Connected and leading disconnected hadronic light-by-light contribution to the muon anomalous magnetic moment with a physical pion mass

DOE PAGES

Blum, Thomas; Christ, Norman; Hayakawa, Masashi; ...

2017-01-11

We report a lattice QCD calculation of the hadronic light-by-light contribution to the muon anomalous magnetic moment at a physical pion mass. The calculation includes the connected diagrams and the leading, quark-line-disconnected diagrams. We incorporate algorithmic improvements developed in our previous work. The calculation was performed on the 48 3 × 96 ensemble generated with a physical pion mass and a 5.5 fm spatial extent by the RBC and UKQCD Collaborations using the chiral, domain wall fermion formulation. We find a HLbL μ = 5.35(1.35) × 10 –10, where the error is statistical only. The finite-volume and finite lattice-spacing errorsmore » could be quite large and are the subject of ongoing research. Finally, the omitted disconnected graphs, while expected to give a correction of order 10%, also need to be computed.« less

Modeling Group Interactions via Open Data Sources

DTIC Science & Technology

2011-08-30

data. The state-of-art search engines are designed to help general query-specific search and not suitable for finding disconnected online groups. The...groups, (2) developing innovative mathematical and statistical models and efficient algorithms that leverage existing search engines and employ

A secure 3-way routing protocols for intermittently connected mobile ad hoc networks.

PubMed

Sekaran, Ramesh; Parasuraman, Ganesh Kumar

2014-01-01

The mobile ad hoc network may be partially connected or it may be disconnected in nature and these forms of networks are termed intermittently connected mobile ad hoc network (ICMANET). The routing in such disconnected network is commonly an arduous task. Many routing protocols have been proposed for routing in ICMANET since decades. The routing techniques in existence for ICMANET are, namely, flooding, epidemic, probabilistic, copy case, spray and wait, and so forth. These techniques achieve an effective routing with minimum latency, higher delivery ratio, lesser overhead, and so forth. Though these techniques generate effective results, in this paper, we propose novel routing algorithms grounded on agent and cryptographic techniques, namely, location dissemination service (LoDiS) routing with agent AES, A-LoDiS with agent AES routing, and B-LoDiS with agent AES routing, ensuring optimal results with respect to various network routing parameters. The algorithm along with efficient routing ensures higher degree of security. The security level is cited testing with respect to possibility of malicious nodes into the network. This paper also aids, with the comparative results of proposed algorithms, for secure routing in ICMANET.

A Secure 3-Way Routing Protocols for Intermittently Connected Mobile Ad Hoc Networks

PubMed Central

Parasuraman, Ganesh Kumar

2014-01-01

The mobile ad hoc network may be partially connected or it may be disconnected in nature and these forms of networks are termed intermittently connected mobile ad hoc network (ICMANET). The routing in such disconnected network is commonly an arduous task. Many routing protocols have been proposed for routing in ICMANET since decades. The routing techniques in existence for ICMANET are, namely, flooding, epidemic, probabilistic, copy case, spray and wait, and so forth. These techniques achieve an effective routing with minimum latency, higher delivery ratio, lesser overhead, and so forth. Though these techniques generate effective results, in this paper, we propose novel routing algorithms grounded on agent and cryptographic techniques, namely, location dissemination service (LoDiS) routing with agent AES, A-LoDiS with agent AES routing, and B-LoDiS with agent AES routing, ensuring optimal results with respect to various network routing parameters. The algorithm along with efficient routing ensures higher degree of security. The security level is cited testing with respect to possibility of malicious nodes into the network. This paper also aids, with the comparative results of proposed algorithms, for secure routing in ICMANET. PMID:25136697

Auditory Responses and Stimulus-Specific Adaptation in Rat Auditory Cortex are Preserved Across NREM and REM Sleep

PubMed Central

Nir, Yuval; Vyazovskiy, Vladyslav V.; Cirelli, Chiara; Banks, Matthew I.; Tononi, Giulio

2015-01-01

Sleep entails a disconnection from the external environment. By and large, sensory stimuli do not trigger behavioral responses and are not consciously perceived as they usually are in wakefulness. Traditionally, sleep disconnection was ascribed to a thalamic “gate,” which would prevent signal propagation along ascending sensory pathways to primary cortical areas. Here, we compared single-unit and LFP responses in core auditory cortex as freely moving rats spontaneously switched between wakefulness and sleep states. Despite robust differences in baseline neuronal activity, both the selectivity and the magnitude of auditory-evoked responses were comparable across wakefulness, Nonrapid eye movement (NREM) and rapid eye movement (REM) sleep (pairwise differences <8% between states). The processing of deviant tones was also compared in sleep and wakefulness using an oddball paradigm. Robust stimulus-specific adaptation (SSA) was observed following the onset of repetitive tones, and the strength of SSA effects (13–20%) was comparable across vigilance states. Thus, responses in core auditory cortex are preserved across sleep states, suggesting that evoked activity in primary sensory cortices is driven by external physical stimuli with little modulation by vigilance state. We suggest that sensory disconnection during sleep occurs at a stage later than primary sensory areas. PMID:24323498

Perils in the adaptation of fire management to a changing world

Treesearch

Armando González-Cabán; M.M. Fernández-Ramiro; Claudio Conese; Francesco Bosello; Jorge Núñez; Victor Otrachshenko; B.J. Orr

2014-01-01

Increased fire load and costs are anticipated under future scenarios of climate and other global changes. This requires increased efficiency in investments in wildfire management operations, and resolving the disconnect problem between science, policy and management.

Cluster decomposition of full configuration interaction wave functions: A tool for chemical interpretation of systems with strong correlation

NASA Astrophysics Data System (ADS)

Lehtola, Susi; Tubman, Norm M.; Whaley, K. Birgitta; Head-Gordon, Martin

2017-10-01

Approximate full configuration interaction (FCI) calculations have recently become tractable for systems of unforeseen size, thanks to stochastic and adaptive approximations to the exponentially scaling FCI problem. The result of an FCI calculation is a weighted set of electronic configurations, which can also be expressed in terms of excitations from a reference configuration. The excitation amplitudes contain information on the complexity of the electronic wave function, but this information is contaminated by contributions from disconnected excitations, i.e., those excitations that are just products of independent lower-level excitations. The unwanted contributions can be removed via a cluster decomposition procedure, making it possible to examine the importance of connected excitations in complicated multireference molecules which are outside the reach of conventional algorithms. We present an implementation of the cluster decomposition analysis and apply it to both true FCI wave functions, as well as wave functions generated from the adaptive sampling CI algorithm. The cluster decomposition is useful for interpreting calculations in chemical studies, as a diagnostic for the convergence of various excitation manifolds, as well as as a guidepost for polynomially scaling electronic structure models. Applications are presented for (i) the double dissociation of water, (ii) the carbon dimer, (iii) the π space of polyacenes, and (iv) the chromium dimer. While the cluster amplitudes exhibit rapid decay with an increasing rank for the first three systems, even connected octuple excitations still appear important in Cr2, suggesting that spin-restricted single-reference coupled-cluster approaches may not be tractable for some problems in transition metal chemistry.

Duplex sampling apparatus and method

DOEpatents

Brown, Paul E.; Lloyd, Robert

1992-01-01

An improved apparatus is provided for sampling a gaseous mixture and for measuring mixture components. The apparatus includes two sampling containers connected in series serving as a duplex sampling apparatus. The apparatus is adapted to independently determine the amounts of condensable and noncondensable gases in admixture from a single sample. More specifically, a first container includes a first port capable of selectively connecting to and disconnecting from a sample source and a second port capable of selectively connecting to and disconnecting from a second container. A second container also includes a first port capable of selectively connecting to and disconnecting from the second port of the first container and a second port capable of either selectively connecting to and disconnecting from a differential pressure source. By cooling a mixture sample in the first container, the condensable vapors form a liquid, leaving noncondensable gases either as free gases or dissolved in the liquid. The condensed liquid is heated to drive out dissolved noncondensable gases, and all the noncondensable gases are transferred to the second container. Then the first and second containers are separated from one another in order to separately determine the amount of noncondensable gases and the amount of condensable gases in the sample.

Adaptability to Changes in Temporal Structure Is Fornix-Dependent

ERIC Educational Resources Information Center

Kwok, Sze Chai; Mitchell, Anna S.; Buckley, Mark J.

2015-01-01

Recognition memory deficits, even after short delays, are sometimes observed following hippocampal damage. One hypothesis links the hippocampus with processes in updating contextual memory representation. Here, we used fornix transection, which partially disconnects the hippocampal system, and compares the performance of fornix-transected monkeys…

Adaptability to Changes Intemporal Structure Is Fornix-Dependent

ERIC Educational Resources Information Center

Kwok, Sze Chai; Mitchell, Anna S.; Buckley, Mark J.

2015-01-01

Recognition memory deficits, even after short delays, are sometimes observed following hippocampal damage. One hypothesis links the hippocampus with processes in updating contextual memory representation. Here, we used fornix transection, which partially disconnects the hippocampal system, and compares the performance of fornix-transected monkeys…

Adapting Assessment Tasks to Support Three-Dimensional Learning

ERIC Educational Resources Information Center

Underwood, Sonia M.; Posey, Lynmarie A.; Herrington, Deborah G.; Carmel, Justin H.; Cooper, Melanie M.

2018-01-01

As chemists, we understand that science is more than a set of disconnected facts. It is a way of investigating and understanding our natural world that involves things like asking questions, analyzing data, identifying patterns, constructing explanations, developing and using models, and applying core concepts to other situations. This paper uses…

Using In Silico Fragmentation to Improve Routine Residue Screening in Complex Matrices.

PubMed

Kaufmann, Anton; Butcher, Patrick; Maden, Kathryn; Walker, Stephan; Widmer, Mirjam

2017-12-01

Targeted residue screening requires the use of reference substances in order to identify potential residues. This becomes a difficult issue when using multi-residue methods capable of analyzing several hundreds of analytes. Therefore, the capability of in silico fragmentation based on a structure database ("suspect screening") instead of physical reference substances for routine targeted residue screening was investigated. The detection of fragment ions that can be predicted or explained by in silico software was utilized to reduce the number of false positives. These "proof of principle" experiments were done with a tool that is integrated into a commercial MS vendor instrument operating software (UNIFI) as well as with a platform-independent MS tool (Mass Frontier). A total of 97 analytes belonging to different chemical families were separated by reversed phase liquid chromatography and detected in a data-independent acquisition (DIA) mode using ion mobility hyphenated with quadrupole time of flight mass spectrometry. The instrument was operated in the MS E mode with alternating low and high energy traces. The fragments observed from product ion spectra were investigated using a "chopping" bond disconnection algorithm and a rule-based algorithm. The bond disconnection algorithm clearly explained more analyte product ions and a greater percentage of the spectral abundance than the rule-based software (92 out of the 97 compounds produced ≥1 explainable fragment ions). On the other hand, tests with a complex blank matrix (bovine liver extract) indicated that the chopping algorithm reports significantly more false positive fragments than the rule based software. Graphical Abstract.

Using In Silico Fragmentation to Improve Routine Residue Screening in Complex Matrices

NASA Astrophysics Data System (ADS)

Kaufmann, Anton; Butcher, Patrick; Maden, Kathryn; Walker, Stephan; Widmer, Mirjam

2017-12-01

Targeted residue screening requires the use of reference substances in order to identify potential residues. This becomes a difficult issue when using multi-residue methods capable of analyzing several hundreds of analytes. Therefore, the capability of in silico fragmentation based on a structure database ("suspect screening") instead of physical reference substances for routine targeted residue screening was investigated. The detection of fragment ions that can be predicted or explained by in silico software was utilized to reduce the number of false positives. These "proof of principle" experiments were done with a tool that is integrated into a commercial MS vendor instrument operating software (UNIFI) as well as with a platform-independent MS tool (Mass Frontier). A total of 97 analytes belonging to different chemical families were separated by reversed phase liquid chromatography and detected in a data-independent acquisition (DIA) mode using ion mobility hyphenated with quadrupole time of flight mass spectrometry. The instrument was operated in the MSE mode with alternating low and high energy traces. The fragments observed from product ion spectra were investigated using a "chopping" bond disconnection algorithm and a rule-based algorithm. The bond disconnection algorithm clearly explained more analyte product ions and a greater percentage of the spectral abundance than the rule-based software (92 out of the 97 compounds produced ≥1 explainable fragment ions). On the other hand, tests with a complex blank matrix (bovine liver extract) indicated that the chopping algorithm reports significantly more false positive fragments than the rule based software. [Figure not available: see fulltext.

Algorithm based on regional separation for automatic grain boundary extraction using improved mean shift method

NASA Astrophysics Data System (ADS)

Zhenying, Xu; Jiandong, Zhu; Qi, Zhang; Yamba, Philip

2018-06-01

Metallographic microscopy shows that the vast majority of metal materials are composed of many small grains; the grain size of a metal is important for determining the tensile strength, toughness, plasticity, and other mechanical properties. In order to quantitatively evaluate grain size in metals, grain boundaries must be identified in metallographic images. Based on the phenomenon of grain boundary blurring or disconnection in metallographic images, this study develops an algorithm based on regional separation for automatically extracting grain boundaries by an improved mean shift method. Experimental observation shows that the grain boundaries obtained by the proposed algorithm are highly complete and accurate. This research has practical value because the proposed algorithm is suitable for grain boundary extraction from most metallographic images.

Real-time management of faulty electrodes in electrical impedance tomography.

PubMed

Hartinger, Alzbeta E; Guardo, Robert; Adler, Andy; Gagnon, Hervé

2009-02-01

Completely or partially disconnected electrodes are a fairly common occurrence in many electrical impedance tomography (EIT) clinical applications. Several factors can contribute to electrode disconnection: patient movement, perspiration, manipulations by clinical staff, and defective electrode leads or electronics. By corrupting several measurements, faulty electrodes introduce significant image artifacts. In order to properly manage faulty electrodes, it is necessary to: 1) account for invalid data in image reconstruction algorithms and 2) automatically detect faulty electrodes. This paper presents a two-part approach for real-time management of faulty electrodes based on the principle of voltage-current reciprocity. The first part allows accounting for faulty electrodes in EIT image reconstruction without a priori knowledge of which electrodes are at fault. The method properly weights each measurement according to its compliance with the principle of voltage-current reciprocity. Results show that the algorithm is able to automatically determine the valid portion of the data and use it to calculate high-quality images. The second part of the approach allows automatic real-time detection of at least one faulty electrode with 100% sensitivity and two faulty electrodes with 80% sensitivity enabling the clinical staff to fix the problem as soon as possible to minimize data loss.

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

PubMed

Sato, João R; Moll, Jorge; Green, Sophie; Deakin, John F W; Thomaz, Carlos E; Zahn, Roland

2015-08-30

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

Leveraging Students' Knowledge to Adapt Science Curricula to Local Context

ERIC Educational Resources Information Center

Minshew, Lana M.; Barber-Lester, Kelly J.; Derry, Sharon J.; Anderson, Janice L.

2017-01-01

Conceptions of ecological processes such as the flow of energy and cycling of matter in an ecosystem are increasingly important understandings in a rapidly changing world. This study utilizes a p-prims, or knowledge in pieces, lens to examine understandings and disconnections in students' conceptualizations of energy flow and matter cycling…

Science Education in Rural America: Adaptations for the Ivory Tower

ERIC Educational Resources Information Center

Van Doren, Gregory S.

2010-01-01

This thesis illustrated what can happen when academic culture disconnects from the cultures surrounding it. It showed that formal school environments are not always the best places to learn. A discussion of the debate between coherence and fragmentation learning theories illustrated academic chasms and a mindset that science education must…

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

PubMed

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

PubMed Central

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

Self-organized multi-species vegetation patterns: the role of connectivity of environmental niches in natural water harvesting ecosystems

NASA Astrophysics Data System (ADS)

Callegaro, Chiara; Ursino, Nadia

2016-04-01

Self-organizing vegetation patterns are natural water harvesting systems in arid and semi-arid regions of the world and should be imitated when designing man-managed water-harvesting systems for rain-fed crop. Disconnected vegetated and bare zones, functioning as a source-sink system of resources, sustain vegetation growth and reduce water and soil losses. Mechanisms such as soil crusting over bare areas and soil loosening in vegetated areas feed back to the local net facilitation effect and contribute to maintain the patterned landscape structure. Dis-connectivity of run-off production and run-on infiltration sites reduces runoff production at the landscape scale, and increases water retention in the vegetated patches. What is the effect of species adaptation to different resource niches on the landscape structure? A minimal model for two coexisting species and soil moisture balance was formulated, to improve our understanding of the effects of species differentiation on the dynamics of plants and water at single-pattern and landscape scale within a tiger bush type ecosystem. A basic assumption of our model was that soil moisture availability is a proxy for the environmental niche of plant species. Connectivity and dis-connectivity of specific niches of adaptation of two differing plant species was an input parameter of our model, in order to test the effect of coexistence on the ecosystem structure. The ecosystem structure is the model outcome, including: patterns persistence of coexisting species; patterns persistence of one species with exclusion of the other; patterns decline with just one species surviving in a non organized structure; bare landscape with loss of both species. Results suggest that pattern-forming-species communities arise as a result of complementary niche adaptation (niche dis-connecivity), whereas niche superposition (niche connectivity) may lead to impoverishment of environmental resources and loss of vegetation cover and diversity.

Shortest multiple disconnected path for the analysis of entanglements in two- and three-dimensional polymeric systems

NASA Astrophysics Data System (ADS)

Kröger, Martin

2005-06-01

We present an algorithm which returns a shortest path and related number of entanglements for a given configuration of a polymeric system in 2 or 3 dimensions. Rubinstein and Helfand, and later Everaers et al. introduced a concept to extract primitive paths for dense polymeric melts made of linear chains (a multiple disconnected multibead 'path'), where each primitive path is defined as a path connecting the (space-fixed) ends of a polymer under the constraint of non-interpenetration (excluded volume) between primitive paths of different chains, such that the multiple disconnected path fulfills a minimization criterion. The present algorithm uses geometrical operations and provides a—model independent—efficient approximate solution to this challenging problem. Primitive paths are treated as 'infinitely' thin (we further allow for finite thickness to model excluded volume), and tensionless lines rather than multibead chains, excluded volume is taken into account without a force law. The present implementation allows to construct a shortest multiple disconnected path (SP) for 2D systems (polymeric chain within spherical obstacles) and an optimal SP for 3D systems (collection of polymeric chains). The number of entanglements is then simply obtained from the SP as either the number of interior kinks, or from the average length of a line segment. Further, information about structure and potentially also the dynamics of entanglements is immediately available from the SP. We apply the method to study the 'concentration' dependence of the degree of entanglement in phantom chain systems. Program summaryTitle of program:Z Catalogue number:ADVG Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADVG Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: Silicon Graphics (Irix), Sun (Solaris), PC (Linux) Operating systems or monitors under which the program has been tested: UNIX, Linux Program language used: USANSI Fortran 77 and Fortran 90 Memory required to execute with typical data: 1 MByte No. of lines in distributed program, including test data, etc.: 10 660 No. of bytes in distributed program, including test data, etc.: 119 551 Distribution formet:tar.gz Nature of physical problem: The problem is to obtain primitive paths substantiating a shortest multiple disconnected path (SP) for a given polymer configuration (chains of particles, with or without additional single particles as obstacles for the 2D case). Primitive paths are here defined as in [M. Rubinstein, E. Helfand, J. Chem. Phys. 82 (1985) 2477; R. Everaers, S.K. Sukumaran, G.S. Grest, C. Svaneborg, A. Sivasubramanian, K. Kremer, Science 303 (2004) 823] as the shortest line (path) respecting 'topological' constraints (from neighboring polymers or point obstacles) between ends of polymers. There is a unique solution for the 2D case. For the 3D case it is unique if we construct a primitive path of a single chain embedded within fixed line obstacles [J.S.B. Mitchell, Geometric shortest paths and network optimization, in: J.-R. Sack, J. Urrutia (Eds.), Handbook of Computational Geometry, Elsevier, Amsterdam, 2000, pp. 633-701]. For a large 3D configuration made of several chains, short is meant to be the Euclidean shortest multiple disconnected path (SP) where primitive paths are constructed for all chains simultaneously. While the latter problem, in general, does not possess a unique solution, the algorithm must return a locally optimal solution, robust against minor displacements of the disconnected path and chain re-labeling. The problem is solved if the number of kinks (or entanglements Z), explicitly deduced from the SP, is quite insensitive to the exact conformation of the SP which allows to estimate Z with a small error. Efficient method of solution: Primitive paths are constructed from the given polymer configuration (a non-shortest multiple disconnected path, including obstacles, if present) by first replacing each polymer contour by a line with a number of 'kinks' (beads, nodes) and 'segments' (edges). To obtain primitive paths, defined to be uncrossable by any other objects (neighboring primitive paths, line or point obstacles), the algorithm minimizes the length of all primitive paths consecutively, until a final minimum Euclidean length of the SP is reached. Fast geometric operations rather than dynamical methods are used to minimize the contour lengths of the primitive paths. Neighbor lists are used to keep track of potentially intersecting segments of other chains. Periodic boundary conditions are employed. A finite small line thickness is used in order to make sure that entanglements are not 'lost' due to finite precision of representation of numbers. Restrictions on the complexity of the problem: For a single chain embedded within fixed line or point obstacles, the algorithm returns the exact SP. For more complex problems, the algorithm returns a locally optimal SP. Except for exotic, probably rare, configurations it turns out that different locally optimal SPs possess quite an identical number of nodes. In general, the problem constructing the SP is known to be NP-hard [J.S.B. Mitchell, Geometric shortest paths and network optimization, in: J.-R. Sack, J. Urrutia (Eds.), Handbook of Computational Geometry, Elsevier, Amsterdam, 2000, pp. 633-701], and we offer a solution which should suffice to analyze physical problems, and gives an estimate about the precision and uniqueness of the result (from a standard deviation by varying the parameter: cyclicswitch). The program is NOT restricted to handle systems for which segment lengths of the SP exceed half the box size. Typical running time: Typical running times are approximately two orders of magnitude shorter compared with the ones needed for a corresponding molecular dynamics approach, and scale mostly linearly with system size. We provide a benchmark table.

Perfectionism, Stress, and Social (Dis)Connection: A Short-Term Study of Hopelessness, Depression, and Academic Adjustment among Honors Students

ERIC Educational Resources Information Center

Rice, Kenneth G.; Leever, Brooke A.; Christopher, John; Porter, J. Diane

2006-01-01

This study tested models of perfectionism predicting psychological distress and academic adjustment and moderators and mediators of those associations in 2 successive cohorts of high-achieving university honors students (N = 499). Participants completed measures earl and late in the semester. Adaptive (high standards) and maladaptive…

The Big Picture: Pre-Service Teachers' Perceptions of "Expert" Science Teachers

ERIC Educational Resources Information Center

McKinnon, Merryn; Perara, Sean

2015-01-01

This study adapted the Draw-A-Science-Teacher Test to compare 22 pre-service teachers' perceptions of their own strengths as science teachers against their perceived strengths of expert science teachers. The drawings identified a disconnection between theory and practice that we revisit in the literature. Our findings from this pilot study are…

Underground localization using dual magnetic field sequence measurement and pose graph SLAM for directional drilling

NASA Astrophysics Data System (ADS)

Park, Byeolteo; Myung, Hyun

2014-12-01

With the development of unconventional gas, the technology of directional drilling has become more advanced. Underground localization is the key technique of directional drilling for real-time path following and system control. However, there are problems such as vibration, disconnection with external infrastructure, and magnetic field distortion. Conventional methods cannot solve these problems in real time or in various environments. In this paper, a novel underground localization algorithm using a re-measurement of the sequence of the magnetic field and pose graph SLAM (simultaneous localization and mapping) is introduced. The proposed algorithm exploits the property of the drilling system that the body passes through the previous pass. By comparing the recorded measurement from one magnetic sensor and the current re-measurement from another magnetic sensor, the proposed algorithm predicts the pose of the drilling system. The performance of the algorithm is validated through simulations and experiments.

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Causes, effects and connectivity changes in MS-related cognitive decline.

PubMed

Rimkus, Carolina de Medeiros; Steenwijk, Martijn D; Barkhof, Frederik

2016-01-01

Cognitive decline is a frequent but undervalued aspect of multiple sclerosis (MS). Currently, it remains unclear what the strongest determinants of cognitive dysfunction are, with grey matter damage most directly related to cognitive impairment. Multi-parametric studies seem to indicate that individual factors of MS-pathology are highly interdependent causes of grey matter atrophy and permanent brain damage. They are associated with intermediate functional effects (e.g. in functional MRI) representing a balance between disconnection and (mal) adaptive connectivity changes. Therefore, a more comprehensive MRI approach is warranted, aiming to link structural changes with functional brain organization. To better understand the disconnection syndromes and cognitive decline in MS, this paper reviews the associations between MRI metrics and cognitive performance, by discussing the interactions between multiple facets of MS pathology as determinants of brain damage and how they affect network efficiency.

Algorithms for accelerated convergence of adaptive PCA.

PubMed

Chatterjee, C; Kang, Z; Roychowdhury, V P

2000-01-01

We derive and discuss new adaptive algorithms for principal component analysis (PCA) that are shown to converge faster than the traditional PCA algorithms due to Oja, Sanger, and Xu. It is well known that traditional PCA algorithms that are derived by using gradient descent on an objective function are slow to converge. Furthermore, the convergence of these algorithms depends on appropriate choices of the gain sequences. Since online applications demand faster convergence and an automatic selection of gains, we present new adaptive algorithms to solve these problems. We first present an unconstrained objective function, which can be minimized to obtain the principal components. We derive adaptive algorithms from this objective function by using: 1) gradient descent; 2) steepest descent; 3) conjugate direction; and 4) Newton-Raphson methods. Although gradient descent produces Xu's LMSER algorithm, the steepest descent, conjugate direction, and Newton-Raphson methods produce new adaptive algorithms for PCA. We also provide a discussion on the landscape of the objective function, and present a global convergence proof of the adaptive gradient descent PCA algorithm using stochastic approximation theory. Extensive experiments with stationary and nonstationary multidimensional Gaussian sequences show faster convergence of the new algorithms over the traditional gradient descent methods.We also compare the steepest descent adaptive algorithm with state-of-the-art methods on stationary and nonstationary sequences.

A Pilot Study of the Effectiveness of Augmented Reality to Enhance the Use of Remote Labs in Electrical Engineering Education

ERIC Educational Resources Information Center

Borrero, A. Mejias; Marquez, J. M. Andujar

2012-01-01

Lab practices are an essential part of teaching in Engineering. However, traditional laboratory lessons developed in classroom labs (CL) must be adapted to teaching and learning strategies that go far beyond the common concept of e-learning, in the sense that completely virtualized distance education disconnects teachers and students from the real…

Connected, Disconnected, or Uncertain: Student Attitudes about Future Writing Contexts and Perceptions of Transfer from First Year Writing to the Disciplines

ERIC Educational Resources Information Center

Driscoll, Dana Lynn

2011-01-01

Transfer, or how much knowledge from one context is used or adapted in new contexts, is a longstanding issue for researchers and teachers of writing in a variety of disciplines. Transfer is of particular concern when examining how effective first-year writing is in preparing students with a foundation for their disciplinary coursework. This…

Teachers' Perceptions and Attitudes of Infusing Technology in High School Classrooms to Strengthen Pedagogy: A Qualitative Embedded Single-Case Study

ERIC Educational Resources Information Center

Singleton, Yolanda

2017-01-01

Traditional teaching methods no longer motivate students to learn, especially with the influence of technology in their daily lives. This disconnect has led to a continual influx of teachers who are forced to adapt to this shift in teaching, embracing technology, and restructuring pedagogy to include technology-driven lessons. Because perception…

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Simulation for noise cancellation using LMS adaptive filter

NASA Astrophysics Data System (ADS)

Lee, Jia-Haw; Ooi, Lu-Ean; Ko, Ying-Hao; Teoh, Choe-Yung

2017-06-01

In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.

Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms.

PubMed

Arts, E E A; Popa, C D; Den Broeder, A A; Donders, R; Sandoo, A; Toms, T; Rollefstad, S; Ikdahl, E; Semb, A G; Kitas, G D; Van Riel, P L C M; Fransen, J

2016-04-01

Predictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm. Data from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated. Of the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer-Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups. This study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Adaptive cockroach swarm algorithm

NASA Astrophysics Data System (ADS)

Obagbuwa, Ibidun C.; Abidoye, Ademola P.

2017-07-01

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.

An adaptive replacement algorithm for paged-memory computer systems.

NASA Technical Reports Server (NTRS)

Thorington, J. M., Jr.; Irwin, J. D.

1972-01-01

A general class of adaptive replacement schemes for use in paged memories is developed. One such algorithm, called SIM, is simulated using a probability model that generates memory traces, and the results of the simulation of this adaptive scheme are compared with those obtained using the best nonlookahead algorithms. A technique for implementing this type of adaptive replacement algorithm with state of the art digital hardware is also presented.

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

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.

Differential and relaxed image foresting transform for graph-cut segmentation of multiple 3D objects.

PubMed

Moya, Nikolas; Falcão, Alexandre X; Ciesielski, Krzysztof C; Udupa, Jayaram K

2014-01-01

Graph-cut algorithms have been extensively investigated for interactive binary segmentation, when the simultaneous delineation of multiple objects can save considerable user's time. We present an algorithm (named DRIFT) for 3D multiple object segmentation based on seed voxels and Differential Image Foresting Transforms (DIFTs) with relaxation. DRIFT stands behind efficient implementations of some state-of-the-art methods. The user can add/remove markers (seed voxels) along a sequence of executions of the DRIFT algorithm to improve segmentation. Its first execution takes linear time with the image's size, while the subsequent executions for corrections take sublinear time in practice. At each execution, DRIFT first runs the DIFT algorithm, then it applies diffusion filtering to smooth boundaries between objects (and background) and, finally, it corrects possible objects' disconnection occurrences with respect to their seeds. We evaluate DRIFT in 3D CT-images of the thorax for segmenting the arterial system, esophagus, left pleural cavity, right pleural cavity, trachea and bronchi, and the venous system.

Prosthetic Tool For Holding Small Ferromagnetic Parts

NASA Technical Reports Server (NTRS)

Norton, William E.; Carden, James R.; Belcher, Jewell G., Jr.; Vest, Thomas W.

1995-01-01

Tool attached to prosthetic hand or arm enables user to hold nails, screws, nuts, rivets, and other small ferromagnetic objects on small magnetic tip. Device adjusted to hold nail or screw at proper angle for hammering or for use of screwdriver, respectively. Includes base connector with threaded outer surface and lower male member inserted in standard spring-action, quick-connect/quick-disconnect wrist adapter on prosthetic hand or arm.

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

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. PMID:23935409

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.

Adaptive Algorithms for Automated Processing of Document Images

DTIC Science & Technology

2011-01-01

ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University

Motion Cueing Algorithm Development: Initial Investigation and Redesign of the Algorithms

NASA Technical Reports Server (NTRS)

Telban, Robert J.; Wu, Weimin; Cardullo, Frank M.; Houck, Jacob A. (Technical Monitor)

2000-01-01

In this project four motion cueing algorithms were initially investigated. The classical algorithm generated results with large distortion and delay and low magnitude. The NASA adaptive algorithm proved to be well tuned with satisfactory performance, while the UTIAS adaptive algorithm produced less desirable results. Modifications were made to the adaptive algorithms to reduce the magnitude of undesirable spikes. The optimal algorithm was found to have the potential for improved performance with further redesign. The center of simulator rotation was redefined. More terms were added to the cost function to enable more tuning flexibility. A new design approach using a Fortran/Matlab/Simulink setup was employed. A new semicircular canals model was incorporated in the algorithm. With these changes results show the optimal algorithm has some advantages over the NASA adaptive algorithm. Two general problems observed in the initial investigation required solutions. A nonlinear gain algorithm was developed that scales the aircraft inputs by a third-order polynomial, maximizing the motion cues while remaining within the operational limits of the motion system. A braking algorithm was developed to bring the simulator to a full stop at its motion limit and later release the brake to follow the cueing algorithm output.

Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.

PubMed

Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu

2015-08-01

This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.

Multiple jeopardy: poor, economically disconnected, and child welfare involved.

PubMed

Marcenko, Maureen O; Hook, Jennifer L; Romich, Jennifer L; Lee, JoAnn S

2012-08-01

Although the welfare literature reveals a growing number of parents who are economically disconnected, meaning neither employed nor receiving cash assistance, little is known about the prevalence and impacts of disconnection among child welfare-involved parents. This study took advantage of a statewide survey of child welfare-involved parents to examine economic disconnection in this population and to explore the relationship between disconnection and parent engagement in child welfare. One fifth of the sample reported that they were economically disconnected, with several patterns differentiating disconnected caregivers from those who received benefits or earned income through employment. Disconnected caregivers were younger and more frequently had children in out-of-home placements as opposed to receiving services in home than economically connected caregivers. They also reported higher unmet needs for basic services, such as housing and medical care, but were more likely to report financial help from their informal network. Finally, disconnected caregivers reported lower engagement in child welfare services even when controlling for demographic characteristics, chronic psychosocial risk factors, placement status, and maltreatment type. The findings document economic disconnection among child welfare-involved parents and raise important questions about the implications of disconnection for families and for child welfare outcomes.

Adaptive Reception for Underwater Communications

DTIC Science & Technology

2011-06-01

Experimental results prove the effectiveness of the receiver. 14. SUBJECT TERMS Underwater acoustic communications, adaptive algorithms , Kalman filter...the update algorithm design and the value of the spatial diversity are addressed. In this research, an adaptive multichannel equalizer made up of a...for the time-varying nature of the channel is to use an Adaptive Decision Feedback Equalizer based on either the RLS or LMS algorithm . Although this

Star adaptation for two-algorithms used on serial computers

NASA Technical Reports Server (NTRS)

Howser, L. M.; Lambiotte, J. J., Jr.

1974-01-01

Two representative algorithms used on a serial computer and presently executed on the Control Data Corporation 6000 computer were adapted to execute efficiently on the Control Data STAR-100 computer. Gaussian elimination for the solution of simultaneous linear equations and the Gauss-Legendre quadrature formula for the approximation of an integral are the two algorithms discussed. A description is given of how the programs were adapted for STAR and why these adaptations were necessary to obtain an efficient STAR program. Some points to consider when adapting an algorithm for STAR are discussed. Program listings of the 6000 version coded in 6000 FORTRAN, the adapted STAR version coded in 6000 FORTRAN, and the STAR version coded in STAR FORTRAN are presented in the appendices.

