAPTIVE ROUTING ALGORITHM FOR PRIORITY FLOWS IN A NETWORK THESIS Presented to the Faculty Department of Electrical and Computer... Thesis 20 Aug 10 – 14 Jun 12 Adaptive Routing Algorithm for Priority Flows in a Network 12629PCarbino, Timothy J, Captain, USAF Air Force Institute of

  6. Adaptive path planning: Algorithm and analysis

    SciTech Connect

    Chen, Pang C.

    1993-03-01

    Path planning has to be fast to support real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To alleviate this problem, we present a learning algorithm that uses past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful subgoals is learned to support faster planning. The algorithm is suitable for both stationary and incrementally-changing environments. To analyze our algorithm, we use a previously developed stochastic model that quantifies experience utility. Using this model, we characterize the situations in which the adaptive planner is useful, and provide quantitative bounds to predict its behavior. The results are demonstrated with problems in manipulator planning. Our algorithm and analysis are sufficiently general that they may also be applied to task planning or other planning domains in which experience is useful.

  7. Adaptive Trajectory Prediction Algorithm for Climbing Flights

    NASA Technical Reports Server (NTRS)

    Schultz, Charles Alexander; Thipphavong, David P.; Erzberger, Heinz

    2012-01-01

    Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

  8. Adapting Aquatic Circuit Training for Special Populations.

    ERIC Educational Resources Information Center

    Thome, Kathleen

    1980-01-01

    The author discusses how land activities can be adapted to water so that individuals with handicapping conditions can participate in circuit training activities. An initial section lists such organizational procedures as providing vocal and/or visual cues for activities, having assistants accompany the performers throughout the circuit, and…

  9. Adaptive Numerical Algorithms in Space Weather Modeling

    NASA Technical Reports Server (NTRS)

    Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav

    2010-01-01

    Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical

  10. Adaptive numerical algorithms in space weather modeling

    NASA Astrophysics Data System (ADS)

    Tóth, Gábor; van der Holst, Bart; Sokolov, Igor V.; De Zeeuw, Darren L.; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Najib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav

    2012-02-01

    Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different relevant physics in different domains. A multi-physics system can be modeled by a software framework comprising several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamic (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit

  11. Motor adaptation training for faster relearning.

    PubMed

    Malone, Laura A; Vasudevan, Erin V L; Bastian, Amy J

    2011-10-19

    Adaptation is an error-driven motor learning process that can account for predictable changes in the environment (e.g., walking on ice) or in ourselves (e.g., injury). Our ability to recall and build upon adapted motor patterns across days is essential to this learning process. We investigated how different training paradigms affect the day-to-day memory of an adapted walking pattern. Healthy human adults walked on a split-belt treadmill, and returned the following day to assess recall, relearning rate, and performance. In the first experiment, one group adapted and de-adapted (i.e., washed-out the learning) several times on day 1 to practice the initial stage of learning where errors are large; another group adapted only one time and then practiced in the adapted ("learned") state where errors were small. On day 2, they performed washout trials before readapting. The group that repeatedly practiced the initial portion of adaptation where errors are large showed the fastest relearning on the second day. In fact, the memory was nearly as strong as that of a third group that was left overnight in the adapted state and was not washed-out before reexposure on the second day. This demonstrates that alternating exposures to early adaptation and washout can enhance readaptation. In the second experiment, we tested whether the opposite split-belt pattern interferes with day 2 relearning. Surprisingly, it did not, and instead was similar to practicing in the adapted state. These results show that the structure of the initial phase of learning influences the ease of motor relearning.

  12. Adaptive RED algorithm based on minority game

    NASA Astrophysics Data System (ADS)

    Wei, Jiaolong; Lei, Ling; Qian, Jingjing

    2007-11-01

    With more and more applications appearing and the technology developing in the Internet, only relying on terminal system can not satisfy the complicated demand of QoS network. Router mechanisms must be participated into protecting responsive flows from the non-responsive. Routers mainly use active queue management mechanism (AQM) to avoid congestion. In the point of interaction between the routers, the paper applies minority game to describe the interaction of the users and observes the affection on the length of average queue. The parameters α, β of ARED being hard to confirm, adaptive RED based on minority game can depict the interactions of main body and amend the parameter α, β of ARED to the best. Adaptive RED based on minority game optimizes ARED and realizes the smoothness of average queue length. At the same time, this paper extends the network simulator plat - NS by adding new elements. Simulation has been implemented and the results show that new algorithm can reach the anticipative objects.

  13. Central nervous system adaptation to exercise training

    NASA Astrophysics Data System (ADS)

    Kaminski, Lois Anne

    Exercise training causes physiological changes in skeletal muscle that results in enhanced performance in humans and animals. Despite numerous studies on exercise effects on skeletal muscle, relatively little is known about adaptive changes in the central nervous system. This study investigated whether spinal pathways that mediate locomotor activity undergo functional adaptation after 28 days of exercise training. Ventral horn spinal cord expression of calcitonin gene-related peptide (CGRP), a trophic factor at the neuromuscular junction, choline acetyltransferase (Chat), the synthetic enzyme for acetylcholine, vesicular acetylcholine transporter (Vacht), a transporter of ACh into synaptic vesicles and calcineurin (CaN), a protein phosphatase that phosphorylates ion channels and exocytosis machinery were measured to determine if changes in expression occurred in response to physical activity. Expression of these proteins was determined by western blot and immunohistochemistry (IHC). Comparisons between sedentary controls and animals that underwent either endurance training or resistance training were made. Control rats received no exercise other than normal cage activity. Endurance-trained rats were exercised 6 days/wk at 31m/min on a treadmill (8% incline) for 100 minutes. Resistance-trained rats supported their weight plus an additional load (70--80% body weight) on a 60° incline (3 x 3 min, 5 days/wk). CGRP expression was measured by radioimmunoassay (RIA). CGRP expression in the spinal dorsal and ventral horn of exercise-trained animals was not significantly different than controls. Chat expression measured by Western blot and IHC was not significantly different between runners and controls but expression in resistance-trained animals assayed by IHC was significantly less than controls and runners. Vacht and CaN immunoreactivity in motor neurons of endurance-trained rats was significantly elevated relative to control and resistance-trained animals. Ventral

  14. Neural adaptations to electrical stimulation strength training.

    PubMed

    Hortobágyi, Tibor; Maffiuletti, Nicola A

    2011-10-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there is substantial evidence to suggest that EST modifies the excitability of specific neural paths and such adaptations contribute to the increases in MVC force. Similar to strength training with voluntary contractions, EST increases MVC force after only a few sessions with some changes in muscle biochemistry but without overt muscle hypertrophy. There is some mixed evidence for spinal neural adaptations in the form of an increase in the amplitude of the interpolated twitch and in the amplitude of the volitional wave, with less evidence for changes in spinal excitability. Cross-sectional and exercise studies also suggest that the barrage of sensory and nociceptive inputs acts at the cortical level and can modify the motor cortical output and interhemispheric paths. The data suggest that neural adaptations mediate initial increases in MVC force after short-term EST.

  15. Enhancing astronaut performance using sensorimotor adaptability training

    PubMed Central

    Bloomberg, Jacob J.; Peters, Brian T.; Cohen, Helen S.; Mulavara, Ajitkumar P.

    2015-01-01

    Astronauts experience disturbances in balance and gait function when they return to Earth. The highly plastic human brain enables individuals to modify their behavior to match the prevailing environment. Subjects participating in specially designed variable sensory challenge training programs can enhance their ability to rapidly adapt to novel sensory situations. This is useful in our application because we aim to train astronauts to rapidly formulate effective strategies to cope with the balance and locomotor challenges associated with new gravitational environments—enhancing their ability to “learn to learn.” We do this by coupling various combinations of sensorimotor challenges with treadmill walking. A unique training system has been developed that is comprised of a treadmill mounted on a motion base to produce movement of the support surface during walking. This system provides challenges to gait stability. Additional sensory variation and challenge are imposed with a virtual visual scene that presents subjects with various combinations of discordant visual information during treadmill walking. This experience allows them to practice resolving challenging and conflicting novel sensory information to improve their ability to adapt rapidly. Information obtained from this work will inform the design of the next generation of sensorimotor countermeasures for astronauts. PMID:26441561

  16. Training product unit neural networks with genetic algorithms

    NASA Technical Reports Server (NTRS)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  17. Adaptive model training system and method

    DOEpatents

    Bickford, Randall L; Palnitkar, Rahul M; Lee, Vo

    2014-04-15

    An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

  18. Adaptive model training system and method

    DOEpatents

    Bickford, Randall L; Palnitkar, Rahul M

    2014-11-18

    An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

  19. Adaptive Mesh and Algorithm Refinement Using Direct Simulation Monte Carlo

    NASA Astrophysics Data System (ADS)

    Garcia, Alejandro L.; Bell, John B.; Crutchfield, William Y.; Alder, Berni J.

    1999-09-01

    Adaptive mesh and algorithm refinement (AMAR) embeds a particle method within a continuum method at the finest level of an adaptive mesh refinement (AMR) hierarchy. The coupling between the particle region and the overlaying continuum grid is algorithmically equivalent to that between the fine and coarse levels of AMR. Direct simulation Monte Carlo (DSMC) is used as the particle algorithm embedded within a Godunov-type compressible Navier-Stokes solver. Several examples are presented and compared with purely continuum calculations.

  20. An Adaptive Unified Differential Evolution Algorithm for Global Optimization

    SciTech Connect

    Qiang, Ji; Mitchell, Chad

    2014-11-03

    In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.

  1. Recurrent neural networks training with stable bounding ellipsoid algorithm.

    PubMed

    Yu, Wen; de Jesús Rubio, José

    2009-06-01

    Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

  2. Adaptive Training Considerations for Use in Simulation-Based Systems

    DTIC Science & Technology

    2010-09-01

    ABSTRACT In this report, we examine theoretical and empirical papers that describe adaptive training (AT). When applied effectively, AT has the...the effectiveness of various instructional techniques and methods. In the current report, we examine theoretical and empirical papers that describe...this report, we examine theoretical and empirical papers that describe one such advanced training method, Adaptive Training (AT). In AT, some

  3. [Endurance training and cardial adaptation (athlete's heart)].

    PubMed

    Dickhuth, Hans-Hermann; Röcker, Kai; Mayer, Frank; König, Daniel; Korsten-Reck, Ulrike

    2004-06-01

    One essential function of the cardiovascular system is to provide an adequate blood supply to all organs, including the skeletal muscles at rest and during exercise. Adaptation to chronic exercise proceeds mainly via the autonomic nervous system. On the one hand, peripheral muscles influence the autonomic reactions through "feedback" control via ergoreceptors, in particular, mechano- and chemoreceptors. On the other hand, there is central control in the sense of a "feed forward" regulation, e. g., the reaction of an athlete before competition. Along with other influential factors, such as circulatory presso-, chemo-, and volume receptors, the incoming impulses are processed in vegetative centers.A cardiovascular reaction, then, is the result of nerval and humoral sympathetic and parasympathetic activity. At rest, the parasympathetic tone dominates. It reduces heart frequency and conduction velocity. The high vagal tone is initially reduced with increasing physical exertion and switches at higher intensity to increasingly sympathetic activation. This mechanism of reaction to exercise is supported by inverse central and peripheral transmissions.Chronic endurance training leads to an improved local aerobic capacity of the exercised musculature. At rest, it augments parasympathetic activity when the muscle mass is sufficiently large, i. e., 20-30% of the skeletal musculature. The extent of the adaptation depends on individual factors, such as scope, intensity of training, and type of muscle fiber. A higher vagal tone delays the increase in the sympathetic tone during physical exertion. The regulatory range of heart rate, contractility, diastolic function, and blood pressure is increased. In addition, adaptation results in functional and structural changes in the vascular system. Cardiocirculatory work is economized, and maximum performance and oxygen uptake are improved. Endurance training exceeding an individual limit causes harmonic enlargement and hypertrophy of the

  4. An algorithmic approach to adaptive state filtering using recurrent neural networks.

    PubMed

    Parlos, A G; Menon, S K; Atiya, A

    2001-01-01

    Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.

  5. Resistance Training: Physiological Responses and Adaptations (Part 3 of 4).

    ERIC Educational Resources Information Center

    Fleck, Steven J.; Kraemer, William J.

    1988-01-01

    The physiological responses and adaptations which occur as a result of resistance training, such as cardiovascular responses, serum lipid count, body composition, and neural adaptations are discussed. Changes in the endocrine system are also described. (JL)

  6. An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

    PubMed Central

    Choi, Tae Jong; Ahn, Chang Wook; An, Jinung

    2013-01-01

    Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems. PMID:23935445

  7. An adaptive Cauchy differential evolution algorithm for global numerical optimization.

    PubMed

    Choi, Tae Jong; Ahn, Chang Wook; An, Jinung

    2013-01-01

    Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

  8. Adaptive Distributed Environment for Procedure Training (ADEPT)

    NASA Technical Reports Server (NTRS)

    Domeshek, Eric; Ong, James; Mohammed, John

    2013-01-01

    ADEPT (Adaptive Distributed Environment for Procedure Training) is designed to provide more effective, flexible, and portable training for NASA systems controllers. When creating a training scenario, an exercise author can specify a representative rationale structure using the graphical user interface, annotating the results with instructional texts where needed. The author's structure may distinguish between essential and optional parts of the rationale, and may also include "red herrings" - hypotheses that are essential to consider, until evidence and reasoning allow them to be ruled out. The system is built from pre-existing components, including Stottler Henke's SimVentive? instructional simulation authoring tool and runtime. To that, a capability was added to author and exploit explicit control decision rationale representations. ADEPT uses SimVentive's Scalable Vector Graphics (SVG)- based interactive graphic display capability as the basis of the tool for quickly noting aspects of decision rationale in graph form. The ADEPT prototype is built in Java, and will run on any computer using Windows, MacOS, or Linux. No special peripheral equipment is required. The software enables a style of student/ tutor interaction focused on the reasoning behind systems control behavior that better mimics proven Socratic human tutoring behaviors for highly cognitive skills. It supports fast, easy, and convenient authoring of such tutoring behaviors, allowing specification of detailed scenario-specific, but content-sensitive, high-quality tutor hints and feedback. The system places relatively light data-entry demands on the student to enable its rationale-centered discussions, and provides a support mechanism for fostering coherence in the student/ tutor dialog by including focusing, sequencing, and utterance tuning mechanisms intended to better fit tutor hints and feedback into the ongoing context.

  9. Adaptive Decision Aiding in Computer-Assisted Instruction: Adaptive Computerized Training System (ACTS).

    ERIC Educational Resources Information Center

    Hopf-Weichel, Rosemarie; And Others

    This report describes results of the first year of a three-year program to develop and evaluate a new Adaptive Computerized Training System (ACTS) for electronics maintenance training. (ACTS incorporates an adaptive computer program that learns the student's diagnostic and decision value structure, compares it to that of an expert, and adapts the…

  10. Adaptive phase aberration correction based on imperialist competitive algorithm.

    PubMed

    Yazdani, R; Hajimahmoodzadeh, M; Fallah, H R

    2014-01-01

    We investigate numerically the feasibility of phase aberration correction in a wavefront sensorless adaptive optical system, based on the imperialist competitive algorithm (ICA). Considering a 61-element deformable mirror (DM) and the Strehl ratio as the cost function of ICA, this algorithm is employed to search the optimum surface profile of DM for correcting the phase aberrations in a solid-state laser system. The correction results show that ICA is a powerful correction algorithm for static or slowly changing phase aberrations in optical systems, such as solid-state lasers. The correction capability and the convergence speed of this algorithm are compared with those of the genetic algorithm (GA) and stochastic parallel gradient descent (SPGD) algorithm. The results indicate that these algorithms have almost the same correction capability. Also, ICA and GA are almost the same in convergence speed and SPGD is the fastest of these algorithms.

  11. Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies.

    PubMed

    Fan, Qinqin; Yan, Xuefeng

    2016-01-01

    The performance of the differential evolution (DE) algorithm is significantly affected by the choice of mutation strategies and control parameters. Maintaining the search capability of various control parameter combinations throughout the entire evolution process is also a key issue. A self-adaptive DE algorithm with zoning evolution of control parameters and adaptive mutation strategies is proposed in this paper. In the proposed algorithm, the mutation strategies are automatically adjusted with population evolution, and the control parameters evolve in their own zoning to self-adapt and discover near optimal values autonomously. The proposed algorithm is compared with five state-of-the-art DE algorithm variants according to a set of benchmark test functions. Furthermore, seven nonparametric statistical tests are implemented to analyze the experimental results. The results indicate that the overall performance of the proposed algorithm is better than those of the five existing improved algorithms.

  12. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

    PubMed Central

    Wu, Haizhou; Luo, Qifang

    2016-01-01

    Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared. PMID:28105044

  13. Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

    NASA Astrophysics Data System (ADS)

    Zirkohi, Majid Moradi

    2015-04-01

    The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

  14. Central line simulation: a new training algorithm.

    PubMed

    Britt, Rebecca C; Reed, Scott F; Britt, L D

    2007-07-01

    Recent development of a partial task simulator for central line placement has altered the training algorithm from one of supervised learning on patients to mannequin-based practice to proficiency before patient interaction. There are little data published on the efficacy of this type of simulator. We reviewed our initial resident experience with central line simulation. Education to proficiency using the CentralLine Man simulator is completed by all interns during orientation. At the completion of training, te residents were asked to complete a voluntary, anonymous questionnaire with a 5-point Likert scale as well as open-ended questions. Additionally, the residents were asked to maintain a log of the initial 10 central lines placed. Retrospective review of the questionnaire and logs were done with analysis of simulator experience as well as initial line experience. Seventeen trainees completed the central line simulation course and returned the initial survey. Before the course, the trainees had placed an average of 0.4 internal jugular (IJ) and 1 subclavian (SC) line. On the simulator, an average of 3 SC attempts and 2.5 IJ attempts led to resident comfort with the procedure. On the first attempt, the vessel was accessed after an average of 1.5 SC and 1.9 IJ needlesticks, which improved to 1 SC and 1.3 IJ by the fifth simulated attempt. A total of 4 pneumothorax and 5 carotid sticks were done. Overall, the residents were highly satisfied with the course with an average score of 4.8 for didactics, 4.8 for equipment, 4.5 for the mannequin, and 4.8 for practice opportunity. Nine of the 11 residents who completed logs felt the simulation improved performance on the patient. On the first patient attempt, an average of 1.8 needlesticks was done with an average of 1.3 by the tenth line. For the first patient line documented in the logs, comfort with the anatomy was rated 3.8 with comfort with the procedure rated 2.8. Central line simulation before actual performance on

  15. Classification of adaptive memetic algorithms: a comparative study.

    PubMed

    Ong, Yew-Soon; Lim, Meng-Hiot; Zhu, Ning; Wong, Kok-Wai

    2006-02-01

    Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

  16. The PCNN adaptive segmentation algorithm based on visual perception

    NASA Astrophysics Data System (ADS)

    Zhao, Yanming

    To solve network adaptive parameter determination problem of the pulse coupled neural network (PCNN), and improve the image segmentation results in image segmentation. The PCNN adaptive segmentation algorithm based on visual perception of information is proposed. Based on the image information of visual perception and Gabor mathematical model of Optic nerve cells receptive field, the algorithm determines adaptively the receptive field of each pixel of the image. And determines adaptively the network parameters W, M, and β of PCNN by the Gabor mathematical model, which can overcome the problem of traditional PCNN parameter determination in the field of image segmentation. Experimental results show that the proposed algorithm can improve the region connectivity and edge regularity of segmentation image. And also show the PCNN of visual perception information for segmentation image of advantage.

  17. Adaptively resizing populations: Algorithm, analysis, and first results

    NASA Technical Reports Server (NTRS)

    Smith, Robert E.; Smuda, Ellen

    1993-01-01

    Deciding on an appropriate population size for a given Genetic Algorithm (GA) application can often be critical to the algorithm's success. Too small, and the GA can fall victim to sampling error, affecting the efficacy of its search. Too large, and the GA wastes computational resources. Although advice exists for sizing GA populations, much of this advice involves theoretical aspects that are not accessible to the novice user. An algorithm for adaptively resizing GA populations is suggested. This algorithm is based on recent theoretical developments that relate population size to schema fitness variance. The suggested algorithm is developed theoretically, and simulated with expected value equations. The algorithm is then tested on a problem where population sizing can mislead the GA. The work presented suggests that the population sizing algorithm may be a viable way to eliminate the population sizing decision from the application of GA's.

  18. Adaptive wavelet transform algorithm for image compression applications

    NASA Astrophysics Data System (ADS)

    Pogrebnyak, Oleksiy B.; Manrique Ramirez, Pablo

    2003-11-01

    A new algorithm of locally adaptive wavelet transform is presented. The algorithm implements the integer-to-integer lifting scheme. It performs an adaptation of the wavelet function at the prediction stage to the local image data activity. The proposed algorithm is based on the generalized framework for the lifting scheme that permits to obtain easily different wavelet coefficients in the case of the (N~,N) lifting. It is proposed to perform the hard switching between (2, 4) and (4, 4) lifting filter outputs according to an estimate of the local data activity. When the data activity is high, i.e., in the vicinity of edges, the (4, 4) lifting is performed. Otherwise, in the plain areas, the (2,4) decomposition coefficients are calculated. The calculations are rather simples that permit the implementation of the designed algorithm in fixed point DSP processors. The proposed adaptive transform possesses the perfect restoration of the processed data and possesses good energy compactation. The designed algorithm was tested on different images. The proposed adaptive transform algorithm can be used for image/signal lossless compression.

  19. Automatic DarkAdaptation Threshold Detection Algorithm.

    PubMed

    G de Azevedo, Dario; Helegda, Sergio; Glock, Flavio; Russomano, Thais

    2005-01-01

    This paper describes an algorithm used to automatically determine the threshold sensitivity in a new dark adaptometer. The new instrument is controlled by a personal computer and can be used in the investigation of several retinal diseases. The stimulus field is delivered to the eye through the modified optics of a fundus camera. An automated light stimulus source was developed to operate together with this fundus camera. New control parameters were developed in this instrument to improve the traditional Goldmann-Weekers dark adaptometer.

  20. Adaptive Training for Voice Conversion Based on Eigenvoices

    NASA Astrophysics Data System (ADS)

    Ohtani, Yamato; Toda, Tomoki; Saruwatari, Hiroshi; Shikano, Kiyohiro

    In this paper, we describe a novel model training method for one-to-many eigenvoice conversion (EVC). One-to-many EVC is a technique for converting a specific source speaker's voice into an arbitrary target speaker's voice. An eigenvoice Gaussian mixture model (EV-GMM) is trained in advance using multiple parallel data sets consisting of utterance-pairs of the source speaker and many pre-stored target speakers. The EV-GMM can be adapted to new target speakers using only a few of their arbitrary utterances by estimating a small number of adaptive parameters. In the adaptation process, several parameters of the EV-GMM to be fixed for different target speakers strongly affect the conversion performance of the adapted model. In order to improve the conversion performance in one-to-many EVC, we propose an adaptive training method of the EV-GMM. In the proposed training method, both the fixed parameters and the adaptive parameters are optimized by maximizing a total likelihood function of the EV-GMMs adapted to individual pre-stored target speakers. We conducted objective and subjective evaluations to demonstrate the effectiveness of the proposed training method. The experimental results show that the proposed adaptive training yields significant quality improvements in the converted speech.

  1. Optimization of genomic selection training populations with a genetic algorithm

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

  2. Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

    NASA Astrophysics Data System (ADS)

    Hentschel, Alexander; Sanders, Barry C.

    2011-12-01

    Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

  3. Adaptations of skeletal muscle mitochondria to exercise training.

    PubMed

    Lundby, Carsten; Jacobs, Robert A

    2016-01-01

    Mitochondrial volume density (Mito(VD)) is composed of two distinct mitochondrial subpopulations--intermyofibrillar mitochondria (Mito(IMF)) and subsarcolemmal mitochondria (Mito(SS)). With exercise training, Mito(VD) may increase by up to 40% and is, for the most part, related to an increase in Mito(IMF). Exercise-induced adaptations in mitochondrial function depend on the intensity of training and appear to be explained predominately by an increased expression of mitochondrial enzymes that facilitate aerobic metabolism. Although mitochondrial content often increases with training, it seems that mitochondrial adaptations are not needed to facilitate maximal oxygen uptake, whereas such adaptations are of greater importance for endurance capacity.

  4. Adaptive Decision Aiding in Computer-Assisted Instruction: Adaptive Computerized Training System (ACTS)

    DTIC Science & Technology

    1980-09-01

    CATALOG NUMBERTechnical Report 475%; , ADAPTIVE DECISION &IDING IN OMPUTER-ASSISTED *JECHNICAL REPOT INSTRUCTION: ADAPTIVE COMPUTERIZED TRAINING )16...alternative. For example, in auto maintenance, the mechanic is trained to adjust the dis- tributor with a "feeler" guage or a dwell tachometer . He

  5. Performance of an Adaptive Matched Filter Using the Griffiths Algorithm

    DTIC Science & Technology

    1988-12-01

    Simon. Introduction to Adaptive Filters. New York: Macmillan Publishing Company, 1984. 11. Sklar , Bernard . Digital Communications Fundamentals and...York: Harper and Row, 1986. 8. Widrow, Bernard and Samuel D. Stearns. Adaptive Signal Processing. Englewood Cliffs, N.J.: Prentice-Hall, 1985. 9...Fourier Transforms. and Optics. New York: John Wiley and Sons, 1978. 15. Widrow, Bernard and others. "The Complex LMS Algorithm," Proceedings of the IEEE

  6. Spatially adaptive regularized iterative high-resolution image reconstruction algorithm

    NASA Astrophysics Data System (ADS)

    Lim, Won Bae; Park, Min K.; Kang, Moon Gi

    2000-12-01

    High resolution images are often required in applications such as remote sensing, frame freeze in video, military and medical imaging. Digital image sensor arrays, which are used for image acquisition in many imaging systems, are not dense enough to prevent aliasing, so the acquired images will be degraded by aliasing effects. To prevent aliasing without loss of resolution, a dense detector array is required. But it may be very costly or unavailable, thus, many imaging systems are designed to allow some level of aliasing during image acquisition. The purpose of our work is to reconstruct an unaliased high resolution image from the acquired aliased image sequence. In this paper, we propose a spatially adaptive regularized iterative high resolution image reconstruction algorithm for blurred, noisy and down-sampled image sequences. The proposed approach is based on a Constrained Least Squares (CLS) high resolution reconstruction algorithm, with spatially adaptive regularization operators and parameters. These regularization terms are shown to improve the reconstructed image quality by forcing smoothness, while preserving edges in the reconstructed high resolution image. Accurate sub-pixel motion registration is the key of the success of the high resolution image reconstruction algorithm. However, sub-pixel motion registration may have some level of registration error. Therefore, a reconstruction algorithm which is robust against the registration error is required. The registration algorithm uses a gradient based sub-pixel motion estimator which provides shift information for each of the recorded frames. The proposed algorithm is based on a technique of high resolution image reconstruction, and it solves spatially adaptive regularized constrained least square minimization functionals. In this paper, we show that the reconstruction algorithm gives dramatic improvements in the resolution of the reconstructed image and is effective in handling the aliased information. The

  7. Adaptive neuron-to-EMG decoder training for FES neuroprostheses

    NASA Astrophysics Data System (ADS)

    Ethier, Christian; Acuna, Daniel; Solla, Sara A.; Miller, Lee E.

    2016-08-01

    Objective. We have previously demonstrated a brain-machine interface neuroprosthetic system that provided continuous control of functional electrical stimulation (FES) and restoration of grasp in a primate model of spinal cord injury (SCI). Predicting intended EMG directly from cortical recordings provides a flexible high-dimensional control signal for FES. However, no peripheral signal such as force or EMG is available for training EMG decoders in paralyzed individuals. Approach. Here we present a method for training an EMG decoder in the absence of muscle activity recordings; the decoder relies on mapping behaviorally relevant cortical activity to the inferred EMG activity underlying an intended action. Monkeys were trained at a 2D isometric wrist force task to control a computer cursor by applying force in the flexion, extension, ulnar, and radial directions and execute a center-out task. We used a generic muscle force-to-endpoint force model based on muscle pulling directions to relate each target force to an optimal EMG pattern that attained the target force while minimizing overall muscle activity. We trained EMG decoders during the target hold periods using a gradient descent algorithm that compared EMG predictions to optimal EMG patterns. Main results. We tested this method both offline and online. We quantified both the accuracy of offline force predictions and the ability of a monkey to use these real-time force predictions for closed-loop cursor control. We compared both offline and online results to those obtained with several other direct force decoders, including an optimal decoder computed from concurrently measured neural and force signals. Significance. This novel approach to training an adaptive EMG decoder could make a brain-control FES neuroprosthesis an effective tool to restore the hand function of paralyzed individuals. Clinical implementation would make use of individualized EMG-to-force models. Broad generalization could be achieved by

  8. Adaptations to swimming training: influence of training volume.

    PubMed

    Costill, D L; Thomas, R; Robergs, R A; Pascoe, D; Lambert, C; Barr, S; Fink, W J

    1991-03-01

    In an effort to assess the contributions of a period of increased training volume on swimming performance, two matched groups of collegiate male swimmers were studied before and during 25 wk of training. For the first 4 wk of this study, the two groups trained together in one session per day for approximately 1.5 h.d-1. During the following 6 wk (weeks 5-11), one group (LONG) trained two sessions per day, 1.5 h in the morning and 1.5 h in the afternoon. The other group (SHORT) continued to train once each day, in the afternoon with the LONG group. Over the final 14 wk of the study, both groups trained together in one session per day (1.5 h.d-1). Although the swimmers experienced significant improvements in swimming power, endurance, and performance throughout the 25 wk study, there were no differences between the groups. However, during the 6 wk period of increased training, the LONG group experienced a decline in sprinting velocity, whereas the SHORT group showed a significant increase in sprinting performance. The test results suggest that a 6 wk period of two 1.5 h training sessions per day does not enhance performance above that experienced with a single training session of 1.5 h each day. It was also noted that both groups showed little change in swimming endurance and power after the first 8 wk of training, though their performances improved significantly after each taper period.

  9. Adapting Eclat algorithm to parallel environments with Charm++ library

    NASA Astrophysics Data System (ADS)

    Puścian, Marek; Grabski, Waldemar

    2016-09-01

    In this paper we describe Eclat algorithm that is adapted to deal with growing data repositories. The presented solution utilizes Master-Slave scheme to distribute data mining tasks among available computation nodes. Several improvements have been proposed and successfully implemented using Charm++ library. This paper introduces optimization techniques to reduce communication cost and synchronization overhead. It also discusses results of the performance of parallel Eclat algorithm against different databases and compares it with parallel Apriori algorithm. The proposed approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer platform.

  10. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    USGS Publications Warehouse

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  11. Sympathetic adaptations to one-legged training

    NASA Technical Reports Server (NTRS)

    Ray, C. A.

    1999-01-01

    The purpose of the present study was to determine the effect of leg exercise training on sympathetic nerve responses at rest and during dynamic exercise. Six men were trained by using high-intensity interval and prolonged continuous one-legged cycling 4 day/wk, 40 min/day, for 6 wk. Heart rate, mean arterial pressure (MAP), and muscle sympathetic nerve activity (MSNA; peroneal nerve) were measured during 3 min of upright dynamic one-legged knee extensions at 40 W before and after training. After training, peak oxygen uptake in the trained leg increased 19 +/- 2% (P < 0.01). At rest, heart rate decreased from 77 +/- 3 to 71 +/- 6 beats/min (P < 0.01) with no significant changes in MAP (91 +/- 7 to 91 +/- 11 mmHg) and MSNA (29 +/- 3 to 28 +/- 1 bursts/min). During exercise, both heart rate and MAP were lower after training (108 +/- 5 to 96 +/- 5 beats/min and 132 +/- 8 to 119 +/- 4 mmHg, respectively, during the third minute of exercise; P < 0.01). MSNA decreased similarly from rest during the first 2 min of exercise both before and after training. However, MSNA was significantly less during the third minute of exercise after training (32 +/- 2 to 22 +/- 3 bursts/min; P < 0.01). This training effect on MSNA remained when MSNA was expressed as bursts per 100 heartbeats. Responses to exercise in five untrained control subjects were not different at 0 and 6 wk. These results demonstrate that exercise training prolongs the decrease in MSNA during upright leg exercise and indicates that attenuation of MSNA to exercise reported with forearm training also occurs with leg training.

  12. An Adaptive Tradeoff Algorithm for Multi-issue SLA Negotiation

    NASA Astrophysics Data System (ADS)

    Son, Seokho; Sim, Kwang Mong

    Since participants in a Cloud may be independent bodies, mechanisms are necessary for resolving different preferences in leasing Cloud services. Whereas there are currently mechanisms that support service-level agreement negotiation, there is little or no negotiation support for concurrent price and timeslot for Cloud service reservations. For the concurrent price and timeslot negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price and timeslot proposal is necessary. The contribution of this work is thus to design an adaptive tradeoff algorithm for multi-issue negotiation mechanism. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed and total utility and to reduce computational load by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using a testbed suggest that due to the concurrent price and timeslot negotiation mechanism with adaptive tradeoff algorithm: 1) both agents achieve the best performance in terms of negotiation speed and utility; 2) the number of evaluations of each proposal is comparatively lower than previous scheme (burst-N).

  13. Adaptive wavelet transform algorithm for lossy image compression

    NASA Astrophysics Data System (ADS)

    Pogrebnyak, Oleksiy B.; Ramirez, Pablo M.; Acevedo Mosqueda, Marco Antonio

    2004-11-01

    A new algorithm of locally adaptive wavelet transform based on the modified lifting scheme is presented. It performs an adaptation of the wavelet high-pass filter at the prediction stage to the local image data activity. The proposed algorithm uses the generalized framework for the lifting scheme that permits to obtain easily different wavelet filter coefficients in the case of the (~N, N) lifting. Changing wavelet filter order and different control parameters, one can obtain the desired filter frequency response. It is proposed to perform the hard switching between different wavelet lifting filter outputs according to the local data activity estimate. The proposed adaptive transform possesses a good energy compaction. The designed algorithm was tested on different images. The obtained simulation results show that the visual and quantitative quality of the restored images is high. The distortions are less in the vicinity of high spatial activity details comparing to the non-adaptive transform, which introduces ringing artifacts. The designed algorithm can be used for lossy image compression and in the noise suppression applications.

  14. Adaptation algorithms for 2-D feedforward neural networks.

    PubMed

    Kaczorek, T

    1995-01-01

    The generalized weight adaptation algorithms presented by J.G. Kuschewski et al. (1993) and by S.H. Zak and H.J. Sira-Ramirez (1990) are extended for 2-D madaline and 2-D two-layer feedforward neural nets (FNNs).

  15. A Procedure for Empirical Initialization of Adaptive Testing Algorithms.

    ERIC Educational Resources Information Center

    van der Linden, Wim J.

    In constrained adaptive testing, the numbers of constraints needed to control the content of the tests can easily run into the hundreds. Proper initialization of the algorithm becomes a requirement because the presence of large numbers of constraints slows down the convergence of the ability estimator. In this paper, an empirical initialization of…

  16. Training to Facilitate Adaptation to Novel Sensory Environments

    NASA Technical Reports Server (NTRS)

    Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Brady, R. A.; Batson, C. D.; Ploutz-Snyder, R. J.; Cohen, H. S.

    2010-01-01

    After spaceflight, the process of readapting to Earth s gravity causes locomotor dysfunction. We are developing a gait training countermeasure to facilitate adaptive responses in locomotor function. Our training system is comprised of a treadmill placed on a motion-base facing a virtual visual scene that provides an unstable walking surface combined with incongruent visual flow designed to train subjects to rapidly adapt their gait patterns to changes in the sensory environment. The goal of our present study was to determine if training improved both the locomotor and dual-tasking ability responses to a novel sensory environment and to quantify the retention of training. Subjects completed three, 30-minute training sessions during which they walked on the treadmill while receiving discordant support surface and visual input. Control subjects walked on the treadmill without any support surface or visual alterations. To determine the efficacy of training, all subjects were then tested using a novel visual flow and support surface movement not previously experienced during training. This test was performed 20 minutes, 1 week, and 1, 3, and 6 months after the final training session. Stride frequency and auditory reaction time were collected as measures of postural stability and cognitive effort, respectively. Subjects who received training showed less alteration in stride frequency and auditory reaction time compared to controls. Trained subjects maintained their level of performance over 6 months. We conclude that, with training, individuals became more proficient at walking in novel discordant sensorimotor conditions and were able to devote more attention to competing tasks.

  17. An Adaptive Immune Genetic Algorithm for Edge Detection

    NASA Astrophysics Data System (ADS)

    Li, Ying; Bai, Bendu; Zhang, Yanning

    An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

  18. An Adaptive Homomorphic Aperture Photometry Algorithm for Merging Galaxies

    NASA Astrophysics Data System (ADS)

    Huang, J. C.; Hwang, C. Y.

    2017-03-01

    We present a novel automatic adaptive aperture photometry algorithm for measuring the total magnitudes of merging galaxies with irregular shapes. First, we use a morphological pattern recognition routine for identifying the shape of an irregular source in a background-subtracted image. Then, we extend the shape of the source by using the Dilation image operation to obtain an aperture that is quasi-homomorphic to the shape of the irregular source. The magnitude measured from the homomorphic aperture would thus have minimal contamination from the nearby background. As a test of our algorithm, we applied our technique to the merging galaxies observed by the Sloan Digital Sky Survey and the Canada–France–Hawaii Telescope. Our results suggest that the adaptive homomorphic aperture algorithm can be very useful for investigating extended sources with irregular shapes and sources in crowded regions.

  19. Flight data processing with the F-8 adaptive algorithm

    NASA Technical Reports Server (NTRS)

    Hartmann, G.; Stein, G.; Petersen, K.

    1977-01-01

    An explicit adaptive control algorithm based on maximum likelihood estimation of parameters has been designed for NASA's DFBW F-8 aircraft. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm has been implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer and surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software. The software and its performance evaluation based on flight data are described

  20. Neuromuscular adaptations following antagonist resisted training.

    PubMed

    MacKenzie, Sasho J; Rannelli, Luke A; Yurchevich, Jordan J

    2010-01-01

    The purpose was to assess a novel form of strength training, antagonist resisted training (ART), with potential use in microgravity and athletic rehabilitation settings. ART uses the force from antagonist muscles, during cocontractions, as the source of resistance for the agonists. Strength and electromyography (EMG) measurements were recorded before and after a 6-week training program during which participants trained the left arm while the right arm served as a control. Training was designed so that the elbow extensors (antagonists) served as resistance for the elbow flexors (agonists). Elbow flexor and extensor strengths were measured during maximal isometric contractions with the elbow fixed at 90 degrees. EMG was recorded from the biceps brachii and lateral head of the triceps brachii during all strength tests. EMG was also recorded from both muscles during a maximal isometric cocontraction of the elbow flexors and extensors. Elbow flexion strength increased significantly for the trained arm (5.8%) relative to the control (0.5%) (p = 0.003). Elbow extension strength of the trained limb also increased significantly (8.5%) relative to the control (4.5%) (p = 0.029). Biceps and triceps EMG, during maximum strength tests, increased significantly for the trained arm (18.5 and 18.6%) relative to the control (0.5 and -5.2%) (p = 0.035 and p = 0.01). Biceps and triceps EMG, during maximum cocontraction tests, increased significantly for the trained arm (30.1 and 61.1%) relative to the control (9.2 and 1.1%) (p = 0.042 and p = 0.0005). ART was found to increase strength and therefore could be an effective form of resistance training. Because it requires no equipment, ART may be especially applicable in microgravity environments, which have space and weight constraints.

  1. A new adaptive GMRES algorithm for achieving high accuracy

    SciTech Connect

    Sosonkina, M.; Watson, L.T.; Kapania, R.K.; Walker, H.F.

    1996-12-31

    GMRES(k) is widely used for solving nonsymmetric linear systems. However, it is inadequate either when it converges only for k close to the problem size or when numerical error in the modified Gram-Schmidt process used in the GMRES orthogonalization phase dramatically affects the algorithm performance. An adaptive version of GMRES (k) which tunes the restart value k based on criteria estimating the GMRES convergence rate for the given problem is proposed here. The essence of the adaptive GMRES strategy is to adapt the parameter k to the problem, similar in spirit to how a variable order ODE algorithm tunes the order k. With FORTRAN 90, which provides pointers and dynamic memory management, dealing with the variable storage requirements implied by varying k is not too difficult. The parameter k can be both increased and decreased-an increase-only strategy is described next followed by pseudocode.

  2. Adaptive process control using fuzzy logic and genetic algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  3. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  4. Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

    NASA Astrophysics Data System (ADS)

    Li, Dongming; Zhang, Lijuan; Wang, Ting; Liu, Huan; Yang, Jinhua; Chen, Guifen

    2016-11-01

    To improve the adaptive optics (AO) image's quality, we study the AO image restoration algorithm based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF). Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution issue. The image restoration experiments are performed to verify the image restoration effect of our proposed algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44% respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is significantly improved.

  5. Adaptive bad pixel correction algorithm for IRFPA based on PCNN

    NASA Astrophysics Data System (ADS)

    Leng, Hanbing; Zhou, Zuofeng; Cao, Jianzhong; Yi, Bo; Yan, Aqi; Zhang, Jian

    2013-10-01

    Bad pixels and response non-uniformity are the primary obstacles when IRFPA is used in different thermal imaging systems. The bad pixels of IRFPA include fixed bad pixels and random bad pixels. The former is caused by material or manufacture defect and their positions are always fixed, the latter is caused by temperature drift and their positions are always changing. Traditional radiometric calibration-based bad pixel detection and compensation algorithm is only valid to the fixed bad pixels. Scene-based bad pixel correction algorithm is the effective way to eliminate these two kinds of bad pixels. Currently, the most used scene-based bad pixel correction algorithm is based on adaptive median filter (AMF). In this algorithm, bad pixels are regarded as image noise and then be replaced by filtered value. However, missed correction and false correction often happens when AMF is used to handle complex infrared scenes. To solve this problem, a new adaptive bad pixel correction algorithm based on pulse coupled neural networks (PCNN) is proposed. Potential bad pixels are detected by PCNN in the first step, then image sequences are used periodically to confirm the real bad pixels and exclude the false one, finally bad pixels are replaced by the filtered result. With the real infrared images obtained from a camera, the experiment results show the effectiveness of the proposed algorithm.

  6. Sensorimotor Adaptability Training Improves Motor and Dual-Task Performance

    NASA Technical Reports Server (NTRS)

    Bloomberg, J.J.; Peters, B.T.; Mulavara, A.P.; Brady, R.; Batson, C.; Cohen, H.S.

    2009-01-01

    The overall objective of our project is to develop a sensorimotor adaptability (SA) training program designed to facilitate recovery of functional capabilities when astronauts transition to different gravitational environments. The goal of our current study was to determine if SA training using variation in visual flow and support surface motion produces improved performance in a novel sensory environment and demonstrate the retention characteristics of SA training.

  7. Automated training for algorithms that learn from genomic data.

    PubMed

    Cilingir, Gokcen; Broschat, Shira L

    2015-01-01

    Supervised machine learning algorithms are used by life scientists for a variety of objectives. Expert-curated public gene and protein databases are major resources for gathering data to train these algorithms. While these data resources are continuously updated, generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction. In this paper, we propose a new model of operation for supervised machine learning algorithms that learn from genomic data. By defining these algorithms in a pipeline in which the training data gathering procedure and the learning process are automated, one can create a system that generates a classifier or predictor using information available from public resources. The proposed model is explained using three case studies on SignalP, MemLoci, and ApicoAP in which existing machine learning models are utilized in pipelines. Given that the vast majority of the procedures described for gathering training data can easily be automated, it is possible to transform valuable machine learning algorithms into self-evolving learners that benefit from the ever-changing data available for gene products and to develop new machine learning algorithms that are similarly capable.

  8. Single muscle fiber adaptations with marathon training.

    PubMed

    Trappe, Scott; Harber, Matthew; Creer, Andrew; Gallagher, Philip; Slivka, Dustin; Minchev, Kiril; Whitsett, David

    2006-09-01

    The purpose of this investigation was to characterize the effects of marathon training on single muscle fiber contractile function in a group of recreational runners. Muscle biopsies were obtained from the gastrocnemius muscle of seven individuals (22 +/- 1 yr, 177 +/- 3 cm, and 68 +/- 2 kg) before, after 13 wk of run training, and after 3 wk of taper. Slow-twitch myosin heavy chain [(MHC) I] and fast-twitch (MHC IIa) muscle fibers were analyzed for size, strength (P(o)), speed (V(o)), and power. The run training program led to the successful completion of a marathon (range 3 h 56 min to 5 h 35 min). Oxygen uptake during submaximal running and citrate synthase activity were improved (P < 0.05) with the training program. Muscle fiber size declined (P < 0.05) by approximately 20% in both fiber types after training. P(o) was maintained in both fiber types with training and increased (P < 0.05) by 18% in the MHC IIa fibers after taper. This resulted in >60% increase (P < 0.05) in force per cross-sectional area in both fiber types. Fiber V(o) increased (P < 0.05) by 28% in MHC I fibers with training and was unchanged in MHC IIa fibers. Peak power increased (P < 0.05) in MHC I and IIa fibers after training with a further increase (P < 0.05) in MHC IIa fiber power after taper. These data show that marathon training decreased slow-twitch and fast-twitch muscle fiber size but that it maintained or improved the functional profile of these fibers. A taper period before the marathon further improved the functional profile of the muscle, which was targeted to the fast-twitch muscle fibers.

  9. Office of Land and Emergency Management (OLEM) Climate Change Adaptation Training

    EPA Pesticide Factsheets

    This training discusses climate vulnerabilities and methods for incorporating adaptation measures into OLEM programs. This training is meant to follow completion of EPA's Introductory Climate Change Training.

  10. Enhancing Functional Performance using Sensorimotor Adaptability Training Programs

    NASA Technical Reports Server (NTRS)

    Bloomberg, J. J.; Mulavara, A. P.; Peters, B. T.; Brady, R.; Audas, C.; Ruttley, T. M.; Cohen, H. S.

    2009-01-01

    During the acute phase of adaptation to novel gravitational environments, sensorimotor disturbances have the potential to disrupt the ability of astronauts to perform functional tasks. The goal of this project is to develop a sensorimotor adaptability (SA) training program designed to facilitate recovery of functional capabilities when astronauts transition to different gravitational environments. The project conducted a series of studies that investigated the efficacy of treadmill training combined with a variety of sensory challenges designed to increase adaptability including alterations in visual flow, body loading, and support surface stability.

  11. Adaptive Flocking of Robot Swarms: Algorithms and Properties

    NASA Astrophysics Data System (ADS)

    Lee, Geunho; Chong, Nak Young

    This paper presents a distributed approach for adaptive flocking of swarms of mobile robots that enables to navigate autonomously in complex environments populated with obstacles. Based on the observation of the swimming behavior of a school of fish, we propose an integrated algorithm that allows a swarm of robots to navigate in a coordinated manner, split into multiple swarms, or merge with other swarms according to the environment conditions. We prove the convergence of the proposed algorithm using Lyapunov stability theory. We also verify the effectiveness of the algorithm through extensive simulations, where a swarm of robots repeats the process of splitting and merging while passing around multiple stationary and moving obstacles. The simulation results show that the proposed algorithm is scalable, and robust to variations in the sensing capability of individual robots.

  12. Neuromuscular adaptations following prepubescent strength training.

    PubMed

    Ozmun, J C; Mikesky, A E; Surburg, P R

    1994-04-01

    Underlying mechanisms of prepubescent strength gains following resistance training are speculative. The purpose of this investigation was to determine the effects of 8 wk of resistance training on muscular strength, integrated EMG amplitude (IEMG), and arm anthropometrics of prepubescent youth. Sixteen subjects (8 males, 8 females) were randomly assigned to trained or control groups. All subjects (mean age = 10.3 yr) were of prepubertal status according to the criteria of Tanner. The trained group performed three sets (7-11 repetitions) of bicep curls with dumbbells three times per week for 8 wk. Pre- and posttraining measurements included isotonic and isokinetic strength of the elbow flexors, arm anthropometrics, and IEMG of the biceps brachii. Planned comparisons for a 2 x 2 (group by test) ANOVA model were used for data analysis. Significant isotonic (22.6%) and isokinetic (27.8%) strength gains were observed in the trained group without corresponding changes in arm circumference or skinfolds. The IEMG amplitude increased 16.8% (P < 0.05). The control group did not demonstrate any significant changes in the parameters measured. Early gains in muscular strength resulting from resistance training prepubescent children may be attributed to increased muscle activation.

  13. Redirected Touching: Training and Adaptation in Warped Virtual Spaces

    PubMed Central

    Kohli, Luv; Whitton, Mary C.; Brooks, Frederick P.

    2014-01-01

    Redirected Touching is a technique in which virtual space is warped to map many virtual objects onto one real object that serves as a passive haptic prop. Recent work suggests that this mapping can often be predictably unnoticeable and have little effect on task performance. We investigated training and adaptation on a rapid aiming task in a real environment, an unwarped virtual environment, and a warped virtual environment. Participants who experienced a warped virtual space reported an initial strange sensation, but adapted to the warped space after short repeated exposure. Our data indicate that all the virtual training was less effective than real-world training, but after adaptation, participants trained as well in a warped virtual space as in an unwarped one. PMID:25621318

  14. An Adaptive Hybrid Genetic Algorithm for Improved Groundwater Remediation Design

    NASA Astrophysics Data System (ADS)

    Espinoza, F. P.; Minsker, B. S.; Goldberg, D. E.