SMI adaptive antenna arrays for weak interfering signals

NASA Technical Reports Server (NTRS)

Gupta, I. J.

1987-01-01

The performance of adaptive antenna arrays is studied when a sample matrix inversion (SMI) algorithm is used to control array weights. It is shown that conventional SMI adaptive antennas, like other adaptive antennas, are unable to suppress weak interfering signals (below thermal noise) encountered in broadcasting satellite communication systems. To overcome this problem, the SMI algorithm is modified. In the modified algorithm, the covariance matrix is modified such that the effect of thermal noise on the weights of the adaptive array is reduced. Thus, the weights are dictated by relatively weak coherent signals. It is shown that the modified algorithm provides the desired interference protection. The use of defocused feeds as auxiliary elements of an SMI adaptive array is also discussed.

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.

Temporal disconnectivity of the energy landscape in glassy systems

NASA Astrophysics Data System (ADS)

Lempesis, Nikolaos; Boulougouris, Georgios C.; Theodorou, Doros N.

2013-03-01

An alternative graphical representation of the potential energy landscape (PEL) has been developed and applied to a binary Lennard-Jones glassy system, providing insight into the unique topology of the system's potential energy hypersurface. With the help of this representation one is able to monitor the different explored basins of the PEL, as well as how - and mainly when - subsets of basins communicate with each other via transitions in such a way that details of the prior temporal history have been erased, i.e., local equilibration between the basins in each subset has been achieved. In this way, apart from detailed information about the structure of the PEL, the system's temporal evolution on the PEL is described. In order to gather all necessary information about the identities of two or more basins that are connected with each other, we consider two different approaches. The first one is based on consideration of the time needed for two basins to mutually equilibrate their populations according to the transition rate between them, in the absence of any effect induced by the rest of the landscape. The second approach is based on an analytical solution of the master equation that explicitly takes into account the entire explored landscape. It is shown that both approaches lead to the same result concerning the topology of the PEL and dynamical evolution on it. Moreover, a "temporal disconnectivity graph" is introduced to represent a lumped system stemming from the initial one. The lumped system is obtained via a specially designed algorithm [N. Lempesis, D. G. Tsalikis, G. C. Boulougouris, and D. N. Theodorou, J. Chem. Phys. 135, 204507 (2011), 10.1063/1.3663207]. The temporal disconnectivity graph provides useful information about both the lumped and the initial systems, including the definition of "metabasins" as collections of basins that communicate with each other via transitions that are fast relative to the observation time. Finally, the two examined approaches are compared to an "on the fly" molecular dynamics-based algorithm [D. G. Tsalikis, N. Lempesis, G. C. Boulougouris, and D. N. Theodorou, J. Chem. Theory Comput. 6, 1307 (2010), 10.1021/ct9004245].

Study on a low complexity adaptive modulation algorithm in OFDM-ROF system with sub-carrier grouping technology

NASA Astrophysics Data System (ADS)

Liu, Chong-xin; Liu, Bo; Zhang, Li-jia; Xin, Xiang-jun; Tian, Qing-hua; Tian, Feng; Wang, Yong-jun; Rao, Lan; Mao, Yaya; Li, Deng-ao

2018-01-01

During the last decade, the orthogonal frequency division multiplexing radio-over-fiber (OFDM-ROF) system with adaptive modulation technology is of great interest due to its capability of raising the spectral efficiency dramatically, reducing the effects of fiber link or wireless channel, and improving the communication quality. In this study, according to theoretical analysis of nonlinear distortion and frequency selective fading on the transmitted signal, a low-complexity adaptive modulation algorithm is proposed in combination with sub-carrier grouping technology. This algorithm achieves the optimal performance of the system by calculating the average combined signal-to-noise ratio of each group and dynamically adjusting the origination modulation format according to the preset threshold and user's requirements. At the same time, this algorithm takes the sub-carrier group as the smallest unit in the initial bit allocation and the subsequent bit adjustment. So, the algorithm complexity is only 1 /M (M is the number of sub-carriers in each group) of Fischer algorithm, which is much smaller than many classic adaptive modulation algorithms, such as Hughes-Hartogs algorithm, Chow algorithm, and is in line with the development direction of green and high speed communication. Simulation results show that the performance of OFDM-ROF system with the improved algorithm is much better than those without adaptive modulation, and the BER of the former achieves 10e1 to 10e2 times lower than the latter when SNR values gets larger. We can obtain that this low complexity adaptive modulation algorithm is extremely useful for the OFDM-ROF system.

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.

POF-Darts: Geometric adaptive sampling for probability of failure

DOE PAGES

Ebeida, Mohamed S.; Mitchell, Scott A.; Swiler, Laura P.; ...

2016-06-18

We introduce a novel technique, POF-Darts, to estimate the Probability Of Failure based on random disk-packing in the uncertain parameter space. POF-Darts uses hyperplane sampling to explore the unexplored part of the uncertain space. We use the function evaluation at a sample point to determine whether it belongs to failure or non-failure regions, and surround it with a protection sphere region to avoid clustering. We decompose the domain into Voronoi cells around the function evaluations as seeds and choose the radius of the protection sphere depending on the local Lipschitz continuity. As sampling proceeds, regions uncovered with spheres will shrink,more » improving the estimation accuracy. After exhausting the function evaluation budget, we build a surrogate model using the function evaluations associated with the sample points and estimate the probability of failure by exhaustive sampling of that surrogate. In comparison to other similar methods, our algorithm has the advantages of decoupling the sampling step from the surrogate construction one, the ability to reach target POF values with fewer samples, and the capability of estimating the number and locations of disconnected failure regions, not just the POF value. Furthermore, we present various examples to demonstrate the efficiency of our novel approach.« less

Skeletal muscle adaptation to fatty acid depends on coordinated actions of the PPARs and PGC1 alpha: implications for metabolic disease.

PubMed

Muoio, Deborah M; Koves, Timothy R

2007-10-01

Dyslipidemia and intramuscular accumulation of fatty acid metabolites are increasingly recognized as core features of obesity and type 2 diabetes. Emerging evidence suggests that normal physiological adaptations to a heavy lipid load depend on the coordinated actions of broad transcriptional regulators such as the peroxisome proliferator activated receptors (PPARs) and PPAR gamma coactivator 1 alpha (PGC1 alpha). The application of transcriptomics and targeted metabolic profiling tools based on mass spectrometry has led to our finding that lipid-induced insulin resistance is a condition in which upregulation of PPAR-targeted genes and high rates of beta-oxidation are not supported by a commensurate upregulation of tricarboxylic acid (TCA) cycle activity. In contrast, exercise training enhances mitochondrial performance, favoring tighter coupling between beta-oxidation and the TCA cycle, and concomitantly restores insulin sensitivity in animals fed a chronic high-fat diet. The exercise-activated transcriptional coactivator, PGC1 alpha, plays a key role in coordinating metabolic flux through these 2 intersecting metabolic pathways, and its suppression by overfeeding may contribute to diet-induced mitochondrial dysfunction. Our emerging model predicts that muscle insulin resistance arises from a mitochondrial disconnect between beta-oxidation and TCA cycle activity. Understanding of this "disconnect" and its molecular basis may lead to new therapeutic approaches to combatting metabolic disease.

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.

A Controlled Study of the Effectiveness of an Adaptive Closed-Loop Algorithm to Minimize Corticosteroid-Induced Stress Hyperglycemia in Type 1 Diabetes

PubMed Central

Youssef, Joseph El; Castle, Jessica R; Branigan, Deborah L; Massoud, Ryan G; Breen, Matthew E; Jacobs, Peter G; Bequette, B Wayne; Ward, W Kenneth

2011-01-01

To be effective in type 1 diabetes, algorithms must be able to limit hyperglycemic excursions resulting from medical and emotional stress. We tested an algorithm that estimates insulin sensitivity at regular intervals and continually adjusts gain factors of a fading memory proportional-derivative (FMPD) algorithm. In order to assess whether the algorithm could appropriately adapt and limit the degree of hyperglycemia, we administered oral hydrocortisone repeatedly to create insulin resistance. We compared this indirect adaptive proportional-derivative (APD) algorithm to the FMPD algorithm, which used fixed gain parameters. Each subject with type 1 diabetes (n = 14) was studied on two occasions, each for 33 h. The APD algorithm consistently identified a fall in insulin sensitivity after hydrocortisone. The gain factors and insulin infusion rates were appropriately increased, leading to satisfactory glycemic control after adaptation (premeal glucose on day 2, 148 ± 6 mg/dl). After sufficient time was allowed for adaptation, the late postprandial glucose increment was significantly lower than when measured shortly after the onset of the steroid effect. In addition, during the controlled comparison, glycemia was significantly lower with the APD algorithm than with the FMPD algorithm. No increase in hypoglycemic frequency was found in the APD-only arm. An afferent system of duplicate amperometric sensors demonstrated a high degree of accuracy; the mean absolute relative difference of the sensor used to control the algorithm was 9.6 ± 0.5%. We conclude that an adaptive algorithm that frequently estimates insulin sensitivity and adjusts gain factors is capable of minimizing corticosteroid-induced stress hyperglycemia. PMID:22226248

SMI adaptive antenna arrays for weak interfering signals. [Sample Matrix Inversion

NASA Technical Reports Server (NTRS)

Gupta, Inder J.

1986-01-01

The performance of adaptive antenna arrays in the presence of weak interfering signals (below thermal noise) is studied. It is shown that a conventional adaptive antenna array sample matrix inversion (SMI) algorithm is unable to suppress such interfering signals. To overcome this problem, the SMI algorithm is modified. In the modified algorithm, the covariance matrix is redefined such that the effect of thermal noise on the weights of adaptive arrays is reduced. Thus, the weights are dictated by relatively weak signals. It is shown that the modified algorithm provides the desired interference protection.

Cross counter-based adaptive assembly scheme in optical burst switching networks

NASA Astrophysics Data System (ADS)

Zhu, Zhi-jun; Dong, Wen; Le, Zi-chun; Chen, Wan-jun; Sun, Xingshu

2009-11-01

A novel adaptive assembly algorithm called Cross-counter Balance Adaptive Assembly Period (CBAAP) is proposed in this paper. The major difference between CBAAP and other adaptive assembly algorithms is that the threshold of CBAAP can be dynamically adjusted according to the cross counter and step length value. In terms of assembly period and the burst loss probability, we compare the performance of CBAAP with those of three typical algorithms FAP (Fixed Assembly Period), FBL (Fixed Burst Length) and MBMAP (Min-Burst length-Max-Assembly-Period) in the simulation part. The simulation results demonstrate the effectiveness of our algorithm.

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

Statistical efficiency of adaptive algorithms.

PubMed

Widrow, Bernard; Kamenetsky, Max

2003-01-01

The statistical efficiency of a learning algorithm applied to the adaptation of a given set of variable weights is defined as the ratio of the quality of the converged solution to the amount of data used in training the weights. Statistical efficiency is computed by averaging over an ensemble of learning experiences. A high quality solution is very close to optimal, while a low quality solution corresponds to noisy weights and less than optimal performance. In this work, two gradient descent adaptive algorithms are compared, the LMS algorithm and the LMS/Newton algorithm. LMS is simple and practical, and is used in many applications worldwide. LMS/Newton is based on Newton's method and the LMS algorithm. LMS/Newton is optimal in the least squares sense. It maximizes the quality of its adaptive solution while minimizing the use of training data. Many least squares adaptive algorithms have been devised over the years, but no other least squares algorithm can give better performance, on average, than LMS/Newton. LMS is easily implemented, but LMS/Newton, although of great mathematical interest, cannot be implemented in most practical applications. Because of its optimality, LMS/Newton serves as a benchmark for all least squares adaptive algorithms. The performances of LMS and LMS/Newton are compared, and it is found that under many circumstances, both algorithms provide equal performance. For example, when both algorithms are tested with statistically nonstationary input signals, their average performances are equal. When adapting with stationary input signals and with random initial conditions, their respective learning times are on average equal. However, under worst-case initial conditions, the learning time of LMS can be much greater than that of LMS/Newton, and this is the principal disadvantage of the LMS algorithm. But the strong points of LMS are ease of implementation and optimal performance under important practical conditions. For these reasons, the LMS algorithm has enjoyed very widespread application. It is used in almost every modem for channel equalization and echo cancelling. Furthermore, it is related to the famous backpropagation algorithm used for training neural networks.

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.

Low complexity adaptive equalizers for underwater acoustic communications

NASA Astrophysics Data System (ADS)

Soflaei, Masoumeh; Azmi, Paeiz

2014-08-01

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor APA(SR-APA), family of selective partial update (SPU) algorithms, family of set-membership (SM) algorithms and selective partial update selective regressor APA (SPU-SR-APA) are compared with conventional algorithms such as the least mean square (LMS) in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA, SPU-APA, SPU-normalized least mean square (SPU-NLMS), SPU-SR-APA, SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

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

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

Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua

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 tomore » 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.« less

Adaptive Control Allocation in the Presence of Actuator Failures

NASA Technical Reports Server (NTRS)

Liu, Yu; Crespo, Luis G.

2010-01-01

In this paper, a novel adaptive control allocation framework is proposed. In the adaptive control allocation structure, cooperative actuators are grouped and treated as an equivalent control effector. A state feedback adaptive control signal is designed for the equivalent effector and allocated to the member actuators adaptively. Two adaptive control allocation algorithms are proposed, which guarantee closed-loop stability and asymptotic state tracking in the presence of uncertain loss of effectiveness and constant-magnitude actuator failures. The proposed algorithms can be shown to reduce the controller complexity with proper grouping of the actuators. The proposed adaptive control allocation schemes are applied to two linearized aircraft models, and the simulation results demonstrate the performance of the proposed algorithms.

30 CFR 75.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2012 CFR

2012-07-01

... disconnecting switches. 75.809 Section 75.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... High-Voltage Distribution § 75.809 Identification of circuit breakers and disconnecting switches. [Statutory Provisions] Circuit breakers and disconnecting switches underground shall be marked for...

30 CFR 75.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2010 CFR

2010-07-01

... disconnecting switches. 75.809 Section 75.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... High-Voltage Distribution § 75.809 Identification of circuit breakers and disconnecting switches. [Statutory Provisions] Circuit breakers and disconnecting switches underground shall be marked for...

30 CFR 75.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2011 CFR

2011-07-01

... disconnecting switches. 75.809 Section 75.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... High-Voltage Distribution § 75.809 Identification of circuit breakers and disconnecting switches. [Statutory Provisions] Circuit breakers and disconnecting switches underground shall be marked for...

30 CFR 75.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2013 CFR

2013-07-01

... disconnecting switches. 75.809 Section 75.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... High-Voltage Distribution § 75.809 Identification of circuit breakers and disconnecting switches. [Statutory Provisions] Circuit breakers and disconnecting switches underground shall be marked for...

30 CFR 75.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2014 CFR

2014-07-01

... disconnecting switches. 75.809 Section 75.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... High-Voltage Distribution § 75.809 Identification of circuit breakers and disconnecting switches. [Statutory Provisions] Circuit breakers and disconnecting switches underground shall be marked for...

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

A Self Adaptive Differential Evolution Algorithm for Global Optimization

NASA Astrophysics Data System (ADS)

Kumar, Pravesh; Pant, Millie

This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

SU-F-J-66: Anatomy Deformation Based Comparison Between One-Step and Two-Step Optimization for Online ART

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

Feng, Z; Yu, G; Qin, S

Purpose: This study investigated that how the quality of adapted plan was affected by inter-fractional anatomy deformation by using one-step and two-step optimization for on line adaptive radiotherapy (ART) procedure. Methods: 10 lung carcinoma patients were chosen randomly to produce IMRT plan by one-step and two-step algorithms respectively, and the prescribed dose was set as 60 Gy on the planning target volume (PTV) for all patients. To simulate inter-fractional target deformation, four specific cases were created by systematic anatomy variation; including target superior shift 0.5 cm, 0.3cm contraction, 0.3 cm expansion and 45-degree rotation. Based on these four anatomy deformation,more » adapted plan, regenerated plan and non-adapted plan were created to evaluate quality of adaptation. Adapted plans were generated automatically by using one-step and two-step algorithms respectively to optimize original plans, and regenerated plans were manually created by experience physicists. Non-adapted plans were produced by recalculating the dose distribution based on corresponding original plans. The deviations among these three plans were statistically analyzed by paired T-test. Results: In PTV superior shift case, adapted plans had significantly better PTV coverage by using two-step algorithm compared with one-step one, and meanwhile there was a significant difference of V95 by comparison with adapted and non-adapted plans (p=0.0025). In target contraction deformation, with almost same PTV coverage, the total lung received lower dose using one-step algorithm than two-step algorithm (p=0.0143,0.0126 for V20, Dmean respectively). In other two deformation cases, there were no significant differences observed by both two optimized algorithms. Conclusion: In geometry deformation such as target contraction, with comparable PTV coverage, one-step algorithm gave better OAR sparing than two-step algorithm. Reversely, the adaptation by using two-step algorithm had higher efficiency and accuracy as target occurred position displacement. We want to thank Dr. Lei Xing and Dr. Yong Yang in the Stanford University School of Medicine for this work. This work was jointly supported by NSFC (61471226), Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201516), and China Postdoctoral Science Foundation (2015T80739, 2014M551949).« less

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.

Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg-Saxton-based phase retrieval algorithm

NASA Astrophysics Data System (ADS)

Chang, Huan; Yin, Xiao-li; Cui, Xiao-zhou; Zhang, Zhi-chao; Ma, Jian-xin; Wu, Guo-hua; Zhang, Li-jia; Xin, Xiang-jun

2017-12-01

Practical orbital angular momentum (OAM)-based free-space optical (FSO) communications commonly experience serious performance degradation and crosstalk due to atmospheric turbulence. In this paper, we propose a wave-front sensorless adaptive optics (WSAO) system with a modified Gerchberg-Saxton (GS)-based phase retrieval algorithm to correct distorted OAM beams. We use the spatial phase perturbation (SPP) GS algorithm with a distorted probe Gaussian beam as the only input. The principle and parameter selections of the algorithm are analyzed, and the performance of the algorithm is discussed. The simulation results show that the proposed adaptive optics (AO) system can significantly compensate for distorted OAM beams in single-channel or multiplexed OAM systems, which provides new insights into adaptive correction systems using OAM beams.

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

NASA Astrophysics Data System (ADS)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

Real-Time Feedback Control of Flow-Induced Cavity Tones. Part 2; Adaptive Control

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cabell, R. H.; Cattafesta, L. N., III

2006-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. Past input-output data and an estimate of the open-loop pulse response sequence are all that is needed to implement the algorithm for application at fixed Mach numbers. Transient measurements made during controller adaptation revealed that the controller coefficients converged to a steady state in the mean, and this implies that adaptation can be turned off at some point with no degradation in control performance. When converged, the control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. However, as in the case of fixed-gain GPC, the adaptive GPC performance was limited by spillover in sidebands around the suppressed Rossiter modes. The algorithm was also able to maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Beyond this range, stable operation of the control algorithm was not possible due to the fixed plant model in the algorithm.

46 CFR 111.50-9 - Disconnecting and guarding.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 4 2012-10-01 2012-10-01 false Disconnecting and guarding. 111.50-9 Section 111.50-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Overcurrent Protection § 111.50-9 Disconnecting and guarding. Disconnecting and...

46 CFR 111.50-9 - Disconnecting and guarding.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 4 2011-10-01 2011-10-01 false Disconnecting and guarding. 111.50-9 Section 111.50-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Overcurrent Protection § 111.50-9 Disconnecting and guarding. Disconnecting and...

46 CFR 111.50-9 - Disconnecting and guarding.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 4 2013-10-01 2013-10-01 false Disconnecting and guarding. 111.50-9 Section 111.50-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Overcurrent Protection § 111.50-9 Disconnecting and guarding. Disconnecting and...

46 CFR 111.50-9 - Disconnecting and guarding.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 4 2014-10-01 2014-10-01 false Disconnecting and guarding. 111.50-9 Section 111.50-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Overcurrent Protection § 111.50-9 Disconnecting and guarding. Disconnecting and...

30 CFR 77.903 - Disconnecting devices.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Disconnecting devices. 77.903 Section 77.903... Medium-Voltage Alternating Current Circuits § 77.903 Disconnecting devices. Disconnecting devices shall be installed in circuits supplying power to portable or mobile equipment and shall provide visual...

Locally adaptive vector quantization: Data compression with feature preservation

NASA Technical Reports Server (NTRS)

Cheung, K. M.; Sayano, M.

1992-01-01

A study of a locally adaptive vector quantization (LAVQ) algorithm for data compression is presented. This algorithm provides high-speed one-pass compression and is fully adaptable to any data source and does not require a priori knowledge of the source statistics. Therefore, LAVQ is a universal data compression algorithm. The basic algorithm and several modifications to improve performance are discussed. These modifications are nonlinear quantization, coarse quantization of the codebook, and lossless compression of the output. Performance of LAVQ on various images using irreversible (lossy) coding is comparable to that of the Linde-Buzo-Gray algorithm, but LAVQ has a much higher speed; thus this algorithm has potential for real-time video compression. Unlike most other image compression algorithms, LAVQ preserves fine detail in images. LAVQ's performance as a lossless data compression algorithm is comparable to that of Lempel-Ziv-based algorithms, but LAVQ uses far less memory during the coding process.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

30 CFR 77.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Identification of circuit breakers and disconnecting switches. 77.809 Section 77.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... circuit breakers and disconnecting switches. Circuit breakers and disconnecting switches shall be labeled...

30 CFR 77.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Identification of circuit breakers and disconnecting switches. 77.809 Section 77.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... circuit breakers and disconnecting switches. Circuit breakers and disconnecting switches shall be labeled...

30 CFR 77.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Identification of circuit breakers and disconnecting switches. 77.809 Section 77.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... circuit breakers and disconnecting switches. Circuit breakers and disconnecting switches shall be labeled...

30 CFR 77.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Identification of circuit breakers and disconnecting switches. 77.809 Section 77.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... circuit breakers and disconnecting switches. Circuit breakers and disconnecting switches shall be labeled...

30 CFR 77.809 - Identification of circuit breakers and disconnecting switches.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Identification of circuit breakers and disconnecting switches. 77.809 Section 77.809 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... circuit breakers and disconnecting switches. Circuit breakers and disconnecting switches shall be labeled...

A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

PubMed Central

Ahmad, Noor Atinah

2014-01-01

An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs. PMID:24688412

30 CFR 77.704-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Operating disconnecting or cutout switches. 77... UNDERGROUND COAL MINES Grounding § 77.704-9 Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage surface lines shall be operated only with insulated sticks...

30 CFR 75.519 - Main power circuits; disconnecting switches.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Main power circuits; disconnecting switches. 75... § 75.519 Main power circuits; disconnecting switches. [Statutory Provision] In all main power circuits, disconnecting switches shall be installed underground within 500 feet of the bottoms of shafts and boreholes...

30 CFR 75.519 - Main power circuits; disconnecting switches.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Main power circuits; disconnecting switches. 75... § 75.519 Main power circuits; disconnecting switches. [Statutory Provision] In all main power circuits, disconnecting switches shall be installed underground within 500 feet of the bottoms of shafts and boreholes...

30 CFR 75.705-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Operating disconnecting or cutout switches. 75... Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage... insulated and maintained to protect the operator from the voltage to which he is exposed. When such switches...

30 CFR 77.704-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Operating disconnecting or cutout switches. 77... UNDERGROUND COAL MINES Grounding § 77.704-9 Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage surface lines shall be operated only with insulated sticks...

30 CFR 75.705-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Operating disconnecting or cutout switches. 75... Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage... insulated and maintained to protect the operator from the voltage to which he is exposed. When such switches...

30 CFR 75.705-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Operating disconnecting or cutout switches. 75... Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage... insulated and maintained to protect the operator from the voltage to which he is exposed. When such switches...

30 CFR 75.519 - Main power circuits; disconnecting switches.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Main power circuits; disconnecting switches. 75... § 75.519 Main power circuits; disconnecting switches. [Statutory Provision] In all main power circuits, disconnecting switches shall be installed underground within 500 feet of the bottoms of shafts and boreholes...

30 CFR 77.704-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Operating disconnecting or cutout switches. 77... UNDERGROUND COAL MINES Grounding § 77.704-9 Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage surface lines shall be operated only with insulated sticks...

30 CFR 77.704-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Operating disconnecting or cutout switches. 77... UNDERGROUND COAL MINES Grounding § 77.704-9 Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage surface lines shall be operated only with insulated sticks...

30 CFR 77.704-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Operating disconnecting or cutout switches. 77... UNDERGROUND COAL MINES Grounding § 77.704-9 Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage surface lines shall be operated only with insulated sticks...

30 CFR 75.519 - Main power circuits; disconnecting switches.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Main power circuits; disconnecting switches. 75... § 75.519 Main power circuits; disconnecting switches. [Statutory Provision] In all main power circuits, disconnecting switches shall be installed underground within 500 feet of the bottoms of shafts and boreholes...

30 CFR 75.705-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Operating disconnecting or cutout switches. 75... Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage... insulated and maintained to protect the operator from the voltage to which he is exposed. When such switches...

30 CFR 75.705-9 - Operating disconnecting or cutout switches.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Operating disconnecting or cutout switches. 75... Operating disconnecting or cutout switches. Disconnecting or cutout switches on energized high-voltage... insulated and maintained to protect the operator from the voltage to which he is exposed. When such switches...

24 CFR 3280.804 - Disconnecting means and branch-circuit protective equipment.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 5 2010-04-01 2010-04-01 false Disconnecting means and branch... SAFETY STANDARDS Electrical Systems § 3280.804 Disconnecting means and branch-circuit protective equipment. (a) The branch-circuit equipment is permitted to be combined with the disconnecting means as a...

Pitch-Learning Algorithm For Speech Encoders

NASA Technical Reports Server (NTRS)

Bhaskar, B. R. Udaya

1988-01-01

Adaptive algorithm detects and corrects errors in sequence of estimates of pitch period of speech. Algorithm operates in conjunction with techniques used to estimate pitch period. Used in such parametric and hybrid speech coders as linear predictive coders and adaptive predictive coders.

An Adaptive Buddy Check for Observational Quality Control

NASA Technical Reports Server (NTRS)

Dee, Dick P.; Rukhovets, Leonid; Todling, Ricardo; DaSilva, Arlindo M.; Larson, Jay W.; Einaudi, Franco (Technical Monitor)

2000-01-01

An adaptive buddy check algorithm is presented that adjusts tolerances for outlier observations based on the variability of surrounding data. The algorithm derives from a statistical hypothesis test combined with maximum-likelihood covariance estimation. Its stability is shown to depend on the initial identification of outliers by a simple background check. The adaptive feature ensures that the final quality control decisions are not very sensitive to prescribed statistics of first-guess and observation errors, nor on other approximations introduced into the algorithm. The implementation of the algorithm in a global atmospheric data assimilation is described. Its performance is contrasted with that of a non-adaptive buddy check, for the surface analysis of an extreme storm that took place in Europe on 27 December 1999. The adaptive algorithm allowed the inclusion of many important observations that differed greatly from the first guess and that would have been excluded on the basis of prescribed statistics. The analysis of the storm development was much improved as a result of these additional observations.

Adaptive Two Dimensional RLS (Recursive Least Squares) Algorithms

DTIC Science & Technology

1989-03-01

in Monterey wonderful. IX I. INTRODUCTION Adaptive algorithms have been used successfully for many years in a wide range of digital signal...SIMULATION RESULTS The 2-D FRLS algorithm was tested both on computer-generated data and on digitized images. For a baseline reference the 2-D L:rv1S...Alexander, S. T. Adaptivt Signal Processing: Theory and Applications. Springer- Verlag, New York. 1986. 7. Bellanger, Maurice G. Adaptive Digital

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Formulation and implementation of nonstationary adaptive estimation algorithm with applications to air-data reconstruction

NASA Technical Reports Server (NTRS)

Whitmore, S. A.

1985-01-01

The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2018-06-01

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

30 CFR 75.519-1 - Main power circuits; disconnecting switches; locations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Main power circuits; disconnecting switches...-General § 75.519-1 Main power circuits; disconnecting switches; locations. Section 75.519 requires (a) that a disconnecting switch be installed on the surface at a point within 500 feet of the place where...

30 CFR 75.519-1 - Main power circuits; disconnecting switches; locations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Main power circuits; disconnecting switches...-General § 75.519-1 Main power circuits; disconnecting switches; locations. Section 75.519 requires (a) that a disconnecting switch be installed on the surface at a point within 500 feet of the place where...

30 CFR 75.519-1 - Main power circuits; disconnecting switches; locations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Main power circuits; disconnecting switches...-General § 75.519-1 Main power circuits; disconnecting switches; locations. Section 75.519 requires (a) that a disconnecting switch be installed on the surface at a point within 500 feet of the place where...

30 CFR 75.519-1 - Main power circuits; disconnecting switches; locations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Main power circuits; disconnecting switches...-General § 75.519-1 Main power circuits; disconnecting switches; locations. Section 75.519 requires (a) that a disconnecting switch be installed on the surface at a point within 500 feet of the place where...

30 CFR 75.519-1 - Main power circuits; disconnecting switches; locations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Main power circuits; disconnecting switches...-General § 75.519-1 Main power circuits; disconnecting switches; locations. Section 75.519 requires (a) that a disconnecting switch be installed on the surface at a point within 500 feet of the place where...

Combination of Adaptive Feedback Cancellation and Binaural Adaptive Filtering in Hearing Aids

NASA Astrophysics Data System (ADS)

Lombard, Anthony; Reindl, Klaus; Kellermann, Walter

2009-12-01

We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.

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

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

NASA Astrophysics Data System (ADS)

Cheng, Sheng-Yi; Liu, Wen-Jin; Chen, Shan-Qiu; Dong, Li-Zhi; Yang, Ping; Xu, Bing

2015-08-01

Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ˜ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ˜ (O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. Project supported by the National Key Scientific and Research Equipment Development Project of China (Grant No. ZDYZ2013-2), the National Natural Science Foundation of China (Grant No. 11173008), and the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program, China (Grant No. 2012JQ0012).

Utilizing community-based participatory research to adapt a mental health intervention for African American emerging adults.

PubMed

Mance, Gishawn A; Mendelson, Tamar; Byrd, Benjamin; Jones, Jahon; Tandon, Darius

2010-01-01

Adapting mental health interventions to heighten their cultural and contextual appropriateness may be critical for engaging ethnic/racial groups that have been traditionally excluded or marginalized. Community-based participatory research (CBPR) is a collaborative research approach that highlights unique strengths and expertise of those involved. Although intervention adaptations have garnered much attention there is little previous work specifically describing the adaptation process of mental health interventions using CBPR. This article summarizes the use of a CBPR approach to adapt a mental health intervention for urban adolescents and young adults disconnected from school and work, a population at elevated risk for poor mental health owing to the presence of numerous chronic stressors. We describe the process undertaken to modify the content and delivery format of an evidence-based intervention. Unique challenges of working with urban African American adolescents and young adults in a job training program are highlighted. By incorporating principles of co-learning and shared responsibility, this partnership was able to achieve positive outcomes. Our experience suggests that a CBPR approach can be used effectively to adapt a mental health intervention in collaboration with African American adolescents and emerging adults in a job training program.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Convergence and objective functions of some fault/noise-injection-based online learning algorithms for RBF networks.

PubMed

Ho, Kevin I-J; Leung, Chi-Sing; Sum, John

2010-06-01

In the last two decades, many online fault/noise injection algorithms have been developed to attain a fault tolerant neural network. However, not much theoretical works related to their convergence and objective functions have been reported. This paper studies six common fault/noise-injection-based online learning algorithms for radial basis function (RBF) networks, namely 1) injecting additive input noise, 2) injecting additive/multiplicative weight noise, 3) injecting multiplicative node noise, 4) injecting multiweight fault (random disconnection of weights), 5) injecting multinode fault during training, and 6) weight decay with injecting multinode fault. Based on the Gladyshev theorem, we show that the convergence of these six online algorithms is almost sure. Moreover, their true objective functions being minimized are derived. For injecting additive input noise during training, the objective function is identical to that of the Tikhonov regularizer approach. For injecting additive/multiplicative weight noise during training, the objective function is the simple mean square training error. Thus, injecting additive/multiplicative weight noise during training cannot improve the fault tolerance of an RBF network. Similar to injective additive input noise, the objective functions of other fault/noise-injection-based online algorithms contain a mean square error term and a specialized regularization term.

46 CFR 169.681 - Disconnect switches and devices.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 7 2012-10-01 2012-10-01 false Disconnect switches and devices. 169.681 Section 169.681... Less Than 100 Gross Tons § 169.681 Disconnect switches and devices. (a) Externally operable switches or... protected by fuses, the disconnect switch required for fuses in § 169.683(b) of this chapter is adequate for...

46 CFR 169.681 - Disconnect switches and devices.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 7 2014-10-01 2014-10-01 false Disconnect switches and devices. 169.681 Section 169.681... Less Than 100 Gross Tons § 169.681 Disconnect switches and devices. (a) Externally operable switches or... protected by fuses, the disconnect switch required for fuses in § 169.683(b) of this chapter is adequate for...

46 CFR 169.681 - Disconnect switches and devices.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 7 2011-10-01 2011-10-01 false Disconnect switches and devices. 169.681 Section 169.681... Less Than 100 Gross Tons § 169.681 Disconnect switches and devices. (a) Externally operable switches or... protected by fuses, the disconnect switch required for fuses in § 169.683(b) of this chapter is adequate for...

46 CFR 169.681 - Disconnect switches and devices.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 7 2013-10-01 2013-10-01 false Disconnect switches and devices. 169.681 Section 169.681... Less Than 100 Gross Tons § 169.681 Disconnect switches and devices. (a) Externally operable switches or... protected by fuses, the disconnect switch required for fuses in § 169.683(b) of this chapter is adequate for...

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. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Synthesis of the adaptive continuous system for the multi-axle wheeled vehicle body oscillation damping

NASA Astrophysics Data System (ADS)

Zhileykin, M. M.; Kotiev, G. O.; Nagatsev, M. V.

2018-02-01

In order to meet the growing mobility requirements for the wheeled vehicles on all types of terrain the engineers have to develop a large number of specialized control algorithms for the multi-axle wheeled vehicle (MWV) suspension improving such qualities as ride comfort, handling and stability. The authors have developed an adaptive algorithm of the dynamic damping of the MVW body oscillations. The algorithm provides high ride comfort and high mobility of the vehicle. The article discloses a method for synthesis of an adaptive dynamic continuous algorithm of the MVW body oscillation damping and provides simulation results proving high efficiency of the developed control algorithm.

Can a Healthcare "Lean Sweep" Deliver on What Matters to Patients? Comment on "Improving Wait Times to Care for Individuals with Multimorbidities and Complex Conditions Using Value Stream Mapping".

PubMed

Verma, Jennifer Y; Amar, Claudia

2015-07-28

Disconnects and defects in care - such as duplication, poor integration between services or avoidable adverse events - are costly to the health system and potentially harmful to patients and families. For patients living with multiple chronic conditions, such disconnects can be particularly detrimental. Lean is an approach to optimizing value by reducing waste (eg, duplication and defects) and containing costs (eg, improving integration of services) as well as focusing on what matters to patients. Lean works particularly well to optimize existing processes and services. However, as the burden of chronic illness and frailty overtake episodic care needs, health systems require far greater complex, adaptive change. Such change ought to take into account outcomes in population health in addition to care experiences and costs (together, comprising the Triple Aim); and involve patients and families in co-designing new models of care that better address complex, longer-term health needs. © 2015 by Kerman University of Medical Sciences.

Properties of an adaptive feedback equalization algorithm.

PubMed

Engebretson, A M; French-St George, M

1993-01-01

This paper describes a new approach to feedback equalization for hearing aids. The method involves the use of an adaptive algorithm that estimates and tracks the characteristic of the hearing aid feedback path. The algorithm is described and the results of simulation studies and bench testing are presented.

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 ...

SIMULATION OF A REACTING POLLUTANT PUFF USING AN ADAPTIVE GRID ALGORITHM

EPA Science Inventory

A new dynamic solution adaptive grid algorithm DSAGA-PPM, has been developed for use in air quality modeling. In this paper, this algorithm is described and evaluated with a test problem. Cone-shaped distributions of various chemical species undergoing chemical reactions are rota...

Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization.

PubMed

Wong, Ieong; Liu, Wenjia; Ho, Chih-Ming; Ding, Xianting

2017-06-01

Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.

An adaptive inverse kinematics algorithm for robot manipulators

NASA Technical Reports Server (NTRS)

Colbaugh, R.; Glass, K.; Seraji, H.

1990-01-01

An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.

Adaptive Identification and Control of Flow-Induced Cavity Oscillations

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cattafesta, L. N.; Ha, C.

2002-01-01

Progress towards an adaptive self-tuning regulator (STR) for the cavity tone problem is discussed in this paper. Adaptive system identification algorithms were applied to an experimental cavity-flow tested as a prerequisite to control. In addition, a simple digital controller and a piezoelectric bimorph actuator were used to demonstrate multiple tone suppression. The control tests at Mach numbers of 0.275, 0.40, and 0.60 indicated approx. = 7dB tone reductions at multiple frequencies. Several different adaptive system identification algorithms were applied at a single freestream Mach number of 0.275. Adaptive finite-impulse response (FIR) filters of orders up to N = 100 were found to be unsuitable for modeling the cavity flow dynamics. Adaptive infinite-impulse response (IIR) filters of comparable order better captured the system dynamics. Two recursive algorithms, the least-mean square (LMS) and the recursive-least square (RLS), were utilized to update the adaptive filter coefficients. Given the sample-time requirements imposed by the cavity flow dynamics, the computational simplicity of the least mean squares (LMS) algorithm is advantageous for real-time control.

Adaptive Interventions in Drug Court: A Pilot Experiment

PubMed Central

Marlowe, Douglas B.; Festinger, David S.; Arabia, Patricia L.; Dugosh, Karen L.; Benasutti, Kathleen M.; Croft, Jason R.; McKay, James R.

2009-01-01

This pilot study (N = 30) experimentally examined the effects of an adaptive intervention in an adult misdemeanor drug court. The adaptive algorithm adjusted the frequency of judicial status hearings and clinical case-management sessions according to pre-specified criteria in response to participants' ongoing performance in the program. Results revealed the adaptive algorithm was acceptable to both clients and staff, feasible to implement with greater than 85% fidelity, and showed promise for eliciting clinically meaningful improvements in drug abstinence and graduation rates. Estimated effect sizes ranged from 0.40 to 0.60 across various dependent measures. Compared to drug court as-usual, participants in the adaptive condition were more likely to receive responses from the drug court team for inadequate performance in the program and received those responses after a substantially shorter period of time. This suggests the adaptive algorithm may have more readily focused the drug court team's attention on poorly-performing individuals, thus allowing the team to “nip problems in the bud” before they developed too fully. These preliminary data justify additional research evaluating the effects of the adaptive algorithm in a fully powered experimental trial. PMID:19724664

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

NASA Technical Reports Server (NTRS)

Hunter, H. E.; Amato, R. A.

1972-01-01

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions.

PubMed

He, Chenlong; Feng, Zuren; Ren, Zhigang

2018-01-01

In this paper, we propose a connectivity-preserving flocking algorithm for multi-agent systems in which the neighbor set of each agent is determined by the hybrid metric-topological distance so that the interaction topology can be represented as the range-limited Delaunay graph, which combines the properties of the commonly used disk graph and Delaunay graph. As a result, the proposed flocking algorithm has the following advantages over the existing ones. First, range-limited Delaunay graph is sparser than the disk graph so that the information exchange among agents is reduced significantly. Second, some links irrelevant to the connectivity can be dynamically deleted during the evolution of the system. Thus, the proposed flocking algorithm is more flexible than existing algorithms, where links are not allowed to be disconnected once they are created. Finally, the multi-agent system spontaneously generates a regular quasi-lattice formation without imposing the constraint on the ratio of the sensing range of the agent to the desired distance between two adjacent agents. With the interaction topology induced by the hybrid distance, the proposed flocking algorithm can still be implemented in a distributed manner. We prove that the proposed flocking algorithm can steer the multi-agent system to a stable flocking motion, provided the initial interaction topology of multi-agent systems is connected and the hysteresis in link addition is smaller than a derived upper bound. The correctness and effectiveness of the proposed algorithm are verified by extensive numerical simulations, where the flocking algorithms based on the disk and Delaunay graph are compared.

A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions

PubMed Central

Feng, Zuren; Ren, Zhigang

2018-01-01

In this paper, we propose a connectivity-preserving flocking algorithm for multi-agent systems in which the neighbor set of each agent is determined by the hybrid metric-topological distance so that the interaction topology can be represented as the range-limited Delaunay graph, which combines the properties of the commonly used disk graph and Delaunay graph. As a result, the proposed flocking algorithm has the following advantages over the existing ones. First, range-limited Delaunay graph is sparser than the disk graph so that the information exchange among agents is reduced significantly. Second, some links irrelevant to the connectivity can be dynamically deleted during the evolution of the system. Thus, the proposed flocking algorithm is more flexible than existing algorithms, where links are not allowed to be disconnected once they are created. Finally, the multi-agent system spontaneously generates a regular quasi-lattice formation without imposing the constraint on the ratio of the sensing range of the agent to the desired distance between two adjacent agents. With the interaction topology induced by the hybrid distance, the proposed flocking algorithm can still be implemented in a distributed manner. We prove that the proposed flocking algorithm can steer the multi-agent system to a stable flocking motion, provided the initial interaction topology of multi-agent systems is connected and the hysteresis in link addition is smaller than a derived upper bound. The correctness and effectiveness of the proposed algorithm are verified by extensive numerical simulations, where the flocking algorithms based on the disk and Delaunay graph are compared. PMID:29462217

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

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

A hybrid adaptive routing algorithm for event-driven wireless sensor networks.

PubMed

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.

Parallel Algorithm Solves Coupled Differential Equations

NASA Technical Reports Server (NTRS)

Hayashi, A.

1987-01-01

Numerical methods adapted to concurrent processing. Algorithm solves set of coupled partial differential equations by numerical integration. Adapted to run on hypercube computer, algorithm separates problem into smaller problems solved concurrently. Increase in computing speed with concurrent processing over that achievable with conventional sequential processing appreciable, especially for large problems.

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...

Positive isolation disconnect

NASA Technical Reports Server (NTRS)

Rosener, A. A.; Jonkoniec, T. G.

1975-01-01

A positive isolation disconnect was developed for component replacement in serviced liquid and gaseous spacecraft systems. Initially a survey of feasible concepts was made to determine the optimum method for fluid isolation, sealing techniques, coupling concepts, and foolproofing techniques. The top concepts were then further evaluated, including the fabrication of a semifunctional model. After all tradeoff analyses were made, a final configuration was designed and fabricated for development testing. This resulted in a 6.35 mm (1/4 inch) line and 12.7 mm (1/2 inch) line positive isolation disconnect, each unit consisting of two coupled disconnect halves, each capable of fluid isolation with essentially zero clearance between them for zero leakage upon disconnect half disengagement. An interlocking foolproofing technique was incorporated that prevents uncoupling of disconnect halves prior to fluid isolation.

Active link selection for efficient semi-supervised community detection

NASA Astrophysics Data System (ADS)

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-03-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches.

Active link selection for efficient semi-supervised community detection

PubMed Central

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-01-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

Quick fuzzy backpropagation algorithm.

PubMed

Nikov, A; Stoeva, S

2001-03-01

A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

Culture, the Crack'd Mirror, and the Neuroethics of Disease.

PubMed

Gillett, Grant

2016-10-01

Human beings are sensorimotor coupled to the actual world and also attuned to the symbolic world of culture and the techniques of adaptation that culture provides. The self-image and self-shaping mediated by that mirror directly affects the neurocognitive structures that integrate human neural activity and reshape its processing capacities through top-down or autopoietic effects. Thus a crack'd mirror, which disrupts the processes of enactive self-configuration, can be disabling for an individual. That is exactly what happens in postcolonial or immigration contexts in which individuals' cultural adaptations are marginalized and disconnected in diverse and often painful and disorienting ways. The crack'd mirror is therefore a powerful trope for neuroethics and helps us understand the social and moral pathologies of many indigenous and immigrant communities.

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).

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.

Extending Comprehensive Maritime Awareness to Disconnected Vessels and Users

DTIC Science & Technology

2007-09-01

system shall: 7. Provide flexible , scalable, and tailorable techniques, processes, and procedures that can be adapted rapidly and securely 8...PS (1 2- 21 ) SO W (2 2- 23 ) St ak eh ol de r C om m un ic at io ns (2 4) Provide secure connection of Node 5 to CMA Node 4 1,4,5 8 19 Provide...6 7 13,24 Provide flexible system - allow user display customization 6 7 13 Provide accurate data and info 2,3 16, 20, 24 Provide accurate local data

Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm

NASA Astrophysics Data System (ADS)

Plattner, A.; Maurer, H. R.; Vorloeper, J.; Dahmen, W.

2010-08-01

Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best-fitting subsurface boundaries. Such algorithms represent the current state-of-the-art in geoelectric modelling. An analysis of the numerical accuracy as a function of the number of degrees of freedom revealed that the adaptive wavelet algorithm outperforms the finite element solver for simple and moderately complex models, whereas the results become comparable for models with high spatial variability of electrical conductivities. The linear dependence of the modelling error and the computing time proved to be model-independent. This feature will allow very efficient computations using large-scale models as soon as our experimental code is optimized in terms of its implementation.

Multi-limit unsymmetrical MLIBD image restoration algorithm

NASA Astrophysics Data System (ADS)

Yang, Yang; Cheng, Yiping; Chen, Zai-wang; Bo, Chen

2012-11-01

A novel multi-limit unsymmetrical iterative blind deconvolution(MLIBD) algorithm was presented to enhance the performance of adaptive optics image restoration.The algorithm enhances the reliability of iterative blind deconvolution by introducing the bandwidth limit into the frequency domain of point spread(PSF),and adopts the PSF dynamic support region estimation to improve the convergence speed.The unsymmetrical factor is automatically computed to advance its adaptivity.Image deconvolution comparing experiments between Richardson-Lucy IBD and MLIBD were done,and the result indicates that the iteration number is reduced by 22.4% and the peak signal-to-noise ratio is improved by 10.18dB with MLIBD method. The performance of MLIBD algorithm is outstanding in the images restoration the FK5-857 adaptive optics and the double-star adaptive optics.

Hardware Acceleration of Adaptive Neural Algorithms.

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

James, Conrad D.

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - worldmore » conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.« less

A chaos wolf optimization algorithm with self-adaptive variable step-size

NASA Astrophysics Data System (ADS)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

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.

Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2

DOE PAGES

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.; ...

2015-12-17

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

A homotopy method based on WENO schemes for solving steady state problems of hyperbolic conservation laws

DTIC Science & Technology

2012-09-03

prac- tice to solve these initial value problems. Additionally, the predictor / corrector methods are combined with adaptive stepsize and adaptive ...for implementing a numerical path tracking algorithm is to decide which predictor / corrector method to employ, how large to take the step ∆t, and what...the endgame algorithm . Output: A steady state solution Set ǫ = 1 while ǫ >= ǫend do set the stepsize ∆ǫ by using adaptive stepsize control algorithm

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

PubMed Central

Tian, Xiaochun; Chen, Jiabin; Han, Yongqiang; Shang, Jianyu; Li, Nan

2016-01-01

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance. PMID:27669266

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

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

Space transportation system disconnect. [replenishing fluids during orbital servicing

NASA Technical Reports Server (NTRS)

1980-01-01

The use of medium duty 300 psi fluid disconnects for orbital servicing was demonstrated to be both practical and technically feasible. A prototype disconnect was designed and tested, based on criteria formulated from a survey of expected usage requirements for orbital servicing concepts. Testing involved the comparison of three seal materials (EPR, Neoprene and Teflon), and two test media (helium and Freon 21), and was conducted over a temperature range of -150 F to +225 F. Results indicate low helium leakage (10 to the -4 power sccs) and extremely low engagement forces (56 lb sub f). Testing was also performed on a new seal design. Design concepts for a cryogenic disconnect and a high pressure disconnect were investigated. Results of an industry survey for usable orbital servicing disconnects and areas needing attention in future studies are discussed.

Flight representative positive isolation disconnect

NASA Technical Reports Server (NTRS)

Rosener, A. A.; Jonkoniec, T. G.

1977-01-01

Resolutions were developed for each problem encountered and a tradeoff analysis was performed to select a final configuration for a flight representative PID (Positive Isolation Disconnect) that is reduced in size and comparable in weight and pressure drop to the developmental PID. A 6.35 mm (1/4-inch) line size PID was fabricated and tested. The flight representative PID consists of two coupled disconnect halves, each capable of fluid isolation with essentially zero clearance between them for zero leakage upon disconnect half disengagement. An interlocking foolproofing technique prevents uncoupling of disconnect halves prior to fluid isolation. Future development efforts for the Space Shuttle subsystems that would benefit from the use of the positive isolation disconnect are also recommended. Customary units were utilized for principal measurements and calculations with conversion factors being inserted in equations to convert the results to the international system of units.

Accelerated Dimension-Independent Adaptive Metropolis

DOE PAGES

Chen, Yuxin; Keyes, David E.; Law, Kody J.; ...

2016-10-27

This work describes improvements from algorithmic and architectural means to black-box Bayesian inference over high-dimensional parameter spaces. The well-known adaptive Metropolis (AM) algorithm [33] is extended herein to scale asymptotically uniformly with respect to the underlying parameter dimension for Gaussian targets, by respecting the variance of the target. The resulting algorithm, referred to as the dimension-independent adaptive Metropolis (DIAM) algorithm, also shows improved performance with respect to adaptive Metropolis on non-Gaussian targets. This algorithm is further improved, and the possibility of probing high-dimensional (with dimension d 1000) targets is enabled, via GPU-accelerated numerical libraries and periodically synchronized concurrent chains (justimore » ed a posteriori). Asymptotically in dimension, this GPU implementation exhibits a factor of four improvement versus a competitive CPU-based Intel MKL parallel version alone. Strong scaling to concurrent chains is exhibited, through a combination of longer time per sample batch (weak scaling) and yet fewer necessary samples to convergence. The algorithm performance is illustrated on several Gaussian and non-Gaussian target examples, in which the dimension may be in excess of one thousand.« less

Accelerated Dimension-Independent Adaptive Metropolis

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

Chen, Yuxin; Keyes, David E.; Law, Kody J.

This work describes improvements from algorithmic and architectural means to black-box Bayesian inference over high-dimensional parameter spaces. The well-known adaptive Metropolis (AM) algorithm [33] is extended herein to scale asymptotically uniformly with respect to the underlying parameter dimension for Gaussian targets, by respecting the variance of the target. The resulting algorithm, referred to as the dimension-independent adaptive Metropolis (DIAM) algorithm, also shows improved performance with respect to adaptive Metropolis on non-Gaussian targets. This algorithm is further improved, and the possibility of probing high-dimensional (with dimension d 1000) targets is enabled, via GPU-accelerated numerical libraries and periodically synchronized concurrent chains (justimore » ed a posteriori). Asymptotically in dimension, this GPU implementation exhibits a factor of four improvement versus a competitive CPU-based Intel MKL parallel version alone. Strong scaling to concurrent chains is exhibited, through a combination of longer time per sample batch (weak scaling) and yet fewer necessary samples to convergence. The algorithm performance is illustrated on several Gaussian and non-Gaussian target examples, in which the dimension may be in excess of one thousand.« less

Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift

PubMed Central

Ortíz Díaz, Agustín; Ramos-Jiménez, Gonzalo; Frías Blanco, Isvani; Caballero Mota, Yailé; Morales-Bueno, Rafael

2015-01-01

The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts. PMID:25879051

Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

NASA Astrophysics Data System (ADS)

Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

2018-06-01

The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals.

PubMed

Deng, Yi; Hung, Karen S Y; Lui, Simon S Y; Chui, William W H; Lee, Joe C W; Wang, Yi; Li, Zhi; Mak, Henry K F; Sham, Pak C; Chan, Raymond C K; Cheung, Eric F C

2018-06-20

Schizophrenia has been characterized as a neurodevelopmental disorder of brain disconnectivity. However, whether disrupted integrity of white matter tracts in schizophrenia can potentially serve as individual discriminative biomarkers remains unclear. A random forest algorithm was applied to tractography-based diffusion properties obtained from a cohort of 65 patients with first-episode schizophrenia (FES) and 60 healthy individuals to investigate the machine-learning discriminative power of white matter disconnectivity. Recursive feature elimination was used to select the ultimate white matter features in the classification. Relationships between algorithm-predicted probabilities and clinical characteristics were also examined in the FES group. The classifier was trained by 80% of the sample. Patients were distinguished from healthy individuals with an overall accuracy of 71.0% (95% confident interval: 61.1%, 79.6%), a sensitivity of 67.3%, a specificity of 75.0%, and the area under receiver operating characteristic curve (AUC) was 79.3% (χ2 p < 0.001). In validation using the held-up 20% of the sample, patients were distinguished from healthy individuals with an overall accuracy of 76.0% (95% confident interval: 54.9%, 90.6%), a sensitivity of 76.9%, a specificity of 75.0%, and an AUC of 73.1% (χ2 p = 0.012). Diffusion properties of inter-hemispheric fibres, the cerebello-thalamo-cortical circuits and the long association fibres were identified to be the most discriminative in the classification. Higher predicted probability scores were found in younger patients. Our findings suggest that the widespread connectivity disruption observed in FES patients, especially in younger patients, might be considered potential individual discriminating biomarkers. Copyright © 2018. Published by Elsevier Inc.

Parameter Estimation for a Hybrid Adaptive Flight Controller

NASA Technical Reports Server (NTRS)

Campbell, Stefan F.; Nguyen, Nhan T.; Kaneshige, John; Krishnakumar, Kalmanje

2009-01-01

This paper expands on the hybrid control architecture developed at the NASA Ames Research Center by addressing issues related to indirect adaptation using the recursive least squares (RLS) algorithm. Specifically, the hybrid control architecture is an adaptive flight controller that features both direct and indirect adaptation techniques. This paper will focus almost exclusively on the modifications necessary to achieve quality indirect adaptive control. Additionally this paper will present results that, using a full non -linear aircraft model, demonstrate the effectiveness of the hybrid control architecture given drastic changes in an aircraft s dynamics. Throughout the development of this topic, a thorough discussion of the RLS algorithm as a system identification technique will be provided along with results from seven well-known modifications to the popular RLS algorithm.

Implementation of LSCMA adaptive array terminal for mobile satellite communications

NASA Astrophysics Data System (ADS)

Zhou, Shun; Wang, Huali; Xu, Zhijun

2007-11-01

This paper considers the application of adaptive array antenna based on the least squares constant modulus algorithm (LSCMA) for interference rejection in mobile SATCOM terminals. A two-element adaptive array scheme is implemented with a combination of ADI TS201S DSP chips and Altera Stratix II FPGA device, which makes a cooperating computation for adaptive beamforming. Its interference suppressing performance is verified via Matlab simulations. Digital hardware system is implemented to execute the operations of LSCMA beamforming algorithm that is represented by an algorithm flowchart. The result of simulations and test indicate that this scheme can improve the anti-jamming performance of terminals.

An adaptive interpolation scheme for molecular potential energy surfaces

NASA Astrophysics Data System (ADS)

Kowalewski, Markus; Larsson, Elisabeth; Heryudono, Alfa

2016-08-01

The calculation of potential energy surfaces for quantum dynamics can be a time consuming task—especially when a high level of theory for the electronic structure calculation is required. We propose an adaptive interpolation algorithm based on polyharmonic splines combined with a partition of unity approach. The adaptive node refinement allows to greatly reduce the number of sample points by employing a local error estimate. The algorithm and its scaling behavior are evaluated for a model function in 2, 3, and 4 dimensions. The developed algorithm allows for a more rapid and reliable interpolation of a potential energy surface within a given accuracy compared to the non-adaptive version.

Robust local search for spacecraft operations using adaptive noise

NASA Technical Reports Server (NTRS)

Fukunaga, Alex S.; Rabideau, Gregg; Chien, Steve

2004-01-01

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

Modeling and Analysis of Remote, Off-grid Microgrids

NASA Astrophysics Data System (ADS)

Madathil, Sreenath Chalil

Over the past century the electric power industry has evolved to support the delivery of power over long distances with highly interconnected transmission systems. Despite this evolution, some remote communities are not connected to these systems. These communities rely on small, disconnected distribution systems, i.e., microgrids, to deliver power. Power distribution in most of these remote communities often depend on a type of microgrid called "off-grid microgrids". However, as microgrids often are not held to the same reliability standards as transmission grids, remote communities can be at risk to experience extended blackouts. Recent trends have also shown an increased use of renewable energy resources in power systems for remote communities. The increased penetration of renewable resources in power generation will require complex decision making when designing a resilient power system. This is mainly due to the stochastic nature of renewable resources that can lead to loss of load or line overload during their operations. In the first part of this thesis, we develop an optimization model and accompanying solution algorithm for capacity planning and operating microgrids that include N-1 security and other practical modeling features (e.g., AC power flow physics, component efficiencies and thermal limits). We demonstrate the effectiveness of our model and solution approach on two test systems: a modified version of the IEEE 13 node test feeder and a model of a distribution system in a remote Alaskan community. Once a tractable algorithm was identified to solve the above problem, we develop a mathematical model that includes topology design of microgrids. The topology design includes building new lines, making redundant lines, and analyzing N-1 contingencies on generators and lines. We develop a rolling horizon algorithm to efficiently analyze the model and demonstrate the strength of our algorithm in the same network. Finally, we develop a stochastic model that considers generation uncertainties along with N-1 security on generation assets. We develop a chance-constrained model to analyze the efficacy of the problem under consideration and present a case study on an adapted IEEE-13 node network. A successful implementation of this research could help remote communities around the world to enhance their quality of life by providing them with cost-effective, reliable electricity.

An improved conscan algorithm based on a Kalman filter

NASA Technical Reports Server (NTRS)

Eldred, D. B.

1994-01-01

Conscan is commonly used by DSN antennas to allow adaptive tracking of a target whose position is not precisely known. This article describes an algorithm that is based on a Kalman filter and is proposed to replace the existing fast Fourier transform based (FFT-based) algorithm for conscan. Advantages of this algorithm include better pointing accuracy, continuous update information, and accommodation of missing data. Additionally, a strategy for adaptive selection of the conscan radius is proposed. The performance of the algorithm is illustrated through computer simulations and compared to the FFT algorithm. The results show that the Kalman filter algorithm is consistently superior.

Adaptive Wavelet Modeling of Geophysical Data

NASA Astrophysics Data System (ADS)

Plattner, A.; Maurer, H.; Dahmen, W.; Vorloeper, J.

2009-12-01

Despite the ever-increasing power of modern computers, realistic modeling of complex three-dimensional Earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modeling approaches includes either finite difference or non-adaptive finite element algorithms, and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behavior of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modeled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet based approach that is applicable to a large scope of problems, also including nonlinear problems. To the best of our knowledge such algorithms have not yet been applied in geophysics. Adaptive wavelet algorithms offer several attractive features: (i) for a given subsurface model, they allow the forward modeling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient, and (iii) the modeling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving three-dimensional geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best fit subsurface boundaries. Such algorithms represent the current state-of-the-art in geoelectrical modeling. An analysis of the numerical accuracy as a function of the number of degrees of freedom revealed that the adaptive wavelet algorithm outperforms the finite element solver for simple and moderately complex models, whereas the results become comparable for models with spatially highly variable electrical conductivities. The linear dependency of the modeling error and the computing time proved to be model-independent. This feature will allow very efficient computations using large-scale models as soon as our experimental code is optimized in terms of its implementation.

Magnetic resonance image restoration via dictionary learning under spatially adaptive constraints.

PubMed

Wang, Shanshan; Xia, Yong; Dong, Pei; Feng, David Dagan; Luo, Jianhua; Huang, Qiu

2013-01-01

This paper proposes a spatially adaptive constrained dictionary learning (SAC-DL) algorithm for Rician noise removal in magnitude magnetic resonance (MR) images. This algorithm explores both the strength of dictionary learning to preserve image structures and the robustness of local variance estimation to remove signal-dependent Rician noise. The magnitude image is first separated into a number of partly overlapping image patches. The statistics of each patch are collected and analyzed to obtain a local noise variance. To better adapt to Rician noise, a correction factor is formulated with the local signal-to-noise ratio (SNR). Finally, the trained dictionary is used to denoise each image patch under spatially adaptive constraints. The proposed algorithm has been compared to the popular nonlocal means (NLM) filtering and unbiased NLM (UNLM) algorithm on simulated T1-weighted, T2-weighted and PD-weighted MR images. Our results suggest that the SAC-DL algorithm preserves more image structures while effectively removing the noise than NLM and it is also superior to UNLM at low noise levels.

Application of adaptive filters in denoising magnetocardiogram signals

NASA Astrophysics Data System (ADS)

Khan, Pathan Fayaz; Patel, Rajesh; Sengottuvel, S.; Saipriya, S.; Swain, Pragyna Parimita; Gireesan, K.

2017-05-01

Magnetocardiography (MCG) is the measurement of weak magnetic fields from the heart using Superconducting QUantum Interference Devices (SQUID). Though the measurements are performed inside magnetically shielded rooms (MSR) to reduce external electromagnetic disturbances, interferences which are caused by sources inside the shielded room could not be attenuated. The work presented here reports the application of adaptive filters to denoise MCG signals. Two adaptive noise cancellation approaches namely least mean squared (LMS) algorithm and recursive least squared (RLS) algorithm are applied to denoise MCG signals and the results are compared. It is found that both the algorithms effectively remove noisy wiggles from MCG traces; significantly improving the quality of the cardiac features in MCG traces. The calculated signal-to-noise ratio (SNR) for the denoised MCG traces is found to be slightly higher in the LMS algorithm as compared to the RLS algorithm. The results encourage the use of adaptive techniques to suppress noise due to power line frequency and its harmonics which occur frequently in biomedical measurements.

Implementation of Real-Time Feedback Flow Control Algorithms on a Canonical Testbed

NASA Technical Reports Server (NTRS)

Tian, Ye; Song, Qi; Cattafesta, Louis

2005-01-01

This report summarizes the activities on "Implementation of Real-Time Feedback Flow Control Algorithms on a Canonical Testbed." The work summarized consists primarily of two parts. The first part summarizes our previous work and the extensions to adaptive ID and control algorithms. The second part concentrates on the validation of adaptive algorithms by applying them to a vibration beam test bed. Extensions to flow control problems are discussed.

Adaptive Control Strategies for Flexible Robotic Arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1996-01-01

The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.

Adaptive mechanism-based congestion control for networked systems

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

2013-03-01

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

PubMed

Jin, Ick Hoon; Yuan, Ying; Liang, Faming

2013-10-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

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

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking †

PubMed Central

Kiku, Daisuke; Okutomi, Masatoshi

2017-01-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking. PMID:29194407

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking.

PubMed

Monno, Yusuke; Kiku, Daisuke; Tanaka, Masayuki; Okutomi, Masatoshi

2017-12-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.

Adaptive multi-resolution Modularity for detecting communities in networks

NASA Astrophysics Data System (ADS)

Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

2018-02-01

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

Linear Temporal Logic (LTL) Based Monitoring of Smart Manufacturing Systems.

PubMed

Heddy, Gerald; Huzaifa, Umer; Beling, Peter; Haimes, Yacov; Marvel, Jeremy; Weiss, Brian; LaViers, Amy

2015-01-01

The vision of Smart Manufacturing Systems (SMS) includes collaborative robots that can adapt to a range of scenarios. This vision requires a classification of multiple system behaviors, or sequences of movement, that can achieve the same high-level tasks. Likewise, this vision presents unique challenges regarding the management of environmental variables in concert with discrete, logic-based programming. Overcoming these challenges requires targeted performance and health monitoring of both the logical controller and the physical components of the robotic system. Prognostics and health management (PHM) defines a field of techniques and methods that enable condition-monitoring, diagnostics, and prognostics of physical elements, functional processes, overall systems, etc. PHM is warranted in this effort given that the controller is vulnerable to program changes, which propagate in unexpected ways, logical runtime exceptions, sensor failure, and even bit rot. The physical component's health is affected by the wear and tear experienced by machines constantly in motion. The controller's source of faults is inherently discrete, while the latter occurs in a manner that builds up continuously over time. Such a disconnect poses unique challenges for PHM. This paper presents a robotic monitoring system that captures and resolves this disconnect. This effort leverages supervisory robotic control and model checking with linear temporal logic (LTL), presenting them as a novel monitoring system for PHM. This methodology has been demonstrated in a MATLAB-based simulator for an industry inspired use-case in the context of PHM. Future work will use the methodology to develop adaptive, intelligent control strategies to evenly distribute wear on the joints of the robotic arms, maximizing the life of the system.

Simulating bioclogging effects on dynamic riverbed permeability and infiltration

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

Newcomer, Michelle E.; Hubbard, Susan S.; Fleckenstein, Jan H.

Bioclogging in rivers can detrimentally impact aquifer recharge. This is particularly so in dry regions, where losing rivers are common, and where disconnection between surface water and groundwater (leading to the development of an unsaturated zone) can occur. Reduction in riverbed permeability due to biomass growth is a time-variable parameter that is often neglected, yet permeability reduction from bioclogging can introduce order of magnitude changes in seepage fluxes from rivers over short (i.e., monthly) timescales. To address the combined effects of bioclogging and disconnection on infiltration, we developed in this paper numerical representations of bioclogging processes within a one-dimensional, variablymore » saturated flow model representing losing-connected and losing-disconnected rivers. We tested these formulations using a synthetic case study informed with biological data obtained from the Russian River, California, USA. Our findings show that modeled biomass growth reduced seepage for losing-connected and losing-disconnected rivers. However, for rivers undergoing disconnection, infiltration declines occurred only after the system was fully disconnected. Before full disconnection, biologically induced permeability declines were not significant enough to offset the infiltration gains introduced by disconnection. The two effects combine to lead to a characteristic infiltration curve where peak infiltration magnitude and timing is controlled by permeability declines relative to hydraulic gradient gains. Biomass growth was found to hasten the onset of full disconnection; a condition we term ‘effective disconnection’. Finally, our results show that river infiltration can respond dynamically to bioclogging and subsequent permeability declines that are highly dependent on river connection status.« less

Simulating bioclogging effects on dynamic riverbed permeability and infiltration

DOE PAGES

Newcomer, Michelle E.; Hubbard, Susan S.; Fleckenstein, Jan H.; ...

2016-03-18

Bioclogging in rivers can detrimentally impact aquifer recharge. This is particularly so in dry regions, where losing rivers are common, and where disconnection between surface water and groundwater (leading to the development of an unsaturated zone) can occur. Reduction in riverbed permeability due to biomass growth is a time-variable parameter that is often neglected, yet permeability reduction from bioclogging can introduce order of magnitude changes in seepage fluxes from rivers over short (i.e., monthly) timescales. To address the combined effects of bioclogging and disconnection on infiltration, we developed in this paper numerical representations of bioclogging processes within a one-dimensional, variablymore » saturated flow model representing losing-connected and losing-disconnected rivers. We tested these formulations using a synthetic case study informed with biological data obtained from the Russian River, California, USA. Our findings show that modeled biomass growth reduced seepage for losing-connected and losing-disconnected rivers. However, for rivers undergoing disconnection, infiltration declines occurred only after the system was fully disconnected. Before full disconnection, biologically induced permeability declines were not significant enough to offset the infiltration gains introduced by disconnection. The two effects combine to lead to a characteristic infiltration curve where peak infiltration magnitude and timing is controlled by permeability declines relative to hydraulic gradient gains. Biomass growth was found to hasten the onset of full disconnection; a condition we term ‘effective disconnection’. Finally, our results show that river infiltration can respond dynamically to bioclogging and subsequent permeability declines that are highly dependent on river connection status.« less

An adaptive SVSF-SLAM algorithm to improve the success and solving the UGVs cooperation problem

NASA Astrophysics Data System (ADS)

Demim, Fethi; Nemra, Abdelkrim; Louadj, Kahina; Hamerlain, Mustapha; Bazoula, Abdelouahab

2018-05-01

This paper aims to present a Decentralised Cooperative Simultaneous Localization and Mapping (DCSLAM) solution based on 2D laser data using an Adaptive Covariance Intersection (ACI). The ACI-DCSLAM algorithm will be validated on a swarm of Unmanned Ground Vehicles (UGVs) receiving features to estimate the position and covariance of shared features before adding them to the global map. With the proposed solution, a group of (UGVs) will be able to construct a large reliable map and localise themselves within this map without any user intervention. The most popular solutions to this problem are the EKF-SLAM, Nonlinear H-infinity ? SLAM and the FAST-SLAM. The former suffers from two important problems which are the poor consistency caused by the linearization problem and the calculation of Jacobian. The second solution is the ? which is a very promising filter because it doesn't make any assumption about noise characteristics, while the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF) is adopted. Cooperative adaptive SVSF-SLAM algorithm is proposed in this paper to solve the UGVs SLAM problem. Our main contribution consists in adapting the SVSF filter to solve the Decentralised Cooperative SLAM problem for multiple UGVs. The algorithms developed in this paper were implemented using two mobile robots Pioneer ?, equiped with 2D laser telemetry sensors. Good results are obtained by the Cooperative adaptive SVSF-SLAM algorithm compared to the Cooperative EKF/?-SLAM algorithms, especially when the noise is colored or affected by a variable bias. Simulation results confirm and show the efficiency of the proposed algorithm which is more robust, stable and adapted to real time applications.