    2001-12-01

    Identifying optimal designs for a groundwater remediation system is computationally intensive, especially for complex, nonlinear problems such as enhanced in situ bioremediation technology. To improve performance, we apply a hybrid genetic algorithm (HGA), which is a two-step solution method: a genetic algorithm (GA) for global search using the entire population and then a local search (LS) to improve search speed for only a few individuals in the population. We implement two types of HGAs: a non-adaptive HGA (NAHGA), whose operations are invariant throughout the run, and a self-adaptive HGA (SAHGA), whose operations adapt to the performance of the algorithm. The best settings of the two HGAs for optimal performance are then investigated for a groundwater remediation problem. The settings include the frequency of LS with respect to the normal GA evaluation, probability of individual selection for LS, evolution criterion for LS (Lamarckian or Baldwinian), and number of local search iterations. A comparison of the algorithms' performance under different settings will be presented.

  15. An adaptive multimeme algorithm for designing HIV multidrug therapies.

    PubMed

    Neri, Ferrante; Toivanen, Jari; Cascella, Giuseppe Leonardo; Ong, Yew-Soon

    2007-01-01

    This paper proposes a period representation for modeling the multidrug HIV therapies and an Adaptive Multimeme Algorithm (AMmA) for designing the optimal therapy. The period representation offers benefits in terms of flexibility and reduction in dimensionality compared to the binary representation. The AMmA is a memetic algorithm which employs a list of three local searchers adaptively activated by an evolutionary framework. These local searchers, having different features according to the exploration logic and the pivot rule, have the role of exploring the decision space from different and complementary perspectives and, thus, assisting the standard evolutionary operators in the optimization process. Furthermore, the AMmA makes use of an adaptation which dynamically sets the algorithmic parameters in order to prevent stagnation and premature convergence. The numerical results demonstrate that the application of the proposed algorithm leads to very efficient medication schedules which quickly stimulate a strong immune response to HIV. The earlier termination of the medication schedule leads to lesser unpleasant side effects for the patient due to strong antiretroviral therapy. A numerical comparison shows that the AMmA is more efficient than three popular metaheuristics. Finally, a statistical test based on the calculation of the tolerance interval confirms the superiority of the AMmA compared to the other methods for the problem under study.

  16. An adaptive grid algorithm for one-dimensional nonlinear equations

    NASA Technical Reports Server (NTRS)

    Gutierrez, William E.; Hills, Richard G.

    1990-01-01

    Richards' equation, which models the flow of liquid through unsaturated porous media, is highly nonlinear and difficult to solve. Step gradients in the field variables require the use of fine grids and small time step sizes. The numerical instabilities caused by the nonlinearities often require the use of iterative methods such as Picard or Newton interation. These difficulties result in large CPU requirements in solving Richards equation. With this in mind, adaptive and multigrid methods are investigated for use with nonlinear equations such as Richards' equation. Attention is focused on one-dimensional transient problems. To investigate the use of multigrid and adaptive grid methods, a series of problems are studied. First, a multigrid program is developed and used to solve an ordinary differential equation, demonstrating the efficiency with which low and high frequency errors are smoothed out. The multigrid algorithm and an adaptive grid algorithm is used to solve one-dimensional transient partial differential equations, such as the diffusive and convective-diffusion equations. The performance of these programs are compared to that of the Gauss-Seidel and tridiagonal methods. The adaptive and multigrid schemes outperformed the Gauss-Seidel algorithm, but were not as fast as the tridiagonal method. The adaptive grid scheme solved the problems slightly faster than the multigrid method. To solve nonlinear problems, Picard iterations are introduced into the adaptive grid and tridiagonal methods. Burgers' equation is used as a test problem for the two algorithms. Both methods obtain solutions of comparable accuracy for similar time increments. For the Burgers' equation, the adaptive grid method finds the solution approximately three times faster than the tridiagonal method. Finally, both schemes are used to solve the water content formulation of the Richards' equation. For this problem, the adaptive grid method obtains a more accurate solution in fewer work units and

  17. Locomotive assignment problem with train precedence using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Noori, Siamak; Ghannadpour, Seyed Farid

    2012-07-01

    This paper aims to study the locomotive assignment problem which is very important for railway companies, in view of high cost of operating locomotives. This problem is to determine the minimum cost assignment of homogeneous locomotives located in some central depots to a set of pre-scheduled trains in order to provide sufficient power to pull the trains from their origins to their destinations. These trains have different degrees of priority for servicing, and the high class of trains should be serviced earlier than others. This problem is modeled using vehicle routing and scheduling problem where trains representing the customers are supposed to be serviced in pre-specified hard/soft fuzzy time windows. A two-phase approach is used which, in the first phase, the multi-depot locomotive assignment is converted to a set of single depot problems, and after that, each single depot problem is solved heuristically by a hybrid genetic algorithm. In the genetic algorithm, various heuristics and efficient operators are used in the evolutionary search. The suggested algorithm is applied to solve the medium sized numerical example to check capabilities of the model and algorithm. Moreover, some of the results are compared with those solutions produced by branch-and-bound technique to determine validity and quality of the model. Results show that suggested approach is rather effective in respect of quality and time.

  18. Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.

    PubMed

    Smith, J E

    2012-01-01

    Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes

  19. Efficient implementation of the adaptive scale pixel decomposition algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, L.; Bhatnagar, S.; Rau, U.; Zhang, M.

    2016-08-01

    Context. Most popular algorithms in use to remove the effects of a telescope's point spread function (PSF) in radio astronomy are variants of the CLEAN algorithm. Most of these algorithms model the sky brightness using the delta-function basis, which results in undesired artefacts when used to image extended emission. The adaptive scale pixel decomposition (Asp-Clean) algorithm models the sky brightness on a scale-sensitive basis and thus gives a significantly better imaging performance when imaging fields that contain both resolved and unresolved emission. Aims: However, the runtime cost of Asp-Clean is higher than that of scale-insensitive algorithms. In this paper, we identify the most expensive step in the original Asp-Clean algorithm and present an efficient implementation of it, which significantly reduces the computational cost while keeping the imaging performance comparable to the original algorithm. The PSF sidelobe levels of modern wide-band telescopes are significantly reduced, allowing us to make approximations to reduce the computational cost, which in turn allows for the deconvolution of larger images on reasonable timescales. Methods: As in the original algorithm, scales in the image are estimated through function fitting. Here we introduce an analytical method to model extended emission, and a modified method for estimating the initial values used for the fitting procedure, which ultimately leads to a lower computational cost. Results: The new implementation was tested with simulated EVLA data and the imaging performance compared well with the original Asp-Clean algorithm. Tests show that the current algorithm can recover features at different scales with lower computational cost.

  20. PHURBAS: AN ADAPTIVE, LAGRANGIAN, MESHLESS, MAGNETOHYDRODYNAMICS CODE. I. ALGORITHM

    SciTech Connect

    Maron, Jason L.; McNally, Colin P.; Mac Low, Mordecai-Mark E-mail: cmcnally@amnh.org

    2012-05-01

    We present an algorithm for simulating the equations of ideal magnetohydrodynamics and other systems of differential equations on an unstructured set of points represented by sample particles. Local, third-order, least-squares, polynomial interpolations (Moving Least Squares interpolations) are calculated from the field values of neighboring particles to obtain field values and spatial derivatives at the particle position. Field values and particle positions are advanced in time with a second-order predictor-corrector scheme. The particles move with the fluid, so the time step is not limited by the Eulerian Courant-Friedrichs-Lewy condition. Full spatial adaptivity is implemented to ensure the particles fill the computational volume, which gives the algorithm substantial flexibility and power. A target resolution is specified for each point in space, with particles being added and deleted as needed to meet this target. Particle addition and deletion is based on a local void and clump detection algorithm. Dynamic artificial viscosity fields provide stability to the integration. The resulting algorithm provides a robust solution for modeling flows that require Lagrangian or adaptive discretizations to resolve. This paper derives and documents the Phurbas algorithm as implemented in Phurbas version 1.1. A following paper presents the implementation and test problem results.

  1. Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description

    USGS Publications Warehouse

    Schmidt, Gail; Jenkerson, Calli; Masek, Jeffrey; Vermote, Eric; Gao, Feng

    2013-01-01

    The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from LandsatThematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product.The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record.This report discusses the LEDAPS algorithm, which was implemented by the USGS.

  2. A Novel Adaptive Frequency Estimation Algorithm Based on Interpolation FFT and Improved Adaptive Notch Filter

    NASA Astrophysics Data System (ADS)

    Shen, Ting-ao; Li, Hua-nan; Zhang, Qi-xin; Li, Ming

    2017-02-01

    The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.

  3. Self-adaptive incremental Newton-Raphson algorithms

    NASA Technical Reports Server (NTRS)

    Padovan, J.

    1980-01-01

    Multilevel self-adaptive Newton-Raphson type strategies are developed to improve the solution efficiency of nonlinear finite element simulations of statically loaded structures. The overall strategy involves three basic levels. The first level involves preliminary solution tunneling via primative operators. Secondly, the solution is constantly monitored via quality/convergence/nonlinearity tests. Lastly, the third level involves self-adaptive algorithmic update procedures aimed at improving the convergence characteristics of the Newton-Raphson strategy. Numerical experiments are included to illustrate the results of the procedure.

  4. Blood Volume: Its Adaptation to Endurance Training

    NASA Technical Reports Server (NTRS)

    Convertino, Victor A.

    1991-01-01

    Expansion of blood volume (hypervolemia) has been well documented in both cross-sectional and longitudinal studies as a consequence of endurance exercise training. Plasma volume expansion can account for nearly all of the exercise-induced hypervolemia up to 2-4 wk; after this time expansion may be distributed equally between plasma and red cell volumes. The exercise stimulus for hypervolemia has both thermal and nonthermal components that increase total circulating plasma levels of electrolytes and proteins. Although protein and fluid shifts from the extravascular to intravascular space may provide a mechanism for rapid hypervolemia immediately after exercise, evidence supports the notion that chronic hypervolemia associated with exercise training represents a net expansion of total body water and solutes. This net increase of body fluids with exercise training is associated with increased water intake and decreased urine volume output. The mechanism of reduced urine output appears to be increased renal tubular reabsorption of sodium through a more sensitive aldosterone action in man. Exercise training-induced hypervolemia appears to be universal among most animal species, although the mechanisms may be quite different. The hypervolemia may provide advantages of greater body fluid for heat dissipation and thermoregulatory stability as well as larger vascular volume and filling pressure for greater cardiac stroke volume and lower heart rates during exercise.

  5. Aging influences adaptations of the neuromuscular junction to endurance training.

    PubMed

    Deschenes, M R; Roby, M A; Glass, E K

    2011-09-08

    This investigation sought to determine if aging affected adaptations of the neuromuscular junction (NMJ) to exercise training. Twenty young adult (8 months) and 20 aged (24 months) rats were assigned to either a program of treadmill exercise, or sedentary conditions. Following the 10-week experimental period, rats were euthanized, and soleus and plantaris muscles were removed and frozen. Longitudinal sections of the muscles were fluorescently stained to visualize pre-synaptic nerve terminals and post-synaptic endplates on both slow- and fast-twitch fibers. Images were collected with confocal microscopy and quantified. Muscle cross-sections were histochemically stained to assess muscle fiber profiles (size and fiber type). Our analysis of NMJs revealed a high degree of specificity and sensitivity to aging, exercise training, and their interaction. In the soleus, slow-twitch NMJs demonstrated significant (P ≤ 0.05) training-induced adaptations in young adult, but not aged rats. In the fast-twitch NMJs of the soleus, aging, but not training, was associated with remodeling. In the plantaris, aging, but not training, remodeled the predominant fast-twitch NMJs, but only pre-synaptically. In contrast, the slow-twitch NMJs of the plantaris displayed morphologic adaptations to both aging and exercise in pre- and post-synaptic components. Muscle fiber profiles indicated that changes in NMJ size were unrelated to adaptations of their fibers. Our data show that aging interferes with the ability of NMJs to adapt to exercise training. Results also reveal complexity in the coordination of synaptic responses among different muscles, and different fiber types within muscles, in their adaptation to aging and exercise training.

  6. Muscular adaptation of horses during intensive training and detraining.

    PubMed

    Essén-Gustavsson, B; McMiken, D; Karlström, K; Lindholm, A; Persson, S; Thornton, J

    1989-01-01

    Five horses were studied during a five-week regime of controlled intensive daily training on a high-speed treadmill followed by five weeks of detraining. Muscle biopsies were taken weekly from both the right and left gluteus muscle and from the sternocephalicus muscle before, and at the end of, the training and detraining periods. Histochemical and biochemical analyses of the sternocephalicus muscle showed no metabolic adaptation with either training or detraining. No significant differences were observed in any of the analysed parameters in the gluteus muscle between contralateral sites. Glycogen levels decreased by 10 to 15 per cent after one to two weeks of training, remained low during the training period and increased to pretraining levels after one week's cessation of training. Citrate synthase activity increased rapidly and was 27 per cent higher after one week and 42 per cent higher after five weeks of training. Lactate dehydrogenase activity decreased by 15 per cent during this period. The changes seen in these enzyme levels persisted during the detraining period. No alterations were seen in fibre type composition but type IIA fibre areas decreased by 19 per cent after five weeks training and capillary density increased by 17 per cent. It is concluded that a period of intensive training will rapidly increase the oxidative capacity and the capillary density in an actively working muscle, and that these metabolic adaptations are well maintained during a subsequent period of detraining.

  7. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales

    SciTech Connect

    Xiu, Dongbin

    2016-06-21

    The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.

  8. Adaptive primal-dual genetic algorithms in dynamic environments.

    PubMed

    Wang, Hongfeng; Yang, Shengxiang; Ip, W H; Wang, Dingwei

    2009-12-01

    Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.

  9. Adaptive Load-Balancing Algorithms Using Symmetric Broadcast Networks

    NASA Technical Reports Server (NTRS)

    Das, Sajal K.; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)

    1997-01-01

    In a distributed-computing environment, it is important to ensure that the processor workloads are adequately balanced. Among numerous load-balancing algorithms, a unique approach due to Dam and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three novel SBN-based load-balancing algorithms, and implement them on an SP2. A thorough experimental study with Poisson-distributed synthetic loads demonstrates that these algorithms are very effective in balancing system load while minimizing processor idle time. They also compare favorably with several other existing load-balancing techniques. Additional experiments performed with real data demonstrate that the SBN approach is effective in adaptive computational science and engineering applications where dynamic load balancing is extremely crucial.

  10. An adaptive gyroscope-based algorithm for temporal gait analysis.

    PubMed

    Greene, Barry R; McGrath, Denise; O'Neill, Ross; O'Donovan, Karol J; Burns, Adrian; Caulfield, Brian

    2010-12-01

    Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was -4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was -75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

  11. Adaptive Firefly Algorithm: Parameter Analysis and its Application

    PubMed Central

    Shen, Hong-Bin

    2014-01-01

    As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithmadaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem — protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise. PMID:25397812

  12. Adaptive firefly algorithm: parameter analysis and its application.

    PubMed

    Cheung, Ngaam J; Ding, Xue-Ming; Shen, Hong-Bin

    2014-01-01

    As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

  13. Adaptive training diminishes distractibility in aging across species.

    PubMed

    Mishra, Jyoti; de Villers-Sidani, Etienne; Merzenich, Michael; Gazzaley, Adam

    2014-12-03

    Aging is associated with deficits in the ability to ignore distractions, which has not yet been remediated by any neurotherapeutic approach. Here, in parallel auditory experiments with older rats and humans, we evaluated a targeted cognitive training approach that adaptively manipulated distractor challenge. Training resulted in enhanced discrimination abilities in the setting of irrelevant information in both species that was driven by selectively diminished distraction-related errors. Neural responses to distractors in auditory cortex were selectively reduced in both species, mimicking the behavioral effects. Sensory receptive fields in trained rats exhibited improved spectral and spatial selectivity. Frontal theta measures of top-down engagement with distractors were selectively restrained in trained humans. Finally, training gains generalized to group and individual level benefits in aspects of working memory and sustained attention. Thus, we demonstrate converging cross-species evidence for training-induced selective plasticity of distractor processing at multiple neural scales, benefitting distractor suppression and cognitive control.

  14. Sensemaking Training Requirements for the Adaptive Battlestaff

    DTIC Science & Technology

    2007-06-01

    Westport, Connecticut: Greenwood Press Hodgkins, R.A. (1992). Michael polanyi on the activity of knowing—the bearing of his ideas on the theory of...know more than we can tell (Polyani, 1966). Polanyi describes the semantic aspect of tacit knowing, how meaning tends to be displaced away from...training systems. The focal knowledge posited by Polanyi (1966) forms the theoretical basis for describing the enactment of sensemaking process into

  15. Generalized pattern search algorithms with adaptive precision function evaluations

    SciTech Connect

    Polak, Elijah; Wetter, Michael

    2003-05-14

    In the literature on generalized pattern search algorithms, convergence to a stationary point of a once continuously differentiable cost function is established under the assumption that the cost function can be evaluated exactly. However, there is a large class of engineering problems where the numerical evaluation of the cost function involves the solution of systems of differential algebraic equations. Since the termination criteria of the numerical solvers often depend on the design parameters, computer code for solving these systems usually defines a numerical approximation to the cost function that is discontinuous with respect to the design parameters. Standard generalized pattern search algorithms have been applied heuristically to such problems, but no convergence properties have been stated. In this paper we extend a class of generalized pattern search algorithms to a form that uses adaptive precision approximations to the cost function. These numerical approximations need not define a continuous function. Our algorithms can be used for solving linearly constrained problems with cost functions that are at least locally Lipschitz continuous. Assuming that the cost function is smooth, we prove that our algorithms converge to a stationary point. Under the weaker assumption that the cost function is only locally Lipschitz continuous, we show that our algorithms converge to points at which the Clarke generalized directional derivatives are nonnegative in predefined directions. An important feature of our adaptive precision scheme is the use of coarse approximations in the early iterations, with the approximation precision controlled by a test. Such an approach leads to substantial time savings in minimizing computationally expensive functions.

  16. Developing Adaptive Training in the Classroom

    DTIC Science & Technology

    2009-09-01

    Developing Adaptive Proficiency in Special Forces Officers (2005), p. A-4. 2-3 Such a critical incident speaks to a performance requirement to...Order of speaking Body language Prepare both cases: your and their arguments Build rapport Main Discussion Agreement is the simultaneous...think you can make a judgment, check “N/A” for not applicable. Adjectives* High Medium Low N/A • Extroverted (outgoing, talkative

  17. Adaptive Mesh Refinement Algorithms for Parallel Unstructured Finite Element Codes

    SciTech Connect

    Parsons, I D; Solberg, J M

    2006-02-03

    This project produced algorithms for and software implementations of adaptive mesh refinement (AMR) methods for solving practical solid and thermal mechanics problems on multiprocessor parallel computers using unstructured finite element meshes. The overall goal is to provide computational solutions that are accurate to some prescribed tolerance, and adaptivity is the correct path toward this goal. These new tools will enable analysts to conduct more reliable simulations at reduced cost, both in terms of analyst and computer time. Previous academic research in the field of adaptive mesh refinement has produced a voluminous literature focused on error estimators and demonstration problems; relatively little progress has been made on producing efficient implementations suitable for large-scale problem solving on state-of-the-art computer systems. Research issues that were considered include: effective error estimators for nonlinear structural mechanics; local meshing at irregular geometric boundaries; and constructing efficient software for parallel computing environments.

  18. Sympathetic neural adaptations to exercise training in humans.

    PubMed

    Carter, Jason R; Ray, Chester A

    2015-03-01

    Physiological adaptations to exercise training are well recognized and contribute importantly to health and fitness. Cardiovascular diseases, such as hypertension and heart failure, are often associated with elevated activity of the sympathetic nervous system. This review aims to provide comprehensive overview on the role of exercise training on muscle sympathetic nerve activity (MSNA) regulation in humans, with a focus on recent advances in at-risk populations. Collectively, these studies converge to demonstrate that aerobic exercise training reduces resting MSNA in populations at heightened cardiovascular risk, but do not appear to alter resting MSNA in healthy adults. We provide directions for future research which might address gaps in our knowledge regarding sympathoneural adaptations to exercise training.

  19. A genetic-based algorithm for personalized resistance training

    PubMed Central

    Kiely, J; Suraci, B; Collins, DJ; de Lorenzo, D; Pickering, C; Grimaldi, KA

    2016-01-01

    Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective

  20. A genetic-based algorithm for personalized resistance training.

    PubMed

    Jones, N; Kiely, J; Suraci, B; Collins, D J; de Lorenzo, D; Pickering, C; Grimaldi, K A

    2016-06-01

    Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective

  1. Adaptive thinking & leadership simulation game training for special forces officers.

    SciTech Connect

    Raybourn, Elaine Marie; Mendini, Kip; Heneghan, Jerry; Deagle, Edwin

    2005-07-01

    Complex problem solving approaches and novel strategies employed by the military at the squad, team, and commander level are often best learned experimentally. Since live action exercises can be costly, advances in simulation game training technology offer exciting ways to enhance current training. Computer games provide an environment for active, critical learning. Games open up possibilities for simultaneous learning on multiple levels; players may learn from contextual information embedded in the dynamics of the game, the organic process generated by the game, and through the risks, benefits, costs, outcomes, and rewards of alternative strategies that result from decision making. In the present paper we discuss a multiplayer computer game simulation created for the Adaptive Thinking & Leadership (ATL) Program to train Special Forces Team Leaders. The ATL training simulation consists of a scripted single-player and an immersive multiplayer environment for classroom use which leverages immersive computer game technology. We define adaptive thinking as consisting of competencies such as negotiation and consensus building skills, the ability to communicate effectively, analyze ambiguous situations, be self-aware, think innovatively, and critically use effective problem solving skills. Each of these competencies is an essential element of leader development training for the U.S. Army Special Forces. The ATL simulation is used to augment experiential learning in the curriculum for the U.S. Army JFK Special Warfare Center & School (SWCS) course in Adaptive Thinking & Leadership. The school is incorporating the ATL simulation game into two additional training pipelines (PSYOPS and Civil Affairs Qualification Courses) that are also concerned with developing cultural awareness, interpersonal communication adaptability, and rapport-building skills. In the present paper, we discuss the design, development, and deployment of the training simulation, and emphasize how the

  2. G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

    NASA Technical Reports Server (NTRS)

    Rogers, David

    1991-01-01

    G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

  3. Task-specific neural adaptations to isoinertial resistance training.

    PubMed

    Buckthorpe, M; Erskine, R M; Fletcher, G; Folland, J P

    2015-10-01

    This study aimed to delineate the contribution of adaptations in agonist, antagonist, and stabilizer muscle activation to changes in isometric and isoinertial lifting strength after short-term isoinertial resistance training (RT). Following familiarization, 45 men (23.2 ± 2.8 years) performed maximal isometric and isoinertial strength tests of the elbow flexors of their dominant arms before and after 3 weeks of isoinertial RT. During these tasks, surface electromyography (EMG) amplitude was recorded from the agonist (biceps brachii short and long heads), antagonist (triceps brachii lateral head), and stabilizer (anterior deltoid, pectoralis major) muscles and normalized to either Mmax (agonists) or to maximum EMG during relevant reference tasks (antagonist, stabilizers). After training, there was more than a twofold greater increase in training task-specific isoinertial than isometric strength (17% vs 7%). There were also task-specific adaptations in agonist EMG, with greater increases during the isoinertial than isometric strength task [analysis of variance (ANOVA), training × task, P = 0.005]. A novel finding of this study was that training increased stabilizer muscle activation during all the elbow flexion strength tasks (P < 0.001), although these were not task-specific training effects. RT elicited specific neural adaptations to the training task that appeared to explain the greater increase in isoinertial than isometric strength.

  4. Training pi-sigma network by online gradient algorithm with penalty for small weight update.

    PubMed

    Xiong, Yan; Wu, Wei; Kang, Xidai; Zhang, Chao

    2007-12-01

    A pi-sigma network is a class of feedforward neural networks with product units in the output layer. An online gradient algorithm is the simplest and most often used training method for feedforward neural networks. But there arises a problem when the online gradient algorithm is used for pi-sigma networks in that the update increment of the weights may become very small, especially early in training, resulting in a very slow convergence. To overcome this difficulty, we introduce an adaptive penalty term into the error function, so as to increase the magnitude of the update increment of the weights when it is too small. This strategy brings about faster convergence as shown by the numerical experiments carried out in this letter.

  5. Analysis of adaptive algorithms for an integrated communication network

    NASA Technical Reports Server (NTRS)

    Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim

    1985-01-01

    Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.

  6. Adaptive Inferential Feedback Partner Training for Depression: A Pilot Study

    ERIC Educational Resources Information Center

    Dobkin, Roseanne DeFronzo; Allen, Lesley A.; Alloy, Lauren B.; Menza, Matthew; Gara, Michael A.; Panzarella, Catherine

    2007-01-01

    Adaptive inferential feedback (AIF) partner training is a cognitive technique that teaches the friends and family members of depressed patients to respond to the patients' dysfunctional thoughts in a targeted manner. These dysfunctional attributions, which AIF addresses, are a common residual feature of depression amongst remitted patients, and…

  7. Training Adaptable Leaders: Lessons from Research and Practice

    DTIC Science & Technology

    2005-10-01

    FIGURE 2. PORTFOLIO DEVELOPMENT PROCESS ........................................... 22 vii TRAINING ADAPTABLE LEADERS: LESSONS FROM RESEARCH AND...Robertson, 2000; Klein, 1997). + Metacognitive skills. Metacognition has been defined as "thinking about thinking", referring essentially to an awareness...and regulation of one’s own thought processes. Often, metacognition is described as mentally checking on one’s progress toward a goal. For example

  8. Nutritional strategies to modulate the adaptive response to endurance training.

    PubMed

    Hawley, John A

    2013-01-01

    In recent years, advances in molecular biology have allowed scientists to elucidate how endurance exercise training stimulates skeletal muscle remodeling (i.e. promotes mitochondrial biogenesis). A growing field of interest directly arising from our understanding of the molecular bases of training adaptation is how nutrient availability can alter the regulation of many contraction-induced events in muscle in response to endurance exercise. Acutely manipulating substrate availability can exert profound effects on muscle energy stores and patterns of fuel metabolism during exercise, as well as many processes activating gene expression and cell signaling. Accordingly, such interventions when repeated over weeks and months have the potential to modulate numerous adaptive processes in skeletal muscle that ultimately drive the phenotype-specific characteristics observed in highly trained athletes. In this review, the molecular and cellular events that occur in skeletal muscle during and after endurance exercise are discussed and evidence provided to demonstrate that nutrient availability plays an important role in modulating many of the adaptive responses to training. Emphasis is on human studies that have determined the regulatory role of muscle glycogen availability on cell metabolism, endurance training capacity and performance.

  9. Statistical behaviour of adaptive multilevel splitting algorithms in simple models

    NASA Astrophysics Data System (ADS)

    Rolland, Joran; Simonnet, Eric

    2015-02-01

    Adaptive multilevel splitting algorithms have been introduced rather recently for estimating tail distributions in a fast and efficient way. In particular, they can be used for computing the so-called reactive trajectories corresponding to direct transitions from one metastable state to another. The algorithm is based on successive selection-mutation steps performed on the system in a controlled way. It has two intrinsic parameters, the number of particles/trajectories and the reaction coordinate used for discriminating good or bad trajectories. We investigate first the convergence in law of the algorithm as a function of the timestep for several simple stochastic models. Second, we consider the average duration of reactive trajectories for which no theoretical predictions exist. The most important aspect of this work concerns some systems with two degrees of freedom. They are studied in detail as a function of the reaction coordinate in the asymptotic regime where the number of trajectories goes to infinity. We show that during phase transitions, the statistics of the algorithm deviate significatively from known theoretical results when using non-optimal reaction coordinates. In this case, the variance of the algorithm is peaking at the transition and the convergence of the algorithm can be much slower than the usual expected central limit behaviour. The duration of trajectories is affected as well. Moreover, reactive trajectories do not correspond to the most probable ones. Such behaviour disappears when using the optimal reaction coordinate called committor as predicted by the theory. We finally investigate a three-state Markov chain which reproduces this phenomenon and show logarithmic convergence of the trajectory durations.

  10. Statistical behaviour of adaptive multilevel splitting algorithms in simple models

    SciTech Connect

    Rolland, Joran Simonnet, Eric

    2015-02-15

    Adaptive multilevel splitting algorithms have been introduced rather recently for estimating tail distributions in a fast and efficient way. In particular, they can be used for computing the so-called reactive trajectories corresponding to direct transitions from one metastable state to another. The algorithm is based on successive selection–mutation steps performed on the system in a controlled way. It has two intrinsic parameters, the number of particles/trajectories and the reaction coordinate used for discriminating good or bad trajectories. We investigate first the convergence in law of the algorithm as a function of the timestep for several simple stochastic models. Second, we consider the average duration of reactive trajectories for which no theoretical predictions exist. The most important aspect of this work concerns some systems with two degrees of freedom. They are studied in detail as a function of the reaction coordinate in the asymptotic regime where the number of trajectories goes to infinity. We show that during phase transitions, the statistics of the algorithm deviate significatively from known theoretical results when using non-optimal reaction coordinates. In this case, the variance of the algorithm is peaking at the transition and the convergence of the algorithm can be much slower than the usual expected central limit behaviour. The duration of trajectories is affected as well. Moreover, reactive trajectories do not correspond to the most probable ones. Such behaviour disappears when using the optimal reaction coordinate called committor as predicted by the theory. We finally investigate a three-state Markov chain which reproduces this phenomenon and show logarithmic convergence of the trajectory durations.

  11. Adaptivity and smart algorithms for fluid-structure interaction

    NASA Technical Reports Server (NTRS)

    Oden, J. Tinsley

    1990-01-01

    This paper reviews new approaches in CFD which have the potential for significantly increasing current capabilities of modeling complex flow phenomena and of treating difficult problems in fluid-structure interaction. These approaches are based on the notions of adaptive methods and smart algorithms, which use instantaneous measures of the quality and other features of the numerical flowfields as a basis for making changes in the structure of the computational grid and of algorithms designed to function on the grid. The application of these new techniques to several problem classes are addressed, including problems with moving boundaries, fluid-structure interaction in high-speed turbine flows, flow in domains with receding boundaries, and related problems.

  12. Thermoregulatory adaptations associated with training and heat acclimation.

    PubMed

    Geor, R J; McCutcheon, L J

    1998-04-01

    The large metabolic heat load generated as a consequence of muscular work requires activation of thermoregulatory mechanisms in order to prevent an excessive and potentially dangerous rise in body temperature during exercise. Although the horse has highly efficient heat dissipatory mechanisms, there are a number of circumstances in which the thermoregulatory system may be overwhelmed, resulting in the development of critical hyperthermia. The risk for development of life-threatening hyperthermia is greatest when (1) the horse is inadequately conditioned for the required level of physical performance; (2) exercise is undertaken in hot and particularly, in hot and humid ambient conditions; and (3) there is an impairment to thermoregulatory mechanisms (e.g., severe dehydration, anhidrosis). Both exercise training under cool to moderate ambient conditions and a period of repeated exposure to, and exercise in, hot ambient conditions (heat acclimation) will result in a number of physiologic adaptations conferring improved thermoregulatory ability. These adaptations include an expanded plasma volume, greater stability of cardiovascular function during exercise, and an improved efficiency of evaporative heat loss as a result of alterations in the sweating response. Collectively, these adjustments serve to attenuate the rise in core body temperature in response to a given intensity of exercise. The magnitude of the physiologic adaptations occurring during exercise training and heat acclimation is a reflection of the thermal load imposed on the horse. Therefore, when compared with a period of training in cool conditions, the larger thermal stimulus associated with repeated exercise in hot ambient conditions will invoke proportionally greater thermoregulatory adaptations. Although it is not possible to eliminate the effects of adverse environmental conditions on exercise performance, it is clear that a thorough exercise training program together with a subsequent period of

  13. ADART: an adaptive algebraic reconstruction algorithm for discrete tomography.

    PubMed

    Maestre-Deusto, F Javier; Scavello, Giovanni; Pizarro, Joaquín; Galindo, Pedro L

    2011-08-01

    In this paper we suggest an algorithm based on the Discrete Algebraic Reconstruction Technique (DART) which is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography. Adaptive DART (ADART) goes a step further than DART on the reduction of the number of unknowns of the associated linear system achieving a significant reduction in the pixel error rate of reconstructed objects. The proposed methodology automatically adapts the border definition criterion at each iteration, resulting in a reduction of the number of pixels belonging to the border, and consequently of the number of unknowns in the general algebraic reconstruction linear system to be solved, being this reduction specially important at the final stage of the iterative process. Experimental results show that reconstruction errors are considerably reduced using ADART when compared to original DART, both in clean and noisy environments.

  14. An adaptive penalty method for DIRECT algorithm in engineering optimization

    NASA Astrophysics Data System (ADS)

    Vilaça, Rita; Rocha, Ana Maria A. C.

    2012-09-01

    The most common approach for solving constrained optimization problems is based on penalty functions, where the constrained problem is transformed into a sequence of unconstrained problem by penalizing the objective function when constraints are violated. In this paper, we analyze the implementation of an adaptive penalty method, within the DIRECT algorithm, in which the constraints that are more difficult to be satisfied will have relatively higher penalty values. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

  15. Adaptations to training at the individual anaerobic threshold.

    PubMed

    Keith, S P; Jacobs, I; McLellan, T M

    1992-01-01

    The individual anaerobic threshold (Th(an)) is the highest metabolic rate at which blood lactate concentrations can be maintained at a steady-state during prolonged exercise. The purpose of this study was to test the hypothesis that training at the Th(an) would cause a greater change in indicators of training adaptation than would training "around" the Th(an). Three groups of subjects were evaluated before, and again after 4 and 8 weeks of training: a control group, a group which trained continuously for 30 min at the Th(an) intensity (SS), and a group (NSS) which divided the 30 min of training into 7.5-min blocks at intensities which alternated between being below the Th(an) [Th(an) -30% of the difference between Th(an) and maximal oxygen consumption (VO2max)] and above the Th(an) (Th(an) +30% of the difference between Th(an) and VO2max). The VO2max increased significantly from 4.06 to 4.27 l.min-1 in SS and from 3.89 to 4.06 l.min-1 in NSS. The power output (W) at Th(an) increased from 70.5 to 79.8% VO2max in SS and from 71.1 to 80.7% VO2max in NSS. The magnitude of change in VO2max, W at Th(an), % VO2max at Th(an) and in exercise time to exhaustion at the pretraining Th(an) was similar in both trained groups. Vastus lateralis citrate synthase and 3-hydroxyacyl-CoA-dehydrogenase activities increased to the same extent in both trained groups. While all of these training-induced adaptations were statistically significant (P < 0.05), there were no significant changes in any of these variables for the control subjects.(ABSTRACT TRUNCATED AT 250 WORDS)

  16. Adaptive testing for psychological assessment: how many items are enough to run an adaptive testing algorithm?

    PubMed

    Wagner-Menghin, Michaela M; Masters, Geoff N

    2013-01-01

    Although the principles of adaptive testing were established in the psychometric literature many years ago (e.g., Weiss, 1977), and practice of adaptive testing is established in educational assessment, it not yet widespread in psychological assessment. One obstacle to adaptive psychological testing is a lack of clarity about the necessary number of items to run an adaptive algorithm. The study explores the relationship between item bank size, test length and measurement precision. Simulated adaptive test runs (allowing a maximum of 30 items per person) out of an item bank with 10 items per ability level (covering .5 logits, 150 items total) yield a standard error of measurement (SEM) of .47 (.39) after an average of 20 (29) items for 85-93% (64-82%) of the simulated rectangular sample. Expanding the bank to 20 items per level (300 items total) did not improve the algorithm's performance significantly. With a small item bank (5 items per ability level, 75 items total) it is possible to reach the same SEM as with a conventional test, but with fewer items or a better SEM with the same number of items.

  17. 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.

  18. Time course of neuromuscular adaptations to knee extensor eccentric training.

    PubMed

    Baroni, B M; Rodrigues, R; Franke, R A; Geremia, J M; Rassier, D E; Vaz, M A

    2013-10-01

    This study investigated the chronology of neural and morphological adaptations to knee extensor eccentric training and their contribution to strength gains in isometric, concentric and eccentric muscle actions. 20 male healthy subjects performed a 12-week eccentric training program on an isokinetic dynamometer, and neuromuscular evaluations of knee extensors were performed every 4 weeks. After 12 training weeks, significant increases were observed for: isometric (24%), concentric (15%) and eccentric (29%) torques; isometric (29%) and eccentric (33%) electromyographic activity; muscle thickness (10%) and anatomical cross-sectional area (19%). Eccentric and isometric torques increased progressively until the end of the program. Concentric torque and muscle mass parameters increased until the eighth training week, but did not change from this point to the twelfth training week. Eccentric and isometric activation increased at 4 and 8 training weeks, respectively, while no change was found in concentric activation. These results suggest that: 1) the relative increment in concentric strength was minor and does not relate to neural effects; 2) eccentric and isometric strength gains up to 8 training weeks are explained by the increased neural activation and muscle mass, whereas the increments in the last 4 training weeks seem to be associated with other mechanisms.

  19. Path Planning Algorithms for the Adaptive Sensor Fleet

    NASA Technical Reports Server (NTRS)

    Stoneking, Eric; Hosler, Jeff

    2005-01-01

    The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

  20. Does living setting influence training adaptations in young girls?

    PubMed

    Gallotta, M C; Guidetti, L; Emerenziani, G P; Franciosi, E; Baldari, C

    2011-04-01

    To assess whether rural or urban setting may influence young girls' fitness and coordinative abilities training adaptations following dance training. Forty-four dancers aged 11-12 years (21 urban, 23 rural) attended a 6-month dance training while continuing to practice their habitual physical activities (PA). Dancers' fitness and motor coordination abilities were assessed by pre- and post-intervention tests (anthropometric measures, 1-mile run/walk, sit and reach, standing long jump, hand grip and four field tests of kinaesthetic discrimination and response orientation). PA was assessed using a self-report recall measure. After the intervention period, rural dancers significantly improved in 1-mile run/walk, lower limb kinaesthetic discrimination and response orientation ability tests. Significant differences between two groups in upper limb response orientation ability test were found. Both groups showed a significant increase in body height and weight. Multiple regression analysis indicated that time in nonorganized PA predicted some fitness and coordinative changes (1-mile run/walk, lower limb response orientation and kinaesthetic discrimination ability tests) following the training period, although the percentage of variance it could explain was moderate. Our results showed that training adaptations of some fitness and coordinative performances could be influenced by setting characteristics that provided opportunities for different types of PA.

  1. Neuromuscular Adaptations to Unilateral vs. Bilateral Strength Training in Women.

    PubMed

    Botton, Cíntia E; Radaelli, Regis; Wilhelm, Eurico N; Rech, Anderson; Brown, Lee E; Pinto, Ronei S

    2016-07-01

    Botton, CE, Radaelli, R, Wilhelm, EN, Rech, A, Brown, LE, and Pinto, RS. Neuromuscular adaptations to unilateral vs. bilateral strength training in women. J Strength Cond Res 30(7): 1924-1932, 2016-Considering the bilateral deficit, the sum of forces produced by each limb in a unilateral condition is generally greater than that produced by them in a bilateral condition. Therefore, it can be speculated that performing unilateral strength exercises may allow greater training workloads and subsequently greater neuromuscular adaptations when compared with bilateral training. Hence, the purpose of this study was to compare neuromuscular adaptations with unilateral vs. bilateral training in the knee extensor muscles. Forty-three recreationally active young women were allocated to a control, unilateral (UG) or bilateral (BG) training group, which performed 2 times strength training sessions a week for 12 weeks. Knee extension one repetition maximum (1RM), maximal isometric strength, muscle electrical activity, and muscle thickness were obtained before and after the study period. Muscle strength was measured in unilateral (right + left) and bilateral tests. Both UG and BG increased similarly their unilateral 1RM (33.3 ± 14.3% vs. 24.6 ± 11.9%, respectively), bilateral 1RM (20.3 ± 6.8% vs. 28.5 ± 12.3%, respectively), and isometric strength (14.7 ± 11.3% vs. 13.1 ± 12.5%, respectively). The UG demonstrated greater unilateral isometric strength increase than the BG (21.4 ± 10.5% vs. 10.3 ± 11.1%, respectively) and only the UG increased muscle electrical activity. Muscle thickness increased similarly for both training groups. Neither group exhibited pretesting 1RM bilateral deficit values, but at post-testing, UG showed a significant bilateral deficit (-6.5 ± 7.8%) whereas BG showed a significant bilateral facilitation (5.9 ± 9.0%). Thus, performing unilateral or bilateral exercises was not a decisive factor for improving morphological adaptations and bilateral

  2. A Competency-Based Guided-Learning Algorithm Applied on Adaptively Guiding E-Learning

    ERIC Educational Resources Information Center

    Hsu, Wei-Chih; Li, Cheng-Hsiu

    2015-01-01

    This paper presents a new algorithm called competency-based guided-learning algorithm (CBGLA), which can be applied on adaptively guiding e-learning. Computational process analysis and mathematical derivation of competency-based learning (CBL) were used to develop the CBGLA. The proposed algorithm could generate an effective adaptively guiding…

  3. Physiological Adaptations to Resistance Training in Prepubertal Boys

    ERIC Educational Resources Information Center

    dos Santos Cunha, Giovani; Sant'anna, Marcelo Morganti; Cadore, Eduardo Lusa; de Oliveira, Norton Luis; dos Santos, Cinara Bos; Pinto, Ronei Silveira; Reischak-Oliveira, Alvaro

    2015-01-01

    Purpose: The purpose of this study was to investigate the physiological adaptations of resistance training (RT) in prepubertal boys. Methods: Eighteen healthy boys were divided into RT (n = 9, M[subscript age] = 10.4 ± 0.5 years) and control (CTR; n = 9, M[subscript age] = 10.9 ± 0.7 years) groups. The RT group underwent a resistance training…

  4. Specific Stimuli Induce Specific Adaptations: Sensorimotor Training vs. Reactive Balance Training

    PubMed Central

    Freyler, Kathrin; Krause, Anne; Gollhofer, Albert; Ritzmann, Ramona

    2016-01-01

    Typically, balance training has been used as an intervention paradigm either as static or as reactive balance training. Possible differences in functional outcomes between the two modalities have not been profoundly studied. The objective of the study was to investigate the specificity of neuromuscular adaptations in response to two balance intervention modalities within test and intervention paradigms containing characteristics of both profiles: classical sensorimotor training (SMT) referring to a static ledger pivoting around the ankle joint vs. reactive balance training (RBT) using externally applied perturbations to deteriorate body equilibrium. Thirty-eight subjects were assigned to either SMT or RBT. Before and after four weeks of intervention training, postural sway and electromyographic activities of shank and thigh muscles were recorded and co-contraction indices (CCI) were calculated. We argue that specificity of training interventions could be transferred into corresponding test settings containing properties of SMT and RBT, respectively. The results revealed that i) postural sway was reduced in both intervention groups in all test paradigms; magnitude of changes and effect sizes differed dependent on the paradigm: when training and paradigm coincided most, effects were augmented (P<0.05). ii) These specificities were accompanied by segmental modulations in the amount of CCI, with a greater reduction within the CCI of thigh muscles after RBT compared to the shank muscles after SMT (P<0.05). The results clearly indicate the relationship between test and intervention specificity in balance performance. Hence, specific training modalities of postural control cause multi-segmental and context-specific adaptations, depending upon the characteristics of the trained postural strategy. In relation to fall prevention, perturbation training could serve as an extension to SMT to include the proximal segment, and thus the control of structures near to the body

  5. Adaptation to physical training in rats orally supplemented with glycerol.

    PubMed

    Andrade, Eric Francelino; Lobato, Raquel Vieira; de Araújo, Ticiana Vasques; Orlando, Débora Ribeiro; Vicente da Costa, Diego; de Oliveira Silva, Víviam; Rogatto, Gustavo Puggina; Zangeronimo, Márcio Gilberto; Rosa, Priscila Vieira; Pereira, Luciano José

    2015-01-01

    We evaluated training adaptation and physical performance parameters in rats orally supplemented with glycerol, glucose, or saline, and submitted to moderate aerobic exercise. Thirty male rats were trained for 6 weeks and administered the supplements during the last 4 weeks of the experiment. Animals were distributed in a completely randomized factorial 2 × 3 design (with or without exercise and 3 substrates). Data were subjected to analysis of variance (ANOVA) and means were compared using the Student-Newmann-Keuls test at 5%. Among the trained animals, none of the substances caused differences in the percentages of protein, fat, or water content in the carcass. Compared with the sedentary animals, the trained animals supplemented with saline and glucose showed a higher protein percentage in the carcass. The relative mass of the heart and adrenal glands was higher in the trained animals. Glycerol improved the protein content in non-trained animals and increased the relative adrenal mass in both groups. Glycerol reduced the variation in levels of lactate and aspartate aminotransferase (AST) during the last exercise session. There was no difference between groups regarding the relative mass of the thymus and gastrocnemius or with the diameter of muscle fibers or the neutrophil-lymphocyte ratio. Supplementation with glycerol was efficient at attenuating variation in AST and lactate levels during exercise.

  6. Training-specific functional, neural, and hypertrophic adaptations to explosive- vs. sustained-contraction strength training.

    PubMed

    Balshaw, Thomas G; Massey, Garry J; Maden-Wilkinson, Thomas M; Tillin, Neale A; Folland, Jonathan P

    2016-06-01

    Training specificity is considered important for strength training, although the functional and underpinning physiological adaptations to different types of training, including brief explosive contractions, are poorly understood. This study compared the effects of 12 wk of explosive-contraction (ECT, n = 13) vs. sustained-contraction (SCT, n = 16) strength training vs. control (n = 14) on the functional, neural, hypertrophic, and intrinsic contractile characteristics of healthy young men. Training involved 40 isometric knee extension repetitions (3 times/wk): contracting as fast and hard as possible for ∼1 s (ECT) or gradually increasing to 75% of maximum voluntary torque (MVT) before holding for 3 s (SCT). Torque and electromyography during maximum and explosive contractions, torque during evoked octet contractions, and total quadriceps muscle volume (QUADSVOL) were quantified pre and post training. MVT increased more after SCT than ECT [23 vs. 17%; effect size (ES) = 0.69], with similar increases in neural drive, but greater QUADSVOL changes after SCT (8.1 vs. 2.6%; ES = 0.74). ECT improved explosive torque at all time points (17-34%; 0.54 ≤ ES ≤ 0.76) because of increased neural drive (17-28%), whereas only late-phase explosive torque (150 ms, 12%; ES = 1.48) and corresponding neural drive (18%) increased after SCT. Changes in evoked torque indicated slowing of the contractile properties of the muscle-tendon unit after both training interventions. These results showed training-specific functional changes that appeared to be due to distinct neural and hypertrophic adaptations. ECT produced a wider range of functional adaptations than SCT, and given the lesser demands of ECT, this type of training provides a highly efficient means of increasing function.

  7. Adaptively wavelet-based image denoising algorithm with edge preserving

    NASA Astrophysics Data System (ADS)

    Tan, Yihua; Tian, Jinwen; Liu, Jian

    2006-02-01

    A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge information. This method is adaptive to local image details, and can achieve better performance than the methods of state of the art.

  8. Algorithms and data structures for adaptive multigrid elliptic solvers

    NASA Technical Reports Server (NTRS)

    Vanrosendale, J.

    1983-01-01

    Adaptive refinement and the complicated data structures required to support it are discussed. These data structures must be carefully tuned, especially in three dimensions where the time and storage requirements of algorithms are crucial. Another major issue is grid generation. The options available seem to be curvilinear fitted grids, constructed on iterative graphics systems, and unfitted Cartesian grids, which can be constructed automatically. On several grounds, including storage requirements, the second option seems preferrable for the well behaved scalar elliptic problems considered here. A variety of techniques for treatment of boundary conditions on such grids are reviewed. A new approach, which may overcome some of the difficulties encountered with previous approaches, is also presented.

  9. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

    PubMed

    Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

    2016-12-19

    The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

  10. An adaptive /N-body algorithm of optimal order

    NASA Astrophysics Data System (ADS)

    Pruett, C. David; Rudmin, Joseph W.; Lacy, Justin M.

    2003-05-01

    Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. Test cases for both benign and chaotic N-body systems reveal that optimal order is dynamic. That is, in addition to dependency upon N and the desired accuracy, optimal order depends upon the configuration of the bodies at any instant.

  11. Cultural Adaptation of the Skills Training Model: Assertion Training with American Indians.

    ERIC Educational Resources Information Center

    LaFromboise, Teresa D.; Rowe, Wayne

    A skills training approach provides a conceptual framework from which human services can be provided for the personal and emotional needs of Indian people without the subtle, culturally erosive effect of traditional psychotherapy. Some 30 tribal groups and agencies participated in a cultural adaptation of an assertive coping-skills training…

  12. Design of infrasound-detection system via adaptive LMSTDE algorithm

    NASA Technical Reports Server (NTRS)

    Khalaf, C. S.; Stoughton, J. W.

    1984-01-01

    A proposed solution to an aviation safety problem is based on passive detection of turbulent weather phenomena through their infrasonic emission. This thesis describes a system design that is adequate for detection and bearing evaluation of infrasounds. An array of four sensors, with the appropriate hardware, is used for the detection part. Bearing evaluation is based on estimates of time delays between sensor outputs. The generalized cross correlation (GCC), as the conventional time-delay estimation (TDE) method, is first reviewed. An adaptive TDE approach, using the least mean square (LMS) algorithm, is then discussed. A comparison between the two techniques is made and the advantages of the adaptive approach are listed. The behavior of the GCC, as a Roth processor, is examined for the anticipated signals. It is shown that the Roth processor has the desired effect of sharpening the peak of the correlation function. It is also shown that the LMSTDE technique is an equivalent implementation of the Roth processor in the time domain. A LMSTDE lead-lag model, with a variable stability coefficient and a convergence criterion, is designed.