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

The Impact of Receiving the Same Items on Consecutive Computer Adaptive Test Administrations.

ERIC Educational Resources Information Center

O'Neill, Thomas; Lunz, Mary E.; Thiede, Keith

2000-01-01

Studied item exposure in a computerized adaptive test when the item selection algorithm presents examinees with questions they were asked in a previous test administration. Results with 178 repeat examinees on a medical technologists' test indicate that the combined use of an adaptive algorithm to select items and latent trait theory to estimate…

Breakdowns in coordinated decision making at and above the incident management team level: an analysis of three large scale Australian wildfires.

PubMed

Bearman, Chris; Grunwald, Jared A; Brooks, Benjamin P; Owen, Christine

2015-03-01

Emergency situations are by their nature difficult to manage and success in such situations is often highly dependent on effective team coordination. Breakdowns in team coordination can lead to significant disruption to an operational response. Breakdowns in coordination were explored in three large-scale bushfires in Australia: the Kilmore East fire, the Wangary fire, and the Canberra Firestorm. Data from these fires were analysed using a top-down and bottom-up qualitative analysis technique. Forty-four breakdowns in coordinated decision making were identified, which yielded 83 disconnects grouped into three main categories: operational, informational and evaluative. Disconnects were specific instances where differences in understanding existed between team members. The reasons why disconnects occurred were largely consistent across the three sets of data. In some cases multiple disconnects occurred in a temporal manner, which suggested some evidence of disconnects creating states that were conducive to the occurrence of further disconnects. In terms of resolution, evaluative disconnects were nearly always resolved however operational and informational disconnects were rarely resolved effectively. The exploratory data analysis and discussion presented here represents the first systematic research to provide information about the reasons why breakdowns occur in emergency management and presents an account of how team processes can act to disrupt coordination and the operational response. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

F-8C adaptive control law refinement and software development

NASA Technical Reports Server (NTRS)

Hartmann, G. L.; Stein, G.

1981-01-01

An explicit adaptive control algorithm based on maximum likelihood estimation of parameters was designed. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm was implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer, 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.

Slower speed and stronger coupling: adaptive mechanisms of chaos synchronization.

PubMed

Wang, Xiao Fan

2002-06-01

We show that two initially weakly coupled chaotic systems can achieve synchronization by adaptively reducing their speed and/or enhancing the coupling strength. Explicit adaptive algorithms for speed reduction and coupling enhancement are provided. We apply these algorithms to the synchronization of two coupled Lorenz systems. It is found that after a long-time adaptive process, the two coupled chaotic systems can achieve synchronization with almost the minimum required coupling-speed ratio.

Multiscale computations with a wavelet-adaptive algorithm

NASA Astrophysics Data System (ADS)

Rastigejev, Yevgenii Anatolyevich

A wavelet-based adaptive multiresolution algorithm for the numerical solution of multiscale problems governed by partial differential equations is introduced. The main features of the method include fast algorithms for the calculation of wavelet coefficients and approximation of derivatives on nonuniform stencils. The connection between the wavelet order and the size of the stencil is established. The algorithm is based on the mathematically well established wavelet theory. This allows us to provide error estimates of the solution which are used in conjunction with an appropriate threshold criteria to adapt the collocation grid. The efficient data structures for grid representation as well as related computational algorithms to support grid rearrangement procedure are developed. The algorithm is applied to the simulation of phenomena described by Navier-Stokes equations. First, we undertake the study of the ignition and subsequent viscous detonation of a H2 : O2 : Ar mixture in a one-dimensional shock tube. Subsequently, we apply the algorithm to solve the two- and three-dimensional benchmark problem of incompressible flow in a lid-driven cavity at large Reynolds numbers. For these cases we show that solutions of comparable accuracy as the benchmarks are obtained with more than an order of magnitude reduction in degrees of freedom. The simulations show the striking ability of the algorithm to adapt to a solution having different scales at different spatial locations so as to produce accurate results at a relatively low computational cost.

On adaptive learning rate that guarantees convergence in feedforward networks.

PubMed

Behera, Laxmidhar; Kumar, Swagat; Patnaik, Awhan

2006-09-01

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.

An adaptive interpolation scheme for molecular potential energy surfaces

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

Kowalewski, Markus, E-mail: mkowalew@uci.edu; Larsson, Elisabeth; Heryudono, Alfa

The calculation of potential energy surfaces for quantum dynamics can be a time consuming task—especially when a high level of theory for the electronic structure calculation is required. We propose an adaptive interpolation algorithm based on polyharmonic splines combined with a partition of unity approach. The adaptive node refinement allows to greatly reduce the number of sample points by employing a local error estimate. The algorithm and its scaling behavior are evaluated for a model function in 2, 3, and 4 dimensions. The developed algorithm allows for a more rapid and reliable interpolation of a potential energy surface within amore » given accuracy compared to the non-adaptive version.« less

Intelligent neural network and fuzzy logic control of industrial and power systems

NASA Astrophysics Data System (ADS)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

Wavefront sensorless adaptive optics OCT with the DONE algorithm for in vivo human retinal imaging [Invited

PubMed Central

Verstraete, Hans R. G. W.; Heisler, Morgan; Ju, Myeong Jin; Wahl, Daniel; Bliek, Laurens; Kalkman, Jeroen; Bonora, Stefano; Jian, Yifan; Verhaegen, Michel; Sarunic, Marinko V.

2017-01-01

In this report, which is an international collaboration of OCT, adaptive optics, and control research, we demonstrate the Data-based Online Nonlinear Extremum-seeker (DONE) algorithm to guide the image based optimization for wavefront sensorless adaptive optics (WFSL-AO) OCT for in vivo human retinal imaging. The ocular aberrations were corrected using a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators. The DONE algorithm succeeded in drastically improving image quality and the OCT signal intensity, up to a factor seven, while achieving a computational time of 1 ms per iteration, making it applicable for many high speed applications. We demonstrate the correction of five aberrations using 70 iterations of the DONE algorithm performed over 2.8 s of continuous volumetric OCT acquisition. Data acquired from an imaging phantom and in vivo from human research volunteers are presented. PMID:28736670

Wavefront sensorless adaptive optics OCT with the DONE algorithm for in vivo human retinal imaging [Invited].

PubMed

Verstraete, Hans R G W; Heisler, Morgan; Ju, Myeong Jin; Wahl, Daniel; Bliek, Laurens; Kalkman, Jeroen; Bonora, Stefano; Jian, Yifan; Verhaegen, Michel; Sarunic, Marinko V

2017-04-01

In this report, which is an international collaboration of OCT, adaptive optics, and control research, we demonstrate the Data-based Online Nonlinear Extremum-seeker (DONE) algorithm to guide the image based optimization for wavefront sensorless adaptive optics (WFSL-AO) OCT for in vivo human retinal imaging. The ocular aberrations were corrected using a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators. The DONE algorithm succeeded in drastically improving image quality and the OCT signal intensity, up to a factor seven, while achieving a computational time of 1 ms per iteration, making it applicable for many high speed applications. We demonstrate the correction of five aberrations using 70 iterations of the DONE algorithm performed over 2.8 s of continuous volumetric OCT acquisition. Data acquired from an imaging phantom and in vivo from human research volunteers are presented.

Implementation of Multispectral Image Classification on a Remote Adaptive Computer

NASA Technical Reports Server (NTRS)

Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

1999-01-01

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

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.

Adaptive image coding based on cubic-spline interpolation

NASA Astrophysics Data System (ADS)

Jiang, Jian-Xing; Hong, Shao-Hua; Lin, Tsung-Ching; Wang, Lin; Truong, Trieu-Kien

2014-09-01

It has been investigated that at low bit rates, downsampling prior to coding and upsampling after decoding can achieve better compression performance than standard coding algorithms, e.g., JPEG and H. 264/AVC. However, at high bit rates, the sampling-based schemes generate more distortion. Additionally, the maximum bit rate for the sampling-based scheme to outperform the standard algorithm is image-dependent. In this paper, a practical adaptive image coding algorithm based on the cubic-spline interpolation (CSI) is proposed. This proposed algorithm adaptively selects the image coding method from CSI-based modified JPEG and standard JPEG under a given target bit rate utilizing the so called ρ-domain analysis. The experimental results indicate that compared with the standard JPEG, the proposed algorithm can show better performance at low bit rates and maintain the same performance at high bit rates.

30 CFR 77.808 - Disconnecting devices.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE WORK AREAS OF UNDERGROUND COAL MINES Surface High-Voltage Distribution § 77.808 Disconnecting devices. Disconnecting devices shall be installed at the...

Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

PubMed

Wilson, Emma D; Assaf, Tareq; Pearson, Martin J; Rossiter, Jonathan M; Dean, Paul; Anderson, Sean R; Porrill, John

2015-01-01

The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

Use of a genetic algorithm for the analysis of eye movements from the linear vestibulo-ocular reflex

NASA Technical Reports Server (NTRS)

Shelhamer, M.

2001-01-01

It is common in vestibular and oculomotor testing to use a single-frequency (sine) or combination of frequencies [sum-of-sines (SOS)] stimulus for head or target motion. The resulting eye movements typically contain a smooth tracking component, which follows the stimulus, in which are interspersed rapid eye movements (saccades or fast phases). The parameters of the smooth tracking--the amplitude and phase of each component frequency--are of interest; many methods have been devised that attempt to identify and remove the fast eye movements from the smooth. We describe a new approach to this problem, tailored to both single-frequency and sum-of-sines stimulation of the human linear vestibulo-ocular reflex. An approximate derivative is used to identify fast movements, which are then omitted from further analysis. The remaining points form a series of smooth tracking segments. A genetic algorithm is used to fit these segments together to form a smooth (but disconnected) wave form, by iteratively removing biases due to the missing fast phases. A genetic algorithm is an iterative optimization procedure; it provides a basis for extending this approach to more complex stimulus-response situations. In the SOS case, the genetic algorithm estimates the amplitude and phase values of the component frequencies as well as removing biases.

Positive isolation disconnect

NASA Technical Reports Server (NTRS)

Friedell, M. V. (Inventor)

1978-01-01

A disconnect composed basically of two halves each consisting of a poppet valve operable to isolate fluid with essentially zero fluid loss is presented. The two halves are coupled together by a quickly releasable coupling which may be either a coupling ring tightened or loosened by a twisting motion, or a clamp operated by a pivoted to prevent disconnecting the two halves until both valves are in closed condition. The positive feature of the device is one requiring a valve closing step before a disconnect step, and takes structural form in an accentric lobe mounted on the valve operating stem. If some obstruction prevents the poppet from moving to its seat, the eccentric lobe cannot be rotated to the closed position, and the interlock prevents a disconnect.

The architecture of adaptive neural network based on a fuzzy inference system for implementing intelligent control in photovoltaic systems

NASA Astrophysics Data System (ADS)

Gimazov, R.; Shidlovskiy, S.

2018-05-01

In this paper, we consider the architecture of the algorithm for extreme regulation in the photovoltaic system. An algorithm based on an adaptive neural network with fuzzy inference is proposed. The implementation of such an algorithm not only allows solving a number of problems in existing algorithms for extreme power regulation of photovoltaic systems, but also creates a reserve for the creation of a universal control system for a photovoltaic system.

Adaptable Binary Programs

DTIC Science & Technology

1994-04-01

a variation of Ziv - Lempel compression [ZL77]. We found that using a standard compression algorithm rather than semantic compression allowed simplified...mentation. In Proceedings of the Conference on Programming Language Design and Implementation, 1993. (ZL77] J. Ziv and A. Lempel . A universal algorithm ...required by adaptable binaries. Our ABS stores adaptable binary information using the conventional binary symbol table and compresses this data using

Digital adaptive controllers for VTOL vehicles. Volume 2: Software documentation

NASA Technical Reports Server (NTRS)

Hartmann, G. L.; Stein, G.; Pratt, S. G.

1979-01-01

The VTOL approach and landing test (VALT) adaptive software is documented. Two self-adaptive algorithms, one based on an implicit model reference design and the other on an explicit parameter estimation technique were evaluated. The organization of the software, user options, and a nominal set of input data are presented along with a flow chart and program listing of each algorithm.

Mouse EEG spike detection based on the adapted continuous wavelet transform

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.

2016-04-01

Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

A set-theoretic model reference adaptive control architecture for disturbance rejection and uncertainty suppression with strict performance guarantees

NASA Astrophysics Data System (ADS)

Arabi, Ehsan; Gruenwald, Benjamin C.; Yucelen, Tansel; Nguyen, Nhan T.

2018-05-01

Research in adaptive control algorithms for safety-critical applications is primarily motivated by the fact that these algorithms have the capability to suppress the effects of adverse conditions resulting from exogenous disturbances, imperfect dynamical system modelling, degraded modes of operation, and changes in system dynamics. Although government and industry agree on the potential of these algorithms in providing safety and reducing vehicle development costs, a major issue is the inability to achieve a-priori, user-defined performance guarantees with adaptive control algorithms. In this paper, a new model reference adaptive control architecture for uncertain dynamical systems is presented to address disturbance rejection and uncertainty suppression. The proposed framework is predicated on a set-theoretic adaptive controller construction using generalised restricted potential functions.The key feature of this framework allows the system error bound between the state of an uncertain dynamical system and the state of a reference model, which captures a desired closed-loop system performance, to be less than a-priori, user-defined worst-case performance bound, and hence, it has the capability to enforce strict performance guarantees. Examples are provided to demonstrate the efficacy of the proposed set-theoretic model reference adaptive control architecture.

Tuning medical education for rural-ready practice: designing and resourcing optimally.

PubMed

Maley, Moira A L; Denz-Penhey, Harriet; Lockyer-Stevens, Vanessa; Murdoch, J Campbell

2006-06-01

In an effort to bring doctors back to the bush the Australian government has resourced a number of rural clinical schools (RCS). At the RCS in the University of Western Australia students were allocated in small groups to rural sites for the entire fifth year of a six-year course, sitting the same final examinations as city students. Key factors guiding the successful outcome were the resourcing and implementation of the infrastructure and teaching and learning pedagogy. In designing support, the disconnection of students from their city colleagues was anticipated as an issue, as was the pedagogical indoctrination of the teachers. The curriculum implementation was adapted in this light. The role of the Web in teaching and learning, and their status as 'student colleagues' and independent learners were pivotal aspects. As students settled at their site, their confidence grew and their anxiety over urban disconnection dissipated. By benchmarking themselves using Web-based formative assessments and in formative 'objective structured clinical examinations' staged for them by the RCS, the students received ongoing feedback on their progress. This model of embedding students in rural centres for an extended period with rural practitioners as teachers was successfully implemented at multiple sites geographically vastly separate.

iss050e059620

NASA Image and Video Library

2017-03-24

iss050e059620 (03/24/2017) --- Expedition 50 Commander Shane Kimbrough of NASA is seen floating into the Quest airlock at the conclusion of a spacewalk. Kimbrough and Flight Engineer Thomas Pesquet of ESA (European Space Agency) conducted a six hour and 34 minute spacewalk on March 24, 2017. The two astronauts successfully disconnected cables and electrical connections on the Pressurized Mating Adapter-3 to prepare for its robotic move, lubricated the latching end effector on the Special Purpose Dexterous Manipulator “extension” for the Canadarm2 robotic arm, inspected a radiator valve and replaced cameras on the Japanese segment of the outpost.

iss050e059613

NASA Image and Video Library

2017-03-24

iss050e059613 (03/24/2017) --- Expedition 50 Commander Shane Kimbrough of NASA is seen floating into the Quest airlock at the conclusion of a spacewalk. Kimbrough and Flight Engineer Thomas Pesquet of ESA (European Space Agency) conducted a six hour and 34 minute spacewalk on March 24, 2017. The two astronauts successfully disconnected cables and electrical connections on the Pressurized Mating Adapter-3 to prepare for its robotic move, lubricated the latching end effector on the Special Purpose Dexterous Manipulator “extension” for the Canadarm2 robotic arm, inspected a radiator valve and replaced cameras on the Japanese segment of the outpost.

iss050e059576

NASA Image and Video Library

2017-03-24

iss050e059576 (03/24/2017) --- Russian cosmonaut Oleg Novitskiy (middle) poses with Expedition 50 Commander Shane Kimbrough of NASA (left) and Flight Engineer Thomas Pesquet of ESA (European Space Agency) (right) prior to their spacewalk. The pair conducted a six hour and 34 minute spacewalk on March 24, 2017. The two astronauts successfully disconnected cables and electrical connections on the Pressurized Mating Adapter-3 to prepare for its robotic move, lubricated the latching end effector on the Special Purpose Dexterous Manipulator “extension” for the Canadarm2 robotic arm, inspected a radiator valve and replaced cameras on the Japanese segment of the outpost.

iss050e059529

NASA Image and Video Library

2017-03-24

iss050e059529 (03/24/2017) --- Flight Engineer Thomas Pesquet of ESA (European Space Agency) is seen performing maintenance on the Dextre robot during a spacewalk. Pesquet and Expedition 50 Commander Shane Kimbrough of NASA conducted a six hour and 34 minute spacewalk on March 24, 2017. The two astronauts successfully disconnected cables and electrical connections on the Pressurized Mating Adapter-3 to prepare for its robotic move, lubricated the latching end effector on the Special Purpose Dexterous Manipulator “extension” for the Canadarm2 robotic arm, inspected a radiator valve and replaced cameras on the Japanese segment of the outpost.

iss050e059608

NASA Image and Video Library

2017-03-24

iss050e059608 (03/24/2017) --- NASA astronaut Peggy Whitson controls the robotic arm aboard the International Space Station during a spacewalk. Expedition 50 Commander Shane Kimbrough of NASA and Flight Engineer Thomas Pesquet of ESA (European Space Agency) conducted a six hour and 34 minute spacewalk on March 24, 2017. The two astronauts successfully disconnected cables and electrical connections on the Pressurized Mating Adapter-3 to prepare for its robotic move, lubricated the latching end effector on the Special Purpose Dexterous Manipulator “extension” for the Canadarm2 robotic arm, inspected a radiator valve and replaced cameras on the Japanese segment of the outpost.

iss050e059579

NASA Image and Video Library

2017-03-24

iss050e059579 (03/24/2017) --- NASA astronaut Peggy Whitson (middle) poses with Expedition 50 Commander Shane Kimbrough of NASA (left) and Flight Engineer Thomas Pesquet of ESA (European Space Agency) (right) prior to their spacewalk. The pair conducted a six hour and 34 minute spacewalk on March 24, 2017. The two astronauts successfully disconnected cables and electrical connections on the Pressurized Mating Adapter-3 to prepare for its robotic move, lubricated the latching end effector on the Special Purpose Dexterous Manipulator “extension” for the Canadarm2 robotic arm, inspected a radiator valve and replaced cameras on the Japanese segment of the outpost.

iss050e059752

NASA Image and Video Library

2017-03-24

iss050e059752 (03/24/2017) --- Flight Engineer Thomas Pesquet of ESA (European Space Agency) is seen floating outside the International Space Station during a spacewalk. Pesquet and Expedition 50 Commander Shane Kimbrough of NASA conducted a six hour and 34 minute spacewalk on March 24, 2017. The two astronauts successfully disconnected cables and electrical connections on the Pressurized Mating Adapter-3 to prepare for its robotic move, lubricated the latching end effector on the Special Purpose Dexterous Manipulator “extension” for the Canadarm2 robotic arm, inspected a radiator valve and replaced cameras on the Japanese segment of the outpost.

Method and apparatus for anti-islanding protection of distributed generations

DOEpatents

Ye, Zhihong; John, Vinod; Wang, Changyong; Garces, Luis Jose; Zhou, Rui; Li, Lei; Walling, Reigh Allen; Premerlani, William James; Sanza, Peter Claudius; Liu, Yan; Dame, Mark Edward

2006-03-21

An apparatus for anti-islanding protection of a distributed generation with respect to a feeder connected to an electrical grid is disclosed. The apparatus includes a sensor adapted to generate a voltage signal representative of an output voltage and/or a current signal representative of an output current at the distributed generation, and a controller responsive to the signals from the sensor. The controller is productive of a control signal directed to the distributed generation to drive an operating characteristic of the distributed generation out of a nominal range in response to the electrical grid being disconnected from the feeder.

Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI.

PubMed

Asif, M Salman; Hamilton, Lei; Brummer, Marijn; Romberg, Justin

2013-09-01

Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. . Copyright © 2012 Wiley Periodicals, Inc.

Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

NASA Technical Reports Server (NTRS)

Kelly, D. A.; Fermelia, A.; Lee, G. K. F.

1990-01-01

An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.

Optimal and adaptive methods of processing hydroacoustic signals (review)

NASA Astrophysics Data System (ADS)

Malyshkin, G. S.; Sidel'nikov, G. B.

2014-09-01

Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

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

30 CFR 75.519 - Main power circuits; disconnecting switches.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Main power circuits; disconnecting switches. 75... MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Electrical Equipment-General § 75.519 Main power circuits; disconnecting switches. [Statutory Provision] In all main power circuits...

30 CFR 75.825 - Power centers.

Code of Federal Regulations, 2014 CFR

2014-07-01

....825 Power centers. (a) Main disconnecting switch. The power center supplying high voltage power to the continuous mining machine must be equipped with a main disconnecting switch that, when in the open position... the main disconnecting switch required in paragraph (a) of this section, the power center must be...

30 CFR 75.825 - Power centers.

Code of Federal Regulations, 2012 CFR

2012-07-01

....825 Power centers. (a) Main disconnecting switch. The power center supplying high voltage power to the continuous mining machine must be equipped with a main disconnecting switch that, when in the open position... the main disconnecting switch required in paragraph (a) of this section, the power center must be...

30 CFR 75.825 - Power centers.

Code of Federal Regulations, 2013 CFR

2013-07-01

....825 Power centers. (a) Main disconnecting switch. The power center supplying high voltage power to the continuous mining machine must be equipped with a main disconnecting switch that, when in the open position... the main disconnecting switch required in paragraph (a) of this section, the power center must be...

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 less computation time than required by the tridiagonal method. The performance of the adaptive grid method tends to degrade as the solution process proceeds in time, but still remains faster than the tridiagonal scheme.

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

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

Aziz, H. M. Abdul; Zhu, Feng; Ukkusuri, Satish V.

Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better atmore » higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO 2, NO x, VOC, PM 10) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.« less

An auto-adaptive optimization approach for targeting nonpoint source pollution control practices.

PubMed

Chen, Lei; Wei, Guoyuan; Shen, Zhenyao

2015-10-21

To solve computationally intensive and technically complex control of nonpoint source pollution, the traditional genetic algorithm was modified into an auto-adaptive pattern, and a new framework was proposed by integrating this new algorithm with a watershed model and an economic module. Although conceptually simple and comprehensive, the proposed algorithm would search automatically for those Pareto-optimality solutions without a complex calibration of optimization parameters. The model was applied in a case study in a typical watershed of the Three Gorges Reservoir area, China. The results indicated that the evolutionary process of optimization was improved due to the incorporation of auto-adaptive parameters. In addition, the proposed algorithm outperformed the state-of-the-art existing algorithms in terms of convergence ability and computational efficiency. At the same cost level, solutions with greater pollutant reductions could be identified. From a scientific viewpoint, the proposed algorithm could be extended to other watersheds to provide cost-effective configurations of BMPs.

Synchronization Of Parallel Discrete Event Simulations

NASA Technical Reports Server (NTRS)

Steinman, Jeffrey S.

1992-01-01

Adaptive, parallel, discrete-event-simulation-synchronization algorithm, Breathing Time Buckets, developed in Synchronous Parallel Environment for Emulation and Discrete Event Simulation (SPEEDES) operating system. Algorithm allows parallel simulations to process events optimistically in fluctuating time cycles that naturally adapt while simulation in progress. Combines best of optimistic and conservative synchronization strategies while avoiding major disadvantages. Algorithm processes events optimistically in time cycles adapting while simulation in progress. Well suited for modeling communication networks, for large-scale war games, for simulated flights of aircraft, for simulations of computer equipment, for mathematical modeling, for interactive engineering simulations, and for depictions of flows of information.

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

PubMed

Huang, X N; Ren, H P

2016-05-13

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.

Adaptive fuzzy system for 3-D vision

NASA Technical Reports Server (NTRS)

Mitra, Sunanda

1993-01-01

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

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.

A selective-update affine projection algorithm with selective input vectors

NASA Astrophysics Data System (ADS)

Kong, NamWoong; Shin, JaeWook; Park, PooGyeon

2011-10-01

This paper proposes an affine projection algorithm (APA) with selective input vectors, which based on the concept of selective-update in order to reduce estimation errors and computations. The algorithm consists of two procedures: input- vector-selection and state-decision. The input-vector-selection procedure determines the number of input vectors by checking with mean square error (MSE) whether the input vectors have enough information for update. The state-decision procedure determines the current state of the adaptive filter by using the state-decision criterion. As the adaptive filter is in transient state, the algorithm updates the filter coefficients with the selected input vectors. On the other hand, as soon as the adaptive filter reaches the steady state, the update procedure is not performed. Through these two procedures, the proposed algorithm achieves small steady-state estimation errors, low computational complexity and low update complexity for colored input signals.

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

PubMed

Semenov, Mikhail A; Terkel, Dmitri A

2003-01-01

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

Self-Avoiding Walks Over Adaptive Triangular Grids

NASA Technical Reports Server (NTRS)

Heber, Gerd; Biswas, Rupak; Gao, Guang R.; Saini, Subhash (Technical Monitor)

1999-01-01

Space-filling curves is a popular approach based on a geometric embedding for linearizing computational meshes. We present a new O(n log n) combinatorial algorithm for constructing a self avoiding walk through a two dimensional mesh containing n triangles. We show that for hierarchical adaptive meshes, the algorithm can be locally adapted and easily parallelized by taking advantage of the regularity of the refinement rules. The proposed approach should be very useful in the runtime partitioning and load balancing of adaptive unstructured grids.

Arbitrary-level hanging nodes for adaptive hphp-FEM approximations in 3D

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

Pavel Kus; Pavel Solin; David Andrs

2014-11-01

In this paper we discuss constrained approximation with arbitrary-level hanging nodes in adaptive higher-order finite element methods (hphp-FEM) for three-dimensional problems. This technique enables using highly irregular meshes, and it greatly simplifies the design of adaptive algorithms as it prevents refinements from propagating recursively through the finite element mesh. The technique makes it possible to design efficient adaptive algorithms for purely hexahedral meshes. We present a detailed mathematical description of the method and illustrate it with numerical examples.

Circuit reliability boosted by soldering pins of disconnect plugs to sockets

NASA Technical Reports Server (NTRS)

Pierce, W. B.

1964-01-01

Where disconnect pins must be used for wiring and testing a circuit, improved system reliability is obtained by making a permanent joint between pins and sockets of the disconnect plug. After the circuit has been tested, contact points may be fused through soldering, brazing, or welding.

Vulnerable Youth: Identifying Their Need for Alternative Educational Settings.

ERIC Educational Resources Information Center

Zweig, Janine M.

This paper examines the extent to which alternative education can meet the needs of disconnected youth who are at risk of economic and social hardship. It summarizes characteristics of disconnected youth, risk factors associated with disconnection, and characteristics of students in selected alternative education settings. The most common factors…

30 CFR 77.600 - Trailing cables; short-circuit protection; disconnecting devices.

Code of Federal Regulations, 2014 CFR

2014-07-01

... capacity in each ungrounded conductor. Disconnecting devices used to disconnect power from trailing cables... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Trailing cables; short-circuit protection... AREAS OF UNDERGROUND COAL MINES Trailing Cables § 77.600 Trailing cables; short-circuit protection...

30 CFR 77.600 - Trailing cables; short-circuit protection; disconnecting devices.

Code of Federal Regulations, 2013 CFR

2013-07-01

... capacity in each ungrounded conductor. Disconnecting devices used to disconnect power from trailing cables... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Trailing cables; short-circuit protection... AREAS OF UNDERGROUND COAL MINES Trailing Cables § 77.600 Trailing cables; short-circuit protection...

30 CFR 77.600 - Trailing cables; short-circuit protection; disconnecting devices.

Code of Federal Regulations, 2012 CFR

2012-07-01

... capacity in each ungrounded conductor. Disconnecting devices used to disconnect power from trailing cables... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Trailing cables; short-circuit protection... AREAS OF UNDERGROUND COAL MINES Trailing Cables § 77.600 Trailing cables; short-circuit protection...

30 CFR 77.600 - Trailing cables; short-circuit protection; disconnecting devices.

Code of Federal Regulations, 2011 CFR

2011-07-01

... capacity in each ungrounded conductor. Disconnecting devices used to disconnect power from trailing cables... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Trailing cables; short-circuit protection... AREAS OF UNDERGROUND COAL MINES Trailing Cables § 77.600 Trailing cables; short-circuit protection...

Quick-disconnect harness system for helmet-mounted displays

NASA Astrophysics Data System (ADS)

Bapu, P. T.; Aulds, M. J.; Fuchs, Steven P.; McCormick, David M.

1992-10-01

We have designed a pilot's harness-mounted, high voltage quick-disconnect connectors with 62 pins, to transmit voltages up to 13.5 kV and video signals with 70 MHz bandwidth, for a binocular helmet-mounted display system. It connects and disconnects with power off, and disconnects 'hot' without pilot intervention and without producing external sparks or exposing hot embers to the explosive cockpit environment. We have implemented a procedure in which the high voltage pins disconnect inside a hermetically-sealed unit before the physical separation of the connector. The 'hot' separation triggers a crowbar circuit in the high voltage power supplies for additional protection. Conductor locations and shields are designed to reduce capacitance in the circuit and avoid crosstalk among adjacent circuits. The quick- disconnect connector and wiring harness are human-engineered to ensure pilot safety and mobility. The connector backshell is equipped with two hybrid video amplifiers to improve the clarity of the video signals. Shielded wires and coaxial cables are molded as a multi-layered ribbon for maximum flexibility between the pilot's harness and helmet. Stiff cabling is provided between the quick-disconnect connector and the aircraft console to control behavior during seat ejection. The components of the system have been successfully tested for safety, performance, ergonomic considerations, and reliability.

An adaptive tracker for ShipIR/NTCS

NASA Astrophysics Data System (ADS)

Ramaswamy, Srinivasan; Vaitekunas, David A.

2015-05-01

A key component in any image-based tracking system is the adaptive tracking algorithm used to segment the image into potential targets, rank-and-select the best candidate target, and the gating of the selected target to further improve tracker performance. This paper will describe a new adaptive tracker algorithm added to the naval threat countermeasure simulator (NTCS) of the NATO-standard ship signature model (ShipIR). The new adaptive tracking algorithm is an optional feature used with any of the existing internal NTCS or user-defined seeker algorithms (e.g., binary centroid, intensity centroid, and threshold intensity centroid). The algorithm segments the detected pixels into clusters, and the smallest set of clusters that meet the detection criterion is obtained by using a knapsack algorithm to identify the set of clusters that should not be used. The rectangular area containing the chosen clusters defines an inner boundary, from which a weighted centroid is calculated as the aim-point. A track-gate is then positioned around the clusters, taking into account the rate of change of the bounding area and compensating for any gimbal displacement. A sequence of scenarios is used to test the new tracking algorithm on a generic unclassified DDG ShipIR model, with and without flares, and demonstrate how some of the key seeker signals are impacted by both the ship and flare intrinsic signatures.

A Demons algorithm for image registration with locally adaptive regularization.

PubMed

Cahill, Nathan D; Noble, J Alison; Hawkes, David J

2009-01-01

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules.

Fast ℓ1-regularized space-time adaptive processing using alternating direction method of multipliers

NASA Astrophysics Data System (ADS)

Qin, Lilong; Wu, Manqing; Wang, Xuan; Dong, Zhen

2017-04-01

Motivated by the sparsity of filter coefficients in full-dimension space-time adaptive processing (STAP) algorithms, this paper proposes a fast ℓ1-regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the convergence and reduce the calculations. The proposed algorithm uses a splitting variable to obtain an equivalent optimization formulation, which is addressed with an augmented Lagrangian method. Using the alternating recursive algorithm, the method can rapidly result in a low minimum mean-square error without a large number of calculations. Through theoretical analysis and experimental verification, we demonstrate that the proposed algorithm provides a better output signal-to-clutter-noise ratio performance than other algorithms.

Development of model reference adaptive control theory for electric power plant control applications

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

Mabius, L.E.