  13. The Adaptive Analysis of Visual Cognition using Genetic Algorithms

    PubMed Central

    Cook, Robert G.; Qadri, Muhammad A. J.

    2014-01-01

    Two experiments used a novel, open-ended, and adaptive test procedure to examine visual cognition in animals. Using a genetic algorithm, a pigeon was tested repeatedly from a variety of different initial conditions for its solution to an intermediate brightness search task. On each trial, the animal had to accurately locate and peck a target element of intermediate brightness from among a variable number of surrounding darker and lighter distractor elements. Displays were generated from six parametric variables, or genes (distractor number, element size, shape, spacing, target brightness, distractor brightness). Display composition changed over time, or evolved, as a function of the bird’s differential accuracy within the population of values for each gene. Testing three randomized initial conditions and one set of controlled initial conditions, element size and number of distractors were identified as the most important factors controlling search accuracy, with distractor brightness, element shape, and spacing making secondary contributions. The resulting changes in this multidimensional stimulus space suggested the existence of a set of conditions that the bird repeatedly converged upon regardless of initial conditions. This psychological “attractor” represents the cumulative action of the cognitive operations used by the pigeon in solving and performing this search task. The results are discussed regarding their implications for visual cognition in pigeons and the usefulness of adaptive, subject-driven experimentation for investigating human and animal cognition more generally. PMID:24000905

  14. Designing Training for Temporal and Adaptive Transfer: A Comparative Evaluation of Three Training Methods for Process Control Tasks

    ERIC Educational Resources Information Center

    Kluge, Annette; Sauer, Juergen; Burkolter, Dina; Ritzmann, Sandrina

    2010-01-01

    Training in process control environments requires operators to be prepared for temporal and adaptive transfer of skill. Three training methods were compared with regard to their effectiveness in supporting transfer: Drill & Practice (D&P), Error Training (ET), and procedure-based and error heuristics training (PHT). Communication…

  15. Behavioral training promotes multiple adaptive processes following acute hearing loss

    PubMed Central

    Keating, Peter; Rosenior-Patten, Onayomi; Dahmen, Johannes C; Bell, Olivia; King, Andrew J

    2016-01-01

    The brain possesses a remarkable capacity to compensate for changes in inputs resulting from a range of sensory impairments. Developmental studies of sound localization have shown that adaptation to asymmetric hearing loss can be achieved either by reinterpreting altered spatial cues or by relying more on those cues that remain intact. Adaptation to monaural deprivation in adulthood is also possible, but appears to lack such flexibility. Here we show, however, that appropriate behavioral training enables monaurally-deprived adult humans to exploit both of these adaptive processes. Moreover, cortical recordings in ferrets reared with asymmetric hearing loss suggest that these forms of plasticity have distinct neural substrates. An ability to adapt to asymmetric hearing loss using multiple adaptive processes is therefore shared by different species and may persist throughout the lifespan. This highlights the fundamental flexibility of neural systems, and may also point toward novel therapeutic strategies for treating sensory disorders. DOI: http://dx.doi.org/10.7554/eLife.12264.001 PMID:27008181

  16. Effect of Vitamin D Supplementation on Training Adaptation in Well-Trained Soccer Players.

    PubMed

    Jastrzębska, Maria; Kaczmarczyk, Mariusz; Jastrzębski, Zbigniew

    2016-09-01

    Jastrzębska, M, Kaczmarczyk, M, and Jastrzębski, Z. Effect of vitamin D supplementation on training adaptation in well-trained soccer players. J Strength Cond Res 30(9): 2648-2655, 2016-There is growing body of evidence implying that vitamin D may be associated with athletic performance, however, studies examining the effects of vitamin D on athletic performance are inconsistent. Moreover, very little literature exists about the vitamin D and training efficiency or adaptation, especially in high-level, well-trained athletes. The purpose of the current study was to investigate the effect of vitamin D supplementation on training adaptation in well-trained football players. The subjects were divided into 2 groups: the placebo group (PG) and the experimental group (SG, supplemented with vitamin D, 5,000 IU per day). Both groups were subjected to High Intensity Interval Training Program. The selection to the groups was based on peak power results attained before the experiment and position on the field. Blood samples for vitamin D level were taken from the players. In addition, total work, 5, 10, 20, and 30 m running speed, squat jump, and countermovement jump height were determined. There were no significant differences between SG and PG groups for any power-related characteristics at baseline. All power-related variables, except the 30 m sprint running time, improved significantly in response to interval training. However, the mean change scores (the differences between posttraining and pretraining values) did not differ significantly between SG and PG groups. In conclusion, an 8-week vitamin D supplementation in highly trained football players was not beneficial in terms of response to High Intensity Interval Training. Given the current level of evidence, the recommendation to use vitamin D supplements in all athletes to improve performance or training gains would be premature. To avoid a seasonal decrease in 25(OH)D level or to obtain optimal vitamin D levels, the

  17. Robust adaptive cruise control of high speed trains.

    PubMed

    Faieghi, Mohammadreza; Jalali, Aliakbar; Mashhadi, Seyed Kamal-e-ddin Mousavi

    2014-03-01

    The cruise control problem of high speed trains in the presence of unknown parameters and external disturbances is considered. In particular a Lyapunov-based robust adaptive controller is presented to achieve asymptotic tracking and disturbance rejection. The system under consideration is nonlinear, MIMO and non-minimum phase. To deal with the limitations arising from the unstable zero-dynamics we do an output redefinition such that the zero-dynamics with respect to new outputs becomes stable. Rigorous stability analyses are presented which establish the boundedness of all the internal states and simultaneously asymptotic stability of the tracking error dynamics. The results are presented for two common configurations of high speed trains, i.e. the DD and PPD designs, based on the multi-body model and are verified by several numerical simulations.

  18. Exercise training - Blood pressure responses in subjects adapted to microgravity

    NASA Technical Reports Server (NTRS)

    Convertino, Victor A.

    1991-01-01

    Conventional endurance exercise training that involves daily workouts of 1-2 hr duration during exposure to microgravity has not proven completely effective in ameliorating postexposure orthostatic hypotension. Single bouts of intense exercise have been shown to increase plasma volume and baroreflex sensitivity in ambulatory subjects through 24 hr postexercise and to reverse decrements in maximal oxygen uptake and syncopal episodes following exposure to simulated microgravity. These physiological adaptations to acute intense exercise were opposite to those observed following exposure to microgravity. These results suggest that the 'exercise training' stimulus used to prevent orthostatic hypotension induced by microgravity may be specific and should be redefined to include single bouts of maximal exercise which may provide an acute effective countermeasure against postflight hypotension.

  19. An Adaptive Mesh Algorithm: Mesh Structure and Generation

    SciTech Connect

    Scannapieco, Anthony J.

    2016-06-21

    The purpose of Adaptive Mesh Refinement is to minimize spatial errors over the computational space not to minimize the number of computational elements. The additional result of the technique is that it may reduce the number of computational elements needed to retain a given level of spatial accuracy. Adaptive mesh refinement is a computational technique used to dynamically select, over a region of space, a set of computational elements designed to minimize spatial error in the computational model of a physical process. The fundamental idea is to increase the mesh resolution in regions where the physical variables are represented by a broad spectrum of modes in k-space, hence increasing the effective global spectral coverage of those physical variables. In addition, the selection of the spatially distributed elements is done dynamically by cyclically adjusting the mesh to follow the spectral evolution of the system. Over the years three types of AMR schemes have evolved; block, patch and locally refined AMR. In block and patch AMR logical blocks of various grid sizes are overlaid to span the physical space of interest, whereas in locally refined AMR no logical blocks are employed but locally nested mesh levels are used to span the physical space. The distinction between block and patch AMR is that in block AMR the original blocks refine and coarsen entirely in time, whereas in patch AMR the patches change location and zone size with time. The type of AMR described herein is a locally refi ned AMR. In the algorithm described, at any point in physical space only one zone exists at whatever level of mesh that is appropriate for that physical location. The dynamic creation of a locally refi ned computational mesh is made practical by a judicious selection of mesh rules. With these rules the mesh is evolved via a mesh potential designed to concentrate the nest mesh in regions where the physics is modally dense, and coarsen zones in regions where the physics is modally

  20. Protein Secondary Structure Prediction Using Local Adaptive Techniques in Training Neural Networks

    NASA Astrophysics Data System (ADS)

    Aik, Lim Eng; Zainuddin, Zarita; Joseph, Annie

    2008-01-01

    One of the most significant problems in computer molecular biology today is how to predict a protein's three-dimensional structure from its one-dimensional amino acid sequence or generally call the protein folding problem and difficult to determine the corresponding protein functions. Thus, this paper involves protein secondary structure prediction using neural network in order to solve the protein folding problem. The neural network used for protein secondary structure prediction is multilayer perceptron (MLP) of the feed-forward variety. The training set are taken from the protein data bank which are 120 proteins while 60 testing set is the proteins which were chosen randomly from the protein data bank. Multiple sequence alignment (MSA) is used to get the protein similar sequence and Position Specific Scoring matrix (PSSM) is used for network input. The training process of the neural network involves local adaptive techniques. Local adaptive techniques used in this paper comprises Learning rate by sign changes, SuperSAB, Quickprop and RPROP. From the simulation, the performance for learning rate by Rprop and Quickprop are superior to all other algorithms with respect to the convergence time. However, the best result was obtained using Rprop algorithm.

  1. Evaluating Knowledge Structure-Based Adaptive Testing Algorithms and System Development

    ERIC Educational Resources Information Center

    Wu, Huey-Min; Kuo, Bor-Chen; Yang, Jinn-Min

    2012-01-01

    In recent years, many computerized test systems have been developed for diagnosing students' learning profiles. Nevertheless, it remains a challenging issue to find an adaptive testing algorithm to both shorten testing time and precisely diagnose the knowledge status of students. In order to find a suitable algorithm, four adaptive testing…

  2. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  3. Fitting of adaptive neuron model to electrophysiological recordings using particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Shan, Bonan; Wang, Jiang; Zhang, Lvxia; Deng, Bin; Wei, Xile

    2017-02-01

    In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multi-timescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.

  4. Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails

    PubMed Central

    2012-01-01

    Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications. PMID:23134742

  5. Multi-element array signal reconstruction with adaptive least-squares algorithms

    NASA Technical Reports Server (NTRS)

    Kumar, R.

    1992-01-01

    Two versions of the adaptive least-squares algorithm are presented for combining signals from multiple feeds placed in the focal plane of a mechanical antenna whose reflector surface is distorted due to various deformations. Coherent signal combining techniques based on the adaptive least-squares algorithm are examined for nearly optimally and adaptively combining the outputs of the feeds. The performance of the two versions is evaluated by simulations. It is demonstrated for the example considered that both of the adaptive least-squares algorithms are capable of offsetting most of the loss in the antenna gain incurred due to reflector surface deformations.

  6. Sparse diffraction imaging method using an adaptive reweighting homotopy algorithm

    NASA Astrophysics Data System (ADS)

    Yu, Caixia; Zhao, Jingtao; Wang, Yanfei; Qiu, Zhen

    2017-02-01

    Seismic diffractions carry valuable information from subsurface small-scale geologic discontinuities, such as faults, cavities and other features associated with hydrocarbon reservoirs. However, seismic imaging methods mainly use reflection theory for constructing imaging models, which means a smooth constraint on imaging conditions. In fact, diffractors occupy a small account of distributions in an imaging model and possess discontinuous characteristics. In mathematics, this kind of phenomena can be described by the sparse optimization theory. Therefore, we propose a diffraction imaging method based on a sparsity-constraint model for studying diffractors. A reweighted L 2-norm and L 1-norm minimization model is investigated, where the L 2 term requests a least-square error between modeled diffractions and observed diffractions and the L 1 term imposes sparsity on the solution. In order to efficiently solve this model, we use an adaptive reweighting homotopy algorithm that updates the solutions by tracking a path along inexpensive homotopy steps. Numerical examples and field data application demonstrate the feasibility of the proposed method and show its significance for detecting small-scale discontinuities in a seismic section. The proposed method has an advantage in improving the focusing ability of diffractions and reducing the migration artifacts.

  7. Adaptive Computer-Assisted Mammography Training for Improved Breast Cancer Screening

    DTIC Science & Technology

    2012-10-01

    11-1-0755 TITLE: Adaptive Computer-Assisted Mammography Training for Improved Breast Cancer Screening PRINCIPAL INVESTIGATOR: Maciej...AND SUBTITLE 5a. CONTRACT NUMBER Adaptive Computer-Assisted Mammography Training for Improved Breast Cancer Screening 5b. GRANT...propose to research the methodology for constructing adaptive computer-aided education systems for mammography . Improved mammography education could

  8. Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm.

    PubMed

    Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua

    2014-03-01

    This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

  9. Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

    NASA Astrophysics Data System (ADS)

    Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua

    2014-03-01

    This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

  10. Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

    SciTech Connect

    Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao

    2014-03-15

    This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

  11. A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study.

    PubMed

    Wu, Jingheng; Mei, Juan; Wen, Sixiang; Liao, Siyan; Chen, Jincan; Shen, Yong

    2010-07-30

    Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

  12. A new adaptive videogame for training attention and executive functions: design principles and initial validation.

    PubMed

    Montani, Veronica; De Filippo De Grazia, Michele; Zorzi, Marco

    2014-01-01

    A growing body of evidence suggests that action videogames could enhance a variety of cognitive skills and more specifically attention skills. The aim of this study was to develop a novel adaptive videogame to support the rehabilitation of the most common consequences of traumatic brain injury (TBI), that is the impairment of attention and executive functions. TBI patients can be affected by psychomotor slowness and by difficulties in dealing with distraction, maintain a cognitive set for a long time, processing different simultaneously presented stimuli, and planning purposeful behavior. Accordingly, we designed a videogame that was specifically conceived to activate those functions. Playing involves visuospatial planning and selective attention, active maintenance of the cognitive set representing the goal, and error monitoring. Moreover, different game trials require to alternate between two tasks (i.e., task switching) or to perform the two tasks simultaneously (i.e., divided attention/dual-tasking). The videogame is controlled by a multidimensional adaptive algorithm that calibrates task difficulty on-line based on a model of user performance that is updated on a trial-by-trial basis. We report simulations of user performance designed to test the adaptive game as well as a validation study with healthy participants engaged in a training protocol. The results confirmed the involvement of the cognitive abilities that the game is supposed to enhance and suggested that training improved attentional control during play.

  13. Adapting the Training Site to Training Needs. Self-Paced Instructional Module. Module Number VII-A.

    ERIC Educational Resources Information Center

    King, Sylvester; Brooks, Kent

    One of 33 self-paced instructional modules for training industry services leaders to provide guidance in the performance of manpower services by public agencies to new and expanding private industry, this module contains three sequential learning activities on adapting the training site to training needs. The first learning activity is designed to…

  14. Measures of complexity in neural spike-trains of the slowly adapting stretch receptor organs.

    PubMed

    Jiménez-Montaño, M A; Penagos, H; Hernández Torres, A; Diez-Martínez, O

    2000-01-01

    Discrete sequence analysis methods were applied to study spike-trains generated by the isolated neuron of the slowly adapting stretch receptor organ. Calculation of the algorithmic complexity and block entropies of digitized individual spike-train forms allowed us to distinguish different classes of neural behavior. While some spike-trains exhibited significant structure, others displayed diverse degrees of randomness. The sequences recorded during the stimulated portions of the intermittent and walk-through forms, differed considerably from their randomly shuffled surrogates. Informational and grammar complexity measures (in two, four and eight-letter alphabets), tell us things about the structure of spike-trains that are not obtained with conventional spike analysis. Comparison of the conditional entropies for the digitized signals showed that the method distinguishes between different stimulated conditions. Additionally, comparison of the different stimulated conditions with their corresponding surrogates showed that, both, conditional entropies and complexities were significantly different for the two groups. Although the original and the randomly shuffled sequences had the same distribution and average firing rate, their complexity values were different. The results obtained with both measures of sequence structure were quite consistent.

  15. Influence of Resistance Training Frequency on Muscular Adaptations in Well-Trained Men.

    PubMed

    Schoenfeld, Brad J; Ratamess, Nicholas A; Peterson, Mark D; Contreras, Bret; Tiryaki-Sonmez, Gul

    2015-07-01

    The purpose of this study was to investigate the effects of training muscle groups 1 day per week using a split-body routine (SPLIT) vs. 3 days per week using a total-body routine (TOTAL) on muscular adaptations in well-trained men. Subjects were 20 male volunteers (height = 1.76 ± 0.05 m; body mass = 78.0 ± 10.7 kg; age = 23.5 ± 2.9 years) recruited from a university population. Participants were pair matched according to baseline strength and then randomly assigned to 1 of the 2 experimental groups: a SPLIT, where multiple exercises were performed for a specific muscle group in a session with 2-3 muscle groups trained per session (n = 10) or a TOTAL, where 1 exercise was performed per muscle group in a session with all muscle groups trained in each session (n = 10). Subjects were tested pre- and poststudy for 1 repetition maximum strength in the bench press and squat, and muscle thickness (MT) of forearm flexors, forearm extensors, and vastus lateralis. Results showed significantly greater increases in forearm flexor MT for TOTAL compared with SPLIT. No significant differences were noted in maximal strength measures. The findings suggest a potentially superior hypertrophic benefit to higher weekly resistance training frequencies.

  16. Adaptive Estimation and Parameter Identification Using Multiple Model Estimation Algorithm

    DTIC Science & Technology

    1976-06-23

    Point Continuous Linear Smoothing ," Proc. Joint Automatic Control Conf., June 1967, pp. 249-257. [26] J. S. Meditch , "On Optimal Linear Smoothing ...Theory," Infor- mation and Control, 10, 598-615 (1967). [27] J. S. Meditch , "A Successive Approximation Procedure for Nonlinear Data Smoothing ," Proc...algorithm Kalman filter algorithms multiple model smoothing algorithm 70. ABSTRACT (Coensnia• en rever.e side if eceossuy Adidonilty by block nu.wbe

  17. Is cognitive adaptation training (CAT) compensatory, restorative, or both?

    PubMed

    Fredrick, Megan M; Mintz, Jim; Roberts, David L; Maples, Natalie J; Sarkar, Sonali; Li, Xueying; Velligan, Dawn I

    2015-08-01

    Cognitive adaptation training (CAT) is a psychosocial treatment incorporating environmental supports including signs, checklists to bypass the cognitive deficits of schizophrenia. Our objective was to examine the association between CAT, functional outcomes, and cognitive test performance (cognition). The two research questions were as follows: 1) Does cognition mediate the effect of CAT intervention on functional outcome? 2) Does CAT impact cognitive test performance? A total of 120 participants with schizophrenia were randomized to one of three treatments: 1) CAT (weekly for 9months; monthly thereafter), 2) generic environmental supports (given to participants on clinic visits to promote adaptive behavior), or 3) treatment as usual (TAU). Assessments of cognition and functional outcome were conducted at baseline, 9 and 24months. Mediation analyses and mixed effects regression were conducted. Mediation analyses revealed that during the initial 9months, the direct path from treatment group to functional outcome on the primary measure was positive and highly significant. CAT significantly improved functional outcome compared to the other treatments. However, paths involving cognition were negligible. There was no evidence that cognition mediated improvement in functional outcomes. At 24months, cognition improved more in CAT compared to other treatment groups. The test for cognition mediating improvement in functional outcomes was not significant at this time point. However, improvement in functional outcome led to better performance on cognitive testing. We concluded that improvement in cognition is not a necessary condition for improvement in functional outcome and that greater engagement in functional behavior has a positive impact on cognition.

  18. On the use of harmony search algorithm in the training of wavelet neural networks

    NASA Astrophysics Data System (ADS)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2015-10-01

    Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.

  19. Experiences with an adaptive mesh refinement algorithm in numerical relativity.

    NASA Astrophysics Data System (ADS)

    Choptuik, M. W.

    An implementation of the Berger/Oliger mesh refinement algorithm for a model problem in numerical relativity is described. The principles of operation of the method are reviewed and its use in conjunction with leap-frog schemes is considered. The performance of the algorithm is illustrated with results from a study of the Einstein/massless scalar field equations in spherical symmetry.

  20. A novel algorithm for real-time adaptive signal detection and identification

    SciTech Connect

    Sleefe, G.E.; Ladd, M.D.; Gallegos, D.E.; Sicking, C.W.; Erteza, I.A.

    1998-04-01

    This paper describes a novel digital signal processing algorithm for adaptively detecting and identifying signals buried in noise. The algorithm continually computes and updates the long-term statistics and spectral characteristics of the background noise. Using this noise model, a set of adaptive thresholds and matched digital filters are implemented to enhance and detect signals that are buried in the noise. The algorithm furthermore automatically suppresses coherent noise sources and adapts to time-varying signal conditions. Signal detection is performed in both the time-domain and the frequency-domain, thereby permitting the detection of both broad-band transients and narrow-band signals. The detection algorithm also provides for the computation of important signal features such as amplitude, timing, and phase information. Signal identification is achieved through a combination of frequency-domain template matching and spectral peak picking. The algorithm described herein is well suited for real-time implementation on digital signal processing hardware. This paper presents the theory of the adaptive algorithm, provides an algorithmic block diagram, and demonstrate its implementation and performance with real-world data. The computational efficiency of the algorithm is demonstrated through benchmarks on specific DSP hardware. The applications for this algorithm, which range from vibration analysis to real-time image processing, are also discussed.

  1. Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging

    NASA Astrophysics Data System (ADS)

    Apolinar Muñoz Rodríguez, J.; Mejía Alanís, Francisco Carlos

    2016-07-01

    An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.

  2. GENNET-Toolbox: An Evolving Genetic Algorithm for Neural Network Training

    NASA Astrophysics Data System (ADS)

    Gómez-Garay, Vicente; Irigoyen, Eloy; Artaza, Fernando

    Genetic Algorithms have been used from 1989 for both Neural Network training and design. Nevertheless, the use of a Genetic Algorithm for adjusting the Neural Network parameters can still be engaging. This work presents the study and validation of a different approach to this matter by introducing a Genetic Algorithm designed for Neural Network training. This algorithm features a mutation operator capable of working on three levels (network, neuron and layer) and with the mutation parameters encoded and evolving within each individual. We also explore the use of three types of hybridization: post-training, Lamarckian and Baldwinian. These proposes in combination with the algorithm, show for a fast and powerful tool for Neural Network training.

  3. A Context-Adaptive Teacher Training Model in a Ubiquitous Learning Environment

    ERIC Educational Resources Information Center

    Chen, Min; Chiang, Feng Kuang; Jiang, Ya Na; Yu, Sheng Quan

    2017-01-01

    In view of the discrepancies in teacher training and teaching practice, this paper put forward a context-adaptive teacher training model in a ubiquitous learning (u-learning) environment. The innovative model provides teachers of different subjects with adaptive and personalized learning content in a u-learning environment, implements intra- and…

  4. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces.

    PubMed

    Dangi, Siddharth; Orsborn, Amy L; Moorman, Helene G; Carmena, Jose M

    2013-07-01

    Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performance. In this article, we present a general framework for the design and analysis of CLDA algorithms and support our results with experimental data of two monkeys performing a BMI task. First, we analyze and compare existing CLDA algorithms to highlight the importance of four critical design elements: the adaptation timescale, selective parameter adaptation, smooth decoder updates, and intuitive CLDA parameters. Second, we introduce mathematical convergence analysis using measures such as mean-squared error and KL divergence as a useful paradigm for evaluating the convergence properties of a prototype CLDA algorithm before experimental testing. By applying these measures to an existing CLDA algorithm, we demonstrate that our convergence analysis is an effective analytical tool that can ultimately inform and improve the design of CLDA algorithms.

  5. Adaptive Load-Balancing Algorithms using Symmetric Broadcast Networks

    NASA Technical Reports Server (NTRS)

    Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    In a distributed computing environment, it is important to ensure that the processor workloads are adequately balanced, Among numerous load-balancing algorithms, a unique approach due to Das and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three efficient SBN-based dynamic load-balancing algorithms, and implement them on an SGI Origin2000. A thorough experimental study with Poisson distributed synthetic loads demonstrates that our algorithms are effective in balancing system load. By optimizing completion time and idle time, the proposed algorithms are shown to compare favorably with several existing approaches.

  6. Comparison of several stochastic parallel optimization algorithms for adaptive optics system without a wavefront sensor

    NASA Astrophysics Data System (ADS)

    Yang, Huizhen; Li, Xinyang

    2011-04-01

    Optimizing the system performance metric directly is an important method for correcting wavefront aberrations in an adaptive optics (AO) system where wavefront sensing methods are unavailable or ineffective. An appropriate "Deformable Mirror" control algorithm is the key to successful wavefront correction. Based on several stochastic parallel optimization control algorithms, an adaptive optics system with a 61-element Deformable Mirror (DM) is simulated. Genetic Algorithm (GA), Stochastic Parallel Gradient Descent (SPGD), Simulated Annealing (SA) and Algorithm Of Pattern Extraction (Alopex) are compared in convergence speed and correction capability. The results show that all these algorithms have the ability to correct for atmospheric turbulence. Compared with least squares fitting, they almost obtain the best correction achievable for the 61-element DM. SA is the fastest and GA is the slowest in these algorithms. The number of perturbation by GA is almost 20 times larger than that of SA, 15 times larger than SPGD and 9 times larger than Alopex.

  7. A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

    PubMed Central

    Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

    2009-01-01

    Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

  8. Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm.

    PubMed

    Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

    2016-01-01

    Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.

  9. Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm

    PubMed Central

    Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

    2016-01-01

    Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938

  10. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

    PubMed Central

    Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

    2016-01-01

    The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

  11. SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM. (R827028)

    EPA Science Inventory

    A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme––the piecewise parabolic method (PPM)––for computing advective solution fields; a weight function capable o...

  12. SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM

    EPA Science Inventory

    A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme?the piecewise parabolic method (PPM)?for computing advective solution fields; a weight function capable of promoting grid node clustering ...

  13. Interactions between exposure to hypoxia and the training-induced autonomic adaptations in a "live high-train low" session.

    PubMed

    Cornolo, Jérémy; Fouillot, Jean-Pierre; Schmitt, Laurent; Povea, Camillo; Robach, Paul; Richalet, Jean-Paul

    2006-03-01

    The autonomic and cardiovascular adaptations to hypoxia are opposite to those resulting from aerobic training. We investigated (1) whether exposure to hypoxia in a live high-train low (LHTL) session limits the autonomic and cardiovascular adaptations to training, and (2) whether such interactions remain 15 days after the end of the LHTL. Eighteen national swimmers trained for 13 days at 1,200 m, living (16 h day(-1)) either at 1,200 m (live low-train low, LLTL) or at a simulated height of 2,500-3,000 m (LHTL). Subjects were investigated at 1,200 m before and at the end of the training session, and after the following 15 days of sea-level training. Cardiovascular parameters and the autonomic control assessed by spectral analysis of R-R and diastolic blood pressure (DBP) variability were obtained in the resting supine position and in response to an orthostatic test. At the end of the 13-day training, resting heart rate (HR) and sympathetic modulation on heart decreased in LLTL (-10.1% and -25.4%, P<0.01, respectively) but not in LHTL (-5.8, -15.5%, respectively). Total peripheral resistance (TPR) and DBP became higher in LHTL than in LLTL (P<0.05). Stroke index decreased in both groups during the tilt test, counteracted by an increase in HR and sympathetic modulation to the heart and vasculature, and a decrease in vagal modulation to the heart. After the following 15-day sea-level training, differences in TPR and DBP between groups disappeared. During an LHTL session, adaptations to hypoxia interacted with the autonomic and cardiovascular adaptations to training. However, these interactions did not limit the adaptations to the following sea-level training.

  14. Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms

    DTIC Science & Technology

    2004-08-06

    identification. Figure 1 shows a very basic example of this type of system . x(n) Figure 1. Basic system identification using adaptive filters block diagram...block diagram of adaptive wavelet filtering system . The main objective of the system shown in Figure 2 is to minimize the error signal, e(k), which is...in Table 1. Daub4 wavelets use filter banks (Vaidyanathan 1992) containing exactly four elements. 5 Figure 4. Time-Domain Representation of

  15. Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

    PubMed

    Mei, Gang; Xu, Nengxiong; Xu, Liangliang

    2016-01-01

    This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

  16. Software Technology for Adaptable, Reliable Systems (STARS) Program: SPMS Training Class: Student Handout. Addendum

    DTIC Science & Technology

    2015-04-02

    Sequence 03705-001B 30 September 1991 (3 SOFTWARE TECHNOLOGY FOR ADAPTABLE, RELIABLE SYSTEMS ( STARS ) PROGRAM ELEGTE m AUG15 1991 Ö I 0 SPMS Training...September 1991 SOFTWARE TECHNOLOGY FOR ADAPTABLE, RELIABLE SYSTEMS ( STARS ) PROGRAM SPMS Training Class: Student Handout Addendum to: Software...document is the student handout prepared for the "SEI/ STARS P.3 Asset Acquisition Sub-task" training class. The student handout covers basic aspects

  17. Exercise training-induced gender-specific heat shock protein adaptations in human skeletal muscle.

    PubMed

    Morton, James P; Holloway, Kathryn; Woods, Paul; Cable, Nigel T; Burniston, Jatin; Evans, Louise; Kayani, Anna C; McArdle, Anne

    2009-02-01

    This study investigates the effects of short-term endurance training on heat shock protein (HSP) adaptations of male and female human skeletal muscle. The data demonstrate that females did not respond to continuous or interval training in terms of increasing HSP content of the vastus lateralis muscle. In contrast, males displayed HSP adaptations to both training interventions. These data provide a platform for future human studies to examine a potential gender-specific stress response to exercise.

  18. An Adaptive Data Collection Algorithm Based on a Bayesian Compressed Sensing Framework

    PubMed Central

    Liu, Zhi; Zhang, Mengmeng; Cui, Jian

    2014-01-01

    For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introducing new target node selection metrics, embedding the routing structure and maximizing the differential entropy for each collection round, an adaptive projection vector is constructed. Simulations show that compared to reference algorithms, the proposed algorithm can decrease computation complexity and improve energy efficiency. PMID:24818659

  19. Personalised Adaptive Task Selection in Air Traffic Control: Effects on Training Efficiency and Transfer

    ERIC Educational Resources Information Center

    Salden, Ron J. C. M.; Paas, Fred; van Merrienboer, Jeroen J. G.

    2006-01-01

    The differential effects of four task selection methods on training efficiency and transfer in a computer-based training for Air Traffic Control were investigated. Two personalised conditions were compared with two corresponding yoked control conditions. The hypothesis that personalised adaptive task selection leads to more efficient training than…

  20. Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique

    PubMed Central

    Darzi, Soodabeh; Islam, Mohammad Tariqul; Tiong, Sieh Kiong; Kibria, Salehin; Singh, Mandeep

    2015-01-01

    In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA. PMID:26552032

  1. Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Koromyslova, A.; Semenkina, M.; Sergienko, R.

    2017-02-01

    The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the feature selection based on self-adaptive GA is considered. k-NN, linear SVM and ANN were used as classification algorithms. The tasks of the research are the following: perform research of text classification for natural language call routing with different term weighting methods and classification algorithms and investigate the feature selection method based on self-adaptive GA. The numerical results showed that the most effective term weighting is TRR. The most effective classification algorithm is ANN. Feature selection with self-adaptive GA provides improvement of classification effectiveness and significant dimensionality reduction with all term weighting methods and with all classification algorithms.

  2. Study on adaptive PID algorithm of hydraulic turbine governing system based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Tang, Liangbao; Bao, Jumin

    2006-11-01

    The conventional hydraulic turbine governing system can't automatically modulate PID parameters according to the dynamic process of the system, the generator speed is unstable and the mains frequency fluctuation results in. To solve the above problem, the fuzzy neural network (FNN) and the adaptive control are combined to design an adaptive PID algorithm based on the fuzzy neural network which can effectively control the hydraulic turbine governing system. Finally, the improved mathematic model is simulated. The simulation results are compared with the conventional hydraulic turbine's. Thus the validity and superiority of the fuzzy neural network PID algorithm have been proved. The simulation results show that the algorithm not only retains the functions of fuzzy control, but also provides the ability to approach to the non-linear system. Also the dynamic process of the system can be reflected more precisely and the on-line adaptive control is implemented. The algorithm is superior to other methods in response and control effect.

  3. An Adaptive Inpainting Algorithm Based on DCT Induced Wavelet Regularization

    DTIC Science & Technology

    2013-01-01

    differentiable and its gradient is Lipschitz continuous. This property is particularly important in developing a fast and efficient numerical algorithm for...with Lipschitz continuous gra- dient L(ψ), i.e., ∥∇ψ(f1) − ∇ψ(f2)∥2 ≤ L(ψ)∥f1 − f2∥2 for every f1, f2 ∈ Rn. The corresponding APG algorithm proposed in...entries are uniformly distributed on the interval [0, 255]; 2) Take u1 = f0 and L = L(ψ) as a Lipschitz constant of ∇ψ; 3) For k = 1, 2, . . ., compute a

  4. Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

    NASA Astrophysics Data System (ADS)

    Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

    2015-12-01

    In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

  5. Adaptive Personalized Training Games for Individual and Collaborative Rehabilitation of People with Multiple Sclerosis

    PubMed Central

    2014-01-01

    Any rehabilitation involves people who are unique individuals with their own characteristics and rehabilitation needs, including patients suffering from Multiple Sclerosis (MS). The prominent variation of MS symptoms and the disease severity elevate a need to accommodate the patient diversity and support adaptive personalized training to meet every patient's rehabilitation needs. In this paper, we focus on integrating adaptivity and personalization in rehabilitation training for MS patients. We introduced the automatic adjustment of difficulty levels as an adaptation that can be provided in individual and collaborative rehabilitation training exercises for MS patients. Two user studies have been carried out with nine MS patients to investigate the outcome of this adaptation. The findings showed that adaptive personalized training trajectories have been successfully provided to MS patients according to their individual training progress, which was appreciated by the patients and the therapist. They considered the automatic adjustment of difficulty levels to provide more variety in the training and to minimize the therapists involvement in setting up the training. With regard to social interaction in the collaborative training exercise, we have observed some social behaviors between the patients and their training partner which indicated the development of social interaction during the training. PMID:24982862

  6. Adaptive personalized training games for individual and collaborative rehabilitation of people with multiple sclerosis.

    PubMed

    Octavia, Johanna Renny; Coninx, Karin

    2014-01-01

    Any rehabilitation involves people who are unique individuals with their own characteristics and rehabilitation needs, including patients suffering from Multiple Sclerosis (MS). The prominent variation of MS symptoms and the disease severity elevate a need to accommodate the patient diversity and support adaptive personalized training to meet every patient's rehabilitation needs. In this paper, we focus on integrating adaptivity and personalization in rehabilitation training for MS patients. We introduced the automatic adjustment of difficulty levels as an adaptation that can be provided in individual and collaborative rehabilitation training exercises for MS patients. Two user studies have been carried out with nine MS patients to investigate the outcome of this adaptation. The findings showed that adaptive personalized training trajectories have been successfully provided to MS patients according to their individual training progress, which was appreciated by the patients and the therapist. They considered the automatic adjustment of difficulty levels to provide more variety in the training and to minimize the therapists involvement in setting up the training. With regard to social interaction in the collaborative training exercise, we have observed some social behaviors between the patients and their training partner which indicated the development of social interaction during the training.

  7. Time-sequenced adaptive filtering using a modified P-vector algorithm

    NASA Astrophysics Data System (ADS)

    Williams, Robert L.

    1996-10-01

    An adaptive algorithm and two stage filter structure were developed for adaptive filtering of certain classes of signals that exhibit cyclostationary characteristics. The new modified P-vector algorithm (mPa) eliminates the need for a separate desired signal which is typically required by conventional adaptive algorithms. It is then implemented in a time-sequenced manner to counteract the nonstationary characteristics typically found in certain radar and bioelectromagnetic signals. Initial algorithm testing is performed on evoked responses generated by the visual cortex of the human brain with the objective, ultimately, to transition the results to radar signals. Each sample of the evoked response is modeled as the sum of three uncorrelated signal components, a time-varying mean (M), a noise component (N), and a random jitter component (Q). A two stage single channel time-sequenced adaptive filter structure was developed which improves convergence characteristics by de coupling the time-varying mean component from the `Q' and noise components in the first stage. The EEG statistics must be known a priori and are adaptively estimated from the pre stimulus data. The performance of the two stage mPa time-sequenced adaptive filter approaches the performance for the ideal case of an adaptive filter having a noiseless desired response.

  8. apGA: An adaptive parallel genetic algorithm

    SciTech Connect

    Liepins, G.E. ); Baluja, S. )

    1991-01-01

    We develop apGA, a parallel variant of the standard generational GA, that combines aggressive search with perpetual novelty, yet is able to preserve enough genetic structure to optimally solve variably scaled, non-uniform block deceptive and hierarchical deceptive problems. apGA combines elitism, adaptive mutation, adaptive exponential scaling, and temporal memory. We present empirical results for six classes of problems, including the DeJong test suite. Although we have not investigated hybrids, we note that apGA could be incorporated into other recent GA variants such as GENITOR, CHC, and the recombination stage of mGA. 12 refs., 2 figs., 2 tabs.

  9. Adaptive working-memory training benefits reading, but not mathematics in middle childhood.

    PubMed

    Karbach, Julia; Strobach, Tilo; Schubert, Torsten

    2015-01-01

    Working memory (WM) capacity is highly correlated with general cognitive ability and has proven to be an excellent predictor for academic success. Given that WM can be improved by training, our aim was to test whether WM training benefited academic abilities in elementary-school children. We examined 28 participants (mean age = 8.3 years, SD = 0.4) in a pretest-training-posttest-follow-up design. Over 14 training sessions, children either performed adaptive WM training (training group, n = 14) or nonadaptive low-level training (active control group, n = 14) on the same tasks. Pretest, posttest, and follow-up at 3 months after posttest included a neurocognitive test battery (WM, task switching, inhibition) and standardized tests for math and reading abilities. Adaptive WM training resulted in larger training gains than nonadaptive low-level training. The benefits induced by the adaptive training transferred to an untrained WM task and a standardized test for reading ability, but not to task switching, inhibition, or performance on a standardized math test. Transfer to the untrained WM task was maintained over 3 months. The analysis of individual differences revealed compensatory effects with larger gains in children with lower WM and reading scores at pretest. These training and transfer effects are discussed against the background of cognitive processing resulting from WM span training and the nature of the intervention.

  10. Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling

    NASA Astrophysics Data System (ADS)

    Mohamadian, Masoumeh; Afarideh, Hossein; Ghergherehchi, Mitra

    2017-01-01

    The cyclotron cavity presented in this paper is modeled by a feed-forward neural network trained by the authors’ optimized back-propagation (BP) algorithm. The training samples were obtained from simulation results that are for a number of defined situations and parameters and were achieved parametrically using MWS CST software; furthermore, the conventional BP algorithm with different hidden-neuron numbers, structures, and other optimal parameters such as learning rate that are applied for our purpose was also used here. The present study shows that an optimized FFN can be used to estimate the cyclotron-model parameters with an acceptable error function. A neural network trained by an optimized algorithm therefore shows a proper approximation and an acceptable ability regarding the modeling of the proposed structure. The cyclotron-cavity parameter-modeling results demonstrate that an FNN that is trained by the optimized algorithm could be a suitable method for the estimation of the design parameters in this case.

  11. Infrared image gray adaptive adjusting enhancement algorithm based on gray redundancy histogram-dealing technique

    NASA Astrophysics Data System (ADS)

    Hao, Zi-long; Liu, Yong; Chen, Ruo-wang

    2016-11-01

    In view of the histogram equalizing algorithm to enhance image in digital image processing, an Infrared Image Gray adaptive adjusting Enhancement Algorithm Based on Gray Redundancy Histogram-dealing Technique is proposed. The algorithm is based on the determination of the entire image gray value, enhanced or lowered the image's overall gray value by increasing appropriate gray points, and then use gray-level redundancy HE method to compress the gray-scale of the image. The algorithm can enhance image detail information. Through MATLAB simulation, this paper compares the algorithm with the histogram equalization method and the algorithm based on gray redundancy histogram-dealing technique , and verifies the effectiveness of the algorithm.

  12. A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning

    NASA Astrophysics Data System (ADS)

    Morales-Esteban, Antonio; Martínez-Álvarez, Francisco; Scitovski, Sanja; Scitovski, Rudolf

    2014-12-01

    In this paper we construct an efficient adaptive Mahalanobis k-means algorithm. In addition, we propose a new efficient algorithm to search for a globally optimal partition obtained by using the adoptive Mahalanobis distance-like function. The algorithm is a generalization of the previously proposed incremental algorithm (Scitovski and Scitovski, 2013). It successively finds optimal partitions with k = 2 , 3 , … clusters. Therefore, it can also be used for the estimation of the most appropriate number of clusters in a partition by using various validity indexes. The algorithm has been applied to the seismic catalogues of Croatia and the Iberian Peninsula. Both regions are characterized by a moderate seismic activity. One of the main advantages of the algorithm is its ability to discover not only circular but also elliptical shapes, whose geometry fits the faults better. Three seismogenic zonings are proposed for Croatia and two for the Iberian Peninsula and adjacent areas, according to the clusters discovered by the algorithm.

  13. The new image segmentation algorithm using adaptive evolutionary programming and fuzzy c-means clustering

    NASA Astrophysics Data System (ADS)

    Liu, Fang

    2011-06-01

    Image segmentation remains one of the major challenges in image analysis and computer vision. Fuzzy clustering, as a soft segmentation method, has been widely studied and successfully applied in mage clustering and segmentation. The fuzzy c-means (FCM) algorithm is the most popular method used in mage segmentation. However, most clustering algorithms such as the k-means and the FCM clustering algorithms search for the final clusters values based on the predetermined initial centers. The FCM clustering algorithms does not consider the space information of pixels and is sensitive to noise. In the paper, presents a new fuzzy c-means (FCM) algorithm with adaptive evolutionary programming that provides image clustering. The features of this algorithm are: 1) firstly, it need not predetermined initial centers. Evolutionary programming will help FCM search for better center and escape bad centers at local minima. Secondly, the spatial distance and the Euclidean distance is also considered in the FCM clustering. So this algorithm is more robust to the noises. Thirdly, the adaptive evolutionary programming is proposed. The mutation rule is adaptively changed with learning the useful knowledge in the evolving process. Experiment results shows that the new image segmentation algorithm is effective. It is providing robustness to noisy images.

  14. Simple and Effective Algorithms: Computer-Adaptive Testing.

    ERIC Educational Resources Information Center

    Linacre, John Michael

    Computer-adaptive testing (CAT) allows improved security, greater scoring accuracy, shorter testing periods, quicker availability of results, and reduced guessing and other undesirable test behavior. Simple approaches can be applied by the classroom teacher, or other content specialist, who possesses simple computer equipment and elementary…

  15. Adaptive quasi-Newton algorithm for source extraction via CCA approach.

    PubMed

    Zhang, Wei-Tao; Lou, Shun-Tian; Feng, Da-Zheng

    2014-04-01

    This paper addresses the problem of adaptive source extraction via the canonical correlation analysis (CCA) approach. Based on Liu's analysis of CCA approach, we propose a new criterion for source extraction, which is proved to be equivalent to the CCA criterion. Then, a fast and efficient online algorithm using quasi-Newton iteration is developed. The stability of the algorithm is also analyzed using Lyapunov's method, which shows that the proposed algorithm asymptotically converges to the global minimum of the criterion. Simulation results are presented to prove our theoretical analysis and demonstrate the merits of the proposed algorithm in terms of convergence speed and successful rate for source extraction.

  16. Passification based simple adaptive control of quadrotor attitude: Algorithms and testbed results

    NASA Astrophysics Data System (ADS)

    Tomashevich, Stanislav; Belyavskyi, Andrey; Andrievsky, Boris

    2017-01-01

    In the paper, the results of the Passification Method with the Implicit Reference Model (IRM) approach are applied for designing the simple adaptive controller for quadrotor attitude. The IRM design technique makes it possible to relax the matching condition, known for habitual MRAC systems, and leads to simple adaptive controllers, ensuring fast tuning the controller gains, high robustness with respect to nonlinearities in the control loop, to the external disturbances and the unmodeled plant dynamics. For experimental evaluation of the adaptive systems performance, the 2DOF laboratory setup has been created. The testbed allows to safely test new control algorithms in the laboratory area with a small space and promptly make changes in cases of failure. The testing results of simple adaptive control of quadrotor attitude are presented, demonstrating efficacy of the applied simple adaptive control method. The experiments demonstrate good performance quality and high adaptation rate of the simple adaptive control system.

  17. Simulated annealing algorithm applied in adaptive near field beam shaping

    NASA Astrophysics Data System (ADS)

    Yu, Zhan; Ma, Hao-tong; Du, Shao-jun

    2010-11-01

    Laser beam shaping is required in many applications for improving the efficiency of the laser systems. In this paper, the near field beam shaping based on the combination of simulated annealing algorithm and Zernike polynomials is demonstrated. Considering phase distribution can be represented by the expansion of Zernike polynomials, the problem of searching appropriate phase distribution can be changed into a problem of optimizing a vector made up of Zernike coefficients. The feasibility of this method is validated theoretically by translating the Gaussian beam into square quasi-flattop beam in the near field. Finally, the closed control loop system constituted by phase only liquid crystal spatial light modulator and simulated annealing algorithm is used to prove the validity of the technique. The experiment results show that the system can generate laser beam with desired intensity distributions.

  18. Concluding remarks: nutritional strategies to support the adaptive response to prolonged exercise training.

    PubMed

    van Loon, Luc J C; Tipton, Kevin D

    2013-01-01

    Nutrition plays a key role in allowing the numerous training hours to be translated into useful adaptive responses of various tissues in the individual athlete. Research over the last decade has shown many examples of the impact of dietary interventions to modulate the skeletal muscle adaptive response to prolonged exercise training. Proper nutritional coaching should be applied throughout both training and competition, each with their specific requirements regarding nutrient provision. Such dietary support will improve exercise training efficiency and, as such, further increase performance capacity. Here, we provide an overview on the properties of various nutritional interventions that may be useful to support the adaptive response to exercise training and competition and, as such, to augment exercise training efficiency.

  19. Astronomical image denoising by means of improved adaptive backtracking-based matching pursuit algorithm.

    PubMed

    Liu, Qianshun; Bai, Jian; Yu, Feihong

    2014-11-10

    In an effort to improve compressive sensing and spare signal reconstruction by way of the backtracking-based adaptive orthogonal matching pursuit (BAOMP), a new sparse coding algorithm called improved adaptive backtracking-based OMP (ABOMP) is proposed in this study. Many aspects have been improved compared to the original BAOMP method, including replacing the fixed threshold with an adaptive one, adding residual feedback and support set verification, and others. Because of these ameliorations, the proposed algorithm can more precisely choose the atoms. By adding the adaptive step-size mechanism, it requires much less iteration and thus executes more efficiently. Additionally, a simple but effective contrast enhancement method is also adopted to further improve the denoising results and visual effect. By combining the IABOMP algorithm with the state-of-art dictionary learning algorithm K-SVD, the proposed algorithm achieves better denoising effects for astronomical images. Numerous experimental results show that the proposed algorithm performs successfully and effectively on Gaussian and Poisson noise removal.

  20. Neural correlates of adaptive working memory training in a glycogen storage disease type-IV patient.

    PubMed

    Lee, Kristin; Ernst, Thomas; Løhaugen, Gro; Zhang, Xin; Chang, Linda

    2017-03-01

    Glycogen storage disease type-IV has varied clinical presentations and subtypes. We evaluated a 38-year-old man with memory complaints, common symptoms in adult polyglucosan body disease subtype, and investigated cognitive and functional MRI changes associated with two 25-sessions of adaptive working memory training. He showed improved trained and nontrained working memory up to 6-months after the training sessions. On functional MRI, he showed increased cortical activation 1-3 months after training, but both increased and decreased activation 6-months later. Working memory training appears to be beneficial to patients with adult polyglucosan body disease, although continued training may be required to maintain improvements.

  1. Physiological and Neural Adaptations to Eccentric Exercise: Mechanisms and Considerations for Training

    PubMed Central

    Hedayatpour, Nosratollah; Falla, Deborah

    2015-01-01

    Eccentric exercise is characterized by initial unfavorable effects such as subcellular muscle damage, pain, reduced fiber excitability, and initial muscle weakness. However, stretch combined with overload, as in eccentric contractions, is an effective stimulus for inducing physiological and neural adaptations to training. Eccentric exercise-induced adaptations include muscle hypertrophy, increased cortical activity, and changes in motor unit behavior, all of which contribute to improved muscle function. In this brief review, neuromuscular adaptations to different forms of exercise are reviewed, the positive training effects of eccentric exercise are presented, and the implications for training are considered. PMID:26543850

  2. Physiological and Neural Adaptations to Eccentric Exercise: Mechanisms and Considerations for Training.

    PubMed

    Hedayatpour, Nosratollah; Falla, Deborah

    2015-01-01

    Eccentric exercise is characterized by initial unfavorable effects such as subcellular muscle damage, pain, reduced fiber excitability, and initial muscle weakness. However, stretch combined with overload, as in eccentric contractions, is an effective stimulus for inducing physiological and neural adaptations to training. Eccentric exercise-induced adaptations include muscle hypertrophy, increased cortical activity, and changes in motor unit behavior, all of which contribute to improved muscle function. In this brief review, neuromuscular adaptations to different forms of exercise are reviewed, the positive training effects of eccentric exercise are presented, and the implications for training are considered.

  3. Almost Sure Convergence of Adaptive Identification Prediction and Control Algorithms.

    DTIC Science & Technology

    1981-03-01

    achievable with known plant parameters, in the Cesaro sense. An additional regularity assumption on the signal model establishes the result that the...the Cesaro sense. Under an additional regularity assumption, the convergence of these errors and also that of the tracking error for the adaptive con...The 4- convergence in all these references is established in the Cesaro sense. The above schemes of [7-10] leave the question unanswered as to

  4. Similarity in adaptations to high-resistance circuit vs. traditional strength training in resistance-trained men.

    PubMed

    Alcaraz, Pedro E; Perez-Gomez, Jorge; Chavarrias, Manuel; Blazevich, Anthony J

    2011-09-01

    To compare the effects of 8 weeks of high-resistance circuit (HRC) training (3-6 sets of 6 exercises, 6 repetition maximum [RM], ∼35-second interset recovery) and traditional strength (TS) training (3-6 sets of 6 exercises, 6RM, 3-minute interset recovery) on physical performance parameters and body composition, 33 healthy men were randomly assigned to HRC, TS, or a control group. Training consisted of weight lifting 3 times a week for 8 weeks. Before and after the training, 1RM strength on bench press and half squat exercises, bench press peak power output, and body composition (dual x-ray absorptiometry ) were determined. Shuttle run and 30-second Wingate tests were also completed. Upper limb (UL) and lower limb 1RM increased equally after both TS and HRC training. The UL peak power at various loads was significantly higher at posttraining for both groups (p ≤ 0.01). Shuttle-run performance was significantly better after both HRC and TS training, however peak cycling power increased only in TS training (p ≤ 0.05). Significant decreases were found in % body fat in the HRC group only; HRC and TS training both resulted in an increased lean but not bone mass. The HRC training was as effective as TS for improving weight lifting 1RM and peak power, shuttle-run performance and lean mass. Thus, HRC training promoted a similar strength-mass adaptation as traditional training while using a shorter training session duration.

  5. Simulation of Biochemical Pathway Adaptability Using Evolutionary Algorithms

    SciTech Connect

    Bosl, W J

    2005-01-26

    The systems approach to genomics seeks quantitative and predictive descriptions of cells and organisms. However, both the theoretical and experimental methods necessary for such studies still need to be developed. We are far from understanding even the simplest collective behavior of biomolecules, cells or organisms. A key aspect to all biological problems, including environmental microbiology, evolution of infectious diseases, and the adaptation of cancer cells is the evolvability of genomes. This is particularly important for Genomes to Life missions, which tend to focus on the prospect of engineering microorganisms to achieve desired goals in environmental remediation and climate change mitigation, and energy production. All of these will require quantitative tools for understanding the evolvability of organisms. Laboratory biodefense goals will need quantitative tools for predicting complicated host-pathogen interactions and finding counter-measures. In this project, we seek to develop methods to simulate how external and internal signals cause the genetic apparatus to adapt and organize to produce complex biochemical systems to achieve survival. This project is specifically directed toward building a computational methodology for simulating the adaptability of genomes. This project investigated the feasibility of using a novel quantitative approach to studying the adaptability of genomes and biochemical pathways. This effort was intended to be the preliminary part of a larger, long-term effort between key leaders in computational and systems biology at Harvard University and LLNL, with Dr. Bosl as the lead PI. Scientific goals for the long-term project include the development and testing of new hypotheses to explain the observed adaptability of yeast biochemical pathways when the myosin-II gene is deleted and the development of a novel data-driven evolutionary computation as a way to connect exploratory computational simulation with hypothesis

  6. Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows.

    PubMed

    Jawarneh, Sana; Abdullah, Salwani

    2015-01-01

    This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.

  7. Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows

    PubMed Central

    Jawarneh, Sana; Abdullah, Salwani

    2015-01-01

    This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158

  8. Training Enhances Both Locomotor and Cognitive Adaptability to a Novel Sensory Environment

    NASA Technical Reports Server (NTRS)

    Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Brady, R. A.; Batson, C. D.; Ploutz-Snyder, R. J.; Cohen, H. S.

    2010-01-01

    During adaptation to novel gravitational environments, sensorimotor disturbances have the potential to disrupt the ability of astronauts to perform required mission tasks. The goal of this project is to develop a sensorimotor adaptability (SA) training program to facilitate rapid adaptation. We have developed a unique training system comprised of a treadmill placed on a motion-base facing a virtual visual scene that provides an unstable walking surface combined with incongruent visual flow designed to enhance sensorimotor adaptability. The goal of our present study was to determine if SA training improved both the locomotor and cognitive responses to a novel sensory environment and to quantify the extent to which training would be retained. Methods: Twenty subjects (10 training, 10 control) completed three, 30-minute training sessions during which they walked on the treadmill while receiving discordant support surface and visual input. Control subjects walked on the treadmill but did not receive any support surface or visual alterations. To determine the efficacy of training all subjects performed the Transfer Test upon completion of training. For this test, subjects were exposed to novel visual flow and support surface movement, not previously experienced during training. The Transfer Test was performed 20 minutes, 1 week, 1, 3 and 6 months after the final training session. Stride frequency, auditory reaction time, and heart rate data were collected as measures of postural stability, cognitive effort and anxiety, respectively. Results: Using mixed effects regression methods we determined that subjects who received SA training showed less alterations in stride frequency, auditory reaction time and heart rate compared to controls. Conclusion: Subjects who received SA training improved performance across a number of modalities including enhanced locomotor function, increased multi-tasking capability and reduced anxiety during adaptation to novel discordant sensory

  9. Adaptive Waveform Correlation Detectors for Arrays: Algorithms for Autonomous Calibration

    DTIC Science & Technology

    2007-09-01

    correlation coefficient , or some comparable detection statistic, exceeds a given threshold. Since these methods exploit characteristic details of the full waveform, they provide exquisitely sensitive detectors with far lower detection thresholds than typical short-term average/long-term average (STA/LTA) algorithms. The drawback is that the form of the sought-after signal needs to be known quite accurately a priori, which limits such methods to instances of seismicity whereby a very similar signal has already been observed by every station used. Such instances include

  10. Adaptive merit function in SPGD algorithm for beam combining

    NASA Astrophysics Data System (ADS)

    Yang, Guo-qing; Liu, Li-sheng; Jiang, Zhen-hua; Wang, Ting-feng; Guo, Jin

    2016-09-01

    The beam pointing is the most crucial issue for beam combining to achieve high energy laser output. In order to meet the turbulence situation, a beam pointing method that cooperates with the stochastic parallel gradient descent (SPGD) algorithm is proposed. The power-in-the-bucket ( PIB) is chosen as the merit function, and its radius changes gradually during the correction process. The linear radius and the exponential radius are simulated. The results show that the exponential radius has great promise for beam pointing.

  11. A geometry-based adaptive unstructured grid generation algorithm for complex geological media

    NASA Astrophysics Data System (ADS)

    Bahrainian, Seyed Saied; Dezfuli, Alireza Daneh

    2014-07-01

    In this paper a novel unstructured grid generation algorithm is presented that considers the effect of geological features and well locations in grid resolution. The proposed grid generation algorithm presents a strategy for definition and construction of an initial grid based on the geological model, geometry adaptation of geological features, and grid resolution control. The algorithm is applied to seismotectonic map of the Masjed-i-Soleiman reservoir. Comparison of grid results with the “Triangle” program shows a more suitable permeability contrast. Immiscible two-phase flow solutions are presented for a fractured porous media test case using different grid resolutions. Adapted grid on the fracture geometry gave identical results with that of a fine grid. The adapted grid employed 88.2% less CPU time when compared to the solutions obtained by the fine grid.

  12. Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations

    SciTech Connect

    Diego Mandelli; Dan Maljovec; Bei Wang; Valerio Pascucci; Peer-Timo Bremer

    2013-09-01

    Nuclear simulations are often computationally expensive, time-consuming, and high-dimensional with respect to the number of input parameters. Thus exploring the space of all possible simulation outcomes is infeasible using finite computing resources. During simulation-based probabilistic risk analysis, it is important to discover the relationship between a potentially large number of input parameters and the output of a simulation using as few simulation trials as possible. This is a typical context for performing adaptive sampling where a few observations are obtained from the simulation, a surrogate model is built to represent the simulation space, and new samples are selected based on the model constructed. The surrogate model is then updated based on the simulation results of the sampled points. In this way, we attempt to gain the most information possible with a small number of carefully selected sampled points, limiting the number of expensive trials needed to understand features of the simulation space. We analyze the specific use case of identifying the limit surface, i.e., the boundaries in the simulation space between system failure and system success. In this study, we explore several techniques for adaptively sampling the parameter space in order to reconstruct the limit surface. We focus on several adaptive sampling schemes. First, we seek to learn a global model of the entire simulation space using prediction models or neighborhood graphs and extract the limit surface as an iso-surface of the global model. Second, we estimate the limit surface by sampling in the neighborhood of the current estimate based on topological segmentations obtained locally. Our techniques draw inspirations from topological structure known as the Morse-Smale complex. We highlight the advantages and disadvantages of using a global prediction model versus local topological view of the simulation space, comparing several different strategies for adaptive sampling in both

  13. The effects of accentuated eccentric loading on strength, muscle hypertrophy, and neural adaptations in trained individuals.

    PubMed

    Brandenburg, Jason P; Docherty, David

    2002-02-01

    The purpose of this study was to compare the strength and neuromuscular adaptations for dynamic constant external resistance (DCER) training and dynamic accentuated external resistance (DAER) training (resistance training employing an accentuated load during eccentric actions). Male subjects active in resistance training were assigned to either a DCER training group (n = 10) or a DAER training group (n = 8) for 9 weeks. Subjects in the DCER group performed 4 sets of 10 repetitions with a load of 75% concentric 1 repetition maximum (RM). Subjects in the DAER group performed 3 sets of 10 repetitions with a concentric load of 75% of 1RM and an eccentric load of approximately 120% of concentric 1RM. Three measures reflecting adaptation of elbow flexors and extensors were recorded pretraining and posttraining: concentric 1RM, muscle cross-sectional area (CSA), and specific tension. Strength was assessed at midtraining periods. No significant changes in muscle CSA were observed in either group. Both training groups experienced significant increases in concentric 1RM and specific tension of both the elbow flexors and extensors, but compared with DCER training, DAER training produced significantly greater increases in concentric 1RM of the elbow extensors. These results suggest that, for some exercises, DAER training may be more effective than DCER training in developing strength within a 9-week training phase. However, for trained subjects, neither protocol is effective in eliciting muscle hypertrophy.

  14. [Curvelet denoising algorithm for medical ultrasound image based on adaptive threshold].

    PubMed

    Zhuang, Zhemin; Yao, Weike; Yang, Jinyao; Li, FenLan; Yuan, Ye

    2014-11-01

    The traditional denoising algorithm for ultrasound images would lost a lot of details and weak edge information when suppressing speckle noise. A new denoising algorithm of adaptive threshold based on curvelet transform is proposed in this paper. The algorithm utilizes differences of coefficients' local variance between texture and smooth region in each layer of ultrasound image to define fuzzy regions and membership functions. In the end, using the adaptive threshold that determine by the membership function to denoise the ultrasound image. The experimental text shows that the algorithm can reduce the speckle noise effectively and retain the detail information of original image at the same time, thus it can greatly enhance the performance of B ultrasound instrument.

  15. Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm

    PubMed Central

    Prado-Velasco, Manuel; Ortiz Marín, Rafael; del Rio Cidoncha, Gloria

    2013-01-01

    Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions. The algorithm works together with an unsupervised real-time learning technique that addresses the adaptive capability, and this is also presented. The work demonstrates the robustness and reliability of our new algorithm, which will be the basis of a smart falling monitor. This is shown in this work to underline the relevance of the results. PMID:24157505

  16. Performance study of LMS based adaptive algorithms for unknown system identification

    NASA Astrophysics Data System (ADS)

    Javed, Shazia; Ahmad, Noor Atinah

    2014-07-01

    Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.

  17. Performance study of LMS based adaptive algorithms for unknown system identification

    SciTech Connect

    Javed, Shazia; Ahmad, Noor Atinah

    2014-07-10

    Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.

  18. Adaptive control and noise suppression by a variable-gain gradient algorithm

    NASA Technical Reports Server (NTRS)

    Merhav, S. J.; Mehta, R. S.

    1987-01-01

    An adaptive control system based on normalized LMS filters is investigated. The finite impulse response of the nonparametric controller is adaptively estimated using a given reference model. Specifically, the following issues are addressed: The stability of the closed loop system is analyzed and heuristically established. Next, the adaptation process is studied for piecewise constant plant parameters. It is shown that by introducing a variable-gain in the gradient algorithm, a substantial reduction in the LMS adaptation rate can be achieved. Finally, process noise at the plant output generally causes a biased estimate of the controller. By introducing a noise suppression scheme, this bias can be substantially reduced and the response of the adapted system becomes very close to that of the reference model. Extensive computer simulations validate these and demonstrate assertions that the system can rapidly adapt to random jumps in plant parameters.

  19. Self-adaptive differential evolution algorithm incorporating local search for protein-ligand docking

    NASA Astrophysics Data System (ADS)

    Chung, Hwan Won; Cho, Seung Joo; Lee, Kwang-Ryeol; Lee, Kyu-Hwan

    2013-02-01

    Differential Evolution (DE) algorithm is powerful in optimization problems over several real parameters. DE depends on strategies to generate new trial solutions and the associated parameter values for searching performance. In self-adaptive DE, the automatic learning about previous evolution was used to determine the best mutation strategy and its parameter settings. By combining the self-adaptive DE and Hooke Jeeves local search, we developed a new docking method named SADock (Strategy Adaptation Dock) with the help of AutoDock4 scoring function. As the accuracy and performance of SADock was evaluated in self-docking using the Astex diverse set, the introduced SADock showed better success ratio (89%) than the success ratio (60%) of the Lamarckian genetic algorithm (LGA) of AutoDock4. The self-adapting scheme enabled our new docking method to converge fast and to be robust through the various docking problems.

  20. A bit-level image encryption algorithm based on spatiotemporal chaotic system and self-adaptive

    NASA Astrophysics Data System (ADS)

    Teng, Lin; Wang, Xingyuan

    2012-09-01

    This paper proposes a bit-level image encryption algorithm based on spatiotemporal chaotic system which is self-adaptive. We use a bit-level encryption scheme to reduce the volume of data during encryption and decryption in order to reduce the execution time. We also use the adaptive encryption scheme to make the ciphered image dependent on the plain image to improve performance. Simulation results show that the performance and security of the proposed encryption algorithm can encrypt plaintext effectively and resist various typical attacks.

  1. A Mass Conservation Algorithm for Adaptive Unrefinement Meshes Used by Finite Element Methods

    DTIC Science & Technology

    2012-01-01

    dimensional mesh generation. In: Proc. 4th ACM-SIAM Symp. on Disc. Algorithms. (1993) 83–92 [9] Weatherill, N., Hassan, O., Marcum, D., Marchant, M.: Grid ...Conference on Computational Science, ICCS 2012 A Mass Conservation Algorithm For Adaptive Unrefinement Meshes Used By Finite Element Methods Hung V. Nguyen...velocity fields, and chemical distribution, as well as conserve mass, especially for water quality applications. Solution accuracy depends highly on mesh

  2. Comparison of adaptive algorithms for the control of tonal disturbances in mechanical systems

    NASA Astrophysics Data System (ADS)

    Zilletti, M.; Elliott, S. J.; Cheer, J.

    2016-09-01

    This paper presents a study on the performance of adaptive control algorithms designed to reduce the vibration of mechanical systems excited by a harmonic disturbance. The mechanical system consists of a mass suspended on a spring and a damper. The system is equipped with a force actuator in parallel with the suspension. The control signal driving the actuator is generated by adjusting the amplitude and phase of a sinusoidal reference signal at the same frequency as the excitation. An adaptive feedforward control algorithm is used to adapt the amplitude and phase of the control signal, to minimise the mean square velocity of the mass. Two adaptation strategies are considered in which the control signal is either updated after each period of the oscillation or at every time sample. The first strategy is traditionally used in vibration control in helicopters for example; the second strategy is normally referred to as the filtered-x least mean square algorithm and is often used to control engine noise in cars. The two adaptation strategies are compared through a parametric study, which investigates the influence of the properties of both the mechanical system and the control system on the convergence speed of the two algorithms.

  3. Effects of different volume-equated resistance training loading strategies on muscular adaptations in well-trained men.

    PubMed

    Schoenfeld, Brad J; Ratamess, Nicholas A; Peterson, Mark D; Contreras, Bret; Sonmez, G T; Alvar, Brent A

    2014-10-01

    Regimented resistance training has been shown to promote marked increases in skeletal muscle mass. Although muscle hypertrophy can be attained through a wide range of resistance training programs, the principle of specificity, which states that adaptations are specific to the nature of the applied stimulus, dictates that some programs will promote greater hypertrophy than others. Research is lacking, however, as to the best combination of variables required to maximize hypertophic gains. The purpose of this study was to investigate muscular adaptations to a volume-equated bodybuilding-type training program vs. a powerlifting-type routine in well-trained subjects. Seventeen young men were randomly assigned to either a hypertrophy-type resistance training group that performed 3 sets of 10 repetition maximum (RM) with 90 seconds rest or a strength-type resistance training (ST) group that performed 7 sets of 3RM with a 3-minute rest interval. After 8 weeks, no significant differences were noted in muscle thickness of the biceps brachii. Significant strength differences were found in favor of ST for the 1RM bench press, and a trend was found for greater increases in the 1RM squat. In conclusion, this study showed that both bodybuilding- and powerlifting-type training promote similar increases in muscular size, but powerlifting-type training is superior for enhancing maximal strength.

  4. Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms.

    PubMed

    Vyas, Bhargav Y; Das, Biswarup; Maheshwari, Rudra Prakash

    2016-08-01

    This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg-Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications.

  5. Absence of Training-Specific Cardiac Adaptation in Paraplegic Athletes.

    ERIC Educational Resources Information Center

    Gates, Phillip E.; Campbell, Ian G.; George, Keith P.

    2002-01-01

    Tested the hypothesis that wall thickness, but not chamber dimension, would be larger in endurance- and power-trained athletes with spinal cord injuries than in sedentary people with spinal cord injuries. Data on 11 power-trained and 5 sedentary participants showed no statistically significant differences between groups, though there was a trend…

  6. A Rural Special Education Teacher Training Program: Successful Adaptations.

    ERIC Educational Resources Information Center

    Prater, Greg; And Others

    The Rural Special Education Program (RSEP), a partnership between Northern Arizona University (NAU) and Kayenta Unified School District (KUSD), provides training for preservice special education teachers to work with Native American students and their families. To date, the program has provided training for 63 preservice special education…

  7. A comparison study between Wiener and adaptive state estimation (STAP-ASE) algorithms for space time adaptive radar processing

    NASA Astrophysics Data System (ADS)

    Malek, Obaidul; Venetsanopoulos, Anastasios; Anpalagan, Alagan

    2010-08-01

    Space Time Adaptive Processing (STAP) is a multi-dimensional adaptive signal processing technique, which processes the signal in spatial and Doppler domains for which a target detection hypothesis is to be formed. It is a sample based technique and based on the assumption of adequate number of Independent and Identically Distributed (i.i.d.) training data set in the surrounding environment. The principal challenge of the radar processing lies when it violates these underlying assumptions due to severe dynamic heterogeneous clutter (hot clutter) and jammer effects. This in turn degrades the Signal to Interference-plus-Noise Ratio (SINR), hence signal detection performance. Classical Wiener filtering theory is inadequate to deal with nonlinear and nonstationary interferences, however Wiener filtering approach is optimal for stationary and linear systems. But, these challenges can be overcome by Adaptive Sequential State Estimation (ASSE) filtering technique.

  8. Multiobjective Image Color Quantization Algorithm Based on Self-Adaptive Hybrid Differential Evolution

    PubMed Central

    Xia, Xuewen

    2016-01-01

    In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm. PMID:27738423

  9. Low-power metabolic equivalents estimation algorithm using adaptive acceleration sampling.

    PubMed

    Tsukahara, Mio; Nakanishi, Motofumi; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Tsukahara, Mio; Nakanishi, Motofumi; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Tsukahara, Mio; Nakanishi, Motofumi

    2016-08-01

    This paper describes a proposed low-power metabolic equivalent estimation algorithm that can calculate the value of metabolic equivalents (METs) from triaxial acceleration at an adaptively changeable sampling rate. This algorithm uses four rates of 32, 16, 8 and 4 Hz. The mode of switching them is decided from synthetic acceleration. Applying this proposed algorithm to acceleration measured for 1 day, we achieved the low root mean squared error (RMSE) of calculated METs, with current consumption that was 41.5 % of the value at 32 Hz, and 75.4 % of the value at 16 Hz.

  10. An adaptive prediction and detection algorithm for multistream syndromic surveillance

    PubMed Central

    Najmi, Amir-Homayoon; Magruder, Steve F

    2005-01-01

    Background Surveillance of Over-the-Counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions, in particular in cases of interest to biosurvellance, has been suggested in the literature. This paper is a continuation of a previous study in which we formulated the problem of estimating clinical data from OTC sales in terms of optimal LMS linear and Finite Impulse Response (FIR) filters. In this paper we extend our results to predict clinical data multiple steps ahead using OTC sales as well as the clinical data itself. Methods The OTC data are grouped into a few categories and we predict the clinical data using a multichannel filter that encompasses all the past OTC categories as well as the past clinical data itself. The prediction is performed using FIR (Finite Impulse Response) filters and the recursive least squares method in order to adapt rapidly to nonstationary behaviour. In addition, we inject simulated events in both clinical and OTC data streams to evaluate the predictions by computing the Receiver Operating Characteristic curves of a threshold detector based on predicted outputs. Results We present all prediction results showing the effectiveness of the combined filtering operation. In addition, we compute and present the performance of a detector using the prediction output. Conclusion Multichannel adaptive FIR least squares filtering provides a viable method of predicting public health conditions, as represented by clinical data, from OTC sales, and/or the clinical data. The potential value to a biosurveillance system cannot, however, be determined without studying this approach in the presence of transient events (nonstationary events of relatively short duration and fast rise times). Our simulated events superimposed on actual OTC and clinical data allow us to provide an upper bound on that potential value under some restricted conditions. Based on our ROC curves we argue that a biosurveillance system can

  11. Effects of Methoxyisoflavone, Ecdysterone, and Sulfo-Polysaccharide Supplementation on Training Adaptations in Resistance-Trained Males

    PubMed Central

    Wilborn, Colin D; Taylor, Lemuel W; Campbell, Bill I; Kerksick, Chad; Rasmussen, Chris J; Greenwood, Michael; Kreider, Richard B

    2006-01-01

    Purpose Methoxyisoflavone (M), 20-hydroxyecdysone (E), and sulfo-polysaccharide (CSP3) have been marketed to athletes as dietary supplements that can increase strength and muscle mass during resistance-training. However, little is known about their potential ergogenic value. The purpose of this study was to determine whether these supplements affect training adaptations and/or markers of muscle anabolism/catabolism in resistance-trained athletes. Methods Forty-five resistance-trained males (20.5 ± 3 yrs; 179 ± 7 cm, 84 ± 16 kg, 17.3 ± 9% body fat) were matched according to FFM and randomly assigned to ingest in a double blind manner supplements containing either a placebo (P); 800 mg/day of M; 200 mg of E; or, 1,000 mg/day of CSP3 for 8-weeks during training. At 0, 4, and 8-weeks, subjects donated fasting blood samples and completed comprehensive muscular strength, muscular endurance, anaerobic capacity, and body composition analysis. Data were analyzed by repeated measures ANOVA. Results No significant differences (p > 0.05) were observed in training adaptations among groups in the variables FFM, percent body fat, bench press 1 RM, leg press 1 RM or sprint peak power. Anabolic/catabolic analysis revealed no significant differences among groups in active testosterone (AT), free testosterone (FT), cortisol, the AT to cortisol ratio, urea nitrogen, creatinine, the blood urea nitrogen to creatinine ratio. In addition, no significant differences were seen from pre to post supplementation and/or training in AT, FT, or cortisol. Conclusion Results indicate that M, E, and CSP3 supplementation do not affect body composition or training adaptations nor do they influence the anabolic/catabolic hormone status or general markers of catabolism in resistance-trained males. PMID:18500969

  12. An Adaptive Weighting Algorithm for Interpolating the Soil Potassium Content

    PubMed Central

    Liu, Wei; Du, Peijun; Zhao, Zhuowen; Zhang, Lianpeng

    2016-01-01

    The concept of spatial interpolation is important in the soil sciences. However, the use of a single global interpolation model is often limited by certain conditions (e.g., terrain complexity), which leads to distorted interpolation results. Here we present a method of adaptive weighting combined environmental variables for soil properties interpolation (AW-SP) to improve accuracy. Using various environmental variables, AW-SP was used to interpolate soil potassium content in Qinghai Lake Basin. To evaluate AW-SP performance, we compared it with that of inverse distance weighting (IDW), ordinary kriging, and OK combined with different environmental variables. The experimental results showed that the methods combined with environmental variables did not always improve prediction accuracy even if there was a strong correlation between the soil properties and environmental variables. However, compared with IDW, OK, and OK combined with different environmental variables, AW-SP is more stable and has lower mean absolute and root mean square errors. Furthermore, the AW-SP maps provided improved details of soil potassium content and provided clearer boundaries to its spatial distribution. In conclusion, AW-SP can not only reduce prediction errors, it also accounts for the distribution and contributions of environmental variables, making the spatial interpolation of soil potassium content more reasonable. PMID:27051998

  13. The new adaptive enhancement algorithm on the degraded color images

    NASA Astrophysics Data System (ADS)

    Xue, Rong Kun; He, Wei; Li, Yufeng

    2016-10-01

    Based on the scene characteristics of frequency distribution in the degraded color images, the MSRCR method and wavelet transform in the paper are introduced respectively to enhance color images and the advantages and disadvantages of them are analyzed combining with the experiment, then the combination of improved MSRCR method and wavelet transform are proposed to enhance color images, it uses wavelet to decompose color images in order to increase the coefficient of low-level details and reduce top-level details to highlight the scene information, meanwhile, the method of improved MSRCR is used to enhance the low-frequency components of degraded images processed by wavelet, then the adaptive equalization is carried on to further enhance images, finally, the enhanced color images are acquired with the reconstruction of all the coefficients brought by the wavelet transform. Through the evaluation of the experimental results and data analysis, it shows that the method proposed in the paper is better than the separate use of wavelet transform and MSRCR method.

  14. Adaptive motion artifact reducing algorithm for wrist photoplethysmography application

    NASA Astrophysics Data System (ADS)

    Zhao, Jingwei; Wang, Guijin; Shi, Chenbo

    2016-04-01

    Photoplethysmography (PPG) technology is widely used in wearable heart pulse rate monitoring. It might reveal the potential risks of heart condition and cardiopulmonary function by detecting the cardiac rhythms in physical exercise. However the quality of wrist photoelectric signal is very sensitive to motion artifact since the thicker tissues and the fewer amount of capillaries. Therefore, motion artifact is the major factor that impede the heart rate measurement in the high intensity exercising. One accelerometer and three channels of light with different wavelengths are used in this research to analyze the coupled form of motion artifact. A novel approach is proposed to separate the pulse signal from motion artifact by exploiting their mixing ratio in different optical paths. There are four major steps of our method: preprocessing, motion artifact estimation, adaptive filtering and heart rate calculation. Five healthy young men are participated in the experiment. The speeder in the treadmill is configured as 12km/h, and all subjects would run for 3-10 minutes by swinging the arms naturally. The final result is compared with chest strap. The average of mean square error (MSE) is less than 3 beats per minute (BPM/min). Proposed method performed well in intense physical exercise and shows the great robustness to individuals with different running style and posture.

  15. An adaptive solution domain algorithm for solving multiphase flow equations

    NASA Astrophysics Data System (ADS)

    Katyal, A. K.; Parker, J. C.

    1992-01-01

    An adaptive solution domain (ASD) finite-element model for simulating hydrocarbon spills has been developed that is computationally more efficient than conventional numerical methods. Coupled flow of water and oil with an air phase at constant pressure is considered. In the ASD formulation, the solution domain for water- and oil-flow equations is restricted by eliminating elements from the global matrix assembly which are not experiencing significant changes in fluid saturations or pressures. When any nodes of an element exhibit changes in fluid pressures more than a stipulated tolerance τ, or changes in fluid saturations greater than tolerance τ 2 during the current time step, it is labeled active and included in the computations for the next iteration. This formulation achieves computational efficiency by solving the flow equations for only the part of the domain where changes in fluid pressure or the saturations take place above stipulated tolerances. Examples involving infiltration and redistribution of oil in 1- and 2-D spatial domains are described to illustrate the application of the ASD method and the savings in the processor time achieved by this formulation. Savings in the computational effort up to 84% during infiltration and 63% during redistribution were achieved for the 2-D example problem.

  16. Systematic Review of Engagement in Culturally Adapted Parent Training for Disruptive Behavior

    ERIC Educational Resources Information Center

    Butler, Ashley M.; Titus, Courtney

    2015-01-01

    This article reviews the literature reporting engagement (enrollment, attendance, and attrition) in culturally adapted parent training for disruptive behavior among racial/ethnic minority parents of children ages 2 to 7 years. The review describes the reported rates of engagement in adapted interventions and how engagement is analyzed in studies,…

  17. Space motion sickness preflight adaptation training: preliminary studies with prototype trainers

    NASA Technical Reports Server (NTRS)

    Parker, D. E.; Rock, J. C.; von Gierke, H. E.; Ouyang, L.; Reschke, M. F.; Arrott, A. P.

    1987-01-01

    Preflight training frequently has been proposed as a potential solution to the problem of space motion sickness. The paper considers successively the otolith reinterpretation, the concept for a preflight adaptation trainer and the research with the Miami University Seesaw, the Wright Patterson Air-Force Base Dynamic Environment Simulator and the Visually Coupled Airborne Systems Simulator prototype adaptation trainers.

  18. The importance of training strategy adaptation: a learner-oriented approach for improving older adults' memory and transfer.

    PubMed

    Bottiroli, Sara; Cavallini, Elena; Dunlosky, John; Vecchi, Tomaso; Hertzog, Christopher

    2013-09-01

    We investigated the benefits of strategy-adaptation training for promoting transfer effects. This learner-oriented approach--which directly encourages the learner to generalize strategic behavior to new tasks--helps older adults appraise new tasks and adapt trained strategies to them. In Experiment 1, older adults in a strategy-adaptation training group used 2 strategies (imagery and sentence generation) while practicing 2 tasks (list and associative learning); they were then instructed on how to do a simple task analysis to help them adapt the trained strategies for 2 different unpracticed tasks (place learning and text learning) that were discussed during training. Two additional criterion tasks (name-face associative learning and grocery-list learning) were never mentioned during training. Two other groups were included: A strategy training group (who received strategy training and transfer instructions but not strategy-adaptation training) and a waiting-list control group. Both training procedures enhanced older adults' performance on the trained tasks and those tasks that were discussed during training, but transfer was greatest after strategy-adaptation training. Experiment 2 found that strategy-adaptation training conducted via a manual that older adults used at home also promoted transfer. These findings demonstrate the importance of adopting a learner-oriented approach to promote transfer of strategy training.

  19. Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.

    PubMed

    Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam

    2005-05-01

    This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures.

  20. Optimization of heterogeneous Bin packing using adaptive genetic algorithm

    NASA Astrophysics Data System (ADS)

    Sridhar, R.; Chandrasekaran, M.; Sriramya, C.; Page, Tom

    2017-03-01

    This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.

  1. Modified fast frequency acquisition via adaptive least squares algorithm

    NASA Technical Reports Server (NTRS)

    Kumar, Rajendra (Inventor)

    1992-01-01

    A method and the associated apparatus for estimating the amplitude, frequency, and phase of a signal of interest are presented. The method comprises the following steps: (1) inputting the signal of interest; (2) generating a reference signal with adjustable amplitude, frequency and phase at an output thereof; (3) mixing the signal of interest with the reference signal and a signal 90 deg out of phase with the reference signal to provide a pair of quadrature sample signals comprising respectively a difference between the signal of interest and the reference signal and a difference between the signal of interest and the signal 90 deg out of phase with the reference signal; (4) using the pair of quadrature sample signals to compute estimates of the amplitude, frequency, and phase of an error signal comprising the difference between the signal of interest and the reference signal employing a least squares estimation; (5) adjusting the amplitude, frequency, and phase of the reference signal from the numerically controlled oscillator in a manner which drives the error signal towards zero; and (6) outputting the estimates of the amplitude, frequency, and phase of the error signal in combination with the reference signal to produce a best estimate of the amplitude, frequency, and phase of the signal of interest. The preferred method includes the step of providing the error signal as a real time confidence measure as to the accuracy of the estimates wherein the closer the error signal is to zero, the higher the probability that the estimates are accurate. A matrix in the estimation algorithm provides an estimate of the variance of the estimation error.

  2. Investigation of Adaptive Robust Kalman Filtering Algorithms for GPS/DR Navigation System Filters

    NASA Astrophysics Data System (ADS)

    Elzoghby, MOSTAFA; Arif, USMAN; Li, FU; Zhi Yu, XI

    2017-03-01

    The conventional Kalman filter (KF) algorithm is suitable if the characteristic noise covariance for states as well as measurements is readily known but in most cases these are unknown. Similarly robustness is required instead of smoothing if states are changing abruptly. Such an adaptive as well as robust Kalman filter is vital for many real time applications, like target tracking and navigating aerial vehicles. A number of adaptive as well as robust Kalman filtering methods are available in the literature. In order to investigate the performance of some of these methods, we have selected three different Kalman filters, namely Sage Husa KF, Modified Adaptive Robust KF and Adaptively Robust KF, which are easily simulate able as well as implementable for real time applications. These methods are simulated for land based vehicle and the results are compared with conventional Kalman filter. Results show that the Modified Adaptive Robust KF is best amongst the selected methods and can be used for Navigation applications.

  3. Variable Resistance Training Promotes Greater Strength and Power Adaptations Than Traditional Resistance Training in Elite Youth Rugby League Players.

    PubMed

    Rivière, Maxence; Louit, Loic; Strokosch, Alasdair; Seitz, Laurent B

    2017-04-01

    Rivière, M, Louit, L, Strokosch, A, and Seitz, LB. Variable resistance training promotes greater strength and power adaptations than traditional resistance training in elite youth rugby league players. J Strength Cond Res 31(4): 947-955, 2017-The purpose of this study was to examine the strength, velocity, and power adaptations in youth rugby league players in response to a variable resistance training (VRT) or traditional free-weight resistance training (TRAD) intervention. Sixteen elite youth players were assigned to a VRT or TRAD group and completed 2 weekly upper- and lower-body strength and power sessions for 6 weeks. Training programs were identical except that the VRT group trained the bench press exercise with 20% of the prescribed load coming from elastic bands. Bench press 1 repetition maximum (1RM) and bench press mean velocity and power at 35, 45, 65, 75, and 85% of 1RM were measured before and after the training intervention, and the magnitude of the changes was determined using effect sizes (ESs). The VRT group experienced larger increases in both absolute (ES = 0.46 vs. 0.20) and relative (ES = 0.41 vs. 0.19) bench press 1RM. Similar results were observed for mean velocity as well as both absolute and relative mean power at 35, 45, 65, 75, and 85% of 1RM. Furthermore, both groups experienced large gains in both velocity and power in the heavier loads but small improvements in the lighter loads. The improvements in both velocity and power against the heavier loads were larger for the VRT group, whereas smaller differences existed between the 2 groups in the lighter loads. Variable resistance training using elastic bands may offer a greater training stimulus than traditional free-weight resistance training to improve upper-body strength, velocity, and power in elite youth rugby league players.

  4. Importance of eccentric actions in performance adaptations to resistance training

    NASA Technical Reports Server (NTRS)

    Dudley, Gary A.; Miller, Bruce J.; Buchanan, Paul; Tesch, Per A.

    1991-01-01

    The importance of eccentric (ecc) muscle actions in resistance training for the maintenance of muscle strength and mass in hypogravity was investigated in experiments in which human subjects, divided into three groups, were asked to perform four-five sets of 6 to 12 repetitions (rep) per set of three leg press and leg extension exercises, 2 days each weeks for 19 weeks. One group, labeled 'con', performed each rep with only concentric (con) actions, while group con/ecc with performed each rep with only ecc actions; the third group, con/con, performed twice as many sets with only con actions. Control subjects did not train. It was found that resistance training wih both con and ecc actions induced greater increases in muscle strength than did training with only con actions.

  5. Resistance Training: Physiological Responses and Adaptations (Part 2 of 4).

    ERIC Educational Resources Information Center

    Fleck, Stephen J.; Kraerner, William J.

    1988-01-01

    Resistance training causes a variety of physiological reactions, including changes in muscle size, connective tissue size, and bone mineral content. This article summarizes data from a variety of studies and research. (JL)

  6. Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network.

    PubMed

    Kusy, Maciej; Zajdel, Roman

    2015-09-01

    In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: Q(0)-learning, Q(λ)-learning, and stateless Q-learning. We regard three types of PNN classifiers: the model that uses single smoothing parameter for the whole network, the model that utilizes single smoothing parameter for each data attribute, and the model that possesses the matrix of smoothing parameters different for each data variable and data class. Reinforcement learning is applied as the method of finding such a value of the smoothing parameter, which ensures the maximization of the prediction ability. PNN models with smoothing parameters computed according to the proposed algorithms are tested on eight databases by calculating the test error with the use of the cross validation procedure. The results are compared with state-of-the-art methods for PNN training published in the literature up to date and, additionally, with PNN whose sigma is determined by means of the conjugate gradient approach. The results demonstrate that the proposed approaches can be used as alternative PNN training procedures.

  7. Does load uncertainty affect adaptation to catch training?

    PubMed

    Berg, William P; Richards, Brian J; Hannigan, Aaron M; Biller, Kelsey L; Hughes, Michael R

    2016-09-01

    Catching relies on anticipatory and compensatory control processes. Load uncertainty increases anticipatory and compensatory neuromotor effort in catching. This experiment tested the effect of load uncertainty in plyometric catch/throw training on elbow flexion reaction time (RT), movement time (MT) and peak torque, as well as the distribution of anticipatory and compensatory neuromotor effort in catching. We expected load uncertainty training to be superior to traditional training for improving elbow flexion MT and peak torque, as well as for reallocating neuromotor effort from compensatory to anticipatory control in catching. Three groups of men (mean age = 21), load knowledge training (K) (n = 14), load uncertainty training (U) (n = 13) and control (C) (n = 14), participated. Groups K and U trained three times/week for 6 weeks using single-arm catch/throw exercises with 0.45-4.08 kg balls. Sets involved 16 repetitions of four different ball masses presented randomly. Group K had knowledge of ball mass on every repetition, whereas group U never did. Change scores were analyzed using Kruskal-Wallis tests and follow-up Wilcoxon rank-sum tests. Group K improved both RT and MT (by 6.2 and 12 %, respectively), whereas group U did not. Both groups K and U improved peak eccentric elbow flexion torque. Group K reallocated neuromotor effort from compensatory to anticipatory processes in the biceps, triceps and the all muscle average, whereas group U did so in the triceps only. In sum, plyometric catch/throw training caused a reallocation of neuromotor effort from compensatory to anticipatory control in catching. However, load uncertainty training did not amplify this effect and in fact appeared to inhibit the reallocation of neuromotor effort from compensatory to anticipatory control.

  8. A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

    PubMed Central

    Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah

    2015-01-01

    A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974

  9. Optimized adaptation algorithm for HEVC/H.265 dynamic adaptive streaming over HTTP using variable segment duration

    NASA Astrophysics Data System (ADS)

    Irondi, Iheanyi; Wang, Qi; Grecos, Christos

    2016-04-01

    Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation algorithm is then proposed to accurately estimate the bandwidth and precisely determine the time to download the next

  10. Adapted Resistance Training Improves Strength in Eight Weeks in Individuals with Multiple Sclerosis.

    PubMed

    Keller, Jennifer L; Fritz, Nora; Chiang, Chen Chun; Jiang, Allen; Thompson, Tziporah; Cornet, Nicole; Newsome, Scott D; Calabresi, Peter A; Zackowski, Kathleen

    2016-01-29

    Hip weakness is a common symptom affecting walking ability in people with multiple sclerosis (MS). It is known that resistance strength training (RST) can improve strength in individuals with MS, however; it remains unclear the duration of RST that is needed to make strength gains and how to adapt hip strengthening exercises for individuals of varying strength using only resistance bands. This paper describes the methodology to set up and implement an adapted resistance strength training program, using resistance bands, for individuals with MS. Directions for pre- and post-strength tests to evaluate efficacy of the strength-training program are included. Safety features and detailed instructions outline the weekly program content and progression. Current evidence is presented showing that significant strength gains can be made within 8 weeks of starting a RST program. Evidence is also presented showing that resistance strength training can be successfully adapted for individuals with MS of varying strength with little equipment.

  11. Search Control Algorithm Based on Random Step Size Hill-Climbing Method for Adaptive PMD Compensation

    NASA Astrophysics Data System (ADS)

    Tanizawa, Ken; Hirose, Akira

    Adaptive polarization mode dispersion (PMD) compensation is required for the speed-up and advancement of the present optical communications. The combination of a tunable PMD compensator and its adaptive control method achieves adaptive PMD compensation. In this paper, we report an effective search control algorithm for the feedback control of the PMD compensator. The algorithm is based on the hill-climbing method. However, the step size changes randomly to prevent the convergence from being trapped at a local maximum or a flat, unlike the conventional hill-climbing method. The randomness depends on the Gaussian probability density functions. We conducted transmission simulations at 160Gb/s and the results show that the proposed method provides more optimal compensator control than the conventional hill-climbing method.

  12. Adaptive and perceptual learning technologies in medical education and training.

    PubMed

    Kellman, Philip J

    2013-10-01

    Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains.

  13. A 6-month analysis of training-intensity distribution and physiological adaptation in Ironman triathletes.

    PubMed

    Neal, Craig M; Hunter, Angus M; Galloway, Stuart D R

    2011-11-01

    In the present study, we analysed the training-intensity distribution and physiological adaptations over a 6-month period preceding an Ironman triathlon race. Ten athletes (mean ± s: age 43 ± 3 years, mass 78.3 ± 10.3 kg, stature 1.79 ± 0.05 m) participated in the study. The study consisted of three training periods (A, B, C), each of approximately 2 months' duration, and four testing weeks. Testing consisted of incremental tests to exhaustion for swimming, cycling and running, and assessments for anthropometry plus cardiovascular and pulmonary measures. The lactate threshold and the lactate turnpoint were used to demarcate three discipline-specific, exercise-intensity zones. The mean percentage of time spent in zones 1, 2, and 3 was 69 ± 9%, 25 ± 8%, and 6 ± 2% for periods A-C combined. Only modest physiological adaptation occurred throughout the 6-month period, with small to moderate effect sizes at best. Relationships between the training volume/training load and the training-intensity distribution with the changes in key measures of adaptation were weak and probably reflect differences in initial training status. Our results suggest that the effects of intensity distribution are small over short-term training periods and future experimental research is needed to clarify the potential impact of intensity distribution on physiological adaptation.

  14. Performance and neuromuscular adaptations following differing ratios of concurrent strength and endurance training.

    PubMed

    Jones, Thomas W; Howatson, Glyn; Russell, Mark; French, Duncan N

    2013-12-01

    The interference effect attenuates strength and hypertrophic responses when strength and endurance training are conducted concurrently; however, the influence of training frequency on these responses remain unclear when varying ratios of concurrent strength and endurance training are performed. Therefore, the purpose of the study was to examine the strength, limb girth, and neuromuscular adaptations to varying ratios of concurrent strength and endurance training. Twenty-four men with >2 years resistance training experience completed 6 weeks of 3 days per week of (a) strength training (ST), (b) concurrent strength and endurance training ratio 3:1 (CT3), (c) concurrent strength and endurance training ratio 1:1 (CT1), or (d) no training (CON) in an isolated limb model. Assessments of maximal voluntary contraction by means of isokinetic dynamometry leg extensions (maximum voluntary suppression [MVC]), limb girth, and neuromuscular responses through electromyography (EMG) were conducted at baseline, mid-intervention, and postintervention. After training, ST and CT3 conditions elicited greater MVC increases than CT1 and CON conditions (p ≤ 0.05). Strength training resulted in significantly greater increases in limb girth than both CT1 and CON conditions (p = 0.05 and 0.004, respectively). The CT3 induced significantly greater limb girth adaptations than CON condition (p = 0.04). No effect of time or intervention was observed for EMG (p > 0.05). In conclusion, greater frequencies of endurance training performed increased the magnitude of the interference response on strength and limb girth responses after 6 weeks of 3 days a week of training. Therefore, the frequency of endurance training should remain low if the primary focus of the training intervention is strength and hypertrophy.

  15. An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication

    NASA Astrophysics Data System (ADS)

    Wang, Pangwei; Wang, Yunpeng; Yu, Guizhen; Tang, Tieqiao

    2014-05-01

    For the Cooperative Adaptive Cruise Control (CACC) Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.

  16. A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems.

    PubMed

    Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro

    2015-06-01

    The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively.

  17. Speech modifications algorithms used for training language learning-impaired children.

    PubMed

    Nagarajan, S S; Wang, X; Merzenich, M M; Schreiner, C E; Johnston, P; Jenkins, W M; Miller, S; Tallal, P

    1998-09-01

    In this paper, the details of processing algorithms used in a training program with language learning-impaired children (LLI's) are described. The training program utilized computer games, speech/language training exercises, books-on-tape and educational CD-ROM's. Speech tracks in these materials were processed using these algorithms. During a four week training period, recognition of both processed and normal speech in these children continually increased to near age-appropriate levels. We conclude that this form of processed speech is subject to profound perceptual learning effects and exhibits widespread generalization to normal speech. This form of learning and generalization contributes to the rehabilitation of temporal processing deficits and language comprehension in this subject population.

  18. A conjugate gradients/trust regions algorithms for training multilayer perceptrons for nonlinear mapping

    NASA Technical Reports Server (NTRS)

    Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.

    1992-01-01

    This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.

  19. Transform Domain Robust Variable Step Size Griffiths' Adaptive Algorithm for Noise Cancellation in ECG

    NASA Astrophysics Data System (ADS)

    Hegde, Veena; Deekshit, Ravishankar; Satyanarayana, P. S.

    2011-12-01

    The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts or noise. Noise severely limits the utility of the recorded ECG and thus needs to be removed, for better clinical evaluation. In the present paper a new noise cancellation technique is proposed for removal of random noise like muscle artifact from ECG signal. A transform domain robust variable step size Griffiths' LMS algorithm (TVGLMS) is proposed for noise cancellation. For the TVGLMS, the robust variable step size has been achieved by using the Griffiths' gradient which uses cross-correlation between the desired signal contaminated with observation or random noise and the input. The algorithm is discrete cosine transform (DCT) based and uses symmetric property of the signal to represent the signal in frequency domain with lesser number of frequency coefficients when compared to that of discrete Fourier transform (DFT). The algorithm is implemented for adaptive line enhancer (ALE) filter which extracts the ECG signal in a noisy environment using LMS filter adaptation. The proposed algorithm is found to have better convergence error/misadjustment when compared to that of ordinary transform domain LMS (TLMS) algorithm, both in the presence of white/colored observation noise. The reduction in convergence error achieved by the new algorithm with desired signal decomposition is found to be lower than that obtained without decomposition. The experimental results indicate that the proposed method is better than traditional adaptive filter using LMS algorithm in the aspects of retaining geometrical characteristics of ECG signal.

  20. 49 CFR 231.23 - Unidirectional passenger-train cars adaptable to van-type semi-trailer use.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false Unidirectional passenger-train cars adaptable to... APPLIANCE STANDARDS § 231.23 Unidirectional passenger-train cars adaptable to van-type semi-trailer use. (a) Hand brakes—(1) Number. Same as specified for “Passenger-Train Cars Without End-Platforms.”...

  1. 49 CFR 231.23 - Unidirectional passenger-train cars adaptable to van-type semi-trailer use.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Unidirectional passenger-train cars adaptable to... APPLIANCE STANDARDS § 231.23 Unidirectional passenger-train cars adaptable to van-type semi-trailer use. (a) Hand brakes—(1) Number. Same as specified for “Passenger-Train Cars Without End-Platforms.”...

  2. 49 CFR 231.23 - Unidirectional passenger-train cars adaptable to van-type semi-trailer use.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Unidirectional passenger-train cars adaptable to... APPLIANCE STANDARDS § 231.23 Unidirectional passenger-train cars adaptable to van-type semi-trailer use. (a) Hand brakes—(1) Number. Same as specified for “Passenger-Train Cars Without End-Platforms.”...

  3. 49 CFR 231.23 - Unidirectional passenger-train cars adaptable to van-type semi-trailer use.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Unidirectional passenger-train cars adaptable to... APPLIANCE STANDARDS § 231.23 Unidirectional passenger-train cars adaptable to van-type semi-trailer use. (a) Hand brakes—(1) Number. Same as specified for “Passenger-Train Cars Without End-Platforms.”...

  4. Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

    SciTech Connect

    Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana

    2008-11-06

    This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

  5. Adaptive search range adjustment and multiframe selection algorithm for motion estimation in H.264/AVC

    NASA Astrophysics Data System (ADS)

    Liu, Yingzhe; Wang, Jinxiang; Fu, Fangfa

    2013-04-01

    The H.264/AVC video standard adopts a fixed search range (SR) and fixed reference frame (RF) for motion estimation. These fixed settings result in a heavy computational load in the video encoder. We propose a dynamic SR and multiframe selection algorithm to improve the computational efficiency of motion estimation. By exploiting the relationship between the predicted motion vector and the SR size, we develop an adaptive SR adjustment algorithm. We also design a RF selection scheme based on the correlation between the different block sizes of the macroblock. Experimental results show that our algorithm can significantly reduce the computational complexity of motion estimation compared with the JM15.1 reference software, with a negligible decrease in peak signal-to-noise ratio and a slight increase in bit rate. Our algorithm also outperforms existing methods in terms of its low complexity and high coding quality.