1982-09-15

The scope of this effort includes the theoretical development of a multi-input, multi-output (MIMO) Model Reference Control (MRC) algorithm, (i.e., model following control law), Model Reference Adaptive Control (MRAC) algorithm and the formulation of a nonlinear model of a typical electric power plant. Previous single-input, single-output MRAC algorithm designs have been generalized to MIMO MRAC designs using the MIMO MRC algorithm. This MRC algorithm, which has been developed using Command Generator Tracker methodologies, represents the steady state behavior (in the adaptive sense) of the MRAC algorithm. The MRC algorithm is a fundamental component in the MRAC design and stability analysis.more » An enhanced MRC algorithm, which has been developed for systems with more controls than regulated outputs, alleviates the MRC stability constraint of stable plant transmission zeroes. The nonlinear power plant model is based on the Cromby model with the addition of a governor valve management algorithm, turbine dynamics and turbine interactions with extraction flows. An application of the MRC algorithm to a linearization of this model demonstrates its applicability to power plant systems. In particular, the generated power changes at 7% per minute while throttle pressure and temperature, reheat temperature and drum level are held constant with a reasonable level of control. The enhanced algorithm reduces significantly control fluctuations without modifying the output response.« less

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.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

PubMed

Wang, Xingmei; Hao, Wenqian; Li, Qiming

2017-12-18

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

Wireless rake-receiver using adaptive filter with a family of partial update algorithms in noise cancellation applications

NASA Astrophysics Data System (ADS)

Fayadh, Rashid A.; Malek, F.; Fadhil, Hilal A.; Aldhaibani, Jaafar A.; Salman, M. K.; Abdullah, Farah Salwani

2015-05-01

For high data rate propagation in wireless ultra-wideband (UWB) communication systems, the inter-symbol interference (ISI), multiple-access interference (MAI), and multiple-users interference (MUI) are influencing the performance of the wireless systems. In this paper, the rake-receiver was presented with the spread signal by direct sequence spread spectrum (DS-SS) technique. The adaptive rake-receiver structure was shown with adjusting the receiver tap weights using least mean squares (LMS), normalized least mean squares (NLMS), and affine projection algorithms (APA) to support the weak signals by noise cancellation and mitigate the interferences. To minimize the data convergence speed and to reduce the computational complexity by the previous algorithms, a well-known approach of partial-updates (PU) adaptive filters were employed with algorithms, such as sequential-partial, periodic-partial, M-max-partial, and selective-partial updates (SPU) in the proposed system. The simulation results of bit error rate (BER) versus signal-to-noise ratio (SNR) are illustrated to show the performance of partial-update algorithms that have nearly comparable performance with the full update adaptive filters. Furthermore, the SPU-partial has closed performance to the full-NLMS and full-APA while the M-max-partial has closed performance to the full-LMS updates algorithms.

Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits.

PubMed

Chakrabartty, Shantanu; Shaga, Ravi K; Aono, Kenji

2013-04-01

Analog circuits that are calibrated using digital-to-analog converters (DACs) use a digital signal processor-based algorithm for real-time adaptation and programming of system parameters. In this paper, we first show that this conventional framework for adaptation yields suboptimal calibration properties because of artifacts introduced by quantization noise. We then propose a novel online stochastic optimization algorithm called noise-shaping or ΣΔ gradient descent, which can shape the quantization noise out of the frequency regions spanning the parameter adaptation trajectories. As a result, the proposed algorithms demonstrate superior parameter search properties compared to floating-point gradient methods and better convergence properties than conventional quantized gradient-methods. In the second part of this paper, we apply the ΣΔ gradient descent algorithm to two examples of real-time digital calibration: 1) balancing and tracking of bias currents, and 2) frequency calibration of a band-pass Gm-C biquad filter biased in weak inversion. For each of these examples, the circuits have been prototyped in a 0.5-μm complementary metal-oxide-semiconductor process, and we demonstrate that the proposed algorithm is able to find the optimal solution even in the presence of spurious local minima, which are introduced by the nonlinear and non-monotonic response of calibration DACs.

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm.

PubMed

Mascolo-Fortin, Julia; Matenine, Dmitri; Archambault, Louis; Després, Philippe

2018-01-01

Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time. The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV). Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs. All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality. The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

Benefit of adaptive FEC in shared backup path protected elastic optical network.

PubMed

Guo, Hong; Dai, Hua; Wang, Chao; Li, Yongcheng; Bose, Sanjay K; Shen, Gangxiang

2015-07-27

We apply an adaptive forward error correction (FEC) allocation strategy to an Elastic Optical Network (EON) operated with shared backup path protection (SBPP). To maximize the protected network capacity that can be carried, an Integer Linear Programing (ILP) model and a spectrum window plane (SWP)-based heuristic algorithm are developed. Simulation results show that the FEC coding overhead required by the adaptive FEC scheme is significantly lower than that needed by a fixed FEC allocation strategy resulting in higher network capacity for the adaptive strategy. The adaptive FEC allocation strategy can also significantly outperform the fixed FEC allocation strategy both in terms of the spare capacity redundancy and the average FEC coding overhead needed per optical channel. The proposed heuristic algorithm is efficient and not only performs closer to the ILP model but also does much better than the shortest-path algorithm.

Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?

PubMed

Béliveau, Audrey; Goring, Sarah; Platt, Robert W; Gustafson, Paul

2017-12-01

In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated. Copyright © 2017 John Wiley & Sons, Ltd.

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.

Adaptive Mesh Refinement in Curvilinear Body-Fitted Grid Systems

NASA Technical Reports Server (NTRS)

Steinthorsson, Erlendur; Modiano, David; Colella, Phillip

1995-01-01

To be truly compatible with structured grids, an AMR algorithm should employ a block structure for the refined grids to allow flow solvers to take advantage of the strengths of unstructured grid systems, such as efficient solution algorithms for implicit discretizations and multigrid schemes. One such algorithm, the AMR algorithm of Berger and Colella, has been applied to and adapted for use with body-fitted structured grid systems. Results are presented for a transonic flow over a NACA0012 airfoil (AGARD-03 test case) and a reflection of a shock over a double wedge.

Sinogram-based adaptive iterative reconstruction for sparse view x-ray computed tomography

NASA Astrophysics Data System (ADS)

Trinca, D.; Zhong, Y.; Wang, Y.-Z.; Mamyrbayev, T.; Libin, E.

2016-10-01

With the availability of more powerful computing processors, iterative reconstruction algorithms have recently been successfully implemented as an approach to achieving significant dose reduction in X-ray CT. In this paper, we propose an adaptive iterative reconstruction algorithm for X-ray CT, that is shown to provide results comparable to those obtained by proprietary algorithms, both in terms of reconstruction accuracy and execution time. The proposed algorithm is thus provided for free to the scientific community, for regular use, and for possible further optimization.

29 CFR 1910.399 - Definitions applicable to this subpart.

Code of Federal Regulations, 2010 CFR

2010-07-01

... assembly of a fuse support with either a fuseholder, fuse carrier, or disconnecting blade. The fuseholder or fuse carrier may include a conducting element (fuse link), or may act as the disconnecting blade... in which all or part of the fuse support and its fuse link or disconnecting blade are mounted in oil...

29 CFR 1910.399 - Definitions applicable to this subpart.

Code of Federal Regulations, 2013 CFR

2013-07-01

... assembly of a fuse support with either a fuseholder, fuse carrier, or disconnecting blade. The fuseholder or fuse carrier may include a conducting element (fuse link), or may act as the disconnecting blade... in which all or part of the fuse support and its fuse link or disconnecting blade are mounted in oil...

29 CFR 1910.399 - Definitions applicable to this subpart.

Code of Federal Regulations, 2014 CFR

2014-07-01

... assembly of a fuse support with either a fuseholder, fuse carrier, or disconnecting blade. The fuseholder or fuse carrier may include a conducting element (fuse link), or may act as the disconnecting blade... in which all or part of the fuse support and its fuse link or disconnecting blade are mounted in oil...

29 CFR 1910.399 - Definitions applicable to this subpart.

Code of Federal Regulations, 2011 CFR

2011-07-01

... assembly of a fuse support with either a fuseholder, fuse carrier, or disconnecting blade. The fuseholder or fuse carrier may include a conducting element (fuse link), or may act as the disconnecting blade... in which all or part of the fuse support and its fuse link or disconnecting blade are mounted in oil...

29 CFR 1910.399 - Definitions applicable to this subpart.

Code of Federal Regulations, 2012 CFR

2012-07-01

... assembly of a fuse support with either a fuseholder, fuse carrier, or disconnecting blade. The fuseholder or fuse carrier may include a conducting element (fuse link), or may act as the disconnecting blade... in which all or part of the fuse support and its fuse link or disconnecting blade are mounted in oil...

30 CFR 75.831 - Electrical work; troubleshooting and testing.

Code of Federal Regulations, 2010 CFR

2010-07-01

... disconnecting device: (i) Remove the plug from the power receptacle and connect it to the grounding receptacle... coupler is used as a disconnecting device: (i) Remove the plug from the power receptacle and connect it to...) or (2): (1) If a trailing cable disconnecting switch is provided: (i) Open and ground the power...

Determining a "Profile" of "Disconnected" Students.

ERIC Educational Resources Information Center

Parish, Thomas S.; Parish, Joycelyn Gay

An attempt was made to create a profile of students at risk of disconnecting from school or being disconnected. In this study, 1,853 grade school students were evaluated by their teachers on the Potential Dropout Checklist (PDOC) (T. Parish and J. Parish, 1993), an instrument that offers 20 items believed to discern trouble spots or problem…

Cultural Connections and Disconnections between Non-Hmong Principals and Hmong Parents

ERIC Educational Resources Information Center

Xiong, Tong T.; Obiakor, Festus E.

2013-01-01

The purpose of the authors in this qualitative research is to explore cultural connections and disconnections between non-Hmong principals and Hmong parents. The results show that cultural disconnections occur because of the lack of culturally sensitive professional development for teachers and principals, and the lack of understanding of Hmong…

Why Subscribers Drop Cable Television: Characteristics of Three Groups.

ERIC Educational Resources Information Center

Burkum, Larry G.; Niebauer, Walter E., Jr.

It would be to the advantage of cable operators to identify, in advance, those subscribers who have a high probability of voluntarily disconnecting their service, in order to develop strategies to anticipate and address the factors that might potentially lead to disconnection. A study developed a method for identifying likely disconnecters.…

A Nonlinear, Human-Centered Approach to Motion Cueing with a Neurocomputing Solver

NASA Technical Reports Server (NTRS)

Telban, Robert J.; Cardullo, Frank M.; Houck, Jacob A.

2002-01-01

This paper discusses the continuation of research into the development of new motion cueing algorithms first reported in 1999. In this earlier work, two viable approaches to motion cueing were identified: the coordinated adaptive washout algorithm or 'adaptive algorithm', and the 'optimal algorithm'. In this study, a novel approach to motion cueing is discussed that would combine features of both algorithms. The new algorithm is formulated as a linear optimal control problem, incorporating improved vestibular models and an integrated visual-vestibular motion perception model previously reported. A control law is generated from the motion platform states, resulting in a set of nonlinear cueing filters. The time-varying control law requires the matrix Riccati equation to be solved in real time. Therefore, in order to meet the real time requirement, a neurocomputing approach is used to solve this computationally challenging problem. Single degree-of-freedom responses for the nonlinear algorithm were generated and compared to the adaptive and optimal algorithms. Results for the heave mode show the nonlinear algorithm producing a motion cue with a time-varying washout, sustaining small cues for a longer duration and washing out larger cues more quickly. The addition of the optokinetic influence from the integrated perception model was shown to improve the response to a surge input, producing a specific force response with no steady-state washout. Improved cues are also observed for responses to a sway input. Yaw mode responses reveal that the nonlinear algorithm improves the motion cues by reducing the magnitude of negative cues. The effectiveness of the nonlinear algorithm as compared to the adaptive and linear optimal algorithms will be evaluated on a motion platform, the NASA Langley Research Center Visual Motion Simulator (VMS), and ultimately the Cockpit Motion Facility (CMF) with a series of pilot controlled maneuvers. A proposed experimental procedure is discussed. The results of this evaluation will be used to assess motion cueing performance.

An adaptive bit synchronization algorithm under time-varying environment.

NASA Technical Reports Server (NTRS)

Chow, L. R.; Owen, H. A., Jr.; Wang, P. P.

1973-01-01

This paper presents an adaptive estimation algorithm for bit synchronization, assuming that the parameters of the incoming data process are time-varying. Experiment results have proved that this synchronizer is workable either judged by the amount of data required or the speed of convergence.

Absolute phase estimation: adaptive local denoising and global unwrapping.

PubMed

Bioucas-Dias, Jose; Katkovnik, Vladimir; Astola, Jaakko; Egiazarian, Karen

2008-10-10

The paper attacks absolute phase estimation with a two-step approach: the first step applies an adaptive local denoising scheme to the modulo-2 pi noisy phase; the second step applies a robust phase unwrapping algorithm to the denoised modulo-2 pi phase obtained in the first step. The adaptive local modulo-2 pi phase denoising is a new algorithm based on local polynomial approximations. The zero-order and the first-order approximations of the phase are calculated in sliding windows of varying size. The zero-order approximation is used for pointwise adaptive window size selection, whereas the first-order approximation is used to filter the phase in the obtained windows. For phase unwrapping, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [IEEE Trans. Image Process.16, 698 (2007)] to the denoised wrapped phase. Simulations give evidence that the proposed algorithm yields state-of-the-art performance, enabling strong noise attenuation while preserving image details. (c) 2008 Optical Society of America

Graph theory data for topological quantum chemistry.

PubMed

Vergniory, M G; Elcoro, L; Wang, Zhijun; Cano, Jennifer; Felser, C; Aroyo, M I; Bernevig, B Andrei; Bradlyn, Barry

2017-08-01

Topological phases of noninteracting particles are distinguished by the global properties of their band structure and eigenfunctions in momentum space. On the other hand, group theory as conventionally applied to solid-state physics focuses only on properties that are local (at high-symmetry points, lines, and planes) in the Brillouin zone. To bridge this gap, we have previously [Bradlyn et al., Nature (London) 547, 298 (2017)NATUAS0028-083610.1038/nature23268] mapped the problem of constructing global band structures out of local data to a graph construction problem. In this paper, we provide the explicit data and formulate the necessary algorithms to produce all topologically distinct graphs. Furthermore, we show how to apply these algorithms to certain "elementary" band structures highlighted in the aforementioned reference, and thus we identified and tabulated all orbital types and lattices that can give rise to topologically disconnected band structures. Finally, we show how to use the newly developed bandrep program on the Bilbao Crystallographic Server to access the results of our computation.

Automatic lung lobe segmentation of COPD patients using iterative B-spline fitting

NASA Astrophysics Data System (ADS)

Shamonin, D. P.; Staring, M.; Bakker, M. E.; Xiao, C.; Stolk, J.; Reiber, J. H. C.; Stoel, B. C.

2012-02-01

We present an automatic lung lobe segmentation algorithm for COPD patients. The method enhances fissures, removes unlikely fissure candidates, after which a B-spline is fitted iteratively through the remaining candidate objects. The iterative fitting approach circumvents the need to classify each object as being part of the fissure or being noise, and allows the fissure to be detected in multiple disconnected parts. This property is beneficial for good performance in patient data, containing incomplete and disease-affected fissures. The proposed algorithm is tested on 22 COPD patients, resulting in accurate lobe-based densitometry, and a median overlap of the fissure (defined 3 voxels wide) with an expert ground truth of 0.65, 0.54 and 0.44 for the three main fissures. This compares to complete lobe overlaps of 0.99, 0.98, 0.98, 0.97 and 0.87 for the five main lobes, showing promise for lobe segmentation on data of patients with moderate to severe COPD.

Laboratory Study on Disconnection Events in Comets

NASA Astrophysics Data System (ADS)

Li, Yan-Fei; Li, Yu-Tong; Wang, Wei-Min; Yuan, Da-Wei; et al.

2018-01-01

When comets interacting with solar wind, straight and narrow plasma tails will be often formed. The most remarkable phenomenon of the plasma tails is the disconnection event, in which a plasma tail is uprooted from the comet's head and moves away from the comet. In this paper, the interaction process between a comet and solar wind is simulated by using a laser-driven plasma cloud to hit a cylinder obstacle. A disconnected plasma tail is observed behind the obstacle by optical shadowgraphy and interferometry. Our particle-in-cell simulations show that the diference in thermal velocity between ions and electrons induces an electrostatic field behind the obstacle. This field can lead to the convergence of ions to the central region, resulting in a disconnected plasma tail. This electrostatic field-induced model may be a possible explanation for the disconnection events of cometary tails.

The arcuate fasciculus and the disconnection theme in language and aphasia: History and current state

PubMed Central

Catani, Marco; Mesulam, Marsel

2009-01-01

Few themes have been more central to neurological models of aphasia than the disconnection paradigm and the role of the arcuate fasciculus. Introduced by luminaries of 19th Century neurology and resurrected by the charismatic work of Norman Geschwind, the disconnection theme has triggered spectacular advances of modern understanding of language and aphasia. But the disconnection paradigm had alternate fortunes, ranging from irrational exuberance to benign neglect, and its followers have not always shared the same view on its functional consequences and anatomical correlates. Our goal in this paper is, first, to survey the 19th Century roots of the connectionist approach to aphasia and, second, to describe emerging imaging technologies based on diffusion tensor imaging (DTI) that promise to consolidate and expand the disconnection approach to language and its disorders. PMID:18614162

The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state.

PubMed

Catani, Marco; Mesulam, Marsel

2008-09-01

Few themes have been more central to neurological models of aphasia than the disconnection paradigm and the role of the arcuate fasciculus. Introduced by luminaries of 19th Century neurology and resurrected by the charismatic work of Norman Geschwind, the disconnection theme has triggered spectacular advances of modern understanding of language and aphasia. But the disconnection paradigm had alternate fortunes, ranging from irrational exuberance to benign neglect, and its followers have not always shared the same view on its functional consequences and anatomical correlates. Our goal in this paper is, first, to survey the 19th Century roots of the connectionist approach to aphasia and, second, to describe emerging imaging technologies based on diffusion tensor imaging (DTI) that promise to consolidate and expand the disconnection approach to language and its disorders.

A PRELIMINARY STUDY ON THE FRACTAL PHENOMENON: “DISCONNECTED+ DISCONNECTED=CONNECTED”

NASA Astrophysics Data System (ADS)

Wang, Da; Liu, Shutang; Zhao, Yang

The well-known Parrondo’s paradox: “losing+losing=winning” [G. P. Harmer and D. Abbott, Parrondo’s paradox, Stat. Sci. 14 (2009) 206-213.] indicated that two games with negative gains can generate a new game with positive gain. By extending the Parrondo’s philosophy into chaos research, it was shown that the periodic alteration of two chaotic dynamics results in an ordered dynamics, that is the phenomenon: “chaos+chaos=order” [J. Almeida, D. Peralta-Salas and M. Romera, Can two chaotic systems give rise to order, Physica D 200 124-132 (2005)]. This paper further extends these researches into fractal research by proposing that two disconnected Julia sets can originate a new connected Julia set via alternating order. This new parrondian paradoxical phenomenon can be stated in the Parrondo’s terms as “disconnected+disconnected=connected”.

Linear Temporal Logic (LTL) Based Monitoring of Smart Manufacturing Systems

PubMed Central

Heddy, Gerald; Huzaifa, Umer; Beling, Peter; Haimes, Yacov; Marvel, Jeremy; Weiss, Brian; LaViers, Amy

2017-01-01

The vision of Smart Manufacturing Systems (SMS) includes collaborative robots that can adapt to a range of scenarios. This vision requires a classification of multiple system behaviors, or sequences of movement, that can achieve the same high-level tasks. Likewise, this vision presents unique challenges regarding the management of environmental variables in concert with discrete, logic-based programming. Overcoming these challenges requires targeted performance and health monitoring of both the logical controller and the physical components of the robotic system. Prognostics and health management (PHM) defines a field of techniques and methods that enable condition-monitoring, diagnostics, and prognostics of physical elements, functional processes, overall systems, etc. PHM is warranted in this effort given that the controller is vulnerable to program changes, which propagate in unexpected ways, logical runtime exceptions, sensor failure, and even bit rot. The physical component’s health is affected by the wear and tear experienced by machines constantly in motion. The controller’s source of faults is inherently discrete, while the latter occurs in a manner that builds up continuously over time. Such a disconnect poses unique challenges for PHM. This paper presents a robotic monitoring system that captures and resolves this disconnect. This effort leverages supervisory robotic control and model checking with linear temporal logic (LTL), presenting them as a novel monitoring system for PHM. This methodology has been demonstrated in a MATLAB-based simulator for an industry inspired use-case in the context of PHM. Future work will use the methodology to develop adaptive, intelligent control strategies to evenly distribute wear on the joints of the robotic arms, maximizing the life of the system. PMID:28730154

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation.

PubMed

Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

2017-09-05

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes.

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation

PubMed Central

Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

2017-01-01

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes. PMID:28872629

Compression of multispectral Landsat imagery using the Embedded Zerotree Wavelet (EZW) algorithm

NASA Technical Reports Server (NTRS)

Shapiro, Jerome M.; Martucci, Stephen A.; Czigler, Martin

1994-01-01

The Embedded Zerotree Wavelet (EZW) algorithm has proven to be an extremely efficient and flexible compression algorithm for low bit rate image coding. The embedding algorithm attempts to order the bits in the bit stream in numerical importance and thus a given code contains all lower rate encodings of the same algorithm. Therefore, precise bit rate control is achievable and a target rate or distortion metric can be met exactly. Furthermore, the technique is fully image adaptive. An algorithm for multispectral image compression which combines the spectral redundancy removal properties of the image-dependent Karhunen-Loeve Transform (KLT) with the efficiency, controllability, and adaptivity of the embedded zerotree wavelet algorithm is presented. Results are shown which illustrate the advantage of jointly encoding spectral components using the KLT and EZW.

Evaluation of the effect of vibration nonlinearity on convergence behavior of adaptive higher harmonic controllers

NASA Technical Reports Server (NTRS)

Molusis, J. A.; Mookerjee, P.; Bar-Shalom, Y.

1983-01-01

Effect of nonlinearity on convergence of the local linear and global linear adaptive controllers is evaluated. A nonlinear helicopter vibration model is selected for the evaluation which has sufficient nonlinearity, including multiple minimum, to assess the vibration reduction capability of the adaptive controllers. The adaptive control algorithms are based upon a linear transfer matrix assumption and the presence of nonlinearity has a significant effect on algorithm behavior. Simulation results are presented which demonstrate the importance of the caution property in the global linear controller. Caution is represented by a time varying rate weighting term in the local linear controller and this improves the algorithm convergence. Nonlinearity in some cases causes Kalman filter divergence. Two forms of the Kalman filter covariance equation are investigated.

KENNEDY SPACE CENTER, FLA. - Workers in the Orbiter Processing Facility insert the liquid oxygen feedline for the 17-inch disconnect in the orbiter Discovery. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

NASA Image and Video Library

2003-11-11

KENNEDY SPACE CENTER, FLA. - Workers in the Orbiter Processing Facility insert the liquid oxygen feedline for the 17-inch disconnect in the orbiter Discovery. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers install the liquid oxygen feedline for the 17-inch disconnect on orbiter Discovery. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

NASA Image and Video Library

2003-11-11

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers install the liquid oxygen feedline for the 17-inch disconnect on orbiter Discovery. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers raise the liquid oxygen feedline for the 17-inch disconnect toward orbiter Discovery for installation. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

NASA Image and Video Library

2003-11-11

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers raise the liquid oxygen feedline for the 17-inch disconnect toward orbiter Discovery for installation. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers lift the liquid oxygen feedline for the 17-inch disconnect toward orbiter Discovery for installation. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

NASA Image and Video Library

2003-11-11

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers lift the liquid oxygen feedline for the 17-inch disconnect toward orbiter Discovery for installation. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers move the liquid oxygen feedline for the 17-inch disconnect toward orbiter Discovery for installation. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

NASA Image and Video Library

2003-11-11

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, workers move the liquid oxygen feedline for the 17-inch disconnect toward orbiter Discovery for installation. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

Low complexity Reed-Solomon-based low-density parity-check design for software defined optical transmission system based on adaptive puncturing decoding algorithm

NASA Astrophysics Data System (ADS)

Pan, Xiaolong; Liu, Bo; Zheng, Jianglong; Tian, Qinghua

2016-08-01

We propose and demonstrate a low complexity Reed-Solomon-based low-density parity-check (RS-LDPC) code with adaptive puncturing decoding algorithm for elastic optical transmission system. Partial received codes and the relevant column in parity-check matrix can be punctured to reduce the calculation complexity by adaptive parity-check matrix during decoding process. The results show that the complexity of the proposed decoding algorithm is reduced by 30% compared with the regular RS-LDPC system. The optimized code rate of the RS-LDPC code can be obtained after five times iteration.

INITIAL APPL;ICATION OF THE ADAPTIVE GRID AIR POLLUTION MODEL

EPA Science Inventory

The paper discusses an adaptive-grid algorithm used in air pollution models. The algorithm reduces errors related to insufficient grid resolution by automatically refining the grid scales in regions of high interest. Meanwhile the grid scales are coarsened in other parts of the d...

Adaptive geodesic transform for segmentation of vertebrae on CT images

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

2014-03-01

Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach

NASA Astrophysics Data System (ADS)

Piretzidis, Dimitrios; Sideris, Michael G.

2017-09-01

Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63-84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10-30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be connected to and employed in the first computational steps of the space-wise approach, where a time-wise Wiener filter is applied at the first stage of GOCE gravity gradient filtering. The results of this work can be extended to using other adaptive filtering algorithms, such as the recursive least-squares and recursive least-squares lattice filters.

On recursive least-squares filtering algorithms and implementations. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Hsieh, Shih-Fu

1990-01-01

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

Active Control of Wind Tunnel Noise

NASA Technical Reports Server (NTRS)

Hollis, Patrick (Principal Investigator)

1991-01-01

The need for an adaptive active control system was realized, since a wind tunnel is subjected to variations in air velocity, temperature, air turbulence, and some other factors such as nonlinearity. Among many adaptive algorithms, the Least Mean Squares (LMS) algorithm, which is the simplest one, has been used in an Active Noise Control (ANC) system by some researchers. However, Eriksson's results, Eriksson (1985), showed instability in the ANC system with an ER filter for random noise input. The Restricted Least Squares (RLS) algorithm, although computationally more complex than the LMS algorithm, has better convergence and stability properties. The ANC system in the present work was simulated by using an FIR filter with an RLS algorithm for different inputs and for a number of plant models. Simulation results for the ANC system with acoustic feedback showed better robustness when used with the RLS algorithm than with the LMS algorithm for all types of inputs. Overall attenuation in the frequency domain was better in the case of the RLS adaptive algorithm. Simulation results with a more realistic plant model and an RLS adaptive algorithm showed a slower convergence rate than the case with an acoustic plant as a delay plant. However, the attenuation properties were satisfactory for the simulated system with the modified plant. The effect of filter length on the rate of convergence and attenuation was studied. It was found that the rate of convergence decreases with increase in filter length, whereas the attenuation increases with increase in filter length. The final design of the ANC system was simulated and found to have a reasonable convergence rate and good attenuation properties for an input containing discrete frequencies and random noise.

Improved liver R2* mapping by pixel-wise curve fitting with adaptive neighborhood regularization.

PubMed

Wang, Changqing; Zhang, Xinyuan; Liu, Xiaoyun; He, Taigang; Chen, Wufan; Feng, Qianjin; Feng, Yanqiu

2018-08-01

To improve liver R2* mapping by incorporating adaptive neighborhood regularization into pixel-wise curve fitting. Magnetic resonance imaging R2* mapping remains challenging because of the serial images with low signal-to-noise ratio. In this study, we proposed to exploit the neighboring pixels as regularization terms and adaptively determine the regularization parameters according to the interpixel signal similarity. The proposed algorithm, called the pixel-wise curve fitting with adaptive neighborhood regularization (PCANR), was compared with the conventional nonlinear least squares (NLS) and nonlocal means filter-based NLS algorithms on simulated, phantom, and in vivo data. Visually, the PCANR algorithm generates R2* maps with significantly reduced noise and well-preserved tiny structures. Quantitatively, the PCANR algorithm produces R2* maps with lower root mean square errors at varying R2* values and signal-to-noise-ratio levels compared with the NLS and nonlocal means filter-based NLS algorithms. For the high R2* values under low signal-to-noise-ratio levels, the PCANR algorithm outperforms the NLS and nonlocal means filter-based NLS algorithms in the accuracy and precision, in terms of mean and standard deviation of R2* measurements in selected region of interests, respectively. The PCANR algorithm can reduce the effect of noise on liver R2* mapping, and the improved measurement precision will benefit the assessment of hepatic iron in clinical practice. Magn Reson Med 80:792-801, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.

Comparison of Reconstruction and Control algorithms on the ESO end-to-end simulator OCTOPUS

NASA Astrophysics Data System (ADS)

Montilla, I.; Béchet, C.; Lelouarn, M.; Correia, C.; Tallon, M.; Reyes, M.; Thiébaut, É.

Extremely Large Telescopes are very challenging concerning their Adaptive Optics requirements. Their diameters, the specifications demanded by the science for which they are being designed for, and the planned use of Extreme Adaptive Optics systems, imply a huge increment in the number of degrees of freedom in the deformable mirrors. It is necessary to study new reconstruction algorithms to implement the real time control in Adaptive Optics at the required speed. We have studied the performance, applied to the case of the European ELT, of three different algorithms: the matrix-vector multiplication (MVM) algorithm, considered as a reference; the Fractal Iterative Method (FrIM); and the Fourier Transform Reconstructor (FTR). The algorithms have been tested on ESO's OCTOPUS software, which simulates the atmosphere, the deformable mirror, the sensor and the closed-loop control. The MVM is the default reconstruction and control method implemented in OCTOPUS, but it scales in O(N2) operations per loop so it is not considered as a fast algorithm for wave-front reconstruction and control on an Extremely Large Telescope. The two other methods are the fast algorithms studied in the E-ELT Design Study. The performance, as well as their response in the presence of noise and with various atmospheric conditions, has been compared using a Single Conjugate Adaptive Optics configuration for a 42 m diameter ELT, with a total amount of 5402 actuators. Those comparisons made on a common simulator allow to enhance the pros and cons of the various methods, and give us a better understanding of the type of reconstruction algorithm that an ELT demands.

An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)

PubMed Central

2011-01-01

Background Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction. Methods A proposed method with anisotropic diffusion as pre-processing and a novel Bounded Area Elimination (BAE) post-processing algorithm to improve the algorithm of ossification site localization technique are designed with the intent of improving the adaptive segmentation result and the region-of interest (ROI) localization accuracy. Results The results are then evaluated by quantitative analysis and qualitative analysis using texture feature evaluation. The result indicates that the image homogeneity after anisotropic diffusion has improved averagely on each age group for 17.59%. Results of experiments showed that the smoothness has been improved averagely 35% after BAE algorithm and the improvement of ROI localization has improved for averagely 8.19%. The MSSIM has improved averagely 10.49% after performing the BAE algorithm on the adaptive segmented hand radiograph. Conclusions The result indicated that hand radiographs which have undergone anisotropic diffusion have greatly reduced the noise in the segmented image and the result as well indicated that the BAE algorithm proposed is capable of removing the artifacts generated in adaptive segmentation. PMID:21952080

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

On the packing measure of the Sierpinski gasket

NASA Astrophysics Data System (ADS)

Llorente, Marta; Mera, M. Eugenia; Morán, Manuel

2018-06-01

We show that the s-dimensional packing measure P s (S) of the Sierpinski gasket S, where is the similarity dimension of S, satisfies . The formula presented in theorem 6 enables the achievement of the above measure bounds for this non-totally disconnected set as it shows that the symmetries of the Sierpinski gasket can be exploited to simplify the density characterization of P s obtained in Morán (2005 Nonlinearity 18 559–70) for self-similar sets satisfying the so-called open set condition. Thanks to the reduction obtained in theorem 6 we are able to handle the problem of computability of P s (S) with a suitable algorithm.

Some design guidelines for discrete-time adaptive controllers

NASA Technical Reports Server (NTRS)

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

1985-01-01

There have been many algorithms proposed for adaptive control which will provide globally asymptotically stable controllers if some stringent conditions on the plant are met. The conditions on the plant cannot be met in practice as all plants will contain high frequency unmolded dynamics therefore, blind implementation of the published algorithms can lead to disastrous results. This paper uses a linearization analysis of a non-linear adaptive controller to demonstrate analytically design guidelines which aleviate some of the problems associated with adaptive control in the presence of unmodeled dynamics.

STAR adaptation of QR algorithm. [program for solving over-determined systems of linear equations

NASA Technical Reports Server (NTRS)

Shah, S. N.

1981-01-01

The QR algorithm used on a serial computer and executed on the Control Data Corporation 6000 Computer was adapted to execute efficiently on the Control Data STAR-100 computer. How the scalar program was adapted for the STAR-100 and why these adaptations yielded an efficient STAR program is described. Program listings of the old scalar version and the vectorized SL/1 version are presented in the appendices. Execution times for the two versions applied to the same system of linear equations, are compared.

MDRC's Current and Recent Projects for Disconnected and Disadvantaged Youth

ERIC Educational Resources Information Center

MDRC, 2013

2013-01-01

MDRC develops and studies programs to help young people who face major barriers in finding a path to stable adult life. Their recent roster of youth-related projects mainly targets three groups: at-risk high school students; dropouts disconnected from school and work; and system-involved youth at risk of disconnection, including youth exiting…

Thermal disconnect for high-temperature batteries

DOEpatents

Jungst, Rudolph George; Armijo, James Rudolph; Frear, Darrel Richard

2000-01-01

A new type of high temperature thermal disconnect has been developed to protect electrical and mechanical equipment from damage caused by operation at extreme temperatures. These thermal disconnects allow continuous operation at temperatures ranging from 250.degree. C. to 450.degree. C., while rapidly terminating operation at temperatures 50.degree. C. to 150.degree. C. higher than the continuous operating temperature.

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

Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II

PubMed Central

Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y.; Fernández, Eduardo

2010-01-01

In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

NASA Technical Reports Server (NTRS)

Kurtoglu, Tolga; Narasimhan, Sriram; Poll, Scott; Garcia, David; Wright, Stephanie

2009-01-01

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.

An adaptive clustering algorithm for image matching based on corner feature

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

Adaptive algorithm of magnetic heading detection

NASA Astrophysics Data System (ADS)

Liu, Gong-Xu; Shi, Ling-Feng

2017-11-01

Magnetic data obtained from a magnetic sensor usually fluctuate in a certain range, which makes it difficult to estimate the magnetic heading accurately. In fact, magnetic heading information is usually submerged in noise because of all kinds of electromagnetic interference and the diversity of the pedestrian’s motion states. In order to solve this problem, a new adaptive algorithm based on the (typically) right-angled corridors of a building or residential buildings is put forward to process heading information. First, a 3D indoor localization platform is set up based on MPU9250. Then, several groups of data are measured by changing the experimental environment and pedestrian’s motion pace. The raw data from the attached inertial measurement unit are calibrated and arranged into a time-stamped array and written to a data file. Later, the data file is imported into MATLAB for processing and analysis using the proposed adaptive algorithm. Finally, the algorithm is verified by comparison with the existing algorithm. The experimental results show that the algorithm has strong robustness and good fault tolerance, which can detect the heading information accurately and in real-time.

[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].

PubMed

Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao

2016-06-01

An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.

Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone.