  6. Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection

    NASA Astrophysics Data System (ADS)

    Gao, Hang; Chen, Qian; Sui, Xiubao; Zeng, Junjie; Zhao, Yao

    2016-09-01

    Restricted by the detector technology and optical diffraction limit, the spatial resolution of infrared imaging system is difficult to achieve significant improvement. Super-Resolution (SR) reconstruction algorithm is an effective way to solve this problem. Among them, the SR algorithm based on multichannel blind deconvolution (MBD) estimates the convolution kernel only by low resolution observation images, according to the appropriate regularization constraints introduced by a priori assumption, to realize the high resolution image restoration. The algorithm has been shown effective when each channel is prime. In this paper, we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism, according to the uncertainty of the convolution kernel size in MBD processing. To reduce the interference of noise, we amend the convolution kernel in an iterative process, and finally restore a clear image. Experimental results show that the algorithm can meet the convergence requirement of the convolution kernel estimation.

  7. Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

    PubMed

    Liu, Derong; Li, Hongliang; Wang, Ding

    2015-06-01

    In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

  8. On an adaptive preconditioned Crank-Nicolson MCMC algorithm for infinite dimensional Bayesian inference

    NASA Astrophysics Data System (ADS)

    Hu, Zixi; Yao, Zhewei; Li, Jinglai

    2017-03-01

    Many scientific and engineering problems require to perform Bayesian inference for unknowns of infinite dimension. In such problems, many standard Markov Chain Monte Carlo (MCMC) algorithms become arbitrary slow under the mesh refinement, which is referred to as being dimension dependent. To this end, a family of dimensional independent MCMC algorithms, known as the preconditioned Crank-Nicolson (pCN) methods, were proposed to sample the infinite dimensional parameters. In this work we develop an adaptive version of the pCN algorithm, where the covariance operator of the proposal distribution is adjusted based on sampling history to improve the simulation efficiency. We show that the proposed algorithm satisfies an important ergodicity condition under some mild assumptions. Finally we provide numerical examples to demonstrate the performance of the proposed method.

  9. A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

    NASA Astrophysics Data System (ADS)

    Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

    MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

  10. Self-adaptive predictor-corrector algorithm for static nonlinear structural analysis

    NASA Technical Reports Server (NTRS)

    Padovan, J.

    1981-01-01

    A multiphase selfadaptive predictor corrector type algorithm was developed. This algorithm enables the solution of highly nonlinear structural responses including kinematic, kinetic and material effects as well as pro/post buckling behavior. The strategy involves three main phases: (1) the use of a warpable hyperelliptic constraint surface which serves to upperbound dependent iterate excursions during successive incremental Newton Ramphson (INR) type iterations; (20 uses an energy constraint to scale the generation of successive iterates so as to maintain the appropriate form of local convergence behavior; (3) the use of quality of convergence checks which enable various self adaptive modifications of the algorithmic structure when necessary. The restructuring is achieved by tightening various conditioning parameters as well as switch to different algorithmic levels to improve the convergence process. The capabilities of the procedure to handle various types of static nonlinear structural behavior are illustrated.

  11. An adaptive metamodel-based global optimization algorithm for black-box type problems

    NASA Astrophysics Data System (ADS)

    Jie, Haoxiang; Wu, Yizhong; Ding, Jianwan

    2015-11-01

    In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.

  12. A parallel second-order adaptive mesh algorithm for incompressible flow in porous media.

    PubMed

    Pau, George S H; Almgren, Ann S; Bell, John B; Lijewski, Michael J

    2009-11-28

    In this paper, we present a second-order accurate adaptive algorithm for solving multi-phase, incompressible flow in porous media. We assume a multi-phase form of Darcy's law with relative permeabilities given as a function of the phase saturation. The remaining equations express conservation of mass for the fluid constituents. In this setting, the total velocity, defined to be the sum of the phase velocities, is divergence free. The basic integration method is based on a total-velocity splitting approach in which we solve a second-order elliptic pressure equation to obtain a total velocity. This total velocity is then used to recast component conservation equations as nonlinear hyperbolic equations. Our approach to adaptive refinement uses a nested hierarchy of logically rectangular grids with simultaneous refinement of the grids in both space and time. The integration algorithm on the grid hierarchy is a recursive procedure in which coarse grids are advanced in time, fine grids are advanced multiple steps to reach the same time as the coarse grids and the data at different levels are then synchronized. The single-grid algorithm is described briefly, but the emphasis here is on the time-stepping procedure for the adaptive hierarchy. Numerical examples are presented to demonstrate the algorithm's accuracy and convergence properties and to illustrate the behaviour of the method.

  13. A Parallel Second-Order Adaptive Mesh Algorithm for Incompressible Flow in Porous Media

    SciTech Connect

    Pau, George Shu Heng; Almgren, Ann S.; Bell, John B.; Lijewski, Michael J.

    2008-04-01

    In this paper we present a second-order accurate adaptive algorithm for solving multiphase, incompressible flows in porous media. We assume a multiphase form of Darcy's law with relative permeabilities given as a function of the phase saturation. The remaining equations express conservation of mass for the fluid constituents. In this setting the total velocity, defined to be the sum of the phase velocities, is divergence-free. The basic integration method is based on a total-velocity splitting approach in which we solve a second-order elliptic pressure equation to obtain a total velocity. This total velocity is then used to recast component conservation equations as nonlinear hyperbolic equations. Our approach to adaptive refinement uses a nested hierarchy of logically rectangular grids with simultaneous refinement of the grids in both space and time. The integration algorithm on the grid hierarchy is a recursive procedure in which coarse grids are advanced in time, fine grids areadvanced multiple steps to reach the same time as the coarse grids and the data atdifferent levels are then synchronized. The single grid algorithm is described briefly,but the emphasis here is on the time-stepping procedure for the adaptive hierarchy. Numerical examples are presented to demonstrate the algorithm's accuracy and convergence properties and to illustrate the behavior of the method.

  14. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods.

    PubMed

    Ergün, Ayla; Barbieri, Riccardo; Eden, Uri T; Wilson, Matthew A; Brown, Emery N

    2007-03-01

    The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.

  15. A structured multi-block solution-adaptive mesh algorithm with mesh quality assessment

    NASA Technical Reports Server (NTRS)

    Ingram, Clint L.; Laflin, Kelly R.; Mcrae, D. Scott

    1995-01-01

    The dynamic solution adaptive grid algorithm, DSAGA3D, is extended to automatically adapt 2-D structured multi-block grids, including adaption of the block boundaries. The extension is general, requiring only input data concerning block structure, connectivity, and boundary conditions. Imbedded grid singular points are permitted, but must be prevented from moving in space. Solutions for workshop cases 1 and 2 are obtained on multi-block grids and illustrate both increased resolution of and alignment with the solution. A mesh quality assessment criteria is proposed to determine how well a given mesh resolves and aligns with the solution obtained upon it. The criteria is used to evaluate the grid quality for solutions of workshop case 6 obtained on both static and dynamically adapted grids. The results indicate that this criteria shows promise as a means of evaluating resolution.

  16. Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring.

    PubMed

    Plews, Daniel J; Laursen, Paul B; Stanley, Jamie; Kilding, Andrew E; Buchheit, Martin

    2013-09-01

    The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how

  17. Virtual Reality as a Medium for Sensorimotor Adaptation Training and Spaceflight Countermeasures

    NASA Technical Reports Server (NTRS)

    Madansingh, S.; Bloomberg, J. J.

    2015-01-01

    With the upcoming shift to extra-long duration missions (1 year) aboard the ISS, sensorimotor adaptations during transitory periods in-and-out of microgravity are more important to understand and prepare for. Advances in virtual reality technology enables everyday adoption of these tools for entertainment and use in training. Experiencing virtual environments (VE) allows for the manipulation of visual flow to elicit automatic motor behavior and produce sensorimotor adaptation (SA). Recently, the ability to train individuals using repeatable and varied exposures to SA challenges has shown success by improving performance during exposure to a novel environment (Batson 2011). This capacity to 'learn to learn' is referred to as sensorimotor adaptive generalizability and, through the use of treadmill training, represents an untapped potential for individualized countermeasures. The goal of this study is to determine the feasibility of present head mounted displays (HMDs) to produce compelling visual flow information and the expected adaptations for use in future SA treadmill-based countermeasures. Participants experience infinite hallways providing congruent (baseline) or incongruent visual information (half or double speed) via HMD while walking on an instrumented treadmill at 1.1m/s. As gait performance approaches baseline levels, an adaptation time constant is derived to establish individual time-to-adapt (TTA). It is hypothesized that decreasing the TTA through SA treadmill training will facilitate sensorimotor adaptation during gravitational transitions. In this way, HMD technology represents a novel platform for SA training using off-the-shelf consumer products for greater training flexibility in astronaut and terrestrial applications alike.

  18. Gait adaptability training improves obstacle avoidance and dynamic stability in patients with cerebellar degeneration.

    PubMed

    Fonteyn, Ella M R; Heeren, Anita; Engels, Jasper-Jan C; Boer, Jasper J Den; van de Warrenburg, Bart P C; Weerdesteyn, Vivian

    2014-01-01

    Balance and gait problems in patients with cerebellar degeneration lead to reduced mobility, loss of independence, and frequent falls. It is currently unclear, however, whether balance and gait capacities can be improved by training in this group of patients. Therefore, the aim of this study was to examine the effects of gait adaptability training on obstacle avoidance and dynamic stability during adaptive gait. Ten patients with degenerative cerebellar ataxia received 10 protocolized gait adaptability training sessions of 1 h each during 5 weeks. Training was performed on a treadmill with visual stepping targets and obstacles projected on the belt's surface. As the primary outcome, we used an obstacle avoidance task while walking on a treadmill. We determined avoidance success rates, as well as dynamic stability during the avoidance manoeuvre. Clinical ratings included the scale for the assessment of ataxia (SARA), 10 m walking test, timed up-and-go test, berg balance scale, and the obstacle subtask of the emory functional ambulation profile (EFAP). Following the intervention, success rates on the obstacle avoidance task had significantly improved compared to pre-intervention. For successful avoidance, participants allowed themselves smaller stability margins in the sagittal plane in the (shortened) pre-crossing step. However, in the subsequent steps they returned to baseline stability values more effectively than before training. SARA scores and the EFAP obstacle subtask improved significantly as well. This pilot study provides preliminary evidence of a beneficial effect of gait adaptability training on obstacle avoidance capacity and dynamic stability in patients with cerebellar degeneration.

  19. Dynamic programming algorithms for comparing multineuronal spike trains via cost-based metrics and alignments.

    PubMed

    Victor, Jonathan D; Goldberg, David H; Gardner, Daniel

    2007-04-15

    Cost-based metrics formalize notions of distance, or dissimilarity, between two spike trains, and are applicable to single- and multineuronal responses. As such, these metrics have been used to characterize neural variability and neural coding. By examining the structure of an efficient algorithm [Aronov D, 2003. Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. J Neurosci Methods 124(2), 175-79] implementing a metric for multineuronal responses, we determine criteria for its generalization, and identify additional efficiencies that are applicable when related dissimilarity measures are computed in parallel. The generalized algorithm provides the means to test a wide range of coding hypotheses.

  20. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    NASA Astrophysics Data System (ADS)

    Masri Husam Fayiz, Al

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

  1. TRAINING-INDUCED VASCULAR ADAPTATIONS TO ISCHEMIC MUSCLE

    PubMed Central

    YANG, H.T.; PRIOR, B.M.; LLOYD, P.G.; TAYLOR, J.C.; LI, Z.; LAUGHLIN, M.H.; TERJUNG, R.L.

    2009-01-01

    Peripheral arterial insufficiency is a progressive degenerative disease associated with an increased morbidity and mortality. It decreases exercise tolerance and often presents with symptoms of intermittent claudication. Enhanced physical activity is one of the most effective means of improving the life of affected patients. While this occurs for a variety of reasons, vascular remodeling can be an important means for improved oxygen exchange and blood flow delivery. Relevant exercise-induced signals stimulate angiogenesis, within the active muscle (e.g. hypoxia), and arteriogenesis (enlargement of pre-existing vessels via increased shear stress) to increase oxygen exchange and blood flow capacity, respectively. Evidence from pre-clinical studies shows that the increase in collateral blood flow observed with exercise progresses over time of training, is accompanied by significant enlargement of isolated collateral vessels, and enhances the responses observed with angiogenic growth factors (e.g. VEGF, FGF-2). Thus, enhanced physical activity can be an effective means of enlarging the structure and function of the collateral circuit. Interestingly, disrupting normal NO production (via L-NAME) eliminates this increase in collateral blood flow induced by training, but does not disturb the increase in muscle capillarity within the active muscle. Similarly, inhibiting VEGF receptor kinase activity eliminates the increase in collateral-dependent blood flow, and lessens, but does not eliminate, angiogenesis within the calf muscle. These findings illustrate distinctions between the processes influencing angiogenesis and arteriogenesis. Further, sympathetic modulation of the collateral circuit does not eliminate the increase in collateral circuit conductance induced by exercise training. These findings indicate that structural enlargement of the collateral vessels is essential to realize the increase in collateral-dependent blood flow capacity caused by exercise training

  2. Development of the TARGET Training Effectiveness Tool and Underlying Algorithms Specifying Training Method - Performance Outcome Relationships

    DTIC Science & Technology

    2014-05-01

    Training aide: Research and guidance for effective training - User guide. (Research Product 2014-02). Fort Belvoir, VA: U.S. Army Research Institute for...hand and right hand on the piano, or strumming and chording on the guitar . Perceptual This skill category involves detecting and interpreting sensory

  3. Neuromuscular adaptations to constant vs. variable resistance training in older men.

    PubMed

    Walker, S; Peltonen, H; Sautel, J; Scaramella, C; Kraemer, W J; Avela, J; Häkkinen, K

    2014-01-01

    This study examined the effects of constant or variable external resistance training on neuromuscular adaptations in the lower limbs of older men. 37 subjects (age 65±4 year) were quasi-randomly assigned to the constant or variable training group, or a non-training control group. Training consisted of a 20-week medium-intensity, high volume resistance training program. Maximum bilateral concentric and isometric force production of the leg extensors as well as repetitions-to-failure test were performed pre-, mid- and post-training. Vastus lateralis muscle cross-sectional area was assessed by ultrasound and lean leg mass was assessed by dual-energy x-ray absorptiometry. Both training groups significantly increased force production of the leg extensors (variable: 26 kg, 95% CI=12-39, P<0.01; constant: 31 kg, 95% CI=19-43, P<0.01) and VL cross-sectional area (variable: 1.5 cm2, 95% CI=0.03-3.1, P=0.046; constant: 3 cm2, 95% CI=1.2-4.8, P=0.002). However, only the variable training group significantly improved repetitions to failure performance (704 kg, 95% CI=45-1 364, P=0.035). Only the variable resistance training group improved fatigue-resistance properties, which may be an important adaptation to maintain exercise and functional capacity in older individuals.

  4. Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm

    PubMed Central

    Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

    2016-01-01

    In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis. PMID:27959895

  5. Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

    PubMed

    Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

    2016-01-01

    In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

  6. Dependence of Adaptive Cross-correlation Algorithm Performance on the Extended Scene Image Quality

    NASA Technical Reports Server (NTRS)

    Sidick, Erkin

    2008-01-01

    Recently, we reported an adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels between two extended-scene sub-images captured by a Shack-Hartmann wavefront sensor. It determines the positions of all extended-scene image cells relative to a reference cell in the same frame using an FFT-based iterative image-shifting algorithm. It works with both point-source spot images as well as extended scene images. We have demonstrated previously based on some measured images that the ACC algorithm can determine image shifts with as high an accuracy as 0.01 pixel for shifts as large 3 pixels, and yield similar results for both point source spot images and extended scene images. The shift estimate accuracy of the ACC algorithm depends on illumination level, background, and scene content in addition to the amount of the shift between two image cells. In this paper we investigate how the performance of the ACC algorithm depends on the quality and the frequency content of extended scene images captured by a Shack-Hatmann camera. We also compare the performance of the ACC algorithm with those of several other approaches, and introduce a failsafe criterion for the ACC algorithm-based extended scene Shack-Hatmann sensors.

  7. A fast image super-resolution algorithm using an adaptive Wiener filter.

    PubMed

    Hardie, Russell

    2007-12-01

    A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.

  8. Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics.

    PubMed

    Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

    2017-04-06

    An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

  9. A novel adaptive multi-focus image fusion algorithm based on PCNN and sharpness

    NASA Astrophysics Data System (ADS)

    Miao, Qiguang; Wang, Baoshu

    2005-05-01

    A novel adaptive multi-focus image fusion algorithm is given in this paper, which is based on the improved pulse coupled neural network(PCNN) model, the fundamental characteristics of the multi-focus image and the properties of visual imaging. Compared with the traditional algorithm where the linking strength, βij, of each neuron in the PCNN model is the same and its value is chosen through experimentation, this algorithm uses the clarity of each pixel of the image as its value, so that the linking strength of each pixel can be chosen adaptively. A fused image is produced by processing through the compare-select operator the objects of each firing mapping image taking part in image fusion, deciding in which image the clear parts is and choosing the clear parts in the image fusion process. By this algorithm, other parameters, for example, Δ, the threshold adjusting constant, only have a slight effect on the new fused image. It therefore overcomes the difficulty in adjusting parameters in the PCNN. Experiments show that the proposed algorithm works better in preserving the edge and texture information than the wavelet transform method and the Laplacian pyramid method do in multi-focus image fusion.

  10. Algorithm for localized adaptive diffuse optical tomography and its application in bioluminescence tomography

    NASA Astrophysics Data System (ADS)

    Naser, Mohamed A.; Patterson, Michael S.; Wong, John W.

    2014-04-01

    A reconstruction algorithm for diffuse optical tomography based on diffusion theory and finite element method is described. The algorithm reconstructs the optical properties in a permissible domain or region-of-interest to reduce the number of unknowns. The algorithm can be used to reconstruct optical properties for a segmented object (where a CT-scan or MRI is available) or a non-segmented object. For the latter, an adaptive segmentation algorithm merges contiguous regions with similar optical properties thereby reducing the number of unknowns. In calculating the Jacobian matrix the algorithm uses an efficient direct method so the required time is comparable to that needed for a single forward calculation. The reconstructed optical properties using segmented, non-segmented, and adaptively segmented 3D mouse anatomy (MOBY) are used to perform bioluminescence tomography (BLT) for two simulated internal sources. The BLT results suggest that the accuracy of reconstruction of total source power obtained without the segmentation provided by an auxiliary imaging method such as x-ray CT is comparable to that obtained when using perfect segmentation.

  11. Parallel paving: An algorithm for generating distributed, adaptive, all-quadrilateral meshes on parallel computers

    SciTech Connect

    Lober, R.R.; Tautges, T.J.; Vaughan, C.T.

    1997-03-01

    Paving is an automated mesh generation algorithm which produces all-quadrilateral elements. It can additionally generate these elements in varying sizes such that the resulting mesh adapts to a function distribution, such as an error function. While powerful, conventional paving is a very serial algorithm in its operation. Parallel paving is the extension of serial paving into parallel environments to perform the same meshing functions as conventional paving only on distributed, discretized models. This extension allows large, adaptive, parallel finite element simulations to take advantage of paving`s meshing capabilities for h-remap remeshing. A significantly modified version of the CUBIT mesh generation code has been developed to host the parallel paving algorithm and demonstrate its capabilities on both two dimensional and three dimensional surface geometries and compare the resulting parallel produced meshes to conventionally paved meshes for mesh quality and algorithm performance. Sandia`s {open_quotes}tiling{close_quotes} dynamic load balancing code has also been extended to work with the paving algorithm to retain parallel efficiency as subdomains undergo iterative mesh refinement.

  12. A semi-supervised classification algorithm using the TAD-derived background as training data

    NASA Astrophysics Data System (ADS)

    Fan, Lei; Ambeau, Brittany; Messinger, David W.

    2013-05-01

    In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

  13. Visuomotor adaptation in head-mounted virtual reality versus conventional training

    PubMed Central

    Anglin, J. M.; Sugiyama, T.; Liew, S.-L.

    2017-01-01

    Immersive, head-mounted virtual reality (HMD-VR) provides a unique opportunity to understand how changes in sensory environments affect motor learning. However, potential differences in mechanisms of motor learning and adaptation in HMD-VR versus a conventional training (CT) environment have not been extensively explored. Here, we investigated whether adaptation on a visuomotor rotation task in HMD-VR yields similar adaptation effects in CT and whether these effects are achieved through similar mechanisms. Specifically, recent work has shown that visuomotor adaptation may occur via both an implicit, error-based internal model and a more cognitive, explicit strategic component. We sought to measure both overall adaptation and balance between implicit and explicit mechanisms in HMD-VR versus CT. Twenty-four healthy individuals were placed in either HMD-VR or CT and trained on an identical visuomotor adaptation task that measured both implicit and explicit components. Our results showed that the overall timecourse of adaption was similar in both HMD-VR and CT. However, HMD-VR participants utilized a greater cognitive strategy than CT, while CT participants engaged in greater implicit learning. These results suggest that while both conditions produce similar results in overall adaptation, the mechanisms by which visuomotor adaption occurs in HMD-VR appear to be more reliant on cognitive strategies. PMID:28374808

  14. Visuomotor adaptation in head-mounted virtual reality versus conventional training.

    PubMed

    Anglin, J M; Sugiyama, T; Liew, S-L

    2017-04-04

    Immersive, head-mounted virtual reality (HMD-VR) provides a unique opportunity to understand how changes in sensory environments affect motor learning. However, potential differences in mechanisms of motor learning and adaptation in HMD-VR versus a conventional training (CT) environment have not been extensively explored. Here, we investigated whether adaptation on a visuomotor rotation task in HMD-VR yields similar adaptation effects in CT and whether these effects are achieved through similar mechanisms. Specifically, recent work has shown that visuomotor adaptation may occur via both an implicit, error-based internal model and a more cognitive, explicit strategic component. We sought to measure both overall adaptation and balance between implicit and explicit mechanisms in HMD-VR versus CT. Twenty-four healthy individuals were placed in either HMD-VR or CT and trained on an identical visuomotor adaptation task that measured both implicit and explicit components. Our results showed that the overall timecourse of adaption was similar in both HMD-VR and CT. However, HMD-VR participants utilized a greater cognitive strategy than CT, while CT participants engaged in greater implicit learning. These results suggest that while both conditions produce similar results in overall adaptation, the mechanisms by which visuomotor adaption occurs in HMD-VR appear to be more reliant on cognitive strategies.

  15. Functional and muscular adaptations in an experimental model for isometric strength training in mice.

    PubMed

    Krüger, Karsten; Gessner, Denise K; Seimetz, Michael; Banisch, Jasmin; Ringseis, Robert; Eder, Klaus; Weissmann, Norbert; Mooren, Frank C

    2013-01-01

    Exercise training induces muscular adaptations that are highly specific to the type of exercise. For a systematic study of the differentiated exercise adaptations on a molecular level mouse models have been used successfully. The aim of the current study was to develop a suitable mouse model of isometric strength exercise training characterized by specific adaptations known from strength training. C57BL/6 mice performed an isometric strength training (ST) for 10 weeks 5 days/week. Additionally, either a sedentary control group (CT) or a regular endurance training group (ET) groups were used as controls. Performance capacity was determined by maximum holding time (MHT) and treadmill spirometry, respectively. Furthermore, muscle fiber types and diameter, muscular concentration of phosphofructokinase 1 (PFK), succinate dehydrogenase (SDHa), and glucose transporter type 4 (GLUT4) were determined. In a further approach, the effect of ST on glucose intolerance was tested in diabetic mice. In mice of the ST group we observed an increase of MHT in isometric strength tests, a type II fiber hypertrophy, and an increased GLUT4 protein content in the membrane fraction. In contrast, in mice of the ET group an increase of VO(2max), a shift to oxidative muscle fiber type and an increase of oxidative enzyme content was measured. Furthermore strength training was effective in reducing glucose intolerance in mice fed a high fat diet. An effective murine strength training model was developed and evaluated, which revealed marked differences in adaptations known from endurance training. This approach seems also suitable to test for therapeutical effects of strength training.

  16. Adaptive-mesh-based algorithm for fluorescence molecular tomography using an analytical solution

    NASA Astrophysics Data System (ADS)

    Wang, Daifa; Song, Xiaolei; Bai, Jing

    2007-07-01

    Fluorescence molecular tomography (FMT) has become an important method for in-vivo imaging of small animals. It has been widely used for tumor genesis, cancer detection, metastasis, drug discovery, and gene therapy. In this study, an algorithm for FMT is proposed to obtain accurate and fast reconstruction by combining an adaptive mesh refinement technique and an analytical solution of diffusion equation. Numerical studies have been performed on a parallel plate FMT system with matching fluid. The reconstructions obtained show that the algorithm is efficient in computation time, and they also maintain image quality.

  17. Algorithme d'adaptation du filtre de Kalman aux variations soudaines de bruit

    NASA Astrophysics Data System (ADS)

    Canciu, Vintila

    This research targets the case of Kalman filtering as applied to linear time-invariant systems having unknown process noise covariance and measurement noise covariance matrices and addresses the problem represented by the incomplete a priori knowledge of these two filter initialization parameters. The goal of this research is to determine in realtime both the process covariance matrix and the noise covariance matrix in the context of adaptive Kalman filtering. The resultant filter, called evolutionary adaptive Kalman filter, is able to adapt to sudden noise variations and constitutes a hybrid solution for adaptive Kalman filtering based on metaheuristic algorithms. MATLAB/Simulink simulation using several processes and covariance matrices plus comparison with other filters was selected as validation method. The Cramer-Rae Lower Bound (CRLB) was used as performance criterion. The thesis begins with a description of the problem under consideration (the design of a Kalman filter that is able to adapt to sudden noise variations) followed by a typical application (INS-GPS integrated navigation system) and by a statistical analysis of publications related to adaptive Kalman filtering. Next, the thesis presents the current architectures of the adaptive Kalman filtering: the innovation adaptive estimator (IAE) and the multiple model adaptive estimator (MMAE). It briefly presents their formulation, their behavior, and the limit of their performances. The thesis continues with the architectural synthesis of the evolutionary adaptive Kalman filter. The steps involved in the solution of the problem under consideration is also presented: an analysis of Kalman filtering and sub-optimal filtering methods, a comparison of current adaptive Kalman and sub-optimal filtering methods, the emergence of evolutionary adaptive Kalman filter as an enrichment of sub-optimal filtering with the help of biological-inspired computational intelligence methods, and the step-by-step architectural

  18. Pulmonary adaptations to swim and inspiratory muscle training.

    PubMed

    Mickleborough, Timothy D; Stager, Joel M; Chatham, Ken; Lindley, Martin R; Ionescu, Alina A

    2008-08-01

    Because the anomalous respiratory characteristics of competitive swimmers have been suggested to be due to inspiratory muscle work, the respiratory muscle and pulmonary function of 30 competitively trained swimmers was assessed at the beginning and end of an intensive 12-week swim training (ST) program. Swimmers (n = 10) combined ST with either inspiratory muscle training (IMT) set at 80% sustained maximal inspiratory pressure (SMIP) with progressively increased work-rest ratios until task failure for 3-days per week (ST + IMT) or ST with sham-IMT (ST + SHAM-IMT, n = 10), or acted as controls (ST only, ST, n = 10). Measures of respiratory and pulmonary function were assessed at the beginning and end of the 12 week study period. There were no significant differences (P > 0.05) in respiratory and pulmonary function between groups (ST + IMT, ST + SHAM-IMT and ST) at baseline and at the end of the 12 week study period. However, within all groups significant increases (P < 0.05) were observed in a number of respiratory and pulmonary function variables at the end of the 12 week study, such as maximal inspiratory and expiratory pressure, inspiratory power output, forced vital capacity, forced expiratory and inspiratory volume in 1-s, total lung capacity and diffusion capacity of the lung. This study has demonstrated that there are no appreciable differences in terms of respiratory changes between elite swimmers undergoing a competitive ST program and those undergoing respiratory muscle training using the flow-resistive IMT device employed in the present study; as yet, the causal mechanisms involved are undefined.

  19. The Pilates method and cardiorespiratory adaptation to training.

    PubMed

    Tinoco-Fernández, Maria; Jiménez-Martín, Miguel; Sánchez-Caravaca, M Angeles; Fernández-Pérez, Antonio M; Ramírez-Rodrigo, Jesús; Villaverde-Gutiérrez, Carmen

    2016-01-01

    Although all authors report beneficial health changes following training based on the Pilates method, no explicit analysis has been performed of its cardiorespiratory effects. The objective of this study was to evaluate possible changes in cardiorespiratory parameters with the Pilates method. A total of 45 university students aged 18-35 years (77.8% female and 22.2% male), who did not routinely practice physical exercise or sports, volunteered for the study and signed informed consent. The Pilates training was conducted over 10 weeks, with three 1-hour sessions per week. Physiological cardiorespiratory responses were assessed using a MasterScreen CPX apparatus. After the 10-week training, statistically significant improvements were observed in mean heart rate (135.4-124.2 beats/min), respiratory exchange ratio (1.1-0.9) and oxygen equivalent (30.7-27.6) values, among other spirometric parameters, in submaximal aerobic testing. These findings indicate that practice of the Pilates method has a positive influence on cardiorespiratory parameters in healthy adults who do not routinely practice physical exercise activities.

  20. Evolving Army Leader Training: Adapting for GWOT Experienced Junior Leaders

    DTIC Science & Technology

    2009-03-10

    and centralized system. If this system does not adapt, flex, and evolve in parallel with the demands of Junior Leaders from the Millennial Generation ...TERMS Junior Leaders: Millennial Generation , Senior Leaders: Generation X, Very Senior Leaders: Baby Boomer Generation 16. SECURITY CLASSIFICATION OF: 17...Research Project DATE: 10 March 2009 WORD COUNT: 6,506 PAGES: 32 KEY TERMS: Junior Leaders: Millennial Generation , Senior Leaders: Generation X, Very

  1. Preflight Adaptation Training for Spatial Orientation and Space Motion Sickness

    NASA Technical Reports Server (NTRS)

    Harm, Deborah L.; Parker, Donald E.

    1994-01-01

    Two part-task preflight adaptation trainers (PATs) are being developed at the NASA Johnson Space Center to preadapt astronauts to novel sensory stimulus conditions similar to those present in microgravity to facilitate adaptation to microgravity and readaptation to Earth. This activity is a major component of a general effort to develop countermeasures aimed at minimizing sensory and sensorimotor disturbances and Space Motion Sickness (SMS) associated with adaptation to microgravity and readaptation to Earth. Design principles for the development of the two trainers are discussed, along with a detailed description of both devices. In addition, a summary of four ground-based investigations using one of the trainers to determine the extent to which various novel sensory stimulus conditions produce changes in compensatory eye movement responses, postural equilibrium, motion sickness symptoms, and electrogastric responses are presented. Finally, a brief description of the general concept of dual-adopted states that underly the development of the PATs, and ongoing and future operational and basic research activities are presented.

  2. Combined evolutionary algorithm and minimum classification error training for DHMM-based land mine detection

    NASA Astrophysics Data System (ADS)

    Zhao, Yunxin; Chen, Ping; Gader, Paul D.; Zhang, Yue

    2002-08-01

    Minimum classification error (MCE) training is proposed to improve performance of a discrete hidden Markov model (DHMM) based landmine detection system. The system (baseline) was proposed previously for detection of both metal and nonmetal mines from ground penetrating radar signatures collected by moving vehicles. An initial DHMM model is trained by conventional methods of vector quantization and Baum-Welch algorithm. A sequential generalized probabilistic descent (GPD) algorithm that minimizes an empirical loss function is then used to estimate the landmine/background DHMM parameters, and an evolutionary algorithm based on fitness score of classification accuracy is used to generate and select codebooks. The landmine data of one geographical site was used for model training, and those of two different sites were used for evaluation of system performance. Three scenarios were studied: apply MCE/GPD alone to DHMM estimation, apply EA alone to codebook generation, first apply EA to codebook generation and then apply MCE/GPD to DHMM estimation. Overall, the combined EA and MCE/GPD training led to the best performance. At the same level of detection rate as the baseline DHMM system, the proposed training reduced false alarm rate by a factor of two, indicating significant performance improvement.

  3. Hormonal and psychological adaptation in elite male rowers during prolonged training.

    PubMed

    Purge, P; Jürimäe, J; Jürimäe, T

    2006-10-01

    In this study, we examined possible hormonal and psychological changes in elite male rowers during a 24-week preparatory period. Eleven elite male rowers were tested on seven occasions over the 6-month training season. Fasting testosterone, growth hormone, cortisol, and creatine kinase activity, together with perceived recovery-stress state were evaluated after a day of rest. Maximal oxygen consumption (VO2max) was determined before and after the training period. Training was mainly organized as low-intensity prolonged training sessions. Significant increases in VO2max (from 6.2 +/- 0.5 to 6.4 +/- 0.6 l x min(-1)) were observed as a result of training. The overall perceived recovery-stress index did not change during the preparatory period. Standardized recovery and stress scores changed during the course of training in comparison with pre-training values. When basal hormone concentrations were compared with the first measurement, significant changes in testosterone and cortisol were observed together with changes in mean weekly training volume. Basal testosterone (r = 0.416; P = 0.010) and cortisol (r = 0.527; P = 0.001) were related to mean weekly training volume. Basal growth hormone did not change during the training. Changes in creatine kinase activity demonstrated similar pattern with changes in mean weekly training volume. The overall perceived recovery-stress index was related to testosterone, cortisol, growth hormone, and creatine kinase activity (r > 0.299; P < 0.015). Our findings indicate that testosterone and cortisol are more sensitive to changes in training volume than either growth hormone or perceived recovery-stress state in elite rowing training. Increases in these stress hormone concentrations represent a positive adaptation to current training load. Significant relationships between hormonal and perceived recovery-stress state suggest that metabolic and psychological changes should be carefully monitored to avoid a negative effect on the

  4. A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

    SciTech Connect

    Zarepisheh, Masoud; Li, Nan; Long, Troy; Romeijn, H. Edwin; Tian, Zhen; Jia, Xun; Jiang, Steve B.

    2014-06-15

    Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment

  5. Adjoint-Based Algorithms for Adaptation and Design Optimizations on Unstructured Grids

    NASA Technical Reports Server (NTRS)

    Nielsen, Eric J.

    2006-01-01

    Schemes based on discrete adjoint algorithms present several exciting opportunities for significantly advancing the current state of the art in computational fluid dynamics. Such methods provide an extremely efficient means for obtaining discretely consistent sensitivity information for hundreds of design variables, opening the door to rigorous, automated design optimization of complex aerospace configuration using the Navier-Stokes equation. Moreover, the discrete adjoint formulation provides a mathematically rigorous foundation for mesh adaptation and systematic reduction of spatial discretization error. Error estimates are also an inherent by-product of an adjoint-based approach, valuable information that is virtually non-existent in today's large-scale CFD simulations. An overview of the adjoint-based algorithm work at NASA Langley Research Center is presented, with examples demonstrating the potential impact on complex computational problems related to design optimization as well as mesh adaptation.

  6. A robust face recognition algorithm under varying illumination using adaptive retina modeling

    NASA Astrophysics Data System (ADS)

    Cheong, Yuen Kiat; Yap, Vooi Voon; Nisar, Humaira

    2013-10-01

    Variation in illumination has a drastic effect on the appearance of a face image. This may hinder the automatic face recognition process. This paper presents a novel approach for face recognition under varying lighting conditions. The proposed algorithm uses adaptive retina modeling based illumination normalization. In the proposed approach, retina modeling is employed along with histogram remapping following normal distribution. Retina modeling is an approach that combines two adaptive nonlinear equations and a difference of Gaussians filter. Two databases: extended Yale B database and CMU PIE database are used to verify the proposed algorithm. For face recognition Gabor Kernel Fisher Analysis method is used. Experimental results show that the recognition rate for the face images with different illumination conditions has improved by the proposed approach. Average recognition rate for Extended Yale B database is 99.16%. Whereas, the recognition rate for CMU-PIE database is 99.64%.

  7. A Study on Adapting the Zoom FFT Algorithm to Automotive Millimetre Wave Radar

    NASA Astrophysics Data System (ADS)

    Kuroda, Hiroshi; Takano, Kazuaki

    The millimetre wave radar has been developed for automotive application such as ACC (Adaptive Cruise Control) and CWS (Collision Warning System). The radar uses MMIC (Monolithic Microwave Integrated Circuits) devices for transmitting and receiving 76 GHz millimetre wave signals. The radar is FSK (Frequency Shift Keying) monopulse type. The radar transmits 2 frequencies in time-duplex manner, and measures distance and relative speed of targets. The monopulse feature detects the azimuth angle of targets without a scanning mechanism. The Zoom FFT (Fast Fourier Transform) algorithm, which analyses frequency domain precisely, has adapted to the radar for discriminating multiple stationary targets. The Zoom FFT algorithm is evaluated in test truck. The evaluation results show good performance on discriminating two stationary vehicles in host lane and adjacent lane.

  8. A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training.

    PubMed

    Yang, Zhiyong; Zhang, Taohong; Zhang, Dezheng

    2016-02-01

    Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.

  9. A multilevel examination of the relationships among training outcomes, mediating regulatory processes, and adaptive performance.

    PubMed

    Chen, Gilad; Thomas, Brian; Wallace, J Craig

    2005-09-01

    This study examined whether cognitive, affective-motivational, and behavioral training outcomes relate to posttraining regulatory processes and adaptive performance similarly at the individual and team levels of analysis. Longitudinal data were collected from 156 individuals composing 78 teams who were trained on and then performed a simulated flight task. Results showed that posttraining regulation processes related similarly to adaptive performance across levels. Also, regulation processes fully mediated the influences of self- and collective efficacy beliefs on individual and team adaptive performance. Finally, knowledge and skill more strongly and directly related to adaptive performance at the individual than the team level of analysis. Implications to theory and practice, limitations, and future directions are discussed.

  10. Combining genetic algorithm and Levenberg-Marquardt algorithm in training neural network for hypoglycemia detection using EEG signals.

    PubMed

    Nguyen, Lien B; Nguyen, Anh V; Ling, Sai Ho; Nguyen, Hung T

    2013-01-01

    Hypoglycemia is the most common but highly feared complication induced by the intensive insulin therapy in patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is dangerous because sleep obscures early symptoms and potentially leads to severe episodes which can cause seizure, coma, or even death. It is shown that the hypoglycemia onset induces early changes in electroencephalography (EEG) signals which can be detected non-invasively. In our research, EEG signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected efficiently using EEG signals from only two channels. This paper demonstrates that by implementing a training process of combining genetic algorithm and Levenberg-Marquardt algorithm, the classification results are improved markedly up to 75% sensitivity and 60% specificity on a separate testing set.

  11. Interval versus continuous training with identical workload: physiological and aerobic capacity adaptations.

    PubMed

    de Araujo, G G; Gobatto, C A; Marcos-Pereira, M; Dos Reis, I G M; Verlengia, R

    2015-01-01

    The interval model training has been more recommended to promote aerobic adaptations due to recovery period that enables the execution of elevated intensity and as consequence, higher workload in relation to continuous training. However, the physiological and aerobic capacity adaptations in interval training with identical workload to continuous are still uncertain. The purpose was to characterize the effects of chronic and acute biomarkers adaptations and aerobic capacity in interval and continuous protocols with equivalent load. Fifty Wistar rats were divided in three groups: Continuous training (GTC), interval training (GTI) and control (CG). The running training lasted 8 weeks (wk) and was based at Anaerobic Threshold (AT) velocity. GTI showed glycogen super-compensation (mg/100 mg) 48 h after training session in relation to CG and GTC (GTI red gastrocnemius (RG)=1.41+/-0.16; GTI white gastrocnemius (WG)=1.78+/-0.20; GTI soleus (S)=0.26+/-0.01; GTI liver (L)=2.72+/-0.36; GTC RG=0.42+/-0.17; GTC WG=0.54+/-0.22; GTC S=0.100+/-0.01; GTC L=1.12+/-0.24; CG RG=0.32+/-0.05; CG WG=0.65+/-0.17; CG S=0.14+/-0.01; CG L=2.28+/-0.33). The volume performed by GTI was higher than GTC. The aerobic capacity reduced 11 % after experimental period in GTC when compared to GTI, but this change was insignificant (19.6+/-5.4 m/min; 17.7+/-2.5 m/min, effect size = 0.59). Free fatty acids and glucose concentration did not show statistical differences among the groups. Corticosterone concentration increased in acute condition for GTI and GTC. Testosterone concentration reduced 71 % in GTC immediately after the exercise in comparison to CG. The GTI allowed positive adaptations when compared to GTC in relation to: glycogen super-compensation, training volume performed and anabolic condition. However, the GTI not improved the aerobic performance.

  12. A Submaximal Running Test With Postexercise Cardiac Autonomic and Neuromuscular Function in Monitoring Endurance Training Adaptation.

    PubMed

    Vesterinen, Ville; Nummela, Ari; Laine, Tanja; Hynynen, Esa; Mikkola, Jussi; Häkkinen, Keijo

    2017-01-01

    Vesterinen, V, Nummela, A, Laine, T, Hynynen, E, Mikkola, J, and Häkkinen, K. A submaximal running test with postexercise cardiac autonomic and neuromuscular function in monitoring endurance training adaptation. J Strength Cond Res 31(1): 233-243, 2017-The aim of this study was to investigate whether a submaximal running test (SRT) with postexercise heart rate recovery (HRR), heart rate variability (HRV), and countermovement jump (CMJ) measurements could be used to monitor endurance training adaptation. Thirty-five endurance-trained men and women completed an 18-week endurance training. Maximal endurance performance and maximal oxygen uptake were measured every 8 weeks. In addition, SRTs with postexercise HRR, HRV, and CMJ measurements were carried out every 4 weeks. Submaximal running test consisted of two 6-minute stages at 70 and 80% of maximum heart rate (HRmax) and a 3-minute stage at 90% HRmax, followed by a 2-minute recovery stage for measuring postexercise HRR, HRV, and CMJ test. The highest responders according to the change of maximal endurance performance showed a significant improvement in running speeds during stages 2 and 3 in SRT, whereas no changes were observed in the lowest responders. The strongest correlation was found between the change of maximal endurance performance and running speed during stage 3, whereas no significant relationships were found between the change of maximal endurance performance and the changes of postexercise HRR, HRV, and CMJ. Running speed at 90% HRmax intensity was the most sensitive variable to monitor adaptation to endurance training. The present submaximal test showed potential to monitor endurance training adaptation. Furthermore, it may serve as a practical tool for athletes and coaches to evaluate weekly the effectiveness of training program without interfering in the normal training habits.

  13. Adaptations to exercise training and contraction-induced muscle injury in animal models of muscular dystrophy.

    PubMed

    Carter, Gregory T; Abresch, R Ted; Fowler, William M

    2002-11-01

    This article reviews the current status of exercise training and contraction-induced muscle-injury investigations in animal models of muscular dystrophy. Most exercise-training studies have compared the adaptations of normal and dystrophic muscles with exercise. Adaptation of diseased muscle to exercise occurs at many levels, starting with the extracellular matrix, but also involves cytoskeletal architecture, muscle contractility, repair mechanisms, and gene regulation. The majority of exercise-injury investigations have attempted to determine the susceptibility of dystrophin-deficient muscles to contraction-induced injury. There is some evidence in animal models that diseased muscle can adapt and respond to mechanical stress. However, exercise-injury studies show that dystrophic muscles have an increased susceptibility to high mechanical forces. Most of the studies involving exercise training have shown that muscle adaptations in dystrophic animals were qualitatively similar to the adaptations observed in control muscle. Deleterious effects of the dystrophy usually occur only in older animals with advanced muscle fiber degeneration or after high-resistive eccentric training. The main limitations in applying these conclusions to humans are the differences in phenotypic expression between humans and genetically homologous animal models and in the significant biomechanical differences between humans and these animal models.

  14. Incrementing data quality of multi-frequency echograms using the Adaptive Wiener Filter (AWF) denoising algorithm

    NASA Astrophysics Data System (ADS)

    Peña, M.

    2016-10-01

    Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.

  15. Adaptive local backlight dimming algorithm based on local histogram and image characteristics

    NASA Astrophysics Data System (ADS)

    Nadernejad, Ehsan; Burini, Nino; Korhonen, Jari; Forchhammer, Søren; Mantel, Claire

    2013-02-01

    Liquid Crystal Display (LCDs) with Light Emitting Diode (LED) backlight is a very popular display technology, used for instance in television sets, monitors and mobile phones. This paper presents a new backlight dimming algorithm that exploits the characteristics of the target image, such as the local histograms and the average pixel intensity of each backlight segment, to reduce the power consumption of the backlight and enhance image quality. The local histogram of the pixels within each backlight segment is calculated and, based on this average, an adaptive quantile value is extracted. A classification into three classes based on the average luminance value is performed and, depending on the image luminance class, the extracted information on the local histogram determines the corresponding backlight value. The proposed method has been applied on two modeled screens: one with a high resolution direct-lit backlight, and the other screen with 16 edge-lit backlight segments placed in two columns and eight rows. We have compared the proposed algorithm against several known backlight dimming algorithms by simulations; and the results show that the proposed algorithm provides better trade-off between power consumption and image quality preservation than the other algorithms representing the state of the art among feature based backlight algorithms.

  16. An adaptive scale factor based MPPT algorithm for changing solar irradiation levels in outer space

    NASA Astrophysics Data System (ADS)

    Kwan, Trevor Hocksun; Wu, Xiaofeng

    2017-03-01

    Maximum power point tracking (MPPT) techniques are popularly used for maximizing the output of solar panels by continuously tracking the maximum power point (MPP) of their P-V curves, which depend both on the panel temperature and the input insolation. Various MPPT algorithms have been studied in literature, including perturb and observe (P&O), hill climbing, incremental conductance, fuzzy logic control and neural networks. This paper presents an algorithm which improves the MPP tracking performance by adaptively scaling the DC-DC converter duty cycle. The principle of the proposed algorithm is to detect the oscillation by checking the sign (ie. direction) of the duty cycle perturbation between the current and previous time steps. If there is a difference in the signs then it is clear an oscillation is present and the DC-DC converter duty cycle perturbation is subsequently scaled down by a constant factor. By repeating this process, the steady state oscillations become negligibly small which subsequently allows for a smooth steady state MPP response. To verify the proposed MPPT algorithm, a simulation involving irradiances levels that are typically encountered in outer space is conducted. Simulation and experimental results prove that the proposed algorithm is fast and stable in comparison to not only the conventional fixed step counterparts, but also to previous variable step size algorithms.

  17. An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

    SciTech Connect

    Li, Weixuan; Lin, Guang

    2015-08-01

    Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.

  18. An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

    PubMed

    Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

    2015-01-01

    Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

  19. An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

    SciTech Connect

    Li, Weixuan; Lin, Guang

    2015-03-21

    Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.

  20. An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

    DOE PAGES

    Li, Weixuan; Lin, Guang

    2015-03-21

    Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less

  1. A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

    NASA Astrophysics Data System (ADS)

    Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar

    2011-12-01

    This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.

  2. Statistical Methods for Tissue Array Images – Algorithmic Scoring and Co-training

    PubMed Central

    Yan, Donghui; Wang, Pei; Knudsen, Beatrice S.; Linden, Michael; Randolph, Timothy W.

    2012-01-01

    Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm—Tissue Array Co-Occurrence Matrix Analysis (TACOMA)—for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists’ input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high dimensional setting when there is “sufficient” redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists’ performance in terms of accuracy and repeatability. PMID:22984376

  3. A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.

    PubMed

    Rapoport, Benjamin I; Wattanapanitch, Woradorn; Penagos, Hector L; Musallam, Sam; Andersen, Richard A; Sarpeshkar, Rahul

    2009-01-01

    Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.

  4. Binary 3D image interpolation algorithm based global information and adaptive curves fitting

    NASA Astrophysics Data System (ADS)

    Zhang, Tian-yi; Zhang, Jin-hao; Guan, Xiang-chen; Li, Qiu-ping; He, Meng

    2013-08-01

    Interpolation is a necessary processing step in 3-D reconstruction because of the non-uniform resolution. Conventional interpolation methods simply use two slices to obtain the missing slices between the two slices .when the key slice is missing, those methods may fail to recover it only employing the local information .And the surface of 3D object especially for the medical tissues may be highly complicated, so a single interpolation can hardly get high-quality 3D image. We propose a novel binary 3D image interpolation algorithm. The proposed algorithm takes advantages of the global information. It chooses the best curve adaptively from lots of curves based on the complexity of the surface of 3D object. The results of this algorithm are compared with other interpolation methods on artificial objects and real breast cancer tumor to demonstrate the excellent performance.

  5. Nutritional strategies to support adaptation to high-intensity interval training in team sports.

    PubMed

    Gibala, Martin J

    2013-01-01

    Team sports are characterized by intermittent high-intensity activity patterns. Typically, play consists of short periods of very intense or all-out efforts interspersed with longer periods of low-intensity activity. Fatigue is a complex, multi-factorial process, but intense intermittent exercise performance can potentially be limited by reduced availability of substrates stored in skeletal muscle and/or metabolic by-products associated with fuel breakdown. High-intensity interval training (HIT) has been shown to induce adaptations in skeletal muscle that enhance the capacity for both oxidative and non-oxidative metabolism. Nutrient availability is a potent modulator of many acute physiological responses to exercise, including various molecular signaling pathways that are believed to regulate cellular adaptation to training. Several nutritional strategies have also been reported to acutely alter metabolism and enhance intermittent high-intensity exercise performance. However, relatively little is known regarding the effect of chronic interventions, and whether supplementation over a period of weeks or months augments HIT-induced physiological remodeling and promotes greater performance adaptations. Theoretically, a nutritional intervention could augment HIT adaptation by improving energy metabolism during exercise, which could facilitate greater total work and an enhanced chronic training stimulus, or promoting some aspect of the adaptive response during recovery, which could lead to enhanced physiological adaptations over time.

  6. An adaptive support driven reweighted L1-regularization algorithm for fluorescence molecular tomography

    PubMed Central

    Shi, Junwei; Liu, Fei; Pu, Huangsheng; Zuo, Simin; Luo, Jianwen; Bai, Jing

    2014-01-01

    Fluorescence molecular tomography (FMT) is a promising in vivo functional imaging modality in preclinical study. When solving the ill-posed FMT inverse problem, L1 regularization can preserve the details and reduce the noise in the reconstruction results effectively. Moreover, compared with the regular L1 regularization, reweighted L1 regularization is recently reported to improve the performance. In order to realize the reweighted L1 regularization for FMT, an adaptive support driven reweighted L1-regularization (ASDR-L1) algorithm is proposed in this work. This algorithm has two integral parts: an adaptive support estimate and the iteratively updated weights. In the iteratively reweighted L1-minimization sub-problem, different weights are equivalent to different regularization parameters at different locations. Thus, ASDR-L1 can be considered as a kind of spatially variant regularization methods for FMT. Physical phantom and in vivo mouse experiments were performed to validate the proposed algorithm. The results demonstrate that the proposed reweighted L1-reguarization algorithm can significantly improve the performance in terms of relative quantitation and spatial resolution. PMID:25426329

  7. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    PubMed Central

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  8. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters.

    PubMed

    Ren, Min; Liu, Peiyu; Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule [Formula: see text] and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

  9. An adaptive left-right eigenvector evolution algorithm for vibration isolation control

    NASA Astrophysics Data System (ADS)

    Wu, T. Y.

    2009-11-01

    The purpose of this research is to investigate the feasibility of utilizing an adaptive left and right eigenvector evolution (ALREE) algorithm for active vibration isolation. As depicted in the previous paper presented by Wu and Wang (2008 Smart Mater. Struct. 17 015048), the structural vibration behavior depends on both the disturbance rejection capability and mode shape distributions, which correspond to the left and right eigenvector distributions of the system, respectively. In this paper, a novel adaptive evolution algorithm is developed for finding the optimal combination of left-right eigenvectors of the vibration isolator, which is an improvement over the simultaneous left-right eigenvector assignment (SLREA) method proposed by Wu and Wang (2008 Smart Mater. Struct. 17 015048). The isolation performance index used in the proposed algorithm is defined by combining the orthogonality index of left eigenvectors and the modal energy ratio index of right eigenvectors. Through the proposed ALREE algorithm, both the left and right eigenvectors evolve such that the isolation performance index decreases, and therefore one can find the optimal combination of left-right eigenvectors of the closed-loop system for vibration isolation purposes. The optimal combination of left-right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active isolation control shows that the proposed method can be utilized to improve the vibration isolation performance compared with the previous approaches.

  10. Effect of the duration of daily aerobic physical training on cardiac autonomic adaptations.

    PubMed

    Sant'Ana, Janaina E; Pereira, Marília G A G; Dias da Silva, Valdo J; Dambrós, Camila; Costa-Neto, Claudio M; Souza, Hugo C D

    2011-01-20

    The present study has investigated in conscious rats the influence of the duration of physical training sessions on cardiac autonomic adaptations by using different approaches; 1) double blockade with methylatropine and propranolol; 2) the baroreflex sensitivity evaluated by alternating bolus injections of phenylephrine and sodium nitroprusside; and 3) the autonomic modulation of HRV in the frequency domain by means of spectral analysis. The animals were divided into four groups: one sedentary group and three training groups submitted to physical exercise (swimming) for 15, 30, and 60min a day during 10 weeks. All training groups showed similar reduction in intrinsic heart rate (IHR) after double blockade with methylatropine and propranolol. However, only 30-min and 60-min physical training presented an increase in the vagal autonomic component for determination of basal heart rate (HR) in relation to group sedentary. Spectral analysis of HR showed that the 30-min and 60-min physical training presented the reduction in low-frequency oscillations (LF=0.20-0.75Hz) and the increase in high-frequency oscillations (HF=0.75-2.5Hz) in normalized units. These both groups only showed an increased baroreflex sensitivity to tachycardiac responses in relation to group sedentary, however when compared, the physical training of 30-min exhibited a greater gain. In conclusion, cardiac autonomic adaptations, characterised by the increased predominance of the vagal autonomic component, were not proportional to the duration of daily physical training sessions. In fact, 30-minute training sessions provided similar cardiac autonomic adaptations, or even more enhanced ones, as in the case of baroreflex sensitivity compared to 60-minute training sessions.

  11. Physiological adaptations to interval training and the role of exercise intensity.

    PubMed

    MacInnis, Martin J; Gibala, Martin J

    2016-10-17

    Interval exercise typically involves repeated bouts of relatively intense exercise interspersed by short periods of recovery. A common classification scheme subdivides this method into high-intensity interval training (HIIT; 'near maximal' efforts) and sprint interval training (SIT; 'supramaximal' efforts). Both forms of interval training induce the classic physiological adaptations characteristic of moderate-intensity continuous training (MICT) such as increased aerobic capacity (V̇O2 max ) and mitochondrial content. This brief review considers the role of exercise intensity in mediating physiological adaptations to training, with a focus on the capacity for aerobic energy metabolism. With respect to skeletal muscle adaptations, cellular stress and the resultant metabolic signals for mitochondrial biogenesis depend largely on exercise intensity, with limited work suggesting that increases in mitochondrial content are superior after HIIT compared to MICT, at least when matched-work comparisons are made within the same individual. It is well established that SIT increases mitochondrial content to a similar extent to MICT despite a reduced exercise volume. At the whole-body level, V̇O2 max is generally increased more by HIIT than MICT for a given training volume, whereas SIT and MICT similarly improve V̇O2 max despite differences in training volume. There is less evidence available regarding the role of exercise intensity in mediating changes in skeletal muscle capillary density, maximum stroke volume and cardiac output, and blood volume. Furthermore, the interactions between intensity and duration and frequency have not been thoroughly explored. While interval training is clearly a potent stimulus for physiological remodelling in humans, the integrative response to this type of exercise warrants further attention, especially in comparison to traditional endurance training.

  12. A optimized context-based adaptive binary arithmetic coding algorithm in progressive H.264 encoder

    NASA Astrophysics Data System (ADS)

    Xiao, Guang; Shi, Xu-li; An, Ping; Zhang, Zhao-yang; Gao, Ge; Teng, Guo-wei

    2006-05-01

    Context-based Adaptive Binary Arithmetic Coding (CABAC) is a new entropy coding method presented in H.264/AVC that is highly efficient in video coding. In the method, the probability of current symbol is estimated by using the wisely designed context model, which is adaptive and can approach to the statistic characteristic. Then an arithmetic coding mechanism largely reduces the redundancy in inter-symbol. Compared with UVLC method in the prior standard, CABAC is complicated but efficiently reduce the bit rate. Based on thorough analysis of coding and decoding methods of CABAC, This paper proposed two methods, sub-table method and stream-reuse methods, to improve the encoding efficiency implemented in H.264 JM code. In JM, the CABAC function produces bits one by one of every syntactic element. Multiplication operating times after times in the CABAC function lead to it inefficient.The proposed algorithm creates tables beforehand and then produce every bits of syntactic element. In JM, intra-prediction and inter-prediction mode selection algorithm with different criterion is based on RDO(rate distortion optimization) model. One of the parameter of the RDO model is bit rate that is produced by CABAC operator. After intra-prediction or inter-prediction mode selection, the CABAC stream is discard and is recalculated to output stream. The proposed Stream-reuse algorithm puts the stream in memory that is created in mode selection algorithm and reuses it in encoding function. Experiment results show that our proposed algorithm can averagely speed up 17 to 78 MSEL higher speed for QCIF and CIF sequences individually compared with the original algorithm of JM at the cost of only a little memory space. The CABAC was realized in our progressive h.264 encoder.

  13. Flutter signal extracting technique based on FOG and self-adaptive sparse representation algorithm

    NASA Astrophysics Data System (ADS)

    Lei, Jian; Meng, Xiangtao; Xiang, Zheng

    2016-10-01

    Due to various moving parts inside, when a spacecraft runs in orbits, its structure could get a minor angular vibration, which results in vague image formation of space camera. Thus, image compensation technique is required to eliminate or alleviate the effect of movement on image formation and it is necessary to realize precise measuring of flutter angle. Due to the advantages such as high sensitivity, broad bandwidth, simple structure and no inner mechanical moving parts, FOG (fiber optical gyro) is adopted in this study to measure minor angular vibration. Then, movement leading to image degeneration is achieved by calculation. The idea of the movement information extracting algorithm based on self-adaptive sparse representation is to use arctangent function approximating L0 norm to construct unconstrained noisy-signal-aimed sparse reconstruction model and then solve the model by a method based on steepest descent algorithm and BFGS algorithm to estimate sparse signal. Then taking the advantage of the principle of random noises not able to be represented by linear combination of elements, useful signal and random noised are separated effectively. Because the main interference of minor angular vibration to image formation of space camera is random noises, sparse representation algorithm could extract useful information to a large extent and acts as a fitting pre-process method of image restoration. The self-adaptive sparse representation algorithm presented in this paper is used to process the measured minor-angle-vibration signal of FOG used by some certain spacecraft. By component analysis of the processing results, we can find out that the algorithm could extract micro angular vibration signal of FOG precisely and effectively, and can achieve the precision degree of 0.1".

  14. Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics

    NASA Technical Reports Server (NTRS)

    Rohrs, C. E.; Valavani, L.; Athans, M.; Stein, G.

    1985-01-01

    This paper examines the robustness properties of existing adaptive control algorithms to unmodeled plant high-frequency dynamics and unmeasurable output disturbances. It is demonstrated that there exist two infinite-gain operators in the nonlinear dynamic system which determines the time-evolution of output and parameter errors. The pragmatic implications of the existence of such infinite-gain operators is that: (1) sinusoidal reference inputs at specific frequencies and/or (2) sinusoidal output disturbances at any frequency (including dc), can cause the loop gain to increase without bound, thereby exciting the unmodeled high-frequency dynamics, and yielding an unstable control system. Hence, it is concluded that existing adaptive control algorithms as they are presented in the literature referenced in this paper, cannot be used with confidence in practical designs where the plant contains unmodeled dynamics because instability is likely to result. Further understanding is required to ascertain how the currently implemented adaptive systems differ from the theoretical systems studied here and how further theoretical development can improve the robustness of adaptive controllers.

  15. Systematic Review of Engagement in Culturally Adapted Parent Training for Disruptive Behavior

    PubMed Central

    Butler, Ashley M.; Titus, Courtney

    2016-01-01

    This article reviews the literature reporting engagement (enrollment, attendance, and attrition) in culturally adapted parent training for disruptive behavior among racial/ethnic minority parents of children ages 2–7 years. The review describes the reported rates of engagement in adapted interventions and how engagement is analyzed in studies, methods to develop adaptations, and adaptations that have been implemented. Seven studies were identified. Parental engagement varied across and within studies. Only one study examined whether adaptations improved engagement compared to non-adapted intervention. Frequent methods to develop adaptations were building partnerships or conducting interviews/focus groups with minority parents or community members. Adaptations included addressing cultural beliefs (perceptions of parenting skills), values (interdependence), or experiences (immigration) that affect parenting or receptivity to interventions; ensuring racial/ethnic diversity of interventionists; and addressing cultural relevancy and literacy level of materials. Future research should examine engagement in adapted interventions compared to non-adapted interventions and examine factors (e.g., immigration status) that may moderate impact on engagement. PMID:27429537

  16. A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps.

    PubMed

    Mao, Wei; Lan, Heng-You; Li, Hao-Ru

    2016-01-01

    As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions.

  17. A surrogate-primary replacement algorithm for response-adaptive randomization in stroke clinical trials.

    PubMed

    Nowacki, Amy S; Zhao, Wenle; Palesch, Yuko Y

    2015-01-12

    Response-adaptive randomization (RAR) offers clinical investigators benefit by modifying the treatment allocation probabilities to optimize the ethical, operational, or statistical performance of the trial. Delayed primary outcomes and their effect on RAR have been studied in the literature; however, the incorporation of surrogate outcomes has not been fully addressed. We explore the benefits and limitations of surrogate outcome utilization in RAR in the context of acute stroke clinical trials. We propose a novel surrogate-primary (S-P) replacement algorithm where a patient's surrogate outcome is used in the RAR algorithm only until their primary outcome becomes available to replace it. Computer simulations investigate the effect of both the delay in obtaining the primary outcome and the underlying surrogate and primary outcome distributional discrepancies on complete randomization, standard RAR and the S-P replacement algorithm methods. Results show that when the primary outcome is delayed, the S-P replacement algorithm reduces the variability of the treatment allocation probabilities and achieves stabilization sooner. Additionally, the S-P replacement algorithm benefit proved to be robust in that it preserved power and reduced the expected number of failures across a variety of scenarios.

  18. A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps

    PubMed Central

    Mao, Wei; Li, Hao-ru

    2016-01-01

    As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions. PMID:27293426

  19. Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

    PubMed

    Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

    2015-02-01

    Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

  20. Effects of systemic hypoxia on human muscular adaptations to resistance exercise training.

    PubMed

    Kon, Michihiro; Ohiwa, Nao; Honda, Akiko; Matsubayashi, Takeo; Ikeda, Tatsuaki; Akimoto, Takayuki; Suzuki, Yasuhiro; Hirano, Yuichi; Russell, Aaron P

    2014-06-01

    Hypoxia is an important modulator of endurance exercise-induced oxidative adaptations in skeletal muscle. However, whether hypoxia affects resistance exercise-induced muscle adaptations remains unknown. Here, we determined the effect of resistance exercise training under systemic hypoxia on muscular adaptations known to occur following both resistance and endurance exercise training, including muscle cross-sectional area (CSA), one-repetition maximum (1RM), muscular endurance, and makers of mitochondrial biogenesis and angiogenesis, such as peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α), citrate synthase (CS) activity, nitric oxide synthase (NOS), vascular endothelial growth factor (VEGF), hypoxia-inducible factor-1 (HIF-1), and capillary-to-fiber ratio. Sixteen healthy male subjects were randomly assigned to either a normoxic resistance training group (NRT, n = 7) or a hypoxic (14.4% oxygen) resistance training group (HRT, n = 9) and performed 8 weeks of resistance training. Blood and muscle biopsy samples were obtained before and after training. After training muscle CSA of the femoral region, 1RM for bench-press and leg-press, muscular endurance, and skeletal muscle VEGF protein levels significantly increased in both groups. The increase in muscular endurance was significantly higher in the HRT group. Plasma VEGF concentration and skeletal muscle capillary-to-fiber ratio were significantly higher in the HRT group than the NRT group following training. Our results suggest that, in addition to increases in muscle size and strength, HRT may also lead to increased muscular endurance and the promotion of angiogenesis in skeletal muscle.

  1. Heuristic-Leadership Model: Adapting to Current Training and Changing Times.

    ERIC Educational Resources Information Center

    Danielson, Mary Ann

    A model was developed for training individuals to adapt better to the changing work environment by focusing on the subordinate to supervisor relationship and providing a heuristic approach to leadership. The model emphasizes a heuristic approach to decision-making through the active participation of both members of the dyad. The demand among…

  2. Predictive Validity of Conventional and Adaptive Tests in an Air Force Training Environment. Interim Report.

    ERIC Educational Resources Information Center

    Sympson, James B.; And Others

    Conventional Armed Services Vocational Aptitude Battery-7 (ASVAB) Arithmetic Reasoning and Word Knowledge tests, were compared with computer-administered adaptive tests as predictors of performance in an Air Force Jet Engine Mechanic training course (n=495). Results supported earlier research in showing somewhat longer examinee response times for…

  3. Effects of Culturally Adapted Parent Management Training on Latino Youth Behavioral Health Outcomes

    ERIC Educational Resources Information Center

    Martinez, Charles R.; Eddy, J. Mark

    2005-01-01

    A randomized experimental test of the implementation feasibility and the efficacy of a culturally adapted Parent Management Training intervention was conducted with a sample of 73 Spanish-speaking Latino parents with middle-school-aged youth at risk for problem behaviors. Intervention feasibility was evaluated through weekly parent satisfaction…

  4. The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

    NASA Technical Reports Server (NTRS)

    Kocurek, Michael J.

    2005-01-01

    The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

  5. Adaptive Virtual Reality Training to Optimize Military Medical Skills Acquisition and Retention.

    PubMed

    Siu, Ka-Chun; Best, Bradley J; Kim, Jong Wook; Oleynikov, Dmitry; Ritter, Frank E

    2016-05-01

    The Department of Defense has pursued the integration of virtual reality simulation into medical training and applications to fulfill the need to train 100,000 military health care personnel annually. Medical personnel transitions, both when entering an operational area and returning to the civilian theater, are characterized by the need to rapidly reacquire skills that are essential but have decayed through disuse or infrequent use. Improved efficiency in reacquiring such skills is critical to avoid the likelihood of mistakes that may result in mortality and morbidity. We focus here on a study testing a theory of how the skills required for minimally invasive surgery for military surgeons are learned and retained. Our adaptive virtual reality surgical training system will incorporate an intelligent mechanism for tracking performance that will recognize skill deficiencies and generate an optimal adaptive training schedule. Our design is modeling skill acquisition based on a skill retention theory. The complexity of appropriate training tasks is adjusted according to the level of retention and/or surgical experience. Based on preliminary work, our system will improve the capability to interactively assess the level of skills learning and decay, optimizes skill relearning across levels of surgical experience, and positively impact skill maintenance. Our system could eventually reduce mortality and morbidity by providing trainees with the reexperience they need to help make a transition between operating theaters. This article reports some data that will support adaptive tutoring of minimally invasive surgery and similar surgical skills.

  6. An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

    NASA Technical Reports Server (NTRS)

    Tan, Bin; Brown de Colstoun, Eric; Wolfe, Robert E.; Tilton, James C.; Huang, Chengquan; Smith, Sarah E.

    2012-01-01

    An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to

  7. Physiological Adaptations to Training in Competitive Swimming: A Systematic Review.

    PubMed

    Costa, Mário J; Balasekaran, Govindasamy; Vilas-Boas, J Paulo; Barbosa, Tiago M

    2015-12-22

    The purpose of this systematic review was to summarize longitudinal studies on swimming physiology and get implications for daily practice. A computerized search of databases according to the PRISMA statement was employed. Studies were screened for eligibility on inclusion criteria: (i) present two testing points; (ii) on swimming physiology; (iii) using adult elite swimmers; (iv) no case-studies or with small sample sizes. Two independent reviewers used a checklist to assess the methodological quality of the studies. Thirty-four studies selected for analysis were gathered into five main categories: blood composition (n=7), endocrine secretion (n=11), muscle biochemistry (n=7), cardiovascular response (n=8) and the energetic profile (n=14). The mean quality index was 10.58 ± 2.19 points demonstrating an almost perfect agreement between reviewers (K = 0.93). It can be concluded that the mixed findings in the literature are due to the diversity of the experimental designs. Micro variables obtained at the cellular or molecular level are sensitive measures and demonstrate overtraining signs and health symptoms. The improvement of macro variables (i.e. main physiological systems) is limited and may depend on the athletes' training background and experience.

  8. Long-term training adaptations in elite male volleyball players.

    PubMed

    Sheppard, Jeremy M; Newton, Robert U

    2012-08-01

    Several investigations have demonstrated differences in anthropometry, jump performance, and strength variables between developmental and elite-level volleyball players. However, within the elite level of play, the magnitude of change that can occur with training is unclear. The purpose of this investigation was to examine the anthropometric, vertical jump, and strength quality changes over 2 years in a group of national team volleyball players. Fourteen national team volleyball players (age, 23.0 ± 4.1 years; height, 1.98 ± 0.07 m; weight, 91.7 ± 7.9 kg) began and completed this study. Participants had all played international matches (representing Australia) before the examination time period and continued to do so during the international season. Anthropometry (stature, mass, and sum of 7 skinfolds), vertical jump measures (countermovement vertical jump; depth jump from 0.35 m, DJ; spike jump, SPJ, all including arm swing), and lower-body power (jump squat at body mass, and jump squat + 50% body weight, JS50) measures were tested before and at the conclusion of the investigation period. Significant (p < 0.05) improvements were observed in sum of 7 skinfolds, DJ, SPJ, and JS50 performance, with large magnitude changes (d > 0.70) in the sum of 7 skinfolds reduction, SPJ, and leg extensor power. This study has demonstrated that elite male volleyball players can improve leanness and power, which contribute to improvements in vertical jump.

  9. Current Practice in Designing Training for Complex Skills: Implications for Design and Evaluation of ADAPT[IT].

    ERIC Educational Resources Information Center

    Eseryel, Deniz; Schuver-van Blanken, Marian J.; Spector, J. Michael

    ADAPT[IT] (Advanced Design Approach for Personalized Training-Interactive Tools is a European project coordinated by the Dutch National Aerospace Laboratory. The aim of ADAPT[IT] is to create and validate an effective training design methodology, based on cognitive science and leading to the integration of advanced technologies, so that the…

  10. Principles of exercise physiology: responses to acute exercise and long-term adaptations to training.

    PubMed

    Rivera-Brown, Anita M; Frontera, Walter R

    2012-11-01

    Physical activity and fitness are associated with a lower prevalence of chronic diseases, such as heart disease, cancer, high blood pressure, and diabetes. This review discusses the body's response to an acute bout of exercise and long-term physiological adaptations to exercise training with an emphasis on endurance exercise. An overview is provided of skeletal muscle actions, muscle fiber types, and the major metabolic pathways involved in energy production. The importance of adequate fluid intake during exercise sessions to prevent impairments induced by dehydration on endurance exercise, muscular power, and strength is discussed. Physiological adaptations that result from regular exercise training such as increases in cardiorespiratory capacity and strength are mentioned. The review emphasizes the cardiovascular and metabolic adaptations that lead to improvements in maximal oxygen capacity.

  11. Skeletal muscle metabolic adaptations to endurance exercise training are attainable in mice with simvastatin treatment

    PubMed Central

    Southern, William M.; Nichenko, Anna S.; Shill, Daniel D.; Spencer, Corey C.; Jenkins, Nathan T.; McCully, Kevin K.

    2017-01-01

    We tested the hypothesis that a 6-week regimen of simvastatin would attenuate skeletal muscle adaptation to low-intensity exercise. Male C57BL/6J wildtype mice were subjected to 6-weeks of voluntary wheel running or normal cage activities with or without simvastatin treatment (20 mg/kg/d, n = 7–8 per group). Adaptations in in vivo fatigue resistance were determined by a treadmill running test, and by ankle plantarflexor contractile assessment. The tibialis anterior, gastrocnemius, and plantaris muscles were evaluated for exercised-induced mitochondrial adaptations (i.e., biogenesis, function, autophagy). There was no difference in weekly wheel running distance between control and simvastatin-treated mice (P = 0.51). Trained mice had greater treadmill running distance (296%, P<0.001), and ankle plantarflexor contractile fatigue resistance (9%, P<0.05) compared to sedentary mice, independent of simvastatin treatment. At the cellular level, trained mice had greater mitochondrial biogenesis (e.g., ~2-fold greater PGC1α expression, P<0.05) and mitochondrial content (e.g., 25% greater citrate synthase activity, P<0.05), independent of simvastatin treatment. Mitochondrial autophagy-related protein contents were greater in trained mice (e.g., 40% greater Bnip3, P<0.05), independent of simvastatin treatment. However, Drp1, a marker of mitochondrial fission, was less in simvastatin treated mice, independent of exercise training, and there was a significant interaction between training and statin treatment (P<0.022) for LC3-II protein content, a marker of autophagy flux. These data indicate that whole body and skeletal muscle adaptations to endurance exercise training are attainable with simvastatin treatment, but simvastatin may have side effects on muscle mitochondrial maintenance via autophagy, which could have long-term implications on muscle health. PMID:28207880

  12. Effects of body temperature during exercise training on myocardial adaptations.

    PubMed

    Harris, M B; Starnes, J W

    2001-05-01

    This study determined the role of body temperature during chronic exercise on myocardial stress proteins and antioxidant enzymes as well as functional recovery after an ischemic insult. Male Sprague-Dawley rats were exercised for 3, 6, or 9 wk in a 23 degrees C room (3WK, 6WK, and 9WK, respectively) or in a 4-8 degrees C environment with wetted fur (3WKC, 6WKC, and 9WKC, respectively). The colder room prevented elevations in core temperature. During weeks 3-9 the animals ran 5 days/wk up a 6% grade at 20 m/min for 60 min. Myocardial heat shock protein 70 (HSP 70) increased 12.3-fold (P < 0.05) in 9WK versus sedentary (SED) rats but was unchanged in the cold-room runners. Compared with SED rats, alphaB-crystallin was 90% higher in 9WKC animals, HSP 90 was 50% higher in 3WKC and 6WKC animals, and catalase was 23% higher in 3WK animals (P < 0.05 for all). Cytosolic superoxide dismutase increased and mitochondrial SOD decreased (P < 0.05) in 3WK and 6WK rats compared with 3WKC and 6WKC rats. Antioxidant enzymes returned to SED values in all runners by 9 wk. No differences were observed among any of the groups for glucose-regulated protein 75, heme oxygenase-1, or glutathione peroxidase. Mechanical recovery of isolated working hearts after 22.5 min of global ischemia was enhanced in 9WK (P < 0.05) but not in 9WKC rats. We conclude that exercise training results in dynamic changes in cardioprotective proteins over time which are influenced by core temperature. In addition, cardioprotection resulting from chronic exercise appears to be due to increased HSP 70.

  13. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors.

    PubMed

    Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

    2016-03-28

    Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

  14. An adaptive reconstruction algorithm for spectral CT regularized by a reference image

    NASA Astrophysics Data System (ADS)

    Wang, Miaoshi; Zhang, Yanbo; Liu, Rui; Guo, Shuxu; Yu, Hengyong

    2016-12-01

    The photon counting detector based spectral CT system is attracting increasing attention in the CT field. However, the spectral CT is still premature in terms of both hardware and software. To reconstruct high quality spectral images from low-dose projections, an adaptive image reconstruction algorithm is proposed that assumes a known reference image (RI). The idea is motivated by the fact that the reconstructed images from different spectral channels are highly correlated. If a high quality image of the same object is known, it can be used to improve the low-dose reconstruction of each individual channel. This is implemented by maximizing the patch-wise correlation between the object image and the RI. Extensive numerical simulations and preclinical mouse study demonstrate the feasibility and merits of the proposed algorithm. It also performs well for truncated local projections, and the surrounding area of the region- of-interest (ROI) can be more accurately reconstructed. Furthermore, a method is introduced to adaptively choose the step length, making the algorithm more feasible and easier for applications.

  15. Differential effects of endurance training and creatine depletion on regional mitochondrial adaptations in rat skeletal muscle.

    PubMed Central

    Roussel, D; Lhenry, F; Ecochard, L; Sempore, B; Rouanet, J L; Favier, R

    2000-01-01

    To examine the combined effects of 2-week endurance training and 3-week feeding with beta-guanidinopropionic acid (GPA) on regional adaptability of skeletal muscle mitochondria, intermyofibrillar mitochondria (IFM) and subsarcolemmal mitochondria (SSM) were isolated from quadriceps muscles of sedentary control, trained control, sedentary GPA-fed and trained GPA-fed rats. Mitochondrial oxidative phosphorylation was assessed polarographically by using pyruvate plus malate, succinate (plus rotenone), and ascorbate plus N,N,N',N'-tetramethyl-p-phenylenediamine (TMPD) (plus antimycin) as respiratory substrates. Assays of cytochrome c oxidase and F(1)-ATPase activities were also performed. In sedentary control rats, IFM exhibited a higher oxidative capacity than SSM, whereas F(1)-ATPase activities were similar. Training increased the oxidative phosphorylation capacity of mitochondria with both pyruvate plus malate and ascorbate plus TMPD as substrates, with no differences between IFM and SSM. In contrast, the GPA diet mainly improved the overall SSM oxidative phosphorylation capacity, irrespective of the substrate used. Finally, the superimposition of training to feeding with GPA strongly increased both oxidase and enzymic activities in SSM, whereas no cumulative effects were found in IFM mitochondria. It therefore seems that endurance training and feeding with GPA, which are both known to alter the energetic status of the muscle cell, might mediate distinct biochemical adaptations in regional skeletal muscle mitochondria. PMID:10947970

  16. Adapting an effective primary care provider STD/HIV prevention training programme.

    PubMed

    Rabin, D L

    1998-04-01

    Sexually acquired human immunodeficiency virus (HIV) infection continues to be the major source of HIV infection in the USA. Preventing sexual transmission of HIV can be accomplished through patient behaviour change. Such behaviour change can also decrease risk of other sexually transmitted diseases (STDs) and unwanted pregnancies, both far more common problems than HIV infection. Primary care physicians and other providers can increase patients' safe sex practices by conducting effective sexual risk assessment (RA) and risk reduction (RR) counselling, but physicians both infrequently and incompletely do sexual RA and RR. A programme was developed to improve primary care physicians' prevention practice using Simulated Patient Instructors (SPIs) and mailed educational materials. Programme evaluations showed improved sexual RA and RR practice both by self-report as well as by observation by Simulated Patient Evaluators (SPEs). This paper briefly reviews these findings and then presents adaptations made to improve the programme's content, decrease its cost and increase its availability for training many other care providers. Evaluation of the adapted programme indicates that content and training methods are highly regarded by a diverse array of trainees. To disseminate the modified programme beyond the local area, a Train-the-Trainer programme and manual have been developed, including discussion of recruiting, training and using SPIs for sexual risk reduction. Wider use of this training, as well as more effective and more readily available STD/HIV prevention training, are needed to attain national goals of provider clinical prevention practice.

  17. Adaptations of the endothelin system after exercise training in a porcine model of ischemic heart disease

    PubMed Central

    Robles, Juan Carlos; Heaps, Cristine L.

    2014-01-01

    Objective Test the hypothesis that exercise training would increase endothelin-mediated vasoconstriction in collateral-dependent arteries via enhanced contribution of ETA. Methods An ameroid constrictor was surgically placed around the proximal LCX artery to induce gradual occlusion in Yucatan miniature swine. Eight-weeks postoperatively, pigs were randomized into sedentary or exercise-training (treadmill; 5 days/wk; 14 wks) groups. Subsequently, arteries (~150 μm diameter) were isolated from collateral-dependent and nonoccluded myocardial regions and studied. Results Following exercise training, ET-1-mediated contraction was significantly enhanced in collateral-dependent arteries. Exercise training induced a disproportionate increase in the ETA contribution to the ET-1 contractile response in collateral-dependent arteries, with negligible contributions by ETB. In collateral-dependent arteries of sedentary pigs, inhibition of ETA or ETB did not significantly alter ET-1 contractile responses in collateral-dependent arteries, suggesting compensation by the functionally active receptor. These adaptations occurred without significant changes in ETA, ETB, or ECE mRNA levels but with significant exercise training-induced elevations in endothelin levels in both nonoccluded and collateral-dependent myocardial regions Conclusions Taken together, these data reveal differential adaptive responses in collateral-dependent arteries based upon physical activity level. ETA and ETB appear to compensate for one another to maintain contraction in sedentary pigs, whereas exercise-training favors enhanced contribution of ETA. PMID:25220869

  18. Effects of Varied Versus Constant Loading Zones on Muscular Adaptations in Trained Men.

    PubMed

    Schoenfeld, B J; Contreras, B; Ogborn, D; Galpin, A; Krieger, J; Sonmez, G T

    2016-06-01

    The purpose of this study was to compare the effects of a protocol employing a combination of loading zones vs. one employing a constant medium-repetition loading zone on muscular adaptations in resistance-trained men. 19 trained men (height=176.9±7.0 cm; body mass=83.1±11.8 kg; age=23.3±2.9 years) were randomly assigned to 1 of 2 experimental groups: a constant-rep resistance training (RT) routine (CONSTANT) that trained using 8-12 RM per set, or a varied-rep RT routine (VARIED) that trained with 2-4 RM per set on Day 1, 8-12 RM per set on Day 2, and 20-30 RM on Day 3 for 8 weeks. Results showed that both groups significantly increased markers of muscle strength, muscle thickness, and local muscular endurance, with no differences noted between groups. Effect sizes favored VARIED over CONSTANT condition for elbow flexor thickness (0.72 vs. 0.57), elbow extensor thickness (0.77 vs. 0.48), maximal bench press strength (0.80 vs. 0.57), and upper body muscle endurance (1.91 vs. 1.28). In conclusion, findings indicate that both varied and constant loading approaches can promote significant improvements in muscular adaptations in trained young men.

  19. Adaptive working memory training improved brain function in human immunodeficiency virus–seropositive patients

    PubMed Central

    Løhaugen, Gro C.; Andres, Tamara; Jiang, Caroline S.; Douet, Vanessa; Tanizaki, Naomi; Walker, Christina; Castillo, Deborrah; Lim, Ahnate; Skranes, Jon; Otoshi, Chad; Miller, Eric N.; Ernst, Thomas M.

    2016-01-01

    Objective We aimed to evaluate the effectiveness of an adaptive working memory (WM) training (WMT) program, the corresponding neural correlates, and LMX1A‐rs4657412 polymorphism on the adaptive WMT, in human immunodeficiency virus (HIV) participants compared to seronegative (SN) controls. Methods A total of 201 of 206 qualified participants completed baseline assessments before randomization to 25 sessions of adaptive WMT or nonadaptive WMT. A total of 74 of 76 (34 HIV, 42 SN) completed adaptive WMT and all 40 completed nonadaptive WMT (20 HIV, 20 SN) and were assessed after 1 month, and 55 adaptive WMT participants were also assessed after 6 months. Nontrained near‐transfer WM tests (Digit‐Span, Spatial‐Span), self‐reported executive functioning, and functional magnetic resonance images during 1‐back and 2‐back tasks were performed at baseline and each follow‐up visit, and LMX1A‐rs4657412 was genotyped in all participants. Results Although HIV participants had slightly lower cognitive performance and start index than SN at baseline, both groups improved on improvement index (>30%; false discovery rate [FDR] corrected p < 0.0008) and nontrained WM tests after adaptive WMT (FDR corrected, p ≤ 0.001), but not after nonadaptive WMT (training by training type corrected, p = 0.01 to p = 0.05) 1 month later. HIV participants (especially LMX1A‐G carriers) also had poorer self‐reported executive functioning than SN, but both groups reported improvements after adaptive WMT (Global: training FDR corrected, p = 0.004), and only HIV participants improved after nonadaptive WMT. HIV participants also had greater frontal activation than SN at baseline, but brain activation decreased in both groups at 1 and 6 months after adaptive WMT (FDR corrected, p < 0.0001), with normalization of brain activation in HIV participants, especially the LMX1A‐AA carriers (LMX1A genotype by HIV status, cluster‐corrected‐p < 0

  20. Kinematic, strength, and stiffness adaptations after a short-term sled towing training in athletes.

    PubMed

    Alcaraz, P E; Elvira, J L L; Palao, J M

    2014-04-01

    One of the most frequently used methods for training the sprint-specific strength is the sled towing. To date, no studies have been conducted to explore the effects of this method after a training period in well-trained athletes. The purpose of this study was to determine the effects of 4 weeks of resisted sprint training with sled towing. Twenty-two trained athletes experienced in the use of weighted sled (WS) participated in the study. They conducted the same 3-week training to level their initial condition. After that they were distributed in two groups, unresisted (UR) and WS training. They carried out the same 4-week, 2 days/week sprint-specific training, only differing in that the experimental group performed sprints with a (WS) which caused a reduction of 7.5% of their maximum velocity. Pre- and posttest were conducted which included the measurement of sprint kinematics, muscular strength (including isoinertial, isokinetic, and jump measurements), and sprinting stiffness (leg and vertical). Results show different adaptations in the groups although no interaction effect was found. The WS group improved the velocity in the transition phase, while the UR group improved the velocity in the maximum velocity phase. No improvements in the height of the jump tests were found.

  1. Gait Adaptability Training Improves Both Postural Stability and Dual-Tasking Ability

    NASA Technical Reports Server (NTRS)

    Brady, Rachel A.; Batson, Crystal D.; Peters, Brian T.; Ploutz-Snyder, Robert J.; Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

    2010-01-01

    After spaceflight, the process of readapting to Earth's gravity commonly presents crewmembers with a variety of locomotor challenges. Our recent work has shown that the ability to adapt to a novel discordant sensorimotor environment can be increased through preflight training, so one focus of our laboratory has been the development of a gait training countermeasure to expedite the return of normal locomotor function after spaceflight. We used a training system comprising a treadmill mounted on a motion base facing a virtual visual scene that provided a variety of sensory challenges. As part of their participation in a larger retention study, 10 healthy adults completed 3 training sessions during which they walked on a treadmill at 1.1 m/s while receiving discordant support-surface and visual manipulations. After a single training session, subjects stride frequencies improved, and after 2 training sessions their auditory reaction times improved, where improvement was indicated by a return toward baseline values. Interestingly, improvements in reaction time came after stride frequency improvements plateaued. This finding suggests that postural stability was given a higher priority than a competing cognitive task. Further, it demonstrates that improvement in both postural stability and dual-tasking can be achieved with multiple training exposures. We conclude that, with training, individuals become more proficient at walking in discordant sensorimotor conditions and are able to devote more attention to competing tasks.

  2. Baroreflex deficit blunts exercise training-induced cardiovascular and autonomic adaptations in hypertensive rats.

    PubMed

    Moraes-Silva, I C; De La Fuente, R N; Mostarda, C; Rosa, K; Flues, K; Damaceno-Rodrigues, N R; Caldini, E G; De Angelis, K; Krieger, E M; Irigoyen, M C

    2010-03-01

    1. Baroreceptors regulate moment-to-moment blood pressure (BP) variations, but their long-term effect on the cardiovascular system remains unclear. Baroreceptor deficit accompanying hypertension contributes to increased BP variability (BPV) and sympathetic activity, whereas exercise training has been associated with an improvement in these baroreflex-mediated changes. The aim of the present study was to evaluate the autonomic, haemodynamic and cardiac morphofunctional effects of long-term sinoaortic baroreceptor denervation (SAD) in trained and sedentary spontaneously hypertensive rats (SHR). 2. Rats were subjected to SAD or sham surgery and were then further divided into sedentary and trained groups. Exercise training was performed on a treadmill (five times per week, 50-70% maximal running speed). All groups were studied after 10 weeks. 3. Sinoaortic baroreceptor denervation in SHR had no effect on basal heart rate (HR) or BP, but did augment BPV, impairing the cardiac function associated with increased cardiac hypertrophy and collagen deposition. Exercise training reduced BP and HR, re-established baroreflex sensitivity and improved both HR variability and BPV. However, SAD in trained SHR blunted all these improvements. Moreover, the systolic and diastolic hypertensive dysfunction, reduced left ventricular chamber diameter and increased cardiac collagen deposition seen in SHR were improved after the training protocol. These benefits were attenuated in trained SAD SHR. 4. In conclusion, the present study has demonstrated that the arterial baroreflex mediates cardiac disturbances associated with hypertension and is crucial for the beneficial cardiovascular morphofunctional and autonomic adaptations induced by chronic exercise in hypertension.

  3. The effects of creatine monohydrate supplementation with and without D-pinitol on resistance training adaptations.

    PubMed

    Kerksick, Chad M; Wilborn, Colin D; Campbell, William I; Harvey, Travis M; Marcello, Brandon M; Roberts, Mike D; Parker, Adam G; Byars, Allyn G; Greenwood, Lori D; Almada, Anthony L; Kreider, Richard B; Greenwood, Mike

    2009-12-01

    Coingestion of D-pinitol with creatine (CR) has been reported to enhance creatine uptake. The purpose of this study was to evaluate whether adding D-pinitol to CR affects training adaptations, body composition, whole-body creatine retention, and/or blood safety markers when compared to CR ingestion alone after 4 weeks of resistance training. Twenty-four resistance trained males were randomly assigned in a double-blind manner to creatine + pinitol (CRP) or creatine monohydrate (CR) prior to beginning a supervised 4-week resistance training program. Subjects ingested a typical loading phase (i.e., 20 g/d-1 for 5 days) before ingesting 5 g/d-1 the remaining 23 days. Performance measures were assessed at baseline (T0), week 1 (T1), and week 4 (T2) and included 1 repetition maximum (1RM) bench press (BP), 1RM leg press (LP), isokinetic knee extension, and a 30-second Wingate anaerobic capacity test. Fasting blood and body composition using dual-energy x-ray absorptiometry (DEXA) were determined at T1 and T3. Data were analyzed by repeated measures analysis of variance (ANOVA). Creatine retention increased (p < 0.001) in both groups as a result of supplementation but was not different between groups (p > 0.05). Significant improvements in upper- and lower-body strength and body composition occurred in both groups. However, significantly greater increases in lean mass and fat-free mass occurred in the CR group when compared to CRP (p <0.05). Adding D-pinitol to creatine monohydrate does not appear to facilitate further physiological adaptations while resistance training. Creatine monohydrate supplementation helps to improve strength and body composition while resistance training. Data from this study assist in determining the potential role the addition of D-pinitol to creatine may aid in facilitating training adaptations to exercise.

  4. Computerized Algorithms: Evaluation of Capability to Predict Graduation from Air Force Training.

    DTIC Science & Technology

    1980-09-01

    Q . .. In order that each case could be classified into a criterion category in a meaningful way, training termination designators were grouped and...computations on the same problem groupings as previously described for the BAYS algorithm; however, comparisons will still be made to point out a...observed for the retention/attrition study (Albert, 1980). For all analyses, the total time required to process a group of BAYS problems was appreciably

  5. Visual pattern recognition network: its training algorithm and its optoelectronic architecture

    NASA Astrophysics Data System (ADS)

    Wang, Ning; Liu, Liren

    1996-07-01

    A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition.

  6. Comparison of a new rapid convergent adaptive control algorithm to least mean square on an active noise control system

    NASA Astrophysics Data System (ADS)

    Koshigoe, Shozo; Gordon, Alan; Teagle, Allen; Tsay, Ching-Hsu

    1995-04-01

    In this paper, an efficient rapid convergent control algorithm will be developed and will be compared with other adaptive control algorithms using an experimental active noise control system. Other control algorithms are Widrow's finite impulse response adaptive control algorithm, and a modified Godard's algorithm. Comparisons of the random noise attenuation capability, transient and convergence performance, and computational requirements of each algorithm will be made as the order of the controller and relevant convergence parameters are varied. The system used for these experiments is a test bed of noise suppression technology for expendable launch vehicles. It consists of a flexible plate backed by a rigid cavity. Piezoelectric actuators are mounted on the plate and polyvinylidene fluoride is used both for microphones and pressure sensors within the cavity. The plate is bombarded with an amplified random noise signal, and the control system is used to suppress the noise inside the cavity generated by the outside sound source.

  7. An adaptive multi-level simulation algorithm for stochastic biological systems.

    PubMed

    Lester, C; Yates, C A; Giles, M B; Baker, R E

    2015-01-14

    Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, "Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics," SIAM Multiscale Model. Simul. 10(1), 146-179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the

  8. Adaptation of a Fast Optimal Interpolation Algorithm to the Mapping of Oceangraphic Data

    NASA Technical Reports Server (NTRS)

    Menemenlis, Dimitris; Fieguth, Paul; Wunsch, Carl; Willsky, Alan

    1997-01-01

    A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This algorithm produces solution and error estimates which are consistent with those obtained from exact least squares methods, but at a small fraction of the computational cost. Problems whose solution would be completely impractical using exact least squares, that is, problems with tens or hundreds of thousands of measurements and estimation grid points, can easily be solved on a small workstation using the multiscale algorithm. In contrast to methods previously proposed for solving large least squares problems, our approach provides estimation error statistics while permitting long-range correlations, using all measurements, and permitting arbitrary measurement locations. The multiscale algorithm itself, published elsewhere, is not the focus of this paper. However, the algorithm requires statistical models having a very particular multiscale structure; it is the development of a class of multiscale statistical models, appropriate for oceanographic mapping problems, with which we concern ourselves in this paper. The approach is illustrated by mapping temperature in the northeastern Pacific. The number of hydrographic stations is kept deliberately small to show that multiscale and exact least squares results are comparable. A portion of the data were not used in the analysis; these data serve to test the multiscale estimates. A major advantage of the present approach is the ability to repeat the estimation procedure a large number of times for sensitivity studies, parameter estimation, and model testing. We have made available by anonymous Ftp a set of MATLAB-callable routines which implement the multiscale algorithm and the statistical models developed in this paper.

  9. Training Regular Educators with Exceptional Kids: A Guide for Adapting the TREK Model of Inservice Training.

    ERIC Educational Resources Information Center

    Marmont, Wendy L.; And Others

    The document describes Project TREK (Training Regular Educators with Exceptional Kids) which provides services to seven school districts within the Portland, Oregon area. Chapter 1 offers an overview of the project's purpose and philosophy, target population, organization and staffing, delivery of services, and staff support and interaction.…

  10. Performance implications of leader briefings and team-interaction training for team adaptation to novel environments.

    PubMed

    Marks, M A; Zaccaro, S J; Mathieu, J E

    2000-12-01

    The authors examined how leader briefings and team-interaction training influence team members' knowledge structures concerning processes related to effective performance in both routine and novel environments. Two-hundred thirty-seven undergraduates from a large mid-Atlantic university formed 79 three-member tank platoon teams and participated in a low-fidelity tank simulation. Team-interaction training, leader briefings, and novelty of performance environment were manipulated. Findings indicated that both leader briefings and team-interaction training affected the development of mental models, which in turn positively influenced team communication processes and team performance. Mental models and communication processes predicted performance more strongly in novel than in routine environments. Implications for the role of team-interaction training, leader briefings, and mental models as mechanisms for team adaptation are discussed.

  11. RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

    NASA Astrophysics Data System (ADS)

    Gui, Guan; Xu, Li; Adachi, Fumiyuki

    2014-12-01

    Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo-based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.

  12. Comparison of Control Algorithms for a MEMS-based Adaptive Optics Scanning Laser Ophthalmoscope

    PubMed Central

    Li, Kaccie Y.; Mishra, Sandipan; Tiruveedhula, Pavan; Roorda, Austin

    2010-01-01

    We compared four algorithms for controlling a MEMS deformable mirror of an adaptive optics (AO) scanning laser ophthalmoscope. Interferometer measurements of the static nonlinear response of the deformable mirror were used to form an equivalent linear model of the AO system so that the classic integrator plus wavefront reconstructor type controller can be implemented. The algorithms differ only in the design of the wavefront reconstructor. The comparisons were made for two eyes (two individuals) via a series of imaging sessions. All four controllers performed similarly according to estimated residual wavefront error not reflecting the actual image quality observed. A metric based on mean image intensity did consistently reflect the qualitative observations of retinal image quality. Based on this metric, the controller most effective for suppressing the least significant modes of the deformable mirror performed the best. PMID:20454552

  13. Validation of elastic registration algorithms based on adaptive irregular grids for medical applications

    NASA Astrophysics Data System (ADS)

    Franz, Astrid; Carlsen, Ingwer C.; Renisch, Steffen; Wischmann, Hans-Aloys

    2006-03-01

    Elastic registration of medical images is an active field of current research. Registration algorithms have to be validated in order to show that they fulfill the requirements of a particular clinical application. Furthermore, validation strategies compare the performance of different registration algorithms and can hence judge which algorithm is best suited for a target application. In the literature, validation strategies for rigid registration algorithms have been analyzed. For a known ground truth they assess the displacement error at a few landmarks, which is not sufficient for elastic transformations described by a huge number of parameters. Hence we consider the displacement error averaged over all pixels in the whole image or in a region-of-interest of clinical relevance. Using artificially, but realistically deformed images of the application domain, we use this quality measure to analyze an elastic registration based on transformations defined on adaptive irregular grids for the following clinical applications: Magnetic Resonance (MR) images of freely moving joints for orthopedic investigations, thoracic Computed Tomography (CT) images for the detection of pulmonary embolisms, and transmission images as used for the attenuation correction and registration of independently acquired Positron Emission Tomography (PET) and CT images. The definition of a region-of-interest allows to restrict the analysis of the registration accuracy to clinically relevant image areas. The behaviour of the displacement error as a function of the number of transformation control points and their placement can be used for identifying the best strategy for the initial placement of the control points.

  14. Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

    PubMed

    Chen, Ying-ping; Chen, Chao-Hong

    2010-01-01

    An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

  15. An Adaptive Reputation-Based Algorithm for Grid Virtual Organization Formation

    NASA Astrophysics Data System (ADS)

    Cui, Yongrui; Li, Mingchu; Ren, Yizhi; Sakurai, Kouichi

    A novel adaptive reputation-based virtual organization formation is proposed. It restrains the bad performers effectively based on the consideration of the global experience of the evaluator and evaluates the direct trust relation between two grid nodes accurately by consulting the previous trust value rationally. It also consults and improves the reputation evaluation process in PathTrust model by taking account of the inter-organizational trust relationship and combines it with direct and recommended trust in a weighted way, which makes the algorithm more robust against collusion attacks. Additionally, the proposed algorithm considers the perspective of the VO creator and takes required VO services as one of the most important fine-grained evaluation criterion, which makes the algorithm more suitable for constructing VOs in grid environments that include autonomous organizations. Simulation results show that our algorithm restrains the bad performers and resists against fake transaction attacks and badmouth attacks effectively. It provides a clear advantage in the design of a VO infrastructure.