PubMed

Jiang, Wen Jun; Wittek, Peter; Zhao, Li; Gao, Shi Chao

2014-01-01

Photoplethysmogram (PPG) signals acquired by smartphone cameras are weaker than those acquired by dedicated pulse oximeters. Furthermore, the signals have lower sampling rates, have notches in the waveform and are more severely affected by baseline drift, leading to specific morphological characteristics. This paper introduces a new feature, the inverted triangular area, to address these specific characteristics. The new feature enables real-time adaptive waveform detection using an algorithm of linear time complexity. It can also recognize notches in the waveform and it is inherently robust to baseline drift. An implementation of the algorithm on Android is available for free download. We collected data from 24 volunteers and compared our algorithm in peak detection with two competing algorithms designed for PPG signals, Incremental-Merge Segmentation (IMS) and Adaptive Thresholding (ADT). A sensitivity of 98.0% and a positive predictive value of 98.8% were obtained, which were 7.7% higher than the IMS algorithm in sensitivity, and 8.3% higher than the ADT algorithm in positive predictive value. The experimental results confirmed the applicability of the proposed method.

Research on adaptive optics image restoration algorithm based on improved joint maximum a posteriori method

NASA Astrophysics Data System (ADS)

Zhang, Lijuan; Li, Yang; Wang, Junnan; Liu, Ying

2018-03-01

In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio ( PSNR) and Laplacian sum ( LS) value than the others. The research results have a certain application values for actual AO image restoration.

Hyperspectral data acquisition and analysis in imaging and real-time active MIR backscattering spectroscopy

NASA Astrophysics Data System (ADS)

Jarvis, Jan; Haertelt, Marko; Hugger, Stefan; Butschek, Lorenz; Fuchs, Frank; Ostendorf, Ralf; Wagner, Joachim; Beyerer, Juergen

2017-04-01

In this work we present data analysis algorithms for detection of hazardous substances in hyperspectral observations acquired using active mid-infrared (MIR) backscattering spectroscopy. We present a novel background extraction algorithm based on the adaptive target generation process proposed by Ren and Chang called the adaptive background generation process (ABGP) that generates a robust and physically meaningful set of background spectra for operation of the well-known adaptive matched subspace detection (AMSD) algorithm. It is shown that the resulting AMSD-ABGP detection algorithm competes well with other widely used detection algorithms. The method is demonstrated in measurement data obtained by two fundamentally different active MIR hyperspectral data acquisition devices. A hyperspectral image sensor applicable in static scenes takes a wavelength sequential approach to hyperspectral data acquisition, whereas a rapid wavelength-scanning single-element detector variant of the same principle uses spatial scanning to generate the hyperspectral observation. It is shown that the measurement timescale of the latter is sufficient for the application of the data analysis algorithms even in dynamic scenarios.

Automatic motor task selection via a bandit algorithm for a brain-controlled button

NASA Astrophysics Data System (ADS)

Fruitet, Joan; Carpentier, Alexandra; Munos, Rémi; Clerc, Maureen

2013-02-01

Objective. Brain-computer interfaces (BCIs) based on sensorimotor rhythms use a variety of motor tasks, such as imagining moving the right or left hand, the feet or the tongue. Finding the tasks that yield best performance, specifically to each user, is a time-consuming preliminary phase to a BCI experiment. This study presents a new adaptive procedure to automatically select (online) the most promising motor task for an asynchronous brain-controlled button. Approach. We develop for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory and design an EEG experiment to test our method. We compare (offline) the adaptive algorithm to a naïve selection strategy which uses uniformly distributed samples from each task. We also run the adaptive algorithm online to fully validate the approach. Main results. By not wasting time on inefficient tasks, and focusing on the most promising ones, this algorithm results in a faster task selection and a more efficient use of the BCI training session. More precisely, the offline analysis reveals that the use of this algorithm can reduce the time needed to select the most appropriate task by almost half without loss in precision, or alternatively, allow us to investigate twice the number of tasks within a similar time span. Online tests confirm that the method leads to an optimal task selection. Significance. This study is the first one to optimize the task selection phase by an adaptive procedure. By increasing the number of tasks that can be tested in a given time span, the proposed method could contribute to reducing ‘BCI illiteracy’.

A heuristic re-mapping algorithm reducing inter-level communication in SAMR applications.

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

Steensland, Johan; Ray, Jaideep

2003-07-01

This paper aims at decreasing execution time for large-scale structured adaptive mesh refinement (SAMR) applications by proposing a new heuristic re-mapping algorithm and experimentally showing its effectiveness in reducing inter-level communication. Tests were done for five different SAMR applications. The overall goal is to engineer a dynamically adaptive meta-partitioner capable of selecting and configuring the most appropriate partitioning strategy at run-time based on current system and application state. Such a metapartitioner can significantly reduce execution times for general SAMR applications. Computer simulations of physical phenomena are becoming increasingly popular as they constitute an important complement to real-life testing. In manymore » cases, such simulations are based on solving partial differential equations by numerical methods. Adaptive methods are crucial to efficiently utilize computer resources such as memory and CPU. But even with adaption, the simulations are computationally demanding and yield huge data sets. Thus parallelization and the efficient partitioning of data become issues of utmost importance. Adaption causes the workload to change dynamically, calling for dynamic (re-) partitioning to maintain efficient resource utilization. The proposed heuristic algorithm reduced inter-level communication substantially. Since the complexity of the proposed algorithm is low, this decrease comes at a relatively low cost. As a consequence, we draw the conclusion that the proposed re-mapping algorithm would be useful to lower overall execution times for many large SAMR applications. Due to its usefulness and its parameterization, the proposed algorithm would constitute a natural and important component of the meta-partitioner.« less

Object-based connectedness facilitates matching.

PubMed

Koning, Arno; van Lier, Rob

2003-10-01

In two matching tasks, participants had to match two images of object pairs. Image-based (IB) connectedness refers to connectedness between the objects in an image. Object-based (OB) connectedness refers to connectedness between the interpreted objects. In Experiment 1, a monocular depth cue (shadow) was used to distinguish different relation types between object pairs. Three relation types were created: IB/OB-connected objects, IB/OB-disconnected objects, and IB-connected/OB-disconnected objects. It was found that IB/OB-connected objects were matched faster than IB/OB-disconnected objects. Objects that were IB-connected/OB-disconnected were matched equally to IB/OB-disconnected objects. In Experiment 2, stereoscopic presentation was used. With relation types comparable to those in Experiment 1, it was again found that OB connectedness determined speed of matching, rather than IB connectedness. We conclude that matching of projections of three-dimensional objects depends more on OB connectedness than on IB connectedness.

Engineering test results for the Moog Single Line Disconnect

NASA Technical Reports Server (NTRS)

Glubke, Scott E.

1990-01-01

New and innovative types of disconnects will be required to service, resupply, and maintain future spacecraft subsystems. Efficiently maintaining orbiting scientific instruments, spacecraft support systems, and a manned space station over a long period of time will require the periodic replenishment of consumables and the replacement of components. To accomplish these tasks, the fluid disconnect must be designed to allow the connection and separation of fluid lines and components with minimal hazard to crew and equipment. The capability to simply connect a refueling line or to easily replace a failed component greatly extends the life of a space based fluid system. A test program was initiated to evaluate the Moog Single Line Disconnect. The objective of the test program was to demonstrate the operational characteristics of the disconnect and to verify compliance with current safety regulations. The results of the program are summarized in the referenced document.

Social Disconnection as a Risk Factor for Health among Cambodian Refugees and Their Offspring in the United States.

PubMed

Berthold, S Megan; Loomis, Alysse Melville; Kuoch, Theanvy; Scully, Mary; Hin-McCormick, Mui Mui; Casavant, Bryce; Buckley, Thomas

2018-05-23

Studies of relatively recently resettled refugees have noted social disconnection, linked to various physical and mental health outcomes, as a concern. Limited studies have examined whether social disconnection and its effects persists within refugee populations resettled more than 3 decades prior. The relationship between social disconnection and self-reported health was explored in a secondary analysis of a cross-sectional needs assessment survey with a snowball sample of 100 Cambodian refugees residing in Connecticut. Social disconnectedness and comorbid health conditions were prevalent. Lack of religious and community engagement were associated with poor health outcomes, while individuals with a lack of ethnic engagement reported better overall health. This study underscores the importance of understanding the specific risks that social disconnection poses to refugees who have resettled many years before and their offspring that may assist in better serving currently settling refugees within the United States.

Simulation Based Evaluation of Integrated Adaptive Control and Flight Planning Technologies

NASA Technical Reports Server (NTRS)

Campbell, Stefan Forrest; Kaneshige, John T.

2008-01-01

The objective of this work is to leverage NASA resources to enable effective evaluation of resilient aircraft technologies through simulation. This includes examining strengths and weaknesses of adaptive controllers, emergency flight planning algorithms, and flight envelope determination algorithms both individually and as an integrated package.

A hand tracking algorithm with particle filter and improved GVF snake model

NASA Astrophysics Data System (ADS)

Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe

2017-07-01

To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.

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.

Adaptive-gain fast super-twisting sliding mode fault tolerant control for a reusable launch vehicle in reentry phase.

PubMed

Zhang, Yao; Tang, Shengjing; Guo, Jie

2017-11-01

In this paper, a novel adaptive-gain fast super-twisting (AGFST) sliding mode attitude control synthesis is carried out for a reusable launch vehicle subject to actuator faults and unknown disturbances. According to the fast nonsingular terminal sliding mode surface (FNTSMS) and adaptive-gain fast super-twisting algorithm, an adaptive fault tolerant control law for the attitude stabilization is derived to protect against the actuator faults and unknown uncertainties. Firstly, a second-order nonlinear control-oriented model for the RLV is established by feedback linearization method. And on the basis a fast nonsingular terminal sliding mode (FNTSM) manifold is designed, which provides fast finite-time global convergence and avoids singularity problem as well as chattering phenomenon. Based on the merits of the standard super-twisting (ST) algorithm and fast reaching law with adaption, a novel adaptive-gain fast super-twisting (AGFST) algorithm is proposed for the finite-time fault tolerant attitude control problem of the RLV without any knowledge of the bounds of uncertainties and actuator faults. The important feature of the AGFST algorithm includes non-overestimating the values of the control gains and faster convergence speed than the standard ST algorithm. A formal proof of the finite-time stability of the closed-loop system is derived using the Lyapunov function technique. An estimation of the convergence time and accurate expression of convergence region are also provided. Finally, simulations are presented to illustrate the effectiveness and superiority of the proposed control scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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.

An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization

NASA Astrophysics Data System (ADS)

Li, Shuo; Jin, Weiqi; Li, Li; Li, Yiyang

2018-05-01

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

Performance study of LMS based adaptive algorithms for unknown system identification

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

Javed, Shazia; Ahmad, Noor Atinah

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) signalmore » 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.« less

Real-Time Adaptive Control of Flow-Induced Cavity Tones

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Analysis of Online DBA Algorithm with Adaptive Sleep Cycle in WDM EPON

NASA Astrophysics Data System (ADS)

Pajčin, Bojan; Matavulj, Petar; Radivojević, Mirjana

2018-05-01

In order to manage Quality of Service (QoS) and energy efficiency in the optical access network, an online Dynamic Bandwidth Allocation (DBA) algorithm with adaptive sleep cycle is presented. This DBA algorithm has the ability to allocate an additional bandwidth to the end user within a single sleep cycle whose duration changes depending on the current buffers occupancy. The purpose of this DBA algorithm is to tune the duration of the sleep cycle depending on the network load in order to provide service to the end user without violating strict QoS requests in all network operating conditions.

EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms.

PubMed

Ahirwal, M K; Kumar, Anil; Singh, G K

2013-01-01

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

Proceedings of the Conference on Moments and Signal

NASA Astrophysics Data System (ADS)

Purdue, P.; Solomon, H.

1992-09-01

The focus of this paper is (1) to describe systematic methodologies for selecting nonlinear transformations for blind equalization algorithms (and thus new types of cumulants), and (2) to give an overview of the existing blind equalization algorithms and point out their strengths as well as weaknesses. It is shown that all blind equalization algorithms belong in one of the following three categories, depending where the nonlinear transformation is being applied on the data: (1) the Bussgang algorithms, where the nonlinearity is in the output of the adaptive equalization filter; (2) the polyspectra (or Higher-Order Spectra) algorithms, where the nonlinearity is in the input of the adaptive equalization filter; and (3) the algorithms where the nonlinearity is inside the adaptive filter, i.e., the nonlinear filter or neural network. We describe methodologies for selecting nonlinear transformations based on various optimality criteria such as MSE or MAP. We illustrate that such existing algorithms as Sato, Benveniste-Goursat, Godard or CMA, Stop-and-Go, and Donoho are indeed special cases of the Bussgang family of techniques when the nonlinearity is memoryless. We present results that demonstrate the polyspectra-based algorithms exhibit faster convergence rate than Bussgang algorithms. However, this improved performance is at the expense of more computations per iteration. We also show that blind equalizers based on nonlinear filters or neural networks are more suited for channels that have nonlinear distortions.

A tunable algorithm for collective decision-making.

PubMed

Pratt, Stephen C; Sumpter, David J T

2006-10-24

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

Model reference adaptive control of robots

NASA Technical Reports Server (NTRS)

Steinvorth, Rodrigo

1991-01-01

This project presents the results of controlling two types of robots using new Command Generator Tracker (CGT) based Direct Model Reference Adaptive Control (MRAC) algorithms. Two mathematical models were used to represent a single-link, flexible joint arm and a Unimation PUMA 560 arm; and these were then controlled in simulation using different MRAC algorithms. Special attention was given to the performance of the algorithms in the presence of sudden changes in the robot load. Previously used CGT based MRAC algorithms had several problems. The original algorithm that was developed guaranteed asymptotic stability only for almost strictly positive real (ASPR) plants. This condition is very restrictive, since most systems do not satisfy this assumption. Further developments to the algorithm led to an expansion of the number of plants that could be controlled, however, a steady state error was introduced in the response. These problems led to the introduction of some modifications to the algorithms so that they would be able to control a wider class of plants and at the same time would asymptotically track the reference model. This project presents the development of two algorithms that achieve the desired results and simulates the control of the two robots mentioned before. The results of the simulations are satisfactory and show that the problems stated above have been corrected in the new algorithms. In addition, the responses obtained show that the adaptively controlled processes are resistant to sudden changes in the load.

Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments

PubMed Central

Yao, Yao; Marchal, Kathleen; Van de Peer, Yves

2014-01-01

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485

Leave No Youth Behind: Opportunities for Congress To Reach Disconnected Youth.

ERIC Educational Resources Information Center

Levin-Epstein, Jodie, Ed.; Greenberg, Mark H., Ed.

This report discusses six programs being considered by the 108th Congress for reauthorization, focusing on policies designed to assist disconnected and at-risk youth. The review seeks to identify how the programs do or do not consider at-risk or disconnected youth ad how such programs might be improved. After an introduction by Alan Houseman, six…

KENNEDY SPACE CENTER, FLA. - Workers in the Orbiter Processing Facility oversee installation of the liquid oxygen feedline for the 17-inch disconnect on the orbiter Discovery. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

NASA Image and Video Library

2003-11-11

KENNEDY SPACE CENTER, FLA. - Workers in the Orbiter Processing Facility oversee installation of the liquid oxygen feedline for the 17-inch disconnect on the orbiter Discovery. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

Indirect learning control for nonlinear dynamical systems

NASA Technical Reports Server (NTRS)

Ryu, Yeong Soon; Longman, Richard W.

1993-01-01

In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

An adaptive DPCM algorithm for predicting contours in NTSC composite video signals

NASA Astrophysics Data System (ADS)

Cox, N. R.

An adaptive DPCM algorithm is proposed for encoding digitized National Television Systems Committee (NTSC) color video signals. This algorithm essentially predicts picture contours in the composite signal without resorting to component separation. The contour parameters (slope thresholds) are optimized using four 'typical' television frames that have been sampled at three times the color subcarrier frequency. Three variations of the basic predictor are simulated and compared quantitatively with three non-adaptive predictors of similar complexity. By incorporating a dual-word-length coder and buffer memory, high quality color pictures can be encoded at 4.0 bits/pel or 42.95 Mbit/s. The effect of channel error propagation is also investigated.

Assessing the clinical effect of residual cortical disconnection after ischemic strokes.

PubMed

Bonilha, Leonardo; Rorden, Chris; Fridriksson, Julius

2014-04-01

Studies assessing the relationship between chronic poststroke language impairment (aphasia) and ischemic brain damage usually rely on measuring the extent of brain necrosis observed on MRI. Nonetheless, clinical observation suggests that patients can exhibit deficits that are more severe than what would be expected based on lesion location and size. This phenomenon is commonly explained as being the result of cortical disconnection. To understand whether disconnection contributes to clinical symptoms, we assessed the relationship between language impairments and structural brain connectivity (the connectome) in patients with chronic aphasia after a stroke. Thirty-nine patients with chronic aphasia underwent language assessment and MRI scanning. Relying on MRI data, we reconstructed the individual connectome from T1-weighted and diffusion tensor imaging. Deterministic fiber tractography was used to assess connectivity between each possible pair of cortical Brodmann areas. Multiple linear regression analyses were performed to evaluate the relationship between language performance and cortical necrosis and cortical disconnection. We observed that structural disconnection of Brodmann area 45 (spared by the necrotic tissue) was independently associated with naming performance, controlling for the extent of Brodmann area 45 necrosis (F=4.62; P<0.01; necrosis: β=0.43; P=0.03; disconnection β=1.21; P<0.001). We suggest that cortical disconnection, as measured by the structural connectome, is an independent predictor of naming impairment in patients with chronic aphasia. The full extent of clinically relevant brain damage after an ischemic stroke may be underappreciated by visual inspection of cortical necrosis alone.

Novel medical image enhancement algorithms

NASA Astrophysics Data System (ADS)

Agaian, Sos; McClendon, Stephen A.

2010-01-01

In this paper, we present two novel medical image enhancement algorithms. The first, a global image enhancement algorithm, utilizes an alpha-trimmed mean filter as its backbone to sharpen images. The second algorithm uses a cascaded unsharp masking technique to separate the high frequency components of an image in order for them to be enhanced using a modified adaptive contrast enhancement algorithm. Experimental results from enhancing electron microscopy, radiological, CT scan and MRI scan images, using the MATLAB environment, are then compared to the original images as well as other enhancement methods, such as histogram equalization and two forms of adaptive contrast enhancement. An image processing scheme for electron microscopy images of Purkinje cells will also be implemented and utilized as a comparison tool to evaluate the performance of our algorithm.

Improvement and implementation for Canny edge detection algorithm

NASA Astrophysics Data System (ADS)

Yang, Tao; Qiu, Yue-hong

2015-07-01

Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.

Modeling, Control, and Estimation of Flexible, Aerodynamic Structures

NASA Astrophysics Data System (ADS)

Ray, Cody W.

Engineers have long been inspired by nature’s flyers. Such animals navigate complex environments gracefully and efficiently by using a variety of evolutionary adaptations for high-performance flight. Biologists have discovered a variety of sensory adaptations that provide flow state feedback and allow flying animals to feel their way through flight. A specialized skeletal wing structure and plethora of robust, adaptable sensory systems together allow nature’s flyers to adapt to myriad flight conditions and regimes. In this work, motivated by biology and the successes of bio-inspired, engineered aerial vehicles, linear quadratic control of a flexible, morphing wing design is investigated, helping to pave the way for truly autonomous, mission-adaptive craft. The proposed control algorithm is demonstrated to morph a wing into desired positions. Furthermore, motivated specifically by the sensory adaptations organisms possess, this work transitions to an investigation of aircraft wing load identification using structural response as measured by distributed sensors. A novel, recursive estimation algorithm is utilized to recursively solve the inverse problem of load identification, providing both wing structural and aerodynamic states for use in a feedback control, mission-adaptive framework. The recursive load identification algorithm is demonstrated to provide accurate load estimate in both simulation and experiment.

An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU

PubMed Central

Xu, Hailong; Cui, Xiaowei; Lu, Mingquan

2016-01-01

Nowadays, software-defined radio (SDR) has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS) adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU) are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP) and Space-Frequency Adaptive Processing (SFAP) are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications. PMID:26978363

An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU.

PubMed

Xu, Hailong; Cui, Xiaowei; Lu, Mingquan

2016-03-11

Nowadays, software-defined radio (SDR) has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS) adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU) are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP) and Space-Frequency Adaptive Processing (SFAP) are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications.

Dynamic grid refinement for partial differential equations on parallel computers

NASA Technical Reports Server (NTRS)

Mccormick, S.; Quinlan, D.

1989-01-01

The fast adaptive composite grid method (FAC) is an algorithm that uses various levels of uniform grids to provide adaptive resolution and fast solution of PDEs. An asynchronous version of FAC, called AFAC, that completely eliminates the bottleneck to parallelism is presented. This paper describes the advantage that this algorithm has in adaptive refinement for moving singularities on multiprocessor computers. This work is applicable to the parallel solution of two- and three-dimensional shock tracking problems.

Performance comparisons on spatial lattice algorithm and direct matrix inverse method with application to adaptive arrays processing

NASA Technical Reports Server (NTRS)

An, S. H.; Yao, K.

1986-01-01

Lattice algorithm has been employed in numerous adaptive filtering applications such as speech analysis/synthesis, noise canceling, spectral analysis, and channel equalization. In this paper the application to adaptive-array processing is discussed. The advantages are fast convergence rate as well as computational accuracy independent of the noise and interference conditions. The results produced by this technique are compared to those obtained by the direct matrix inverse method.

Adaption of unstructured meshes using node movement

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

Carpenter, J.G.; McRae, V.D.S.

1996-12-31

The adaption algorithm of Benson and McRae is modified for application to unstructured grids. The weight function generation was modified for application to unstructured grids and movement was limited to prevent cross over. A NACA 0012 airfoil is used as a test case to evaluate the modified algorithm when applied to unstructured grids and compared to results obtained by Warren. An adaptive mesh solution for the Sudhoo and Hall four element airfoil is included as a demonstration case.

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.

A Monte Carlo Approach for Adaptive Testing with Content Constraints

ERIC Educational Resources Information Center

Belov, Dmitry I.; Armstrong, Ronald D.; Weissman, Alexander

2008-01-01

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the…

Real-time blind deconvolution of retinal images in adaptive optics scanning laser ophthalmoscopy

NASA Astrophysics Data System (ADS)

Li, Hao; Lu, Jing; Shi, Guohua; Zhang, Yudong

2011-06-01

With the use of adaptive optics (AO), the ocular aberrations can be compensated to get high-resolution image of living human retina. However, the wavefront correction is not perfect due to the wavefront measure error and hardware restrictions. Thus, it is necessary to use a deconvolution algorithm to recover the retinal images. In this paper, a blind deconvolution technique called Incremental Wiener filter is used to restore the adaptive optics confocal scanning laser ophthalmoscope (AOSLO) images. The point-spread function (PSF) measured by wavefront sensor is only used as an initial value of our algorithm. We also realize the Incremental Wiener filter on graphics processing unit (GPU) in real-time. When the image size is 512 × 480 pixels, six iterations of our algorithm only spend about 10 ms. Retinal blood vessels as well as cells in retinal images are restored by our algorithm, and the PSFs are also revised. Retinal images with and without adaptive optics are both restored. The results show that Incremental Wiener filter reduces the noises and improve the image quality.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

Certification Considerations for Adaptive Systems

NASA Technical Reports Server (NTRS)

Bhattacharyya, Siddhartha; Cofer, Darren; Musliner, David J.; Mueller, Joseph; Engstrom, Eric

2015-01-01

Advanced capabilities planned for the next generation of aircraft, including those that will operate within the Next Generation Air Transportation System (NextGen), will necessarily include complex new algorithms and non-traditional software elements. These aircraft will likely incorporate adaptive control algorithms that will provide enhanced safety, autonomy, and robustness during adverse conditions. Unmanned aircraft will operate alongside manned aircraft in the National Airspace (NAS), with intelligent software performing the high-level decision-making functions normally performed by human pilots. Even human-piloted aircraft will necessarily include more autonomy. However, there are serious barriers to the deployment of new capabilities, especially for those based upon software including adaptive control (AC) and artificial intelligence (AI) algorithms. Current civil aviation certification processes are based on the idea that the correct behavior of a system must be completely specified and verified prior to operation. This report by Rockwell Collins and SIFT documents our comprehensive study of the state of the art in intelligent and adaptive algorithms for the civil aviation domain, categorizing the approaches used and identifying gaps and challenges associated with certification of each approach.

Generalized sidelobe canceller beamforming method for ultrasound imaging.

PubMed

Wang, Ping; Li, Na; Luo, Han-Wu; Zhu, Yong-Kun; Cui, Shi-Gang

2017-03-01

A modified generalized sidelobe canceller (IGSC) algorithm is proposed to enhance the resolution and robustness against the noise of the traditional generalized sidelobe canceller (GSC) and coherence factor combined method (GSC-CF). In the GSC algorithm, weighting vector is divided into adaptive and non-adaptive parts, while the non-adaptive part does not block all the desired signal. A modified steer vector of the IGSC algorithm is generated by the projection of the non-adaptive vector on the signal space constructed by the covariance matrix of received data. The blocking matrix is generated based on the orthogonal complementary space of the modified steer vector and the weighting vector is updated subsequently. The performance of IGSC was investigated by simulations and experiments. Through simulations, IGSC outperformed GSC-CF in terms of spatial resolution by 0.1 mm regardless there is noise or not, as well as the contrast ratio respect. The proposed IGSC can be further improved by combining with CF. The experimental results also validated the effectiveness of the proposed algorithm with dataset provided by the University of Michigan.

Moire measuring technology for three-dimensional profile of the object

NASA Astrophysics Data System (ADS)

Fu, Yanjun; Yang, Kuntao

2006-02-01

An optical system is designed to get projection of the transmission grating, the deformed grating is obtained on surface of the object. The image of the deformed grating is given by the lens, the reference grating is put on the place of the image, and then the moire fringe is obtained. The amplify principle of the moire fringe is used to measure the profile of the object. The optical principle of the projection is analyzed. And the relation between the phase and the height of object is deduced. From the different point of geometry optics and the physics opticsl, the optical system is analyzed, the factors that influence the image equality and the measuring result are obtained. So the betterment of improving the measuring precision is brought forward, and in the later information processing, because of the diffuse reflection, the image equality is not very well. In order to get a good image, the digital filter is used to filter the noise and smooth the image firstly. Then in order to improve the measure precision, the subdivision technology is applied. The Fourier transform profilometry and phase shifting technology is used in the calculation. A detail analyses is done both in time field and frequency field. And the method of improving the measuring precision is put forward. A good digital filter algorithm is brought forward in the Fourier transform profilometry. In the phase shifting technology, the detail formula of three-step and four-step is given. At last the phase that is relational with the high information of the object is get, but the phase is disconnected phase, after the unwrapping algorithm,the disconnected phase is changed to be the continuous phase. Taking use of the relation between the phase and height, the height is obtained. Then the three-dimensional profile of the measured object can be reconstructed. The system is very convenient for non-contact measure of profile of some objects.

Massively parallel algorithms for real-time wavefront control of a dense adaptive optics system

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

Fijany, A.; Milman, M.; Redding, D.

1994-12-31

In this paper massively parallel algorithms and architectures for real-time wavefront control of a dense adaptive optic system (SELENE) are presented. The authors have already shown that the computation of a near optimal control algorithm for SELENE can be reduced to the solution of a discrete Poisson equation on a regular domain. Although, this represents an optimal computation, due the large size of the system and the high sampling rate requirement, the implementation of this control algorithm poses a computationally challenging problem since it demands a sustained computational throughput of the order of 10 GFlops. They develop a novel algorithm,more » designated as Fast Invariant Imbedding algorithm, which offers a massive degree of parallelism with simple communication and synchronization requirements. Due to these features, this algorithm is significantly more efficient than other Fast Poisson Solvers for implementation on massively parallel architectures. The authors also discuss two massively parallel, algorithmically specialized, architectures for low-cost and optimal implementation of the Fast Invariant Imbedding algorithm.« less

Reengaging New York City's Disconnected Youth through Work: Implementation and Early Impacts of the Young Adult Internship Program. OPRE Report 2017-22

ERIC Educational Resources Information Center

Skemer, Melanie; Sherman, Arielle; Williams, Sonya; Cummings, Danielle

2017-01-01

This report presents implementation and early impact results from a random assignment evaluation of the Young Adult Internship Program (YAIP), a subsidized employment program for young people in New York City who have become disconnected from school and work. Operated by various provider agencies, YAIP offers disconnected youth between the ages of…

Socioeconomic Disconnection as a Risk Factor for Increased HIV Infection in Young Men Who Have Sex with Men.

PubMed

Gayles, Travis A; Kuhns, Lisa M; Kwon, Soyang; Mustanski, Brian; Garofalo, Robert

2016-06-01

HIV disproportionately affects young men who have sex with men (YMSM), particularly black YMSM. Increasingly, researchers are turning to social, economic, and structural factors to explain these disproportionate rates. In this study, we explore the relationship between socioeconomic disconnection and HIV status and factors related to HIV infection, including drug use, condomless anal sex, and binge drinking. We operationalize socioeconomic disconnection in this young population as lack of engagement in educational and employment opportunities. Baseline data were analyzed from a longitudinal cohort study of YMSM aged 16-20 years recruited from the Chicago area (N = 450). Bivariate analyses of the association of socioeconomic disconnection and HIV-positive status, drug and alcohol use, and condomless anal sex were assessed using chi-square tests. The relationship of socioeconomic disconnection and HIV-positive status was then examined in multivariate logistic regression models, controlling for age and race/ethnicity and significant behavioral factors. Among study participants, 112 (25%) were not in school, 310 (69%) were not currently working, and 81 (18%) were neither in school nor working. Black MSM were more likely to be socioeconomically disconnected (neither in school nor working; n = 56, 23.3%). The results revealed that disconnected YMSM were more likely to binge drink (AOR = 2.34; 95% CI = 1.16, 4.74) and be HIV positive (AOR = 2.24; 95% CI = 1.04, 4.83). Subpopulation analysis for black participants revealed similar associations (AOR of binge drinking = 2.92; 95% CI = 1.07, 8.01; AOR of HIV positive = 2.38; 95% CI = 1.03, 5.51). Controlling for substance use, the association between disconnection and HIV-positive status remained significant (AOR = 2.37; 95% CI = 1.08, 5.20). Socioeconomic disconnection is significantly and positively associated with HIV status among YMSM, suggesting that the two factors are related. Socioeconomic factors present an important area for future research focusing on HIV infection in this high-risk group.

An experimental SMI adaptive antenna array simulator for weak interfering signals

NASA Technical Reports Server (NTRS)

Dilsavor, Ronald S.; Gupta, Inder J.

1991-01-01

An experimental sample matrix inversion (SMI) adaptive antenna array for suppressing weak interfering signals is described. The experimental adaptive array uses a modified SMI algorithm to increase the interference suppression. In the modified SMI algorithm, the sample covariance matrix is redefined to reduce the effect of thermal noise on the weights of an adaptive array. This is accomplished by subtracting a fraction of the smallest eigenvalue of the original covariance matrix from its diagonal entries. The test results obtained using the experimental system are compared with theoretical results. The two show a good agreement.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Angular-contact ball-bearing internal load estimation algorithm using runtime adaptive relaxation

NASA Astrophysics Data System (ADS)

Medina, H.; Mutu, R.

2017-07-01

An algorithm to estimate internal loads for single-row angular contact ball bearings due to externally applied thrust loads and high-operating speeds is presented. A new runtime adaptive relaxation procedure and blending function is proposed which ensures algorithm stability whilst also reducing the number of iterations needed to reach convergence, leading to an average reduction in computation time in excess of approximately 80%. The model is validated based on a 218 angular contact bearing and shows excellent agreement compared to published results.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

PubMed

Ravindran, Sindhu; Jambek, Asral Bahari; Muthusamy, Hariharan; Neoh, Siew-Chin

2015-01-01

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

NASA Astrophysics Data System (ADS)

Cai, Jingxiao; Zhang, Yan

2017-07-01

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

CFA-aware features for steganalysis of color images

NASA Astrophysics Data System (ADS)

Goljan, Miroslav; Fridrich, Jessica

2015-03-01

Color interpolation is a form of upsampling, which introduces constraints on the relationship between neighboring pixels in a color image. These constraints can be utilized to substantially boost the accuracy of steganography detectors. In this paper, we introduce a rich model formed by 3D co-occurrences of color noise residuals split according to the structure of the Bayer color filter array to further improve detection. Some color interpolation algorithms, AHD and PPG, impose pixel constraints so tight that extremely accurate detection becomes possible with merely eight features eliminating the need for model richification. We carry out experiments on non-adaptive LSB matching and the content-adaptive algorithm WOW on five different color interpolation algorithms. In contrast to grayscale images, in color images that exhibit traces of color interpolation the security of WOW is significantly lower and, depending on the interpolation algorithm, may even be lower than non-adaptive LSB matching.

Quality Scalability Aware Watermarking for Visual Content.

PubMed

Bhowmik, Deepayan; Abhayaratne, Charith

2016-11-01

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.

Experiment on a three-beam adaptive array for EHF frequency-hopped signals using a fast algorithm, phase-D

NASA Astrophysics Data System (ADS)

Yen, J. L.; Kremer, P.; Amin, N.; Fung, J.

1989-05-01

The Department of National Defence (Canada) has been conducting studies into multi-beam adaptive arrays for extremely high frequency (EHF) frequency hopped signals. A three-beam 43 GHz adaptive antenna and a beam control processor is under development. An interactive software package for the operation of the array, capable of applying different control algorithms is being written. A maximum signal to jammer plus noise ratio (SJNR) was found to provide superior performance in preventing degradation of user signals in the presence of nearby jammers. A new fast algorithm using a modified conjugate gradient approach was found to be a very efficient way to implement anti-jamming arrays based on maximum SJNR criterion. The present study was intended to refine and simplify this algorithm and to implement the algorithm on an experimental array for real-time evaluation of anti-jamming performance. A three-beam adaptive array was used. A simulation package was used in the evaluation of multi-beam systems using more than three beams and different user-jammer scenarios. An attempt to further reduce the computation burden through continued analysis of maximum SJNR met with limited success. A method to acquire and track an incoming laser beam is proposed.

Experiment on a three-beam adaptive array for EHF frequency-hopped signals using a fast algorithm, phase E

NASA Astrophysics Data System (ADS)

Yen, J. L.; Kremer, P.; Fung, J.

1990-05-01

The Department of National Defence (Canada) has been conducting studies into multi-beam adaptive arrays for extremely high frequency (EHF) frequency hopped signals. A three-beam 43 GHz adaptive antenna and a beam control processor is under development. An interactive software package for the operation of the array, capable of applying different control algorithms is being written. A maximum signal to jammer plus noise ratio (SJNR) has been found to provide superior performance in preventing degradation of user signals in the presence of nearby jammers. A new fast algorithm using a modified conjugate gradient approach has been found to be a very efficient way to implement anti-jamming arrays based on maximum SJNR criterion. The present study was intended to refine and simplify this algorithm and to implement the algorithm on an experimental array for real-time evaluation of anti-jamming performance. A three-beam adaptive array was used. A simulation package was used in the evaluation of multi-beam systems using more than three beams and different user-jammer scenarios. An attempt to further reduce the computation burden through further analysis of maximum SJNR met with limited success. The investigation of a new angle detector for spatial tracking in heterodyne laser space communications was completed.

Expanded envelope concepts for aircraft control-element failure detection and identification

NASA Technical Reports Server (NTRS)

Weiss, Jerold L.; Hsu, John Y.

1988-01-01

The purpose of this effort was to develop and demonstrate concepts for expanding the envelope of failure detection and isolation (FDI) algorithms for aircraft-path failures. An algorithm which uses analytic-redundancy in the form of aerodynamic force and moment balance equations was used. Because aircraft-path FDI uses analytical models, there is a tradeoff between accuracy and the ability to detect and isolate failures. For single flight condition operation, design and analysis methods are developed to deal with this robustness problem. When the departure from the single flight condition is significant, algorithm adaptation is necessary. Adaptation requirements for the residual generation portion of the FDI algorithm are interpreted as the need for accurate, large-motion aero-models, over a broad range of velocity and altitude conditions. For the decision-making part of the algorithm, adaptation may require modifications to filtering operations, thresholds, and projection vectors that define the various hypothesis tests performed in the decision mechanism. Methods of obtaining and evaluating adequate residual generation and decision-making designs have been developed. The application of the residual generation ideas to a high-performance fighter is demonstrated by developing adaptive residuals for the AFTI-F-16 and simulating their behavior under a variety of maneuvers using the results of a NASA F-16 simulation.

Control algorithms for aerobraking in the Martian atmosphere

NASA Technical Reports Server (NTRS)

Ward, Donald T.; Shipley, Buford W., Jr.

1991-01-01

The Analytic Predictor Corrector (APC) and Energy Controller (EC) atmospheric guidance concepts were adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. Changes are made to the APC to improve its robustness to density variations. These changes include adaptation of a new exit phase algorithm, an adaptive transition velocity to initiate the exit phase, refinement of the reference dynamic pressure calculation and two improved density estimation techniques. The modified controller with the hybrid density estimation technique is called the Mars Hybrid Predictor Corrector (MHPC), while the modified controller with a polynomial density estimator is called the Mars Predictor Corrector (MPC). A Lyapunov Steepest Descent Controller (LSDC) is adapted to control the vehicle. The LSDC lacked robustness, so a Lyapunov tracking exit phase algorithm is developed to guide the vehicle along a reference trajectory. This algorithm, when using the hybrid density estimation technique to define the reference path, is called the Lyapunov Hybrid Tracking Controller (LHTC). With the polynomial density estimator used to define the reference trajectory, the algorithm is called the Lyapunov Tracking Controller (LTC). These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. The MHPC, MPC, LHTC, and LTC show dramatic improvements in robustness over the APC and EC.

Interleaved diffusion-weighted EPI improved by adaptive partial-Fourier and multi-band multiplexed sensitivity-encoding reconstruction

PubMed Central

Chang, Hing-Chiu; Guhaniyogi, Shayan; Chen, Nan-kuei

2014-01-01

Purpose We report a series of techniques to reliably eliminate artifacts in interleaved echo-planar imaging (EPI) based diffusion weighted imaging (DWI). Methods First, we integrate the previously reported multiplexed sensitivity encoding (MUSE) algorithm with a new adaptive Homodyne partial-Fourier reconstruction algorithm, so that images reconstructed from interleaved partial-Fourier DWI data are free from artifacts even in the presence of either a) motion-induced k-space energy peak displacement, or b) susceptibility field gradient induced fast phase changes. Second, we generalize the previously reported single-band MUSE framework to multi-band MUSE, so that both through-plane and in-plane aliasing artifacts in multi-band multi-shot interleaved DWI data can be effectively eliminated. Results The new adaptive Homodyne-MUSE reconstruction algorithm reliably produces high-quality and high-resolution DWI, eliminating residual artifacts in images reconstructed with previously reported methods. Furthermore, the generalized MUSE algorithm is compatible with multi-band and high-throughput DWI. Conclusion The integration of the multi-band and adaptive Homodyne-MUSE algorithms significantly improves the spatial-resolution, image quality, and scan throughput of interleaved DWI. We expect that the reported reconstruction framework will play an important role in enabling high-resolution DWI for both neuroscience research and clinical uses. PMID:24925000

Recovery Act: Energy Efficiency of Data Networks through Rate Adaptation (EEDNRA) - Final Technical Report

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

Matthew Andrews; Spyridon Antonakopoulos; Steve Fortune

2011-07-12

This Concept Definition Study focused on developing a scientific understanding of methods to reduce energy consumption in data networks using rate adaptation. Rate adaptation is a collection of techniques that reduce energy consumption when traffic is light, and only require full energy when traffic is at full provisioned capacity. Rate adaptation is a very promising technique for saving energy: modern data networks are typically operated at average rates well below capacity, but network equipment has not yet been designed to incorporate rate adaptation. The Study concerns packet-switching equipment, routers and switches; such equipment forms the backbone of the modern Internet.more » The focus of the study is on algorithms and protocols that can be implemented in software or firmware to exploit hardware power-control mechanisms. Hardware power-control mechanisms are widely used in the computer industry, and are beginning to be available for networking equipment as well. Network equipment has different performance requirements than computer equipment because of the very fast rate of packet arrival; hence novel power-control algorithms are required for networking. This study resulted in five published papers, one internal report, and two patent applications, documented below. The specific technical accomplishments are the following: • A model for the power consumption of switching equipment used in service-provider telecommunication networks as a function of operating state, and measured power-consumption values for typical current equipment. • An algorithm for use in a router that adapts packet processing rate and hence power consumption to traffic load while maintaining performance guarantees on delay and throughput. • An algorithm that performs network-wide traffic routing with the objective of minimizing energy consumption, assuming that routers have less-than-ideal rate adaptivity. • An estimate of the potential energy savings in service-provider networks using feasibly-implementable rate adaptivity. • A buffer-management algorithm that is designed to reduce the size of router buffers, and hence energy consumed. • A packet-scheduling algorithm designed to minimize packet-processing energy requirements. Additional research is recommended in at least two areas: further exploration of rate-adaptation in network switching equipment, including incorporation of rate-adaptation in actual hardware, allowing experimentation in operational networks; and development of control protocols that allow parts of networks to be shut down while minimizing disruption to traffic flow in the network. The research is an integral part of a large effort within Bell Laboratories, Alcatel-Lucent, aimed at dramatic improvements in the energy efficiency of telecommunication networks. This Study did not explicitly consider any commercialization opportunities.« less

Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.

PubMed

Elaziz, Mohamed Abd; Hemdan, Ahmed Monem; Hassanien, AboulElla; Oliva, Diego; Xiong, Shengwu

2017-09-07

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

KENNEDY SPACE CENTER, FLA. - Viewed from inside the aft section of the orbiter Discovery, a worker installs the liquid oxygen feedline for the 17-inch disconnect, coming up from below. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

NASA Image and Video Library

2003-11-11

KENNEDY SPACE CENTER, FLA. - Viewed from inside the aft section of the orbiter Discovery, a worker installs the liquid oxygen feedline for the 17-inch disconnect, coming up from below. The 17-inch liquid oxygen and liquid hydrogen disconnects provide the propellant feed interface from the external tank to the orbiter main propulsion system and the three Shuttle main engines.

Wavelet methods in multi-conjugate adaptive optics

NASA Astrophysics Data System (ADS)

Helin, T.; Yudytskiy, M.

2013-08-01

The next generation ground-based telescopes rely heavily on adaptive optics for overcoming the limitation of atmospheric turbulence. In the future adaptive optics modalities, like multi-conjugate adaptive optics (MCAO), atmospheric tomography is the major mathematical and computational challenge. In this severely ill-posed problem, a fast and stable reconstruction algorithm is needed that can take into account many real-life phenomena of telescope imaging. We introduce a novel reconstruction method for the atmospheric tomography problem and demonstrate its performance and flexibility in the context of MCAO. Our method is based on using locality properties of compactly supported wavelets, both in the spatial and frequency domains. The reconstruction in the atmospheric tomography problem is obtained by solving the Bayesian MAP estimator with a conjugate-gradient-based algorithm. An accelerated algorithm with preconditioning is also introduced. Numerical performance is demonstrated on the official end-to-end simulation tool OCTOPUS of European Southern Observatory.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2017-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2016-12-01

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

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.

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.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2016-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered-reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

An on-line equivalent system identification scheme for adaptive control. Ph.D. Thesis - Stanford Univ.

NASA Technical Reports Server (NTRS)

Sliwa, S. M.

1984-01-01

A prime obstacle to the widespread use of adaptive control is the degradation of performance and possible instability resulting from the presence of unmodeled dynamics. The approach taken is to explicitly include the unstructured model uncertainty in the output error identification algorithm. The order of the compensator is successively increased by including identified modes. During this model building stage, heuristic rules are used to test for convergence prior to designing compensators. Additionally, the recursive identification algorithm as extended to multi-input, multi-output systems. Enhancements were also made to reduce the computational burden of an algorithm for obtaining minimal state space realizations from the inexact, multivariate transfer functions which result from the identification process. A number of potential adaptive control applications for this approach are illustrated using computer simulations. Results indicated that when speed of adaptation and plant stability are not critical, the proposed schemes converge to enhance system performance.

Genetic algorithms in adaptive fuzzy control

NASA Technical Reports Server (NTRS)

Karr, C. Lucas; Harper, Tony R.

1992-01-01

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 fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

Robust Adaptive Thresholder For Document Scanning Applications

NASA Astrophysics Data System (ADS)

Hsing, To R.

1982-12-01

In document scanning applications, thresholding is used to obtain binary data from a scanner. However, due to: (1) a wide range of different color backgrounds; (2) density variations of printed text information; and (3) the shading effect caused by the optical systems, the use of adaptive thresholding to enhance the useful information is highly desired. This paper describes a new robust adaptive thresholder for obtaining valid binary images. It is basically a memory type algorithm which can dynamically update the black and white reference level to optimize a local adaptive threshold function. The results of high image quality from different types of simulate test patterns can be obtained by this algorithm. The software algorithm is described and experiment results are present to describe the procedures. Results also show that the techniques described here can be used for real-time signal processing in the varied applications.

Processing of fetal heart rate through non-invasive adaptive system based on recursive least squares algorithm

NASA Astrophysics Data System (ADS)

Fajkus, Marcel; Nedoma, Jan; Martinek, Radek; Vasinek, Vladimir

2017-10-01

In this article, we describe an innovative non-invasive method of Fetal Phonocardiography (fPCG) using fiber-optic sensors and adaptive algorithm for the measurement of fetal heart rate (fHR). Conventional PCG is based on a noninvasive scanning of acoustic signals by means of a microphone placed on the thorax. As for fPCG, the microphone is placed on the maternal abdomen. Our solution is based on patent pending non-invasive scanning of acoustic signals by means of a fiber-optic interferometer. Fiber-optic sensors are resistant to technical artifacts such as electromagnetic interferences (EMI), thus they can be used in situations where it is impossible to use conventional EFM methods, e.g. during Magnetic Resonance Imaging (MRI) examination or in case of delivery in water. The adaptive evaluation system is based on Recursive least squares (RLS) algorithm. Based on real measurements provided on five volunteers with their written consent, we created a simplified dynamic signal model of a distribution of heartbeat sounds (HS) through the human body. Our created model allows us to verification of the proposed adaptive system RLS algorithm. The functionality of the proposed non-invasive adaptive system was verified by objective parameters such as Sensitivity (S+) and Signal to Noise Ratio (SNR).

Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

PubMed Central

Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.

2014-01-01

There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269

Adaptive Cross-correlation Algorithm and Experiment of Extended Scene Shack-Hartmann Wavefront Sensing

NASA Technical Reports Server (NTRS)

Sidick, Erkin; Morgan, Rhonda M.; Green, Joseph J.; Ohara, Catherine M.; Redding, David C.

2007-01-01

We have developed a new, adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels in two extended-scene images captured by a Shack-Hartmann wavefront sensor (SH-WFS). It determines the positions of all of the extended-scene image cells relative to a reference cell using an FFT-based iterative image shifting algorithm. It works with both point-source spot images as well as extended scene images. We have also set up a testbed for extended0scene SH-WFS, and tested the ACC algorithm with the measured data of both point-source and extended-scene images. In this paper we describe our algorithm and present out experimental results.

Selection method of terrain matching area for TERCOM algorithm

NASA Astrophysics Data System (ADS)

Zhang, Qieqie; Zhao, Long

2017-10-01

The performance of terrain aided navigation is closely related to the selection of terrain matching area. The different matching algorithms have different adaptability to terrain. This paper mainly studies the adaptability to terrain of TERCOM algorithm, analyze the relation between terrain feature and terrain characteristic parameters by qualitative and quantitative methods, and then research the relation between matching probability and terrain characteristic parameters by the Monte Carlo method. After that, we propose a selection method of terrain matching area for TERCOM algorithm, and verify the method correctness with real terrain data by simulation experiment. Experimental results show that the matching area obtained by the method in this paper has the good navigation performance and the matching probability of TERCOM algorithm is great than 90%

Single-pass incremental force updates for adaptively restrained molecular dynamics.

PubMed

Singh, Krishna Kant; Redon, Stephane

2018-03-30

Adaptively restrained molecular dynamics (ARMD) allows users to perform more integration steps in wall-clock time by switching on and off positional degrees of freedoms. This article presents new, single-pass incremental force updates algorithms to efficiently simulate a system using ARMD. We assessed different algorithms for speedup measurements and implemented them in the LAMMPS MD package. We validated the single-pass incremental force update algorithm on four different benchmarks using diverse pair potentials. The proposed algorithm allows us to perform simulation of a system faster than traditional MD in both NVE and NVT ensembles. Moreover, ARMD using the new single-pass algorithm speeds up the convergence of observables in wall-clock time. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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.

Blind deconvolution post-processing of images corrected by adaptive optics

NASA Astrophysics Data System (ADS)

Christou, Julian C.

1995-08-01

Experience with the adaptive optics system at the Starfire Optical Range has shown that the point spread function is non-uniform and varies both spatially and temporally as well as being object dependent. Because of this, the application of a standard linear and non-linear deconvolution algorithms make it difficult to deconvolve out the point spread function. In this paper we demonstrate the application of a blind deconvolution algorithm to adaptive optics compensated data where a separate point spread function is not needed.

Lossless Video Sequence Compression Using Adaptive Prediction

NASA Technical Reports Server (NTRS)

Li, Ying; Sayood, Khalid

2007-01-01

We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies.

Filtered-x generalized mixed norm (FXGMN) algorithm for active noise control

NASA Astrophysics Data System (ADS)

Song, Pucha; Zhao, Haiquan

2018-07-01

The standard adaptive filtering algorithm with a single error norm exhibits slow convergence rate and poor noise reduction performance under specific environments. To overcome this drawback, a filtered-x generalized mixed norm (FXGMN) algorithm for active noise control (ANC) system is proposed. The FXGMN algorithm is developed by using a convex mixture of lp and lq norms as the cost function that it can be viewed as a generalized version of the most existing adaptive filtering algorithms, and it will reduce to a specific algorithm by choosing certain parameters. Especially, it can be used to solve the ANC under Gaussian and non-Gaussian noise environments (including impulsive noise with symmetric α -stable (SαS) distribution). To further enhance the algorithm performance, namely convergence speed and noise reduction performance, a convex combination of the FXGMN algorithm (C-FXGMN) is presented. Moreover, the computational complexity of the proposed algorithms is analyzed, and a stability condition for the proposed algorithms is provided. Simulation results show that the proposed FXGMN and C-FXGMN algorithms can achieve better convergence speed and higher noise reduction as compared to other existing algorithms under various noise input conditions, and the C-FXGMN algorithm outperforms the FXGMN.

The Association of Afro-Caribbean Immigrants' Feelings of Disconnection from the Community with the Metabolic Syndrome.

PubMed

Tull, Eugene S; Cort, Malcolm A; Yarandi, Hossein N

2015-12-01

Many immigrants can feel like outsiders in their adopted country. The objective of this study was to determine if psychosocial stress associated with feelings of disconnection from the community is associated with the metabolic syndrome (Met-S) among Afro-Caribbean immigrants. The frequency of the Met-S, based on International Diabetes Federation criteria, was determined for a population-based sample of 406 English-speaking Afro-Caribbean immigrants ages twenty and older in the Virgin Islands of the United States (USVI). Feelings of disconnection were assess with Roger's Life Attitude Inventory. Logistic regression analyses showed that a high level of disconnection was significantly associated with the Met-S [Odds Ratio = 1.64 (95% CI 1.10-2.44)] after adjusting for conventional risk factors and for body mass index. Afro-Caribbean immigrants in the USVI who express a high level of disconnection from the community have an increased risk for the Met-S.

Observations of disconnection of open coronal magnetic structures

NASA Technical Reports Server (NTRS)

Mccomas, D. J.; Phillips, J. L.; Hundhausen, A. J.; Burkepile, J. T.

1991-01-01

The solar maximum mission coronagraph/polarimeter observations are surveyed for evidence of magnetic disconnection of previously open magnetic structures and several sequences of images consistent with this interpretation are identified. Such disconnection occurs when open field lines above helmet streamers reconnect, in contrast to previously suggested disconnections of CMEs into closed plasmoids. In this paper a clear example of open field disconnection is shown in detail. The event, on June 27, 1988, is preceded by compression of a preexisting helmet streamer and the open coronal field around it. The compressed helmet streamer and surrounding open field region detach in a large U-shaped structure which subsequently accelerates outward from the sun. The observed sequence of events is consistent with reconnection across the heliospheric current sheet and the creation of a detached U-shaped magnetic structure. Unlike CMEs, which may open new magnetic flux into interplanetary space, this process could serve to close off previously open flux, perhaps helping to maintain the roughly constant amount of open magnetic flux observed in interplanetary space.

Historical evidence for nature disconnection in a 70-year time series of Disney animated films.

PubMed

Prévot-Julliard, Anne-Caroline; Julliard, Romain; Clayton, Susan

2015-08-01

The assumed ongoing disconnection between humans and nature in Western societies represents a profoundly challenging conservation issue. Here, we demonstrate one manifestation of this nature disconnection, via an examination of the representation of natural settings in a 70-year time series of Disney animated films. We found that natural settings are increasingly less present as a representation of outdoor environments in these films. Moreover, these drawn natural settings tend to be more and more human controlled and are less and less complex in terms of the biodiversity they depict. These results demonstrate the increasing nature disconnection of the filmmaking teams, which we consider as a proxy of the Western relation to nature. Additionally, because nature experience of children is partly based on movies, the depleted representation of biodiversity in outdoor environments of Disney films may amplify the current disconnection from nature for children. This reduction in exposure to nature may hinder the implementation of biodiversity conservation measures. © The Author(s) 2014.

Parallel processors and nonlinear structural dynamics algorithms and software

NASA Technical Reports Server (NTRS)

Belytschko, Ted; Gilbertsen, Noreen D.; Neal, Mark O.; Plaskacz, Edward J.

1989-01-01

The adaptation of a finite element program with explicit time integration to a massively parallel SIMD (single instruction multiple data) computer, the CONNECTION Machine is described. The adaptation required the development of a new algorithm, called the exchange algorithm, in which all nodal variables are allocated to the element with an exchange of nodal forces at each time step. The architectural and C* programming language features of the CONNECTION Machine are also summarized. Various alternate data structures and associated algorithms for nonlinear finite element analysis are discussed and compared. Results are presented which demonstrate that the CONNECTION Machine is capable of outperforming the CRAY XMP/14.

Glint-induced false alarm reduction in signature adaptive target detection

NASA Astrophysics Data System (ADS)

Crosby, Frank J.

2002-07-01

The signal adaptive target detection algorithm developed by Crosby and Riley uses target geometry to discern anomalies in local backgrounds. Detection is not restricted based on specific target signatures. The robustness of the algorithm is limited by an increased false alarm potential. The base algorithm is extended to eliminate one common source of false alarms in a littoral environment. This common source is glint reflected on the surface of water. The spectral and spatial transience of glint prevent straightforward characterization and complicate exclusion. However, the statistical basis of the detection algorithm and its inherent computations allow for glint discernment and the removal of its influence.

Direct adaptive performance optimization of subsonic transports: A periodic perturbation technique

NASA Technical Reports Server (NTRS)

Espana, Martin D.; Gilyard, Glenn

1995-01-01

Aircraft performance can be optimized at the flight condition by using available redundancy among actuators. Effective use of this potential allows improved performance beyond limits imposed by design compromises. Optimization based on nominal models does not result in the best performance of the actual aircraft at the actual flight condition. An adaptive algorithm for optimizing performance parameters, such as speed or fuel flow, in flight based exclusively on flight data is proposed. The algorithm is inherently insensitive to model inaccuracies and measurement noise and biases and can optimize several decision variables at the same time. An adaptive constraint controller integrated into the algorithm regulates the optimization constraints, such as altitude or speed, without requiring and prior knowledge of the autopilot design. The algorithm has a modular structure which allows easy incorporation (or removal) of optimization constraints or decision variables to the optimization problem. An important part of the contribution is the development of analytical tools enabling convergence analysis of the algorithm and the establishment of simple design rules. The fuel-flow minimization and velocity maximization modes of the algorithm are demonstrated on the NASA Dryden B-720 nonlinear flight simulator for the single- and multi-effector optimization cases.

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

Reconciling the Disconnect between Information Technology and Information Systems Using an Organizational Epistemology: A Framework to Improve Success with Technology

ERIC Educational Resources Information Center

Powell, Christopher R.

2010-01-01

There is a disconnect between information technology (IT) and information systems (IS) that lies at the foundation of frequent failure in cost, schedule, and/or performance of IT/IS. This disconnect can perhaps be reconciled through a focus on the socially constructed and emergent nature of IT as it enters and is used by an organization. The…

Addition of fornix transection to frontal-temporal disconnection increases the impairment in object-in-place memory in macaque monkeys.

PubMed

Wilson, C R E; Baxter, M G; Easton, A; Gaffan, D

2008-04-01

Both frontal-inferotemporal disconnection and fornix transection (Fx) in the monkey impair object-in-place scene learning, a model of human episodic memory. If the contribution of the fornix to scene learning is via interaction with or modulation of frontal-temporal interaction--that is, if they form a unitary system--then Fx should have no further effect when added to frontal-temporal disconnection. However, if the contribution of the fornix is to some extent distinct, then fornix lesions may produce an additional deficit in scene learning beyond that caused by frontal-temporal disconnection. To distinguish between these possibilities, we trained three male rhesus monkeys on the object-in-place scene-learning task. We tested their learning on the task following frontal-temporal disconnection, achieved by crossed unilateral aspiration of the frontal cortex in one hemisphere and the inferotemporal cortex in the other, and again following the addition of Fx. The monkeys were significantly impaired in scene learning following frontal-temporal disconnection, and furthermore showed a significant increase in this impairment following the addition of Fx, from 32.8% error to 40.5% error (chance = 50%). The increased impairment following the addition of Fx provides evidence that the fornix and frontal-inferotemporal interaction make distinct contributions to episodic memory.

Optimizing the learning rate for adaptive estimation of neural encoding models

PubMed Central

2018-01-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069

Gradient maintenance: A new algorithm for fast online replanning

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

Ahunbay, Ergun E., E-mail: eahunbay@mcw.edu; Li, X. Allen

2015-06-15

Purpose: Clinical use of online adaptive replanning has been hampered by the unpractically long time required to delineate volumes based on the image of the day. The authors propose a new replanning algorithm, named gradient maintenance (GM), which does not require the delineation of organs at risk (OARs), and can enhance automation, drastically reducing planning time and improving consistency and throughput of online replanning. Methods: The proposed GM algorithm is based on the hypothesis that if the dose gradient toward each OAR in daily anatomy can be maintained the same as that in the original plan, the intended plan qualitymore » of the original plan would be preserved in the adaptive plan. The algorithm requires a series of partial concentric rings (PCRs) to be automatically generated around the target toward each OAR on the planning and the daily images. The PCRs are used in the daily optimization objective function. The PCR dose constraints are generated with dose–volume data extracted from the original plan. To demonstrate this idea, GM plans generated using daily images acquired using an in-room CT were compared to regular optimization and image guided radiation therapy repositioning plans for representative prostate and pancreatic cancer cases. Results: The adaptive replanning using the GM algorithm, requiring only the target contour from the CT of the day, can be completed within 5 min without using high-power hardware. The obtained adaptive plans were almost as good as the regular optimization plans and were better than the repositioning plans for the cases studied. Conclusions: The newly proposed GM replanning algorithm, requiring only target delineation, not full delineation of OARs, substantially increased planning speed for online adaptive replanning. The preliminary results indicate that the GM algorithm may be a solution to improve the ability for automation and may be especially suitable for sites with small-to-medium size targets surrounded by several critical structures.« less

Optimizing the learning rate for adaptive estimation of neural encoding models.

PubMed

Hsieh, Han-Lin; Shanechi, Maryam M

2018-05-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Structural disconnection is responsible for increased functional connectivity in multiple sclerosis.

PubMed

Patel, Kevin R; Tobyne, Sean; Porter, Daria; Bireley, John Daniel; Smith, Victoria; Klawiter, Eric

2018-06-01

Increased synchrony within neuroanatomical networks is often observed in neurophysiologic studies of human brain disease. Most often, this phenomenon is ascribed to a compensatory process in the face of injury, though evidence supporting such accounts is limited. Given the known dependence of resting-state functional connectivity (rsFC) on underlying structural connectivity (SC), we examine an alternative hypothesis: that topographical changes in SC, specifically particular patterns of disconnection, contribute to increased network rsFC. We obtain measures of rsFC using fMRI and SC using probabilistic tractography in 50 healthy and 28 multiple sclerosis subjects. Using a computational model of neuronal dynamics, we simulate BOLD using healthy subject SC to couple regions. We find that altering the model by introducing structural disconnection patterns observed in those multiple sclerosis subjects with high network rsFC generates simulations with high rsFC as well, suggesting that disconnection itself plays a role in producing high network functional connectivity. We then examine SC data in individuals. In multiple sclerosis subjects with high network rsFC, we find a preferential disconnection between the relevant network and wider system. We examine the significance of such network isolation by introducing random disconnection into the model. As observed empirically, simulated network rsFC increases with removal of connections bridging a community with the remainder of the brain. We thus show that structural disconnection known to occur in multiple sclerosis contributes to network rsFC changes in multiple sclerosis and further that community isolation is responsible for elevated network functional connectivity.

Genetic algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steven E.

1991-01-01

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

An implicit adaptation algorithm for a linear model reference control system

NASA Technical Reports Server (NTRS)

Mabius, L.; Kaufman, H.

1975-01-01

This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.

Adaptive controller for a strength testbed for aircraft structures

NASA Astrophysics Data System (ADS)

Laperdin, A. I.; Yurkevich, V. D.

2017-07-01

The problem of control system design for a strength testbed of aircraft structures is considered. A method for calculating the parameters of a proportional-integral controller (control algorithm) using the time-scale separation method for the testbed taking into account the dead time effect in the control loop is presented. An adaptive control algorithm structure is proposed which limits the amplitude of high-frequency oscillations in the control system with a change in the direction of motion of the rod of the hydraulic cylinders and provides the desired accuracy and quality of transients at all stages of structural loading history. The results of tests of the developed control system with the adaptive control algorithm on an experimental strength testbed for aircraft structures are given.

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

Fast frequency acquisition via adaptive least squares algorithm

NASA Technical Reports Server (NTRS)

Kumar, R.

1986-01-01

A new least squares algorithm is proposed and investigated for fast frequency and phase acquisition of sinusoids in the presence of noise. This algorithm is a special case of more general, adaptive parameter-estimation techniques. The advantages of the algorithms are their conceptual simplicity, flexibility and applicability to general situations. For example, the frequency to be acquired can be time varying, and the noise can be nonGaussian, nonstationary and colored. As the proposed algorithm can be made recursive in the number of observations, it is not necessary to have a priori knowledge of the received signal-to-noise ratio or to specify the measurement time. This would be required for batch processing techniques, such as the fast Fourier transform (FFT). The proposed algorithm improves the frequency estimate on a recursive basis as more and more observations are obtained. When the algorithm is applied in real time, it has the extra advantage that the observations need not be stored. The algorithm also yields a real time confidence measure as to the accuracy of the estimator.

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

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.

Control algorithm implementation for a redundant degree of freedom manipulator

NASA Technical Reports Server (NTRS)

Cohan, Steve

1991-01-01

This project's purpose is to develop and implement control algorithms for a kinematically redundant robotic manipulator. The manipulator is being developed concurrently by Odetics Inc., under internal research and development funding. This SBIR contract supports algorithm conception, development, and simulation, as well as software implementation and integration with the manipulator hardware. The Odetics Dexterous Manipulator is a lightweight, high strength, modular manipulator being developed for space and commercial applications. It has seven fully active degrees of freedom, is electrically powered, and is fully operational in 1 G. The manipulator consists of five self-contained modules. These modules join via simple quick-disconnect couplings and self-mating connectors which allow rapid assembly/disassembly for reconfiguration, transport, or servicing. Each joint incorporates a unique drive train design which provides zero backlash operation, is insensitive to wear, and is single fault tolerant to motor or servo amplifier failure. The sensing system is also designed to be single fault tolerant. Although the initial prototype is not space qualified, the design is well-suited to meeting space qualification requirements. The control algorithm design approach is to develop a hierarchical system with well defined access and interfaces at each level. The high level endpoint/configuration control algorithm transforms manipulator endpoint position/orientation commands to joint angle commands, providing task space motion. At the same time, the kinematic redundancy is resolved by controlling the configuration (pose) of the manipulator, using several different optimizing criteria. The center level of the hierarchy servos the joints to their commanded trajectories using both linear feedback and model-based nonlinear control techniques. The lowest control level uses sensed joint torque to close torque servo loops, with the goal of improving the manipulator dynamic behavior. The control algorithms are subjected to a dynamic simulation before implementation.

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.

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 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. PMID:25397812

Accelerated optimization and automated discovery with covariance matrix adaptation for experimental quantum control

NASA Astrophysics Data System (ADS)

Roslund, Jonathan; Shir, Ofer M.; Bäck, Thomas; Rabitz, Herschel

2009-10-01

Optimization of quantum systems by closed-loop adaptive pulse shaping offers a rich domain for the development and application of specialized evolutionary algorithms. Derandomized evolution strategies (DESs) are presented here as a robust class of optimizers for experimental quantum control. The combination of stochastic and quasi-local search embodied by these algorithms is especially amenable to the inherent topology of quantum control landscapes. Implementation of DES in the laboratory results in efficiency gains of up to ˜9 times that of the standard genetic algorithm, and thus is a promising tool for optimization of unstable or fragile systems. The statistical learning upon which these algorithms are predicated also provide the means for obtaining a control problem’s Hessian matrix with no additional experimental overhead. The forced optimal covariance adaptive learning (FOCAL) method is introduced to enable retrieval of the Hessian matrix, which can reveal information about the landscape’s local structure and dynamic mechanism. Exploitation of such algorithms in quantum control experiments should enhance their efficiency and provide additional fundamental insights.

Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.

PubMed

Zhao, Shengkui; Jones, Douglas L; Khoo, Suiyang; Man, Zhihong

2014-09-01

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.

Control algorithms and applications of the wavefront sensorless adaptive optics

NASA Astrophysics Data System (ADS)

Ma, Liang; Wang, Bin; Zhou, Yuanshen; Yang, Huizhen

2017-10-01

Compared with the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system need not to measure the wavefront and reconstruct it. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. Based on the analysis of principle and system model of the WFSless AO system, wavefront correction methods of the WFSless AO system were divided into two categories: model-free-based and model-based control algorithms. The WFSless AO system based on model-free-based control algorithms commonly considers the performance metric as a function of the control parameters and then uses certain control algorithm to improve the performance metric. The model-based control algorithms include modal control algorithms, nonlinear control algorithms and control algorithms based on geometrical optics. Based on the brief description of above typical control algorithms, hybrid methods combining the model-free-based control algorithm with the model-based control algorithm were generalized. Additionally, characteristics of various control algorithms were compared and analyzed. We also discussed the extensive applications of WFSless AO system in free space optical communication (FSO), retinal imaging in the human eye, confocal microscope, coherent beam combination (CBC) techniques and extended objects.

A Compound Algorithm for Maximum Power Point Tracking Used in Laser Power Beaming

NASA Astrophysics Data System (ADS)

Chen, Cheng; Liu, Qiang; Gao, Shan; Teng, Yun; Cheng, Lin; Yu, Chengtao; Peng, Kai

2018-03-01

With the high voltage intelligent substation developing in a pretty high speed, more and more artificial intelligent techniques have been incorporated into the power devices to meet the automation needs. For the sake of the line maintenance staff’s safety, the high voltage isolating switch draws great attention among the most important power devices because of its capability of connecting and disconnecting the high voltage circuit. However, due to the very high level voltage of the high voltage isolating switch’s working environment, the power supply system of the surveillance devices could suffer from great electromagnetic interference. Laser power beaming exhibits its merits in such situation because it can provide steady power from a distance despite the day or the night. Then the energy conversion efficiency arises as a new concern. To make as much use of the laser power as possible, our work mainly focuses on extracting maximum power from the photovoltaic (PV) panel. In this paper, we proposed a neural network based algorithm which relates both the intrinsic and the extrinsic features of the PV panel to the proportion of the voltage at the maximum power point (MPP) to the open circuit voltage of the PV panel. Simulations and experiments were carried out to verify the validness of our algorithm.

Automatic segmentation of tumor-laden lung volumes from the LIDC database

NASA Astrophysics Data System (ADS)

O'Dell, Walter G.