  16. Three-dimensional microscope vision system based on micro laser line scanning and adaptive genetic algorithms

    NASA Astrophysics Data System (ADS)

    Apolinar, J.; Rodríguez, Muñoz

    2017-02-01

    A microscope vision system to retrieve small metallic surface via micro laser line scanning and genetic algorithms is presented. In this technique, a 36 μm laser line is projected on the metallic surface through a laser diode head, which is placed to a small distance away from the target. The micro laser line is captured by a CCD camera, which is attached to the microscope. The surface topography is computed by triangulation by means of the line position and microscope vision parameters. The calibration of the microscope vision system is carried out by an adaptive genetic algorithm based on the line position. In this algorithm, an objective function is constructed from the microscope geometry to determine the microscope vision parameters. Also, the genetic algorithm provides the search space to calculate the microscope vision parameters with high accuracy in fast form. This procedure avoids errors produced by the missing of references and physical measurements, which are employed by the traditional microscope vision systems. The contribution of the proposed system is corroborated by an evaluation via accuracy and speed of the traditional microscope vision systems, which retrieve micro-scale surface topography.

  17. A baseline correction algorithm for Raman spectroscopy by adaptive knots B-spline

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Fan, Xian-guang; Xu, Ying-jie; Wang, Xiu-fen; He, Hao; Zuo, Yong

    2015-11-01

    The Raman spectroscopy technique is a powerful and non-invasive technique for molecular fingerprint detection which has been widely used in many areas, such as food safety, drug safety, and environmental testing. But Raman signals can be easily corrupted by a fluorescent background, therefore we presented a baseline correction algorithm to suppress the fluorescent background in this paper. In this algorithm, the background of the Raman signal was suppressed by fitting a curve called a baseline using a cyclic approximation method. Instead of the traditional polynomial fitting, we used the B-spline as the fitting algorithm due to its advantages of low-order and smoothness, which can avoid under-fitting and over-fitting effectively. In addition, we also presented an automatic adaptive knot generation method to replace traditional uniform knots. This algorithm can obtain the desired performance for most Raman spectra with varying baselines without any user input or preprocessing step. In the simulation, three kinds of fluorescent background lines were introduced to test the effectiveness of the proposed method. We showed that two real Raman spectra (parathion-methyl and colza oil) can be detected and their baselines were also corrected by the proposed method.

  18. An adaptive guidance algorithm for an aerodynamically assisted orbital plane change maneuver. M.S. Thesis

    NASA Technical Reports Server (NTRS)

    Blissit, J. A.

    1986-01-01

    Using analysis results from the post trajectory optimization program, an adaptive guidance algorithm is developed to compensate for density, aerodynamic and thrust perturbations during an atmospheric orbital plane change maneuver. The maneuver offers increased mission flexibility along with potential fuel savings for future reentry vehicles. Although designed to guide a proposed NASA Entry Research Vehicle, the algorithm is sufficiently generic for a range of future entry vehicles. The plane change analysis provides insight suggesting a straight-forward algorithm based on an optimized nominal command profile. Bank angle, angle of attack, and engine thrust level, ignition and cutoff times are modulated to adjust the vehicle's trajectory to achieve the desired end-conditions. A performance evaluation of the scheme demonstrates a capability to guide to within 0.05 degrees of the desired plane change and five nautical miles of the desired apogee altitude while maintaining heating constraints. The algorithm is tested under off-nominal conditions of + or -30% density biases, two density profile models, + or -15% aerodynamic uncertainty, and a 33% thrust loss and for various combinations of these conditions.

  19. Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

    NASA Technical Reports Server (NTRS)

    Matthews, Bryan L.; Srivastava, Ashok N.

    2010-01-01

    Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

  20. Effects of a Peer Tutor Training Program on Tutors and Tutees with Severe Disabilities in Adapted Physical Education

    ERIC Educational Resources Information Center

    Vonlintel, Drew James

    2015-01-01

    This dissertation examines the efficacy of peer tutor training in adapted physical education (APE). A peer tutor evaluation form was created to assess the skills of untrained peer tutors (n = 12). Once skills were assessed, a peer tutor training protocol was created. The protocol was implemented in a peer tutor training program. After peer tutors…

  1. Optimization design of wind turbine drive train based on Matlab genetic algorithm toolbox

    NASA Astrophysics Data System (ADS)

    Li, R. N.; Liu, X.; Liu, S. J.

    2013-12-01

    In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission.

  2. Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm.

    PubMed

    Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei

    2014-04-01

    In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.

  3. Envelope analysis with a genetic algorithm-based adaptive filter bank for bearing fault detection.

    PubMed

    Kang, Myeongsu; Kim, Jaeyoung; Choi, Byeong-Keun; Kim, Jong-Myon

    2015-07-01

    This paper proposes a fault detection methodology for bearings using envelope analysis with a genetic algorithm (GA)-based adaptive filter bank. Although a bandpass filter cooperates with envelope analysis for early identification of bearing defects, no general consensus has been reached as to which passband is optimal. This study explores the impact of various passbands specified by the GA in terms of a residual frequency components-to-defect frequency components ratio, which evaluates the degree of defectiveness in bearings and finally outputs an optimal passband for reliable bearing fault detection.

  4. Reliability Optimization Design for Contact Springs of AC Contactors Based on Adaptive Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Zhao, Sheng; Su, Xiuping; Wu, Ziran; Xu, Chengwen

    The paper illustrates the procedure of reliability optimization modeling for contact springs of AC contactors under nonlinear multi-constraint conditions. The adaptive genetic algorithm (AGA) is utilized to perform reliability optimization on the contact spring parameters of a type of AC contactor. A method that changes crossover and mutation rates at different times in the AGA can effectively avoid premature convergence, and experimental tests are performed after optimization. The experimental result shows that the mass of each optimized spring is reduced by 16.2%, while the reliability increases to 99.9% from 94.5%. The experimental result verifies the correctness and feasibility of this reliability optimization designing method.

  5. Ibuprofen administration during endurance training cancels running-distance-dependent adaptations of skeletal muscle in mice.

    PubMed

    Machida, M; Takemasa, T

    2010-10-01

    Exercise training induces many adaptations in skeletal muscle, representative examples of which include an increase in the IIa myofibre and an increase in the capillary-to-fibre ratio (C:F ratio). Moreover, these phenomena are thought to be dependent on running distance. Ibuprofen is one non-steroidal anti-inflammatory drug that is often used as an analgesic, but its effect on skeletal muscle adaptation during endurance training is unclear. In the present study, therefore, we administered ibuprofen to mice during running wheel exercise for four weeks, and examined its effects on the increase in the IIa myofibre and the C:F ratio in skeletal muscle. We observed a significant increase of the IIa myofibre and C:F ratio even in the presence of ibuprofen. Moreover, in untreated mice, there was a significant positive and strong correlation between these parameters and running distance. These results indicate that the increase in the IIa myofibre and the C:F ratio in skeletal muscle usually depend on running distance. Interestingly, we observed no significant correlation between these parameters and running distance in ibuprofen-administered mice. Moreover, we found no significant increase of these parameters when the running distance was significantly increased, in comparison with untreated mice. These results indicate that ibuprofen administration during endurance training cancels running-distance-dependent adaptations in skeletal muscle. This suggests that even if ibuprofen administration facilitates longer-distance running, no further effects of training on skeletal muscle can be expected.

  6. Experimental Evaluation of a Braille-Reading-Inspired Finger Motion Adaptive Algorithm.

    PubMed

    Ulusoy, Melda; Sipahi, Rifat

    2016-01-01

    Braille reading is a complex process involving intricate finger-motion patterns and finger-rubbing actions across Braille letters for the stimulation of appropriate nerves. Although Braille reading is performed by smoothly moving the finger from left-to-right, research shows that even fluent reading requires right-to-left movements of the finger, known as "reversal". Reversals are crucial as they not only enhance stimulation of nerves for correctly reading the letters, but they also show one to re-read the letters that were missed in the first pass. Moreover, it is known that reversals can be performed as often as in every sentence and can start at any location in a sentence. Here, we report experimental results on the feasibility of an algorithm that can render a machine to automatically adapt to reversal gestures of one's finger. Through Braille-reading-analogous tasks, the algorithm is tested with thirty sighted subjects that volunteered in the study. We find that the finger motion adaptive algorithm (FMAA) is useful in achieving cooperation between human finger and the machine. In the presence of FMAA, subjects' performance metrics associated with the tasks have significantly improved as supported by statistical analysis. In light of these encouraging results, preliminary experiments are carried out with five blind subjects with the aim to put the algorithm to test. Results obtained from carefully designed experiments showed that subjects' Braille reading accuracy in the presence of FMAA was more favorable then when FMAA was turned off. Utilization of FMAA in future generation Braille reading devices thus holds strong promise.

  7. Experimental Evaluation of a Braille-Reading-Inspired Finger Motion Adaptive Algorithm

    PubMed Central

    2016-01-01

    Braille reading is a complex process involving intricate finger-motion patterns and finger-rubbing actions across Braille letters for the stimulation of appropriate nerves. Although Braille reading is performed by smoothly moving the finger from left-to-right, research shows that even fluent reading requires right-to-left movements of the finger, known as “reversal”. Reversals are crucial as they not only enhance stimulation of nerves for correctly reading the letters, but they also show one to re-read the letters that were missed in the first pass. Moreover, it is known that reversals can be performed as often as in every sentence and can start at any location in a sentence. Here, we report experimental results on the feasibility of an algorithm that can render a machine to automatically adapt to reversal gestures of one’s finger. Through Braille-reading-analogous tasks, the algorithm is tested with thirty sighted subjects that volunteered in the study. We find that the finger motion adaptive algorithm (FMAA) is useful in achieving cooperation between human finger and the machine. In the presence of FMAA, subjects’ performance metrics associated with the tasks have significantly improved as supported by statistical analysis. In light of these encouraging results, preliminary experiments are carried out with five blind subjects with the aim to put the algorithm to test. Results obtained from carefully designed experiments showed that subjects’ Braille reading accuracy in the presence of FMAA was more favorable then when FMAA was turned off. Utilization of FMAA in future generation Braille reading devices thus holds strong promise. PMID:26849058

  8. Adding learning to cellular genetic algorithms for training recurrent neural networks.

    PubMed

    Ku, K W; Mak, M W; Siu, W C

    1999-01-01

    This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA's) for training recurrent neural networks (RNN's). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GA's and learning have been compared. Different hill-climbing algorithms are incorporated into the cellular GA's as learning methods. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. The RTRL algorithm has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, which is the simplest form of learning, has been implemented by considering the RNN's as feedforward networks during learning. The hybrid algorithms are used to train the RNN's to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations required for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA's has been found to be the fastest method. It is also concluded that learning should not be too extensive if the hybrid algorithm is to be benefit from learning.

  9. Simulation of the electrically stimulated cochlear neuron: modeling adaptation to trains of electric pulses.

    PubMed

    Woo, Jihwan; Miller, Charles A; Abbas, Paul J

    2009-05-01

    The Hodgkin-Huxley (HH) model does not simulate the significant changes in auditory nerve fiber (ANF) responses to sustained stimulation that are associated with neural adaptation. Given that the electric stimuli used by cochlear prostheses can result in adapted responses, a computational model incorporating an adaptation process is warranted if such models are to remain relevant and contribute to related research efforts. In this paper, we describe the development of a modified HH single-node model that includes potassium ion ( K(+)) concentration changes in response to each action potential. This activity-related change results in an altered resting potential, and hence, excitability. Our implementation of K(+)-related changes uses a phenomenological approach based upon K(+) accumulation and dissipation time constants. Modeled spike times were computed using repeated presentations of modeled pulse-train stimuli. Spike-rate adaptation was characterized by rate decrements and time constants and compared against ANF data from animal experiments. Responses to relatively low (250 pulse/s) and high rate (5000 pulse/s) trains were evaluated and the novel adaptation model results were compared against model results obtained without the adaptation mechanism. In addition to spike-rate changes, jitter and spike intervals were evaluated and found to change with the addition of modeled adaptation. These results provide one means of incorporating a heretofore neglected (although important) aspect of ANF responses to electric stimuli. Future studies could include evaluation of alternative versions of the adaptation model elements and broadening the model to simulate a complete axon, and eventually, a spatially realistic model of the electrically stimulated nerve within extracochlear tissues.

  10. Concurrent strength and endurance training exercise sequence does not affect neuromuscular adaptations in older men.

    PubMed

    Wilhelm, Eurico Nestor; Rech, Anderson; Minozzo, Felipe; Botton, Cintia Ehlers; Radaelli, Regis; Teixeira, Bruno Costa; Reischak-Oliveira, Alvaro; Pinto, Ronei Silveira

    2014-12-01

    Concurrent training is an effective method for increasing skeletal muscle performance in aging individuals, but controversy exists as to whether chronic neuromuscular and functional adaptations are affected by the intra-session exercise sequence. Therefore the aim of this study was to evaluate the effect of concurrent endurance and power-like strength training exercise sequence on muscular and functional adaptations of older participants. Thirty-six healthy older men not engaged in systematic exercise training programs for at least 6 months were divided into a control group (CON; 65.8±5.3 years), or in the training groups: endurance-strength (ES; 63.2±3.3 years), or strength-endurance (SE; 67.1±6.1 years). Training groups underwent 12 weeks of concurrent endurance and power-like strength training, starting every exercise session with either endurance (in ES) or strength (in SE) exercises. Measurements included knee extension one repetition maximum (1RM), knee extension power, 30 second sit-to-stand test (30SS), maximum vastus lateralis surface electromyographic activity, and rectus femoris echo intensity (RFEI). Significant increases in maximal strength (ES +18±11.3%; SE +14.2±6.0%; p≤0.05), peak power (ES +22.2±19.4%; SE +26.3±31.3%; p≤0.05), and 30SS performance (ES +15.2±7.2%; SE +13.2±11.8%; p≤0.05) were observed only in the training groups, with no differences between ES and SE. Maximum muscular activity was greater after 12weeks at training groups (p≤0.05), and reductions in RFEI were found only in ES and SE (p≤0.05). These results demonstrate that concurrent strength and endurance training performed twice a week effectively increases muscular performance and functional capacity in older men, independent of the intra-session exercise sequence. Additionally, the RFEI decreases indicate an additional adaptation to concurrent training.

  11. A Low-cost Adaptive Balance Training Platform for Stroke Patients: A Usability Study.

    PubMed

    Verma, Sunny; Kumar, Deepesh; Kumawat, Animesh; Dutta, Anirban; Lahiri, Uttama

    2017-02-09

    Stroke patients usually suffer from asymmetric posture due to hemi-paresis that can result in reduced postural controllability leading to a balance deficit. This deficit increases the risk of falls, which often makes them dependent on caregivers for community ambulation, thus deteriorating their quality of life. Conventional balance training involves rehabilitation exercises performed under physiotherapist's supervision where the scarcity of trained professionals as well as the cost of clinic-based rehabilitation programs can deter stroke survivors from undergoing regular balance training. Thus, researchers have been exploring technology-assisted solutions, e.g. home-based Virtual Reality (VR) setup. In this study, we developed a VBaT (VR-based Balance Training) platform where VR-augmented user-interface using Nintendo Wii Balance Board was tested in a laboratory setting for its feasibility. The VBaT offered tasks of varying difficulties to the participants that adapted to individual performance capability during balance training. We performed a preliminary usability study with 7 stroke survivors (post-stroke period>6 months). Preliminary results indicate the potential of the VBaT system to cause improvement in overall average task performance over the course of training while using the VBaT. Thus the VBaT system is proposed to be a step towards an effective balance training platform for people with balance disorder.

  12. Evidence of muscular adaptations within four weeks of barbell training in women.

    PubMed

    Stock, Matt S; Olinghouse, Kendra D; Drusch, Alexander S; Mota, Jacob A; Hernandez, Jennah M; Akalonu, Chibuzo C; Thompson, Brennan J

    2016-02-01

    We investigated the time course of neuromuscular and hypertrophic adaptations associated with only four weeks of barbell squat and deadlift training. Forty-seven previously untrained women (mean±SD, age=21±3years) were randomly assigned to low volume training (n=15), moderate volume training (n=16), and control (n=16) groups. The low and moderate volume training groups performed two and four sets, respectively, of five repetitions per exercise, twice a week. Testing was performed weekly, and included dual X-ray absorptiometry and vastus lateralis and rectus femoris B-mode ultrasonography. Bipolar surface electromyographic (EMG) signals were detected from the vastus lateralis and biceps femoris during isometric maximal voluntary contractions of the leg extensors. Significant increases in lean mass for the combined gynoid and leg regions for the low (+0.68kg) and moderate volume (+0.47kg) groups were demonstrated within three weeks. Small-to-moderate effect sizes were shown for leg lean mass, vastus lateralis thickness and pennation angle, and peak torque, but EMG amplitude was unaffected. These findings demonstrated rapid muscular adaptations in response to only eight sessions of back squat and deadlift training in women despite the absence of changes in agonist-antagonist EMG amplitude.

  13. Adaptive ILC algorithms of nonlinear continuous systems with non-parametric uncertainties for non-repetitive trajectory tracking

    NASA Astrophysics Data System (ADS)

    Li, Xiao-Dong; Lv, Mang-Mang; Ho, John K. L.

    2016-07-01

    In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.

  14. Learning, Not Adaptation, Characterizes Stroke Motor Recovery: Evidence From Kinematic Changes Induced by Robot-Assisted Therapy in Trained and Untrained Task in the Same Workspace

    PubMed Central

    Dipietro, L.; Krebs, H. I.; Volpe, B. T.; Stein, J.; Bever, C.; Mernoff, S. T.; Fasoli, S. E.; Hogan, N.

    2015-01-01

    Both the American Heart Association and the VA/DoD endorse upper-extremity robot-mediated rehabilitation therapy for stroke care. However, we do not know yet how to optimize therapy for a particular patient’s needs. Here, we explore whether we must train patients for each functional task that they must perform during their activities of daily living or alternatively capacitate patients to perform a class of tasks and have therapists assist them later in translating the observed gains into activities of daily living. The former implies that motor adaptation is a better model for motor recovery. The latter implies that motor learning (which allows for generalization) is a better model for motor recovery. We quantified trained and untrained movements performed by 158 recovering stroke patients via 13 metrics, including movement smoothness and submovements. Improvements were observed both in trained and untrained movements suggesting that generalization occurred. Our findings suggest that, as motor recovery progresses, an internal representation of the task is rebuilt by the brain in a process that better resembles motor learning than motor adaptation. Our findings highlight possible improvements for therapeutic algorithms design, suggesting sparse-activity-set training should suffice over exhaustive sets of task specific training. PMID:22186963

  15. An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures

    PubMed Central

    Zandi, Kasra; Butler, Gregory; Kharma, Nawwaf

    2016-01-01

    Computational design of RNA sequences that fold into targeted secondary structures has many applications in biomedicine, nanotechnology and synthetic biology. An RNA molecule is made of different types of secondary structure elements and an important RNA element named pseudoknot plays a key role in stabilizing the functional form of the molecule. However, due to the computational complexities associated with characterizing pseudoknotted RNA structures, most of the existing RNA sequence designer algorithms generally ignore this important structural element and therefore limit their applications. In this paper we present a new algorithm to design RNA sequences for pseudoknotted secondary structures. We use NUPACK as the folding algorithm to compute the equilibrium characteristics of the pseudoknotted RNAs, and describe a new adaptive defect weighted sampling algorithm named Enzymer to design low ensemble defect RNA sequences for targeted secondary structures including pseudoknots. We used a biological data set of 201 pseudoknotted structures from the Pseudobase library to benchmark the performance of our algorithm. We compared the quality characteristics of the RNA sequences we designed by Enzymer with the results obtained from the state of the art MODENA and antaRNA. Our results show our method succeeds more frequently than MODENA and antaRNA do, and generates sequences that have lower ensemble defect, lower probability defect and higher thermostability. Finally by using Enzymer and by constraining the design to a naturally occurring and highly conserved Hammerhead motif, we designed 8 sequences for a pseudoknotted cis-acting Hammerhead ribozyme. Enzymer is available for download at https://bitbucket.org/casraz/enzymer. PMID:27499762

  16. How can computerized interpretation algorithms adapt to gender/age differences in ECG measurements?

    PubMed

    Xue, Joel; Farrell, Robert M

    2014-01-01

    It is well known that there are gender differences in 12 lead ECG measurements, some of which can be statistically significant. It is also an accepted practice that we should consider those differences when we interpret ECGs, by either a human overreader or a computerized algorithm. There are some major gender differences in 12 lead ECG measurements based on automatic algorithms, including global measurements such as heart rate, QRS duration, QT interval, and lead-by-lead measurements like QRS amplitude, ST level, etc. The interpretation criteria used in the automatic algorithms can be adapted to the gender differences in the measurements. The analysis of a group of 1339 patients with acute inferior MI showed that for patients under age 60, women had lower ST elevations at the J point in lead II than men (57±91μV vs. 86±117μV, p<0.02). This trend was reversed for patients over age 60 (lead aVF: 102±126μV vs. 84±117μV, p<0.04; lead III: 130±146μV vs. 103±131μV, p<0.007). Therefore, the ST elevation thresholds were set based on available gender and age information, which resulted in 25% relative sensitivity improvement for women under age 60, while maintaining a high specificity of 98%. Similar analyses were done for prolonged QT interval and LVH cases. The paper uses several design examples to demonstrate (1) how to design a gender-specific algorithm, and (2) how to design a robust ECG interpretation algorithm which relies less on absolute threshold-based criteria and is instead more reliant on overall morphology features, which are especially important when gender information is unavailable for automatic analysis.

  17. Neuromuscular adaptations to different modes of combined strength and endurance training.

    PubMed

    Eklund, D; Pulverenti, T; Bankers, S; Avela, J; Newton, R; Schumann, M; Häkkinen, K

    2015-02-01

    The present study investigated neuromuscular adaptations between same-session combined strength and endurance training with 2 loading orders and different day combined training over 24 weeks. 56 subjects were divided into different day (DD) combined strength and endurance training (4-6 d·wk(-1)) and same-session combined training: endurance preceding strength (E+S) or vice versa (S+E) (2-3 d·wk(-1)). Dynamic and isometric strength, EMG, voluntary activation, muscle cross-sectional area and endurance performance were measured. All groups increased dynamic one-repetition maximum (p<0.001; DD 13±7%, E+S 12±9% and S+E 17±12%) and isometric force (p<0.05-0.01), muscle cross-sectional area (p<0.001) and maximal power output during cycling (p<0.001). DD and S+E increased voluntary activation during training (p<0.05-0.01). In E+S no increase in voluntary activation was detected after 12 or 24 weeks. E+S also showed unchanged and S+E increased maximum EMG after 24 weeks during maximal isometric muscle actions. A high correlation (p<0.001, r=0.83) between the individual changes in voluntary activation and maximal knee extension force was found for E+S during weeks 13-24. Neural adaptations showed indications of being compromised and highly individual relating to changes in isometric strength when E+S-training was performed, while gains in one-repetition maximum, endurance performance and hypertrophy did not differ between the training modes.

  18. Optical Cluster-Finding with an Adaptive Matched-Filter Technique: Algorithm and Comparison with Simulations

    SciTech Connect

    Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.

    2007-10-29

    We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.

  19. Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.

    PubMed

    Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi

    2011-04-01

    Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system.

  20. Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

    NASA Technical Reports Server (NTRS)

    Mitra, Sunanda; Pemmaraju, Surya

    1992-01-01

    Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

  1. Strategies to overcome photobleaching in algorithm-based adaptive optics for nonlinear in-vivo imaging.

    PubMed

    Caroline Müllenbroich, M; McGhee, Ewan J; Wright, Amanda J; Anderson, Kurt I; Mathieson, Keith

    2014-01-01

    We have developed a nonlinear adaptive optics microscope utilizing a deformable membrane mirror (DMM) and demonstrated its use in compensating for system- and sample-induced aberrations. The optimum shape of the DMM was determined with a random search algorithm optimizing on either two photon fluorescence or second harmonic signals as merit factors. We present here several strategies to overcome photobleaching issues associated with lengthy optimization routines by adapting the search algorithm and the experimental methodology. Optimizations were performed on extrinsic fluorescent dyes, fluorescent beads loaded into organotypic tissue cultures and the intrinsic second harmonic signal of these cultures. We validate the approach of using these preoptimized mirror shapes to compile a robust look-up table that can be applied for imaging over several days and through a variety of tissues. In this way, the photon exposure to the fluorescent cells under investigation is limited to imaging. Using our look-up table approach, we show signal intensity improvement factors ranging from 1.7 to 4.1 in organotypic tissue cultures and freshly excised mouse tissue. Imaging zebrafish in vivo, we demonstrate signal improvement by a factor of 2. This methodology is easily reproducible and could be applied to many photon starved experiments, for example fluorescent life time imaging, or when photobleaching is a concern.

  2. Adaptive phase extraction: incorporating the Gabor transform in the matching pursuit algorithm.

    PubMed

    Wacker, Matthias; Witte, Herbert

    2011-10-01

    Short-time Fourier transform (STFT), Gabor transform (GT), wavelet transform (WT), and the Wigner-Ville distribution (WVD) are just some examples of time-frequency analysis methods which are frequently applied in biomedical signal analysis. However, all of these methods have their individual drawbacks. The STFT, GT, and WT have a time-frequency resolution that is determined by algorithm parameters and the WVD is contaminated by cross terms. In 1993, Mallat and Zhang introduced the matching pursuit (MP) algorithm that decomposes a signal into a sum of atoms and uses a cross-term free pseudo-WVD to generate a data-adaptive power distribution in the time-frequency space. Thus, it solved some of the problems of the GT and WT but lacks phase information that is crucial e.g., for synchronization analysis. We introduce a new time-frequency analysis method that combines the MP with a pseudo-GT. Therefore, the signal is decomposed into a set of Gabor atoms. Afterward, each atom is analyzed with a Gabor analysis, where the time-domain gaussian window of the analysis matches that of the specific atom envelope. A superposition of the single time-frequency planes gives the final result. This is the first time that a complete analysis of the complex time-frequency plane can be performed in a fully data-adaptive and frequency-selective manner. We demonstrate the capabilities of our approach on a simulation and on real-life magnetoencephalogram data.

  3. Jacobi-like method for a control algorithm in adaptive-optics imaging

    NASA Astrophysics Data System (ADS)

    Pitsianis, Nikos P.; Ellerbroek, Brent L.; Van Loan, Charles; Plemmons, Robert J.

    1998-10-01

    A study is made of a non-smooth optimization problem arising in adaptive-optics, which involves the real-time control of a deformable mirror designed to compensate for atmospheric turbulence and other dynamic image degradation factors. One formulation of this problem yields a functional f(U) equals (Sigma) iequals1n maxj[(UTMjU)ii] to be maximized over orthogonal matrices U for a fixed collection of n X n symmetric matrices Mj. We consider first the situation which can arise in practical applications where the matrices Mj are nearly pairwise commutative. Besides giving useful bounds, results for this case lead to a simple corollary providing a theoretical closed-form solution for globally maximizing f if the Mj are simultaneously diagonalizable. However, even here conventional optimization methods for maximizing f are not practical in a real-time environment. The genal optimization problem is quite difficult and is approached using a heuristic Jacobi-like algorithm. Numerical test indicate that the algorithm provides an effective means to optimize performance for some important adaptive-optics systems.

  4. Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle.

    PubMed

    Wilson, Emma D; Assaf, Tareq; Pearson, Martin J; Rossiter, Jonathan M; Anderson, Sean R; Porrill, John; Dean, Paul

    2016-09-01

    Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training.

  5. Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle

    PubMed Central

    Assaf, Tareq; Rossiter, Jonathan M.; Porrill, John

    2016-01-01

    Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training. PMID:27655667

  6. Self-adapting root-MUSIC algorithm and its real-valued formulation for acoustic vector sensor array

    NASA Astrophysics Data System (ADS)

    Wang, Peng; Zhang, Guo-jun; Xue, Chen-yang; Zhang, Wen-dong; Xiong, Ji-jun

    2012-12-01

    In this paper, based on the root-MUSIC algorithm for acoustic pressure sensor array, a new self-adapting root-MUSIC algorithm for acoustic vector sensor array is proposed by self-adaptive selecting the lead orientation vector, and its real-valued formulation by Forward-Backward(FB) smoothing and real-valued inverse covariance matrix is also proposed, which can reduce the computational complexity and distinguish the coherent signals. The simulation experiment results show the better performance of two new algorithm with low Signal-to-Noise (SNR) in direction of arrival (DOA) estimation than traditional MUSIC algorithm, and the experiment results using MEMS vector hydrophone array in lake trails show the engineering practicability of two new algorithms.

  7. Exercise training-induced adaptations associated with increases in skeletal muscle glycogen content.

    PubMed

    Manabe, Yasuko; Gollisch, Katja S C; Holton, Laura; Kim, Young-Bum; Brandauer, Josef; Fujii, Nobuharu L; Hirshman, Michael F; Goodyear, Laurie J

    2013-02-01

    Chronic exercise training results in numerous skeletal muscle adaptations, including increases in insulin sensitivity and glycogen content. To understand the mechanism leading to increased muscle glycogen, we studied the effects of exercise training on glycogen regulatory proteins in rat skeletal muscle. Female Sprague Dawley rats performed voluntary wheel running for 1, 4 or 7 weeks. After 7 weeks of training, insulin-stimulated glucose uptake was increased in epitrochlearis muscle. As compared with sedentary control rats, muscle glycogen did not change after 1 week of training, but increased significantly after 4 and 7 weeks. The increases in muscle glycogen were accompanied by elevated glycogen synthase activity and protein expression. To assess the regulation of glycogen synthase, we examined its major activator, protein phosphatase 1 (PP1), and its major deactivator, glycogen synthase kinase (GSK)-3. Consistent with glycogen synthase activity, PP1 activity was unchanged after 1 week of training but significantly increased after 4 and 7 weeks of training. Protein expression of R(GL)(G(M)), another regulatory PP1 subunit, significantly decreased after 4 and 7 weeks of training. Unlike PP1 activity, GSK-3 phosphorylation did not follow the pattern of glycogen synthase activity. The ~ 40% decrease in GSK-3α phosphorylation after 1 week of exercise training persisted until 7 weeks, and may function as a negative feedback mechanism in response to elevated glycogen. Our findings suggest that exercise training-induced increases in muscle glycogen content could be regulated by multiple mechanisms, including enhanced insulin sensitivity, glycogen synthase expression, allosteric activation of glycogen synthase, and PP1 activity.

  8. Ergometric and metabolic adaptation to a 5-s sprint training programme.

    PubMed

    Linossier, M T; Denis, C; Dormois, D; Geyssant, A; Lacour, J R

    1993-01-01

    The effects of 7 weeks of sprint training (repeated 5-s all-out sprints) on maximal power output (Wv,max) determined during a force-velocity test and a 30-s Wingate test (Wpeak) were studied in ten students [22 (SD 2) years] exercising on a cycle ergometer. Before and after training, muscle biopsies were taken from vastus lateralis muscle at rest for the ten subjects and immediately after a training session for five of them. Sprint training induced an improvement both in peak performances by 25% (Wv,max and Wpeak) and in the 30-s total work by 16%. Before sprint training, the velocity reached with no load (v0) was related to the resting muscle phosphocreatine (PCr) stores (r = 0.87, P < 0.001). The training-induced changes in v0 were observed only when these PCr stores were lowest. This pointed to a possible limiting role of low PCr concentrations in the ability to reach a high velocity. The improvement in performances was linked to an increase in the energy production from anaerobic glycolysis. This result was suggested in muscle by the increase in lactate production measured after a training session associated with the 20% higher activity of both phosphofructokinase and lactate dehydrogenase. The sprint training also increased the proportion of slow twitch fibres closely related to the decrease in fast twitch b fibres. This result would appear to demonstrate an appropriate adaptive reaction following high-intensity intermittent training for the slow twitch fibres which exhibit a greater oxidative capacity.

  9. Accelerating the Development of Adaptive Performance: Validating the Think Like a Commander Training

    DTIC Science & Technology

    2007-02-01

    U.S. Army Research Institute for the Behavioral and Social Sciences Research Report 1868 Accelerating the Development of Adaptive Performance...REPORT TYPE 3. DATES COVERED (from... to) February 2007 Final March 2005 to March 2006 4. TITLE AND SUBTITLE 5a. CONTRACT OR GRANT NUMBER Accelerating ...deliberate training methods may be more effective and efficient than live, virtual, or constructive experiential learning environments. 15. SUBJECT TERMS

  10. Group Interpersonal Psychotherapy for depressed youth in IDP camps in Northern Uganda: adaptation and training.

    PubMed

    Verdeli, Helen; Clougherty, Kathleen; Onyango, Grace; Lewandowski, Eric; Speelman, Liesbeth; Betancourt, Teresa S; Neugebauer, Richard; Stein, Traci R; Bolton, Paul

    2008-07-01

    This article reviews the use of Interpersonal Psychotherapy (IPT) with depressed youth living in Internally Displaced Persons (IDP) camps in North Uganda. This youth has been exposed to severe losses and disruptions in relationships with caregivers, family, and community members; limited access to formal education; exposure to malnutrition and infections; and pressure to prematurely assume adult family roles. The process of adaptation to the content and training of IPT for these youth is presented and illustrated with case examples.

  11. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

    PubMed Central

    Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

    2016-01-01

    Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. PMID:27043559

  12. A local anisotropic adaptive algorithm for the solution of low-Mach transient combustion problems

    NASA Astrophysics Data System (ADS)

    Carpio, Jaime; Prieto, Juan Luis; Vera, Marcos

    2016-02-01

    A novel numerical algorithm for the simulation of transient combustion problems at low Mach and moderately high Reynolds numbers is presented. These problems are often characterized by the existence of a large disparity of length and time scales, resulting in the development of directional flow features, such as slender jets, boundary layers, mixing layers, or flame fronts. This makes local anisotropic adaptive techniques quite advantageous computationally. In this work we propose a local anisotropic refinement algorithm using, for the spatial discretization, unstructured triangular elements in a finite element framework. For the time integration, the problem is formulated in the context of semi-Lagrangian schemes, introducing the semi-Lagrange-Galerkin (SLG) technique as a better alternative to the classical semi-Lagrangian (SL) interpolation. The good performance of the numerical algorithm is illustrated by solving a canonical laminar combustion problem: the flame/vortex interaction. First, a premixed methane-air flame/vortex interaction with simplified transport and chemistry description (Test I) is considered. Results are found to be in excellent agreement with those in the literature, proving the superior performance of the SLG scheme when compared with the classical SL technique, and the advantage of using anisotropic adaptation instead of uniform meshes or isotropic mesh refinement. As a more realistic example, we then conduct simulations of non-premixed hydrogen-air flame/vortex interactions (Test II) using a more complex combustion model which involves state-of-the-art transport and chemical kinetics. In addition to the analysis of the numerical features, this second example allows us to perform a satisfactory comparison with experimental visualizations taken from the literature.

  13. Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm.

    PubMed

    Zhu, Min; Xia, Jing; Yan, Molei; Cai, Guolong; Yan, Jing; Ning, Gangmin

    2015-01-01

    With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods.

  14. Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm

    PubMed Central

    Zhu, Min; Xia, Jing; Yan, Molei; Cai, Guolong; Yan, Jing; Ning, Gangmin

    2015-01-01

    With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods. PMID:26649071

  15. Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data.

    PubMed

    Elbert, Yevgeniy; Burkom, Howard S

    2009-11-20

    This paper discusses further advances in making robust predictions with the Holt-Winters forecasts for a variety of syndromic time series behaviors and introduces a control-chart detection approach based on these forecasts. Using three collections of time series data, we compare biosurveillance alerting methods with quantified measures of forecast agreement, signal sensitivity, and time-to-detect. The study presents practical rules for initialization and parameterization of biosurveillance time series. Several outbreak scenarios are used for detection comparison. We derive an alerting algorithm from forecasts using Holt-Winters-generalized smoothing for prospective application to daily syndromic time series. The derived algorithm is compared with simple control-chart adaptations and to more computationally intensive regression modeling methods. The comparisons are conducted on background data from both authentic and simulated data streams. Both types of background data include time series that vary widely by both mean value and cyclic or seasonal behavior. Plausible, simulated signals are added to the background data for detection performance testing at signal strengths calculated to be neither too easy nor too hard to separate the compared methods. Results show that both the sensitivity and the timeliness of the Holt-Winters-based algorithm proved to be comparable or superior to that of the more traditional prediction methods used for syndromic surveillance.

  16. [A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

    PubMed

    Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

    2011-10-01

    Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

  17. Effects of directional microphone and adaptive multichannel noise reduction algorithm on cochlear implant performance.

    PubMed

    Chung, King; Zeng, Fan-Gang; Acker, Kyle N

    2006-10-01

    Although cochlear implant (CI) users have enjoyed good speech recognition in quiet, they still have difficulties understanding speech in noise. We conducted three experiments to determine whether a directional microphone and an adaptive multichannel noise reduction algorithm could enhance CI performance in noise and whether Speech Transmission Index (STI) can be used to predict CI performance in various acoustic and signal processing conditions. In Experiment I, CI users listened to speech in noise processed by 4 hearing aid settings: omni-directional microphone, omni-directional microphone plus noise reduction, directional microphone, and directional microphone plus noise reduction. The directional microphone significantly improved speech recognition in noise. Both directional microphone and noise reduction algorithm improved overall preference. In Experiment II, normal hearing individuals listened to the recorded speech produced by 4- or 8-channel CI simulations. The 8-channel simulation yielded similar speech recognition results as in Experiment I, whereas the 4-channel simulation produced no significant difference among the 4 settings. In Experiment III, we examined the relationship between STIs and speech recognition. The results suggested that STI could predict actual and simulated CI speech intelligibility with acoustic degradation and the directional microphone, but not the noise reduction algorithm. Implications for intelligibility enhancement are discussed.

  18. An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

    PubMed

    Yook, Sunhyun; Nam, Kyoung Won; Kim, Heepyung; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young

    2015-04-01

    In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments.

  19. Neuromuscular adaptations to training, injury and passive interventions: implications for running economy.

    PubMed

    Bonacci, Jason; Chapman, Andrew; Blanch, Peter; Vicenzino, Bill

    2009-01-01

    Performance in endurance sports such as running, cycling and triathlon has long been investigated from a physiological perspective. A strong relationship between running economy and distance running performance is well established in the literature. From this established base, improvements in running economy have traditionally been achieved through endurance training. More recently, research has demonstrated short-term resistance and plyometric training has resulted in enhanced running economy. This improvement in running economy has been hypothesized to be a result of enhanced neuromuscular characteristics such as improved muscle power development and more efficient use of stored elastic energy during running. Changes in indirect measures of neuromuscular control (i.e. stance phase contact times, maximal forward jumps) have been used to support this hypothesis. These results suggest that neuromuscular adaptations in response to training (i.e. neuromuscular learning effects) are an important contributor to enhancements in running economy. However, there is no direct evidence to suggest that these adaptations translate into more efficient muscle recruitment patterns during running. Optimization of training and run performance may be facilitated through direct investigation of muscle recruitment patterns before and after training interventions. There is emerging evidence that demonstrates neuromuscular adaptations during running and cycling vary with training status. Highly trained runners and cyclists display more refined patterns of muscle recruitment than their novice counterparts. In contrast, interference with motor learning and neuromuscular adaptation may occur as a result of ongoing multidiscipline training (e.g. triathlon). In the sport of triathlon, impairments in running economy are frequently observed after cycling. This impairment is related mainly to physiological stress, but an alteration in lower limb muscle coordination during running after cycling

  20. Locomotor, cardiocirculatory and metabolic adaptations to training in Andalusian and Anglo-Arabian horses.

    PubMed

    Muñoz, A; Santisteban, R; Rubio, M D; Agüera, E I; Escribano, B M; Castejón, F M

    1999-02-01

    The effects of two training programmes in 20 Andalusian and 12 Anglo-Arabian horses were evaluated by an increasing intensity work test at velocities of 4, 5, 6, 7 and 8 m sec(-1). Heart rate was monitored and blood samples were drawn at rest and after each velocity to analyse packed cell volume, haemoglobin concentration, plasma lactate and potassium levels. Furthermore, the programmes were video-taped and stride length, duration and frequency, stance (restraint and propulsion), swing phase durations and stride vertical component were measured. The training protocol of the Andalusian horses produced significant decreases in the cardiovascular, haematological and metabolic responses to exercise. Locomotory training adaptation consisted of an increased stride frequency and a reduced stride length and vertical stride component. The last variable was the limiting factor of stride length both before and after training in the Andalusian horses. A different training protocol for show-jumping competition in Anglo-Arabian horses failed to show significant differences in the studied parameters to the work test, although an increase in stride length at velocities of over 6 m sec(-1) was observed. Stride vertical component did not have an effect on the physiological response to exercise, either before or after training.

  1. Effects of strength training with eccentric overload on muscle adaptation in male athletes.

    PubMed

    Friedmann-Bette, Birgit; Bauer, Timm; Kinscherf, Ralf; Vorwald, Silke; Klute, Konstanze; Bischoff, Dirk; Müller, Helmut; Weber, Marc-André; Metz, Jürgen; Kauczor, Hans-Ulrich; Bärtsch, Peter; Billeter, Rudolf

    2010-03-01

    In classic concentric/eccentric exercise, the same absolute load is applied in concentric and eccentric actions, which infers a smaller relative eccentric load. We compared the effects of 6 weeks of classic concentric/eccentric quadriceps strength training (CON/ECC, 11 subjects) to eccentric overload training (CON/ECC+, 14 subjects) in athletes accustomed to regular strength training. The parameters determined included functional tests, quadriceps and fibre cross-sectional area (CSA), fibre type distribution by ATPase staining, localisation of myosin heavy chain (MHC) isoform mRNAs by situ hybridization and the steady-state levels of 48 marker mRNAs (RT-PCR) in vastus lateralis biopsies taken before and after training. Both training forms had anabolic effects with significant increases in quadriceps CSA, maximal strength, ribosomal RNA content and the levels of mRNAs involved in growth and regeneration. Only the CON/ECC+ training led to significantly increased height in a squat jump test. This was accompanied by significant increases in IIX fibre CSA, in the percentage of type IIA fibres expressing MHC IIx mRNA, in the level of mRNAs preferentially expressed in fast, glycolytic fibres, and in post-exercise capillary lactate. The enhanced eccentric load apparently led to a subtly faster gene expression pattern and induced a shift towards a faster muscle phenotype plus associated adaptations that make a muscle better suited for fast, explosive movements.

  2. Muscular adaptations after two different volumes of blood flow-restricted training.

    PubMed

    Martín-Hernández, J; Marín, P J; Menéndez, H; Ferrero, C; Loenneke, J P; Herrero, A J

    2013-03-01

    This study aimed to gain an insight into the adaptations of muscle strength and skeletal muscle thickness after two different volumes of blood flow restriction training (BFRT), and compare them with high-intensity training. The sample was divided into four groups: low-volume, low-intensity BFRT (BFRT LV); high-volume, low-intensity BFRT (BFRT HV); traditional high-intensity resistance training (HIT); and a control group, which maintained their routine activities (CON). Leg extension one repetition maximum (1RM), isokinetic peak knee extension, and flexion torques at 60°/s and 180°/s as well as muscle thickness of the rectus femoris (RF) and vastus lateralis (VL) were assessed at baseline and after 5 weeks of training BFRT LV (7.03%, P < 0.05), BFRT HV (6.24%, P < 0.05) and HIT (18.86%, P < 0.001) groups increased 1RM performance, while no changes were observed in the CON group. Muscle thickness of the RF and VL was increased irrespective of the training group (7.5%, P < 0.001; and 9.9%, P < 0.001, respectively). We conclude that doubling the exercise volume with BFRT causes no further benefit with muscular size or strength. Although similar increases in muscle thickness were observed between training groups, HIT increased 1RM performance to a greater extent compared to either volume of BFRT.

  3. About Training and Memory: NK-Cell Adaptation to Viral Infections.

    PubMed

    Hammer, Q; Romagnani, C

    2017-01-01

    Viral infections continuously challenge and shape our immune system. Due to their fine antigen recognition ability, adaptive lymphocytes protect against pathogen reencounter by generating specific immunological memory. Innate cells such as macrophages also adapt to pathogen challenge and mount resistance to reinfection, a phenomenon termed trained immunity. As part of the innate immunity, natural killer (NK) cells can display rapid effector functions and play a crucial role in the control of viral infections, especially by the β-herpesvirus cytomegalovirus (CMV). CMV activates the NK-cell pool by inducing proinflammatory signals, which prime NK cells, paralleling macrophage training. In addition, CMV dramatically shapes the NK-cell repertoire due to its ability to trigger specific NK cell-activating receptors, and enables the expansion and persistence of a specific NK-cell subset displaying adaptive and memory features. In this chapter, we will discuss how different signals during CMV infection contribute to NK-cell training and acquisition of classical memory properties and how these events can impact on reinfection and cross-resistance.

  4. The effects of statin medications on aerobic exercise capacity and training adaptations.

    PubMed

    Murlasits, Zsolt; Radák, Zsolt

    2014-11-01

    The incidence of myopathy increases dramatically in statin users who also exercise, likely limiting the positive impact of this lifesaving medication. New evidence also indicates that statin use can directly compromise aerobic exercise capacity; however, we are just beginning to understand the interactions of statins with exercise training and adaptations. This review focuses on the interactions of statins with aerobic exercise capacity and training adaptations to summarize the available information and draw attention to the gaps in our current knowledge in this area. PubMed, Web of knowledge, and Google scholar databases were searched between January 2000 and December 2013 using the following terms and their combinations: statins, exercise, aerobic capacity, endurance training, adaptations. The reference lists of the relevant articles were also scanned for additional information. Considering the widespread use of statins and the need for exercise for cardiovascular health, a better understanding of the interactions of these interventions as well as practical solutions are needed to reduce statin adverse effects associated with exercise.

  5. Pre-Sleep Protein Ingestion to Improve the Skeletal Muscle Adaptive Response to Exercise Training.

    PubMed

    Trommelen, Jorn; van Loon, Luc J C

    2016-11-28

    Protein ingestion following resistance-type exercise stimulates muscle protein synthesis rates, and enhances the skeletal muscle adaptive response to prolonged resistance-type exercise training. As the adaptive response to a single bout of resistance exercise extends well beyond the first couple of hours of post-exercise recovery, recent studies have begun to investigate the impact of the timing and distribution of protein ingestion during more prolonged recovery periods. Recent work has shown that overnight muscle protein synthesis rates are restricted by the level of amino acid availability. Protein ingested prior to sleep is effectively digested and absorbed, and thereby stimulates muscle protein synthesis rates during overnight recovery. When applied during a prolonged period of resistance-type exercise training, protein supplementation prior to sleep can further augment gains in muscle mass and strength. Recent studies investigating the impact of pre-sleep protein ingestion suggest that at least 40 g of protein is required to display a robust increase in muscle protein synthesis rates throughout overnight sleep. Furthermore, prior exercise allows more of the pre-sleep protein-derived amino acids to be utilized for de novo muscle protein synthesis during sleep. In short, pre-sleep protein ingestion represents an effective dietary strategy to improve overnight muscle protein synthesis, thereby improving the skeletal muscle adaptive response to exercise training.

  6. Cooperative adaptive bidirectional control of a train platoon for efficient utility and string stability

    NASA Astrophysics Data System (ADS)

    Gao, Shi-Gen; Dong, Hai-Rong; Ning, Bin; Roberts, Clive; Chen, Lei; Sun, Xu-Bin

    2015-09-01

    This paper proposes cooperative adaptive control schemes for a train platoon to improve efficient utility and guarantee string stability. The control schemes are developed based on a bidirectional strategy, i.e., the information of proximal (preceding and following) trains is used in the controller design. Based on available proximal information (prox-info) of location, speed, and acceleration, a direct adaptive control is designed to maintain the tracking interval at the minimum safe distance. Based on available prox-info of location, an observer-based adaptive control is designed to achieve the same target, which alleviates the requirements of equipped sensors to measure prox-info of speed and acceleration. The developed schemes are capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system, the string stability of train platoon is guaranteed on the basis of Lyapunov stability theorem. Numerical simulation results are presented to verify the effectiveness of the proposed control laws. Project supported by the Beijing Jiaotong University Research Program, China (Grant No. RCS2014ZT18), the Fundamental Research Funds for Central Universities, China (Grant No. 2015JBZ007), and the National Natural Science Foundation of China (Grant Nos. 61233001, 61322307, and 61304196).

  7. Pre-Sleep Protein Ingestion to Improve the Skeletal Muscle Adaptive Response to Exercise Training

    PubMed Central

    Trommelen, Jorn; van Loon, Luc J. C.

    2016-01-01

    Protein ingestion following resistance-type exercise stimulates muscle protein synthesis rates, and enhances the skeletal muscle adaptive response to prolonged resistance-type exercise training. As the adaptive response to a single bout of resistance exercise extends well beyond the first couple of hours of post-exercise recovery, recent studies have begun to investigate the impact of the timing and distribution of protein ingestion during more prolonged recovery periods. Recent work has shown that overnight muscle protein synthesis rates are restricted by the level of amino acid availability. Protein ingested prior to sleep is effectively digested and absorbed, and thereby stimulates muscle protein synthesis rates during overnight recovery. When applied during a prolonged period of resistance-type exercise training, protein supplementation prior to sleep can further augment gains in muscle mass and strength. Recent studies investigating the impact of pre-sleep protein ingestion suggest that at least 40 g of protein is required to display a robust increase in muscle protein synthesis rates throughout overnight sleep. Furthermore, prior exercise allows more of the pre-sleep protein-derived amino acids to be utilized for de novo muscle protein synthesis during sleep. In short, pre-sleep protein ingestion represents an effective dietary strategy to improve overnight muscle protein synthesis, thereby improving the skeletal muscle adaptive response to exercise training. PMID:27916799

  8. A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.

    PubMed

    Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim

    2015-01-01

    The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.