2012-03-01

The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

Device for remote operation of electrical disconnect

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

Coleman, Jody Rustyn; Bobbitt, III, John Thomas

Provided is a device for remote operation of an electrical disconnect. The device can include a handle clamp configured to be secured to an extending member of the electrical disconnect. The device can further include a case clamp configured to be secured to a rigid portion of the electrical disconnect. The device can further include a cable having an exterior sheath coaxially surrounding an inner cable. The inner cable can be coaxially slidable with respect to the exterior sheath. The inner cable can extend through an opening of the case clamp and be secured to the handle clamp. The devicemore » can further include an actuator configured to coaxially slide the inner cable such that the handle clamp is actuated towards the case clamp.« less

Adaptive jammer nulling in EHF communications satellites

NASA Astrophysics Data System (ADS)

Bhagwan, Jai; Kavanagh, Stephen; Yen, J. L.

A preliminary investigation is reviewed concerning adaptive null steering multibeam uplink receiving system concepts for future extremely high frequency communications satellites. Primary alternatives in the design of the uplink antenna, the multibeam adaptive nulling receiver, and the processing algorithm and optimization criterion are discussed. The alternatives are phased array, lens or reflector antennas, nulling at radio frequency or an intermediate frequency, wideband versus narrowband nulling, and various adaptive nulling algorithms. A primary determinant of the hardware complexity is the receiving system architecture, which is described for the alternative antenna and nulling concepts. The final concept chosen will be influenced by the nulling performance requirements, cost, and technological readiness.

A multi-block adaptive solving technique based on lattice Boltzmann method

NASA Astrophysics Data System (ADS)

Zhang, Yang; Xie, Jiahua; Li, Xiaoyue; Ma, Zhenghai; Zou, Jianfeng; Zheng, Yao

2018-05-01

In this paper, a CFD parallel adaptive algorithm is self-developed by combining the multi-block Lattice Boltzmann Method (LBM) with Adaptive Mesh Refinement (AMR). The mesh refinement criterion of this algorithm is based on the density, velocity and vortices of the flow field. The refined grid boundary is obtained by extending outward half a ghost cell from the coarse grid boundary, which makes the adaptive mesh more compact and the boundary treatment more convenient. Two numerical examples of the backward step flow separation and the unsteady flow around circular cylinder demonstrate the vortex structure of the cold flow field accurately and specifically.

The method for froth floatation condition recognition based on adaptive feature weighted

NASA Astrophysics Data System (ADS)

Wang, Jieran; Zhang, Jun; Tian, Jinwen; Zhang, Daimeng; Liu, Xiaomao

2018-03-01

The fusion of foam characteristics can play a complementary role in expressing the content of foam image. The weight of foam characteristics is the key to make full use of the relationship between the different features. In this paper, an Adaptive Feature Weighted Method For Froth Floatation Condition Recognition is proposed. Foam features without and with weights are both classified by using support vector machine (SVM).The classification accuracy and optimal equaling algorithm under the each ore grade are regarded as the result of the adaptive feature weighting algorithm. At the same time the effectiveness of adaptive weighted method is demonstrated.

Hyper-heuristics with low level parameter adaptation.

PubMed

Ren, Zhilei; Jiang, He; Xuan, Jifeng; Luo, Zhongxuan

2012-01-01

Recent years have witnessed the great success of hyper-heuristics applying to numerous real-world applications. Hyper-heuristics raise the generality of search methodologies by manipulating a set of low level heuristics (LLHs) to solve problems, and aim to automate the algorithm design process. However, those LLHs are usually parameterized, which may contradict the domain independent motivation of hyper-heuristics. In this paper, we show how to automatically maintain low level parameters (LLPs) using a hyper-heuristic with LLP adaptation (AD-HH), and exemplify the feasibility of AD-HH by adaptively maintaining the LLPs for two hyper-heuristic models. Furthermore, aiming at tackling the search space expansion due to the LLP adaptation, we apply a heuristic space reduction (SAR) mechanism to improve the AD-HH framework. The integration of the LLP adaptation and the SAR mechanism is able to explore the heuristic space more effectively and efficiently. To evaluate the performance of the proposed algorithms, we choose the p-median problem as a case study. The empirical results show that with the adaptation of the LLPs and the SAR mechanism, the proposed algorithms are able to achieve competitive results over the three heterogeneous classes of benchmark instances.

Adaptive sleep-wake discrimination for wearable devices.

PubMed

Karlen, Walter; Floreano, Dario

2011-04-01

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human-rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description

USGS Publications Warehouse

Schmidt, Gail; Jenkerson, Calli B.; 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.

An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

PubMed Central

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments. PMID:24701158

An adaptive evolutionary algorithm for traveling salesman problem with precedence constraints.

PubMed

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.

Flight Validation of a Metrics Driven L(sub 1) Adaptive Control

NASA Technical Reports Server (NTRS)

Dobrokhodov, Vladimir; Kitsios, Ioannis; Kaminer, Isaac; Jones, Kevin D.; Xargay, Enric; Hovakimyan, Naira; Cao, Chengyu; Lizarraga, Mariano I.; Gregory, Irene M.

2008-01-01

The paper addresses initial steps involved in the development and flight implementation of new metrics driven L1 adaptive flight control system. The work concentrates on (i) definition of appropriate control driven metrics that account for the control surface failures; (ii) tailoring recently developed L1 adaptive controller to the design of adaptive flight control systems that explicitly address these metrics in the presence of control surface failures and dynamic changes under adverse flight conditions; (iii) development of a flight control system for implementation of the resulting algorithms onboard of small UAV; and (iv) conducting a comprehensive flight test program that demonstrates performance of the developed adaptive control algorithms in the presence of failures. As the initial milestone the paper concentrates on the adaptive flight system setup and initial efforts addressing the ability of a commercial off-the-shelf AP with and without adaptive augmentation to recover from control surface failures.

An adaptive tracking observer for failure-detection systems

NASA Technical Reports Server (NTRS)

Sidar, M.

1982-01-01

The design problem of adaptive observers applied to linear, constant and variable parameters, multi-input, multi-output systems, is considered. It is shown that, in order to keep the observer's (or Kalman filter) false-alarm rate (FAR) under a certain specified value, it is necessary to have an acceptable proper matching between the observer (or KF) model and the system parameters. An adaptive observer algorithm is introduced in order to maintain desired system-observer model matching, despite initial mismatching and/or system parameter variations. Only a properly designed adaptive observer is able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability and FAR. Conditions for convergence for the adaptive process were obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors and accurate and fast parameter identification, in both deterministic and stochastic cases.

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.

Communication-avoiding symmetric-indefinite factorization

DOE PAGES

Ballard, Grey Malone; Becker, Dulcenia; Demmel, James; ...

2014-11-13

We describe and analyze a novel symmetric triangular factorization algorithm. The algorithm is essentially a block version of Aasen's triangular tridiagonalization. It factors a dense symmetric matrix A as the product A=PLTL TP T where P is a permutation matrix, L is lower triangular, and T is block tridiagonal and banded. The algorithm is the first symmetric-indefinite communication-avoiding factorization: it performs an asymptotically optimal amount of communication in a two-level memory hierarchy for almost any cache-line size. Adaptations of the algorithm to parallel computers are likely to be communication efficient as well; one such adaptation has been recently published. Asmore » a result, the current paper describes the algorithm, proves that it is numerically stable, and proves that it is communication optimal.« less

Communication-avoiding symmetric-indefinite factorization

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

Ballard, Grey Malone; Becker, Dulcenia; Demmel, James

We describe and analyze a novel symmetric triangular factorization algorithm. The algorithm is essentially a block version of Aasen's triangular tridiagonalization. It factors a dense symmetric matrix A as the product A=PLTL TP T where P is a permutation matrix, L is lower triangular, and T is block tridiagonal and banded. The algorithm is the first symmetric-indefinite communication-avoiding factorization: it performs an asymptotically optimal amount of communication in a two-level memory hierarchy for almost any cache-line size. Adaptations of the algorithm to parallel computers are likely to be communication efficient as well; one such adaptation has been recently published. Asmore » a result, the current paper describes the algorithm, proves that it is numerically stable, and proves that it is communication optimal.« less

An adaptive, object oriented strategy for base calling in DNA sequence analysis.

PubMed Central

Giddings, M C; Brumley, R L; Haker, M; Smith, L M

1993-01-01

An algorithm has been developed for the determination of nucleotide sequence from data produced in fluorescence-based automated DNA sequencing instruments employing the four-color strategy. This algorithm takes advantage of object oriented programming techniques for modularity and extensibility. The algorithm is adaptive in that data sets from a wide variety of instruments and sequencing conditions can be used with good results. Confidence values are provided on the base calls as an estimate of accuracy. The algorithm iteratively employs confidence determinations from several different modules, each of which examines a different feature of the data for accurate peak identification. Modules within this system can be added or removed for increased performance or for application to a different task. In comparisons with commercial software, the algorithm performed well. Images PMID:8233787

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.

Overview of implementation of DARPA GPU program in SAIC

NASA Astrophysics Data System (ADS)

Braunreiter, Dennis; Furtek, Jeremy; Chen, Hai-Wen; Healy, Dennis

2008-04-01

This paper reviews the implementation of DARPA MTO STAP-BOY program for both Phase I and II conducted at Science Applications International Corporation (SAIC). The STAP-BOY program conducts fast covariance factorization and tuning techniques for space-time adaptive process (STAP) Algorithm Implementation on Graphics Processor unit (GPU) Architectures for Embedded Systems. The first part of our presentation on the DARPA STAP-BOY program will focus on GPU implementation and algorithm innovations for a prototype radar STAP algorithm. The STAP algorithm will be implemented on the GPU, using stream programming (from companies such as PeakStream, ATI Technologies' CTM, and NVIDIA) and traditional graphics APIs. This algorithm will include fast range adaptive STAP weight updates and beamforming applications, each of which has been modified to exploit the parallel nature of graphics architectures.

Automatic Data Distribution for CFD Applications on Structured Grids

NASA Technical Reports Server (NTRS)

Frumkin, Michael; Yan, Jerry

1999-01-01

Data distribution is an important step in implementation of any parallel algorithm. The data distribution determines data traffic, utilization of the interconnection network and affects the overall code efficiency. In recent years a number data distribution methods have been developed and used in real programs for improving data traffic. We use some of the methods for translating data dependence and affinity relations into data distribution directives. We describe an automatic data alignment and placement tool (ADAPT) which implements these methods and show it results for some CFD codes (NPB and ARC3D). Algorithms for program analysis and derivation of data distribution implemented in ADAPT are efficient three pass algorithms. Most algorithms have linear complexity with the exception of some graph algorithms having complexity O(n(sup 4)) in the worst case.

An adaptive deep Q-learning strategy for handwritten digit recognition.

PubMed

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

2018-02-22

Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

Improved artificial bee colony algorithm for wavefront sensor-less system in free space optical communication

NASA Astrophysics Data System (ADS)

Niu, Chaojun; Han, Xiang'e.

2015-10-01

Adaptive optics (AO) technology is an effective way to alleviate the effect of turbulence on free space optical communication (FSO). A new adaptive compensation method can be used without a wave-front sensor. Artificial bee colony algorithm (ABC) is a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm with the advantage of simple, good convergence rate, robust and less parameter setting. In this paper, we simulate the application of the improved ABC to correct the distorted wavefront and proved its effectiveness. Then we simulate the application of ABC algorithm, differential evolution (DE) algorithm and stochastic parallel gradient descent (SPGD) algorithm to the FSO system and analyze the wavefront correction capabilities by comparison of the coupling efficiency, the error rate and the intensity fluctuation in different turbulence before and after the correction. The results show that the ABC algorithm has much faster correction speed than DE algorithm and better correct ability for strong turbulence than SPGD algorithm. Intensity fluctuation can be effectively reduced in strong turbulence, but not so effective in week turbulence.

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

Cololla, P.

This review describes a structured approach to adaptivity. The Automated Mesh Refinement (ARM) algorithms developed by M Berger are described, touching on hyperbolic and parabolic applications. Adaptivity is achieved by overlaying finer grids only in areas flagged by a generalized error criterion. The author discusses some of the issues involved in abutting disparate-resolution grids, and demonstrates that suitable algorithms exist for dissipative as well as hyperbolic systems.

An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

PubMed

Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan

2015-05-13

We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

Implementation of an Adaptive Controller System from Concept to Flight Test

NASA Technical Reports Server (NTRS)

Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

2009-01-01

The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) is used to test and develop these algorithms. Modifications to this airplane include adding canards and changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals include demonstration of revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions and advancement of neural-network-based flight control technology for new aerospace system designs. This report presents an overview of the processes utilized to develop adaptive controller algorithms during a flight-test program, including a description of initial adaptive controller concepts and a discussion of modeling formulation and performance testing. Design finalization led to integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness; these are also discussed.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

PubMed

Maharlou, Hamidreza; Niakan Kalhori, Sharareh R; Shahbazi, Shahrbanoo; Ravangard, Ramin

2018-04-01

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

Advancing X-ray scattering metrology using inverse genetic algorithms.

PubMed

Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph

2016-01-01

We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

PubMed

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

PubMed Central

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

From honeybees to Internet servers: biomimicry for distributed management of Internet hosting centers.

PubMed

Nakrani, Sunil; Tovey, Craig

2007-12-01

An Internet hosting center hosts services on its server ensemble. The center must allocate servers dynamically amongst services to maximize revenue earned from hosting fees. The finite server ensemble, unpredictable request arrival behavior and server reallocation cost make server allocation optimization difficult. Server allocation closely resembles honeybee forager allocation amongst flower patches to optimize nectar influx. The resemblance inspires a honeybee biomimetic algorithm. This paper describes details of the honeybee self-organizing model in terms of information flow and feedback, analyzes the homology between the two problems and derives the resulting biomimetic algorithm for hosting centers. The algorithm is assessed for effectiveness and adaptiveness by comparative testing against benchmark and conventional algorithms. Computational results indicate that the new algorithm is highly adaptive to widely varying external environments and quite competitive against benchmark assessment algorithms. Other swarm intelligence applications are briefly surveyed, and some general speculations are offered regarding their various degrees of success.

Real time optimization algorithm for wavefront sensorless adaptive optics OCT (Conference Presentation)

NASA Astrophysics Data System (ADS)

Verstraete, Hans R. G. W.; Heisler, Morgan; Ju, Myeong Jin; Wahl, Daniel J.; Bliek, Laurens; Kalkman, Jeroen; Bonora, Stefano; Sarunic, Marinko V.; Verhaegen, Michel; Jian, Yifan

2017-02-01

Optical Coherence Tomography (OCT) has revolutionized modern ophthalmology, providing depth resolved images of the retinal layers in a system that is suited to a clinical environment. A limitation of the performance and utilization of the OCT systems has been the lateral resolution. Through the combination of wavefront sensorless adaptive optics with dual variable optical elements, we present a compact lens based OCT system that is capable of imaging the photoreceptor mosaic. We utilized a commercially available variable focal length lens to correct for a wide range of defocus commonly found in patient eyes, and a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators for aberration correction to obtain near diffraction limited imaging at the retina. A parallel processing computational platform permitted real-time image acquisition and display. The Data-based Online Nonlinear Extremum seeker (DONE) algorithm was used for real time optimization of the wavefront sensorless adaptive optics OCT, and the performance was compared with a coordinate search algorithm. Cross sectional images of the retinal layers and en face images of the cone photoreceptor mosaic acquired in vivo from research volunteers before and after WSAO optimization are presented. Applying the DONE algorithm in vivo for wavefront sensorless AO-OCT demonstrates that the DONE algorithm succeeds in drastically improving the signal while achieving a computational time of 1 ms per iteration, making it applicable for high speed real time applications.

Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Orr, Jeb S.; Miller, Christopher J.; Hanson, Curtis E.

2014-01-01

The NASA 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 by adapting an otherwise welltuned classical control algorithm to unexpected environments or variations in vehicle dynamics. This AAC algorithm is currently part of the baseline design for the SLS Flight Control System (FCS), but prior to this series of research flights it was the only component of the autopilot design that had not been flight tested. The Space Launch System (SLS) flight software prototype, including the adaptive component, was recently tested on a piloted aircraft at Dryden Flight Research Center (DFRC) which has the capability to achieve a high level of dynamic similarity to a launch vehicle. Scenarios for the flight test campaign were designed specifically to evaluate the AAC algorithm to ensure that it is able to achieve the expected performance improvements with no adverse impacts in nominal or nearnominal scenarios. Having completed the recent series of flight characterization experiments on DFRC's F/A-18, the AAC algorithm's capability, robustness, and reproducibility, have been successfully demonstrated. Thus, the entire SLS control architecture has been successfully flight tested in a relevant environment. This has increased NASA's confidence that the autopilot design is ready to fly on the SLS Block I vehicle and will exceed the performance of previous architectures.

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring.

PubMed

Shu, Tongxin; Xia, Min; Chen, Jiahong; Silva, Clarence de

2017-11-05

Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

PubMed

Trong Bui, Duong; Nguyen, Nhan Duc; Jeong, Gu-Min

2018-06-25

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

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.

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring

PubMed Central

Shu, Tongxin; Xia, Min; Chen, Jiahong; de Silva, Clarence

2017-01-01

Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy. PMID:29113087

Wavelet tree structure based speckle noise removal for optical coherence tomography

NASA Astrophysics Data System (ADS)

Yuan, Xin; Liu, Xuan; Liu, Yang

2018-02-01

We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.

Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application

NASA Astrophysics Data System (ADS)

Yang, Jian; Yang, Feng; Xi, Hong-Sheng; Guo, Wei; Sheng, Yanmin

2007-12-01

We propose a robust adaptive algorithm for generalized eigendecomposition problems that arise in modern signal processing applications. To that extent, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Starting from the proposed cost function and making use of an approximation of the Hessian matrix, a robust modified Newton algorithm is derived. A rigorous analysis of its convergence properties is presented by using stochastic approximation theory. We also apply this theory to solve the signal reception problem of multicarrier DS-CDMA to illustrate its practical application. The simulation results show that the proposed algorithm has fast convergence and excellent tracking capability, which are important in a practical time-varying communication environment.

Trampling, defoliation and physiological integration affect growth, morphological and mechanical properties of a root-suckering clonal tree.

PubMed

Xu, Liang; Yu, Fei-Hai; van Drunen, Elles; Schieving, Feike; Dong, Ming; Anten, Niels P R

2012-04-01

Grazing is a complex process involving the simultaneous occurrence of both trampling and defoliation. Clonal plants are a common feature of heavily grazed ecosystems where large herbivores inflict the simultaneous pressures of trampling and defoliation on the vegetation. We test the hypothesis that physiological integration (resource sharing between interconnected ramets) may help plants to deal with the interactive effects of trampling and defoliation. In a field study, small and large ramets of the root-suckering clonal tree Populus simonii were subjected to two levels of trampling and defoliation, while connected or disconnected to other ramets. Plant responses were quantified via survival, growth, morphological and stem mechanical traits. Disconnection and trampling increased mortality, especially in small ramets. Trampling increased stem length, basal diameter, fibrous root mass, stem stiffness and resistance to deflection in connected ramets, but decreased them in disconnected ones. Trampling decreased vertical height more in disconnected than in connected ramets, and reduced stem mass in disconnected ramets but not in connected ramets. Defoliation reduced basal diameter, leaf mass, stem mass and leaf area ratio, but did not interact with trampling or disconnection. Although clonal integration did not influence defoliation response, it did alleviate the effects of trampling. We suggest that by facilitating resource transport between ramets, clonal integration compensates for trampling-induced damage to fine roots.

Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model With Cloud Computing for Blood Leakage Detection During Hemodialysis.

PubMed

Huang, Ping-Tzan; Jong, Tai-Lang; Li, Chien-Ming; Chen, Wei-Ling; Lin, Chia-Hung

2017-08-01

Blood leakage and blood loss are serious complications during hemodialysis. From the hemodialysis survey reports, these life-threatening events occur to attract nephrology nurses and patients themselves. When the venous needle and blood line are disconnected, it takes only a few minutes for an adult patient to lose over 40% of his / her blood, which is a sufficient amount of blood loss to cause the patient to die. Therefore, we propose integrating a flexible sensor and self-organizing algorithm to design a cloud computing-based warning device for blood leakage detection. The flexible sensor is fabricated via a screen-printing technique using metallic materials on a soft substrate in an array configuration. The self-organizing algorithm constructs a virtual direct current grid-based alarm unit in an embedded system. This warning device is employed to identify blood leakage levels via a wireless network and cloud computing. It has been validated experimentally, and the experimental results suggest specifications for its commercial designs. The proposed model can also be implemented in an embedded system.

Managing and capturing the physics of robotic systems

NASA Astrophysics Data System (ADS)

Werfel, Justin

Algorithmic and other theoretical analyses of robotic systems often use a discretized or otherwise idealized framework, while the real world is continuous-valued and noisy. This disconnect can make theoretical work sometimes problematic to apply successfully to real-world systems. One approach to bridging the separation can be to design hardware to take advantage of simple physical effects mechanically, in order to guide elements into a desired set of discrete attracting states. As a result, the system behavior can effectively approximate a discretized formalism, so that proofs based on an idealization remain directly relevant, while control can be made simpler. It is important to note, conversely, that such an approach does not make a physical instantiation unnecessary nor a purely theoretical treatment sufficient. Experiments with hardware in practice always reveal physical effects not originally accounted for in simulation or analytic modeling, which lead to unanticipated results and require nontrivial modifications to control algorithms in order to achieve desired outcomes. I will discuss these points in the context of swarm robotic systems recently developed at the Self-Organizing Systems Research Group at Harvard.

Stable adaptive neurocontrollers for spacecraft and space robots

NASA Technical Reports Server (NTRS)

Sanner, Robert M.

1995-01-01

This paper reviews recently developed techniques of adaptive nonlinear control using neural networks, and demonstrates their application to two important practical problems in orbital operations. An adaptive neurocontroller is first developed for spacecraft attitude control applications, and then the same design, slightly modified, is shown to be effective in the control of free-floating orbital manipulators. The algorithms discussed have guaranteed stability and convergence properties, and thus constitute viable alternatives to existing control methodologies. Simulation results are presented demonstrating the performance of each algorithm with representative dynamic models.

A Comprehensive Study of Three Delay Compensation Algorithms for Flight Simulators

NASA Technical Reports Server (NTRS)

Guo, Liwen; Cardullo, Frank M.; Houck, Jacob A.; Kelly, Lon C.; Wolters, Thomas E.

2005-01-01

This paper summarizes a comprehensive study of three predictors used for compensating the transport delay in a flight simulator; The McFarland, Adaptive and State Space Predictors. The paper presents proof that the stochastic approximation algorithm can achieve the best compensation among all four adaptive predictors, and intensively investigates the relationship between the state space predictor s compensation quality and its reference model. Piloted simulation tests show that the adaptive predictor and state space predictor can achieve better compensation of transport delay than the McFarland predictor.

A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

PubMed

Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

2011-01-01

To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays.

PubMed

Lai, Rui; Yang, Yin-tang; Zhou, Duan; Li, Yue-jin

2008-08-20

An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors' parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.

Self-Avoiding Walks over Adaptive Triangular Grids

NASA Technical Reports Server (NTRS)

Heber, Gerd; Biswas, Rupak; Gao, Guang R.; Saini, Subhash (Technical Monitor)

1998-01-01

In this paper, we present a new approach to constructing a "self-avoiding" walk through a triangular mesh. Unlike the popular approach of visiting mesh elements using space-filling curves which is based on a geometric embedding, our approach is combinatorial in the sense that it uses the mesh connectivity only. We present an algorithm for constructing a self-avoiding walk which can be applied to any unstructured triangular mesh. The complexity of the algorithm is O(n x log(n)), where n is the number of triangles in the mesh. We show that for hierarchical adaptive meshes, the algorithm can be easily parallelized by taking advantage of the regularity of the refinement rules. The proposed approach should be very useful in the run-time partitioning and load balancing of adaptive unstructured grids.

An adaptive tensor voting algorithm combined with texture spectrum

NASA Astrophysics Data System (ADS)

Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

2015-01-01

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

Dynamic game balancing implementation using adaptive algorithm in mobile-based Safari Indonesia game

NASA Astrophysics Data System (ADS)

Yuniarti, Anny; Nata Wardanie, Novita; Kuswardayan, Imam

2018-03-01

In developing a game there is one method that should be applied to maintain the interest of players, namely dynamic game balancing. Dynamic game balancing is a process to match a player’s playing style with the behaviour, attributes, and game environment. This study applies dynamic game balancing using adaptive algorithm in scrolling shooter game type called Safari Indonesia which developed using Unity. The game of this type is portrayed by a fighter aircraft character trying to defend itself from insistent enemy attacks. This classic game is chosen to implement adaptive algorithms because it has quite complex attributes to be developed using dynamic game balancing. Tests conducted by distributing questionnaires to a number of players indicate that this method managed to reduce frustration and increase the pleasure factor in playing.

We really need to talk: adapting FDA processes to rapid change.

PubMed

Lykken, Sara

2013-01-01

The rapidly evolving realm of modern commerce strains traditional regulatory paradigms. This paper traces the historical evolution of FDA crisis-response regulation and provides examples of ways in which the definitions and procedures resulting from that past continue to be challenged by new products as market entrants, some in good faith and others not, take actions that create disconnects between actual product and marketing controls and those that consumers might expect. The paper then explores some of the techniques used by other federal agencies that have faced similar challenges in environments characterized by rapid innovation, and draws from this analysis suggestions for improvement of the FDA's warning letter system.

The Aeta – Pinatubo Loop

PubMed Central

2011-01-01

The impact of Mount Pinatubo’s 1991 eruption on the traditional use of natural resources by the indigenous Aeta was devastating. The damage resulted in the immediate and sustained disconnection of traditional knowledge from the biological resources integral to practice that knowledge. The relatively slow ecosystem recovery a full 20 years after the event hinders the transfer of traditional knowledge to younger generations of Aeta. Their traditional knowledge is at risk of disappearing from the cultural fabric of the Philippines. In seeking to adapt, decisions by the Aeta to accept the development of foreign-designed ecotourism enterprises may negatively affect natural ecosystem recovery. Alternatives to the existing ecotourism practices may be warranted to safeguard Aeta traditional knowledge. PMID:22446557

The aeta - pinatubo loop.

PubMed

Marler, Thomas E

2011-11-01

The impact of Mount Pinatubo's 1991 eruption on the traditional use of natural resources by the indigenous Aeta was devastating. The damage resulted in the immediate and sustained disconnection of traditional knowledge from the biological resources integral to practice that knowledge. The relatively slow ecosystem recovery a full 20 years after the event hinders the transfer of traditional knowledge to younger generations of Aeta. Their traditional knowledge is at risk of disappearing from the cultural fabric of the Philippines. In seeking to adapt, decisions by the Aeta to accept the development of foreign-designed ecotourism enterprises may negatively affect natural ecosystem recovery. Alternatives to the existing ecotourism practices may be warranted to safeguard Aeta traditional knowledge.

Blocking reduction of Landsat Thematic Mapper JPEG browse images using optimal PSNR estimated spectra adaptive postfiltering

NASA Technical Reports Server (NTRS)

Linares, Irving; Mersereau, Russell M.; Smith, Mark J. T.

1994-01-01

Two representative sample images of Band 4 of the Landsat Thematic Mapper are compressed with the JPEG algorithm at 8:1, 16:1 and 24:1 Compression Ratios for experimental browsing purposes. We then apply the Optimal PSNR Estimated Spectra Adaptive Postfiltering (ESAP) algorithm to reduce the DCT blocking distortion. ESAP reduces the blocking distortion while preserving most of the image's edge information by adaptively postfiltering the decoded image using the block's spectral information already obtainable from each block's DCT coefficients. The algorithm iteratively applied a one dimensional log-sigmoid weighting function to the separable interpolated local block estimated spectra of the decoded image until it converges to the optimal PSNR with respect to the original using a 2-D steepest ascent search. Convergence is obtained in a few iterations for integer parameters. The optimal logsig parameters are transmitted to the decoder as a negligible byte of overhead data. A unique maxima is guaranteed due to the 2-D asymptotic exponential overshoot shape of the surface generated by the algorithm. ESAP is based on a DFT analysis of the DCT basis functions. It is implemented with pixel-by-pixel spatially adaptive separable FIR postfilters. PSNR objective improvements between 0.4 to 0.8 dB are shown together with their corresponding optimal PSNR adaptive postfiltered images.

Infrared and visible images registration with adaptable local-global feature integration for rail inspection

NASA Astrophysics Data System (ADS)

Tang, Chaoqing; Tian, Gui Yun; Chen, Xiaotian; Wu, Jianbo; Li, Kongjing; Meng, Hongying

2017-12-01

Active thermography provides infrared images that contain sub-surface defect information, while visible images only reveal surface information. Mapping infrared information to visible images offers more comprehensive visualization for decision-making in rail inspection. However, the common information for registration is limited due to different modalities in both local and global level. For example, rail track which has low temperature contrast reveals rich details in visible images, but turns blurry in the infrared counterparts. This paper proposes a registration algorithm called Edge-Guided Speeded-Up-Robust-Features (EG-SURF) to address this issue. Rather than sequentially integrating local and global information in matching stage which suffered from buckets effect, this algorithm adaptively integrates local and global information into a descriptor to gather more common information before matching. This adaptability consists of two facets, an adaptable weighting factor between local and global information, and an adaptable main direction accuracy. The local information is extracted using SURF while the global information is represented by shape context from edges. Meanwhile, in shape context generation process, edges are weighted according to local scale and decomposed into bins using a vector decomposition manner to provide more accurate descriptor. The proposed algorithm is qualitatively and quantitatively validated using eddy current pulsed thermography scene in the experiments. In comparison with other algorithms, better performance has been achieved.

Using Link Disconnection Entropy Disorder to Detect Fast Moving Nodes in MANETs.

PubMed

Alvarez, Carlos F; Palafox, Luis E; Aguilar, Leocundo; Sanchez, Mauricio A; Martinez, Luis G

2016-01-01

Mobile ad-hoc networks (MANETs) are dynamic by nature; this dynamism comes from node mobility, traffic congestion, and other transmission conditions. Metrics to evaluate the effects of those conditions shine a light on node's behavior in an ad-hoc network, helping to identify the node or nodes with better conditions of connection. In this paper, we propose a relative index to evaluate a single node reliability, based on the link disconnection entropy disorder using neighboring nodes as reference. Link disconnection entropy disorder is best used to identify fast moving nodes or nodes with unstable communications, this without the need of specialized sensors such as GPS. Several scenarios were studied to verify the index, measuring the effects of Speed and traffic density on the link disconnection entropy disorder. Packet delivery ratio is associated to the metric detecting a strong relationship, enabling the use of the link disconnection entropy disorder to evaluate the stability of a node to communicate with other nodes. To expand the utilization of the link entropy disorder, we identified nodes with higher speeds in network simulations just by using the link entropy disorder.

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.

On-sky demonstration of matched filters for wavefront measurements using ELT-scale elongated laser guide stars

NASA Astrophysics Data System (ADS)

Basden, A. G.; Bardou, L.; Bonaccini Calia, D.; Buey, T.; Centrone, M.; Chemla, F.; Gach, J. L.; Gendron, E.; Gratadour, D.; Guidolin, I.; Jenkins, D. R.; Marchetti, E.; Morris, T. J.; Myers, R. M.; Osborn, J.; Reeves, A. P.; Reyes, M.; Rousset, G.; Lombardi, G.; Townson, M. J.; Vidal, F.

2017-04-01

The performance of adaptive optics systems is partially dependent on the algorithms used within the real-time control system to compute wavefront slope measurements. We demonstrate the use of a matched filter algorithm for the processing of elongated laser guide star (LGS) Shack-Hartmann images, using the CANARY adaptive optics instrument on the 4.2 m William Herschel Telescope and the European Southern Observatory Wendelstein LGS Unit placed 40 m away. This algorithm has been selected for use with the forthcoming Thirty Meter Telescope, but until now had not been demonstrated on-sky. From the results of a first observing run, we show that the use of matched filtering improves our adaptive optics system performance, with increases in on-sky H-band Strehl measured up to about a factor of 1.1 with respect to a conventional centre of gravity approach. We describe the algorithm used, and the methods that we implemented to enable on-sky demonstration.

Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

NASA Astrophysics Data System (ADS)

Liu, Qiaoyuan; Wang, Yuru; Yin, Minghao; Ren, Jinchang; Li, Ruizhi

2017-11-01

Although various visual tracking algorithms have been proposed in the last 2-3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion, etc. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy causing low efficiency and ambiguity causing poor performance. An effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, the "curse of dimensionality" has been avoided while the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.

A proposed study of multiple scattering through clouds up to 1 THz

NASA Technical Reports Server (NTRS)

Gerace, G. C.; Smith, E. K.

1992-01-01

A rigorous computation of the electromagnetic field scattered from an atmospheric liquid water cloud is proposed. The recent development of a fast recursive algorithm (Chew algorithm) for computing the fields scattered from numerous scatterers now makes a rigorous computation feasible. A method is presented for adapting this algorithm to a general case where there are an extremely large number of scatterers. It is also proposed to extend a new binary PAM channel coding technique (El-Khamy coding) to multiple levels with non-square pulse shapes. The Chew algorithm can be used to compute the transfer function of a cloud channel. Then the transfer function can be used to design an optimum El-Khamy code. In principle, these concepts can be applied directly to the realistic case of a time-varying cloud (adaptive channel coding and adaptive equalization). A brief review is included of some preliminary work on cloud dispersive effects on digital communication signals and on cloud liquid water spectra and correlations.

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

PubMed Central

Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi

2016-01-01

Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

PubMed

Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

2017-01-01

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

Selection of floating-point or fixed-point for adaptive noise canceller in somatosensory evoked potential measurement.

PubMed

Shen, Chongfei; Liu, Hongtao; Xie, Xb; Luk, Keith Dk; Hu, Yong

2007-01-01

Adaptive noise canceller (ANC) has been used to improve signal to noise ratio (SNR) of somsatosensory evoked potential (SEP). In order to efficiently apply the ANC in hardware system, fixed-point algorithm based ANC can achieve fast, cost-efficient construction, and low-power consumption in FPGA design. However, it is still questionable whether the SNR improvement performance by fixed-point algorithm is as good as that by floating-point algorithm. This study is to compare the outputs of ANC by floating-point and fixed-point algorithm ANC when it was applied to SEP signals. The selection of step-size parameter (micro) was found different in fixed-point algorithm from floating-point algorithm. In this simulation study, the outputs of fixed-point ANC showed higher distortion from real SEP signals than that of floating-point ANC. However, the difference would be decreased with increasing micro value. In the optimal selection of micro, fixed-point ANC can get as good results as floating-point algorithm.

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

PubMed

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.