  9. A high precision position sensor design and its signal processing algorithm for a maglev train.

    PubMed

    Xue, Song; Long, Zhiqiang; He, Ning; Chang, Wensen

    2012-01-01

    High precision positioning technology for a kind of high speed maglev train with an electromagnetic suspension (EMS) system is studied. At first, the basic structure and functions of the position sensor are introduced and some key techniques to enhance the positioning precision are designed. Then, in order to further improve the positioning signal quality and the fault-tolerant ability of the sensor, a new kind of discrete-time tracking differentiator (TD) is proposed based on nonlinear optimal control theory. This new TD has good filtering and differentiating performances and a small calculation load. It is suitable for real-time signal processing. The stability, convergence property and frequency characteristics of the TD are studied and analyzed thoroughly. The delay constant of the TD is figured out and an effective time delay compensation algorithm is proposed. Based on the TD technology, a filtering process is introduced in to improve the positioning signal waveform when the sensor is under bad working conditions, and a two-sensor switching algorithm is designed to eliminate the positioning errors caused by the joint gaps of the long stator. The effectiveness and stability of the sensor and its signal processing algorithms are proved by the experiments on a test train during a long-term test run.

  10. A High Precision Position Sensor Design and Its Signal Processing Algorithm for a Maglev Train

    PubMed Central

    Xue, Song; Long, Zhiqiang; He, Ning; Chang, Wensen

    2012-01-01

    High precision positioning technology for a kind of high speed maglev train with an electromagnetic suspension (EMS) system is studied. At first, the basic structure and functions of the position sensor are introduced and some key techniques to enhance the positioning precision are designed. Then, in order to further improve the positioning signal quality and the fault-tolerant ability of the sensor, a new kind of discrete-time tracking differentiator (TD) is proposed based on nonlinear optimal control theory. This new TD has good filtering and differentiating performances and a small calculation load. It is suitable for real-time signal processing. The stability, convergence property and frequency characteristics of the TD are studied and analyzed thoroughly. The delay constant of the TD is figured out and an effective time delay compensation algorithm is proposed. Based on the TD technology, a filtering process is introduced in to improve the positioning signal waveform when the sensor is under bad working conditions, and a two-sensor switching algorithm is designed to eliminate the positioning errors caused by the joint gaps of the long stator. The effectiveness and stability of the sensor and its signal processing algorithms are proved by the experiments on a test train during a long-term test run. PMID:22778582

  11. Adaptive particle-cell algorithm for Fokker-Planck based rarefied gas flow simulations

    NASA Astrophysics Data System (ADS)

    Pfeiffer, M.; Gorji, M. H.

    2017-04-01

    Recently, the Fokker-Planck (FP) kinetic model has been devised on the basis of the Boltzmann equation (Jenny et al., 2010; Gorji et al., 2011). Particle Monte-Carlo schemes are then introduced for simulations of rarefied gas flows based on the FP kinetics. Here the particles follow independent stochastic paths and thus a spatio-temporal resolution coarser than the collisional scales becomes possible. In contrast to the direct simulation Monte-Carlo (DSMC), the computational cost is independent of the Knudsen number resulting in efficient simulations at moderate/low Knudsen flows. In order to further exploit the efficiency of the FP method, the required particle-cell resolutions should be found, and a cell refinement strategy has to be developed accordingly. In this study, an adaptive particle-cell scheme applicable to a general unstructured mesh is derived for the FP model. Virtual sub cells are introduced for the adaptive mesh refinement. Moreover a sub cell-merging algorithm is provided to honor the minimum required number of particles per cell. For assessments, the 70 degree blunted cone reentry flow (Allgre et al., 1997) is studied. Excellent agreement between the introduced adaptive FP method and DSMC is achieved.

  12. Engaging African American Fathers in Behavioral Parent Training: To Adapt or Not Adapt

    PubMed Central

    Kohl, Patricia L.; Seay, Kristen D.

    2015-01-01

    The Positive Parenting Program, Triple P, is an evidence-based parenting program with strong empirical support that increases parenting skills and decreases child behavior problems. Few studies on Triple P include fathers or African American fathers. This study was undertaken to determine if adaptation to Triple P level 4 is necessary to ensure fit with urban African American fathers. Qualitative focus groups and interviews were conducted with African American fathers. Some received a brief overview of the program before giving feedback (series A) and others received the entire intervention (series B). Inductive thematic analysis was used to analyze transcripts and codebooks were developed through an iterative process. Series B fathers had fewer negative perceptions and a more detailed perspective. Limited exposure to an intervention may cause participants to provide inaccurate data on intervention acceptability. The fathers’ initial perceptions of interventions, regardless of accuracy, will affect recruitment and engagement and must be addressed. One strategy is to tailor program examples and language to reflect the experiences of African American fathers. PMID:26190952

  13. Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans.

    PubMed

    Burgomaster, Kirsten A; Howarth, Krista R; Phillips, Stuart M; Rakobowchuk, Mark; Macdonald, Maureen J; McGee, Sean L; Gibala, Martin J

    2008-01-01

    Low-volume 'sprint' interval training (SIT) stimulates rapid improvements in muscle oxidative capacity that are comparable to levels reached following traditional endurance training (ET) but no study has examined metabolic adaptations during exercise after these different training strategies. We hypothesized that SIT and ET would induce similar adaptations in markers of skeletal muscle carbohydrate (CHO) and lipid metabolism and metabolic control during exercise despite large differences in training volume and time commitment. Active but untrained subjects (23 +/- 1 years) performed a constant-load cycling challenge (1 h at 65% of peak oxygen uptake (.VO(2peak)) before and after 6 weeks of either SIT or ET (n = 5 men and 5 women per group). SIT consisted of four to six repeats of a 30 s 'all out' Wingate Test (mean power output approximately 500 W) with 4.5 min recovery between repeats, 3 days per week. ET consisted of 40-60 min of continuous cycling at a workload that elicited approximately 65% (mean power output approximately 150 W) per day, 5 days per week. Weekly time commitment (approximately 1.5 versus approximately 4.5 h) and total training volume (approximately 225 versus approximately 2250 kJ week(-1)) were substantially lower in SIT versus ET. Despite these differences, both protocols induced similar increases (P < 0.05) in mitochondrial markers for skeletal muscle CHO (pyruvate dehydrogenase E1alpha protein content) and lipid oxidation (3-hydroxyacyl CoA dehydrogenase maximal activity) and protein content of peroxisome proliferator-activated receptor-gamma coactivator-1alpha. Glycogen and phosphocreatine utilization during exercise were reduced after training, and calculated rates of whole-body CHO and lipid oxidation were decreased and increased, respectively, with no differences between groups (all main effects, P < 0.05). Given the markedly lower training volume in the SIT group, these data suggest that high-intensity interval training is a time

  14. Active learning: effects of core training design elements on self-regulatory processes, learning, and adaptability.

    PubMed

    Bell, Bradford S; Kozlowski, Steve W J

    2008-03-01

    This article describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches; their effects on learning and transfer; and the core training design elements (exploration, training frame, emotion control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex, computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees' metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for development of an integrated theory of active learning, learner-centered design, and research extensions are discussed.

  15. Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

    NASA Astrophysics Data System (ADS)

    Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

    2014-06-01

    Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

  16. Adapted Prescription Dose for Monte Carlo Algorithm in Lung SBRT: Clinical Outcome on 205 Patients

    PubMed Central

    Bibault, Jean-Emmanuel; Mirabel, Xavier; Lacornerie, Thomas; Tresch, Emmanuelle; Reynaert, Nick; Lartigau, Eric

    2015-01-01

    Purpose SBRT is the standard of care for inoperable patients with early-stage lung cancer without lymph node involvement. Excellent local control rates have been reported in a large number of series. However, prescription doses and calculation algorithms vary to a great extent between studies, even if most teams prescribe to the D95 of the PTV. Type A algorithms are known to produce dosimetric discrepancies in heterogeneous tissues such as lungs. This study was performed to present a Monte Carlo (MC) prescription dose for NSCLC adapted to lesion size and location and compare the clinical outcomes of two cohorts of patients treated with a standard prescription dose calculated by a type A algorithm or the proposed MC protocol. Patients and Methods Patients were treated from January 2011 to April 2013 with a type B algorithm (MC) prescription with 54 Gy in three fractions for peripheral lesions with a diameter under 30 mm, 60 Gy in 3 fractions for lesions with a diameter over 30 mm, and 55 Gy in five fractions for central lesions. Clinical outcome was compared to a series of 121 patients treated with a type A algorithm (TA) with three fractions of 20 Gy for peripheral lesions and 60 Gy in five fractions for central lesions prescribed to the PTV D95 until January 2011. All treatment plans were recalculated with both algorithms for this study. Spearman’s rank correlation coefficient was calculated for GTV and PTV. Local control, overall survival and toxicity were compared between the two groups. Results 205 patients with 214 lesions were included in the study. Among these, 93 lesions were treated with MC and 121 were treated with TA. Overall survival rates were 86% and 94% at one and two years, respectively. Local control rates were 79% and 93% at one and two years respectively. There was no significant difference between the two groups for overall survival (p = 0.785) or local control (p = 0.934). Fifty-six patients (27%) developed grade I lung fibrosis without

  17. Noise-induced interspike interval correlations and spike train regularization in spike-triggered adapting neurons

    NASA Astrophysics Data System (ADS)

    Urdapilleta, Eugenio

    2016-09-01

    Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.

  18. The Effects of Short-Lasting Anti-Saccade Training in Homonymous Hemianopia with and without Saccadic Adaptation

    PubMed Central

    Lévy-Bencheton, Delphine; Pélisson, Denis; Prost, Myriam; Jacquin-Courtois, Sophie; Salemme, Roméo; Pisella, Laure; Tilikete, Caroline

    2016-01-01

    Homonymous Visual Field Defects (HVFD) are common following stroke and can be highly debilitating for visual perception and higher level cognitive functions such as exploring visual scene or reading a text. Rehabilitation using oculomotor compensatory methods with automatic training over a short duration (~15 days) have been shown as efficient as longer voluntary training methods (>1 month). Here, we propose to evaluate and compare the effect of an original HVFD rehabilitation method based on a single 15 min voluntary anti-saccades task (AS) toward the blind hemifield, with automatic sensorimotor adaptation to increase AS amplitude. In order to distinguish between adaptation and training effect, 14 left- or right-HVFD patients were exposed, 1 month apart, to three trainings, two isolated AS task (Delayed-shift and No-shift paradigm), and one combined with AS adaptation (Adaptation paradigm). A quality of life questionnaire (NEI-VFQ 25) and functional measurements (reading speed, visual exploration time in pop-out and serial tasks) as well as oculomotor measurements were assessed before and after each training. We could not demonstrate significant adaptation at the group level, but we identified a group of nine adapted patients. While AS training itself proved to demonstrate significant functional improvements in the overall patient group, we could also demonstrate in the sub-group of adapted patients and specifically following the adaptation training, an increase of saccade amplitude during the reading task (left-HVFD patients) and the Serial exploration task, and improvement of the visual quality of life. We conclude that short-lasting AS training combined with adaptation could be implemented in rehabilitation methods of cognitive dysfunctions following HVFD. Indeed, both voluntary and automatic processes have shown interesting effects on the control of visually guided saccades in different cognitive tasks. PMID:26778986

  19. Metabolic and respiratory adaptations during intense exercise following long-sprint training of short duration.

    PubMed

    Thomas, Claire; Bernard, Olivier; Enea, Carina; Jalab, Chadi; Hanon, Christine

    2012-02-01

    This study aimed to determine metabolic and respiratory adaptations during intense exercise and improvement of long-sprint performance following six sessions of long-sprint training. Nine subjects performed before and after training (1) a 300-m test, (2) an incremental exercise up to exhaustion to determine the velocity associated with maximal oxygen uptake (v-VO(2max)), (3) a 70-s constant exercise at intensity halfway between the v-VO(2max) and the velocity performed during the 300-m test, followed by a 60-min passive recovery to determine an individual blood lactate recovery curve fitted to the bi-exponential time function: [Formula: see text], and blood metabolic and gas exchange responses. The training program consisted of 3-6 repetitions of 150-250 m interspersed with rest periods with a duration ratio superior or equal to 1:10, 3 days a week, for 2 weeks. After sprint training, reduced metabolic disturbances, characterized by a lower peak expired ventilation and carbon dioxide output, in addition to a reduced peak lactate (P < 0.05), was observed. Training also induced significant decrease in the net amount of lactate released at the beginning of recovery (P < 0.05), and significant decrease in the net lactate release rate (NLRR) (P < 0.05). Lastly, a significant improvement of the 300-m performance was observed after training. These results suggest that long-sprint training of short durations was effective to rapidly prevent metabolic disturbances, with alterations in lactate accumulation and gas exchange, and improvement of the NLRR. Furthermore, only six long-sprint training sessions allow long-sprint performance improvement in active subjects.

  20. Beneficial metabolic adaptations due to endurance exercise training in the fasted state.

    PubMed

    Van Proeyen, Karen; Szlufcik, Karolina; Nielens, Henri; Ramaekers, Monique; Hespel, Peter

    2011-01-01

    Training with limited carbohydrate availability can stimulate adaptations in muscle cells to facilitate energy production via fat oxidation. Here we investigated the effect of consistent training in the fasted state, vs. training in the fed state, on muscle metabolism and substrate selection during fasted exercise. Twenty young male volunteers participated in a 6-wk endurance training program (1-1.5 h cycling at ∼70% Vo(₂max), 4 days/wk) while receiving isocaloric carbohydrate-rich diets. Half of the subjects trained in the fasted state (F; n = 10), while the others ingested ample carbohydrates before (∼160 g) and during (1 g·kg body wt⁻¹·h⁻¹) the training sessions (CHO; n = 10). The training similarly increased Vo(₂max) (+9%) and performance in a 60-min simulated time trial (+8%) in both groups (P < 0.01). Metabolic measurements were made during a 2-h constant-load exercise bout in the fasted state at ∼65% pretraining Vo(₂max). In F, exercise-induced intramyocellular lipid (IMCL) breakdown was enhanced in type I fibers (P < 0.05) and tended to be increased in type IIa fibers (P = 0.07). Training did not affect IMCL breakdown in CHO. In addition, F (+21%) increased the exercise intensity corresponding to the maximal rate of fat oxidation more than did CHO (+6%) (P < 0.05). Furthermore, maximal citrate synthase (+47%) and β-hydroxyacyl coenzyme A dehydrogenase (+34%) activity was significantly upregulated in F (P < 0.05) but not in CHO. Also, only F prevented the development exercise-induced drop in blood glucose concentration (P < 0.05). In conclusion, F is more effective than CHO to increase muscular oxidative capacity and at the same time enhances exercise-induced net IMCL degradation. In addition, F but not CHO prevented drop of blood glucose concentration during fasting exercise.

  1. Beneficial metabolic adaptations due to endurance exercise training in the fasted state

    PubMed Central

    Van Proeyen, Karen; Szlufcik, Karolina; Nielens, Henri; Ramaekers, Monique

    2011-01-01

    Training with limited carbohydrate availability can stimulate adaptations in muscle cells to facilitate energy production via fat oxidation. Here we investigated the effect of consistent training in the fasted state, vs. training in the fed state, on muscle metabolism and substrate selection during fasted exercise. Twenty young male volunteers participated in a 6-wk endurance training program (1–1.5 h cycling at ∼70% V̇o2max, 4 days/wk) while receiving isocaloric carbohydrate-rich diets. Half of the subjects trained in the fasted state (F; n = 10), while the others ingested ample carbohydrates before (∼160 g) and during (1 g·kg body wt−1·h−1) the training sessions (CHO; n = 10). The training similarly increased V̇o2max (+9%) and performance in a 60-min simulated time trial (+8%) in both groups (P < 0.01). Metabolic measurements were made during a 2-h constant-load exercise bout in the fasted state at ∼65% pretraining V̇o2max. In F, exercise-induced intramyocellular lipid (IMCL) breakdown was enhanced in type I fibers (P < 0.05) and tended to be increased in type IIa fibers (P = 0.07). Training did not affect IMCL breakdown in CHO. In addition, F (+21%) increased the exercise intensity corresponding to the maximal rate of fat oxidation more than did CHO (+6%) (P < 0.05). Furthermore, maximal citrate synthase (+47%) and β-hydroxyacyl coenzyme A dehydrogenase (+34%) activity was significantly upregulated in F (P < 0.05) but not in CHO. Also, only F prevented the development exercise-induced drop in blood glucose concentration (P < 0.05). In conclusion, F is more effective than CHO to increase muscular oxidative capacity and at the same time enhances exercise-induced net IMCL degradation. In addition, F but not CHO prevented drop of blood glucose concentration during fasting exercise. PMID:21051570

  2. Research on the Filtering Algorithm in Speed and Position Detection of Maglev Trains

    PubMed Central

    Dai, Chunhui; Long, Zhiqiang; Xie, Yunde; Xue, Song

    2011-01-01

    This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train’s structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally. PMID:22164012

  3. Training the neural networks by electromagnetism-like mechanism based algorithm

    NASA Astrophysics Data System (ADS)

    Jalab, Hamid A.; Shaker, Khalid

    2014-12-01

    Recently, medical data mining has become one of the most popular topics in the data mining community. This is due to the societal importance of the field and also the particular computational challenges posed in this domain of data mining. Early researches concentrated on sequential heuristics and later moved to meta-heuristic approaches due to the ability of these approaches to generate better solutions. The aim of this paper is to introduce the basic principles of a new meta-heuristic algorithm called Electromagnetism-like Mechanism (EMag) for neural network training. EMag simulates the electromagnetism theory of physics by considering each data sample to be an electrical charge. For neural network, EMag simulates the attraction-repulsion mechanism of each weight connection as charge partials to move towards the optimum without being trapped into local minimum. The performance of the proposed algorithm is evaluated in 12 of benchmark classification problems, and the computational results show that the proposed algorithm performs better than the standard back propagation algorithm.

  4. Appropriate training area selection for supervised texture classification by using the genetic algorithms

    NASA Astrophysics Data System (ADS)

    Okumura, Hiroshi; Maeda, Masaru; Arai, Kohei

    2003-03-01

    A new method for selection of appropriate training areas which are used for supervised texture classification is proposed. In the method, the genetic algorithms (GA) are employed to determine the appropriate location and the appropriate size of each texture category's training area. The proposed method consists of the following procedures: 1) the determination of the number of classification category and those kinds; 2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed; 6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. Some experiments are conducted to evaluate searching capability of appropriate training areas of the proposed method by using images from Brodatz's photo album and their rotated images. The experimental results show that the proposed method can select appropriate training areas much faster than conventional try-and-error method. The proposed method has been also applied to supervised texture classification of airborne multispectral scanner images. The experimental results show that the proposed method can provide appropriate training areas for reasonable classification results.

  5. Increased Adaptation Rates and Reduction in Trial-by-Trial Variability in Subjects with Cerebral Palsy Following a Multi-session Locomotor Adaptation Training

    PubMed Central

    Mawase, Firas; Bar-Haim, Simona; Joubran, Katherin; Rubin, Lihi; Karniel, Amir; Shmuelof, Lior

    2016-01-01

    Cerebral Palsy (CP) results from an insult to the developing brain and is associated with deficits in locomotor and manual skills and in sensorimotor adaptation. We hypothesized that the poor sensorimotor adaptation in persons with CP is related to their high execution variability and does not reflect a general impairment in adaptation learning. We studied the interaction between performance variability and adaptation deficits using a multi-session locomotor adaptation design in persons with CP. Six adolescents with diplegic CP were exposed, during a period of 15 weeks, to a repeated split-belt treadmill perturbation spread over 30 sessions and were tested again 6 months after the end of training. Compared to age-matched healthy controls, subjects with CP showed poor adaptation and high execution variability in the first exposure to the perturbation. Following training they showed marked reduction in execution variability and an increase in learning rates. The reduction in variability and the improvement in adaptation were highly correlated in the CP group and were retained 6 months after training. Interestingly, despite reducing their variability in the washout phase, subjects with CP did not improve learning rates during washout phases that were introduced only four times during the experiment. Our results suggest that locomotor adaptation in subjects with CP is related to their execution variability. Nevertheless, while variability reduction is generalized to other locomotor contexts, the development of savings requires both reduction in execution variability and multiple exposures to the perturbation. PMID:27199721

  6. Development of a new time domain-based algorithm for train detection and axle counting

    NASA Astrophysics Data System (ADS)

    Allotta, B.; D'Adamio, P.; Meli, E.; Pugi, L.

    2015-12-01

    This paper presents an innovative train detection algorithm, able to perform the train localisation and, at the same time, to estimate its speed, the crossing times on a fixed point of the track and the axle number. The proposed solution uses the same approach to evaluate all these quantities, starting from the knowledge of generic track inputs directly measured on the track (for example, the vertical forces on the sleepers, the rail deformation and the rail stress). More particularly, all the inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. This approach has the advantage to be simple and less invasive than the standard ones (it requires less equipment) and represents a more reliable and robust solution against numerical noise because it exploits the whole shape of the input signal and not only the peak values. A suitable and accurate multibody model of railway vehicle and flexible track has also been developed by the authors to test the algorithm when experimental data are not available and in general, under any operating conditions (fundamental to verify the algorithm accuracy and robustness). The railway vehicle chosen as benchmark is the Manchester Wagon, modelled in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed to verify the performance and the robustness of the proposed algorithm, and the results are quite promising. The research has been carried out in cooperation with Ansaldo STS and ECM Spa.

  7. Adaptation of Perceptual Responses to Low-Load Blood Flow Restriction Training.

    PubMed

    Martín-Hernández, Juan; Ruiz-Aguado, Jorge; Herrero, Azael J; Loenneke, Jeremy P; Aagaard, Per; Cristi-Montero, Carlos; Menéndez, Héctor; Marín, Pedro J

    2017-03-01

    Martín-Hernández, J, Ruiz-Aguado, J, Herrero, AJ, Loenneke, JP, Aagaard, P, Cristi-Montero, C, Menéndez, H, and Marín, PJ. Adaptation of perceptual responses to low-load blood flow restriction training. J Strength Cond Res 31(3): 765-772, 2017-The purpose of this study was to determine the adaptive response of ratings of perceived exertion (RPE) and pain over 6 consecutive training sessions. Thirty subjects were assigned to either a blood flow restriction training (BFRT) group or a high-intensity resistance training (HIT) group. Blood flow-restricted training group performed 4 sets (30 + 15 + 15 + 15, respectively) of unilateral leg extension at an intensity of 20% one repetition maximum (1RM) while a restrictive cuff was applied to the most proximal part of the leg. The HIT group performed 3 sets of 8 repetitions with 85% 1RM. Ratings of perceived exertion and pain were assessed immediately after each exercise set along the 6 training sessions and were then averaged to obtain the overall RPE and pain per session. Statistical analyses showed significant main effects for group (p ≤ 0.05) and time (p < 0.001). Ratings of perceived exertion values dropped from session 1 to session 6 in both BFRT (8.12 ± 1.3 to 5.7 ± 1.1, p < 0.001) and HIT (8.5 ± 1.2 to 6.40 ± 1.2, p < 0.001). Similar results were observed regarding pain ratings (BFRT: 8.12 ± 1.3 to 5.90 ± 1.55, p < 0.001; HIT: 6.22 ± 1.7 to 5.14 ± 1.42, p < 0.01). Our results indicate that RPE was higher after HIT, whereas differences did not reach significance regarding pain. These perceptual responses were attenuated over time, and the time course of this adaptive response was similar between BFRT and HIT. In summary, BFRT induces a marked perceptual response to training, comparable with that observed with HIT. However, this response becomes attenuated with continuous practice, leading to moderate values of RPE and pain. Perceptual responses may not limit the application of BFRT to highly motivated

  8. Heat acclimation and physical training adaptations of young women using different contraceptive hormones.

    PubMed

    Armstrong, Lawrence E; Maresh, Carl M; Keith, Nicole R; Elliott, Tabatha A; Vanheest, Jaci L; Scheett, Timothy P; Stoppani, James; Judelson, Daniel A; De Souza, Mary Jane

    2005-05-01

    Although endogenous and exogenous steroid hormones affect numerous physiological processes, the interactions of reproductive hormones, chronic exercise training, and heat acclimation are unknown. This investigation evaluated the responses and adaptations of 36 inactive females [age 21 +/- 3 (SD) yr] as they undertook a 7- to 8-wk program [heat acclimation and physical training (HAPT)] of indoor heat acclimation (90 min/day, 3 days/wk) and outdoor physical training (3 days/wk) while using either an oral estradiol-progestin contraceptive (ORAL, n = 15), a contraceptive injection of depot medroxyprogesterone acetate (DEPO, n = 7), or no contraceptive (EU-OV, n = 14; control). Standardized physical fitness and exercise-heat tolerance tests (36.5 degrees C, 37% relative humidity), administered before and after HAPT, demonstrated that the three subject groups successfully (P < 0.05) acclimated to heat (i.e., rectal temperature, heart rate) and improved muscular endurance (i.e., sit-ups, push-ups, 4.6-km run time) and body composition characteristics. The stress of HAPT did not disrupt the menstrual cycle length/phase characteristics, ovulation, or plasma hormone concentrations of EU-OV. No between-group differences (P > 0.05) existed for rectal and skin temperatures or metabolic, cardiorespiratory, muscular endurance, or body composition variables. A significant difference post-HAPT in the onset temperature of local sweating, ORAL (37.2 +/- 0.4 degrees C) vs. DEPO (37.7 +/- 0.2 degrees C), suggested that steroid hormones influenced this adaptation. In summary, virtually all adaptations of ORAL and DEPO were similar to EU-OV, suggesting that exogenous reproductive hormones neither enhanced nor impaired the ability of women to complete 7-8 wk of strenuous physical training and heat acclimation.

  9. A solution-adaptive mesh algorithm for dynamic/static refinement of two and three dimensional grids

    NASA Technical Reports Server (NTRS)

    Benson, Rusty A.; Mcrae, D. S.

    1991-01-01

    An adaptive grid algorithm has been developed in two and three dimensions that can be used dynamically with a solver or as part of a grid refinement process. The algorithm employs a transformation from the Cartesian coordinate system to a general coordinate space, which is defined as a parallelepiped in three dimensions. A weighting function, independent for each coordinate direction, is developed that will provide the desired refinement criteria in regions of high solution gradient. The adaptation is performed in the general coordinate space and the new grid locations are returned to the Cartesian space via a simple, one-step inverse mapping. The algorithm for relocation of the mesh points in the parametric space is based on the center of mass for distributed weights. Dynamic solution-adaptive results are presented for laminar flows in two and three dimensions.

  10. A model of professional development for practicing genetic counselors: adaptation of communication skills training in oncology.

    PubMed

    Dunlop, Kate L; Barlow-Stewart, Kristine; Butow, Phyllis; Heinrich, Paul

    2011-06-01

    Ongoing professional development for practicing genetic counselors is critical in maintaining best practice. Communication skills training (CST) workshops for doctors in oncology, utilizing trained actors in role plays, have been implemented for many years to improve patient-centred communication. This model was adapted to provide professional development in counseling skills for practicing genetic counselors, already highly trained in counseling skills. Detailed evidence based scenarios were developed. Evaluation of participants' experience and perceived outcomes on practice included surveys immediately post workshops (2002, 2004, 2005, 2008 (×2); n = 88/97), 2-5 years later (2007; n = 21/38) and a focus group (2007; n = 7). All rated workshops as effective training. Aspects highly valued included facilitator feedback, actors rather than role-playing with peers and being able to stop and try doing things differently. Perceived outcomes included the opportunity to reflect on practice; bring focus to communication; motivation and confidence. The high level of satisfaction is a strong endorsement for ongoing communication skills training in this format as part of professional development.

  11. Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter.

    PubMed

    Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo

    2015-09-01

    This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.

  12. An adaptive alignment algorithm for quality-controlled label-free LC-MS.

    PubMed

    Sandin, Marianne; Ali, Ashfaq; Hansson, Karin; Månsson, Olle; Andreasson, Erik; Resjö, Svante; Levander, Fredrik

    2013-05-01

    Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.

  13. Adaptive polarimetric sensing for optimum radar signature classification using a genetic search algorithm.

    PubMed

    Sadjadi, Firooz A

    2006-08-01

    An automated technique for adaptive radar polarimetric pattern classification is described. The approach is based on a genetic algorithm that uses a probabilistic pattern separation distance function and searches for those transmit and receive states of polarization sensing angles that optimize this function. Seven pattern separation distance functions--the Rayleigh quotient, the Bhattacharyya, divergence, Kolmogorov, Matusta, Kullback-Leibler distances, and the Bayesian probability of error--are used on real, fully polarimetric synthetic aperture radar target signatures. Each of these signatures is represented as functions of transmit and receive polarization ellipticity angles and the angle of polarization ellipse. The results indicate that, based on the majority of the distance functions used, there is a unique set of state of polarization angles whose use will lead to improved classification performance.

  14. Aberration correction in an adaptive free-space optical interconnect with an error diffusion algorithm

    NASA Astrophysics Data System (ADS)

    Gil-Leyva, Diego; Robertson, Brian; Wilkinson, Timothy D.; Henderson, Charley J.

    2006-06-01

    Aberration correction within a free-space optical interconnect based on a spatial light modulator for beam steering and holographic wavefront correction is presented. The wavefront sensing technique is based on an extension of a modal wavefront sensor described by Neil et al. [J. Opt. Soc. Am. A 17, 1098 (2000)], which uses a diffractive element. In this analysis such a wavefront sensor is adapted with an error diffusion algorithm that yields a low reconstruction error and fast reconfigurability. Improvement of the beam propagation quality (Strehl ratio) for different channels across the input plane is achieved. However, due to the space invariancy of the system, a trade-off among the beam propagation quality for channels is obtained. Experimental results are presented and discussed.

  15. Quantitative analysis of terahertz spectra for illicit drugs using adaptive-range micro-genetic algorithm

    NASA Astrophysics Data System (ADS)

    Chen, Yi; Ma, Yong; Lu, Zheng; Peng, Bei; Chen, Qin

    2011-08-01

    In the field of anti-illicit drug applications, many suspicious mixture samples might consist of various drug components—for example, a mixture of methamphetamine, heroin, and amoxicillin—which makes spectral identification very difficult. A terahertz spectroscopic quantitative analysis method using an adaptive range micro-genetic algorithm with a variable internal population (ARVIPɛμGA) has been proposed. Five mixture cases are discussed using ARVIPɛμGA driven quantitative terahertz spectroscopic analysis in this paper. The devised simulation results show agreement with the previous experimental results, which suggested that the proposed technique has potential applications for terahertz spectral identifications of drug mixture components. The results show agreement with the results obtained using other experimental and numerical techniques.

  16. An Adaptive Numeric Predictor-corrector Guidance Algorithm for Atmospheric Entry Vehicles. M.S. Thesis - MIT, Cambridge

    NASA Technical Reports Server (NTRS)

    Spratlin, Kenneth Milton

    1987-01-01

    An adaptive numeric predictor-corrector guidance is developed for atmospheric entry vehicles which utilize lift to achieve maximum footprint capability. Applicability of the guidance design to vehicles with a wide range of performance capabilities is desired so as to reduce the need for algorithm redesign with each new vehicle. Adaptability is desired to minimize mission-specific analysis and planning. The guidance algorithm motivation and design are presented. Performance is assessed for application of the algorithm to the NASA Entry Research Vehicle (ERV). The dispersions the guidance must be designed to handle are presented. The achievable operational footprint for expected worst-case dispersions is presented. The algorithm performs excellently for the expected dispersions and captures most of the achievable footprint.

  17. Demonstration of the use of ADAPT to derive predictive maintenance algorithms for the KSC central heat plant

    NASA Technical Reports Server (NTRS)

    Hunter, H. E.

    1972-01-01

    The Avco Data Analysis and Prediction Techniques (ADAPT) were employed to determine laws capable of detecting failures in a heat plant up to three days in advance of the occurrence of the failure. The projected performance of algorithms yielded a detection probability of 90% with false alarm rates of the order of 1 per year for a sample rate of 1 per day with each detection, followed by 3 hourly samplings. This performance was verified on 173 independent test cases. The program also demonstrated diagnostic algorithms and the ability to predict the time of failure to approximately plus or minus 8 hours up to three days in advance of the failure. The ADAPT programs produce simple algorithms which have a unique possibility of a relatively low cost updating procedure. The algorithms were implemented on general purpose computers at Kennedy Space Flight Center and tested against current data.

  18. Flight Testing of the Space Launch System (SLS) Adaptive Augmenting Control (AAC) Algorithm on an F/A-18

    NASA Technical Reports Server (NTRS)

    Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

    2014-01-01

    The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.

  19. Muscular adaptations to depth jump plyometric training: Comparison of sand vs. land surface

    PubMed Central

    Arazi, Hamid; Mohammadi, Mahdi

    2014-01-01

    The purpose of this study was to compare the effects of plyometric training on sand vs. land surface on muscular performance adaptations in men. Fourteen healthy men were randomly assigned to two training groups: a) Sand Depth Jump (SDJ; N = 7) and b) Land Depth Jump (LDJ; N = 7). Training was performed for 6 weeks and consisted of 5 × 20 repetitions of DJ training on 20-cm dry sand or 3-cm hard court surface twice weekly. Vertical Jump Test (VJT), Standing Long Jump Test (SLJT), 20-m and 40-m sprint, T-test (TT) and one repetition maximum leg press (1RMLP) were performed before and after training. Significant improvements in VJT [4 (ES = 0.63) vs. 5.4 (ES = 0.85) cm], SLJT [8.3 (ES = 0.3) vs. 12.7 (ES = 0.57) cm], and 1RMLP [23.5 (ES = 0.56) vs. 15.3 (ES = 0.49) kg] were seen for both the groups. Likewise, significant decreases were observed for both SDJ and LDJ groups in 20-m [0.3 (ES = 0.72) vs. 0.4 (ES = 1.98) s] and 40-m sprint times [0.2 (ES = 0.4) vs. 0.5 (ES = 0.71) s], and TT [0.5 (ES = 0.62) vs. 0.9 (ES = 0.57) s]. With regard to ES, it can be recommended that athletes used LDJ training for enhancing sprint and jump and SDJ training for improving agility and strength. PMID:25243078

  20. Neuromuscular adaptations to concurrent training in the elderly: effects of intrasession exercise sequence.

    PubMed

    Cadore, Eduardo Lusa; Izquierdo, Mikel; Pinto, Stephanie Santana; Alberton, Cristine Lima; Pinto, Ronei Silveira; Baroni, Bruno Manfredini; Vaz, Marco Aurélio; Lanferdini, Fábio Juner; Radaelli, Régis; González-Izal, Miriam; Bottaro, Martim; Kruel, Luiz Fernando Martins

    2013-06-01

    The aim of this study was investigate the effects of different intrasession exercise orders in the neuromuscular adaptations induced by concurrent training in elderly. Twenty-six healthy elderly men (64.7 ± 4.1 years), were placed into two concurrent training groups: strength prior to (SE, n = 13) or after (ES, n = 13) endurance training. Subjects trained strength and endurance training during 12 weeks, three times per week performing both exercise types in the same training session. Upper and lower body one maximum repetition test (1RM) and lower-body isometric peak torque (PTiso) and rate of force development were evaluated as strength parameters. Upper and lower body muscle thickness (MT) was determined by ultrasonography. Lower-body maximal surface electromyographic activity of vastus lateralis and rectus femoris muscles (maximal electromyographic (EMG) amplitude) and neuromuscular economy (normalized EMG at 50 % of pretraining PTiso) were determined. Both SE and ES groups increased the upper- and lower-body 1RM, but the lower-body 1RM increases observed in the SE was higher than ES (35.1 ± 12.8 vs. 21.9 ± 10.6 %, respectively; P < 0.01). Both SE and ES showed MT increases in all muscles evaluated, with no differences between groups. In addition, there were increases in the maximal EMG and neuromuscular economy of vastus lateralis in both SE and ES, but the neuromuscular economy of rectus femoris was improved only in SE (P < 0.001). Performing strength prior to endurance exercise during concurrent training resulted in greater lower-body strength gains as well as greater changes in the neuromuscular economy (rectus femoris) in elderly.

  1. Neuromuscular adaptations to water-based concurrent training in postmenopausal women: effects of intrasession exercise sequence.

    PubMed

    Pinto, Stephanie S; Alberton, Cristine L; Bagatini, Natália C; Zaffari, Paula; Cadore, Eduardo L; Radaelli, Régis; Baroni, Bruno M; Lanferdini, Fábio J; Ferrari, Rodrigo; Kanitz, Ana Carolina; Pinto, Ronei S; Vaz, Marco Aurélio; Kruel, Luiz Fernando M

    2015-02-01

    This study investigated the effects of different exercise sequences on the neuromuscular adaptations induced by water-based concurrent training in postmenopausal women. Twenty-one healthy postmenopausal women (57.14 ± 2.43 years) were randomly placed into two water-based concurrent training groups: resistance training prior to (RA, n = 10) or after (AR, n = 11) aerobic training. Subjects performed resistance and aerobic training twice a week over 12 weeks, performing both exercise types in the same training session. Upper (elbow flexors) and lower-body (knee extensors) one-repetition maximal test (1RM) and peak torque (PT) (knee extensors) were evaluated. The muscle thickness (MT) of upper (biceps brachii) and lower-body (vastus lateralis) was determined by ultrasonography. Moreover, the maximal and submaximal (neuromuscular economy) electromyographic activity (EMG) of lower-body (vastus lateralis and rectus femoris) was measured. Both RA and AR groups increased the upper- and lower-body 1RM and PT, while the lower-body 1RM increases observed in the RA was greater than AR (34.62 ± 13.51 vs. 14.16 ± 13.68 %). RA and AR showed similar MT increases in upper- and lower-body muscles evaluated. In addition, significant improvements in the maximal and submaximal EMG of lower-body muscles in both RA and AR were found, with no differences between groups. Both exercise sequences in water-based concurrent training presented relevant improvements to promote health and physical fitness in postmenopausal women. However, the exercise sequence resistance-aerobic optimizes the strength gains in lower limbs.

  2. Muscular adaptations to depth jump plyometric training: Comparison of sand vs. land surface.

    PubMed

    Arazi, Hamid; Mohammadi, Mahdi; Asadi, Abbas

    2014-09-01

    The purpose of this study was to compare the effects of plyometric training on sand vs. land surface on muscular performance adaptations in men. Fourteen healthy men were randomly assigned to two training groups: a) Sand Depth Jump (SDJ; N = 7) and b) Land Depth Jump (LDJ; N = 7). Training was performed for 6 weeks and consisted of 5 × 20 repetitions of DJ training on 20-cm dry sand or 3-cm hard court surface twice weekly. Vertical Jump Test (VJT), Standing Long Jump Test (SLJT), 20-m and 40-m sprint, T-test (TT) and one repetition maximum leg press (1RMLP) were performed before and after training. Significant improvements in VJT [4 (ES = 0.63) vs. 5.4 (ES = 0.85) cm], SLJT [8.3 (ES = 0.3) vs. 12.7 (ES = 0.57) cm], and 1RMLP [23.5 (ES = 0.56) vs. 15.3 (ES = 0.49) kg] were seen for both the groups. Likewise, significant decreases were observed for both SDJ and LDJ groups in 20-m [0.3 (ES = 0.72) vs. 0.4 (ES = 1.98) s] and 40-m sprint times [0.2 (ES = 0.4) vs. 0.5 (ES = 0.71) s], and TT [0.5 (ES = 0.62) vs. 0.9 (ES = 0.57) s]. With regard to ES, it can be recommended that athletes used LDJ training for enhancing sprint and jump and SDJ training for improving agility and strength.

  3. Cornu cervi pantotrichum supplementation improves physiological adaptions during intensive endurance training

    PubMed Central

    HUANG, Wen-Ching; HUANG, Chi-Chang; CHUANG, Hsiao-Li; CHIU, Chien-Chao; CHEN, Wen-Chyuan; HSU, Mei-Chich

    2017-01-01

    Cornu cervi pantotrichum (CCP), used in traditional Chinese medicine, is a well-known yang-invigorating agent with multifunctional bioactivities. We previously showed, through an acute exercise challenge, that short-term CCP supplementation improved physical activities and fatigue-associated biochemical indices. Questions about the long-term effects of CCP treatment on exercise performance and physical fatigue, as well as safety, with intensive exercise training need further research. ICR-strain mice were randomly assigned to three groups: (1) sedentary control and vehicle treatment (SC); (2) exercise training with vehicle treatment (ET); and (3) ET with CCP treatment at 4,108 mg/kg/day (ET+CCP). We assessed the physical performance, body compositions, and serum levels of lactate, ammonia, glucose and creatine kinase (CK) after an acute exercise challenge. The ET and ET+CCP groups had significantly increased grip strength and endurance swimming time, and decreased serum lactate and ammonia levels after the acute exercise challenge than the SC group. Moreover, serum ammonia and CK levels in the ET+CCP group were significantly decreased when compared to that of the ET only group. In regard to the body composition, the ET+CCP group inhibits the decrease in fat tissue, and related biochemical changes induced by the high intensity endurance training CCP supplementation combined with high-intensity endurance exercise could significantly improve the physiological adaptions related to fatigue or energy consumption and maintain the fat composition when compared to treatment with training only. Therefore, CCP may potentially improve the physiological adaptions in intensive exercise training. PMID:28163267

  4. Influence of dietary nitrate supplementation on physiological and muscle metabolic adaptations to sprint interval training.

    PubMed

    Thompson, Christopher; Wylie, Lee J; Blackwell, Jamie R; Fulford, Jonathan; Black, Matthew I; Kelly, James; McDonagh, Sinead T J; Carter, James; Bailey, Stephen J; Vanhatalo, Anni; Jones, Andrew M

    2017-03-01

    We hypothesized that 4 wk of dietary nitrate supplementation would enhance exercise performance and muscle metabolic adaptations to sprint interval training (SIT). Thirty-six recreationally active subjects, matched on key variables at baseline, completed a series of exercise tests before and following a 4-wk period in which they were allocated to one of the following groups: 1) SIT and [Formula: see text]-depleted beetroot juice as a placebo (SIT+PL); 2) SIT and [Formula: see text]-rich beetroot juice (~13 mmol [Formula: see text]/day; SIT+BR); or 3) no training and [Formula: see text]-rich beetroot juice (NT+BR). During moderate-intensity exercise, pulmonary oxygen uptake was reduced by 4% following 4 wk of SIT+BR and NT+BR (P < 0.05) but not SIT+PL. The peak work rate attained during incremental exercise increased more in SIT+BR than in SIT+PL (P < 0.05) or NT+BR (P < 0.001). The reduction in muscle and blood [lactate] and the increase in muscle pH from preintervention to postintervention were greater at 3 min of severe-intensity exercise in SIT+BR compared with SIT+PL and NT+BR (P < 0.05). However, the change in severe-intensity exercise performance was not different between SIT+BR and SIT+PL (P > 0.05). The relative proportion of type IIx muscle fibers in the vastus lateralis muscle was reduced in SIT+BR only (P < 0.05). These findings suggest that BR supplementation may enhance some aspects of the physiological adaptations to SIT.NEW & NOTEWORTHY We investigated the influence of nitrate-rich and nitrate-depleted beetroot juice on the muscle metabolic and physiological adaptations to 4 wk of sprint interval training. Compared with placebo, dietary nitrate supplementation reduced the O2 cost of submaximal exercise, resulted in greater improvement in incremental (but not severe-intensity) exercise performance, and augmented some muscle metabolic adaptations to training. Nitrate supplementation may facilitate some of the physiological responses to sprint interval

  5. Robust image transmission using a new joint source channel coding algorithm and dual adaptive OFDM

    NASA Astrophysics Data System (ADS)

    Farshchian, Masoud; Cho, Sungdae; Pearlman, William A.

    2004-01-01

    In this paper we consider the problem of robust image coding and packetization for the purpose of communications over slow fading frequency selective channels and channels with a shaped spectrum like those of digital subscribe lines (DSL). Towards this end, a novel and analytically based joint source channel coding (JSCC) algorithm to assign unequal error protection is presented. Under a block budget constraint, the image bitstream is de-multiplexed into two classes with different error responses. The algorithm assigns unequal error protection (UEP) in a way to minimize the expected mean square error (MSE) at the receiver while minimizing the probability of catastrophic failure. In order to minimize the expected mean square error at the receiver, the algorithm assigns unequal protection to the value bit class (VBC) stream. In order to minimizes the probability of catastrophic error which is a characteristic of progressive image coders, the algorithm assigns more protection to the location bit class (LBC) stream than the VBC stream. Besides having the advantage of being analytical and also numerically solvable, the algorithm is based on a new formula developed to estimate the distortion rate (D-R) curve for the VBC portion of SPIHT. The major advantage of our technique is that the worst case instantaneous minimum peak signal to noise ratio (PSNR) does not differ greatly from the averge MSE while this is not the case for the optimal single stream (UEP) system. Although both average PSNR of our method and the optimal single stream UEP are about the same, our scheme does not suffer erratic behavior because we have made the probability of catastrophic error arbitarily small. The coded image is sent via orthogonal frequency division multiplexing (OFDM) which is a known and increasing popular modulation scheme to combat ISI (Inter Symbol Interference) and impulsive noise. Using dual adaptive energy OFDM, we use the minimum energy necessary to send each bit stream at a

  6. Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery

    PubMed Central

    Tuna, E. Erdem; Franke, Timothy J.; Bebek, Özkan; Shiose, Akira; Fukamachi, Kiyotaka; Çavuşoğlu, M. Cenk

    2013-01-01

    Robotic assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface—a process called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-square based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a 3 degrees of freedom test-bed and prerecorded in-vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an Extended Kalman Filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized. PMID:23976889

  7. SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association.

    PubMed

    Dai, James Y; Leblanc, Michael; Smith, Nicholas L; Psaty, Bruce; Kooperberg, Charles

    2009-10-01

    Association studies have been widely used to identify genetic liability variants for complex diseases. While scanning the chromosomal region 1 single nucleotide polymorphism (SNP) at a time may not fully explore linkage disequilibrium, haplotype analyses tend to require a fairly large number of parameters, thus potentially losing power. Clustering algorithms, such as the cladistic approach, have been proposed to reduce the dimensionality, yet they have important limitations. We propose a SNP-Haplotype Adaptive REgression (SHARE) algorithm that seeks the most informative set of SNPs for genetic association in a targeted candidate region by growing and shrinking haplotypes with 1 more or less SNP in a stepwise fashion, and comparing prediction errors of different models via cross-validation. Depending on the evolutionary history of the disease mutations and the markers, this set may contain a single SNP or several SNPs that lay a foundation for haplotype analyses. Haplotype phase ambiguity is effectively accounted for by treating haplotype reconstruction as a part of the learning procedure. Simulations and a data application show that our method has improved power over existing methodologies and that the results are informative in the search for disease-causal loci.

  8. Refinement and evaluation of helicopter real-time self-adaptive active vibration controller algorithms

    NASA Technical Reports Server (NTRS)

    Davis, M. W.

    1984-01-01

    A Real-Time Self-Adaptive (RTSA) active vibration controller was used as the framework in developing a computer program for a generic controller that can be used to alleviate helicopter vibration. Based upon on-line identification of system parameters, the generic controller minimizes vibration in the fuselage by closed-loop implementation of higher harmonic control in the main rotor system. The new generic controller incorporates a set of improved algorithms that gives the capability to readily define many different configurations by selecting one of three different controller types (deterministic, cautious, and dual), one of two linear system models (local and global), and one or more of several methods of applying limits on control inputs (external and/or internal limits on higher harmonic pitch amplitude and rate). A helicopter rotor simulation analysis was used to evaluate the algorithms associated with the alternative controller types as applied to the four-bladed H-34 rotor mounted on the NASA Ames Rotor Test Apparatus (RTA) which represents the fuselage. After proper tuning all three controllers provide more effective vibration reduction and converge more quickly and smoothly with smaller control inputs than the initial RTSA controller (deterministic with external pitch-rate limiting). It is demonstrated that internal limiting of the control inputs a significantly improves the overall performance of the deterministic controller.

  9. An Adaptive Multigrid Algorithm for Simulating Solid Tumor Growth Using Mixture Models

    PubMed Central

    Wise, S.M.; Lowengrub, J.S.; Cristini, V.

    2010-01-01

    In this paper we give the details of the numerical solution of a three-dimensional multispecies diffuse interface model of tumor growth, which was derived in (Wise et al., J. Theor. Biol. 253 (2008)) and used to study the development of glioma in (Frieboes et al., NeuroImage 37 (2007) and tumor invasion in (Bearer et al., Cancer Research, 69 (2009)) and (Frieboes et al., J. Theor. Biol. 264 (2010)). The model has a thermodynamic basis, is related to recently developed mixture models, and is capable of providing a detailed description of tumor progression. It utilizes a diffuse interface approach, whereby sharp tumor boundaries are replaced by narrow transition layers that arise due to differential adhesive forces among the cell-species. The model consists of fourth-order nonlinear advection-reaction-diffusion equations (of Cahn-Hilliard-type) for the cell-species coupled with reaction-diffusion equations for the substrate components. Numerical solution of the model is challenging because the equations are coupled, highly nonlinear, and numerically stiff. In this paper we describe a fully adaptive, nonlinear multigrid/finite difference method for efficiently solving the equations. We demonstrate the convergence of the algorithm and we present simulations of tumor growth in 2D and 3D that demonstrate the capabilities of the algorithm in accurately and efficiently simulating the progression of tumors with complex morphologies. PMID:21076663

  10. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    PubMed Central

    Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel