Robust Global Image Registration Based on a Hybrid Algorithm Combining Fourier and Spatial Domain Techniques

DTIC Science & Technology

2012-09-01

Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain techniques Peter N. Crabtree, Collin Seanor...00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain...demonstrate performance of a hybrid algorithm . These results are from analysis of a set of images of an ISO 12233 [12] resolution chart captured in the

Design optimization for cost and quality: The robust design approach

NASA Technical Reports Server (NTRS)

Unal, Resit

1990-01-01

Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.

Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence images.

PubMed

Kopriva, Ivica; Persin, Antun; Puizina-Ivić, Neira; Mirić, Lina

2010-07-02

This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude. Copyright 2010 Elsevier B.V. All rights reserved.

Active Fault Tolerant Control for Ultrasonic Piezoelectric Motor

NASA Astrophysics Data System (ADS)

Boukhnifer, Moussa

2012-07-01

Ultrasonic piezoelectric motor technology is an important system component in integrated mechatronics devices working on extreme operating conditions. Due to these constraints, robustness and performance of the control interfaces should be taken into account in the motor design. In this paper, we apply a new architecture for a fault tolerant control using Youla parameterization for an ultrasonic piezoelectric motor. The distinguished feature of proposed controller architecture is that it shows structurally how the controller design for performance and robustness may be done separately which has the potential to overcome the conflict between performance and robustness in the traditional feedback framework. A fault tolerant control architecture includes two parts: one part for performance and the other part for robustness. The controller design works in such a way that the feedback control system will be solely controlled by the proportional plus double-integral PI2 performance controller for a nominal model without disturbances and H∞ robustification controller will only be activated in the presence of the uncertainties or an external disturbances. The simulation results demonstrate the effectiveness of the proposed fault tolerant control architecture.

Demonstration of Robustness and Integrated Operation of a Series-Bosch System

NASA Technical Reports Server (NTRS)

Abney, Morgan B.; Mansell, Matthew J.; Stanley, Christine; Barnett, Bill; Junaedi, Christian; Vilekar, Saurabh A.; Ryan, Kent

2016-01-01

Manned missions beyond low Earth orbit will require highly robust, reliable, and maintainable life support systems that maximize recycling of water and oxygen. Bosch technology is one option to maximize oxygen recovery, in the form of water, from metabolically-produced carbon dioxide (CO2). A two stage approach to Bosch, called Series-Bosch, reduces metabolic CO2 with hydrogen (H2) to produce water and solid carbon using two reactors: a Reverse Water-Gas Shift (RWGS) reactor and a carbon formation (CF) reactor. Previous development efforts demonstrated the stand-alone performance of a NASA-designed RWGS reactor designed for robustness against carbon formation, two membrane separators intended to maximize single pass conversion of reactants, and a batch CF reactor with both transit and surface catalysts. In the past year, Precision Combustion, Inc. (PCI) developed and delivered a RWGS reactor for testing at NASA. The reactor design was based on their patented Microlith® technology and was first evaluated under a Phase I Small Business Innovative Research (SBIR) effort in 2010. The RWGS reactor was recently evaluated at NASA to compare its performance and operating conditions with NASA's RWGS reactor. The test results will be provided in this paper. Separately, in 2015, a semi-continuous CF reactor was designed and fabricated at NASA based on the results from batch CF reactor testing. The batch CF reactor and the semi-continuous CF reactor were individually integrated with an upstream RWGS reactor to demonstrate the system operation and to evaluate performance. Here, we compare the performance and robustness to carbon formation of both RWGS reactors. We report the results of the integrated operation of a Series-Bosch system and we discuss the technology readiness level.

Robust H ∞ Control for Spacecraft Rendezvous with a Noncooperative Target

PubMed Central

Wu, Shu-Nan; Zhou, Wen-Ya; Tan, Shu-Jun; Wu, Guo-Qiang

2013-01-01

The robust H ∞ control for spacecraft rendezvous with a noncooperative target is addressed in this paper. The relative motion of chaser and noncooperative target is firstly modeled as the uncertain system, which contains uncertain orbit parameter and mass. Then the H ∞ performance and finite time performance are proposed, and a robust H ∞ controller is developed to drive the chaser to rendezvous with the non-cooperative target in the presence of control input saturation, measurement error, and thrust error. The linear matrix inequality technology is used to derive the sufficient condition of the proposed controller. An illustrative example is finally provided to demonstrate the effectiveness of the controller. PMID:24027446

Robust control of electrostatic torsional micromirrors using adaptive sliding-mode control

NASA Astrophysics Data System (ADS)

Sane, Harshad S.; Yazdi, Navid; Mastrangelo, Carlos H.

2005-01-01

This paper presents high-resolution control of torsional electrostatic micromirrors beyond their inherent pull-in instability using robust sliding-mode control (SMC). The objectives of this paper are two-fold - firstly, to demonstrate the applicability of SMC for MEMS devices; secondly - to present a modified SMC algorithm that yields improved control accuracy. SMC enables compact realization of a robust controller tolerant of device characteristic variations and nonlinearities. Robustness of the control loop is demonstrated through extensive simulations and measurements on MEMS with a wide range in their characteristics. Control of two-axis gimbaled micromirrors beyond their pull-in instability with overall 10-bit pointing accuracy is confirmed experimentally. In addition, this paper presents an analysis of the sources of errors in discrete-time implementation of the control algorithm. To minimize these errors, we present an adaptive version of the SMC algorithm that yields substantial performance improvement without considerably increasing implementation complexity.

Computational methods of robust controller design for aerodynamic flutter suppression

NASA Technical Reports Server (NTRS)

Anderson, L. R.

1981-01-01

The development of Riccati iteration, a tool for the design and analysis of linear control systems is examined. First, Riccati iteration is applied to the problem of pole placement and order reduction in two-time scale control systems. Order reduction, yielding a good approximation to the original system, is demonstrated using a 16th order linear model of a turbofan engine. Next, a numerical method for solving the Riccati equation is presented and demonstrated for a set of eighth order random examples. A literature review of robust controller design methods follows which includes a number of methods for reducing the trajectory and performance index sensitivity in linear regulators. Lastly, robust controller design for large parameter variations is discussed.

Design of Robust Adaptive Unbalance Response Controllers for Rotors with Magnetic Bearings

NASA Technical Reports Server (NTRS)

Knospe, Carl R.; Tamer, Samir M.; Fedigan, Stephen J.

1996-01-01

Experimental results have recently demonstrated that an adaptive open loop control strategy can be highly effective in the suppression of unbalance induced vibration on rotors supported in active magnetic bearings. This algorithm, however, relies upon a predetermined gain matrix. Typically, this matrix is determined by an optimal control formulation resulting in the choice of the pseudo-inverse of the nominal influence coefficient matrix as the gain matrix. This solution may result in problems with stability and performance robustness since the estimated influence coefficient matrix is not equal to the actual influence coefficient matrix. Recently, analysis tools have been developed to examine the robustness of this control algorithm with respect to structured uncertainty. Herein, these tools are extended to produce a design procedure for determining the adaptive law's gain matrix. The resulting control algorithm has a guaranteed convergence rate and steady state performance in spite of the uncertainty in the rotor system. Several examples are presented which demonstrate the effectiveness of this approach and its advantages over the standard optimal control formulation.

Design for robustness of unique, multi-component engineering systems

NASA Astrophysics Data System (ADS)

Shelton, Kenneth A.

2007-12-01

The purpose of this research is to advance the science of conceptual designing for robustness in unique, multi-component engineering systems. Robustness is herein defined as the ability of an engineering system to operate within a desired performance range even if the actual configuration has differences from specifications within specified tolerances. These differences are caused by three sources, namely manufacturing errors, system degradation (operational wear and tear), and parts availability. Unique, multi-component engineering systems are defined as systems produced in unique or very small production numbers. They typically have design and manufacturing costs on the order of billions of dollars, and have multiple, competing performance objectives. Design time for these systems must be minimized due to competition, high manpower costs, long manufacturing times, technology obsolescence, and limited available manpower expertise. Most importantly, design mistakes cannot be easily corrected after the systems are operational. For all these reasons, robustness of these systems is absolutely critical. This research examines the space satellite industry in particular. Although inherent robustness assurance is absolutely critical, it is difficult to achieve in practice. The current state of the art for robustness in the industry is to overdesign components and subsystems with redundancy and margin. The shortfall is that it is not known if the added margins were either necessary or sufficient given the risk management preferences of the designer or engineering system customer. To address this shortcoming, new assessment criteria to evaluate robustness in design concepts have been developed. The criteria are comprised of the "Value Distance", addressing manufacturing errors and system degradation, and "Component Distance", addressing parts availability. They are based on an evolutionary computation format that uses a string of alleles to describe the components in the design concept. These allele values are unitless themselves, but map to both configuration descriptions and attribute values. The Value Distance and Component Distance are metrics that measure the relative differences between two design concepts using the allele values, and all differences in a population of design concepts are calculated relative to a reference design, called the "base design". The base design is the top-ranked member of the population in weighted terms of robustness and performance. Robustness is determined based on the change in multi-objective performance as Value Distance and Component Distance (and thus differences in design) increases. It is assessed as acceptable if differences in design configurations up to specified tolerances result in performance changes that remain within a specified performance range. The design configuration difference tolerances and performance range together define the designer's risk management preferences for the final design concepts. Additionally, a complementary visualization capability was developed, called the "Design Solution Topography". This concept allows the visualization of a population of design concepts, and is a 3-axis plot where each point represents an entire design concept. The axes are the Value Distance, Component Distance and Performance Objective. The key benefit of the Design Solution Topography is that it allows the designer to visually identify and interpret the overall robustness of the current population of design concepts for a particular performance objective. In a multi-objective problem, each performance objective has its own Design Solution Topography view. These new concepts are implemented in an evolutionary computation-based conceptual designing method called the "Design for Robustness Method" that produces robust design concepts. The design procedures associated with this method enable designers to evaluate and ensure robustness in selected designs that also perform within a desired performance range. The method uses an evolutionary computation-based procedure to generate populations of large numbers of alternative design concepts, which are assessed for robustness using the Value Distance, Component Distance and Design Solution Topography procedures. The Design for Robustness Method provides a working conceptual designing structure in which to implement and gain the benefits of these new concepts. In the included experiments, the method was used on several mathematical examples to demonstrate feasibility, which showed favorable results as compared to existing known methods. Furthermore, it was tested on a real-world satellite conceptual designing problem to illustrate the applicability and benefits to industry. Risk management insights were demonstrated for the robustness-related issues of manufacturing errors, operational degradation, parts availability, and impacts based on selections of particular types of components.

A Conceptual Methodology for Assessing Acquisition Requirements Robustness against Technology Uncertainties

NASA Astrophysics Data System (ADS)

Chou, Shuo-Ju

2011-12-01

In recent years the United States has shifted from a threat-based acquisition policy that developed systems for countering specific threats to a capabilities-based strategy that emphasizes the acquisition of systems that provide critical national defense capabilities. This shift in policy, in theory, allows for the creation of an "optimal force" that is robust against current and future threats regardless of the tactics and scenario involved. In broad terms, robustness can be defined as the insensitivity of an outcome to "noise" or non-controlled variables. Within this context, the outcome is the successful achievement of defense strategies and the noise variables are tactics and scenarios that will be associated with current and future enemies. Unfortunately, a lack of system capability, budget, and schedule robustness against technology performance and development uncertainties has led to major setbacks in recent acquisition programs. This lack of robustness stems from the fact that immature technologies have uncertainties in their expected performance, development cost, and schedule that cause to variations in system effectiveness and program development budget and schedule requirements. Unfortunately, the Technology Readiness Assessment process currently used by acquisition program managers and decision-makers to measure technology uncertainty during critical program decision junctions does not adequately capture the impact of technology performance and development uncertainty on program capability and development metrics. The Technology Readiness Level metric employed by the TRA to describe program technology elements uncertainties can only provide a qualitative and non-descript estimation of the technology uncertainties. In order to assess program robustness, specifically requirements robustness, against technology performance and development uncertainties, a new process is needed. This process should provide acquisition program managers and decision-makers with the ability to assess or measure the robustness of program requirements against such uncertainties. A literature review of techniques for forecasting technology performance and development uncertainties and subsequent impacts on capability, budget, and schedule requirements resulted in the conclusion that an analysis process that coupled a probabilistic analysis technique such as Monte Carlo Simulations with quantitative and parametric models of technology performance impact and technology development time and cost requirements would allow the probabilities of meeting specific constraints of these requirements to be established. These probabilities of requirements success metrics can then be used as a quantitative and probabilistic measure of program requirements robustness against technology uncertainties. Combined with a Multi-Objective Genetic Algorithm optimization process and computer-based Decision Support System, critical information regarding requirements robustness against technology uncertainties can be captured and quantified for acquisition decision-makers. This results in a more informed and justifiable selection of program technologies during initial program definition as well as formulation of program development and risk management strategies. To meet the stated research objective, the ENhanced TEchnology Robustness Prediction and RISk Evaluation (ENTERPRISE) methodology was formulated to provide a structured and transparent process for integrating these enabling techniques to provide a probabilistic and quantitative assessment of acquisition program requirements robustness against technology performance and development uncertainties. In order to demonstrate the capabilities of the ENTERPRISE method and test the research Hypotheses, an demonstration application of this method was performed on a notional program for acquiring the Carrier-based Suppression of Enemy Air Defenses (SEAD) using Unmanned Combat Aircraft Systems (UCAS) and their enabling technologies. The results of this implementation provided valuable insights regarding the benefits and inner workings of this methodology as well as its limitations that should be addressed in the future to narrow the gap between current state and the desired state.

Intelligent, Robust Control of Deteriorated Turbofan Engines via Linear Parameter Varying Quadratic Lyapunov Function Design

NASA Technical Reports Server (NTRS)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A method for accommodating engine deterioration via a scheduled Linear Parameter Varying Quadratic Lyapunov Function (LPVQLF)-Based controller is presented. The LPVQLF design methodology provides a means for developing unconditionally stable, robust control of Linear Parameter Varying (LPV) systems. The controller is scheduled on the Engine Deterioration Index, a function of estimated parameters that relate to engine health, and is computed using a multilayer feedforward neural network. Acceptable thrust response and tight control of exhaust gas temperature (EGT) is accomplished by adjusting the performance weights on these parameters for different levels of engine degradation. Nonlinear simulations demonstrate that the controller achieves specified performance objectives while being robust to engine deterioration as well as engine-to-engine variations.

Demonstration of Robustness and Integrated Operation of a Series-Bosch System

NASA Technical Reports Server (NTRS)

Abney, Morgan B.; Mansell, J. Matthew; Barnett, Bill; Stanley, Christine M.; Junaedi, Christian; Vilekar, Saurabh A.; Kent, Ryan

2016-01-01

Manned missions beyond low Earth orbit will require highly robust, reliable, and maintainable life support systems that maximize recycling of water and oxygen. Bosch technology is one option to maximize oxygen recovery, in the form of water, from metabolically-produced carbon dioxide (CO2). A two stage approach to Bosch, called Series-Bosch, reduces metabolic CO2 with hydrogen (H2) to produce water and solid carbon using two reactors: a Reverse Water-Gas Shift (RWGS) reactor and a carbon formation (CF) reactor. Previous development efforts demonstrated the stand-alone performance of a RWGS reactor containing Incofoam(TradeMark) catalyst and designed for robustness against carbon formation, two membrane separators intended to maximize single pass conversion of reactants, and a batch CF reactor with both transit and surface catalysts. In the past year, Precision Combustion, Inc. (PCI) developed and delivered a RWGS reactor for testing at NASA. The reactor design was based on their patented Microlith(TradeMark) technology and was first evaluated under a Phase I Small Business Innovative Research (SBIR) effort in 2010. The Microlith(TradeMark) RWGS reactor was recently evaluated at NASA to compare its performance and operating conditions with the Incofoam(TradeMark) RWGS reactor. Separately, in 2015, a fully integrated demonstration of an S-Bosch system was conducted. In an effort to mitigate risk, a second integrated test was conducted to evaluate the effect of membrane failure on a closed-loop Bosch system. Here, we report and discuss the performance and robustness to carbon formation of both RWGS reactors. We report the results of the integrated operation of a Series-Bosch system and we discuss the technology readiness level. 1

Off-target model based OPC

NASA Astrophysics Data System (ADS)

Lu, Mark; Liang, Curtis; King, Dion; Melvin, Lawrence S., III

2005-11-01

Model-based Optical Proximity correction has become an indispensable tool for achieving wafer pattern to design fidelity at current manufacturing process nodes. Most model-based OPC is performed considering the nominal process condition, with limited consideration of through process manufacturing robustness. This study examines the use of off-target process models - models that represent non-nominal process states such as would occur with a dose or focus variation - to understands and manipulate the final pattern correction to a more process robust configuration. The study will first examine and validate the process of generating an off-target model, then examine the quality of the off-target model. Once the off-target model is proven, it will be used to demonstrate methods of generating process robust corrections. The concepts are demonstrated using a 0.13 μm logic gate process. Preliminary indications show success in both off-target model production and process robust corrections. With these off-target models as tools, mask production cycle times can be reduced.

Boeing's variable geometry chevron: morphing aerospace structures for jet noise reduction

NASA Astrophysics Data System (ADS)

Calkins, Frederick T.; Mabe, James H.; Butler, George W.

2006-03-01

Boeing is applying cutting edge smart material actuators to the next generation morphing technologies for aircraft. This effort has led to the Variable Geometry Chevrons (VGC), which utilize compact, light weight, and robust shape memory alloy (SMA) actuators. These actuators morph the shape of chevrons on the trailing edge of a jet engine in order to optimize acoustic and performance objectives at multiple flight conditions. We have demonstrated a technical readiness level of 7 by successfully flight testing the VGCs on a Boeing 777-300ER with GE-115B engines. In this paper we describe the VGC design, development and performance during flight test. Autonomous operation of the VGCs, which did not require a control system or aircraft power, was demonstrated. A parametric study was conducted showing the influence of VGC configurations on shockcell generated cabin noise reduction during cruise. The VGC system provided a robust test vehicle to explore chevron configurations for community and shockcell noise reduction. Most importantly, the VGC concept demonstrated an exciting capability to optimize jet nozzle performance at multiple flight conditions.

Design and Analysis of Morpheus Lander Flight Control System

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; Yang, Lee; Fritz, Mathew; Nguyen, Louis H.; Johnson, Wyatt R.; Hart, Jeremy J.

2014-01-01

The Morpheus Lander is a vertical takeoff and landing test bed vehicle developed to demonstrate the system performance of the Guidance, Navigation and Control (GN&C) system capability for the integrated autonomous landing and hazard avoidance system hardware and software. The Morpheus flight control system design must be robust to various mission profiles. This paper presents a design methodology for employing numerical optimization to develop the Morpheus flight control system. The design objectives include attitude tracking accuracy and robust stability with respect to rigid body dynamics and propellant slosh. Under the assumption that the Morpheus time-varying dynamics and control system can be frozen over a short period of time, the flight controllers are designed to stabilize all selected frozen-time control systems in the presence of parametric uncertainty. Both control gains in the inner attitude control loop and guidance gains in the outer position control loop are designed to maximize the vehicle performance while ensuring robustness. The flight control system designs provided herein have been demonstrated to provide stable control systems in both Draper Ares Stability Analysis Tool (ASAT) and the NASA/JSC Trick-based Morpheus time domain simulation.

Robust Satisficing Decision Making for Unmanned Aerial Vehicle Complex Missions under Severe Uncertainty

PubMed Central

Ji, Xiaoting; Niu, Yifeng; Shen, Lincheng

2016-01-01

This paper presents a robust satisficing decision-making method for Unmanned Aerial Vehicles (UAVs) executing complex missions in an uncertain environment. Motivated by the info-gap decision theory, we formulate this problem as a novel robust satisficing optimization problem, of which the objective is to maximize the robustness while satisfying some desired mission requirements. Specifically, a new info-gap based Markov Decision Process (IMDP) is constructed to abstract the uncertain UAV system and specify the complex mission requirements with the Linear Temporal Logic (LTL). A robust satisficing policy is obtained to maximize the robustness to the uncertain IMDP while ensuring a desired probability of satisfying the LTL specifications. To this end, we propose a two-stage robust satisficing solution strategy which consists of the construction of a product IMDP and the generation of a robust satisficing policy. In the first stage, a product IMDP is constructed by combining the IMDP with an automaton representing the LTL specifications. In the second, an algorithm based on robust dynamic programming is proposed to generate a robust satisficing policy, while an associated robustness evaluation algorithm is presented to evaluate the robustness. Finally, through Monte Carlo simulation, the effectiveness of our algorithms is demonstrated on an UAV search mission under severe uncertainty so that the resulting policy can maximize the robustness while reaching the desired performance level. Furthermore, by comparing the proposed method with other robust decision-making methods, it can be concluded that our policy can tolerate higher uncertainty so that the desired performance level can be guaranteed, which indicates that the proposed method is much more effective in real applications. PMID:27835670

Robust Satisficing Decision Making for Unmanned Aerial Vehicle Complex Missions under Severe Uncertainty.

PubMed

Ji, Xiaoting; Niu, Yifeng; Shen, Lincheng

2016-01-01

This paper presents a robust satisficing decision-making method for Unmanned Aerial Vehicles (UAVs) executing complex missions in an uncertain environment. Motivated by the info-gap decision theory, we formulate this problem as a novel robust satisficing optimization problem, of which the objective is to maximize the robustness while satisfying some desired mission requirements. Specifically, a new info-gap based Markov Decision Process (IMDP) is constructed to abstract the uncertain UAV system and specify the complex mission requirements with the Linear Temporal Logic (LTL). A robust satisficing policy is obtained to maximize the robustness to the uncertain IMDP while ensuring a desired probability of satisfying the LTL specifications. To this end, we propose a two-stage robust satisficing solution strategy which consists of the construction of a product IMDP and the generation of a robust satisficing policy. In the first stage, a product IMDP is constructed by combining the IMDP with an automaton representing the LTL specifications. In the second, an algorithm based on robust dynamic programming is proposed to generate a robust satisficing policy, while an associated robustness evaluation algorithm is presented to evaluate the robustness. Finally, through Monte Carlo simulation, the effectiveness of our algorithms is demonstrated on an UAV search mission under severe uncertainty so that the resulting policy can maximize the robustness while reaching the desired performance level. Furthermore, by comparing the proposed method with other robust decision-making methods, it can be concluded that our policy can tolerate higher uncertainty so that the desired performance level can be guaranteed, which indicates that the proposed method is much more effective in real applications.

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

Robust Learning Control Design for Quantum Unitary Transformations.

PubMed

Wu, Chengzhi; Qi, Bo; Chen, Chunlin; Dong, Daoyi

2017-12-01

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

Periodic Pulay method for robust and efficient convergence acceleration of self-consistent field iterations

DOE PAGES

Banerjee, Amartya S.; Suryanarayana, Phanish; Pask, John E.

2016-01-21

Pulay's Direct Inversion in the Iterative Subspace (DIIS) method is one of the most widely used mixing schemes for accelerating the self-consistent solution of electronic structure problems. In this work, we propose a simple generalization of DIIS in which Pulay extrapolation is performed at periodic intervals rather than on every self-consistent field iteration, and linear mixing is performed on all other iterations. Lastly, we demonstrate through numerical tests on a wide variety of materials systems in the framework of density functional theory that the proposed generalization of Pulay's method significantly improves its robustness and efficiency.

Robust neural network with applications to credit portfolio data analysis.

PubMed

Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

2010-01-01

In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

Guidance and Control of a Small Unmanned Aerial Vehicle and Autonomous Flight Experiments

NASA Astrophysics Data System (ADS)

Fujinaga, Jin; Tokutake, Hiroshi; Sunada, Shigeru

This paper describes the development of a fixed-wing small-size UAV and the design of its flight controllers. The developed UAV’s wing span is 0.6m, and gross weight is 0.27kg. In order to ensure robust performances of the longitudinal and lateral-directional motions of the UAV, flight controllers are designed for these motions with μ-synthesis. Numerical simulations show that the designed controllers attain good robust stabilities and performances, and have good tracking performance for command. After an order-reduction and discretization, the designed flight controllers were implemented in the UAV. A flight test was performed, and the ability of the UAV to fly autonomously, passing over waypoints, was demonstrated.

Performance impact of dynamic surface coatings on polymeric insulator-based dielectrophoretic particle separators.

PubMed

Davalos, Rafael V; McGraw, Gregory J; Wallow, Thomas I; Morales, Alfredo M; Krafcik, Karen L; Fintschenko, Yolanda; Cummings, Eric B; Simmons, Blake A

2008-02-01

Efficient and robust particle separation and enrichment techniques are critical for a diverse range of lab-on-a-chip analytical devices including pathogen detection, sample preparation, high-throughput particle sorting, and biomedical diagnostics. Previously, using insulator-based dielectrophoresis (iDEP) in microfluidic glass devices, we demonstrated simultaneous particle separation and concentration of various biological organisms, polymer microbeads, and viruses. As an alternative to glass, we evaluate the performance of similar iDEP structures produced in polymer-based microfluidic devices. There are numerous processing and operational advantages that motivate our transition to polymers such as the availability of numerous innate chemical compositions for tailoring performance, mechanical robustness, economy of scale, and ease of thermoforming and mass manufacturing. The polymer chips we have evaluated are fabricated through an injection molding process of the commercially available cyclic olefin copolymer Zeonor 1060R. This publication is the first to demonstrate insulator-based dielectrophoretic biological particle differentiation in a polymeric device injection molded from a silicon master. The results demonstrate that the polymer devices achieve the same performance metrics as glass devices. We also demonstrate an effective means of enhancing performance of these microsystems in terms of system power demand through the use of a dynamic surface coating. We demonstrate that the commercially available nonionic block copolymer surfactant, Pluronic F127, has a strong interaction with the cyclic olefin copolymer at very low concentrations, positively impacting performance by decreasing the electric field necessary to achieve particle trapping by an order of magnitude. The presence of this dynamic surface coating, therefore, lowers the power required to operate such devices and minimizes Joule heating. The results of this study demonstrate that iDEP polymeric microfluidic devices with surfactant coatings provide an affordable engineering strategy for selective particle enrichment and sorting.

Robust adaptive multichannel SAR processing based on covariance matrix reconstruction

NASA Astrophysics Data System (ADS)

Tan, Zhen-ya; He, Feng

2018-04-01

With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar(SAR) systems in azimuth promise well in high-resolution and wide-swath imaging, whereas conventional processing methods don't take the nonuniformity of scattering coefficient into consideration. This paper brings up a robust adaptive Multichannel SAR processing method which utilizes the Capon spatial spectrum estimator to obtain the spatial spectrum distribution over all ambiguous directions first, and then the interference-plus-noise covariance Matrix is reconstructed based on definition to acquire the Multichannel SAR processing filter. The performance of processing under nonuniform scattering coefficient is promoted by this novel method and it is robust again array errors. The experiments with real measured data demonstrate the effectiveness and robustness of the proposed method.

Robust infrared targets tracking with covariance matrix representation

NASA Astrophysics Data System (ADS)

Cheng, Jian

2009-07-01

Robust infrared target tracking is an important and challenging research topic in many military and security applications, such as infrared imaging guidance, infrared reconnaissance, scene surveillance, etc. To effectively tackle the nonlinear and non-Gaussian state estimation problems, particle filtering is introduced to construct the theory framework of infrared target tracking. Under this framework, the observation probabilistic model is one of main factors for infrared targets tracking performance. In order to improve the tracking performance, covariance matrices are introduced to represent infrared targets with the multi-features. The observation probabilistic model can be constructed by computing the distance between the reference target's and the target samples' covariance matrix. Because the covariance matrix provides a natural tool for integrating multiple features, and is scale and illumination independent, target representation with covariance matrices can hold strong discriminating ability and robustness. Two experimental results demonstrate the proposed method is effective and robust for different infrared target tracking, such as the sensor ego-motion scene, and the sea-clutter scene.

Robust modulation of arousal regulation, performance, and frontostriatal activity through central thalamic deep brain stimulation in healthy nonhuman primates

PubMed Central

Ryou, Jae-Wook; Wei, Xuefeng F.; Butson, Christopher R.; Schiff, Nicholas D.; Purpura, Keith P.

2016-01-01

The central thalamus (CT) is a key component of the brain-wide network underlying arousal regulation and sensory-motor integration during wakefulness in the mammalian brain. Dysfunction of the CT, typically a result of severe brain injury (SBI), leads to long-lasting impairments in arousal regulation and subsequent deficits in cognition. Central thalamic deep brain stimulation (CT-DBS) is proposed as a therapy to reestablish and maintain arousal regulation to improve cognition in select SBI patients. However, a mechanistic understanding of CT-DBS and an optimal method of implementing this promising therapy are unknown. Here we demonstrate in two healthy nonhuman primates (NHPs), Macaca mulatta, that location-specific CT-DBS improves performance in visuomotor tasks and is associated with physiological effects consistent with enhancement of endogenous arousal. Specifically, CT-DBS within the lateral wing of the central lateral nucleus and the surrounding medial dorsal thalamic tegmental tract (DTTm) produces a rapid and robust modulation of performance and arousal, as measured by neuronal activity in the frontal cortex and striatum. Notably, the most robust and reliable behavioral and physiological responses resulted when we implemented a novel method of CT-DBS that orients and shapes the electric field within the DTTm using spatially separated DBS leads. Collectively, our results demonstrate that selective activation within the DTTm of the CT robustly regulates endogenous arousal and enhances cognitive performance in the intact NHP; these findings provide insights into the mechanism of CT-DBS and further support the development of CT-DBS as a therapy for reestablishing arousal regulation to support cognition in SBI patients. PMID:27582298

A Triblock Copolymer Design Leads to Robust Hybrid Hydrogels for High-Performance Flexible Supercapacitors.

PubMed

Zhang, Guangzhao; Chen, Yunhua; Deng, Yonghong; Wang, Chaoyang

2017-10-18

We report here an intriguing hybrid conductive hydrogel as electrode for high-performance flexible supercapacitor. The key is using a rationally designed water-soluble ABA triblock copolymer (termed as IAOAI) containing a central poly(ethylene oxide) block (A) and terminal poly(acrylamide) (PAAm) block with aniline moieties randomly incorporated (B), which was synthesized by reversible additional fragment transfer polymerization. The subsequent copolymerization of aniline monomers with the terminated aniline moieties on the IAOAI polymer generates a three-dimensional cross-linking hybrid network. The hybrid hydrogel electrode demonstrates robust mechanical flexibility, remarkable electrochemical capacitance (919 F/g), and cyclic stability (90% capacitance retention after 1000 cycles). Moreover, the flexible supercapacitor based on this hybrid hydrogel electrode presents a large specific capacitance (187 F/g), superior to most reported conductive hydrogel-based supercapacitors. With the demonstrated additional favorable cyclic stability and excellent capacitive and rate performance, this hybrid hydrogel-based supercapacitor holds great promise for flexible energy-storage device.

High-efficiency robust perovskite solar cells on ultrathin flexible substrates

PubMed Central

Li, Yaowen; Meng, Lei; Yang, Yang (Michael); Xu, Guiying; Hong, Ziruo; Chen, Qi; You, Jingbi; Li, Gang; Yang, Yang; Li, Yongfang

2016-01-01

Wide applications of personal consumer electronics have triggered tremendous need for portable power sources featuring light-weight and mechanical flexibility. Perovskite solar cells offer a compelling combination of low-cost and high device performance. Here we demonstrate high-performance planar heterojunction perovskite solar cells constructed on highly flexible and ultrathin silver-mesh/conducting polymer substrates. The device performance is comparable to that of their counterparts on rigid glass/indium tin oxide substrates, reaching a power conversion efficiency of 14.0%, while the specific power (the ratio of power to device weight) reaches 1.96 kW kg−1, given the fact that the device is constructed on a 57-μm-thick polyethylene terephthalate based substrate. The flexible device also demonstrates excellent robustness against mechanical deformation, retaining >95% of its original efficiency after 5,000 times fully bending. Our results confirmed that perovskite thin films are fully compatible with our flexible substrates, and are thus promising for future applications in flexible and bendable solar cells. PMID:26750664

On a methodology for robust segmentation of nonideal iris images.

PubMed

Schmid, Natalia A; Zuo, Jinyu

2010-06-01

Iris biometric is one of the most reliable biometrics with respect to performance. However, this reliability is a function of the ideality of the data. One of the most important steps in processing nonideal data is reliable and precise segmentation of the iris pattern from remaining background. In this paper, a segmentation methodology that aims at compensating various nonidealities contained in iris images during segmentation is proposed. The virtue of this methodology lies in its capability to reliably segment nonideal imagery that is simultaneously affected with such factors as specular reflection, blur, lighting variation, occlusion, and off-angle images. We demonstrate the robustness of our segmentation methodology by evaluating ideal and nonideal data sets, namely, the Chinese Academy of Sciences iris data version 3 interval subdirectory, the iris challenge evaluation data, the West Virginia University (WVU) data, and the WVU off-angle data. Furthermore, we compare our performance to that of our implementation of Camus and Wildes's algorithm and Masek's algorithm. We demonstrate considerable improvement in segmentation performance over the formerly mentioned algorithms.

Discriminant locality preserving projections based on L1-norm maximization.

PubMed

Zhong, Fujin; Zhang, Jiashu; Li, Defang

2014-11-01

Conventional discriminant locality preserving projection (DLPP) is a dimensionality reduction technique based on manifold learning, which has demonstrated good performance in pattern recognition. However, because its objective function is based on the distance criterion using L2-norm, conventional DLPP is not robust to outliers which are present in many applications. This paper proposes an effective and robust DLPP version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based locality preserving between-class dispersion and the L1-norm-based locality preserving within-class dispersion. The proposed method is proven to be feasible and also robust to outliers while overcoming the small sample size problem. The experimental results on artificial datasets, Binary Alphadigits dataset, FERET face dataset and PolyU palmprint dataset have demonstrated the effectiveness of the proposed method.

Deep learning and model predictive control for self-tuning mode-locked lasers

NASA Astrophysics Data System (ADS)

Baumeister, Thomas; Brunton, Steven L.; Nathan Kutz, J.

2018-03-01

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\\em intelligent} algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a {\\em deep learning} (DL) architecture with {\\em model predictive control} (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.

A Case Study on the Application of a Structured Experimental Method for Optimal Parameter Design of a Complex Control System

NASA Technical Reports Server (NTRS)

Torres-Pomales, Wilfredo

2015-01-01

This report documents a case study on the application of Reliability Engineering techniques to achieve an optimal balance between performance and robustness by tuning the functional parameters of a complex non-linear control system. For complex systems with intricate and non-linear patterns of interaction between system components, analytical derivation of a mathematical model of system performance and robustness in terms of functional parameters may not be feasible or cost-effective. The demonstrated approach is simple, structured, effective, repeatable, and cost and time efficient. This general approach is suitable for a wide range of systems.

Vehicle logo recognition using multi-level fusion model

NASA Astrophysics Data System (ADS)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

NASA Astrophysics Data System (ADS)

Cannon, A. J.; Hsieh, W. W.

2008-02-01

Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.

Fuzzy logic-based flight control system design

NASA Astrophysics Data System (ADS)

Nho, Kyungmoon

The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzy controller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzy controller. For a simple demonstration of the easy design and control capability of a fuzzy controller, a proportional-derivative (PD) fuzzy control system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzy control systems by considering the key features of a fuzzy control system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

PubMed Central

Wang, Yaping; Nie, Jingxin; Yap, Pew-Thian; Li, Gang; Shi, Feng; Geng, Xiujuan; Guo, Lei; Shen, Dinggang

2014-01-01

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness. PMID:24489639

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

PubMed

Betthauser, Joseph L; Hunt, Christopher L; Osborn, Luke E; Masters, Matthew R; Levay, Gyorgy; Kaliki, Rahul R; Thakor, Nitish V

2018-04-01

Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. We report significant performance improvements () in untrained limb positions across all subject groups. The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Reversible Oxygen Gas Sensor Based On Electrochemiluminescence

PubMed Central

Zhang, Lihua; Tsow, Francis

2013-01-01

A novel and robust oxygen gas sensor based on electrochemiluminescence of Ru(bpy)33+/+ ion annihilation in an ionic liquid is presented. Real-time detection of environmental oxygen concentration together with selective, sensitive and reversible performance is demonstrated. PMID:20386795

High Performance, Robust Control of Flexible Space Structures: MSFC Center Director's Discretionary Fund

NASA Technical Reports Server (NTRS)

Whorton, M. S.

1998-01-01

Many spacecraft systems have ambitious objectives that place stringent requirements on control systems. Achievable performance is often limited because of difficulty of obtaining accurate models for flexible space structures. To achieve sufficiently high performance to accomplish mission objectives may require the ability to refine the control design model based on closed-loop test data and tune the controller based on the refined model. A control system design procedure is developed based on mixed H2/H(infinity) optimization to synthesize a set of controllers explicitly trading between nominal performance and robust stability. A homotopy algorithm is presented which generates a trajectory of gains that may be implemented to determine maximum achievable performance for a given model error bound. Examples show that a better balance between robustness and performance is obtained using the mixed H2/H(infinity) design method than either H2 or mu-synthesis control design. A second contribution is a new procedure for closed-loop system identification which refines parameters of a control design model in a canonical realization. Examples demonstrate convergence of the parameter estimation and improved performance realized by using the refined model for controller redesign. These developments result in an effective mechanism for achieving high-performance control of flexible space structures.

Low noise erbium fiber fs frequency comb based on a tapered-fiber carbon nanotube design.

PubMed

Wu, Tsung-Han; Kieu, K; Peyghambarian, N; Jones, R J

2011-03-14

We report on a low noise all-fiber erbium fs frequency comb based on a simple and robust tapered-fiber carbon nanotube (tf-CNT) design. We mitigate dominant noise sources to show that the free-running linewidth of the carrier-envelope offset frequency (fceo) can be comparable to the best reported performance to date for fiber-based frequency combs. A free-running fceo linewidth of ~20 kHz is demonstrated, corresponding to an improvement of ~30 times over previous work based on a CNT mode-locked fiber laser [Opt. Express 18, 1667 (2010)]. We also demonstrate the use of an acousto-optic modulator external to the laser cavity to stabilize fceo, enabling a 300 kHz feedback control bandwidth. The offset frequency is phase-locked with an in-loop integrated phase noise of ~0.8 rad from 10Hz to 400kHz. We show a resolution-limited linewidth of ~1 Hz, demonstrating over 90% of the carrier power within the coherent fceo signal. The results demonstrate that the relatively simple tf-CNT fiber laser design can provide a compact, robust and high-performance fs frequency comb.

A μ analysis-based, controller-synthesis framework for robust bioinspired visual navigation in less-structured environments.

PubMed

Keshavan, J; Gremillion, G; Escobar-Alvarez, H; Humbert, J S

2014-06-01

Safe, autonomous navigation by aerial microsystems in less-structured environments is a difficult challenge to overcome with current technology. This paper presents a novel visual-navigation approach that combines bioinspired wide-field processing of optic flow information with control-theoretic tools for synthesis of closed loop systems, resulting in robustness and performance guarantees. Structured singular value analysis is used to synthesize a dynamic controller that provides good tracking performance in uncertain environments without resorting to explicit pose estimation or extraction of a detailed environmental depth map. Experimental results with a quadrotor demonstrate the vehicle's robust obstacle-avoidance behaviour in a straight line corridor, an S-shaped corridor and a corridor with obstacles distributed in the vehicle's path. The computational efficiency and simplicity of the current approach offers a promising alternative to satisfying the payload, power and bandwidth constraints imposed by aerial microsystems.

Robust detection, isolation and accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Emami-Naeini, A.; Akhter, M. M.; Rock, S. M.

1986-01-01

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Strain-Dependent Transcriptome Signatures for Robustness in Lactococcus lactis

PubMed Central

Dijkstra, Annereinou R.; Alkema, Wynand; Starrenburg, Marjo J. C.; van Hijum, Sacha A. F. T.; Bron, Peter A.

2016-01-01

Recently, we demonstrated that fermentation conditions have a strong impact on subsequent survival of Lactococcus lactis strain MG1363 during heat and oxidative stress, two important parameters during spray drying. Moreover, employment of a transcriptome-phenotype matching approach revealed groups of genes associated with robustness towards heat and/or oxidative stress. To investigate if other strains have similar or distinct transcriptome signatures for robustness, we applied an identical transcriptome-robustness phenotype matching approach on the L. lactis strains IL1403, KF147 and SK11, which have previously been demonstrated to display highly diverse robustness phenotypes. These strains were subjected to an identical fermentation regime as was performed earlier for strain MG1363 and consisted of twelve conditions, varying in the level of salt and/or oxygen, as well as fermentation temperature and pH. In the exponential phase of growth, cells were harvested for transcriptome analysis and assessment of heat and oxidative stress survival phenotypes. The variation in fermentation conditions resulted in differences in heat and oxidative stress survival of up to five 10-log units. Effects of the fermentation conditions on stress survival of the L. lactis strains were typically strain-dependent, although the fermentation conditions had mainly similar effects on the growth characteristics of the different strains. By association of the transcriptomes and robustness phenotypes highly strain-specific transcriptome signatures for robustness towards heat and oxidative stress were identified, indicating that multiple mechanisms exist to increase robustness and, as a consequence, robustness of each strain requires individual optimization. However, a relatively small overlap in the transcriptome responses of the strains was also identified and this generic transcriptome signature included genes previously associated with stress (ctsR and lplL) and novel genes, including nanE and genes encoding transport proteins. The transcript levels of these genes can function as indicators of robustness and could aid in selection of fermentation parameters, potentially resulting in more optimal robustness during spray drying. PMID:27973578

Distributed robust finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2016-04-01

This paper investigates the robust finite-time consensus problem of multi-agent systems in networks with undirected topology. Global nonlinear consensus protocols augmented with a variable structure are constructed with the aid of Lyapunov functions for each single-integrator agent dynamics in the presence of external disturbances. In particular, it is shown that the finite settling time of the proposed general framework for robust consensus design is upper bounded for any initial condition. This makes it possible for network consensus problems to design and estimate the convergence time offline for a multi-agent team with a given undirected information flow. Finally, simulation results are presented to demonstrate the performance and effectiveness of our finite-time protocols.

Linear quadratic servo control of a reusable rocket engine

NASA Technical Reports Server (NTRS)

Musgrave, Jeffrey L.

1991-01-01

The paper deals with the development of a design method for a servo component in the frequency domain using singular values and its application to a reusable rocket engine. A general methodology used to design a class of linear multivariable controllers for intelligent control systems is presented. Focus is placed on performance and robustness characteristics, and an estimator design performed in the framework of the Kalman-filter formalism with emphasis on using a sensor set different from the commanded values is discussed. It is noted that loop transfer recovery modifies the nominal plant noise intensities in order to obtain the desired degree of robustness to uncertainty reflected at the plant input. Simulation results demonstrating the performance of the linear design on a nonlinear engine model over all power levels during mainstage operation are discussed.

H(2)- and H(infinity)-design tools for linear time-invariant systems

NASA Technical Reports Server (NTRS)

Ly, Uy-Loi

1989-01-01

Recent advances in optimal control have brought design techniques based on optimization of H(2) and H(infinity) norm criteria, closer to be attractive alternatives to single-loop design methods for linear time-variant systems. Significant steps forward in this technology are the deeper understanding of performance and robustness issues of these design procedures and means to perform design trade-offs. However acceptance of the technology is hindered by the lack of convenient design tools to exercise these powerful multivariable techniques, while still allowing single-loop design formulation. Presented is a unique computer tool for designing arbitrary low-order linear time-invarient controllers than encompasses both performance and robustness issues via the familiar H(2) and H(infinity) norm optimization. Application to disturbance rejection design for a commercial transport is demonstrated.

Brain limbic system-based intelligent controller application to lane change manoeuvre

NASA Astrophysics Data System (ADS)

Kim, Changwon; Langari, Reza

2011-12-01

This paper presents the application of a novel neuromorphic control strategy for lane change manoeuvres in the highway environment. The lateral dynamics of a vehicle with and without wind disturbance are derived and utilised to implement a control strategy based on the brain limbic system. To show the robustness of the proposed controller, several disturbance conditions including wind, uncertainty in the cornering stiffness, and changes in the vehicle mass are investigated. To demonstrate the performance of the suggested strategy, simulation results of the proposed method are compared with the human driver model-based control scheme, which has been discussed in the literature. The simulation results demonstrate the superiority of the proposed controller in energy efficiency, driving comfort, and robustness.

Demonstration of a Corner-cube-interferometer LWIR Hyperspectral Imager

NASA Astrophysics Data System (ADS)

Renhorn, Ingmar G. E.; Svensson, Thomas; Cronström, Staffan; Hallberg, Tomas; Persson, Rolf; Lindell, Roland; Boreman, Glenn D.

2010-01-01

An interferometric long-wavelength infrared (LWIR) hyperspectral imager is demonstrated, based on a Michelson corner-cube interferometer. This class of system is inherently mechanically robust, and should have advantages over Sagnac-interferometer systems in terms of relaxed beamsplitter-coating specifications, and wider unvignetted field of view. Preliminary performance data from the laboratory prototype system are provided regarding imaging, spectral resolution, and fidelity of acquired spectra.

Robust Sliding Mode Control of PMSM Based on Rapid Nonlinear Tracking Differentiator and Disturbance Observer

PubMed Central

Zhou, Zhanmin; Zhang, Bao; Mao, Dapeng

2018-01-01

Torque ripples caused by cogging torque, flux harmonics, and current measurement error seriously restrict the application of a permanent magnet synchronous motor (PMSM), which has been paid more and more attention for the use in inertial stabilized platforms. Sliding mode control (SMC), in parallel with the classical proportional integral (PI) controller, has a high advantage to suppress the torque ripples as its invariance to disturbances. However, since the high switching gain tends to cause chattering and it requires derivative of signals which is not readily obtainable without an acceleration signal sensor. Therefore, this paper proposes a robust SMC scheme based on a rapid nonlinear tracking differentiator (NTD) and a disturbance observer (DOB) to further improve the performance of the SMC. The NTD is employed to providing the derivative of the signal, and the DOB is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. On the one hand, DOB can compensate the robust SMC speed controller, it can reduce the chattering of SMC on the other hand. Experiments were carried out on an ARM and DSP-based platform. The obtained experimental results demonstrate that the robust SMC scheme has an improved performance with inertia stability and it exhibits a satisfactory anti-disturbance performance compared to the traditional methods. PMID:29596387

Robust Sliding Mode Control of PMSM Based on a Rapid Nonlinear Tracking Differentiator and Disturbance Observer.

PubMed

Zhou, Zhanmin; Zhang, Bao; Mao, Dapeng

2018-03-29

Torque ripples caused by cogging torque, flux harmonics, and current measurement error seriously restrict the application of a permanent magnet synchronous motor (PMSM), which has been paid more and more attention for the use in inertial stabilized platforms. Sliding mode control (SMC), in parallel with the classical proportional integral (PI) controller, has a high advantage to suppress the torque ripples as its invariance to disturbances. However, since the high switching gain tends to cause chattering and it requires derivative of signals which is not readily obtainable without an acceleration signal sensor. Therefore, this paper proposes a robust SMC scheme based on a rapid nonlinear tracking differentiator (NTD) and a disturbance observer (DOB) to further improve the performance of the SMC. The NTD is employed to providing the derivative of the signal, and the DOB is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. On the one hand, DOB can compensate the robust SMC speed controller, it can reduce the chattering of SMC on the other hand. Experiments were carried out on an ARM and DSP-based platform. The obtained experimental results demonstrate that the robust SMC scheme has an improved performance with inertia stability and it exhibits a satisfactory anti-disturbance performance compared to the traditional methods.

Learning-based image preprocessing for robust computer-aided detection

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Devarakota, Pandu R.; Wolf, Matthias

2013-03-01

Recent studies have shown that low dose computed tomography (LDCT) can be an effective screening tool to reduce lung cancer mortality. Computer-aided detection (CAD) would be a beneficial second reader for radiologists in such cases. Studies demonstrate that while iterative reconstructions (IR) improve LDCT diagnostic quality, it however degrades CAD performance significantly (increased false positives) when applied directly. For improving CAD performance, solutions such as retraining with newer data or applying a standard preprocessing technique may not be suffice due to high prevalence of CT scanners and non-uniform acquisition protocols. Here, we present a learning-based framework that can adaptively transform a wide variety of input data to boost an existing CAD performance. This not only enhances their robustness but also their applicability in clinical workflows. Our solution consists of applying a suitable pre-processing filter automatically on the given image based on its characteristics. This requires the preparation of ground truth (GT) of choosing an appropriate filter resulting in improved CAD performance. Accordingly, we propose an efficient consolidation process with a novel metric. Using key anatomical landmarks, we then derive consistent feature descriptors for the classification scheme that then uses a priority mechanism to automatically choose an optimal preprocessing filter. We demonstrate CAD prototype∗ performance improvement using hospital-scale datasets acquired from North America, Europe and Asia. Though we demonstrated our results for a lung nodule CAD, this scheme is straightforward to extend to other post-processing tools dedicated to other organs and modalities.

A constrained robust least squares approach for contaminant release history identification

NASA Astrophysics Data System (ADS)

Sun, Alexander Y.; Painter, Scott L.; Wittmeyer, Gordon W.

2006-04-01

Contaminant source identification is an important type of inverse problem in groundwater modeling and is subject to both data and model uncertainty. Model uncertainty was rarely considered in the previous studies. In this work, a robust framework for solving contaminant source recovery problems is introduced. The contaminant source identification problem is first cast into one of solving uncertain linear equations, where the response matrix is constructed using a superposition technique. The formulation presented here is general and is applicable to any porous media flow and transport solvers. The robust least squares (RLS) estimator, which originated in the field of robust identification, directly accounts for errors arising from model uncertainty and has been shown to significantly reduce the sensitivity of the optimal solution to perturbations in model and data. In this work, a new variant of RLS, the constrained robust least squares (CRLS), is formulated for solving uncertain linear equations. CRLS allows for additional constraints, such as nonnegativity, to be imposed. The performance of CRLS is demonstrated through one- and two-dimensional test problems. When the system is ill-conditioned and uncertain, it is found that CRLS gave much better performance than its classical counterpart, the nonnegative least squares. The source identification framework developed in this work thus constitutes a reliable tool for recovering source release histories in real applications.

Enhanced H-filter based on Fåhræus-Lindqvist effect for efficient and robust dialysis without membrane

PubMed Central

Zheng, Wei-Chao; Xie, Rui; He, Li-Qun; Xi, Yue-Heng; Liu, Ying-Mei; Meng, Zhi-Jun; Wang, Wei; Ju, Xiao-Jie; Chen, Gang; Chu, Liang-Yin

2015-01-01

A novel microfluidic device for highly efficient and robust dialysis without membrane is highly desired for the development of portable or wearable microdialyzer. Here we report an enhanced H-filter with pillar array based on Fåhræus-Lindqvist effect (F-L effect) for highly efficient and robust membraneless dialysis of simplified blood for the first time. The H-filter employs two fluids laminarly flowing in the microchannel for continuously membraneless dialysis. With pillar array in the microchannel, the two laminar flows, with one containing blood cells and small molecules and another containing dialyzate solution, can form a cell-free layer at the interface as selective zones for separation. This provides enhanced mixing yet extremely low shear for extraction of small molecules from the blood-cell-containing flow into the dialyzate flow, resulting in robust separation with reduced cell loss and improved efficiency. We demonstrate this by first using Chlorella pyrenoidosa as model cells to quantitatively study the separation performances, and then using simplified human blood for dialysis. The advanced H-filter, with highly efficient and robust performance for membraneless dialysis, shows great potential as promising candidate for rapid blood analysis/separation, and as fundamental structure for portable dialyzer. PMID:26339313

Enhanced H-filter based on Fåhræus-Lindqvist effect for efficient and robust dialysis without membrane.

PubMed

Zheng, Wei-Chao; Xie, Rui; He, Li-Qun; Xi, Yue-Heng; Liu, Ying-Mei; Meng, Zhi-Jun; Wang, Wei; Ju, Xiao-Jie; Chen, Gang; Chu, Liang-Yin

2015-07-01

A novel microfluidic device for highly efficient and robust dialysis without membrane is highly desired for the development of portable or wearable microdialyzer. Here we report an enhanced H-filter with pillar array based on Fåhræus-Lindqvist effect (F-L effect) for highly efficient and robust membraneless dialysis of simplified blood for the first time. The H-filter employs two fluids laminarly flowing in the microchannel for continuously membraneless dialysis. With pillar array in the microchannel, the two laminar flows, with one containing blood cells and small molecules and another containing dialyzate solution, can form a cell-free layer at the interface as selective zones for separation. This provides enhanced mixing yet extremely low shear for extraction of small molecules from the blood-cell-containing flow into the dialyzate flow, resulting in robust separation with reduced cell loss and improved efficiency. We demonstrate this by first using Chlorella pyrenoidosa as model cells to quantitatively study the separation performances, and then using simplified human blood for dialysis. The advanced H-filter, with highly efficient and robust performance for membraneless dialysis, shows great potential as promising candidate for rapid blood analysis/separation, and as fundamental structure for portable dialyzer.

Virtual sensors for active noise control in acoustic-structural coupled enclosures using structural sensing: robust virtual sensor design.

PubMed

Halim, Dunant; Cheng, Li; Su, Zhongqing

2011-03-01

The work was aimed to develop a robust virtual sensing design methodology for sensing and active control applications of vibro-acoustic systems. The proposed virtual sensor was designed to estimate a broadband acoustic interior sound pressure using structural sensors, with robustness against certain dynamic uncertainties occurring in an acoustic-structural coupled enclosure. A convex combination of Kalman sub-filters was used during the design, accommodating different sets of perturbed dynamic model of the vibro-acoustic enclosure. A minimax optimization problem was set up to determine an optimal convex combination of Kalman sub-filters, ensuring an optimal worst-case virtual sensing performance. The virtual sensing and active noise control performance was numerically investigated on a rectangular panel-cavity system. It was demonstrated that the proposed virtual sensor could accurately estimate the interior sound pressure, particularly the one dominated by cavity-controlled modes, by using a structural sensor. With such a virtual sensing technique, effective active noise control performance was also obtained even for the worst-case dynamics. © 2011 Acoustical Society of America

Metabolic robustness in young roots underpins a predictive model of maize hybrid performance in the field.

PubMed

de Abreu E Lima, Francisco; Westhues, Matthias; Cuadros-Inostroza, Álvaro; Willmitzer, Lothar; Melchinger, Albrecht E; Nikoloski, Zoran

2017-04-01

Heterosis has been extensively exploited for yield gain in maize (Zea mays L.). Here we conducted a comparative metabolomics-based analysis of young roots from in vitro germinating seedlings and from leaves of field-grown plants in a panel of inbred lines from the Dent and Flint heterotic patterns as well as selected F 1 hybrids. We found that metabolite levels in hybrids were more robust than in inbred lines. Using state-of-the-art modeling techniques, the most robust metabolites from roots and leaves explained up to 37 and 44% of the variance in the biomass from plants grown in two distinct field trials. In addition, a correlation-based analysis highlighted the trade-off between defense-related metabolites and hybrid performance. Therefore, our findings demonstrated the potential of metabolic profiles from young maize roots grown under tightly controlled conditions to predict hybrid performance in multiple field trials, thus bridging the greenhouse-field gap. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

A robust nonlinear skid-steering control design applied to the MULE (6x6) unmanned ground vehicle

NASA Astrophysics Data System (ADS)

Kaloust, Joseph

2006-05-01

The paper presents a robust nonlinear skid-steering control design concept. The control concept is based on the recursive/backstepping control design technique and is capable of compensating for uncertainties associated with sensor noise measurements and/or system dynamic state uncertainties. The objective of this control design is to demonstrate the performance of the nonlinear controller under uncertainty associate with road traction (rough off-road and on-road terrain). The MULE vehicle is used in the simulation modeling and results.

Robust Lee local statistic filter for removal of mixed multiplicative and impulse noise

NASA Astrophysics Data System (ADS)

Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Egiazarian, Karen O.; Astola, Jaakko T.

2004-05-01

A robust version of Lee local statistic filter able to effectively suppress the mixed multiplicative and impulse noise in images is proposed. The performance of the proposed modification is studied for a set of test images, several values of multiplicative noise variance, Gaussian and Rayleigh probability density functions of speckle, and different characteris-tics of impulse noise. The advantages of the designed filter in comparison to the conventional Lee local statistic filter and some other filters able to cope with mixed multiplicative+impulse noise are demonstrated.

Control of nonlinear systems using terminal sliding modes

NASA Technical Reports Server (NTRS)

Venkataraman, S. T.; Gulati, S.

1992-01-01

The development of an approach to control synthesis for robust robot operations in unstructured environments is discussed. To enhance control performance with full model information, the authors introduce the notion of terminal convergence and develop control laws based on a class of sliding modes, denoted as terminal sliders. They demonstrate that terminal sliders provide robustness to parametric uncertainty without having to resort to high-frequency control switching, as in the case of conventional sliders. It is shown that the proposed method leads to greater guaranteed precision in all control cases discussed.

Bend compensated large-mode-area fibers: achieving robust single-modedness with transformation optics.

PubMed

Fini, John M; Nicholson, Jeffrey W

2013-08-12

Fibers with symmetric bend compensated claddings are proposed, and demonstrate performance much better than conventional designs. These fibers can simultaneously achieve complete HOM suppression, negligible bend loss, and mode area >1000 square microns. The robust single-modedness of these fibers offers a path to overcoming mode instability limits on high-power amplifiers and lasers. The proposed designs achieve many of the advantages of our previous (asymmetric) bend compensation strategy in the regime of moderately large area, and are much easier to fabricate and utilize.

Enhanced efficiency and stability of inverted perovskite solar cells using highly crystalline SnO 2 nanocrystals as the robust electron-transporting layer

DOE PAGES

Zhu, Zonglong; Bai, Yang; Liu, Xiao; ...

2016-05-11

Here highly crystalline SnO 2 is demonstrated to serve as a stable and robust electron-transporting layer for high-performance perovskite solar cells. Benefiting from its high crystallinity, the relatively thick SnO 2 electron-transporting layer (≈120 nm) provides a respectable electron-transporting property to yield a promising power conversion efficiency (PCE)(18.8%) Over 90% of the initial PCE can be retained after 30 d storage in ambient with ≈70% relative humidity.

384 hanging drop arrays give excellent Z-factors and allow versatile formation of co-culture spheroids.

PubMed

Hsiao, Amy Y; Tung, Yi-Chung; Qu, Xianggui; Patel, Lalit R; Pienta, Kenneth J; Takayama, Shuichi

2012-05-01

We previously reported the development of a simple, user-friendly, and versatile 384 hanging drop array plate for 3D spheroid culture and the importance of utilizing 3D cellular models in anti-cancer drug sensitivity testing. The 384 hanging drop array plate allows for high-throughput capabilities and offers significant improvements over existing 3D spheroid culture methods. To allow for practical 3D cell-based high-throughput screening and enable broader use of the plate, we characterize the robustness of the 384 hanging drop array plate in terms of assay performance and demonstrate the versatility of the plate. We find that the 384 hanging drop array plate performance is robust in fluorescence- and colorimetric-based assays through Z-factor calculations. Finally, we demonstrate different plate capabilities and applications, including: spheroid transfer and retrieval for Janus spheroid formation, sequential addition of cells for concentric layer patterning of different cell types, and culture of a wide variety of cell types. Copyright © 2011 Wiley Periodicals, Inc.

384 Hanging Drop Arrays Give Excellent Z-factors and Allow Versatile Formation of Co-culture Spheroids

PubMed Central

Hsiao, Amy Y.; Tung, Yi-Chung; Qu, Xianggui; Patel, Lalit R.; Pienta, Kenneth J.; Takayama, Shuichi

2012-01-01

We previously reported the development of a simple, user-friendly, and versatile 384 hanging drop array plate for 3D spheroid culture and the importance of utilizing 3D cellular models in anti-cancer drug sensitivity testing. The 384 hanging drop array plate allows for high-throughput capabilities and offers significant improvements over existing 3D spheroid culture methods. To allow for practical 3D cell-based high-throughput screening and enable broader use of the plate, we characterize the robustness of the 384 hanging drop array plate in terms of assay performance and demonstrate the versatility of the plate. We find that the 384 hanging drop array plate performance is robust in fluorescence- and colorimetric-based assays through z-factor calculations. Finally, we demonstrate different plate capabilities and applications, including: spheroid transfer and retrieval for Janus spheroid formation, sequential addition of cells for concentric layer patterning of different cell types, and culture of a wide variety of cell types. PMID:22161651

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

A robust color image watermarking algorithm against rotation attacks

NASA Astrophysics Data System (ADS)

Han, Shao-cheng; Yang, Jin-feng; Wang, Rui; Jia, Gui-min

2018-01-01

A robust digital watermarking algorithm is proposed based on quaternion wavelet transform (QWT) and discrete cosine transform (DCT) for copyright protection of color images. The luminance component Y of a host color image in YIQ space is decomposed by QWT, and then the coefficients of four low-frequency subbands are transformed by DCT. An original binary watermark scrambled by Arnold map and iterated sine chaotic system is embedded into the mid-frequency DCT coefficients of the subbands. In order to improve the performance of the proposed algorithm against rotation attacks, a rotation detection scheme is implemented before watermark extracting. The experimental results demonstrate that the proposed watermarking scheme shows strong robustness not only against common image processing attacks but also against arbitrary rotation attacks.

Design and Demonstration of Emergency Control Modes for Enhanced Engine Performance

NASA Technical Reports Server (NTRS)

Liu, Yuan; Litt, Jonathan S.; Guo, Ten-Huei

2013-01-01

A design concept is presented for developing control modes that enhance aircraft engine performance during emergency flight scenarios. The benefits of increased engine performance to overall vehicle survivability during these situations may outweigh the accompanied elevated risk of engine failure. The objective involves building control logic that can consistently increase engine performance beyond designed maximum levels based on an allowable heightened probability of failure. This concept is applied to two previously developed control modes: an overthrust mode that increases maximum engine thrust output and a faster response mode that improves thrust response to dynamic throttle commands. This paper describes the redesign of these control modes and presents simulation results demonstrating both enhanced engine performance and robust maintenance of the desired elevated risk level.

Robust, nonlinear, high angle-of-attack control design for a supermaneuverable vehicle

NASA Technical Reports Server (NTRS)

Adams, Richard J.

1993-01-01

High angle-of-attack flight control laws are developed for a supermaneuverable fighter aircraft. The methods of dynamic inversion and structured singular value synthesis are combined into an approach which addresses both the nonlinearity and robustness problems of flight at extreme operating conditions. The primary purpose of the dynamic inversion control elements is to linearize the vehicle response across the flight envelope. Structured singular value synthesis is used to design a dynamic controller which provides robust tracking to pilot commands. The resulting control system achieves desired flying qualities and guarantees a large margin of robustness to uncertainties for high angle-of-attack flight conditions. The results of linear simulation and structured singular value stability analysis are presented to demonstrate satisfaction of the design criteria. High fidelity nonlinear simulation results show that the combined dynamics inversion/structured singular value synthesis control law achieves a high level of performance in a realistic environment.

Hydraulically amplified self-healing electrostatic actuators with muscle-like performance

NASA Astrophysics Data System (ADS)

Acome, E.; Mitchell, S. K.; Morrissey, T. G.; Emmett, M. B.; Benjamin, C.; King, M.; Radakovitz, M.; Keplinger, C.

2018-01-01

Existing soft actuators have persistent challenges that restrain the potential of soft robotics, highlighting a need for soft transducers that are powerful, high-speed, efficient, and robust. We describe a class of soft actuators, termed hydraulically amplified self-healing electrostatic (HASEL) actuators, which harness a mechanism that couples electrostatic and hydraulic forces to achieve a variety of actuation modes. We introduce prototypical designs of HASEL actuators and demonstrate their robust, muscle-like performance as well as their ability to repeatedly self-heal after dielectric breakdown—all using widely available materials and common fabrication techniques. A soft gripper handling delicate objects and a self-sensing artificial muscle powering a robotic arm illustrate the wide potential of HASEL actuators for next-generation soft robotic devices.

Tuning HDF5 for Lustre File Systems

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

Howison, Mark; Koziol, Quincey; Knaak, David

2010-09-24

HDF5 is a cross-platform parallel I/O library that is used by a wide variety of HPC applications for the flexibility of its hierarchical object-database representation of scientific data. We describe our recent work to optimize the performance of the HDF5 and MPI-IO libraries for the Lustre parallel file system. We selected three different HPC applications to represent the diverse range of I/O requirements, and measured their performance on three different systems to demonstrate the robustness of our optimizations across different file system configurations and to validate our optimization strategy. We demonstrate that the combined optimizations improve HDF5 parallel I/O performancemore » by up to 33 times in some cases running close to the achievable peak performance of the underlying file system and demonstrate scalable performance up to 40,960-way concurrency.« less

Two Reconfigurable Flight-Control Design Methods: Robust Servomechanism and Control Allocation

NASA Technical Reports Server (NTRS)

Burken, John J.; Lu, Ping; Wu, Zheng-Lu; Bahm, Cathy

2001-01-01

Two methods for control system reconfiguration have been investigated. The first method is a robust servomechanism control approach (optimal tracking problem) that is a generalization of the classical proportional-plus-integral control to multiple input-multiple output systems. The second method is a control-allocation approach based on a quadratic programming formulation. A globally convergent fixed-point iteration algorithm has been developed to make onboard implementation of this method feasible. These methods have been applied to reconfigurable entry flight control design for the X-33 vehicle. Examples presented demonstrate simultaneous tracking of angle-of-attack and roll angle commands during failures of the fight body flap actuator. Although simulations demonstrate success of the first method in most cases, the control-allocation method appears to provide uniformly better performance in all cases.

Robust, frequency-stable and accurate mid-IR laser spectrometer based on frequency comb metrology of quantum cascade lasers up-converted in orientation-patterned GaAs.

PubMed

Hansen, Michael G; Ernsting, Ingo; Vasilyev, Sergey V; Grisard, Arnaud; Lallier, Eric; Gérard, Bruno; Schiller, Stephan

2013-11-04

We demonstrate a robust and simple method for measurement, stabilization and tuning of the frequency of cw mid-infrared (MIR) lasers, in particular of quantum cascade lasers. The proof of principle is performed with a quantum cascade laser at 5.4 µm, which is upconverted to 1.2 µm by sum-frequency generation in orientation-patterned GaAs with the output of a standard high-power cw 1.5 µm fiber laser. Both the 1.2 µm and the 1.5 µm waves are measured by a standard Er:fiber frequency comb. Frequency measurement at the 100 kHz-level, stabilization to sub-10 kHz level, controlled frequency tuning and long-term stability are demonstrated.

Robust and Efficient Biomolecular Clustering of Tumor Based on ${p}$ -Norm Singular Value Decomposition.

PubMed

Kong, Xiang-Zhen; Liu, Jin-Xing; Zheng, Chun-Hou; Hou, Mi-Xiao; Wang, Juan

2017-07-01

High dimensionality has become a typical feature of biomolecular data. In this paper, a novel dimension reduction method named p-norm singular value decomposition (PSVD) is proposed to seek the low-rank approximation matrix to the biomolecular data. To enhance the robustness to outliers, the Lp-norm is taken as the error function and the Schatten p-norm is used as the regularization function in the optimization model. To evaluate the performance of PSVD, the Kmeans clustering method is then employed for tumor clustering based on the low-rank approximation matrix. Extensive experiments are carried out on five gene expression data sets including two benchmark data sets and three higher dimensional data sets from the cancer genome atlas. The experimental results demonstrate that the PSVD-based method outperforms many existing methods. Especially, it is experimentally proved that the proposed method is more efficient for processing higher dimensional data with good robustness, stability, and superior time performance.

Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms

PubMed Central

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets. PMID:25110755

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

PubMed

Hu, Yi-Chung

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

NASA Astrophysics Data System (ADS)

Li, Zhifu; Hu, Yueming; Li, Di

2016-08-01

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems.

PubMed

Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng

2018-05-31

For a nonlinear system, the cubature Kalman filter (CKF) and its square-root version are useful methods to solve the state estimation problems, and both can obtain good performance in Gaussian noises. However, their performances often degrade significantly in the face of non-Gaussian noises, particularly when the measurements are contaminated by some heavy-tailed impulsive noises. By utilizing the maximum correntropy criterion (MCC) to improve the robust performance instead of traditional minimum mean square error (MMSE) criterion, a new square-root nonlinear filter is proposed in this study, named as the maximum correntropy square-root cubature Kalman filter (MCSCKF). The new filter not only retains the advantage of square-root cubature Kalman filter (SCKF), but also exhibits robust performance against heavy-tailed non-Gaussian noises. A judgment condition that avoids numerical problem is also given. The results of two illustrative examples, especially the SINS/GPS integrated systems, demonstrate the desirable performance of the proposed filter. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates.

PubMed

Leung, Kaston; Klaus, Anders; Lin, Bill K; Laks, Emma; Biele, Justina; Lai, Daniel; Bashashati, Ali; Huang, Yi-Fei; Aniba, Radhouane; Moksa, Michelle; Steif, Adi; Mes-Masson, Anne-Marie; Hirst, Martin; Shah, Sohrab P; Aparicio, Samuel; Hansen, Carl L

2016-07-26

The genomes of large numbers of single cells must be sequenced to further understanding of the biological significance of genomic heterogeneity in complex systems. Whole genome amplification (WGA) of single cells is generally the first step in such studies, but is prone to nonuniformity that can compromise genomic measurement accuracy. Despite recent advances, robust performance in high-throughput single-cell WGA remains elusive. Here, we introduce droplet multiple displacement amplification (MDA), a method that uses commercially available liquid dispensing to perform high-throughput single-cell MDA in nanoliter volumes. The performance of droplet MDA is characterized using a large dataset of 129 normal diploid cells, and is shown to exceed previously reported single-cell WGA methods in amplification uniformity, genome coverage, and/or robustness. We achieve up to 80% coverage of a single-cell genome at 5× sequencing depth, and demonstrate excellent single-nucleotide variant (SNV) detection using targeted sequencing of droplet MDA product to achieve a median allelic dropout of 15%, and using whole genome sequencing to achieve false and true positive rates of 9.66 × 10(-6) and 68.8%, respectively, in a G1-phase cell. We further show that droplet MDA allows for the detection of copy number variants (CNVs) as small as 30 kb in single cells of an ovarian cancer cell line and as small as 9 Mb in two high-grade serous ovarian cancer samples using only 0.02× depth. Droplet MDA provides an accessible and scalable method for performing robust and accurate CNV and SNV measurements on large numbers of single cells.

The Cluster Sensitivity Index: A Basic Measure of Classification Robustness

ERIC Educational Resources Information Center

Hom, Willard C.

2010-01-01

Analysts of institutional performance have occasionally used a peer grouping approach in which they compared institutions only to other institutions with similar characteristics. Because analysts historically have used cluster analysis to define peer groups (i.e., the group of comparable institutions), the author proposes and demonstrates with…

Adaptive control of a manipulator with a flexible link

NASA Technical Reports Server (NTRS)

Yang, Y. P.; Gibson, J. S.

1988-01-01

An adaptive controller for a manipulator with one rigid link and one flexible link is presented. The performance and robustness of the controller are demonstrated by numerical simulation results. In the simulations, the manipulator moves in a gravitational field and a finite element model represents the flexible link.

Performance of a wireless sensor network for crop monitoring and irrigation control

USDA-ARS?s Scientific Manuscript database

Robust automatic irrigation scheduling has been demonstrated using wired sensors and sensor network systems with subsurface drip and moving irrigation systems. However, there are limited studies that report on crop yield and water use efficiency resulting from the use of wireless networks to automat...

Standardized UXO Technology Demonstration Site, Blind Grid Scoring Record No. 919

DTIC Science & Technology

2008-07-01

provided by demonstrator) a. The core component of the electromagnetic (EM) AMOS metal detector is a linear multichannel sensor array consisting of a...Attainable accuracy of depth (z) +0.3 m h. Detection performance for ferrous and nonferrous metals : Will detect ammunition components 20-mm caliber...2-meter-wide transmitter coil and 16 receiver coils, mounted on a robust, all-terrain trailer (fig. 1). b. The AMOS detector unit consists of the

Robust broad spectral photodetection (UV-NIR) and ultra high responsivity investigated in nanosheets and nanowires of Bi2Te3 under harsh nano-milling conditions.

PubMed

Sharma, Alka; Srivastava, A K; Senguttuvan, T D; Husale, Sudhir

2017-12-20

Due to miniaturization of device dimensions, the next generation's photodetector based devices are expected to be fabricated from robust nanostructured materials. Hence there is an utmost requirement of investigating exotic optoelectronic properties of nanodevices fabricated from new novel materials and testing their performances at harsh conditions. The recent advances on 2D layered materials indicate exciting progress on broad spectral photodetection (BSP) but still there is a great demand for fabricating ultra-high performance photodetectors made from single material sensing broad electromagnetic spectrum since the detection range 325 nm-1550 nm is not covered by the conventional Si or InGaAs photodetectors. Alternatively, Bi 2 Te 3 is a layered material, possesses exciting optoelectronic, thermoelectric, plasmonics properties. Here we report robust photoconductivity measurements on Bi 2 Te 3 nanosheets and nanowires demonstrating BSP from UV to NIR. The nanosheets of Bi 2 Te 3 show the best ultra-high photoresponsivity (~74 A/W at 1550 nm). Further these nanosheets when transform into nanowires using harsh FIB milling conditions exhibit about one order enhancement in the photoresponsivity without affecting the performance of the device even after 4 months of storage at ambient conditions. An ultra-high photoresponsivity and BSP indicate exciting robust nature of topological insulator based nanodevices for optoelectronic applications.

Space Launch System Implementation of Adaptive Augmenting Control

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Wall, John H.; Orr, Jeb S.

2014-01-01

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to robustly demonstrate stable and high performance flight. On its development path to preliminary design review (PDR), the stability of the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant dynamics. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an adaptive augmenting control (AAC) algorithm previously presented by Orr and VanZwieten, has been shown to extend the envelope of failures and flight anomalies for which the SLS control system can accommodate while maintaining a direct link to flight control stability criteria (e.g. gain & phase margin). In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the SLS digital 3-axis autopilot, including existing load-relief elements, and necessary steps for integration with the production flight software prototype have been implemented. Several updates to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

Research in robust control for hypersonic aircraft

NASA Technical Reports Server (NTRS)

Calise, A. J.

1994-01-01

The research during the third reporting period focused on fixed order robust control design for hypersonic vehicles. A new technique was developed to synthesize fixed order H(sub infinity) controllers. A controller canonical form is imposed on the compensator structure and a homotopy algorithm is employed to perform the controller design. Various reduced order controllers are designed for a simplified version of the hypersonic vehicle model used in our previous studies to demonstrate the capabilities of the code. However, further work is needed to investigate the issue of numerical ill-conditioning for large order systems and to make the numerical approach more reliable.

Ranking of stopping criteria for log domain diffeomorphic demons application in clinical radiation therapy.

PubMed

Peroni, M; Golland, P; Sharp, G C; Baroni, G

2011-01-01

Deformable Image Registration is a complex optimization algorithm with the goal of modeling a non-rigid transformation between two images. A crucial issue in this field is guaranteeing the user a robust but computationally reasonable algorithm. We rank the performances of four stopping criteria and six stopping value computation strategies for a log domain deformable registration. The stopping criteria we test are: (a) velocity field update magnitude, (b) vector field Jacobian, (c) mean squared error, and (d) harmonic energy. Experiments demonstrate that comparing the metric value over the last three iterations with the metric minimum of between four and six previous iterations is a robust and appropriate strategy. The harmonic energy and vector field update magnitude metrics give the best results in terms of robustness and speed of convergence.

Robust Speech Enhancement Using Two-Stage Filtered Minima Controlled Recursive Averaging

NASA Astrophysics Data System (ADS)

Ghourchian, Negar; Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

In this paper we propose an algorithm for estimating noise in highly non-stationary noisy environments, which is a challenging problem in speech enhancement. This method is based on minima-controlled recursive averaging (MCRA) whereby an accurate, robust and efficient noise power spectrum estimation is demonstrated. We propose a two-stage technique to prevent the appearance of musical noise after enhancement. This algorithm filters the noisy speech to achieve a robust signal with minimum distortion in the first stage. Subsequently, it estimates the residual noise using MCRA and removes it with spectral subtraction. The proposed Filtered MCRA (FMCRA) performance is evaluated using objective tests on the Aurora database under various noisy environments. These measures indicate the higher output SNR and lower output residual noise and distortion.

A robust nonparametric framework for reconstruction of stochastic differential equation models

NASA Astrophysics Data System (ADS)

Rajabzadeh, Yalda; Rezaie, Amir Hossein; Amindavar, Hamidreza

2016-05-01

In this paper, we employ a nonparametric framework to robustly estimate the functional forms of drift and diffusion terms from discrete stationary time series. The proposed method significantly improves the accuracy of the parameter estimation. In this framework, drift and diffusion coefficients are modeled through orthogonal Legendre polynomials. We employ the least squares regression approach along with the Euler-Maruyama approximation method to learn coefficients of stochastic model. Next, a numerical discrete construction of mean squared prediction error (MSPE) is established to calculate the order of Legendre polynomials in drift and diffusion terms. We show numerically that the new method is robust against the variation in sample size and sampling rate. The performance of our method in comparison with the kernel-based regression (KBR) method is demonstrated through simulation and real data. In case of real dataset, we test our method for discriminating healthy electroencephalogram (EEG) signals from epilepsy ones. We also demonstrate the efficiency of the method through prediction in the financial data. In both simulation and real data, our algorithm outperforms the KBR method.

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob; Englander, Arnold

2014-01-01

Trajectory optimization methods using MBH have become well developed during the past decade. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing RVs from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by Englander significantly improves MBH performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness, where efficiency is finding better solutions in less time, and robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive RWs originally developed in the field of statistical physics.

A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters.

PubMed

Liu, Hong; Wang, Jie; Xu, Xiangyang; Song, Enmin; Wang, Qian; Jin, Renchao; Hung, Chih-Cheng; Fei, Baowei

2014-11-01

A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod. Copyright © 2014 Elsevier Inc. All rights reserved.

Topological networks for quantum communication between distant qubits

NASA Astrophysics Data System (ADS)

Lang, Nicolai; Büchler, Hans Peter

2017-11-01

Efficient communication between qubits relies on robust networks, which allow for fast and coherent transfer of quantum information. It seems natural to harvest the remarkable properties of systems characterized by topological invariants to perform this task. Here, we show that a linear network of coupled bosonic degrees of freedom, characterized by topological bands, can be employed for the efficient exchange of quantum information over large distances. Important features of our setup are that it is robust against quenched disorder, all relevant operations can be performed by global variations of parameters, and the time required for communication between distant qubits approaches linear scaling with their distance. We demonstrate that our concept can be extended to an ensemble of qubits embedded in a two-dimensional network to allow for communication between all of them.

Real-time target tracking of soft tissues in 3D ultrasound images based on robust visual information and mechanical simulation.

PubMed

Royer, Lucas; Krupa, Alexandre; Dardenne, Guillaume; Le Bras, Anthony; Marchand, Eric; Marchal, Maud

2017-01-01

In this paper, we present a real-time approach that allows tracking deformable structures in 3D ultrasound sequences. Our method consists in obtaining the target displacements by combining robust dense motion estimation and mechanical model simulation. We perform evaluation of our method through simulated data, phantom data, and real-data. Results demonstrate that this novel approach has the advantage of providing correct motion estimation regarding different ultrasound shortcomings including speckle noise, large shadows and ultrasound gain variation. Furthermore, we show the good performance of our method with respect to state-of-the-art techniques by testing on the 3D databases provided by MICCAI CLUST'14 and CLUST'15 challenges. Copyright © 2016 Elsevier B.V. All rights reserved.

Fault Accommodation in Control of Flexible Systems

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.; Lim, Kyong B.

1998-01-01

New synthesis techniques for the design of fault accommodating controllers for flexible systems are developed. Three robust control design strategies, static dissipative, dynamic dissipative and mu-synthesis, are used in the approach. The approach provides techniques for designing controllers that maximize, in some sense, the tolerance of the closed-loop system against faults in actuators and sensors, while guaranteeing performance robustness at a specified performance level, measured in terms of the proximity of the closed-loop poles to the imaginary axis (the degree of stability). For dissipative control designs, nonlinear programming is employed to synthesize the controllers, whereas in mu-synthesis, the traditional D-K iteration is used. To demonstrate the feasibility of the proposed techniques, they are applied to the control design of a structural model of a flexible laboratory test structure.

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

Scalable Robust Principal Component Analysis Using Grassmann Averages.

PubMed

Hauberg, Sren; Feragen, Aasa; Enficiaud, Raffi; Black, Michael J

2016-11-01

In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average ( GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average ( TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

Performance of the ICAO standard core service modulation and coding techniques

NASA Technical Reports Server (NTRS)

Lodge, John; Moher, Michael

1988-01-01

Aviation binary phase shift keying (A-BPSK) is described and simulated performance results are given that demonstrate robust performance in the presence of hardlimiting amplifiers. The performance of coherently-detected A-BPSK with rate 1/2 convolutional coding are given. The performance loss due to the Rician fading was shown to be less than 1 dB over the simulated range. A partially coherent detection scheme that does not require carrier phase recovery was described. This scheme exhibits similiar performance to coherent detection, at high bit error rates, while it is superior at lower bit error rates.

Proposed biomimetic molecular sensor array for astrobiology applications

NASA Astrophysics Data System (ADS)

Cullen, D. C.; Grant, W. D.; Piletsky, S.; Sims, M. R.

2001-08-01

A key objective of future astrobiology lander missions, e.g. to Mars and Europa, is the detection of biomarkers - molecules whose presence indicates the existence of either current or extinct life. To address limitations of current analytical methods for biomarker detection, we describe the methodology of a new project for demonstration of a robust molecular-recognition sensor array for astrobiology biomarkers. The sensor array will be realised by assembling components that have been demonstrated individually in previous or current research projects. The major components are (1) robust artificial molecular receptors comprised of molecular imprinted polymer (MIP) recognition systems and (2) a sensor array comprised of both optical and electrochemical sensor elements. These components will be integrated together using ink-jet printing technology coupled with in situ photo-polymerisation of MIPs. For demonstration, four model biomarkers are chosen as targets and represent various classes of potential biomarkers. Objectives of the proposed work include (1) demonstration of practical proof-of-concept, (2) identify areas for further development and (3) provide performance and design data for follow-up projects leading to astrobiology missions.

Young Children Are Not Underconfident with Practice: The Benefit of Ignoring a Fallible Memory Heuristic

ERIC Educational Resources Information Center

Lipko, Amanda R.; Dunlosky, John; Lipowski, Stacy L.; Merriman, William E.

2012-01-01

In this study the authors investigated whether children demonstrated the "underconfidence-with-practice" (UWP) effect. This effect is a highly robust metacognitive illusion in which adults become underconfident in their memory performance when asked to predict their memory for the same items across multiple study-test trials. One…

Whole genome structural analysis of Caribbean hair sheep reveals quantitative link to West African ancestry

USDA-ARS?s Scientific Manuscript database

Hair sheep of Caribbean origin have become an important part of the U.S. sheep industry. Lack of wool eliminates a number of health concerns and drastically reduces the cost of production. More importantly, Caribbean hair sheep demonstrate robust performance even in the presence of drug resistant ga...

Lignosulfonate and elevated pH can enhance enzymatic saccharification of lignocelluloses

Treesearch

ZJ Wang; TQ Lan; JY Zhu

2013-01-01

Nonspecific (nonproductive) binding (adsorption) of cellulase by lignin has been identified as a key barrier to reduce cellulase loading for economical sugar and biofuel production from lignocellulosic biomass. Sulfite Pretreatment to Overcome Recalcitrance of Lignocelluloses (SPORL) is a relatively new process, but demonstrated robust performance for sugar and biofuel...

Defending A+: how to maintain a high credit rating during a facilities overhaul.

PubMed

Ollie, Ed; Kolb-Collier, Deborah; Clay, Scott

2007-11-01

To defend its strong credit rating, New Hanover Regional Medical Center: Demonstrated a clear and cohesive vision. Improved short-term financial performance. Developed robust financial projections. Avoided reliance on a "build it and they will come" strategy. Managed construction cost risks. Minimized construction disruption. Anticipated financial market concerns and questions

Aerocapture Guidance Algorithm Comparison Campaign

NASA Technical Reports Server (NTRS)

Rousseau, Stephane; Perot, Etienne; Graves, Claude; Masciarelli, James P.; Queen, Eric

2002-01-01

The aerocapture is a promising technique for the future human interplanetary missions. The Mars Sample Return was initially based on an insertion by aerocapture. A CNES orbiter Mars Premier was developed to demonstrate this concept. Mainly due to budget constraints, the aerocapture was cancelled for the French orbiter. A lot of studies were achieved during the three last years to develop and test different guidance algorithms (APC, EC, TPC, NPC). This work was shared between CNES and NASA, with a fruitful joint working group. To finish this study an evaluation campaign has been performed to test the different algorithms. The objective was to assess the robustness, accuracy, capability to limit the load, and the complexity of each algorithm. A simulation campaign has been specified and performed by CNES, with a similar activity on the NASA side to confirm the CNES results. This evaluation has demonstrated that the numerical guidance principal is not competitive compared to the analytical concepts. All the other algorithms are well adapted to guaranty the success of the aerocapture. The TPC appears to be the more robust, the APC the more accurate, and the EC appears to be a good compromise.

Microflow1, a sheathless fiber-optic flow cytometry biomedical platform: demonstration onboard the international space station.

PubMed

Dubeau-Laramée, Geneviève; Rivière, Christophe; Jean, Isabelle; Mermut, Ozzy; Cohen, Luchino Y

2014-04-01

A fiber-optic based flow cytometry platform was designed to build a portable and robust instrument for space applications. At the core of the Microflow1 is a unique fiber-optic flow cell fitted to a fluidic system and fiber coupled to the source and detection channels. A Microflow1 engineering unit was first tested and benchmarked against a commercial flow cytometer as a reference in a standard laboratory environment. Testing in parabolic flight campaigns was performed to establish Microflow1's performance in weightlessness, before operating the new platform on the International Space Station. Microflow1 had comparable performances to commercial systems, and operated remarkably and robustly in weightlessness (microgravity). Microflow1 supported immunophenotyping as well as microbead-based multiplexed cytokine assays in the space environment and independently of gravity levels. Results presented here provide evidence that this fiber-optic cytometer technology is inherently compatible with the space environment with negligible compromise to analytical performance. © 2013 International Society for Advancement of Cytometry.

The Role of Design-of-Experiments in Managing Flow in Compact Air Vehicle Inlets

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Miller, Daniel N.; Gridley, Marvin C.; Agrell, Johan

2003-01-01

It is the purpose of this study to demonstrate the viability and economy of Design-of-Experiments methodologies to arrive at microscale secondary flow control array designs that maintain optimal inlet performance over a wide range of the mission variables and to explore how these statistical methods provide a better understanding of the management of flow in compact air vehicle inlets. These statistical design concepts were used to investigate the robustness properties of low unit strength micro-effector arrays. Low unit strength micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion. The term robustness is used in this paper in the same sense as it is used in the industrial problem solving community. It refers to minimizing the effects of the hard-to-control factors that influence the development of a product or process. In Robustness Engineering, the effects of the hard-to-control factors are often called noise , and the hard-to-control factors themselves are referred to as the environmental variables or sometimes as the Taguchi noise variables. Hence Robust Optimization refers to minimizing the effects of the environmental or noise variables on the development (design) of a product or process. In the management of flow in compact inlets, the environmental or noise variables can be identified with the mission variables. Therefore this paper formulates a statistical design methodology that minimizes the impact of variations in the mission variables on inlet performance and demonstrates that these statistical design concepts can lead to simpler inlet flow management systems.

The PROPEL Electrodynamic Tether Demonstration Mission

NASA Technical Reports Server (NTRS)

Bilen, Sven G.; Johnson, C. Les; Wiegmann, Bruce M.; Alexander, Leslie; Gilchrist, Brian E.; Hoyt, Robert P.; Elder, Craig H.; Fuhrhop, Keith P.; Scadera, Michael

2012-01-01

The PROPEL ("Propulsion using Electrodynamics") mission will demonstrate the operation of an electrodynamic tether propulsion system in low Earth orbit and advance its technology readiness level for multiple applications. The PROPEL mission has two primary objectives: first, to demonstrate the capability of electrodynamic tether technology to provide robust and safe, near-propellantless propulsion for orbit-raising, de-orbit, plane change, and station keeping, as well as to perform orbital power harvesting and formation flight; and, second, to fully characterize and validate the performance of an integrated electrodynamic tether propulsion system, qualifying it for infusion into future multiple satellite platforms and missions with minimal modification. This paper provides an overview of the PROPEL system and design reference missions; mission goals and required measurements; and ongoing PROPEL mission design efforts.

A Robust Inner and Outer Loop Control Method for Trajectory Tracking of a Quadrotor

PubMed Central

Xia, Dunzhu; Cheng, Limei; Yao, Yanhong

2017-01-01

In order to achieve the complicated trajectory tracking of quadrotor, a geometric inner and outer loop control scheme is presented. The outer loop generates the desired rotation matrix for the inner loop. To improve the response speed and robustness, a geometric SMC controller is designed for the inner loop. The outer loop is also designed via sliding mode control (SMC). By Lyapunov theory and cascade theory, the closed-loop system stability is guaranteed. Next, the tracking performance is validated by tracking three representative trajectories. Then, the robustness of the proposed control method is illustrated by trajectory tracking in presence of model uncertainty and disturbances. Subsequently, experiments are carried out to verify the method. In the experiment, ultra wideband (UWB) is used for indoor positioning. Extended Kalman Filter (EKF) is used for fusing inertial measurement unit (IMU) and UWB measurements. The experimental results show the feasibility of the designed controller in practice. The comparative experiments with PD and PD loop demonstrate the robustness of the proposed control method. PMID:28925984

Robust Frequency-Domain Constrained Feedback Design via a Two-Stage Heuristic Approach.

PubMed

Li, Xianwei; Gao, Huijun

2015-10-01

Based on a two-stage heuristic method, this paper is concerned with the design of robust feedback controllers with restricted frequency-domain specifications (RFDSs) for uncertain linear discrete-time systems. Polytopic uncertainties are assumed to enter all the system matrices, while RFDSs are motivated by the fact that practical design specifications are often described in restricted finite frequency ranges. Dilated multipliers are first introduced to relax the generalized Kalman-Yakubovich-Popov lemma for output feedback controller synthesis and robust performance analysis. Then a two-stage approach to output feedback controller synthesis is proposed: at the first stage, a robust full-information (FI) controller is designed, which is used to construct a required output feedback controller at the second stage. To improve the solvability of the synthesis method, heuristic iterative algorithms are further formulated for exploring the feedback gain and optimizing the initial FI controller at the individual stage. The effectiveness of the proposed design method is finally demonstrated by the application to active control of suspension systems.

A novel binary shape context for 3D local surface description

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Liu, Yuan; Liang, Fuxun; Li, Bijun; Zang, Yufu

2017-08-01

3D local surface description is now at the core of many computer vision technologies, such as 3D object recognition, intelligent driving, and 3D model reconstruction. However, most of the existing 3D feature descriptors still suffer from low descriptiveness, weak robustness, and inefficiency in both time and memory. To overcome these challenges, this paper presents a robust and descriptive 3D Binary Shape Context (BSC) descriptor with high efficiency in both time and memory. First, a novel BSC descriptor is generated for 3D local surface description, and the performance of the BSC descriptor under different settings of its parameters is analyzed. Next, the descriptiveness, robustness, and efficiency in both time and memory of the BSC descriptor are evaluated and compared to those of several state-of-the-art 3D feature descriptors. Finally, the performance of the BSC descriptor for 3D object recognition is also evaluated on a number of popular benchmark datasets, and an urban-scene dataset is collected by a terrestrial laser scanner system. Comprehensive experiments demonstrate that the proposed BSC descriptor obtained high descriptiveness, strong robustness, and high efficiency in both time and memory and achieved high recognition rates of 94.8%, 94.1% and 82.1% on the considered UWA, Queen, and WHU datasets, respectively.

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

PubMed

Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing

2018-03-07

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

PubMed Central

Zheng, Binqi; Yuan, Xiaobing

2018-01-01

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results. PMID:29518960

Self-synchronization for spread spectrum audio watermarks after time scale modification

NASA Astrophysics Data System (ADS)

Nadeau, Andrew; Sharma, Gaurav

2014-02-01

De-synchronizing operations such as insertion, deletion, and warping pose significant challenges for watermarking. Because these operations are not typical for classical communications, watermarking techniques such as spread spectrum can perform poorly. Conversely, specialized synchronization solutions can be challenging to analyze/ optimize. This paper addresses desynchronization for blind spread spectrum watermarks, detected without reference to any unmodified signal, using the robustness properties of short blocks. Synchronization relies on dynamic time warping to search over block alignments to find a sequence with maximum correlation to the watermark. This differs from synchronization schemes that must first locate invariant features of the original signal, or estimate and reverse desynchronization before detection. Without these extra synchronization steps, analysis for the proposed scheme builds on classical SS concepts and allows characterizes the relationship between the size of search space (number of detection alignment tests) and intrinsic robustness (continuous search space region covered by each individual detection test). The critical metrics that determine the search space, robustness, and performance are: time-frequency resolution of the watermarking transform, and blocklength resolution of the alignment. Simultaneous robustness to (a) MP3 compression, (b) insertion/deletion, and (c) time-scale modification is also demonstrated for a practical audio watermarking scheme developed in the proposed framework.

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

PubMed Central

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches. PMID:26927111

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

PubMed

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-02-25

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

A robust rotation-invariance displacement measurement method for a micro-/nano-positioning system

NASA Astrophysics Data System (ADS)

Zhang, Xiang; Zhang, Xianmin; Wu, Heng; Li, Hai; Gan, Jinqiang

2018-05-01

A robust and high-precision displacement measurement method for a compliant mechanism-based micro-/nano-positioning system is proposed. The method is composed of an integer-pixel and a sub-pixel matching procedure. In the proposed algorithm (Pro-A), an improved ring projection transform (IRPT) and gradient information are used as features for approximating the coarse candidates and fine locations, respectively. Simulations are conducted and the results show that the Pro-A has the ability of rotation-invariance and strong robustness, with a theoretical accuracy of 0.01 pixel. To validate the practical performance, a series of experiments are carried out using a computer micro-vision and laser interferometer system (LIMS). The results demonstrate that both the LIMS and Pro-A can achieve high precision, while the Pro-A has better stability and adaptability.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

PubMed

Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

2016-01-01

In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Robust Kalman filtering cooperated Elman neural network learning for vision-sensing-based robotic manipulation with global stability.

PubMed

Zhong, Xungao; Zhong, Xunyu; Peng, Xiafu

2013-10-08

In this paper, a global-state-space visual servoing scheme is proposed for uncalibrated model-independent robotic manipulation. The scheme is based on robust Kalman filtering (KF), in conjunction with Elman neural network (ENN) learning techniques. The global map relationship between the vision space and the robotic workspace is learned using an ENN. This learned mapping is shown to be an approximate estimate of the Jacobian in global space. In the testing phase, the desired Jacobian is arrived at using a robust KF to improve the ENN learning result so as to achieve robotic precise convergence of the desired pose. Meanwhile, the ENN weights are updated (re-trained) using a new input-output data pair vector (obtained from the KF cycle) to ensure robot global stability manipulation. Thus, our method, without requiring either camera or model parameters, avoids the corrupted performances caused by camera calibration and modeling errors. To demonstrate the proposed scheme's performance, various simulation and experimental results have been presented using a six-degree-of-freedom robotic manipulator with eye-in-hand configurations.

Comparison between two methodologies for urban drainage decision aid.

PubMed

Moura, P M; Baptista, M B; Barraud, S

2006-01-01

The objective of the present work is to compare two methodologies based on multicriteria analysis for the evaluation of stormwater systems. The first methodology was developed in Brazil and is based on performance-cost analysis, the second one is ELECTRE III. Both methodologies were applied to a case study. Sensitivity and robustness analyses were then carried out. These analyses demonstrate that both methodologies have equivalent results, and present low sensitivity and high robustness. These results prove that the Brazilian methodology is consistent and can be used safely in order to select a good solution or a small set of good solutions that could be compared with more detailed methods afterwards.

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance.

PubMed

Wang, Minlin; Ren, Xuemei; Chen, Qiang

2018-01-01

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Learning to assign binary weights to binary descriptor

NASA Astrophysics Data System (ADS)

Huang, Zhoudi; Wei, Zhenzhong; Zhang, Guangjun

2016-10-01

Constructing robust binary local feature descriptors are receiving increasing interest due to their binary nature, which can enable fast processing while requiring significantly less memory than their floating-point competitors. To bridge the performance gap between the binary and floating-point descriptors without increasing the computational cost of computing and matching, optimal binary weights are learning to assign to binary descriptor for considering each bit might contribute differently to the distinctiveness and robustness. Technically, a large-scale regularized optimization method is applied to learn float weights for each bit of the binary descriptor. Furthermore, binary approximation for the float weights is performed by utilizing an efficient alternatively greedy strategy, which can significantly improve the discriminative power while preserve fast matching advantage. Extensive experimental results on two challenging datasets (Brown dataset and Oxford dataset) demonstrate the effectiveness and efficiency of the proposed method.

The Nitrogen Balancing Act: Tracking the Environmental Performance of Food Production

PubMed Central

McLellan, Eileen L; Cassman, Kenneth G; Eagle, Alison J; Woodbury, Peter B; Sela, Shai; Tonitto, Christina; Marjerison, Rebecca D; van Es, Harold M

2018-01-01

Abstract Farmers, food supply-chain entities, and policymakers need a simple but robust indicator to demonstrate progress toward reducing nitrogen pollution associated with food production. We show that nitrogen balance—the difference between nitrogen inputs and nitrogen outputs in an agricultural production system—is a robust measure of nitrogen losses that is simple to calculate, easily understood, and based on readily available farm data. Nitrogen balance provides farmers with a means of demonstrating to an increasingly concerned public that they are succeeding in reducing nitrogen losses while also improving the overall sustainability of their farming operation. Likewise, supply-chain companies and policymakers can use nitrogen balance to track progress toward sustainability goals. We describe the value of nitrogen balance in translating environmental targets into actionable goals for farmers and illustrate the potential roles of science, policy, and agricultural support networks in helping farmers achieve them. PMID:29662247

Phase magnification by two-axis countertwisting for detection-noise robust interferometry

NASA Astrophysics Data System (ADS)

Anders, Fabian; Pezzè, Luca; Smerzi, Augusto; Klempt, Carsten

2018-04-01

Entanglement-enhanced atom interferometry has the potential of surpassing the standard quantum limit and eventually reaching the ultimate Heisenberg bound. The experimental progress is, however, hindered by various technical noise sources, including the noise in the detection of the output quantum state. The influence of detection noise can be largely overcome by exploiting echo schemes, where the entanglement-generating interaction is repeated after the interferometer sequence. Here, we propose an echo protocol that uses two-axis countertwisting as the main nonlinear interaction. We demonstrate that the scheme is robust to detection noise and its performance is superior compared to the already demonstrated one-axis twisting echo scheme. In particular, the sensitivity maintains the Heisenberg scaling in the limit of a large particle number. Finally, we show that the protocol can be implemented with spinor Bose-Einstein condensates. Our results thus outline a realistic approach to mitigate the detection noise in quantum-enhanced interferometry.

Efficient Parallel Levenberg-Marquardt Model Fitting towards Real-Time Automated Parametric Imaging Microscopy

PubMed Central

Zhu, Xiang; Zhang, Dianwen

2013-01-01

We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy. PMID:24130785

Discrimination of portraits using a hybrid parallel joint transform correlator system

NASA Astrophysics Data System (ADS)

Inaba, Rieko; Hashimoto, Asako; Kodate, Kashiko

1999-05-01

A hybrid parallel joint transform correlation system is demonstrated through the introduction of a five-channel binary zone plate array and is applied to the discrimination of portraits for a presumed criminal investigation. In order to improve performance, we adopt pe-processing of images with white area of 20%. Furthermore, we discuss the robustness.

Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis under Conditions of Multivariate Nonnormality.

ERIC Educational Resources Information Center

Fouladi, Rachel T.

2000-01-01

Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…

Reexamining the Impact of Nonnormality in Two-Group Comparison Procedures

ERIC Educational Resources Information Center

Kang, Yoonjeong; Harring, Jeffrey R.; Li, Ming

2015-01-01

The authors performed a Monte Carlo simulation to empirically investigate the robustness and power of 4 methods in testing mean differences for 2 independent groups under conditions in which 2 populations may not demonstrate the same pattern of nonnormality. The approaches considered were the t test, Wilcoxon rank-sum test, Welch-James test with…

Do frailty and cognitive impairment affect dual-task cost during walking in the oldest old institutionalized patients?

PubMed

Cadore, Eduardo L; Casas-Herrero, Alvaro; Zambom-Ferraresi, Fabricio; Martínez-Ramírez, Alicia; Millor, Nora; Gómez, Marisol; Moneo, Ana B Bays; Izquierdo, Mikel

2015-12-01

The objective of this study was to investigate dual-task costs in several elderly populations, including robust oldest old, frail oldest old with MCI, frail oldest old without MCI, and frail elderly with dementia. Sixty-four elderly men and women categorized into frail without MCI (age 93.4 ± 3.2 years, n = 20), frail with MCI (age 92.4 ± 4.2 years, n = 13), robust (age 88.2 ± 4.1 years, n = 10), and patients with dementia (age 88.1 ± 5.1 years, n = 21). Five-meter gait ability and timed-up-and-go (TUG) tests with single and dual-task performance were assessed in the groups. Dual-task cost in both 5-m habitual gait velocity test and TUG test was calculated by the time differences between single and dual-task performance. The robust group exhibited better 5-m gait and TUG test performances in the single and dual-task conditions compared with the other three groups (P < 0.001), and the frail and frail + MCI groups exhibited better performances than the dementia group (P < 0.001). No significant differences were observed between the frail and frail + MCI groups. However, all groups exhibited lower gait velocities in the verbal and arithmetic task conditions, but the dual-task cost of the groups were similar. Robust individuals exhibited superior single and dual-task walking performances than the other three groups, and the frail and frail + MCI individuals exhibited performances that were superior to those of the patients with dementia. However, the dual-task costs, i.e., the changes in gait performance when elderly participants switch from a single to a dual task, were similar among all four of the investigated groups. Therefore, these results demonstrated that the magnitude of the impairment in gait pattern is independent of frailty and cognitive impairment status.

Adaptive Core Simulation Employing Discrete Inverse Theory - Part II: Numerical Experiments

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

Abdel-Khalik, Hany S.; Turinsky, Paul J.

2005-07-15

Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. The companion paper, ''Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory,'' describes in detail the theoretical background of the proposed adaptive techniques. This paper, Part II, demonstrates several computational experiments conducted to assess the fidelity and robustness of the proposed techniques. The intentmore » is to check the ability of the adapted core simulator model to predict future core observables that are not included in the adaption or core observables that are recorded at core conditions that differ from those at which adaption is completed. Also, this paper demonstrates successful utilization of an efficient sensitivity analysis approach to calculate the sensitivity information required to perform the adaption for millions of input core parameters. Finally, this paper illustrates a useful application for adaptive simulation - reducing the inconsistencies between two different core simulator code systems, where the multitudes of input data to one code are adjusted to enhance the agreement between both codes for important core attributes, i.e., core reactivity and power distribution. Also demonstrated is the robustness of such an application.« less

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob A.; Englander, Arnold C.

2014-01-01

Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Wang, S.; Ancell, B. C.; Huang, G. H.; Baetz, B. W.

2018-03-01

Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

Integrated Demonstration of Instrument Placement , Robust Execution and Contingent Planning

NASA Technical Reports Server (NTRS)

Pedersen, L.; Bualat, M.; Lees, D.; Smith, D. E.; Korsmeyer, David (Technical Monitor); Washington, R.

2003-01-01

This paper describes an integrated demonstration of ground-based contingent planning, robust execution and autonomous instrument placement for the efficient exploration of a site by a prototype Mars rover.

A Highly Stretchable and Robust Non-fluorinated Superhydrophobic Surface.

PubMed

Ju, Jie; Yao, Xi; Hou, Xu; Liu, Qihan; Zhang, Yu Shrike; Khademhosseini, Ali

2017-08-21

Superhydrophobic surface simultaneously possessing exceptional stretchability, robustness, and non-fluorination is highly desirable in applications ranging from wearable devices to artificial skins. While conventional superhydrophobic surfaces typically feature stretchability, robustness, or non-fluorination individually, co-existence of all these features still remains a great challenge. Here we report a multi-performance superhydrophobic surface achieved through incorporating hydrophilic micro-sized particles with pre-stretched silicone elastomer. The commercial silicone elastomer (Ecoflex) endowed the resulting surface with high stretchability; the densely packed micro-sized particles in multi-layers contributed to the preservation of the large surface roughness even under large strains; and the physical encapsulation of the microparticles by silicone elastomer due to the capillary dragging effect and the chemical interaction between the hydrophilic silica and the elastomer gave rise to the robust and non-fluorinated superhydrophobicity. It was demonstrated that the as-prepared fluorine-free surface could preserve the superhydrophobicity under repeated stretching-relaxing cycles. Most importantly, the surface's superhydrophobicity can be well maintained after severe rubbing process, indicating wear-resistance. Our novel superhydrophobic surface integrating multiple key properties, i.e. stretchability, robustness, and non-fluorination, is expected to provide unique advantages for a wide range of applications in biomedicine, energy, and electronics.

Robust and Blind 3D Mesh Watermarking in Spatial Domain Based on Faces Categorization and Sorting

NASA Astrophysics Data System (ADS)

Molaei, Amir Masoud; Ebrahimnezhad, Hossein; Sedaaghi, Mohammad Hossein

2016-06-01

In this paper, a 3D watermarking algorithm in spatial domain is presented with blind detection. In the proposed method, a negligible visual distortion is observed in host model. Initially, a preprocessing is applied on the 3D model to make it robust against geometric transformation attacks. Then, a number of triangle faces are determined as mark triangles using a novel systematic approach in which faces are categorized and sorted robustly. In order to enhance the capability of information retrieval by attacks, block watermarks are encoded using Reed-Solomon block error-correcting code before embedding into the mark triangles. Next, the encoded watermarks are embedded in spherical coordinates. The proposed method is robust against additive noise, mesh smoothing and quantization attacks. Also, it is stout next to geometric transformation, vertices and faces reordering attacks. Moreover, the proposed algorithm is designed so that it is robust against the cropping attack. Simulation results confirm that the watermarked models confront very low distortion if the control parameters are selected properly. Comparison with other methods demonstrates that the proposed method has good performance against the mesh smoothing attacks.

Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources.

PubMed

Ge, Ruiyang; Wang, Yubao; Zhang, Jipeng; Yao, Li; Zhang, Hang; Long, Zhiying

2016-04-01

As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Although either temporal or spatial prior information has been introduced into the constrained ICA and semi-blind ICA methods to improve the performance of ICA in fMRI data analysis, certain types of additional prior information, such as the sparsity, has seldom been added to the ICA algorithms as constraints. In this study, we proposed a SparseFastICA method by adding the source sparsity as a constraint to the FastICA algorithm to improve the performance of the widely used FastICA. The source sparsity is estimated through a smoothed ℓ0 norm method. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of SparseFastICA and made a performance comparison between SparseFastICA, FastICA and Infomax ICA. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of SparseFastICA for the source separation in fMRI data. Both the simulated and real fMRI experimental results showed that SparseFastICA has better robustness to noise and better spatial detection power than FastICA. Although the spatial detection power of SparseFastICA and Infomax did not show significant difference, SparseFastICA had faster computation speed than Infomax. SparseFastICA was comparable to the Infomax algorithm with a faster computation speed. More importantly, SparseFastICA outperformed FastICA in robustness and spatial detection power and can be used to identify more accurate brain networks than FastICA algorithm. Copyright © 2016 Elsevier B.V. All rights reserved.

One-step solvothermal deposition of ZnO nanorod arrays on a wood surface for robust superamphiphobic performance and superior ultraviolet resistance

PubMed Central

Yao, Qiufang; Wang, Chao; Fan, Bitao; Wang, Hanwei; Sun, Qingfeng; Jin, Chunde; Zhang, Hong

2016-01-01

In the present paper, uniformly large-scale wurtzite-structured ZnO nanorod arrays (ZNAs) were deposited onto a wood surface through a one-step solvothermal method. The as-prepared samples were characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetry (TG), and differential thermal analysis (DTA). ZNAs with a diameter of approximately 85 nm and a length of approximately 1.5 μm were chemically bonded onto the wood surface through hydrogen bonds. The superamphiphobic performance and ultraviolet resistance were measured and evaluated by water or oil contact angles (WCA or OCA) and roll-off angles, sand abrasion tests and an artificially accelerated ageing test. The results show that the ZNA-treated wood demonstrates a robust superamphiphobic performance under mechanical impact, corrosive liquids, intermittent and transpositional temperatures, and water spray. Additionally, the as-prepared wood sample shows superior ultraviolet resistance. PMID:27775091

Linear quadratic servo control of a reusable rocket engine

NASA Technical Reports Server (NTRS)

Musgrave, Jeffrey L.

1991-01-01

A design method for a servo compensator is developed in the frequency domain using singular values. The method is applied to a reusable rocket engine. An intelligent control system for reusable rocket engines was proposed which includes a diagnostic system, a control system, and an intelligent coordinator which determines engine control strategies based on the identified failure modes. The method provides a means of generating various linear multivariable controllers capable of meeting performance and robustness specifications and accommodating failure modes identified by the diagnostic system. Command following with set point control is necessary for engine operation. A Kalman filter reconstructs the state while loop transfer recovery recovers the required degree of robustness while maintaining satisfactory rejection of sensor noise from the command error. The approach is applied to the design of a controller for a rocket engine satisfying performance constraints in the frequency domain. Simulation results demonstrate the performance of the linear design on a nonlinear engine model over all power levels during mainstage operation.

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

PubMed

Owen, Julia P; Wipf, David P; Attias, Hagai T; Sekihara, Kensuke; Nagarajan, Srikantan S

2012-03-01

In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data. Copyright © 2011 Elsevier Inc. All rights reserved.

Robust Multivariable Estimation of the Relevant Information Coming from a Wheel Speed Sensor and an Accelerometer Embedded in a Car under Performance Tests

PubMed Central

Hernandez, Wilmar

2005-01-01

In the present paper, in order to estimate the response of both a wheel speed sensor and an accelerometer placed in a car under performance tests, robust and optimal multivariable estimation techniques are used. In this case, the disturbances and noises corrupting the relevant information coming from the sensors' outputs are so dangerous that their negative influence on the electrical systems impoverish the general performance of the car. In short, the solution to this problem is a safety related problem that deserves our full attention. Therefore, in order to diminish the negative effects of the disturbances and noises on the car's electrical and electromechanical systems, an optimum observer is used. The experimental results show a satisfactory improvement in the signal-to-noise ratio of the relevant signals and demonstrate the importance of the fusion of several intelligent sensor design techniques when designing the intelligent sensors that today's cars need.

One-step solvothermal deposition of ZnO nanorod arrays on a wood surface for robust superamphiphobic performance and superior ultraviolet resistance

NASA Astrophysics Data System (ADS)

Yao, Qiufang; Wang, Chao; Fan, Bitao; Wang, Hanwei; Sun, Qingfeng; Jin, Chunde; Zhang, Hong

2016-10-01

In the present paper, uniformly large-scale wurtzite-structured ZnO nanorod arrays (ZNAs) were deposited onto a wood surface through a one-step solvothermal method. The as-prepared samples were characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetry (TG), and differential thermal analysis (DTA). ZNAs with a diameter of approximately 85 nm and a length of approximately 1.5 μm were chemically bonded onto the wood surface through hydrogen bonds. The superamphiphobic performance and ultraviolet resistance were measured and evaluated by water or oil contact angles (WCA or OCA) and roll-off angles, sand abrasion tests and an artificially accelerated ageing test. The results show that the ZNA-treated wood demonstrates a robust superamphiphobic performance under mechanical impact, corrosive liquids, intermittent and transpositional temperatures, and water spray. Additionally, the as-prepared wood sample shows superior ultraviolet resistance.

Hierarchical Ada robot programming system (HARPS)- A complete and working telerobot control system based on the NASREM model

NASA Technical Reports Server (NTRS)

Leake, Stephen; Green, Tom; Cofer, Sue; Sauerwein, Tim

1989-01-01

HARPS is a telerobot control system that can perform some simple but useful tasks. This capability is demonstrated by performing the ORU exchange demonstration. HARPS is based on NASREM (NASA Standard Reference Model). All software is developed in Ada, and the project incorporates a number of different CASE (computer-aided software engineering) tools. NASREM was found to be a valid and useful model for building a telerobot control system. Its hierarchical and distributed structure creates a natural and logical flow for implementing large complex robust control systems. The ability of Ada to create and enforce abstraction enhanced the implementation of such control systems.

High performance felt-metal-wick heat pipe for solar receivers

NASA Astrophysics Data System (ADS)

Andraka, Charles E.; Moss, Timothy A.; Baturkin, Volodymyr; Zaripov, Vladlen; Nishchyk, Oleksandr

2016-05-01

Sodium heat pipes have been identified as a potentially effective heat transport approach for CSP systems that require near-isothermal input to power cycles or storage, such as dish Stirling and highly recuperated reheat-cycle supercritical CO2 turbines. Heat pipes offer high heat flux capabilities, leading to small receivers, as well as low exergetic losses through isothermal coupling with the engine. Sandia developed a felt metal wick approach in the 1990's, and demonstrated very high performance1. However, multiple durability issues arose, primarily the structural collapse of the wick at temperature over short time periods. NTUU developed several methods of improving robustness of the wick2, but the resulting wick had limited performance capabilities. For application to CSP systems, the wick structures must retain high heat pipe performance with robustness for long term operation. In this paper we present our findings in developing an optimal balance between performance and ruggedness, including operation of a laboratory-scale heat pipe for over 5500 hours so far. Application of heat pipes to dish-Stirling systems has been shown to increase performance as much as 20%3, and application to supercritical CO2 systems has been proposed.

Remodeling of Hippocampal Mossy Fibers Is Selectively Induced Seven Days after the Acquisition of a Spatial but Not a Cued Reference Memory Task

ERIC Educational Resources Information Center

Rekart, Jerome L.; Sandoval, C. Jimena; Bermudez-Rattoni, Federico; Routtenberg, Aryeh

2007-01-01

Relating storage of specific information to a particular neuromorphological change is difficult because behavioral performance factors are not readily disambiguated from underlying cognitive processes. This issue is addressed here by demonstrating robust reorganization of the hippocampal mossy fiber terminal field (MFTF) when adult rats learn the…

Position and attitude tracking control for a quadrotor UAV.

PubMed

Xiong, Jing-Jing; Zheng, En-Hui

2014-05-01

A synthesis control method is proposed to perform the position and attitude tracking control of the dynamical model of a small quadrotor unmanned aerial vehicle (UAV), where the dynamical model is underactuated, highly-coupled and nonlinear. Firstly, the dynamical model is divided into a fully actuated subsystem and an underactuated subsystem. Secondly, a controller of the fully actuated subsystem is designed through a novel robust terminal sliding mode control (TSMC) algorithm, which is utilized to guarantee all state variables converge to their desired values in short time, the convergence time is so small that the state variables are acted as time invariants in the underactuated subsystem, and, a controller of the underactuated subsystem is designed via sliding mode control (SMC), in addition, the stabilities of the subsystems are demonstrated by Lyapunov theory, respectively. Lastly, in order to demonstrate the robustness of the proposed control method, the aerodynamic forces and moments and air drag taken as external disturbances are taken into account, the obtained simulation results show that the synthesis control method has good performance in terms of position and attitude tracking when faced with external disturbances. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Stretchable Electronic Sensors of Nanocomposite Network Films for Ultrasensitive Chemical Vapor Sensing.

PubMed

Yan, Hong; Zhong, Mengjuan; Lv, Ze; Wan, Pengbo

2017-11-01

A stretchable, transparent, and body-attachable chemical sensor is assembled from the stretchable nanocomposite network film for ultrasensitive chemical vapor sensing. The stretchable nanocomposite network film is fabricated by in situ preparation of polyaniline/MoS 2 (PANI/MoS 2 ) nanocomposite in MoS 2 suspension and simultaneously nanocomposite deposition onto prestrain elastomeric polydimethylsiloxane substrate. The assembled stretchable electronic sensor demonstrates ultrasensitive sensing performance as low as 50 ppb, robust sensing stability, and reliable stretchability for high-performance chemical vapor sensing. The ultrasensitive sensing performance of the stretchable electronic sensors could be ascribed to the synergistic sensing advantages of MoS 2 and PANI, higher specific surface area, the reliable sensing channels of interconnected network, and the effectively exposed sensing materials. It is expected to hold great promise for assembling various flexible stretchable chemical vapor sensors with ultrasensitive sensing performance, superior sensing stability, reliable stretchability, and robust portability to be potentially integrated into wearable electronics for real-time monitoring of environment safety and human healthcare. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Retrospective multi-phase non-contrast-enhanced magnetic resonance angiography (ROMANCE MRA) for robust angiogram separation in the presence of cardiac arrhythmia.

PubMed

Kim, Hahnsung; Park, Suhyung; Kim, Eung Yeop; Park, Jaeseok

2018-09-01

To develop a novel, retrospective multi-phase non-contrast-enhanced MRA (ROMANCE MRA) in a single acquisition for robust angiogram separation even in the presence of cardiac arrhythmia. In the proposed ROMANCE MRA, data were continuously acquired over all cardiac phases using retrospective, multi-phase flow-sensitive single-slab 3D fast spin echo (FSE) with variable refocusing flip angles, while an external pulse oximeter was in sync with pulse repetitions in FSE to record real-time information on cardiac cycles. Data were then sorted into k-bin space using the real-time cardiac information. Angiograms were reconstructed directly from k-bin space by solving a constrained optimization problem with both subtraction-induced sparsity and low rank priors. Peripheral MRA was performed in normal volunteers with/without caffeine consumption and a volunteer with cardiac arrhythmia using conventional fresh blood imaging (FBI) and the proposed ROMANCE MRA for comparison. The proposed ROMANCE MRA shows superior performance in accurately delineating both major and small vessel branches with robust background suppression if compared with conventional FBI. Even in the presence of irregular heartbeats, the proposed method exhibits clear depiction of angiograms over conventional methods within clinically reasonable imaging time. We successfully demonstrated the feasibility of the proposed ROMANCE MRA in generating robust angiograms with background suppression. © 2018 International Society for Magnetic Resonance in Medicine.

Robust and Flexible Aramid Nanofiber/Graphene Layer-by-Layer Electrodes.

PubMed

Kwon, Se Ra; Elinski, Meagan B; Batteas, James D; Lutkenhaus, Jodie L

2017-05-24

Aramid nanofibers (ANFs), or nanoscale Kevlar fibers, are of interest for their high mechanical performance and functional nanostructure. The dispersible nature of ANFs opens up processing opportunities for creating mechanically robust and flexible nanocomposites, particularly for energy and power applications. The challenge is to manipulate ANFs into an electrode structure that balances mechanical and electrochemical performance to yield a robust and flexible electrode. Here, ANFs and graphene oxide (GO) sheets are blended using layer-by-layer (LbL) assembly to achieve mechanically flexible supercapacitor electrodes. After reduction, the resulting electrodes exhibit an ANF-rich structure where ANFs act as a polymer matrix that interfacially interacts with reduced graphene oxide sheets. It is shown that ANF/GO deposition proceeds by hydrogen bonding and π-π interactions, leading to linear growth (1.2 nm/layer pairs) and a composition of 75 wt % ANFs and 25 wt % GO sheets. Chemical reduction leads to a high areal capacitance of 221 μF/cm 2 , corresponding to 78 F/cm 3 . Nanomechanical testing shows that the electrodes have a modulus intermediate between those of the two native materials. No cracks or defects are observed upon flexing ANF/GO films 1000 times at a radius of 5 mm, whereas a GO control shows extensive cracking. These results demonstrate that electrodes containing ANFs and reduced GO sheets are promising for flexible, mechanically robust energy and power.

Reliable numerical computation in an optimal output-feedback design

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm is presented for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters. The algorithm is a part of a design algorithm for optimal linear dynamic output-feedback controller that minimizes a finite-time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control-law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed-loop eigensystem. This approach through the use of an accurate Pade series approximation does not require the closed-loop system matrix to be diagonalizable. The algorithm was included in a control design package for optimal robust low-order controllers. Usefulness of the proposed numerical algorithm was demonstrated using numerous practical design cases where degeneracies occur frequently in the closed-loop system under an arbitrary controller design initialization and during the numerical search.

UWE-3, in-orbit performance and lessons learned of a modular and flexible satellite bus for future pico-satellite formations

NASA Astrophysics Data System (ADS)

Busch, S.; Bangert, P.; Dombrovski, S.; Schilling, K.

2015-12-01

Formations of small satellites offer promising perspectives due to improved temporal and spatial coverage and resolution at reasonable costs. The UWE-program addresses in-orbit demonstrations of key technologies to enable formations of cooperating distributed spacecraft at pico-satellite level. In this context, the CubeSat UWE-3 addresses experiments for evaluation of real-time attitude determination and control. UWE-3 introduces also a modular and flexible pico-satellite bus as a robust and extensible base for future missions. Technical objective was a very low power consumption of the COTS-based system, nevertheless providing a robust performance of this miniature satellite by advanced microprocessor redundancy and fault detection, identification and recovery software. This contribution addresses the UWE-3 design and mission results with emphasis on the operational experiences of the attitude determination and control system.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Advanced rotorcraft control using parameter optimization

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters is presented. The algorithm is part of a design algorithm for an optimal linear dynamic output feedback controller that minimizes a finite time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed loop eigensystem. This approach through the use of a accurate Pade series approximation does not require the closed loop system matrix to be diagonalizable. The algorithm has been included in a control design package for optimal robust low order controllers. Usefulness of the proposed numerical algorithm has been demonstrated using numerous practical design cases where degeneracies occur frequently in the closed loop system under an arbitrary controller design initialization and during the numerical search.

Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

PubMed

Zaghian, Maryam; Cao, Wenhua; Liu, Wei; Kardar, Laleh; Randeniya, Sharmalee; Mohan, Radhe; Lim, Gino

2017-03-01

Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so-called "worst case dose" and "minmax" robust optimization approaches and conventional planning target volume (PTV)-based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull-based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP-PTV-based, NLP-PTV-based, LP-worst case dose, NLP-worst case dose, LP-minmax, and NLP-minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP-based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP-based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP-based methods was superior for the skull-based and head and neck cancer patients. Overall, LP-based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull-based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV-based optimization, and the NLP-PTV-based optimization outperformed the LP-PTV-based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP-based methods had notably fewer scanning spots than did those generated using NLP-based methods. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Robust matching for voice recognition

NASA Astrophysics Data System (ADS)

Higgins, Alan; Bahler, L.; Porter, J.; Blais, P.

1994-10-01

This paper describes an automated method of comparing a voice sample of an unknown individual with samples from known speakers in order to establish or verify the individual's identity. The method is based on a statistical pattern matching approach that employs a simple training procedure, requires no human intervention (transcription, work or phonetic marketing, etc.), and makes no assumptions regarding the expected form of the statistical distributions of the observations. The content of the speech material (vocabulary, grammar, etc.) is not assumed to be constrained in any way. An algorithm is described which incorporates frame pruning and channel equalization processes designed to achieve robust performance with reasonable computational resources. An experimental implementation demonstrating the feasibility of the concept is described.

Robust Airfoil Optimization in High Resolution Design Space

NASA Technical Reports Server (NTRS)

Li, Wu; Padula, Sharon L.

2003-01-01

The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of B-spline control points as design variables yet the resulting airfoil shape is fairly smooth, and (3) it allows the user to make a trade-off between the level of optimization and the amount of computing time consumed. The robust optimization method is demonstrated by solving a lift-constrained drag minimization problem for a two-dimensional airfoil in viscous flow with a large number of geometric design variables. Our experience with robust optimization indicates that our strategy produces reasonable airfoil shapes that are similar to the original airfoils, but these new shapes provide drag reduction over the specified range of Mach numbers. We have tested this strategy on a number of advanced airfoil models produced by knowledgeable aerodynamic design team members and found that our strategy produces airfoils better or equal to any designs produced by traditional design methods.

Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method.

PubMed

Ye, Jinzuo; Chi, Chongwei; Xue, Zhenwen; Wu, Ping; An, Yu; Xu, Han; Zhang, Shuang; Tian, Jie

2014-02-01

Fluorescence molecular tomography (FMT), as a promising imaging modality, can three-dimensionally locate the specific tumor position in small animals. However, it remains challenging for effective and robust reconstruction of fluorescent probe distribution in animals. In this paper, we present a novel method based on sparsity adaptive subspace pursuit (SASP) for FMT reconstruction. Some innovative strategies including subspace projection, the bottom-up sparsity adaptive approach, and backtracking technique are associated with the SASP method, which guarantees the accuracy, efficiency, and robustness for FMT reconstruction. Three numerical experiments based on a mouse-mimicking heterogeneous phantom have been performed to validate the feasibility of the SASP method. The results show that the proposed SASP method can achieve satisfactory source localization with a bias less than 1mm; the efficiency of the method is much faster than mainstream reconstruction methods; and this approach is robust even under quite ill-posed condition. Furthermore, we have applied this method to an in vivo mouse model, and the results demonstrate the feasibility of the practical FMT application with the SASP method.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

On the robustness of EC-PC spike detection method for online neural recording.

PubMed

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

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

Liu, Wenyang; Cheung, Yam; Sawant, Amit

2016-05-15

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparsemore » regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.« less

A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system.

PubMed

Liu, Wenyang; Cheung, Yam; Sawant, Amit; Ruan, Dan

2016-05-01

To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.

A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

PubMed Central

Liu, Wenyang; Cheung, Yam; Sawant, Amit; Ruan, Dan

2016-01-01

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications. PMID:27147347

Estimating nonrigid motion from inconsistent intensity with robust shape features

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

Liu, Wenyang; Ruan, Dan, E-mail: druan@mednet.ucla.edu; Department of Radiation Oncology, University of California, Los Angeles, California 90095

2013-12-15

Purpose: To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Methods: Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, andmore » regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. Results: To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. Conclusions: The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.« less

Estimating nonrigid motion from inconsistent intensity with robust shape features.

PubMed

Liu, Wenyang; Ruan, Dan

2013-12-01

To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.

Directional selection causes decanalization in a group I ribozyme.

PubMed

Hayden, Eric J; Weikert, Christian; Wagner, Andreas

2012-01-01

A canalized genotype is robust to environmental or genetic perturbations. Canalization is expected to result from stabilizing selection on a well-adapted phenotype. Decanalization, the loss of robustness, might follow periods of directional selection toward a new optimum. The evolutionary forces causing decanalization are still unknown, in part because it is difficult to determine the fitness effects of mutations in populations of organisms with complex genotypes and phenotypes. Here, we report direct experimental measurements of robustness in a system with a simple genotype and phenotype, the catalytic activity of an RNA enzyme. We find that the robustness of a population of RNA enzymes decreases during a period of directional selection in the laboratory. The decrease in robustness is primarily caused by the selective sweep of a genotype that is decanalized relative to the wild-type, both in terms of mutational robustness and environmental robustness (thermodynamic stability). Our results experimentally demonstrate that directional selection can cause decanalization on short time scales, and demonstrate co-evolution of mutational and environmental robustness.

Discriminative object tracking via sparse representation and online dictionary learning.

PubMed

Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

2014-04-01

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, Mark; Klimeck, Gerhard; Hanks, David; Hua, Hook

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Out preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates.

Adaptive Wing Camber Optimization: A Periodic Perturbation Approach

NASA Technical Reports Server (NTRS)

Espana, Martin; Gilyard, Glenn

1994-01-01

Available redundancy among aircraft control surfaces allows for effective wing camber modifications. As shown in the past, this fact can be used to improve aircraft performance. To date, however, algorithm developments for in-flight camber optimization have been limited. This paper presents a perturbational approach for cruise optimization through in-flight camber adaptation. The method uses, as a performance index, an indirect measurement of the instantaneous net thrust. As such, the actual performance improvement comes from the integrated effects of airframe and engine. The algorithm, whose design and robustness properties are discussed, is demonstrated on the NASA Dryden B-720 flight simulator.

Demonstration of a Robust Sensor System for Remote Condition Monitoring of Heat-Distribution System Manholes

DTIC Science & Technology

2016-02-01

15 Figure 16.Temperature sensor wires routed into galvanized steel piping...The technical monitors were Daniel J. Dunmire (OUSD(AT&L)), Bernie Rodriguez (IMPW-FM), and Valerie D. Hines (DAIM-ODF). The work was performed...or result in severe corrosion of steel HDS components, and must be corrected immediately to avoid costly collateral impacts on energy costs or HDS

Ionic liquid-mediated synthesis of meso-scale porous lanthanum-transition-metal perovskites with high CO oxidation performance

DOE PAGES

Lu, Hanfeng; Zhang, Pengfei; Qiao, Zhen-An; ...

2015-02-19

Lanthanum-transition-metal perovskites with robust meso-scale porous frameworks (meso-LaMO 3) are synthesized through use of ionic liquids. The resultant samples demonstrate a rather high activity for CO oxidation, by taking advantage of unique nanostructure-derived benefits. This synthesis strategy opens up a new opportunity for preparing functional mesoporous complex oxides of various compositions.

Ionic liquid-mediated synthesis of meso-scale porous lanthanum-transition-metal perovskites with high CO oxidation performance

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

Lu, Hanfeng; Zhang, Pengfei; Qiao, Zhen-An

Lanthanum-transition-metal perovskites with robust meso-scale porous frameworks (meso-LaMO 3) are synthesized through use of ionic liquids. The resultant samples demonstrate a rather high activity for CO oxidation, by taking advantage of unique nanostructure-derived benefits. This synthesis strategy opens up a new opportunity for preparing functional mesoporous complex oxides of various compositions.

Performance and robustness of hybrid model predictive control for controllable dampers in building models

NASA Astrophysics Data System (ADS)

Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.

2016-04-01

A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.

Selective robust optimization: A new intensity-modulated proton therapy optimization strategy

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

Li, Yupeng; Niemela, Perttu; Siljamaki, Sami

2015-08-15

Purpose: To develop a new robust optimization strategy for intensity-modulated proton therapy as an important step in translating robust proton treatment planning from research to clinical applications. Methods: In selective robust optimization, a worst-case-based robust optimization algorithm is extended, and terms of the objective function are selectively computed from either the worst-case dose or the nominal dose. Two lung cancer cases and one head and neck cancer case were used to demonstrate the practical significance of the proposed robust planning strategy. The lung cancer cases had minimal tumor motion less than 5 mm, and, for the demonstration of the methodology,more » are assumed to be static. Results: Selective robust optimization achieved robust clinical target volume (CTV) coverage and at the same time increased nominal planning target volume coverage to 95.8%, compared to the 84.6% coverage achieved with CTV-based robust optimization in one of the lung cases. In the other lung case, the maximum dose in selective robust optimization was lowered from a dose of 131.3% in the CTV-based robust optimization to 113.6%. Selective robust optimization provided robust CTV coverage in the head and neck case, and at the same time improved controls over isodose distribution so that clinical requirements may be readily met. Conclusions: Selective robust optimization may provide the flexibility and capability necessary for meeting various clinical requirements in addition to achieving the required plan robustness in practical proton treatment planning settings.« less

Titanium MEMS Technology Development for Drug Delivery and Microfluidic Applications

NASA Astrophysics Data System (ADS)

Khandan, Omid

The use of microelectromechanical systems (MEMS) technology in medical and biological applications has increased dramatically in the past decade due to the potential for enhanced sensitivity, functionality, and performance associated with the miniaturization of devices, as well as the market potential for low-cost, personalized medicine. However, the utility of such devices in clinical medicine is ultimately limited due to factors associated with prevailing micromachined materials such as silicon, as it poses concerns of safety and reliability due to its intrinsically brittle properties, making it prone to catastrophic failure. Recent advances in titanium (Ti) micromachining provides an opportunity to create devices with enhanced safety and performance due to its proven biocompatibility and high fracture toughness, which causes it to fail by means of graceful, plasticity-based deformation. Motivated by this opportunity, we discuss our efforts to advance Ti MEMS technology in two ways: 1) Through the development of titanium-based microneedles (MNs) that seek to provide a safer, simpler, and more efficacious means of ocular drug delivery, and 2) Through the advancement of Ti anodic bonding for future realization of robust microfluidic devices for photocatalysis applications. As for the first of these thrusts, we show that MN devices with in-plane geometry and through-thickness fenestrations that serve as drug reservoirs for passive delivery via diffusive transport from fast-dissolving coatings can be fabricated utilizing Ti deep reactive ion etching (Ti DRIE). Our mechanical testing and finite element analysis (FEA) results suggest that these devices possess sufficient stiffness for reliable corneal insertion. Our MN coating studies show that, relative to solid MNs of identical shank dimension, fenestrated devices can increase drug carrying capacity by 5-fold. Furthermore, we demonstrate that through-etched fenestrations provide a protective cavity for delivering drugs subsurface, thereby enhancing delivery efficiencies in an ex vivo rabbit cornea model. Collectively, these results show the potential embodied in developing Ti MNs for effective, minimally invasive, and low-cost ocular drug delivery. Additionally, or the second of these thrusts, we report the development of an anodic bonding process that allows, for the first time, high-strength joining of bulk Ti and glass substrates at the wafer-scale, without need for interlayers or adhesives. We demonstrate that uniform, full-wafer bonding can be achieved at temperatures as low as 250°C, and that failure during burst pressure testing occurs via crack propagation through the glass, rather than the Ti/glass interface, thus demonstrating the robustness of the bonding. Moreover, using optimized bonding conditions, we demonstrate the fabrication of rudimentary Ti/glass-based microfluidic devices at the wafer-scale, and their leak-free operation under pressure-driven flow. Finally, we demonstrate the monolithic integration of nanoporous titanium dioxide within such devices, thus illustrating the promise embodied in Ti anodic bonding for future realization of robust microfluidic devices for photocatalysis applications. Together, these results demonstrate the potential embodied in utilizing Ti MEMS technology for the fabrication of novel drug delivery and microfluidic systems with enhanced robustness, safety, and performance.

Assessing the Robustness of Green Infrastructure under Stochastic Design Storms and Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Chui, T. F. M.; Yang, Y.

2017-12-01

Green infrastructures (GI) have been widely used to mitigate flood risk, improve surface water quality, and to restore predevelopment hydrologic regimes. Commonly-used GI include, bioretention system, porous pavement and green roof, etc. They are normally sized to fulfil different design criteria (e.g. providing certain storage depths, limiting peak surface flow rates) that are formulated for current climate conditions. While GI commonly have long lifespan, the sensitivity of their performance to climate change is however unclear. This study first proposes a method to formulate suitable design criteria to meet different management interests (e.g. different levels of first flush reduction and peak flow reduction). Then typical designs of GI are proposed. In addition, a high resolution stochastic design storm generator using copulas and random cascade model is developed, which is calibrated using recorded rainfall time series. Then, few climate change scenarios are generated by varying the duration and depth of design storms, and changing the parameters of the calibrated storm generator. Finally, the performance of GI with typical designs under the random synthesized design storms are then assessed using numerical modeling. The robustness of the designs is obtained by the comparing their performance in the future scenarios to the current one. This study overall examines the robustness of the current GI design criteria under uncertain future climate conditions, demonstrating whether current GI design criteria should be modified to account for climate change.

Development of robust building energy demand-side control strategy under uncertainty

NASA Astrophysics Data System (ADS)

Kim, Sean Hay

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.

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.

MPD thruster technology

NASA Technical Reports Server (NTRS)

Myers, Roger M.; Mantenieks, Maris A.; Lapointe, Michael R.

1991-01-01

MPD (MagnetoPlasmaDynamic) thrusters demonstrated between 2000 and 7000 seconds specific impulse at efficiencies approaching 40 percent, and were operated continuously at power levels over 500 kW. These demonstrated capabilities, combined with the simplicity and robustness of the thruster, make them attractive candidates for application to both unmanned and manned orbit raising, lunar, and planetary missions. To date, however, only a limited number of thruster configurations, propellants, and operating conditions were studied. The present status of MPD research is reviewed, including developments in the measured performance levels and electrode erosion rates. Theoretical studies of the thruster dynamics are also described. Significant progress was made in establishing empirical scaling laws, performance and lifetime limitations and in the development of numerical codes to simulate the flow field and electrode processes.

Compensation for Unconstrained Catheter Shaft Motion in Cardiac Catheters

PubMed Central

Degirmenci, Alperen; Loschak, Paul M.; Tschabrunn, Cory M.; Anter, Elad; Howe, Robert D.

2016-01-01

Cardiac catheterization with ultrasound (US) imaging catheters provides real time US imaging from within the heart, but manually navigating a four degree of freedom (DOF) imaging catheter is difficult and requires extensive training. Existing work has demonstrated robotic catheter steering in constrained bench top environments. Closed-loop control in an unconstrained setting, such as patient vasculature, remains a significant challenge due to friction, backlash, and physiological disturbances. In this paper we present a new method for closed-loop control of the catheter tip that can accurately and robustly steer 4-DOF cardiac catheters and other flexible manipulators despite these effects. The performance of the system is demonstrated in a vasculature phantom and an in vivo porcine animal model. During bench top studies the robotic system converged to the desired US imager pose with sub-millimeter and sub-degree-level accuracy. During animal trials the system achieved 2.0 mm and 0.65° accuracy. Accurate and robust robotic navigation of flexible manipulators will enable enhanced visualization and treatment during procedures. PMID:27525170

A robust molecular probe for Ångstrom-scale analytics in liquids

PubMed Central

Nirmalraj, Peter; Thompson, Damien; Dimitrakopoulos, Christos; Gotsmann, Bernd; Dumcenco, Dumitru; Kis, Andras; Riel, Heike

2016-01-01

Traditionally, nanomaterial profiling using a single-molecule-terminated scanning probe is performed at the vacuum–solid interface often at a few Kelvin, but is not a notion immediately associated with liquid–solid interface at room temperature. Here, using a scanning tunnelling probe functionalized with a single C60 molecule stabilized in a high-density liquid, we resolve low-dimensional surface defects, atomic interfaces and capture Ångstrom-level bond-length variations in single-layer graphene and MoS2. Atom-by-atom controllable imaging contrast is demonstrated at room temperature and the electronic structure of the C60–metal probe complex within the encompassing liquid molecules is clarified using density functional theory. Our findings demonstrates that operating a robust single-molecular probe is not restricted to ultra-high vacuum and cryogenic settings. Hence the scope of high-precision analytics can be extended towards resolving sub-molecular features of organic elements and gauging ambient compatibility of emerging layered materials with atomic-scale sensitivity under experimentally less stringent conditions. PMID:27516157

A Robust Symmetry-Based Approach to Exploit Terra-SAR-X Dual-Pol Data for Targets at Sea Observation

NASA Astrophysics Data System (ADS)

Velotto, D.; Nunziata, F.; Migliaccio, M.; Lehner, S.

2013-08-01

In this study a simple physical property, known as reflection symmetry, is exploited to differentiate the objects in marine scenes, i.e. sea surface and metallic targets. First the reflection property is verified and demonstrated against actual SAR images, by measuring the magnitude of the correlation between cross- polarized channels (i.e. HH/HV or VH/VV). Then, a sensitivity study is performed to show the potential of the proposed method in observing man-made metallic target at sea. The robustness of the proposed technique is demonstrated using coherent dual-polarimetric X- band SAR data acquired by the satellite TerraSAR-X in both cross-polarization combinations, with different incidence angle and weather conditions. Co-located ground truth information provided by Automatic Identification System (AIS) reports and harbor charts are used to locate ships, navigation aids and buoys. The proposed method outperforms standard single polarization SAR targets at sea observation independently of the radar geometry and oceanic phenomena.

A robust quantitative near infrared modeling approach for blend monitoring.

PubMed

Mohan, Shikhar; Momose, Wataru; Katz, Jeffrey M; Hossain, Md Nayeem; Velez, Natasha; Drennen, James K; Anderson, Carl A

2018-01-30

This study demonstrates a material sparing Near-Infrared modeling approach for powder blend monitoring. In this new approach, gram scale powder mixtures are subjected to compression loads to simulate the effect of scale using an Instron universal testing system. Models prepared by the new method development approach (small-scale method) and by a traditional method development (blender-scale method) were compared by simultaneously monitoring a 1kg batch size blend run. Both models demonstrated similar model performance. The small-scale method strategy significantly reduces the total resources expended to develop Near-Infrared calibration models for on-line blend monitoring. Further, this development approach does not require the actual equipment (i.e., blender) to which the method will be applied, only a similar optical interface. Thus, a robust on-line blend monitoring method can be fully developed before any large-scale blending experiment is viable, allowing the blend method to be used during scale-up and blend development trials. Copyright © 2017. Published by Elsevier B.V.

How robust is a robust policy? A comparative analysis of alternative robustness metrics for supporting robust decision analysis.

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2015-04-01

In response to climate and socio-economic change, in various policy domains there is increasingly a call for robust plans or policies. That is, plans or policies that performs well in a very large range of plausible futures. In the literature, a wide range of alternative robustness metrics can be found. The relative merit of these alternative conceptualizations of robustness has, however, received less attention. Evidently, different robustness metrics can result in different plans or policies being adopted. This paper investigates the consequences of several robustness metrics on decision making, illustrated here by the design of a flood risk management plan. A fictitious case, inspired by a river reach in the Netherlands is used. The performance of this system in terms of casualties, damages, and costs for flood and damage mitigation actions is explored using a time horizon of 100 years, and accounting for uncertainties pertaining to climate change and land use change. A set of candidate policy options is specified up front. This set of options includes dike raising, dike strengthening, creating more space for the river, and flood proof building and evacuation options. The overarching aim is to design an effective flood risk mitigation strategy that is designed from the outset to be adapted over time in response to how the future actually unfolds. To this end, the plan will be based on the dynamic adaptive policy pathway approach (Haasnoot, Kwakkel et al. 2013) being used in the Dutch Delta Program. The policy problem is formulated as a multi-objective robust optimization problem (Kwakkel, Haasnoot et al. 2014). We solve the multi-objective robust optimization problem using several alternative robustness metrics, including both satisficing robustness metrics and regret based robustness metrics. Satisficing robustness metrics focus on the performance of candidate plans across a large ensemble of plausible futures. Regret based robustness metrics compare the performance of a candidate plan with the performance of other candidate plans across a large ensemble of plausible futures. Initial results suggest that the simplest satisficing metric, inspired by the signal to noise ratio, results in very risk averse solutions. Other satisficing metrics, which handle the average performance and the dispersion around the average separately, provide substantial additional insights into the trade off between the average performance, and the dispersion around this average. In contrast, the regret-based metrics enhance insight into the relative merits of candidate plans, while being less clear on the average performance or the dispersion around this performance. These results suggest that it is beneficial to use multiple robustness metrics when doing a robust decision analysis study. Haasnoot, M., J. H. Kwakkel, W. E. Walker and J. Ter Maat (2013). "Dynamic Adaptive Policy Pathways: A New Method for Crafting Robust Decisions for a Deeply Uncertain World." Global Environmental Change 23(2): 485-498. Kwakkel, J. H., M. Haasnoot and W. E. Walker (2014). "Developing Dynamic Adaptive Policy Pathways: A computer-assisted approach for developing adaptive strategies for a deeply uncertain world." Climatic Change.

A vanadium-doped ZnO nanosheets-polymer composite for flexible piezoelectric nanogenerators

NASA Astrophysics Data System (ADS)

Shin, Sung-Ho; Kwon, Yang Hyeog; Lee, Min Hyung; Jung, Joo-Yun; Seol, Jae Hun; Nah, Junghyo

2016-01-01

We report high performance flexible piezoelectric nanogenerators (PENGs) by employing vanadium (V)-doped ZnO nanosheets (NSs) and the polydimethylsiloxane (PDMS) composite structure. The V-doped ZnO NSs were synthesized to overcome the inherently low piezoelectric properties of intrinsic ZnO. Ferroelectric phase transition induced in the V-doped ZnO NSs contributed to significantly improve the performance of the PENGs after the poling process. Consequently, the PENGs exhibited high output voltage and current up to ~32 V and ~6.2 μA, respectively, under the applied strain, which are sufficient to directly turn on a number of light emitting diodes (LEDs). The composite approach for PENG fabrication is scalable, robust, and reproducible during periodic bending/releasing over extended cycles. The approach introduced here extends the performance limits of ZnO-based PENGs and demonstrates their potential as energy harvesting devices.We report high performance flexible piezoelectric nanogenerators (PENGs) by employing vanadium (V)-doped ZnO nanosheets (NSs) and the polydimethylsiloxane (PDMS) composite structure. The V-doped ZnO NSs were synthesized to overcome the inherently low piezoelectric properties of intrinsic ZnO. Ferroelectric phase transition induced in the V-doped ZnO NSs contributed to significantly improve the performance of the PENGs after the poling process. Consequently, the PENGs exhibited high output voltage and current up to ~32 V and ~6.2 μA, respectively, under the applied strain, which are sufficient to directly turn on a number of light emitting diodes (LEDs). The composite approach for PENG fabrication is scalable, robust, and reproducible during periodic bending/releasing over extended cycles. The approach introduced here extends the performance limits of ZnO-based PENGs and demonstrates their potential as energy harvesting devices. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr07185b

Comprehensive Profiling of Immune Responses in MARV Survivors Demonstrates Robust Th1-Skewing with Short Lived Neutralizing Antibody Responses

DTIC Science & Technology

2017-03-29

Beyond IgG or IgM ELISAs performed for diagnostic purposes, virtually the entirety of the literature available regarding filovirus immune responses in...supernatants for an expanded cytokine analysis by ELISA . A representative set of flow plots for CD4 and CD8 T cell responses from a MARV survivor is shown in...performed a multiplex ELISA assay with the culture supernatants to analyze a broader range of cytokines. We focused on five cytokines that are germane

Micro-Structured Sapphire Fiber Sensors for Simultaneous Measurements of High-T and Dynamic Gas Pressure in Harsh Environments

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

Xiao, Hai; Tsai, Hai-Lung; Dong, Junhang

2014-09-30

This is the final report for the program “Micro-Structured Sapphire Fiber Sensors for Simultaneous Measurements of High Temperature and Dynamic Gas Pressure in Harsh Environments”, funded by NETL, and performed by Missouri University of Science and Technology, Clemson University and University of Cincinnati from October 1, 2009 to September 30, 2014. Securing a sustainable energy economy by developing affordable and clean energy from coal and other fossil fuels is a central element to the mission of The U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL). To further this mission, NETL funds research and development of novel sensor technologiesmore » that can function under the extreme operating conditions often found in advanced power systems. The main objective of this research program is to conduct fundamental and applied research that will lead to successful development and demonstration of robust, multiplexed, microstructured silica and single-crystal sapphire fiber sensors to be deployed into the hot zones of advanced power and fuel systems for simultaneous measurements of high temperature and gas pressure. The specific objectives of this research program include: 1) Design, fabrication and demonstration of multiplexed, robust silica and sapphire fiber temperature and dynamic gas pressure sensors that can survive and maintain fully operational in high-temperature harsh environments. 2) Development and demonstration of a novel method to demodulate the multiplexed interferograms for simultaneous measurements of temperature and gas pressure in harsh environments. 3) Development and demonstration of novel sapphire fiber cladding and low numerical aperture (NA) excitation techniques to assure high signal integrity and sensor robustness.« less

A Robust Damage-Reporting Strategy for Polymeric Materials Enabled by Aggregation-Induced Emission.

PubMed

Robb, Maxwell J; Li, Wenle; Gergely, Ryan C R; Matthews, Christopher C; White, Scott R; Sottos, Nancy R; Moore, Jeffrey S

2016-09-28

Microscopic damage inevitably leads to failure in polymers and composite materials, but it is difficult to detect without the aid of specialized equipment. The ability to enhance the detection of small-scale damage prior to catastrophic material failure is important for improving the safety and reliability of critical engineering components, while simultaneously reducing life cycle costs associated with regular maintenance and inspection. Here, we demonstrate a simple, robust, and sensitive fluorescence-based approach for autonomous detection of damage in polymeric materials and composites enabled by aggregation-induced emission (AIE). This simple, yet powerful system relies on a single active component, and the general mechanism delivers outstanding performance in a wide variety of materials with diverse chemical and mechanical properties.

A robust variable sampling time BLDC motor control design based upon μ-synthesis.

PubMed

Hung, Chung-Wen; Yen, Jia-Yush

2013-01-01

The variable sampling rate system is encountered in many applications. When the speed information is derived from the position marks along the trajectory, one would have a speed dependent sampling rate system. The conventional fixed or multisampling rate system theory may not work in these cases because the system dynamics include the uncertainties which resulted from the variable sampling rate. This paper derived a convenient expression for the speed dependent sampling rate system. The varying sampling rate effect is then translated into multiplicative uncertainties to the system. The design then uses the popular μ-synthesis process to achieve a robust performance controller design. The implementation on a BLDC motor demonstrates the effectiveness of the design approach.

A Robust Variable Sampling Time BLDC Motor Control Design Based upon μ-Synthesis

PubMed Central

Yen, Jia-Yush

2013-01-01

The variable sampling rate system is encountered in many applications. When the speed information is derived from the position marks along the trajectory, one would have a speed dependent sampling rate system. The conventional fixed or multisampling rate system theory may not work in these cases because the system dynamics include the uncertainties which resulted from the variable sampling rate. This paper derived a convenient expression for the speed dependent sampling rate system. The varying sampling rate effect is then translated into multiplicative uncertainties to the system. The design then uses the popular μ-synthesis process to achieve a robust performance controller design. The implementation on a BLDC motor demonstrates the effectiveness of the design approach. PMID:24327804

Management of Total Pressure Recovery, Distortion and High Cycle Fatigue in Compact Air Vehicle Inlets

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Baust, Henry D.; Agrell, Johan

2002-01-01

It is the purpose of this study to demonstrate the viability and economy of Response Surface Methods (RSM) and Robustness Design Concepts (RDC) to arrive at micro-secondary flow control installation designs that maintain optimal inlet performance over a range of the mission variables. These statistical design concepts were used to investigate the robustness properties of 'low unit strength' micro-effector installations. 'Low unit strength' micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion.

Noise properties of a corner-cube Michelson interferometer LWIR hyperspectral imager

NASA Astrophysics Data System (ADS)

Bergstrom, D.; Renhorn, I.; Svensson, T.; Persson, R.; Hallberg, T.; Lindell, R.; Boreman, G.

2010-04-01

Interferometric hyperspectral imagers using infrared focal plane array (FPA) sensors have received increasing interest within the field of security and defence. Setups are commonly based upon either the Sagnac or the Michelson configuration, where the former is usually preferred due to its mechanical robustness. However, the Michelson configuration shows advantages in larger FOV due to better vignetting performance and improved signal-to-noise ratio and cost reduction due to relaxation of beamsplitter specifications. Recently, a laboratory prototype of a more robust and easy-to-align corner-cube Michelson hyperspectral imager has been demonstrated. The prototype is based upon an uncooled bolometric FPA in the LWIR (8-14 μm) spectral band and in this paper the noise properties of this hyperspectral imager are discussed.

An efficient and robust MRI-guided radiotherapy planning approach for targeting abdominal organs and tumours in the mouse

PubMed Central

Bird, Luke; Tullis, Iain D. C.; Newman, Robert G.; Corroyer-Dulmont, Aurelien; Falzone, Nadia; Azad, Abul; Vallis, Katherine A.; Sansom, Owen J.; Muschel, Ruth J.; Vojnovic, Borivoj; Hill, Mark A.; Fokas, Emmanouil; Smart, Sean C.

2017-01-01

Introduction Preclinical CT-guided radiotherapy platforms are increasingly used but the CT images are characterized by poor soft tissue contrast. The aim of this study was to develop a robust and accurate method of MRI-guided radiotherapy (MR-IGRT) delivery to abdominal targets in the mouse. Methods A multimodality cradle was developed for providing subject immobilisation and its performance was evaluated. Whilst CT was still used for dose calculations, target identification was based on MRI. Each step of the radiotherapy planning procedure was validated initially in vitro using BANG gel dosimeters. Subsequently, MR-IGRT of normal adrenal glands with a size-matched collimated beam was performed. Additionally, the SK-N-SH neuroblastoma xenograft model and the transgenic KPC model of pancreatic ductal adenocarcinoma were used to demonstrate the applicability of our methods for the accurate delivery of radiation to CT-invisible abdominal tumours. Results The BANG gel phantoms demonstrated a targeting efficiency error of 0.56 ± 0.18 mm. The in vivo stability tests of body motion during MR-IGRT and the associated cradle transfer showed that the residual body movements are within this MR-IGRT targeting error. Accurate MR-IGRT of the normal adrenal glands with a size-matched collimated beam was confirmed by γH2AX staining. Regression in tumour volume was observed almost immediately post MR-IGRT in the neuroblastoma model, further demonstrating accuracy of x-ray delivery. Finally, MR-IGRT in the KPC model facilitated precise contouring and comparison of different treatment plans and radiotherapy dose distributions not only to the intra-abdominal tumour but also to the organs at risk. Conclusion This is, to our knowledge, the first study to demonstrate preclinical MR-IGRT in intra-abdominal organs. The proposed MR-IGRT method presents a state-of-the-art solution to enabling robust, accurate and efficient targeting of extracranial organs in the mouse and can operate with a sufficiently high throughput to allow fractionated treatments to be given. PMID:28453537

Uncertainty, robustness, and the value of information in managing a population of northern bobwhites

USGS Publications Warehouse

Johnson, Fred A.; Hagan, Greg; Palmer, William E.; Kemmerer, Michael

2014-01-01

The abundance of northern bobwhites (Colinus virginianus) has decreased throughout their range. Managers often respond by considering improvements in harvest and habitat management practices, but this can be challenging if substantial uncertainty exists concerning the cause(s) of the decline. We were interested in how application of decision science could be used to help managers on a large, public management area in southwestern Florida where the bobwhite is a featured species and where abundance has severely declined. We conducted a workshop with managers and scientists to elicit management objectives, alternative hypotheses concerning population limitation in bobwhites, potential management actions, and predicted management outcomes. Using standard and robust approaches to decision making, we determined that improved water management and perhaps some changes in hunting practices would be expected to produce the best management outcomes in the face of uncertainty about what is limiting bobwhite abundance. We used a criterion called the expected value of perfect information to determine that a robust management strategy may perform nearly as well as an optimal management strategy (i.e., a strategy that is expected to perform best, given the relative importance of different management objectives) with all uncertainty resolved. We used the expected value of partial information to determine that management performance could be increased most by eliminating uncertainty over excessive-harvest and human-disturbance hypotheses. Beyond learning about the factors limiting bobwhites, adoption of a dynamic management strategy, which recognizes temporal changes in resource and environmental conditions, might produce the greatest management benefit. Our research demonstrates that robust approaches to decision making, combined with estimates of the value of information, can offer considerable insight into preferred management approaches when great uncertainty exists about system dynamics and the effects of management.

Robust control design with real parameter uncertainty using absolute stability theory. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

How, Jonathan P.; Hall, Steven R.

1993-01-01

The purpose of this thesis is to investigate an extension of mu theory for robust control design by considering systems with linear and nonlinear real parameter uncertainties. In the process, explicit connections are made between mixed mu and absolute stability theory. In particular, it is shown that the upper bounds for mixed mu are a generalization of results from absolute stability theory. Both state space and frequency domain criteria are developed for several nonlinearities and stability multipliers using the wealth of literature on absolute stability theory and the concepts of supply rates and storage functions. The state space conditions are expressed in terms of Riccati equations and parameter-dependent Lyapunov functions. For controller synthesis, these stability conditions are used to form an overbound of the H2 performance objective. A geometric interpretation of the equivalent frequency domain criteria in terms of off-axis circles clarifies the important role of the multiplier and shows that both the magnitude and phase of the uncertainty are considered. A numerical algorithm is developed to design robust controllers that minimize the bound on an H2 cost functional and satisfy an analysis test based on the Popov stability multiplier. The controller and multiplier coefficients are optimized simultaneously, which avoids the iteration and curve-fitting procedures required by the D-K procedure of mu synthesis. Several benchmark problems and experiments on the Middeck Active Control Experiment at M.I.T. demonstrate that these controllers achieve good robust performance and guaranteed stability bounds.

Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.

Development of a robust framework for controlling high performance turbofan engines

NASA Astrophysics Data System (ADS)

Miklosovic, Robert

This research involves the development of a robust framework for controlling complex and uncertain multivariable systems. Where mathematical modeling is often tedious or inaccurate, the new method uses an extended state observer (ESO) to estimate and cancel dynamic information in real time and dynamically decouple the system. As a result, controller design and tuning become transparent as the number of required model parameters is reduced. Much research has been devoted towards the application of modern multivariable control techniques on aircraft engines. However, few, if any, have been implemented on an operational aircraft, partially due to the difficulty in tuning the controller for satisfactory performance. The new technique is applied to a modern two-spool, high-pressure ratio, low-bypass turbofan with mixed-flow afterburning. A realistic Modular Aero-Propulsion System Simulation (MAPSS) package, developed by NASA, is used to demonstrate the new design process and compare its performance with that of a supplied nominal controller. This approach is expected to reduce gain scheduling over the full operating envelope of the engine and allow a controller to be tuned for engine-to-engine variations.

Sensitivity of Rainfall-runoff Model Parametrization and Performance to Potential Evaporation Inputs

NASA Astrophysics Data System (ADS)

Jayathilake, D. I.; Smith, T. J.

2017-12-01

Many watersheds of interest are confronted with insufficient data and poor process understanding. Therefore, understanding the relative importance of input data types and the impact of different qualities on model performance, parameterization, and fidelity is critically important to improving hydrologic models. In this paper, the change in model parameterization and performance are explored with respect to four different potential evapotranspiration (PET) products of varying quality. For each PET product, two widely used, conceptual rainfall-runoff models are calibrated with multiple objective functions to a sample of 20 basins included in the MOPEX data set and analyzed to understand how model behavior varied. Model results are further analyzed by classifying catchments as energy- or water-limited using the Budyko framework. The results demonstrated that model fit was largely unaffected by the quality of the PET inputs. However, model parameterizations were clearly sensitive to PET inputs, as their production parameters adjusted to counterbalance input errors. Despite this, changes in model robustness were not observed for either model across the four PET products, although robustness was affected by model structure.

Strong and Robust Polyaniline-Based Supramolecular Hydrogels for Flexible Supercapacitors.

PubMed

Li, Wanwan; Gao, Fengxian; Wang, Xiaoqian; Zhang, Ning; Ma, Mingming

2016-08-01

We report a supramolecular strategy to prepare conductive hydrogels with outstanding mechanical and electrochemical properties, which are utilized for flexible solid-state supercapacitors (SCs) with high performance. The supramolecular assembly of polyaniline and polyvinyl alcohol through dynamic boronate bond yields the polyaniline-polyvinyl alcohol hydrogel (PPH), which shows remarkable tensile strength (5.3 MPa) and electrochemical capacitance (928 F g(-1) ). The flexible solid-state supercapacitor based on PPH provides a large capacitance (306 mF cm(-2) and 153 F g(-1) ) and a high energy density of 13.6 Wh kg(-1) , superior to other flexible supercapacitors. The robustness of the PPH-based supercapacitor is demonstrated by the 100 % capacitance retention after 1000 mechanical folding cycles, and the 90 % capacitance retention after 1000 galvanostatic charge-discharge cycles. The high activity and robustness enable the PPH-based supercapacitor as a promising power device for flexible electronics. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluating the performance of vehicular platoon control under different network topologies of initial states

NASA Astrophysics Data System (ADS)

Li, Yongfu; Li, Kezhi; Zheng, Taixiong; Hu, Xiangdong; Feng, Huizong; Li, Yinguo

2016-05-01

This study proposes a feedback-based platoon control protocol for connected autonomous vehicles (CAVs) under different network topologies of initial states. In particularly, algebraic graph theory is used to describe the network topology. Then, the leader-follower approach is used to model the interactions between CAVs. In addition, feedback-based protocol is designed to control the platoon considering the longitudinal and lateral gaps simultaneously as well as different network topologies. The stability and consensus of the vehicular platoon is analyzed using the Lyapunov technique. Effects of different network topologies of initial states on convergence time and robustness of platoon control are investigated. Results from numerical experiments demonstrate the effectiveness of the proposed protocol with respect to the position and velocity consensus in terms of the convergence time and robustness. Also, the findings of this study illustrate the convergence time of the control protocol is associated with the initial states, while the robustness is not affected by the initial states significantly.

Robustness of quantum key distribution with discrete and continuous variables to channel noise

NASA Astrophysics Data System (ADS)

Lasota, Mikołaj; Filip, Radim; Usenko, Vladyslav C.

2017-06-01

We study the robustness of quantum key distribution protocols using discrete or continuous variables to the channel noise. We introduce the model of such noise based on coupling of the signal to a thermal reservoir, typical for continuous-variable quantum key distribution, to the discrete-variable case. Then we perform a comparison of the bounds on the tolerable channel noise between these two kinds of protocols using the same noise parametrization, in the case of implementation which is perfect otherwise. Obtained results show that continuous-variable protocols can exhibit similar robustness to the channel noise when the transmittance of the channel is relatively high. However, for strong loss discrete-variable protocols are superior and can overcome even the infinite-squeezing continuous-variable protocol while using limited nonclassical resources. The requirement on the probability of a single-photon production which would have to be fulfilled by a practical source of photons in order to demonstrate such superiority is feasible thanks to the recent rapid development in this field.

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

Coordination of contractility, adhesion and flow in migrating Physarum amoebae.

PubMed

Lewis, Owen L; Zhang, Shun; Guy, Robert D; del Álamo, Juan C

2015-05-06

This work examines the relationship between spatio-temporal coordination of intracellular flow and traction stress and the speed of amoeboid locomotion of microplasmodia of Physarum polycephalum. We simultaneously perform particle image velocimetry and traction stress microscopy to measure the velocity of cytoplasmic flow and the stresses applied to the substrate by migrating Physarum microamoebae. In parallel, we develop a mathematical model of a motile cell which includes forces from the viscous cytosol, a poro-elastic, contractile cytoskeleton and adhesive interactions with the substrate. Our experiments show that flow and traction stress exhibit back-to-front-directed waves with a distinct phase difference. The model demonstrates that the direction and speed of locomotion are determined by this coordination between contraction, flow and adhesion. Using the model, we identify forms of coordination that generate model predictions consistent with experiments. We demonstrate that this coordination produces near optimal migration speed and is insensitive to heterogeneity in substrate adhesiveness. While it is generally thought that amoeboid motility is robust to changes in extracellular geometry and the nature of extracellular adhesion, our results demonstrate that coordination of adhesive forces is essential to producing robust migration. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Multishot Targeted PROPELLER Magnetic Resonance Imaging: Description of the Technique and Initial Applications

PubMed Central

Deng, Jie; Larson, Andrew C.

2010-01-01

Objectives To test the feasibility of combining inner-volume imaging (IVI) techniques with conventional multishot periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) techniques for targeted-PROPELLER magnetic resonance imaging. Materials and Methods Perpendicular section-selective gradients for spatially selective excitation and refocusing RF pulses were applied to limit the refocused field-of-view (FOV) along the phase-encoding direction for each rectangular blade image. We performed comparison studies in phantoms and normal volunteers by using targeted-PROPELLER methods for a wide range of imaging applications that commonly use turbo-spin-echo (TSE) approaches (brain, abdominal, vessel wall, cardiac). Results In these initial studies, we demonstrated the feasibility of using targeted-PROPELLER approaches to limit the imaging FOV thereby reducing the number of blades or permitting increased spatial resolution without commensurate increases in scan time. Both phantom and in vivo motion studies demonstrated the potential for more robust regional self-navigated motion correction compared with conventional full FOV PROPELLER methods. Conclusion We demonstrated that the reduced FOV targeted-PROPELLER technique offers the potential for reducing imaging time, increasing spatial resolution, and targeting specific areas for robust regional motion correction. PMID:19465860

Joint Biological Standoff Detection System increment II: Field Demonstration - SINBAHD Performances

DTIC Science & Technology

2007-12-01

of a dispersive element and a range-gated ICCD that limits the spectral information within the selected volume. This technique has showed an...bioaerosols. This LIF signal is spectrally collected by the combination of a dispersive element and a range-gated ICCD that records spectral...2001 in order to underline the robustness of the spectral signature of a particular biomaterial but of different origin, preparation and dispersion

Tracking and recognition face in videos with incremental local sparse representation model

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Yunhong; Zhang, Zhaoxiang

2013-10-01

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. First a robust face tracking algorithm is proposed via employing local sparse appearance and covariance pooling method. In the following face recognition stage, with the employment of a novel template update strategy, which combines incremental subspace learning, our recognition algorithm adapts the template to appearance changes and reduces the influence of occlusion and illumination variation. This leads to a robust video-based face tracking and recognition with desirable performance. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database, which includes 47 celebrities. Our proposed method produces a high face recognition rate at 95% of all videos. The proposed face tracking and recognition algorithms are also tested on a set of noisy videos under heavy occlusion and illumination variation. The tracking results on challenging benchmark videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. In the case of the challenging dataset in which faces undergo occlusion and illumination variation, and tracking and recognition experiments under significant pose variation on the University of California, San Diego (Honda/UCSD) database, our proposed method also consistently demonstrates a high recognition rate.

Concentric Rings K-Space Trajectory for Hyperpolarized 13C MR Spectroscopic Imaging

PubMed Central

Jiang, Wenwen; Lustig, Michael; Larson, Peder E.Z.

2014-01-01

Purpose To develop a robust and rapid imaging technique for hyperpolarized 13C MR Spectroscopic Imaging (MRSI) and investigate its performance. Methods A concentric rings readout trajectory with constant angular velocity is proposed for hyperpolarized 13C spectroscopic imaging and its properties are analyzed. Quantitative analyses of design tradeoffs are presented for several imaging scenarios. The first application of concentric rings on 13C phantoms and in vivo animal hyperpolarized 13C MRSI studies were performed to demonstrate the feasibility of the proposed method. Finally, a parallel imaging accelerated concentric rings study is presented. Results The concentric rings MRSI trajectory has the advantages of acquisition timesaving compared to echo-planar spectroscopic imaging (EPSI). It provides sufficient spectral bandwidth with relatively high SNR efficiency compared to EPSI and spiral techniques. Phantom and in vivo animal studies showed good image quality with half the scan time and reduced pulsatile flow artifacts compared to EPSI. Parallel imaging accelerated concentric rings showed advantages over Cartesian sampling in g-factor simulations and demonstrated aliasing-free image quality in a hyperpolarized 13C in vivo study. Conclusion The concentric rings trajectory is a robust and rapid imaging technique that fits very well with the speed, bandwidth, and resolution requirements of hyperpolarized 13C MRSI. PMID:25533653

Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species

NASA Astrophysics Data System (ADS)

Zhang, Linna; Li, Gang; Sun, Meixiu; Li, Hongxiao; Wang, Zhennan; Li, Yingxin; Lin, Ling

2017-11-01

Identifying whole bloods to be either human or nonhuman is an important responsibility for import-export ports and inspection and quarantine departments. Analytical methods and DNA testing methods are usually destructive. Previous studies demonstrated that visible diffuse reflectance spectroscopy method can realize noncontact human and nonhuman blood discrimination. An appropriate method for calibration set selection was very important for a robust quantitative model. In this paper, Random Selection (RS) method and Kennard-Stone (KS) method was applied in selecting samples for calibration set. Moreover, proper stoichiometry method can be greatly beneficial for improving the performance of classification model or quantification model. Partial Least Square Discrimination Analysis (PLSDA) method was commonly used in identification of blood species with spectroscopy methods. Least Square Support Vector Machine (LSSVM) was proved to be perfect for discrimination analysis. In this research, PLSDA method and LSSVM method was used for human blood discrimination. Compared with the results of PLSDA method, this method could enhance the performance of identified models. The overall results convinced that LSSVM method was more feasible for identifying human and animal blood species, and sufficiently demonstrated LSSVM method was a reliable and robust method for human blood identification, and can be more effective and accurate.

A robust optimization model for distribution and evacuation in the disaster response phase

NASA Astrophysics Data System (ADS)

Fereiduni, Meysam; Shahanaghi, Kamran

2017-03-01

Natural disasters, such as earthquakes, affect thousands of people and can cause enormous financial loss. Therefore, an efficient response immediately following a natural disaster is vital to minimize the aforementioned negative effects. This research paper presents a network design model for humanitarian logistics which will assist in location and allocation decisions for multiple disaster periods. At first, a single-objective optimization model is presented that addresses the response phase of disaster management. This model will help the decision makers to make the most optimal choices in regard to location, allocation, and evacuation simultaneously. The proposed model also considers emergency tents as temporary medical centers. To cope with the uncertainty and dynamic nature of disasters, and their consequences, our multi-period robust model considers the values of critical input data in a set of various scenarios. Second, because of probable disruption in the distribution infrastructure (such as bridges), the Monte Carlo simulation is used for generating related random numbers and different scenarios; the p-robust approach is utilized to formulate the new network. The p-robust approach can predict possible damages along pathways and among relief bases. We render a case study of our robust optimization approach for Tehran's plausible earthquake in region 1. Sensitivity analysis' experiments are proposed to explore the effects of various problem parameters. These experiments will give managerial insights and can guide DMs under a variety of conditions. Then, the performances of the "robust optimization" approach and the "p-robust optimization" approach are evaluated. Intriguing results and practical insights are demonstrated by our analysis on this comparison.

Flight Control Laws for NASA's Hyper-X Research Vehicle

NASA Technical Reports Server (NTRS)

Davidson, J.; Lallman, F.; McMinn, J. D.; Martin, J.; Pahle, J.; Stephenson, M.; Selmon, J.; Bose, D.

1999-01-01

The goal of the Hyper-X program is to demonstrate and validate technology for design and performance predictions of hypersonic aircraft with an airframe-integrated supersonic-combustion ramjet propulsion system. Accomplishing this goal requires flight demonstration of a hydrogen-fueled scramjet powered hypersonic aircraft. A key enabling technology for this flight demonstration is flight controls. Closed-loop flight control is required to enable a successful stage separation, to achieve and maintain the design condition during the engine test, and to provide a controlled descent. Before the contract award, NASA developed preliminary flight control laws for the Hyper-X to evaluate the feasibility of the proposed scramjet test sequence and descent trajectory. After the contract award, a Boeing/NASA partnership worked to develop the current control laws. This paper presents a description of the Hyper-X Research Vehicle control law architectures with performance and robustness analyses. Assessments of simulated flight trajectories and stability margin analyses demonstrate that these control laws meet the flight test requirements.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots.

PubMed

Duarte, Miguel; Costa, Vasco; Gomes, Jorge; Rodrigues, Tiago; Silva, Fernando; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

PubMed Central

Duarte, Miguel; Costa, Vasco; Gomes, Jorge; Rodrigues, Tiago; Silva, Fernando; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers. PMID:26999614

A robust demonstration of the cognate facilitation effect in first-language and second-language naming.

PubMed

Sheng, Li; Lam, Boji Pak Wing; Cruz, Diana; Fulton, Aislynn

2016-01-01

The cognate facilitation effect refers to the phenomenon that in bilinguals performance on various vocabulary tasks is enhanced for cross-linguistic cognates as opposed to noncognates. However, research investigating the presence of the cognate advantage in bilingual children remains limited. Most studies with children conducted to date has not included a control group or rigorously designed stimuli, which may jeopardize the validity and robustness of the emerging evidence. The current study addressed these methodological problems by examining performance in picture naming tasks in 34 4- to 7-year-old Spanish-English bilinguals and 52 Mandarin-English bilinguals as well as 37 English-speaking monolinguals who served as controls. Stimuli were controlled for phonology, word frequency, and length. The Spanish-English bilinguals performed better for cognates than for noncognates and exhibited a greater number of doublet responses (i.e., providing correct responses in both languages) in naming cognate targets than in naming noncognates. The control groups did not show differences in performance between the two sets of words. These findings provide compelling evidence that cross-linguistic similarities at the phonological level allow bootstrapping of vocabulary learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Robust Optimal Adaptive Control Method with Large Adaptive Gain

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2009-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

Gradient descent for robust kernel-based regression

NASA Astrophysics Data System (ADS)

Guo, Zheng-Chu; Hu, Ting; Shi, Lei

2018-06-01

In this paper, we study the gradient descent algorithm generated by a robust loss function over a reproducing kernel Hilbert space (RKHS). The loss function is defined by a windowing function G and a scale parameter σ, which can include a wide range of commonly used robust losses for regression. There is still a gap between theoretical analysis and optimization process of empirical risk minimization based on loss: the estimator needs to be global optimal in the theoretical analysis while the optimization method can not ensure the global optimality of its solutions. In this paper, we aim to fill this gap by developing a novel theoretical analysis on the performance of estimators generated by the gradient descent algorithm. We demonstrate that with an appropriately chosen scale parameter σ, the gradient update with early stopping rules can approximate the regression function. Our elegant error analysis can lead to convergence in the standard L 2 norm and the strong RKHS norm, both of which are optimal in the mini-max sense. We show that the scale parameter σ plays an important role in providing robustness as well as fast convergence. The numerical experiments implemented on synthetic examples and real data set also support our theoretical results.

Robust Rate Maximization for Heterogeneous Wireless Networks under Channel Uncertainties

PubMed Central

Xu, Yongjun; Hu, Yuan; Li, Guoquan

2018-01-01

Heterogeneous wireless networks are a promising technology in next generation wireless communication networks, which has been shown to efficiently reduce the blind area of mobile communication and improve network coverage compared with the traditional wireless communication networks. In this paper, a robust power allocation problem for a two-tier heterogeneous wireless networks is formulated based on orthogonal frequency-division multiplexing technology. Under the consideration of imperfect channel state information (CSI), the robust sum-rate maximization problem is built while avoiding sever cross-tier interference to macrocell user and maintaining the minimum rate requirement of each femtocell user. To be practical, both of channel estimation errors from the femtocells to the macrocell and link uncertainties of each femtocell user are simultaneously considered in terms of outage probabilities of users. The optimization problem is analyzed under no CSI feedback with some cumulative distribution function and partial CSI with Gaussian distribution of channel estimation error. The robust optimization problem is converted into the convex optimization problem which is solved by using Lagrange dual theory and subgradient algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm by the impact of channel uncertainties on the system performance. PMID:29466315

Can non‐clinical repolarization assays predict the results of clinical thorough QT studies? Results from a research consortium

PubMed Central

Park, Eunjung; Gintant, Gary A; Bi, Daoqin; Kozeli, Devi; Pettit, Syril D; Skinner, Matthew; Willard, James; Wisialowski, Todd; Koerner, John; Valentin, Jean‐Pierre

2018-01-01

Background and Purpose Translation of non‐clinical markers of delayed ventricular repolarization to clinical prolongation of the QT interval corrected for heart rate (QTc) (a biomarker for torsades de pointes proarrhythmia) remains an issue in drug discovery and regulatory evaluations. We retrospectively analysed 150 drug applications in a US Food and Drug Administration database to determine the utility of established non‐clinical in vitro IKr current human ether‐à‐go‐go‐related gene (hERG), action potential duration (APD) and in vivo (QTc) repolarization assays to detect and predict clinical QTc prolongation. Experimental Approach The predictive performance of three non‐clinical assays was compared with clinical thorough QT study outcomes based on free clinical plasma drug concentrations using sensitivity and specificity, receiver operating characteristic (ROC) curves, positive (PPVs) and negative predictive values (NPVs) and likelihood ratios (LRs). Key Results Non‐clinical assays demonstrated robust specificity (high true negative rate) but poor sensitivity (low true positive rate) for clinical QTc prolongation at low‐intermediate (1×–30×) clinical exposure multiples. The QTc assay provided the most robust PPVs and NPVs (ability to predict clinical QTc prolongation). ROC curves (overall test accuracy) and LRs (ability to influence post‐test probabilities) demonstrated overall marginal performance for hERG and QTc assays (best at 30× exposures), while the APD assay demonstrated minimal value. Conclusions and Implications The predictive value of hERG, APD and QTc assays varies, with drug concentrations strongly affecting translational performance. While useful in guiding preclinical candidates without clinical QT prolongation, hERG and QTc repolarization assays provide greater value compared with the APD assay. PMID:29181850

Vertical-cavity surface-emitting lasers: present and future

NASA Astrophysics Data System (ADS)

Morgan, Robert A.

1997-04-01

This manuscript reviews the present status of 'commercial- grade,' state-of-the-art planar, batch-fabricable, vertical- cavity surface-emitting lasers (VCSELs). Commercial-grade performance on all fronts for high-speed data communications is clearly established. In discussing the 'present,' we focus on the entrenched proton-implanted AlGaAs-based (emitting near 850 nm) technology. Renditions of this VCSEL design exist in commercial products and have enabled numerous application demonstrations. Our designs more than adequately meet producibility, performance, and robustness stipulations. Producibility milestones include greater than 99% device yield across 3-in-dia metal-organic vapor phase epitaxy (MOVPE)-grown wafers and wavelength operation across greater than 100-nm range. Progress in performance includes the elimination of the excessive voltage-drop that plagued VCSELs as recently as 2 to 3 years ago. Threshold voltages as low as Vth equals 1.53 V (and routinely less than 1.6 V) are now commonplace. Submilliamp threshold currents (Ith equals 0.68 mA) have even been demonstrated with this planar structure. Moreover, continuous wave (cw) power Pcw greater than 59 mW and respectable wall-plug efficiencies ((eta) wp equals 28%) have been demonstrated. VCSEL robustness is evidenced by maximum cw lasing temperature T equals 200 degrees Celsius and temperature ranges of 10 K to 400 K and minus 55 degrees Celsius to 155 degrees Celsius on a single VCSEL. These characteristics should enable great advances in VCSEL-based technologies and beckon the notion that 'commercial-grade' VCSELs are viable in cryogenic and avionics/military environments. We also discuss what the future may hold in extensions of this platform to different wavelengths, increased integration, and advanced structures. This includes low-threshold, high- speed, single-mode VCSELs, hybrid VCSEL transceivers, and self-pulsating VCSELs.

Performance Analysis of HF Band FB-MC-SS

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

Hussein Moradi; Stephen Andrew Laraway; Behrouz Farhang-Boroujeny

Abstract—In a recent paper [1] the filter bank multicarrier spread spectrum (FB-MC-SS) waveform was proposed for wideband spread spectrum HF communications. A significant benefit of this waveform is robustness against narrow and partial band interference. Simulation results in [1] demonstrated good performance in a wideband HF channel over a wide range of conditions. In this paper we present a theoretical analysis of the bit error probably for this system. Our analysis tailors the results from [2] where BER performance was analyzed for maximum ration combining systems that accounted for correlation between subcarriers and channel estimation error. Equations are give formore » BER that closely match the simulated performance in most situations.« less

A novel shape from focus method based on 3D steerable filters for improved performance on treating textureless region

NASA Astrophysics Data System (ADS)

Fan, Tiantian; Yu, Hongbin

2018-03-01

A novel shape from focus method combining 3D steerable filter for improved performance on treating textureless region was proposed in this paper. Different from conventional spatial methods focusing on the search of maximum edges' response to estimate the depth map, the currently proposed method took both of the edges' response and the axial imaging blur degree into consideration during treatment. As a result, more robust and accurate identification for the focused location can be achieved, especially when treating textureless objects. Improved performance in depth measurement has been successfully demonstrated from both of the simulation and experiment results.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles

NASA Astrophysics Data System (ADS)

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles.

PubMed

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Reliability of simulated robustness testing in fast liquid chromatography, using state-of-the-art column technology, instrumentation and modelling software.

PubMed

Kormány, Róbert; Fekete, Jenő; Guillarme, Davy; Fekete, Szabolcs

2014-02-01

The goal of this study was to evaluate the accuracy of simulated robustness testing using commercial modelling software (DryLab) and state-of-the-art stationary phases. For this purpose, a mixture of amlodipine and its seven related impurities was analyzed on short narrow bore columns (50×2.1mm, packed with sub-2μm particles) providing short analysis times. The performance of commercial modelling software for robustness testing was systematically compared to experimental measurements and DoE based predictions. We have demonstrated that the reliability of predictions was good, since the predicted retention times and resolutions were in good agreement with the experimental ones at the edges of the design space. In average, the retention time relative errors were <1.0%, while the predicted critical resolution errors were comprised between 6.9 and 17.2%. Because the simulated robustness testing requires significantly less experimental work than the DoE based predictions, we think that robustness could now be investigated in the early stage of method development. Moreover, the column interchangeability, which is also an important part of robustness testing, was investigated considering five different C8 and C18 columns packed with sub-2μm particles. Again, thanks to modelling software, we proved that the separation was feasible on all columns within the same analysis time (less than 4min), by proper adjustments of variables. Copyright © 2013 Elsevier B.V. All rights reserved.

A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition.

PubMed

Zhang, Xiaorong; Huang, He

2015-02-19

Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances. This paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels. The proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly "zero-delay" SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system's classification performance when there was no disturbance. This paper presented a real-time, lightweight, and automatic SFTM, which paved the way for reliable and robust EMG PR for prosthesis control.

Cooperative vehicles for robust traffic congestion reduction: An analysis based on algorithmic, environmental and agent behavioral factors

PubMed Central

Desai, Prajakta; Desai, Aniruddha

2017-01-01

Traffic congestion continues to be a persistent problem throughout the world. As vehicle-to-vehicle communication develops, there is an opportunity of using cooperation among close proximity vehicles to tackle the congestion problem. The intuition is that if vehicles could cooperate opportunistically when they come close enough to each other, they could, in effect, spread themselves out among alternative routes so that vehicles do not all jam up on the same roads. Our previous work proposed a decentralized multiagent based vehicular congestion management algorithm entitled Congestion Avoidance and Route Allocation using Virtual Agent Negotiation (CARAVAN), wherein the vehicles acting as intelligent agents perform cooperative route allocation using inter-vehicular communication. This paper focuses on evaluating the practical applicability of this approach by testing its robustness and performance (in terms of travel time reduction), across variations in: (a) environmental parameters such as road network topology and configuration; (b) algorithmic parameters such as vehicle agent preferences and route cost/preference multipliers; and (c) agent-related parameters such as equipped/non-equipped vehicles and compliant/non-compliant agents. Overall, the results demonstrate the adaptability and robustness of the decentralized cooperative vehicles approach to providing global travel time reduction using simple local coordination strategies. PMID:28792513

Cooperative vehicles for robust traffic congestion reduction: An analysis based on algorithmic, environmental and agent behavioral factors.

PubMed

Desai, Prajakta; Loke, Seng W; Desai, Aniruddha

2017-01-01

Traffic congestion continues to be a persistent problem throughout the world. As vehicle-to-vehicle communication develops, there is an opportunity of using cooperation among close proximity vehicles to tackle the congestion problem. The intuition is that if vehicles could cooperate opportunistically when they come close enough to each other, they could, in effect, spread themselves out among alternative routes so that vehicles do not all jam up on the same roads. Our previous work proposed a decentralized multiagent based vehicular congestion management algorithm entitled Congestion Avoidance and Route Allocation using Virtual Agent Negotiation (CARAVAN), wherein the vehicles acting as intelligent agents perform cooperative route allocation using inter-vehicular communication. This paper focuses on evaluating the practical applicability of this approach by testing its robustness and performance (in terms of travel time reduction), across variations in: (a) environmental parameters such as road network topology and configuration; (b) algorithmic parameters such as vehicle agent preferences and route cost/preference multipliers; and (c) agent-related parameters such as equipped/non-equipped vehicles and compliant/non-compliant agents. Overall, the results demonstrate the adaptability and robustness of the decentralized cooperative vehicles approach to providing global travel time reduction using simple local coordination strategies.

Intelligent nonsingular terminal sliding-mode control using MIMO Elman neural network for piezo-flexural nanopositioning stage.

PubMed

Lin, Faa-Jeng; Lee, Shih-Yang; Chou, Po-Huan

2012-12-01

The objective of this study is to develop an intelligent nonsingular terminal sliding-mode control (INTSMC) system using an Elman neural network (ENN) for the threedimensional motion control of a piezo-flexural nanopositioning stage (PFNS). First, the dynamic model of the PFNS is derived in detail. Then, to achieve robust, accurate trajectory-tracking performance, a nonsingular terminal sliding-mode control (NTSMC) system is proposed for the tracking of the reference contours. The steady-state response of the control system can be improved effectively because of the addition of the nonsingularity in the NTSMC. Moreover, to relax the requirements of the bounds and discard the switching function in NTSMC, an INTSMC system using a multi-input-multioutput (MIMO) ENN estimator is proposed to improve the control performance and robustness of the PFNS. The ENN estimator is proposed to estimate the hysteresis phenomenon and lumped uncertainty, including the system parameters and external disturbance of the PFNS online. Furthermore, the adaptive learning algorithms for the training of the parameters of the ENN online are derived using the Lyapunov stability theorem. In addition, two robust compensators are proposed to confront the minimum reconstructed errors in INTSMC. Finally, some experimental results for the tracking of various contours are given to demonstrate the validity of the proposed INTSMC system for PFNS.

Modified artificial bee colony algorithm for reactive power optimization

NASA Astrophysics Data System (ADS)

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-05-01

Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.

Enhancing the Properties of Conductive Polymer Hydrogels by Freeze-Thaw Cycles for High-Performance Flexible Supercapacitors.

PubMed

Li, Wanwan; Lu, Han; Zhang, Ning; Ma, Mingming

2017-06-14

We report that a postsynthesis physical process (freeze-thaw cycles) can reform the microstructure of conductive polymer hydrogels from clustered nanoparticles to interconnected nanosheets, leading to enhanced mechanical and electrochemical properties. The polyaniline-poly(vinyl alcohol) hydrogel after five freeze-thaw cycles (PPH-5) showed remarkable tensile strength (16.3 MPa), large elongation at break (407%), and high electrochemical capacitance (1053 F·g -1 ). The flexible supercapacitor based on PPH-5 provided a large capacitance (420 mF·cm -2 and 210 F·g -1 ) and high energy density (18.7 W·h·kg -1 ), whose robustness was demonstrated by its 100% capacitance retention after 1000 galvanostatic charge-discharge cycles or after 1000 mechanical folding cycles. The outstanding performance enables PPH-5 based supercapacitor as a promising power device for flexible electronics, which also demonstrates the merit of freeze-thaw cycles for enhancing the performance of functional hydrogels.

Evaluation of procedures for prediction of unconventional gas in the presence of geologic trends

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.

2009-01-01

This study extends the application of local spatial nonparametric prediction models to the estimation of recoverable gas volumes in continuous-type gas plays to regimes where there is a single geologic trend. A transformation is presented, originally proposed by Tomczak, that offsets the distortions caused by the trend. This article reports on numerical experiments that compare predictive and classification performance of the local nonparametric prediction models based on the transformation with models based on Euclidean distance. The transformation offers improvement in average root mean square error when the trend is not severely misspecified. Because of the local nature of the models, even those based on Euclidean distance in the presence of trends are reasonably robust. The tests based on other model performance metrics such as prediction error associated with the high-grade tracts and the ability of the models to identify sites with the largest gas volumes also demonstrate the robustness of both local modeling approaches. ?? International Association for Mathematical Geology 2009.

Experimental Validation of L1 Adaptive Control: Rohrs' Counterexample in Flight

NASA Technical Reports Server (NTRS)

Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Issac; Kitsios, Ioannis; Cao, Chengyu; Gregory, Irene M.; Valavani, Lena

2010-01-01

The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems.

Scalable Production of Mechanically Robust Antireflection Film for Omnidirectional Enhanced Flexible Thin Film Solar Cells.

PubMed

Wang, Min; Ma, Pengsha; Yin, Min; Lu, Linfeng; Lin, Yinyue; Chen, Xiaoyuan; Jia, Wei; Cao, Xinmin; Chang, Paichun; Li, Dongdong

2017-09-01

Antireflection (AR) at the interface between the air and incident window material is paramount to boost the performance of photovoltaic devices. 3D nanostructures have attracted tremendous interest to reduce reflection, while the structure is vulnerable to the harsh outdoor environment. Thus the AR film with improved mechanical property is desirable in an industrial application. Herein, a scalable production of flexible AR films is proposed with microsized structures by roll-to-roll imprinting process, which possesses hydrophobic property and much improved robustness. The AR films can be potentially used for a wide range of photovoltaic devices whether based on rigid or flexible substrates. As a demonstration, the AR films are integrated with commercial Si-based triple-junction thin film solar cells. The AR film works as an effective tool to control the light travel path and utilize the light inward more efficiently by exciting hybrid optical modes, which results in a broadband and omnidirectional enhanced performance.

Computing by robust transience: How the fronto-parietal network performs sequential category-based decisions

PubMed Central

Chaisangmongkon, Warasinee; Swaminathan, Sruthi K.; Freedman, David J.; Wang, Xiao-Jing

2017-01-01

Summary Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a ‘neural landscape’, consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally-relevant circuit motifs and generalize the framework to solve other categorization tasks. PMID:28334612

Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

NASA Astrophysics Data System (ADS)

Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun

2018-05-01

The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

Scalable Production of Mechanically Robust Antireflection Film for Omnidirectional Enhanced Flexible Thin Film Solar Cells

PubMed Central

Wang, Min; Ma, Pengsha; Lu, Linfeng; Lin, Yinyue; Chen, Xiaoyuan; Jia, Wei; Cao, Xinmin; Chang, Paichun

2017-01-01

Antireflection (AR) at the interface between the air and incident window material is paramount to boost the performance of photovoltaic devices. 3D nanostructures have attracted tremendous interest to reduce reflection, while the structure is vulnerable to the harsh outdoor environment. Thus the AR film with improved mechanical property is desirable in an industrial application. Herein, a scalable production of flexible AR films is proposed with microsized structures by roll‐to‐roll imprinting process, which possesses hydrophobic property and much improved robustness. The AR films can be potentially used for a wide range of photovoltaic devices whether based on rigid or flexible substrates. As a demonstration, the AR films are integrated with commercial Si‐based triple‐junction thin film solar cells. The AR film works as an effective tool to control the light travel path and utilize the light inward more efficiently by exciting hybrid optical modes, which results in a broadband and omnidirectional enhanced performance. PMID:28932667

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

PubMed

Kauppi, Jukka-Pekka; Martikainen, Kalle; Ruotsalainen, Ulla

2010-12-01

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns. Copyright © 2010 Elsevier Ltd. All rights reserved.

Robust gene selection methods using weighting schemes for microarray data analysis.

PubMed

Kang, Suyeon; Song, Jongwoo

2017-09-02

A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

Performance enhancement for audio-visual speaker identification using dynamic facial muscle model.

PubMed

Asadpour, Vahid; Towhidkhah, Farzad; Homayounpour, Mohammad Mehdi

2006-10-01

Science of human identification using physiological characteristics or biometry has been of great concern in security systems. However, robust multimodal identification systems based on audio-visual information has not been thoroughly investigated yet. Therefore, the aim of this work to propose a model-based feature extraction method which employs physiological characteristics of facial muscles producing lip movements. This approach adopts the intrinsic properties of muscles such as viscosity, elasticity, and mass which are extracted from the dynamic lip model. These parameters are exclusively dependent on the neuro-muscular properties of speaker; consequently, imitation of valid speakers could be reduced to a large extent. These parameters are applied to a hidden Markov model (HMM) audio-visual identification system. In this work, a combination of audio and video features has been employed by adopting a multistream pseudo-synchronized HMM training method. Noise robust audio features such as Mel-frequency cepstral coefficients (MFCC), spectral subtraction (SS), and relative spectra perceptual linear prediction (J-RASTA-PLP) have been used to evaluate the performance of the multimodal system once efficient audio feature extraction methods have been utilized. The superior performance of the proposed system is demonstrated on a large multispeaker database of continuously spoken digits, along with a sentence that is phonetically rich. To evaluate the robustness of algorithms, some experiments were performed on genetically identical twins. Furthermore, changes in speaker voice were simulated with drug inhalation tests. In 3 dB signal to noise ratio (SNR), the dynamic muscle model improved the identification rate of the audio-visual system from 91 to 98%. Results on identical twins revealed that there was an apparent improvement on the performance for the dynamic muscle model-based system, in which the identification rate of the audio-visual system was enhanced from 87 to 96%.

Robust control of flexible space vehicles with minimum structural excitation: On-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1992-01-01

Both feedback and feedforward control approaches for uncertain dynamical systems (in particular, with uncertainty in structural mode frequency) are investigated. The control objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant uncertainty. Preshaping of an ideal, time optimal control input using a tapped-delay filter is shown to provide a fast settling time with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. It is shown that a properly designed, feedback controller performs well, as compared with a time optimal open loop controller with special preshaping for performance robustness. Also included are two separate papers by the same authors on this subject.

Wire rope tension control of hoisting systems using a robust nonlinear adaptive backstepping control scheme.

PubMed

Zhu, Zhen-Cai; Li, Xiang; Shen, Gang; Zhu, Wei-Dong

2018-01-01

This paper concerns wire rope tension control of a double-rope winding hoisting system (DRWHS), which consists of a hoisting system employed to realize a transportation function and an electro-hydraulic servo system utilized to adjust wire rope tensions. A dynamic model of the DRWHS is developed in which parameter uncertainties and external disturbances are considered. A comparison between simulation results using the dynamic model and experimental results using a double-rope winding hoisting experimental system is given in order to demonstrate accuracy of the dynamic model. In order to improve the wire rope tension coordination control performance of the DRWHS, a robust nonlinear adaptive backstepping controller (RNABC) combined with a nonlinear disturbance observer (NDO) is proposed. Main features of the proposed combined controller are: (1) using the RNABC to adjust wire rope tensions with consideration of parameter uncertainties, whose parameters are designed online by adaptive laws derived from Lyapunov stability theory to guarantee the control performance and stability of the closed-loop system; and (2) introducing the NDO to deal with uncertain external disturbances. In order to demonstrate feasibility and effectiveness of the proposed controller, experimental studies have been conducted on the DRWHS controlled by an xPC rapid prototyping system. Experimental results verify that the proposed controller exhibits excellent performance on wire rope tension coordination control compared with a conventional proportional-integral (PI) controller and adaptive backstepping controller. Copyright © 2017 ISA. All rights reserved.

Generalized internal model robust control for active front steering intervention

NASA Astrophysics Data System (ADS)

Wu, Jian; Zhao, Youqun; Ji, Xuewu; Liu, Yahui; Zhang, Lipeng

2015-03-01

Because of the tire nonlinearity and vehicle's parameters' uncertainties, robust control methods based on the worst cases, such as H ∞, µ synthesis, have been widely used in active front steering control, however, in order to guarantee the stability of active front steering system (AFS) controller, the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control. In this paper, a generalized internal model robust control (GIMC) that can overcome the contradiction between performance and stability is used in the AFS control. In GIMC, the Youla parameterization is used in an improved way. And GIMC controller includes two sections: a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters' uncertainties and some external disturbances. Simulations of double lane change (DLC) maneuver and that of braking on split- µ road are conducted to compare the performance and stability of the GIMC control, the nominal performance PID controller and the H ∞ controller. Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle's parameters variations, H ∞ controller is conservative so that the performance is a little low, and only the GIMC controller overcomes the contradiction between performance and robustness, which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller. Therefore, the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system, that is, can solve the instability of PID or LQP control methods and the low performance of the standard H ∞ controller.

Taguchi Based Performance and Reliability Improvement of an Ion Chamber Amplifier for Enhanced Nuclear Reactor Safety

NASA Astrophysics Data System (ADS)

Kulkarni, R. D.; Agarwal, Vivek

2008-08-01

An ion chamber amplifier (ICA) is used as a safety device for neutronic power (flux) measurement in regulation and protection systems of nuclear reactors. Therefore, performance reliability of an ICA is an important issue. Appropriate quality engineering is essential to achieve a robust design and performance of the ICA circuit. It is observed that the low input bias current operational amplifiers used in the input stage of the ICA circuit are the most critical devices for proper functioning of the ICA. They are very sensitive to the gamma radiation present in their close vicinity. Therefore, the response of the ICA deteriorates with exposure to gamma radiation resulting in a decrease in the overall reliability, unless desired performance is ensured under all conditions. This paper presents a performance enhancement scheme for an ICA operated in the nuclear environment. The Taguchi method, which is a proven technique for reliability enhancement, has been used in this work. It is demonstrated that if a statistical, optimal design approach, like the Taguchi method is used, the cost of high quality and reliability may be brought down drastically. The complete methodology and statistical calculations involved are presented, as are the experimental and simulation results to arrive at a robust design of the ICA.

High speed bus technology development

NASA Astrophysics Data System (ADS)

Modrow, Marlan B.; Hatfield, Donald W.

1989-09-01

The development and demonstration of the High Speed Data Bus system, a 50 Million bits per second (Mbps) local data network intended for avionics applications in advanced military aircraft is described. The Advanced System Avionics (ASA)/PAVE PILLAR program provided the avionics architecture concept and basic requirements. Designs for wire and fiber optic media were produced and hardware demonstrations were performed. An efficient, robust token-passing protocol was developed and partially demonstrated. The requirements specifications, the trade-offs made, and the resulting designs for both a coaxial wire media system and a fiber optics design are examined. Also, the development of a message-oriented media access protocol is described, from requirements definition through analysis, simulation and experimentation. Finally, the testing and demonstrations conducted on the breadboard and brassboard hardware is presented.

The effect of complementary therapies on post-operative pain control in ambulatory knee surgery: a systematic review.

PubMed

Barlow, Timothy; Downham, Christopher; Barlow, David

2013-10-01

Ambulatory knee surgery is a common procedure with over 100,000 knee arthroscopies performed in the U.K. in 2010-2011. Pain after surgery can decrease patient satisfaction, delay discharge, and decrease cost effectiveness. Multi-modal therapies, including complementary therapies, to improve pain control after surgery have been recommended. However, a comprehensive review of the literature regarding the use of complementary therapies to enhance pain control after ambulatory knee surgery is lacking, and this article aims to address this deficit. CINHAL, EMBASE, MEDLINE, AMED and CENTRAL databases were searched. Only Randomised Controlled Trials were included. All eligible papers were quality assessed using the Jadad system, and data was extracted using piloted forms. Two independent reviewers performed each stage of the review. Full details of the study methodology can be found on Prospero, a systematic review register. Five studies satisfied our eligibility criteria: three reporting on acupuncture, one on homeopathy, and one on acupoints. Acupoint pressure was the only study that demonstrated reduced pain compared with placebo. This study was the least methodologically robust. Arnica, although demonstrating a significant reduction in swelling, did not affect post-operative pain. Acupuncture did not affect post-operative pain; however, a reduction in ibuprofen use was demonstrated in two studies. Before recommending complementary therapy for routine use in ambulatory knee surgery, further work is required. Two areas of future research likely to bear fruit are demonstrating robust evidence for the effect of acupoint pressure on post-operative pain, and quantifying the positive effect of homeopathic arnica on post-operative swelling. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sliding Mode Control of the X-33 with an Engine Failure

NASA Technical Reports Server (NTRS)

Shtessel, Yuri B.; Hall, Charles E.

2000-01-01

Ascent flight control of the X-3 is performed using two XRS-2200 linear aerospike engines. in addition to aerosurfaces. The baseline control algorithms are PID with gain scheduling. Flight control using an innovative method. Sliding Mode Control. is presented for nominal and engine failed modes of flight. An easy to implement, robust controller. requiring no reconfiguration or gain scheduling is demonstrated through high fidelity flight simulations. The proposed sliding mode controller utilizes a two-loop structure and provides robust. de-coupled tracking of both orientation angle command profiles and angular rate command profiles in the presence of engine failure, bounded external disturbances (wind gusts) and uncertain matrix of inertia. Sliding mode control causes the angular rate and orientation angle tracking error dynamics to be constrained to linear, de-coupled, homogeneous, and vector valued differential equations with desired eigenvalues. Conditions that restrict engine failures to robustness domain of the sliding mode controller are derived. Overall stability of a two-loop flight control system is assessed. Simulation results show that the designed controller provides robust, accurate, de-coupled tracking of the orientation angle command profiles in the presence of external disturbances and vehicle inertia uncertainties, as well as the single engine failed case. The designed robust controller will significantly reduce the time and cost associated with flying new trajectory profiles or orbits, with new payloads, and with modified vehicles

Fast and robust control of nanopositioning systems: Performance limits enabled by field programmable analog arrays.

PubMed

Baranwal, Mayank; Gorugantu, Ram S; Salapaka, Srinivasa M

2015-08-01

This paper aims at control design and its implementation for robust high-bandwidth precision (nanoscale) positioning systems. Even though modern model-based control theoretic designs for robust broadband high-resolution positioning have enabled orders of magnitude improvement in performance over existing model independent designs, their scope is severely limited by the inefficacies of digital implementation of the control designs. High-order control laws that result from model-based designs typically have to be approximated with reduced-order systems to facilitate digital implementation. Digital systems, even those that have very high sampling frequencies, provide low effective control bandwidth when implementing high-order systems. In this context, field programmable analog arrays (FPAAs) provide a good alternative to the use of digital-logic based processors since they enable very high implementation speeds, moreover with cheaper resources. The superior flexibility of digital systems in terms of the implementable mathematical and logical functions does not give significant edge over FPAAs when implementing linear dynamic control laws. In this paper, we pose the control design objectives for positioning systems in different configurations as optimal control problems and demonstrate significant improvements in performance when the resulting control laws are applied using FPAAs as opposed to their digital counterparts. An improvement of over 200% in positioning bandwidth is achieved over an earlier digital signal processor (DSP) based implementation for the same system and same control design, even when for the DSP-based system, the sampling frequency is about 100 times the desired positioning bandwidth.

Robust flow of light in three-dimensional dielectric photonic crystals.

PubMed

Chen, Wen-Jie; Jiang, Shao-Ji; Dong, Jian-Wen

2013-09-01

Chiral defect waveguides and waveguide bend geometry were designed in diamond photonic crystal to mold the flow of light in three dimensions. Propagations of electromagnetic waves in chiral waveguides are robust against isotropic obstacles, which would suppress backscattering in waveguides or integrated devices. Finite-difference time-domain simulations demonstrate that high coupling efficiency through the bend corner is preserved in the polarization gap, as it provides an additional constraint on the polarization state of the backscattered wave. Transport robustness is also demonstrated by inserting two metallic slabs into the waveguide bend.

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2016-01-01

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance. PMID:27303271

Robust adaptive relative position and attitude control for spacecraft autonomous proximity.

PubMed

Sun, Liang; Huo, Wei; Jiao, Zongxia

2016-07-01

This paper provides new results of the dynamical modeling and controller designing for autonomous close proximity phase during rendezvous and docking in the presence of kinematic couplings and model uncertainties. A globally defined relative motion mechanical model for close proximity operations is introduced firstly. Then, in spite of the kinematic couplings and thrust misalignment between relative rotation and relative translation, robust adaptive relative position and relative attitude controllers are designed successively. Finally, stability of the overall system is proved that the relative position and relative attitude are uniformly ultimately bounded, and the size of the ultimate bound can be regulated small enough by control system parameters. Performance of the controlled overall system is demonstrated via a representative numerical example. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Continuum topology optimization considering uncertainties in load locations based on the cloud model

NASA Astrophysics Data System (ADS)

Liu, Jie; Wen, Guilin

2018-06-01

Few researchers have paid attention to designing structures in consideration of uncertainties in the loading locations, which may significantly influence the structural performance. In this work, cloud models are employed to depict the uncertainties in the loading locations. A robust algorithm is developed in the context of minimizing the expectation of the structural compliance, while conforming to a material volume constraint. To guarantee optimal solutions, sufficient cloud drops are used, which in turn leads to low efficiency. An innovative strategy is then implemented to enormously improve the computational efficiency. A modified soft-kill bi-directional evolutionary structural optimization method using derived sensitivity numbers is used to output the robust novel configurations. Several numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed algorithm.

HIFU Transducer Characterization Using a Robust Needle Hydrophone

NASA Astrophysics Data System (ADS)

Howard, Samuel M.; Zanelli, Claudio I.

2007-05-01

A robust needle hydrophone has been developed for HIFU transducer characterization and reported on earlier. After a brief review of the hydrophone design and performance, we demonstrate its use to characterize a 1.5 MHz, 10 cm diameter, F-number 1.5 spherically focused source driven to exceed an intensity of 1400 W/cm2at its focus. Quantitative characterization of this source at high powers is assisted by deconvolving the hydrophone's calibrated frequency response in order to accurately reflect the contribution of harmonics generated by nonlinear propagation in the water testing environment. Results are compared to measurements with a membrane hydrophone at 0.3% duty cycle and to theoretical calculations, using measurements of the field at the source's radiating surface as input to a numerical solution of the KZK equation.

Internal Model-Based Robust Tracking Control Design for the MEMS Electromagnetic Micromirror.

PubMed

Tan, Jiazheng; Sun, Weijie; Yeow, John T W

2017-05-26

The micromirror based on micro-electro-mechanical systems (MEMS) technology is widely employed in different areas, such as scanning, imaging and optical switching. This paper studies the MEMS electromagnetic micromirror for scanning or imaging application. In these application scenarios, the micromirror is required to track the command sinusoidal signal, which can be converted to an output regulation problem theoretically. In this paper, based on the internal model principle, the output regulation problem is solved by designing a robust controller that is able to force the micromirror to track the command signal accurately. The proposed controller relies little on the accuracy of the model. Further, the proposed controller is implemented, and its effectiveness is examined by experiments. The experimental results demonstrate that the performance of the proposed controller is satisfying.

Internal Model-Based Robust Tracking Control Design for the MEMS Electromagnetic Micromirror

PubMed Central

Tan, Jiazheng; Sun, Weijie; Yeow, John T. W.

2017-01-01

The micromirror based on micro-electro-mechanical systems (MEMS) technology is widely employed in different areas, such as scanning, imaging and optical switching. This paper studies the MEMS electromagnetic micromirror for scanning or imaging application. In these application scenarios, the micromirror is required to track the command sinusoidal signal, which can be converted to an output regulation problem theoretically. In this paper, based on the internal model principle, the output regulation problem is solved by designing a robust controller that is able to force the micromirror to track the command signal accurately. The proposed controller relies little on the accuracy of the model. Further, the proposed controller is implemented, and its effectiveness is examined by experiments. The experimental results demonstrate that the performance of the proposed controller is satisfying. PMID:28587105

Robust myoelectric signal detection based on stochastic resonance using multiple-surface-electrode array made of carbon nanotube composite paper

NASA Astrophysics Data System (ADS)

Shirata, Kento; Inden, Yuki; Kasai, Seiya; Oya, Takahide; Hagiwara, Yosuke; Kaeriyama, Shunichi; Nakamura, Hideyuki

2016-04-01

We investigated the robust detection of surface electromyogram (EMG) signals based on the stochastic resonance (SR) phenomenon, in which the response to weak signals is optimized by adding noise, combined with multiple surface electrodes. Flexible carbon nanotube composite paper (CNT-cp) was applied to the surface electrode, which showed good performance that is comparable to that of conventional Ag/AgCl electrodes. The SR-based EMG signal system integrating an 8-Schmitt-trigger network and the multiple-CNT-cp-electrode array successfully detected weak EMG signals even when the subject’s body is in the motion, which was difficult to achieve using the conventional technique. The feasibility of the SR-based EMG detection technique was confirmed by demonstrating its applicability to robot hand control.

Robust adaptive uniform exact tracking control for uncertain Euler-Lagrange system

NASA Astrophysics Data System (ADS)

Yang, Yana; Hua, Changchun; Li, Junpeng; Guan, Xinping

2017-12-01

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler-Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.

Correlation of Phosphorus Cross-Linking to Hydration Rates in Sodium Starch Glycolate Tablet Disintegrants Using MRI.

PubMed

Abraham, Anuji; Olusanmi, Dolapo; Ilott, Andrew J; Good, David; Murphy, Denette; Mcnamara, Daniel; Jerschow, Alexej; Mantri, Rao V

2016-06-01

Understanding the behavior of tablet disintegrants is valuable in the development of pharmaceutical solid dosage formulations. In this study, high-resolution magnetic resonance imaging has been used to understand the hydration behavior of a series of commercial sodium starch glycolate (SSG) samples, providing robust estimates of tablet disintegration rate that could be correlated with physicochemical properties of the SSGs, such as the extent of phosphorus (P) cross-linking as obtained from infra-red spectroscopy. Furthermore, elemental analysis together with powder X-ray diffraction has been used to quantify the presence of carboxymethyl groups and salt impurities, which also contribute to the disintegration behavior. The utility of Fast Low Angle SHot magnetic resonance imaging has been demonstrated as an approach to rapidly acquire approximations of the volume of a disintegrating tablet and, together with a robust voxel analysis routine, extract tablet disintegration rates. In this manner, a complete characterization of a series of SSG grades from different sources has been performed, showing the variability in their physicochemical properties and demonstrating a correlation between their disintegration rates and intrinsic characteristics. The insights obtained will be a valuable aid in the choice of disintegrant source as well as in managing SSG variability to ensure robustness of drug products containing SSG. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Robust Expandable Carbon Nanotube Scaffold for Ultrahigh-Capacity Lithium-Metal Anodes.

PubMed

Sun, Zhaowei; Jin, Song; Jin, Hongchang; Du, Zhenzhen; Zhu, Yanwu; Cao, Anyuan; Ji, Hengxing; Wan, Li-Jun

2018-06-19

There has been a renewed interest in using lithium (Li) metal as an anode material for rechargeable batteries owing to its high theoretical capacity of 3860 mA h g -1 . Despite extensive research, modifications to effectively inhibit Li dendrite growth still result in decreased Li loading and Li utilization. As a result, real capacities are often lower than values expected, if the total mass of the electrode is taken into consideration. Herein, a lightweight yet mechanically robust carbon nanotube (CNT) paper is demonstrated as a freestanding framework to accommodate Li metal with a Li mass fraction of 80.7 wt%. The highly conductive network made of sp2-hybridized carbon effectively inhibits formation of Li dendrites and affords a favorable coulombic efficiency of >97.5%. Moreover, the Li/CNT electrode retains practical areal and gravimetric capacities of 10 mA h cm -2 and 2830 mA h g -1 (vs the mass of electrode), respectively, with 90.9% Li utilization for 1000 cycles at a current density of 10 mA cm -2 . It is demonstrated that the robust and expandable nature is a distinguishing feature of the CNT paper as compared to other 3D scaffolds, and is a key factor that leads to the improved electrochemical performance of the Li/CNT anodes. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Reliability of Free School Meal Eligibility as a Measure of Socio-Economic Disadvantage: Evidence from the Millennium Cohort Study in Wales

ERIC Educational Resources Information Center

Taylor, Chris

2018-01-01

Over the last 20 years, the use of administrative data has become central to understanding pupil attainment and school performance. Of most importance has been its use to robustly demonstrate the impact of socio-economic status (SES) on pupil attainment. Much of this analysis in England and Wales has relied on whether pupils are eligible for free…

Feedforward/feedback control synthesis for performance and robustness

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1990-01-01

Both feedforward and feedback control approaches for uncertain dynamical systems are investigated. The control design objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant modeling uncertainty. Preshapong of an ideal, time-optimal control input using a 'tapped-delay' filter is shown to provide a rapid maneuver with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. The proposed feedforward/feedback control approach is robust for a certain class of uncertain dynamical systems, since the control input command computed for a given desired output does not depend on the plant parameters.

All-organic superhydrophobic coatings with mechanochemical robustness and liquid impalement resistance

NASA Astrophysics Data System (ADS)

Peng, Chaoyi; Chen, Zhuyang; Tiwari, Manish K.

2018-03-01

Superhydrophobicity is a remarkable evolutionary adaption manifested by several natural surfaces. Artificial superhydrophobic coatings with good mechanical robustness, substrate adhesion and chemical robustness have been achieved separately. However, a simultaneous demonstration of these features along with resistance to liquid impalement via high-speed drop/jet impact is challenging. Here, we describe all-organic, flexible superhydrophobic nanocomposite coatings that demonstrate strong mechanical robustness under cyclic tape peels and Taber abrasion, sustain exposure to highly corrosive media, namely aqua regia and sodium hydroxide solutions, and can be applied to surfaces through scalable techniques such as spraying and brushing. In addition, the mechanical flexibility of our coatings enables impalement resistance to high-speed drops and turbulent jets at least up to 35 m s-1 and a Weber number of 43,000. With multifaceted robustness and scalability, these coatings should find potential usage in harsh chemical engineering as well as infrastructure, transport vehicles and communication equipment.

Robust Planning for Effects-Based Operations

DTIC Science & Technology

2006-06-01

Algorithm ......................................... 34 2.6 Robust Optimization Literature ..................................... 36 2.6.1 Protecting Against...Model Formulation ...................... 55 3.1.5 Deterministic EBO Model Example and Performance ............. 59 3.1.6 Greedy Algorithm ...111 4.1.9 Conclusions on Robust EBO Model Performance .................... 116 4.2 Greedy Algorithm versus EBO Models

Robust, Optimal Water Infrastructure Planning Under Deep Uncertainty Using Metamodels

NASA Astrophysics Data System (ADS)

Maier, H. R.; Beh, E. H. Y.; Zheng, F.; Dandy, G. C.; Kapelan, Z.

2015-12-01

Optimal long-term planning plays an important role in many water infrastructure problems. However, this task is complicated by deep uncertainty about future conditions, such as the impact of population dynamics and climate change. One way to deal with this uncertainty is by means of robustness, which aims to ensure that water infrastructure performs adequately under a range of plausible future conditions. However, as robustness calculations require computationally expensive system models to be run for a large number of scenarios, it is generally computationally intractable to include robustness as an objective in the development of optimal long-term infrastructure plans. In order to overcome this shortcoming, an approach is developed that uses metamodels instead of computationally expensive simulation models in robustness calculations. The approach is demonstrated for the optimal sequencing of water supply augmentation options for the southern portion of the water supply for Adelaide, South Australia. A 100-year planning horizon is subdivided into ten equal decision stages for the purpose of sequencing various water supply augmentation options, including desalination, stormwater harvesting and household rainwater tanks. The objectives include the minimization of average present value of supply augmentation costs, the minimization of average present value of greenhouse gas emissions and the maximization of supply robustness. The uncertain variables are rainfall, per capita water consumption and population. Decision variables are the implementation stages of the different water supply augmentation options. Artificial neural networks are used as metamodels to enable all objectives to be calculated in a computationally efficient manner at each of the decision stages. The results illustrate the importance of identifying optimal staged solutions to ensure robustness and sustainability of water supply into an uncertain long-term future.

A new robust adaptive controller for vibration control of active engine mount subjected to large uncertainties

NASA Astrophysics Data System (ADS)

Fakhari, Vahid; Choi, Seung-Bok; Cho, Chang-Hyun

2015-04-01

This work presents a new robust model reference adaptive control (MRAC) for vibration control caused from vehicle engine using an electromagnetic type of active engine mount. Vibration isolation performances of the active mount associated with the robust controller are evaluated in the presence of large uncertainties. As a first step, an active mount with linear solenoid actuator is prepared and its dynamic model is identified via experimental test. Subsequently, a new robust MRAC based on the gradient method with σ-modification is designed by selecting a proper reference model. In designing the robust adaptive control, structured (parametric) uncertainties in the stiffness of the passive part of the mount and in damping ratio of the active part of the mount are considered to investigate the robustness of the proposed controller. Experimental and simulation results are presented to evaluate performance focusing on the robustness behavior of the controller in the face of large uncertainties. The obtained results show that the proposed controller can sufficiently provide the robust vibration control performance even in the presence of large uncertainties showing an effective vibration isolation.

Robust Control Systems.

DTIC Science & Technology

1981-12-01

time control system algorithms that will perform adequately (i.e., at least maintain closed-loop system stability) when ucertain parameters in the...system design models vary significantly. Such a control algorithm is said to have stability robustness-or more simply is said to be "robust". This...cas6s above, the performance is analyzed using a covariance analysis. The development of all the controllers and the performance analysis algorithms is

Centaur Test Bed (CTB) for Cryogenic Fluid Management

NASA Technical Reports Server (NTRS)

Sakla, Steven; Kutter, Bernard; Wall, John

2006-01-01

Future missions such as NASA s space exploration vision and DOD satellite servicing will require significant increases in the understanding and knowledge of space based cryogenic fluid management (CFM), including the transfer and storage of cryogenic fluids. Existing CFM capabilities are based on flight of upper stage cryogenic vehicles, scientific dewars, a few dedicated flight demonstrations and ground testing. This current capability is inadequate to support development of the CEV cryogenic propulsion system, other aspects of robust space exploration or the refueling of satellite cryo propulsion systems with reasonable risk. In addition, these technologies can provide significant performance increases for missions beyond low-earth orbit to enable manned missions to the Moon and beyond. The Centaur upper-stage vehicle can provide a low cost test platform for performing numerous flight demonstrations of the full breadth of required CFM technologies to support CEV development. These flight demonstrations can be performed as secondary mission objectives using excess LH2 and/or LO2 from the main vehicle propellant tanks following primary spacecraft separation at minimal cost and risk.

Robust nanogenerators based on graft copolymers via control of dielectrics for remarkable output power enhancement

PubMed Central

Lee, Jae Won; Cho, Hye Jin; Chun, Jinsung; Kim, Kyeong Nam; Kim, Seongsu; Ahn, Chang Won; Kim, Ill Won; Kim, Ju-Young; Kim, Sang-Woo; Yang, Changduk; Baik, Jeong Min

2017-01-01

A robust nanogenerator based on poly(tert-butyl acrylate) (PtBA)–grafted polyvinylidene difluoride (PVDF) copolymers via dielectric constant control through an atom-transfer radical polymerization technique, which can markedly increase the output power, is demonstrated. The copolymer is mainly composed of α phases with enhanced dipole moments due to the π-bonding and polar characteristics of the ester functional groups in the PtBA, resulting in the increase of dielectric constant values by approximately twice, supported by Kelvin probe force microscopy measurements. This increase in the dielectric constant significantly increased the density of the charges that can be accumulated on the copolymer during physical contact. The nanogenerator generates output signals of 105 V and 25 μA/cm2, a 20-fold enhancement in output power, compared to pristine PVDF–based nanogenerator after tuning the surface potential using a poling method. The markedly enhanced output performance is quite stable and reliable in harsh mechanical environments due to the high flexibility of the films. On the basis of these results, a much faster charging characteristic is demonstrated in this study. PMID:28560339

Edge magnetism of Heisenberg model on honeycomb lattice.

PubMed

Huang, Wen-Min; Hikihara, Toshiya; Lee, Yen-Chen; Lin, Hsiu-Hau

2017-03-07

Edge magnetism in graphene sparks intense theoretical and experimental interests. In the previous study, we demonstrated the existence of collective excitations at the zigzag edge of the honeycomb lattice with long-ranged Néel order. By employing the Schwinger-boson approach, we show that the edge magnons remain robust even when the long-ranged order is destroyed by spin fluctuations. Furthermore, in the effective field-theory limit, the dynamics of the edge magnon is captured by the one-dimensional relativistic Klein-Gordon equation. It is intriguing that the boundary field theory for the edge magnon is tied up with its bulk counterpart. By performing density-matrix renormalization group calculations, we show that the robustness may be attributed to the closeness between the ground state and the Néel state. The existence of edge magnon is not limited to the honeycomb structure, as demonstrated in the rotated-square lattice with zigzag edges as well. The universal behavior indicates that the edge magnons may attribute to the uncompensated edges and can be detected in many two-dimensional materials.

The Utility of Robust Means in Statistics

ERIC Educational Resources Information Center

Goodwyn, Fara

2012-01-01

Location estimates calculated from heuristic data were examined using traditional and robust statistical methods. The current paper demonstrates the impact outliers have on the sample mean and proposes robust methods to control for outliers in sample data. Traditional methods fail because they rely on the statistical assumptions of normality and…

Intelligent and robust optimization frameworks for smart grids

NASA Astrophysics Data System (ADS)

Dhansri, Naren Reddy

A smart grid implies a cyberspace real-time distributed power control system to optimally deliver electricity based on varying consumer characteristics. Although smart grids solve many of the contemporary problems, they give rise to new control and optimization problems with the growing role of renewable energy sources such as wind or solar energy. Under highly dynamic nature of distributed power generation and the varying consumer demand and cost requirements, the total power output of the grid should be controlled such that the load demand is met by giving a higher priority to renewable energy sources. Hence, the power generated from renewable energy sources should be optimized while minimizing the generation from non renewable energy sources. This research develops a demand-based automatic generation control and optimization framework for real-time smart grid operations by integrating conventional and renewable energy sources under varying consumer demand and cost requirements. Focusing on the renewable energy sources, the intelligent and robust control frameworks optimize the power generation by tracking the consumer demand in a closed-loop control framework, yielding superior economic and ecological benefits and circumvent nonlinear model complexities and handles uncertainties for superior real-time operations. The proposed intelligent system framework optimizes the smart grid power generation for maximum economical and ecological benefits under an uncertain renewable wind energy source. The numerical results demonstrate that the proposed framework is a viable approach to integrate various energy sources for real-time smart grid implementations. The robust optimization framework results demonstrate the effectiveness of the robust controllers under bounded power plant model uncertainties and exogenous wind input excitation while maximizing economical and ecological performance objectives. Therefore, the proposed framework offers a new worst-case deterministic optimization algorithm for smart grid automatic generation control.

Parallel In Vivo DNA Assembly by Recombination: Experimental Demonstration and Theoretical Approaches

PubMed Central

Shi, Zhenyu; Wedd, Anthony G.; Gras, Sally L.

2013-01-01

The development of synthetic biology requires rapid batch construction of large gene networks from combinations of smaller units. Despite the availability of computational predictions for well-characterized enzymes, the optimization of most synthetic biology projects requires combinational constructions and tests. A new building-brick-style parallel DNA assembly framework for simple and flexible batch construction is presented here. It is based on robust recombination steps and allows a variety of DNA assembly techniques to be organized for complex constructions (with or without scars). The assembly of five DNA fragments into a host genome was performed as an experimental demonstration. PMID:23468883

Design and implementation of robust decentralized control laws for the ACES structure at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G., Jr.; Phillips, Douglas J.; Hyland, David C.

1990-01-01

Many large space system concepts will require active vibration control to satisfy critical performance requirements such as line-of-sight accuracy. In order for these concepts to become operational it is imperative that the benefits of active vibration control be practically demonstrated in ground based experiments. The results of the experiment successfully demonstrate active vibration control for a flexible structure. The testbed is the Active Control Technique Evaluation for Spacecraft (ACES) structure at NASA Marshall Space Flight Center. The ACES structure is dynamically traceable to future space systems and especially allows the study of line-of-sight control issues.

Validation of the Filovirus Plaque Assay for Use in Preclinical Studies

PubMed Central

Shurtleff, Amy C.; Bloomfield, Holly A.; Mort, Shannon; Orr, Steven A.; Audet, Brian; Whitaker, Thomas; Richards, Michelle J.; Bavari, Sina

2016-01-01

A plaque assay for quantitating filoviruses in virus stocks, prepared viral challenge inocula and samples from research animals has recently been fully characterized and standardized for use across multiple institutions performing Biosafety Level 4 (BSL-4) studies. After standardization studies were completed, Good Laboratory Practices (GLP)-compliant plaque assay method validation studies to demonstrate suitability for reliable and reproducible measurement of the Marburg Virus Angola (MARV) variant and Ebola Virus Kikwit (EBOV) variant commenced at the United States Army Medical Research Institute of Infectious Diseases (USAMRIID). The validation parameters tested included accuracy, precision, linearity, robustness, stability of the virus stocks and system suitability. The MARV and EBOV assays were confirmed to be accurate to ±0.5 log10 PFU/mL. Repeatability precision, intermediate precision and reproducibility precision were sufficient to return viral titers with a coefficient of variation (%CV) of ≤30%, deemed acceptable variation for a cell-based bioassay. Intraclass correlation statistical techniques for the evaluation of the assay’s precision when the same plaques were quantitated by two analysts returned values passing the acceptance criteria, indicating high agreement between analysts. The assay was shown to be accurate and specific when run on Nonhuman Primates (NHP) serum and plasma samples diluted in plaque assay medium, with negligible matrix effects. Virus stocks demonstrated stability for freeze-thaw cycles typical of normal usage during assay retests. The results demonstrated that the EBOV and MARV plaque assays are accurate, precise and robust for filovirus titration in samples associated with the performance of GLP animal model studies. PMID:27110807

Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error.

PubMed

Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui

2017-06-13

The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.

Robust Control Design for Systems With Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a reliability- and robustness-based formulation for robust control synthesis for systems with probabilistic uncertainty. In a reliability-based formulation, the probability of violating design requirements prescribed by inequality constraints is minimized. In a robustness-based formulation, a metric which measures the tendency of a random variable/process to cluster close to a target scalar/function is minimized. A multi-objective optimization procedure, which combines stability and performance requirements in time and frequency domains, is used to search for robustly optimal compensators. Some of the fundamental differences between the proposed strategy and conventional robust control methods are: (i) unnecessary conservatism is eliminated since there is not need for convex supports, (ii) the most likely plants are favored during synthesis allowing for probabilistic robust optimality, (iii) the tradeoff between robust stability and robust performance can be explored numerically, (iv) the uncertainty set is closely related to parameters with clear physical meaning, and (v) compensators with improved robust characteristics for a given control structure can be synthesized.

Angular velocity estimation from measurement vectors of star tracker.

PubMed

Liu, Hai-bo; Yang, Jun-cai; Yi, Wen-jun; Wang, Jiong-qi; Yang, Jian-kun; Li, Xiu-jian; Tan, Ji-chun

2012-06-01

In most spacecraft, there is a need to know the craft's angular rate. Approaches with least squares and an adaptive Kalman filter are proposed for estimating the angular rate directly from the star tracker measurements. In these approaches, only knowledge of the vector measurements and sampling interval is required. The designed adaptive Kalman filter can filter out noise without information of the dynamic model and inertia dyadic. To verify the proposed estimation approaches, simulations based on the orbit data of the challenging minisatellite payload (CHAMP) satellite and experimental tests with night-sky observation are performed. Both the simulations and experimental testing results have demonstrated that the proposed approach performs well in terms of accuracy, robustness, and performance.

NASA's Preparations for ESA's L3 Gravitational Wave Mission

NASA Technical Reports Server (NTRS)

Stebbins, Robin

2016-01-01

Telescope Subsystem - Jeff Livas (GSFC): Demonstrate pathlength stability, straylight and manufacturability. Phase Measurement System - Bill Klipstein (JPL): Key measurement functions demonstrated. Incorporate full flight functionality. Laser Subsystem - Jordan Camp (GSFC): ECL master oscillator, phase noise of fiber power amplifier, demonstrate end-to-end performance in integrated system, lifetime. Micronewton Thrusters - John Ziemer (JPL): Propellant storage and distribution, system robustness, manufacturing yield, lifetime. Arm-locking Demonstration - Kirk McKenzie (JPL): Studying a demonstration of laser frequency stabilization with GRACE Follow-On. Torsion Pendulum - John Conklin (UF): Develop U.S. capability with GRS and torsion pendulum test bed. Multi-Axis Heterodyne Interferometry - Ira Thorpe (GSFC): Investigate test mass/optical bench interface. UV LEDs - John Conklin+ (UF): Flight qualify UV LEDs to replace mercury lamps in discharging system. Optical Bench - Guido Mueller (UF): Investigate alternate designs and fabrication processes to ease manufacturability. LISA researchers at JPL are leading the Laser Ranging Interferometer instrument on the GRACE Follow-On mission.

A robust and high-performance queue management controller for large round trip time networks

NASA Astrophysics Data System (ADS)

Khoshnevisan, Ladan; Salmasi, Farzad R.

2016-05-01

Congestion management for transmission control protocol is of utmost importance to prevent packet loss within a network. This necessitates strategies for active queue management. The most applied active queue management strategies have their inherent disadvantages which lead to suboptimal performance and even instability in the case of large round trip time and/or external disturbance. This paper presents an internal model control robust queue management scheme with two degrees of freedom in order to restrict the undesired effects of large and small round trip time and parameter variations in the queue management. Conventional approaches such as proportional integral and random early detection procedures lead to unstable behaviour due to large delay. Moreover, internal model control-Smith scheme suffers from large oscillations due to the large round trip time. On the other hand, other schemes such as internal model control-proportional integral and derivative show excessive sluggish performance for small round trip time values. To overcome these shortcomings, we introduce a system entailing two individual controllers for queue management and disturbance rejection, simultaneously. Simulation results based on Matlab/Simulink and also Network Simulator 2 (NS2) demonstrate the effectiveness of the procedure and verify the analytical approach.

The integrated effects of future climate and hydrologic uncertainty on sustainable flood risk management

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Wi, S.; Brown, C. M.

2013-12-01

Flood risk management performance is investigated within the context of integrated climate and hydrologic modeling uncertainty to explore system robustness. The research question investigated is whether structural and hydrologic parameterization uncertainties are significant relative to other uncertainties such as climate change when considering water resources system performance. Two hydrologic models are considered, a conceptual, lumped parameter model that preserves the water balance and a physically-based model that preserves both water and energy balances. In the conceptual model, parameter and structural uncertainties are quantified and propagated through the analysis using a Bayesian modeling framework with an innovative error model. Mean climate changes and internal climate variability are explored using an ensemble of simulations from a stochastic weather generator. The approach presented can be used to quantify the sensitivity of flood protection adequacy to different sources of uncertainty in the climate and hydrologic system, enabling the identification of robust projects that maintain adequate performance despite the uncertainties. The method is demonstrated in a case study for the Coralville Reservoir on the Iowa River, where increased flooding over the past several decades has raised questions about potential impacts of climate change on flood protection adequacy.

SUBOPT: A CAD program for suboptimal linear regulators

NASA Technical Reports Server (NTRS)

Fleming, P. J.

1985-01-01

An interactive software package which provides design solutions for both standard linear quadratic regulator (LQR) and suboptimal linear regulator problems is described. Intended for time-invariant continuous systems, the package is easily modified to include sampled-data systems. LQR designs are obtained by established techniques while the large class of suboptimal problems containing controller and/or performance index options is solved using a robust gradient minimization technique. Numerical examples demonstrate features of the package and recent developments are described.

A Palmprint Recognition Algorithm Using Phase-Only Correlation

NASA Astrophysics Data System (ADS)

Ito, Koichi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.

COxSwAIN: Compressive Sensing for Advanced Imaging and Navigation

NASA Technical Reports Server (NTRS)

Kurwitz, Richard; Pulley, Marina; LaFerney, Nathan; Munoz, Carlos

2015-01-01

The COxSwAIN project focuses on building an image and video compression scheme that can be implemented in a small or low-power satellite. To do this, we used Compressive Sensing, where the compression is performed by matrix multiplications on the satellite and reconstructed on the ground. Our paper explains our methodology and demonstrates the results of the scheme, being able to achieve high quality image compression that is robust to noise and corruption.

All-Printed, Foldable Organic Thin-Film Transistors on Glassine Paper.

PubMed

Hyun, Woo Jin; Secor, Ethan B; Rojas, Geoffrey A; Hersam, Mark C; Francis, Lorraine F; Frisbie, C Daniel

2015-11-25

All-printed, foldable organic thin-film transistors are demonstrated on glassine paper with a combination of advanced materials and processing techniques. Glassine paper provides a suitable surface for high-performance printing methods, while graphene electrodes and an ion-gel gate dielectric enable robust stability over 100 folding cycles. Altogether, this study features a practical platform for low-cost, large-area, and foldable electronics. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2005-01-01

In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place.

Wearable, wireless gas sensors using highly stretchable and transparent structures of nanowires and graphene

NASA Astrophysics Data System (ADS)

Park, Jihun; Kim, Joohee; Kim, Kukjoo; Kim, So-Yun; Cheong, Woon Hyung; Park, Kyeongmin; Song, Joo Hyeb; Namgoong, Gyeongho; Kim, Jae Joon; Heo, Jaeyeong; Bien, Franklin; Park, Jang-Ung

2016-05-01

Herein, we report the fabrication of a highly stretchable, transparent gas sensor based on silver nanowire-graphene hybrid nanostructures. Due to its superb mechanical and optical characteristics, the fabricated sensor demonstrates outstanding and stable performances even under extreme mechanical deformation (stable until 20% of strain). The integration of a Bluetooth system or an inductive antenna enables the wireless operation of the sensor. In addition, the mechanical robustness of the materials allows the device to be transferred onto various nonplanar substrates, including a watch, a bicycle light, and the leaves of live plants, thereby achieving next-generation sensing electronics for the `Internet of Things' area.Herein, we report the fabrication of a highly stretchable, transparent gas sensor based on silver nanowire-graphene hybrid nanostructures. Due to its superb mechanical and optical characteristics, the fabricated sensor demonstrates outstanding and stable performances even under extreme mechanical deformation (stable until 20% of strain). The integration of a Bluetooth system or an inductive antenna enables the wireless operation of the sensor. In addition, the mechanical robustness of the materials allows the device to be transferred onto various nonplanar substrates, including a watch, a bicycle light, and the leaves of live plants, thereby achieving next-generation sensing electronics for the `Internet of Things' area. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr01468b

Sparse alignment for robust tensor learning.

PubMed

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

CALiPER Report 20.3: Robustness of LED PAR38 Lamps

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

Poplawski, Michael E.; Royer, Michael P.; Brown, Charles C.

2014-12-01

Three samples of 40 of the Series 20 PAR38 lamps underwent multi-stress testing, whereby samples were subjected to increasing levels of simultaneous thermal, humidity, electrical, and vibrational stress. The results do not explicitly predict expected lifetime or reliability, but they can be compared with one another, as well as with benchmark conventional products, to assess the relative robustness of the product designs. On average, the 32 LED lamp models tested were substantially more robust than the conventional benchmark lamps. As with other performance attributes, however, there was great variability in the robustness and design maturity of the LED lamps. Severalmore » LED lamp samples failed within the first one or two levels of the ten-level stress plan, while all three samples of some lamp models completed all ten levels. One potential area of improvement is design maturity, given that more than 25% of the lamp models demonstrated a difference in failure level for the three samples that was greater than or equal to the maximum for the benchmarks. At the same time, the fact that nearly 75% of the lamp models exhibited better design maturity than the benchmarks is noteworthy, given the relative stage of development for the technology.« less

Robust Design of a Particle-Free Silver-Organo-Complex Ink with High Conductivity and Inkjet Stability for Flexible Electronics.

PubMed

Vaseem, Mohammad; McKerricher, Garret; Shamim, Atif

2016-01-13

Currently, silver-nanoparticle-based inkjet ink is commercially available. This type of ink has several serious problems such as a complex synthesis protocol, high cost, high sintering temperatures (∼200 °C), particle aggregation, nozzle clogging, poor shelf life, and jetting instability. For the emerging field of printed electronics, these shortcomings in conductive inks are barriers for their widespread use in practical applications. Formulating particle-free silver inks has potential to solve these issues and requires careful design of the silver complexation. The ink complex must meet various requirements, such as in situ reduction, optimum viscosity, storage and jetting stability, smooth uniform sintered films, excellent adhesion, and high conductivity. This study presents a robust formulation of silver-organo-complex (SOC) ink, where complexing molecules act as reducing agents. The 17 wt % silver loaded ink was printed and sintered on a wide range of substrates with uniform surface morphology and excellent adhesion. The jetting stability was monitored for 5 months to confirm that the ink was robust and highly stable with consistent jetting performance. Radio frequency inductors, which are highly sensitive to metal quality, were demonstrated as a proof of concept on flexible PEN substrate. This is a major step toward producing high-quality electronic components with a robust inkjet printing process.

Efficient, flexible and mechanically robust perovskite solar cells on inverted nanocone plastic substrates.

PubMed

Tavakoli, Mohammad Mahdi; Lin, Qingfeng; Leung, Siu-Fung; Lui, Ga Ching; Lu, Hao; Li, Liang; Xiang, Bin; Fan, Zhiyong

2016-02-21

Utilization of nanostructures on photovoltaic devices can significantly improve the device energy conversion efficiency by enhancing the device light harvesting capability as well as carrier collection efficiency. However, improvements in device mechanical robustness and reliability, particularly for flexible devices, have rarely been reported with in-depth understanding. In this work, we fabricated efficient, flexible and mechanically robust organometallic perovskite solar cells on plastic substrates with inverted nanocone (i-cone) structures. Compared with the reference cell that was fabricated on a flat substrate, it was shown that the device power conversion efficiency could be improved by 37%, and reached up to 11.29% on i-cone substrates. More interestingly, it was discovered that the performance of an i-cone device remained more than 90% of the initial value even after 200 mechanical bending cycles, which is remarkably better than for the flat reference device, which degraded down to only 60% in the same test. Our experiments, coupled with mechanical simulation, demonstrated that a nanostructured template can greatly help in relaxing stress and strain upon device bending, which suppresses crack nucleation in different layers of a perovskite solar cell. This essentially leads to much improved device reliability and robustness and will have significant impact on practical applications.

Designing Flood Management Systems for Joint Economic and Ecological Robustness

NASA Astrophysics Data System (ADS)

Spence, C. M.; Grantham, T.; Brown, C. M.; Poff, N. L.

2015-12-01

Freshwater ecosystems across the United States are threatened by hydrologic change caused by water management operations and non-stationary climate trends. Nonstationary hydrology also threatens flood management systems' performance. Ecosystem managers and flood risk managers need tools to design systems that achieve flood risk reduction objectives while sustaining ecosystem functions and services in an uncertain hydrologic future. Robust optimization is used in water resources engineering to guide system design under climate change uncertainty. Using principles introduced by Eco-Engineering Decision Scaling (EEDS), we extend robust optimization techniques to design flood management systems that meet both economic and ecological goals simultaneously across a broad range of future climate conditions. We use three alternative robustness indices to identify flood risk management solutions that preserve critical ecosystem functions in a case study from the Iowa River, where recent severe flooding has tested the limits of the existing flood management system. We seek design modifications to the system that both reduce expected cost of flood damage while increasing ecologically beneficial inundation of riparian floodplains across a wide range of plausible climate futures. The first robustness index measures robustness as the fraction of potential climate scenarios in which both engineering and ecological performance goals are met, implicitly weighting each climate scenario equally. The second index builds on the first by using climate projections to weight each climate scenario, prioritizing acceptable performance in climate scenarios most consistent with climate projections. The last index measures robustness as mean performance across all climate scenarios, but penalizes scenarios with worse performance than average, rewarding consistency. Results stemming from alternate robustness indices reflect implicit assumptions about attitudes toward risk and reveal the tradeoffs between using structural and non-structural flood management strategies to ensure economic and ecological robustness.

Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

2018-01-01

Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.

Integrating operation design into infrastructure planning to foster robustness of planned water systems

NASA Astrophysics Data System (ADS)

Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over the past years, many studies have looked at the planning and management of water infrastructure systems as two separate problems, where the dynamic component (i.e., operations) is considered only after the static problem (i.e., planning) has been resolved. Most recent works have started to investigate planning and management as two strictly interconnected faces of the same problem, where the former is solved jointly with the latter in an integrated framework. This brings advantages to multi-purpose water reservoir systems, where several optimal operating strategies exist and similar system designs might perform differently on the long term depending on the considered short-term operating tradeoff. An operationally robust design will be therefore one performing well across multiple feasible tradeoff operating policies. This work aims at studying the interaction between short-term operating strategies and their impacts on long-term structural decisions, when long-lived infrastructures with complex ecological impacts and multi-sectoral demands to satisfy (i.e., reservoirs) are considered. A parametric reinforcement learning approach is adopted for nesting optimization and control yielding to both optimal reservoir design and optimal operational policies for water reservoir systems. The method is demonstrated on a synthetic reservoir that must be designed and operated for ensuring reliable water supply to downstream users. At first, the optimal design capacity derived is compared with the 'no-fail storage' computed through Rippl, a capacity design function that returns the minimum storage needed to satisfy specified water demands without allowing supply shortfall. Then, the optimal reservoir volume is used to simulate the simplified case study under other operating objectives than water supply, in order to assess whether and how the system performance changes. The more robust the infrastructural design, the smaller the difference between the performances of different operating strategies.

Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INS.

PubMed

Zhou, Dapeng; Guo, Lei

2017-11-18

The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estimation accuracy of misalignment angles. This study aims to develop a novel robust nonlinear filter, namely the stochastic integration H ∞ filter (SIH ∞ F) for improving both the accuracy and robustness of RTA. In this new nonlinear H ∞ filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H ∞ filter for the first time, and the resulting SIH ∞ F simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIH ∞ F and previously well-known methodologies are carried out by means of numerical simulation and a van test. The results demonstrate that the newly-proposed method outperforms the cubature H ∞ filter. Moreover, the SIH ∞ F inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty.

Robustness of NMR-based metabolomics to generate comparable data sets for olive oil cultivar classification. An inter-laboratory study on Apulian olive oils.

PubMed

Piccinonna, Sara; Ragone, Rosa; Stocchero, Matteo; Del Coco, Laura; De Pascali, Sandra Angelica; Schena, Francesco Paolo; Fanizzi, Francesco Paolo

2016-05-15

Nuclear Magnetic Resonance (NMR) spectroscopy is emerging as a powerful technique in olive oil fingerprinting, but its analytical robustness has to be proved. Here, we report a comparative study between two laboratories on olive oil (1)H NMR fingerprinting, aiming to demonstrate the robustness of NMR-based metabolomics in generating comparable data sets for cultivar classification. Sample preparation and data acquisition were performed independently in two laboratories, equipped with different resolution spectrometers (400 and 500 MHz), using two identical sets of mono-varietal olive oils. Partial Least Squares (PLS)-based techniques were applied to compare the data sets produced by the two laboratories. Despite differences in spectrum baseline, and in intensity and shape of peaks, the amount of shared information was significant (almost 70%) and related to cultivar (same metabolites discriminated between cultivars). In conclusion, regardless of the variability due to operator and machine, the data sets from the two participating units were comparable for the purpose of classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.

PubMed

Ping-Keng Jao; Yuan-Pin Lin; Yi-Hsuan Yang; Tzyy-Ping Jung

2015-08-01

An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.

Robust output tracking control of a laboratory helicopter for automatic landing

NASA Astrophysics Data System (ADS)

Liu, Hao; Lu, Geng; Zhong, Yisheng

2014-11-01

In this paper, robust output tracking control problem of a laboratory helicopter for automatic landing in high seas is investigated. The motion of the helicopter is required to synchronise with that of an oscillating platform, e.g. the deck of a vessel subject to wave-induced motions. A robust linear time-invariant output feedback controller consisting of a nominal controller and a robust compensator is designed. The robust compensator is introduced to restrain the influences of parametric uncertainties, nonlinearities and external disturbances. It is shown that robust stability and robust tracking property can be achieved simultaneously. Experimental results on the laboratory helicopter for automatic landing demonstrate the effectiveness of the designed control approach.

Robust control of integrated motor-transmission powertrain system over controller area network for automotive applications

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyuan; Zhang, Hui; Cao, Dongpu; Fang, Zongde

2015-06-01

Integrated motor-transmission (IMT) powertrain system with directly coupled motor and gearbox is a good choice for electric commercial vehicles (e.g., pure electric buses) due to its potential in motor size reduction and energy efficiency improvement. However, the controller design for powertrain oscillation damping becomes challenging due to the elimination of damping components. On the other hand, as controller area network (CAN) is commonly adopted in modern vehicle system, the network-induced time-varying delays that caused by bandwidth limitation will further lead to powertrain vibration or even destabilize the powertrain control system. Therefore, in this paper, a robust energy-to-peak controller is proposed for the IMT powertrain system to address the oscillation damping problem and also attenuate the external disturbance. The control law adopted here is based on a multivariable PI control, which ensures the applicability and performance of the proposed controller in engineering practice. With the linearized delay uncertainties characterized by polytopic inclusions, a delay-free closed-loop augmented system is established for the IMT powertrain system under discrete-time framework. The proposed controller design problem is then converted to a static output feedback (SOF) controller design problem where the feedback control gains are obtained by solving a set of linear matrix inequalities (LMIs). The effectiveness as well as robustness of the proposed controller is demonstrated by comparing its performance against that of a conventional PI controller.

A H∞/μ solution for microvibration mitigation in satellites: A case study

NASA Astrophysics Data System (ADS)

Preda, Valentin; Cieslak, Jérôme; Henry, David; Bennani, Samir; Falcoz, Alexandre

2017-07-01

The research work presented in this paper focuses on the development of a mixed active-passive microvibration mitigation solution capable of attenuating the transmitted vibrations generated by reaction wheels to a satellite structure. A representative benchmark provided by the European Space Agency (ESA) and Airbus Defence and Space, serves as a support for testing the proposed solution. The paper also covers modeling and design issues as well as a deep analysis of the solution within the H∞ / μ setting. Especially, an uncertainty modeling strategy is proposed to extract a Linear Fractional Transformation (LFT) model. Insight is naturally provided into various dynamical interactions between the plant elements such as bearing and isolator flexibility, gyroscopic effects, actuator dynamics and feedback-loop delays. The design of the mitigation solution is formulated into the H∞ / μ framework leading to a robust H∞ control strategy capable of achieving exemplary active attenuation performance across a wide range of reaction wheel speeds. A systematic analysis procedure based on the structured singular value μ is used to assess and demonstrate the robust stability and robust performance of the microvibration mitigation strategy. The proposed analysis method is also shown to be a powerful and reliable solution to identify worst-case scenarios without relying on traditional Monte Carlo campaigns. Time domain simulations based on a nonlinear high-fidelity industrial simulator are included as a validation step.

Uncalibrated stereo rectification and disparity range stabilization: a comparison of different feature detectors

NASA Astrophysics Data System (ADS)

Luo, Xiongbiao; Jayarathne, Uditha L.; McLeod, A. Jonathan; Pautler, Stephen E.; Schlacta, Christopher M.; Peters, Terry M.

2016-03-01

This paper studies uncalibrated stereo rectification and stable disparity range determination for surgical scene three-dimensional (3-D) reconstruction. Stereoscopic endoscope calibration sometimes is not available and also increases the complexity of the operating-room environment. Stereo from uncalibrated endoscopic cameras is an alternative to reconstruct the surgical field visualized by binocular endoscopes within the body. Uncalibrated rectification is usually performed on the basis of a number of matched feature points (semi-dense correspondence) between the left and the right images of stereo pairs. After uncalibrated rectification, the corresponding feature points can be used to determine the proper disparity range that helps to improve the reconstruction accuracy and reduce the computational time of disparity map estimation. Therefore, the corresponding or matching accuracy and robustness of feature point descriptors is important to surgical field 3-D reconstruction. This work compares four feature detectors: (1) scale invariant feature transform (SIFT), (2) speeded up robust features (SURF), (3) affine scale invariant feature transform (ASIFT), and (4) gauge speeded up robust features (GSURF) with applications to uncalibrated rectification and stable disparity range determination. We performed our experiments on surgical endoscopic video images that were collected during robotic prostatectomy. The experimental results demonstrate that ASIFT outperforms other feature detectors in the uncalibrated stereo rectification and also provides a stable stable disparity range for surgical scene reconstruction.

Manufacturing Execution Systems: Examples of Performance Indicator and Operational Robustness Tools.

PubMed

Gendre, Yannick; Waridel, Gérard; Guyon, Myrtille; Demuth, Jean-François; Guelpa, Hervé; Humbert, Thierry

Manufacturing Execution Systems (MES) are computerized systems used to measure production performance in terms of productivity, yield, and quality. In the first part, performance indicator and overall equipment effectiveness (OEE), process robustness tools and statistical process control are described. The second part details some tools to help process robustness and control by operators by preventing deviations from target control charts. MES was developed by Syngenta together with CIMO for automation.

Color image watermarking against fog effects

NASA Astrophysics Data System (ADS)

Chotikawanid, Piyanart; Amornraksa, Thumrongrat

2017-07-01

Fog effects in various computer and camera software can partially or fully damage the watermark information within the watermarked image. In this paper, we propose a color image watermarking based on the modification of reflectance component against fog effects. The reflectance component is extracted from the blue color channel in the RGB color space of a host image, and then used to carry a watermark signal. The watermark extraction is blindly achieved by subtracting the estimation of the original reflectance component from the watermarked component. The performance of the proposed watermarking method in terms of wPSNR and NC is evaluated, and then compared with the previous method. The experimental results on robustness against various levels of fog effect, from both computer software and mobile application, demonstrated a higher robustness of our proposed method, compared to the previous one.

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

PubMed

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

Research and development to prepare and characterize robust coal/biomass mixtures for direct co-feeding into gasification systems

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

Felix, Larry; Farthing, William; Hoekman, S. Kent

This project was initiated on October 1, 2010 and utilizes equipment and research supported by the Department of Energy, National Energy Technology Laboratory, under Award Number DE- FE0005349. It is also based upon previous work supported by the Department of Energy, National Energy Technology Laboratory, under Award Numbers DOE-DE-FG36-01GOl1082, DE-FG36-02G012011 or DE-EE0000272. The overall goal of the work performed was to demonstrate and assess the economic viability of fast hydrothermal carbonization (HTC) for transforming lignocellulosic biomass into a densified, friable fuel to gasify like coal that can be easily blended with ground coal and coal fines and then be formedmore » into robust, weather-resistant pellets and briquettes.« less

Robust nonlinear variable selective control for networked systems

NASA Astrophysics Data System (ADS)

Rahmani, Behrooz

2016-10-01

This paper is concerned with the networked control of a class of uncertain nonlinear systems. In this way, Takagi-Sugeno (T-S) fuzzy modelling is used to extend the previously proposed variable selective control (VSC) methodology to nonlinear systems. This extension is based upon the decomposition of the nonlinear system to a set of fuzzy-blended locally linearised subsystems and further application of the VSC methodology to each subsystem. To increase the applicability of the T-S approach for uncertain nonlinear networked control systems, this study considers the asynchronous premise variables in the plant and the controller, and then introduces a robust stability analysis and control synthesis. The resulting optimal switching-fuzzy controller provides a minimum guaranteed cost on an H2 performance index. Simulation studies on three nonlinear benchmark problems demonstrate the effectiveness of the proposed method.

H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

NASA Astrophysics Data System (ADS)

Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

2016-07-01

This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

Real-time driver fatigue detection based on face alignment

NASA Astrophysics Data System (ADS)

Tao, Huanhuan; Zhang, Guiying; Zhao, Yong; Zhou, Yi

2017-07-01

The performance and robustness of fatigue detection largely decrease if the driver with glasses. To address this issue, this paper proposes a practical driver fatigue detection method based on face alignment at 3000 FPS algorithm. Firstly, the eye regions of the driver are localized by exploiting 6 landmarks surrounding each eye. Secondly, the HOG features of the extracted eye regions are calculated and put into SVM classifier to recognize the eye state. Finally, the value of PERCLOS is calculated to determine whether the driver is drowsy or not. An alarm will be generated if the eye is closed for a specified period of time. The accuracy and real-time on testing videos with different drivers demonstrate that the proposed algorithm is robust and obtain better accuracy for driver fatigue detection compared with some previous method.

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter.

PubMed

Fei, Juntao; Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.

Reliable quantification of BOLD fMRI cerebrovascular reactivity despite poor breath-hold performance.

PubMed

Bright, Molly G; Murphy, Kevin

2013-12-01

Cerebrovascular reactivity (CVR) can be mapped using BOLD fMRI to provide a clinical insight into vascular health that can be used to diagnose cerebrovascular disease. Breath-holds are a readily accessible method for producing the required arterial CO2 increases but their implementation into clinical studies is limited by concerns that patients will demonstrate highly variable performance of breath-hold challenges. This study assesses the repeatability of CVR measurements despite poor task performance, to determine if and how robust results could be achieved with breath-holds in patients. Twelve healthy volunteers were scanned at 3 T. Six functional scans were acquired, each consisting of 6 breath-hold challenges (10, 15, or 20 s duration) interleaved with periods of paced breathing. These scans simulated the varying breath-hold consistency and ability levels that may occur in patient data. Uniform ramps, time-scaled ramps, and end-tidal CO2 data were used as regressors in a general linear model in order to measure CVR at the grey matter, regional, and voxelwise level. The intraclass correlation coefficient (ICC) quantified the repeatability of the CVR measurement for each breath-hold regressor type and scale of interest across the variable task performances. The ramp regressors did not fully account for variability in breath-hold performance and did not achieve acceptable repeatability (ICC<0.4) in several regions analysed. In contrast, the end-tidal CO2 regressors resulted in "excellent" repeatability (ICC=0.82) in the average grey matter data, and resulted in acceptable repeatability in all smaller regions tested (ICC>0.4). Further analysis of intra-subject CVR variability across the brain (ICCspatial and voxelwise correlation) supported the use of end-tidal CO2 data to extract robust whole-brain CVR maps, despite variability in breath-hold performance. We conclude that the incorporation of end-tidal CO2 monitoring into scanning enables robust, repeatable measurement of CVR that makes breath-hold challenges suitable for routine clinical practice. © 2013.

Frequent video game players resist perceptual interference.

PubMed

Berard, Aaron V; Cain, Matthew S; Watanabe, Takeo; Sasaki, Yuka

2015-01-01

Playing certain types of video games for a long time can improve a wide range of mental processes, from visual acuity to cognitive control. Frequent gamers have also displayed generalized improvements in perceptual learning. In the Texture Discrimination Task (TDT), a widely used perceptual learning paradigm, participants report the orientation of a target embedded in a field of lines and demonstrate robust over-night improvement. However, changing the orientation of the background lines midway through TDT training interferes with overnight improvements in overall performance on TDT. Interestingly, prior research has suggested that this effect will not occur if a one-hour break is allowed in between the changes. These results have suggested that after training is over, it may take some time for learning to become stabilized and resilient against interference. Here, we tested whether frequent gamers have faster stabilization of perceptual learning compared to non-gamers and examined the effect of daily video game playing on interference of training of TDT with one background orientation on perceptual learning of TDT with a different background orientation. As a result, we found that non-gamers showed overnight performance improvement only on one background orientation, replicating previous results with the interference in TDT. In contrast, frequent gamers demonstrated overnight improvements in performance with both background orientations, suggesting that they are better able to overcome interference in perceptual learning. This resistance to interference suggests that video game playing not only enhances the amplitude and speed of perceptual learning but also leads to faster and/or more robust stabilization of perceptual learning.

Simulation Based Low-Cost Composite Process Development at the US Air Force Research Laboratory

NASA Technical Reports Server (NTRS)

Rice, Brian P.; Lee, C. William; Curliss, David B.

2003-01-01

Low-cost composite research in the US Air Force Research Laboratory, Materials and Manufacturing Directorate, Organic Matrix Composites Branch has focused on the theme of affordable performance. Practically, this means that we use a very broad view when considering the affordability of composites. Factors such as material costs, labor costs, recurring and nonrecurring manufacturing costs are balanced against performance to arrive at the relative affordability vs. performance measure of merit. The research efforts discussed here are two projects focused on affordable processing of composites. The first topic is the use of a neural network scheme to model cure reaction kinetics, then utilize the kinetics coupled with simple heat transport models to predict, in real-time, future exotherms and control them. The neural network scheme is demonstrated to be very robust and a much more efficient method that mechanistic cure modeling approach. This enables very practical low-cost processing of thick composite parts. The second project is liquid composite molding (LCM) process simulation. LCM processing of large 3D integrated composite parts has been demonstrated to be a very cost effective way to produce large integrated aerospace components specific examples of LCM processes are resin transfer molding (RTM), vacuum assisted resin transfer molding (VARTM), and other similar approaches. LCM process simulation is a critical part of developing an LCM process approach. Flow simulation enables the development of the most robust approach to introducing resin into complex preforms. Furthermore, LCM simulation can be used in conjunction with flow front sensors to control the LCM process in real-time to account for preform or resin variability.

Robust differences in antisaccade performance exist between COGS schizophrenia cases and controls regardless of recruitment strategies.

PubMed

Radant, Allen D; Millard, Steven P; Braff, David L; Calkins, Monica E; Dobie, Dorcas J; Freedman, Robert; Green, Michael F; Greenwood, Tiffany A; Gur, Raquel E; Gur, Ruben C; Lazzeroni, Laura C; Light, Gregory A; Meichle, Sean P; Nuechterlein, Keith H; Olincy, Ann; Seidman, Larry J; Siever, Larry J; Silverman, Jeremy M; Stone, William S; Swerdlow, Neal R; Sugar, Catherine A; Tsuang, Ming T; Turetsky, Bruce I; Tsuang, Debby W

2015-04-01

The impaired ability to make correct antisaccades (i.e., antisaccade performance) is well documented among schizophrenia subjects, and researchers have successfully demonstrated that antisaccade performance is a valid schizophrenia endophenotype that is useful for genetic studies. However, it is unclear how the ascertainment biases that unavoidably result from recruitment differences in schizophrenia subjects identified in family versus case-control studies may influence patient-control differences in antisaccade performance. To assess the impact of ascertainment bias, researchers from the Consortium on the Genetics of Schizophrenia (COGS) compared antisaccade performance and antisaccade metrics (latency and gain) in schizophrenia and control subjects from COGS-1, a family-based schizophrenia study, to schizophrenia and control subjects from COGS-2, a corresponding case-control study. COGS-2 schizophrenia subjects were substantially older; had lower education status, worse psychosocial function, and more severe symptoms; and were three times more likely to be a member of a multiplex family than COGS-1 schizophrenia subjects. Despite these variations, which were likely the result of ascertainment differences (as described in the introduction to this special issue), the effect sizes of the control-schizophrenia differences in antisaccade performance were similar in both studies (Cohen's d effect size of 1.06 and 1.01 in COGS-1 and COGS-2, respectively). This suggests that, in addition to the robust, state-independent schizophrenia-related deficits described in endophenotype studies, group differences in antisaccade performance do not vary based on subject ascertainment and recruitment factors. Published by Elsevier B.V.

Robust differences in antisaccade performance exist between COGS schizophrenia cases and controls regardless of recruitment strategies

PubMed Central

Radant, Allen D.; Millard, Steven P.; Braff, David; Calkins, Monica E.; Dobie, Dorcas J.; Freedman, Robert; Green, Michael F.; Greenwood, Tiffany A.; Gur, Raquel E.; Gur, Ruben C.; Lazzeroni, Laura; Light, Gregory A.; Meichle, Sean; Nuechterlein, Keith H.; Olincy, Ann; Seidman, Larry J.; Siever, Larry; Silverman, Jeremy; Stone, William S.; Swerdlow, Neal R.; Sugar, Catherine; Tsuang, Ming T.; Turetsky, Bruce I.; Tsuang, Debby W.

2015-01-01

The impaired ability to make correct antisaccades (i.e., antisaccade performance) is well documented among schizophrenia subjects, and researchers have successfully demonstrated that antisaccade performance is a valid schizophrenia endophenotype that is useful for genetic studies. However, it is unclear how the ascertainment biases that unavoidably result from recruitment differences in schizophrenia subjects identified in family versus case-control studies may influence patient-control differences in antisaccade performance. To assess the impact of ascertainment bias, researchers from the Consortium on the Genetics of Schizophrenia (COGS) compared antisaccade performance and antisaccade metrics (latency and gain) in schizophrenia and control subjects from COGS-1, a family-based schizophrenia study, to schizophrenia and control subjects from COGS-2, a corresponding case-control study. COGS-2 schizophrenia subjects were substantially older; had lower education status, worse psychosocial function, and more severe symptoms; and were three times more likely to be a member of a multiplex family than COGS-1 schizophrenia subjects. Despite these variations, which were likely the result of ascertainment differences (as described in the introduction to this special issue), the effect sizes of the control-schizophrenia differences in antisaccade performance were similar in both studies (Cohen’s d effect size of 1.06 and 1.01 in COGS-1 and COGS-2, respectively). This suggests that, in addition to the robust, state-independent schizophrenia-related deficits described in endophenotype studies, group differences in antisaccade performance do not vary based on subject ascertainment and recruitment factors. PMID:25553977

Robustness of speckle imaging techniques applied to horizontal imaging scenarios

NASA Astrophysics Data System (ADS)

Bos, Jeremy P.

Atmospheric turbulence near the ground severely limits the quality of imagery acquired over long horizontal paths. In defense, surveillance, and border security applications, there is interest in deploying man-portable, embedded systems incorporating image reconstruction to improve the quality of imagery available to operators. To be effective, these systems must operate over significant variations in turbulence conditions while also subject to other variations due to operation by novice users. Systems that meet these requirements and are otherwise designed to be immune to the factors that cause variation in performance are considered robust. In addition to robustness in design, the portable nature of these systems implies a preference for systems with a minimum level of computational complexity. Speckle imaging methods are one of a variety of methods recently been proposed for use in man-portable horizontal imagers. In this work, the robustness of speckle imaging methods is established by identifying a subset of design parameters that provide immunity to the expected variations in operating conditions while minimizing the computation time necessary for image recovery. This performance evaluation is made possible using a novel technique for simulating anisoplanatic image formation. I find that incorporate as few as 15 image frames and 4 estimates of the object phase per reconstructed frame provide an average reduction of 45% reduction in Mean Squared Error (MSE) and 68% reduction in deviation in MSE. In addition, the Knox-Thompson phase recovery method is demonstrated to produce images in half the time required by the bispectrum. Finally, it is shown that certain blind image quality metrics can be used in place of the MSE to evaluate reconstruction quality in field scenarios. Using blind metrics rather depending on user estimates allows for reconstruction quality that differs from the minimum MSE by as little as 1%, significantly reducing the deviation in performance due to user action.

Extracting information in spike time patterns with wavelets and information theory.

PubMed

Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

2015-02-01

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

Validation of a motion-robust 2D sequential technique for quantification of hepatic proton density fat fraction during free breathing.

PubMed

Pooler, B Dustin; Hernando, Diego; Ruby, Jeannine A; Ishii, Hiroshi; Shimakawa, Ann; Reeder, Scott B

2018-04-17

Current chemical-shift-encoded (CSE) MRI techniques for measuring hepatic proton density fat fraction (PDFF) are sensitive to motion artifacts. Initial validation of a motion-robust 2D-sequential CSE-MRI technique for quantification of hepatic PDFF. Phantom study and prospective in vivo cohort. Fifty adult patients (27 women, 23 men, mean age 57.2 years). 3D, 2D-interleaved, and 2D-sequential CSE-MRI acquisitions at 1.5T. Three CSE-MRI techniques (3D, 2D-interleaved, 2D-sequential) were performed in a PDFF phantom and in vivo. Reference standards were 3D CSE-MRI PDFF measurements for the phantom study and single-voxel MR spectroscopy hepatic PDFF measurements (MRS-PDFF) in vivo. In vivo hepatic MRI-PDFF measurements were performed during a single breath-hold (BH) and free breathing (FB), and were repeated by a second reader for the FB 2D-sequential sequence to assess interreader variability. Correlation plots to validate the 2D-sequential CSE-MRI against the phantom and in vivo reference standards. Bland-Altman analysis of FB versus BH CSE-MRI acquisitions to evaluate robustness to motion. Bland-Altman analysis to assess interreader variability. Phantom 2D-sequential CSE-MRI PDFF measurements demonstrated excellent agreement and correlation (R 2 > 0.99) with 3D CSE-MRI. In vivo, the mean (±SD) hepatic PDFF was 8.8 ± 8.7% (range 0.6-28.5%). Compared with BH acquisitions, FB hepatic PDFF measurements demonstrated bias of +0.15% for 2D-sequential compared with + 0.53% for 3D and +0.94% for 2D-interleaved. 95% limits of agreement (LOA) were narrower for 2D-sequential (±0.99%), compared with 3D (±3.72%) and 2D-interleaved (±3.10%). All CSE-MRI techniques had excellent correlation with MRS (R 2 > 0.97). The FB 2D-sequential acquisition demonstrated little interreader variability, with mean bias of +0.07% and 95% LOA of ± 1.53%. This motion-robust 2D-sequential CSE-MRI can accurately measure hepatic PDFF during free breathing in a patient population with a range of PDFF values of 0.6-28.5%, permitting accurate quantification of liver fat content without the need for suspended respiration. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Including robustness in multi-criteria optimization for intensity-modulated proton therapy

NASA Astrophysics Data System (ADS)

Chen, Wei; Unkelbach, Jan; Trofimov, Alexei; Madden, Thomas; Kooy, Hanne; Bortfeld, Thomas; Craft, David

2012-02-01

We present a method to include robustness in a multi-criteria optimization (MCO) framework for intensity-modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade-off between different objectives as well as the trade-off between robustness and nominal plan quality. In MCO, a database of plans each emphasizing different treatment planning objectives, is pre-computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. In our approach, robustness is integrated into MCO by adding robustified objectives and constraints to the MCO problem. Uncertainties (or errors) of the robust problem are modeled by pre-calculated dose-influence matrices for a nominal scenario and a number of pre-defined error scenarios (shifted patient positions, proton beam undershoot and overshoot). Objectives and constraints can be defined for the nominal scenario, thus characterizing nominal plan quality. A robustified objective represents the worst objective function value that can be realized for any of the error scenarios and thus provides a measure of plan robustness. The optimization method is based on a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams. A base-of-skull case is used to demonstrate the robust optimization method. It is demonstrated that the robust optimization method reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved trade-offs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The latter illustrates the advantage of MCO in the context of robust planning. For all cases examined, the robust optimization for each Pareto optimal plan takes less than 5 min on a standard computer, making a computationally friendly interface possible to the planner. In conclusion, the uncertainty pertinent to the IMPT procedure can be reduced during treatment planning by optimizing plans that emphasize different treatment objectives, including robustness, and then interactively seeking for a most-preferred one from the solution Pareto surface.

Respiratory motion correction in dynamic MRI using robust data decomposition registration - application to DCE-MRI.

PubMed

Hamy, Valentin; Dikaios, Nikolaos; Punwani, Shonit; Melbourne, Andrew; Latifoltojar, Arash; Makanyanga, Jesica; Chouhan, Manil; Helbren, Emma; Menys, Alex; Taylor, Stuart; Atkinson, David

2014-02-01

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Synaptic activation patterns of the perirhinal-entorhinal inter-connections.

PubMed

de Villers-Sidani, E; Tahvildari, B; Alonso, A

2004-01-01

Ample neuropsychological evidence supports the role of rhinal cortices in memory. The perirhinal cortex (PRC) represents one of the main conduits for the bi-directional flow of information between the entorhinal-hippocampal network and the cortical mantle, a process essential in memory formation. However, despite anatomical evidence for a robust reciprocal connectivity between the perirhinal and entorhinal cortices, neurophysiological understanding of this circuitry is lacking. We now present the results of a series of electrophysiological experiments in rats that demonstrate robust synaptic activation patterns of the perirhinal-entorhinal inter-connections. First, using silicon multi-electrode arrays placed under visual guidance in vivo we performed current source density (CSD) analysis of lateral entorhinal cortex (LEC) responses to PRC stimulation, which demonstrated a current sink in layers II-III of the LEC with a latency consistent with monosynaptic activation. To further substantiate and extend this conclusion, we developed a PRC-LEC slice preparation where CSD analysis also revealed a current sink in superficial LEC layers in response to PRC stimulation. Importantly, intracellular recording of superficial LEC layer neurons confirmed that they receive a major monosynaptic excitatory input from the PRC. Finally, CSD analysis of the LEC to PRC projection in vivo also allowed us to document robust feedback synaptic activation of PRC neurons to deep LEC layer activation. We conclude that a clear bidirectional pattern of synaptic interactions exists between the PRC and LEC that would support a dynamic flow of information subserving memory function in the temporal lobe.

Stretchable Materials for Robust Soft Actuators towards Assistive Wearable Devices

NASA Astrophysics Data System (ADS)

Agarwal, Gunjan; Besuchet, Nicolas; Audergon, Basile; Paik, Jamie

2016-09-01

Soft actuators made from elastomeric active materials can find widespread potential implementation in a variety of applications ranging from assistive wearable technologies targeted at biomedical rehabilitation or assistance with activities of daily living, bioinspired and biomimetic systems, to gripping and manipulating fragile objects, and adaptable locomotion. In this manuscript, we propose a novel two-component soft actuator design and design tool that produces actuators targeted towards these applications with enhanced mechanical performance and manufacturability. Our numerical models developed using the finite element method can predict the actuator behavior at large mechanical strains to allow efficient design iterations for system optimization. Based on two distinctive actuator prototypes’ (linear and bending actuators) experimental results that include free displacement and blocked-forces, we have validated the efficacy of the numerical models. The presented extensive investigation of mechanical performance for soft actuators with varying geometric parameters demonstrates the practical application of the design tool, and the robustness of the actuator hardware design, towards diverse soft robotic systems for a wide set of assistive wearable technologies, including replicating the motion of several parts of the human body.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

PubMed

Liu, Zhi; Xu, Shuqiong; Zhang, Yun; Chen, Chun Lung Philip

2014-11-01

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

High precision tracking of a piezoelectric nano-manipulator with parameterized hysteresis compensation

NASA Astrophysics Data System (ADS)

Yan, Peng; Zhang, Yangming

2018-06-01

High performance scanning of nano-manipulators is widely deployed in various precision engineering applications such as SPM (scanning probe microscope), where trajectory tracking of sophisticated reference signals is an challenging control problem. The situation is further complicated when rate dependent hysteresis of the piezoelectric actuators and the stress-stiffening induced nonlinear stiffness of the flexure mechanism are considered. In this paper, a novel control framework is proposed to achieve high precision tracking of a piezoelectric nano-manipulator subjected to hysteresis and stiffness nonlinearities. An adaptive parameterized rate-dependent Prandtl-Ishlinskii model is constructed and the corresponding adaptive inverse model based online compensation is derived. Meanwhile a robust adaptive control architecture is further introduced to improve the tracking accuracy and robustness of the compensated system, where the parametric uncertainties of the nonlinear dynamics can be well eliminated by on-line estimations. Comparative experimental studies of the proposed control algorithm are conducted on a PZT actuated nano-manipulating stage, where hysteresis modeling accuracy and excellent tracking performance are demonstrated in real-time implementations, with significant improvement over existing results.

Facile fabrication of robust TiO2@SnO2@C hollow nanobelts for outstanding lithium storage

NASA Astrophysics Data System (ADS)

Tian, Qinghua; Li, Lingxiangyu; Chen, Jizhang; Yang, Li; Hirano, Shin-ichi

2018-02-01

Elaborate fabrication of state-of-the-art nanostructure SnO2@C-based composites greatly contributes to alleviate the huge volume expansion issue of the SnO2 anodes. But the preparation processes of most of them are complicated and tedious, which is generally adverse to the development of SnO2@C-based composite anodes. Herein, a unique nanostructure of TiO2@SnO2@C hollow nanobelts (TiO2@SnO2@C HNBs), including the characteristics of one-dimensional architecture, sandwich protection, hollow structure, carbon coating, and a mechanically robust TiO2 support, has been fabricated by a facile approach for the first time. As anodes for lithium-ion batteries, the as-fabricated TiO2@SnO2@C HNBs exhibit an outstanding lithium storage performance, delivering capacity of 804.6 and 384. 5 mAh g-1 at 200 and even 1000 mA g-1 after 500 cycles, respectively. It is demonstrated that thus outstanding performance is mainly attributed to the unique nanostructure of TiO2@SnO2@C HNBs.

Robust back-stepping output feedback trajectory tracking for quadrotors via extended state observer and sigmoid tracking differentiator

NASA Astrophysics Data System (ADS)

Shao, Xingling; Liu, Jun; Wang, Honglun

2018-05-01

In this paper, a robust back-stepping output feedback trajectory tracking controller is proposed for quadrotors subject to parametric uncertainties and external disturbances. Based on the hierarchical control principle, the quadrotor dynamics is decomposed into translational and rotational subsystems to facilitate the back-stepping control design. With given model information incorporated into observer design, a high-order extended state observer (ESO) that relies only on position measurements is developed to estimate the remaining unmeasurable states and the lumped disturbances in rotational subsystem simultaneously. To overcome the problem of "explosion of complexity" in the back-stepping design, the sigmoid tracking differentiator (STD) is introduced to compute the derivative of virtual control laws. The advantage is that the proposed controller via output-feedback scheme not only can ensure good tracking performance using very limited information of quadrotors, but also has the ability of handling the undesired uncertainties. The stability analysis is established using the Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed control scheme in achieving a guaranteed tracking performance with respect to an 8-shaped reference trajectory.

Vision Sensor-Based Road Detection for Field Robot Navigation

PubMed Central

Lu, Keyu; Li, Jian; An, Xiangjing; He, Hangen

2015-01-01

Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA)-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF) framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art. PMID:26610514

Stretchable Materials for Robust Soft Actuators towards Assistive Wearable Devices

PubMed Central

Agarwal, Gunjan; Besuchet, Nicolas; Audergon, Basile; Paik, Jamie

2016-01-01

Soft actuators made from elastomeric active materials can find widespread potential implementation in a variety of applications ranging from assistive wearable technologies targeted at biomedical rehabilitation or assistance with activities of daily living, bioinspired and biomimetic systems, to gripping and manipulating fragile objects, and adaptable locomotion. In this manuscript, we propose a novel two-component soft actuator design and design tool that produces actuators targeted towards these applications with enhanced mechanical performance and manufacturability. Our numerical models developed using the finite element method can predict the actuator behavior at large mechanical strains to allow efficient design iterations for system optimization. Based on two distinctive actuator prototypes’ (linear and bending actuators) experimental results that include free displacement and blocked-forces, we have validated the efficacy of the numerical models. The presented extensive investigation of mechanical performance for soft actuators with varying geometric parameters demonstrates the practical application of the design tool, and the robustness of the actuator hardware design, towards diverse soft robotic systems for a wide set of assistive wearable technologies, including replicating the motion of several parts of the human body. PMID:27670953

A Robust Kalman Framework with Resampling and Optimal Smoothing

PubMed Central

Kautz, Thomas; Eskofier, Bjoern M.

2015-01-01

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies. PMID:25734647

Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study.

PubMed

Lee, Seokho; Shin, Hyejin; Lee, Sang Han

2016-12-01

Alzheimer's disease (AD) is usually diagnosed by clinicians through cognitive and functional performance test with a potential risk of misdiagnosis. Since the progression of AD is known to cause structural changes in the corpus callosum (CC), the CC thickness can be used as a functional covariate in AD classification problem for a diagnosis. However, misclassified class labels negatively impact the classification performance. Motivated by AD-CC association studies, we propose a logistic regression for functional data classification that is robust to misdiagnosis or label noise. Specifically, our logistic regression model is constructed by adopting individual intercepts to functional logistic regression model. This approach enables to indicate which observations are possibly mislabeled and also lead to a robust and efficient classifier. An effective algorithm using MM algorithm provides simple closed-form update formulas. We test our method using synthetic datasets to demonstrate its superiority over an existing method, and apply it to differentiating patients with AD from healthy normals based on CC from MRI. © 2016, The International Biometric Society.

Sagittal fresh blood imaging with interleaved acquisition of systolic and diastolic data for improved robustness to motion.

PubMed

Atanasova, Iliyana P; Kim, Daniel; Storey, Pippa; Rosenkrantz, Andrew B; Lim, Ruth P; Lee, Vivian S

2013-02-01

To improve robustness to patient motion of "fresh blood imaging" (FBI) for lower extremity noncontrast MR angiography. In FBI, two sets of three-dimensional fast spin echo images are acquired at different cardiac phases and subtracted to generate bright-blood angiograms. Routinely performed with a single coronal slab and sequential acquisition of systolic and diastolic data, FBI is prone to subtraction errors due to patient motion. In this preliminary feasibility study, FBI was implemented with two sagittal imaging slabs, and the systolic and diastolic acquisitions were interleaved to minimize sensitivity to motion. The proposed technique was evaluated in volunteers and patients. In 10 volunteers, imaged while performing controlled movements, interleaved FBI demonstrated better tolerance to subject motion than sequential FBI. In one patient with peripheral arterial disease, interleaved FBI offered better depiction of collateral flow by reducing sensitivity to inadvertent motion. FBI with interleaved acquisition of diastolic and systolic data in two sagittal imaging slabs offers improved tolerance to patient motion. Copyright © 2013 Wiley Periodicals, Inc.

Sagittal Fresh Blood Imaging with Interleaved Acquisition of Systolic and Diastolic Data for Improved Robustness to Motion

PubMed Central

Atanasova, Iliyana P.; Kim, Daniel; Storey, Pippa; Rosenkrantz, Andrew B; Lim, Ruth P.; Lee, Vivian S.

2012-01-01

Purpose To improve robustness to patient motion of ‘fresh blood imaging’ (FBI) for lower extremity non-contrast MRA. Methods In FBI, two sets of 3D fast spin echo images are acquired at different cardiac phases and subtracted to generate bright-blood angiograms. Routinely performed with a single coronal slab and sequential acquisition of systolic and diastolic data, FBI is prone to subtraction errors due to patient motion. In this preliminary feasibility study, FBI was implemented with two sagittal imaging slabs, and the systolic and diastolic acquisitions were interleaved to minimize sensitivity to motion. The proposed technique was evaluated in volunteers and patients. Results In ten volunteers, imaged while performing controlled movements, interleaved FBI demonstrated better tolerance to subject motion than sequential FBI. In one patient with peripheral arterial disease, interleaved FBI offered better depiction of collateral flow by reducing sensitivity to inadvertent motion. Conclusions FBI with interleaved acquisition of diastolic and systolic data in two sagittal imaging slabs offers improved tolerance to patient motion. PMID:23300129

Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies.

PubMed

Li, Zhixun; Zhang, Yingtao; Gong, Huiling; Li, Weimin; Tang, Xianglong

2016-12-01

Coronary artery disease has become the most dangerous diseases to human life. And coronary artery segmentation is the basis of computer aided diagnosis and analysis. Existing segmentation methods are difficult to handle the complex vascular texture due to the projective nature in conventional coronary angiography. Due to large amount of data and complex vascular shapes, any manual annotation has become increasingly unrealistic. A fully automatic segmentation method is necessary in clinic practice. In this work, we study a method based on reliable boundaries via multi-domains remapping and robust discrepancy correction via distance balance and quantile regression for automatic coronary artery segmentation of angiography images. The proposed method can not only segment overlapping vascular structures robustly, but also achieve good performance in low contrast regions. The effectiveness of our approach is demonstrated on a variety of coronary blood vessels compared with the existing methods. The overall segmentation performances si, fnvf, fvpf and tpvf were 95.135%, 3.733%, 6.113%, 96.268%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Robust terahertz self-heterodyne system using a phase noise compensation technique.

PubMed

Song, Hajun; Song, Jong-In

2015-08-10

We propose and demonstrate a robust terahertz self-heterodyne system using a phase noise compensation technique. Conventional terahertz self-heterodyne systems suffer from degraded phase noise performance due to phase noise of the laser sources. The proposed phase noise compensation technique uses an additional photodiode and a simple electric circuit to produce phase noise identical to that observed in the terahertz signal produced by the self-heterodyne system. The phase noise is subsequently subtracted from the terahertz signal produced by the self-heterodyne system using a lock-in amplifier. While the terahertz self-heterodyne system using a phase noise compensation technique offers improved phase noise performance, it also provides a reduced phase drift against ambient temperature variations. The terahertz self-heterodyne system using a phase noise compensation technique shows a phase noise of 0.67 degree in terms of a standard deviation value even without using overall delay balance control. It also shows a phase drift of as small as approximately 10 degrees in an open-to-air measurement condition without any strict temperature control.

Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images

PubMed Central

Phan, Thanh Vân; Seoud, Lama; Chakor, Hadi; Cheriet, Farida

2016-01-01

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality. PMID:27190636

The Mixed Finite Element Multigrid Method for Stokes Equations

PubMed Central

Muzhinji, K.; Shateyi, S.; Motsa, S. S.

2015-01-01

The stable finite element discretization of the Stokes problem produces a symmetric indefinite system of linear algebraic equations. A variety of iterative solvers have been proposed for such systems in an attempt to construct efficient, fast, and robust solution techniques. This paper investigates one of such iterative solvers, the geometric multigrid solver, to find the approximate solution of the indefinite systems. The main ingredient of the multigrid method is the choice of an appropriate smoothing strategy. This study considers the application of different smoothers and compares their effects in the overall performance of the multigrid solver. We study the multigrid method with the following smoothers: distributed Gauss Seidel, inexact Uzawa, preconditioned MINRES, and Braess-Sarazin type smoothers. A comparative study of the smoothers shows that the Braess-Sarazin smoothers enhance good performance of the multigrid method. We study the problem in a two-dimensional domain using stable Hood-Taylor Q 2-Q 1 pair of finite rectangular elements. We also give the main theoretical convergence results. We present the numerical results to demonstrate the efficiency and robustness of the multigrid method and confirm the theoretical results. PMID:25945361

Stretchable Materials for Robust Soft Actuators towards Assistive Wearable Devices.

PubMed

Agarwal, Gunjan; Besuchet, Nicolas; Audergon, Basile; Paik, Jamie

2016-09-27

Soft actuators made from elastomeric active materials can find widespread potential implementation in a variety of applications ranging from assistive wearable technologies targeted at biomedical rehabilitation or assistance with activities of daily living, bioinspired and biomimetic systems, to gripping and manipulating fragile objects, and adaptable locomotion. In this manuscript, we propose a novel two-component soft actuator design and design tool that produces actuators targeted towards these applications with enhanced mechanical performance and manufacturability. Our numerical models developed using the finite element method can predict the actuator behavior at large mechanical strains to allow efficient design iterations for system optimization. Based on two distinctive actuator prototypes' (linear and bending actuators) experimental results that include free displacement and blocked-forces, we have validated the efficacy of the numerical models. The presented extensive investigation of mechanical performance for soft actuators with varying geometric parameters demonstrates the practical application of the design tool, and the robustness of the actuator hardware design, towards diverse soft robotic systems for a wide set of assistive wearable technologies, including replicating the motion of several parts of the human body.

Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

2001-01-01

This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

Large planar maneuvers for articulated flexible manipulators

NASA Technical Reports Server (NTRS)

Huang, Jen-Kuang; Yang, Li-Farn

1988-01-01

An articulated flexible manipulator carried on a translational cart is maneuvered by an active controller to perform certain position control tasks. The nonlinear dynamics of the articulated flexible manipulator are derived and a transformation matrix is formulated to localize the nonlinearities within the inertia matrix. Then a feedback linearization scheme is introduced to linearize the dynamic equations for controller design. Through a pole placement technique, a robust controller design is obtained by properly assigning a set of closed-loop desired eigenvalues to meet performance requirements. Numerical simulations for the articulated flexible manipulators are given to demonstrate the feasibility and effectiveness of the proposed position control algorithms.

Optimization of flexible substrate by gradient elastic modulus design for performance improvement of flexible electronic devices

NASA Astrophysics Data System (ADS)

Xia, Minggang; Liang, Chunping; Hu, Ruixue; Cheng, Zhaofang; Liu, Shiru; Zhang, Shengli

2018-05-01

It is imperative and highly desirable to buffer the stress in flexible electronic devices. In this study, we designed and fabricated lamellate poly(dimethylsiloxane) (PDMS) samples with gradient elastic moduli, motivated by the protection of the pomelo pulp by its skin, followed by the measurements of their elastic moduli. We demonstrated that the electrical and fatigue performances of a Ag-nanowire thin film device on the PDMS substrate with a gradient elastic modulus are significantly better than those of a device on a substrate with a monolayer PDMS. This study provides a robust scheme to effectively protect flexible electronic devices.

Flight Test Comparison of Different Adaptive Augmentations for Fault Tolerant Control Laws for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Hanson, Curtis E.; Lee, James A.; Kaneshige, John T.

2009-01-01

This report describes the improvements and enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research is a follow-on effort to flight tests performed on the NASA F-15 aircraft as part of the Intelligent Flight Control System research effort. Previous flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures.

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

PubMed

Fu, Jian; He, Haibo; Zhou, Xinmin

2011-07-01

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

Robust estimation for partially linear models with large-dimensional covariates

PubMed Central

Zhu, LiPing; Li, RunZe; Cui, HengJian

2014-01-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures. PMID:24955087

Robust estimation for partially linear models with large-dimensional covariates.

PubMed

Zhu, LiPing; Li, RunZe; Cui, HengJian

2013-10-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of [Formula: see text], where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures.

Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution.

PubMed

Slater, Graham J; Pennell, Matthew W

2014-05-01

A central prediction of much theory on adaptive radiations is that traits should evolve rapidly during the early stages of a clade's history and subsequently slowdown in rate as niches become saturated--a so-called "Early Burst." Although a common pattern in the fossil record, evidence for early bursts of trait evolution in phylogenetic comparative data has been equivocal at best. We show here that this may not necessarily be due to the absence of this pattern in nature. Rather, commonly used methods to infer its presence perform poorly when when the strength of the burst--the rate at which phenotypic evolution declines--is small, and when some morphological convergence is present within the clade. We present two modifications to existing comparative methods that allow greater power to detect early bursts in simulated datasets. First, we develop posterior predictive simulation approaches and show that they outperform maximum likelihood approaches at identifying early bursts at moderate strength. Second, we use a robust regression procedure that allows for the identification and down-weighting of convergent taxa, leading to moderate increases in method performance. We demonstrate the utility and power of these approach by investigating the evolution of body size in cetaceans. Model fitting using maximum likelihood is equivocal with regards the mode of cetacean body size evolution. However, posterior predictive simulation combined with a robust node height test return low support for Brownian motion or rate shift models, but not the early burst model. While the jury is still out on whether early bursts are actually common in nature, our approach will hopefully facilitate more robust testing of this hypothesis. We advocate the adoption of similar posterior predictive approaches to improve the fit and to assess the adequacy of macroevolutionary models in general.

MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses

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

Nakayasu, Ernesto S.; Nicora, Carrie D.; Sims, Amy C.

2016-05-03

ABSTRACT Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. Themetabolite,protein, andlipidextraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of thismore » protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental,in vitro, and clinical). IMPORTANCEIn systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample.« less

Robust Fixed-Structure Controller Synthesis

NASA Technical Reports Server (NTRS)

Corrado, Joseph R.; Haddad, Wassim M.; Gupta, Kajal (Technical Monitor)

2000-01-01

The ability to develop an integrated control system design methodology for robust high performance controllers satisfying multiple design criteria and real world hardware constraints constitutes a challenging task. The increasingly stringent performance specifications required for controlling such systems necessitates a trade-off between controller complexity and robustness. The principle challenge of the minimal complexity robust control design is to arrive at a tractable control design formulation in spite of the extreme complexity of such systems. Hence, design of minimal complexitY robust controllers for systems in the face of modeling errors has been a major preoccupation of system and control theorists and practitioners for the past several decades.

Controller partitioning for integrated flight/propulsion control implementation

NASA Technical Reports Server (NTRS)

Garg, Sanjay

1993-01-01

The notion of partitioning a centralized controller into a decentralized, hierarchical structure suitable for integrated flight/propulsion control (IFPC) implementation is discussed. A systematic procedure is developed for determining partitioned airframe and engine subsystem controllers (subcontrollers), with the desired interconnection structure, that approximate the closed-loop performance and robustness characteristics of a given centralized controller. The procedure is demonstrated by application to IFPC design for a Short Take-Off and Vertical Landing (STOVL) aircraft in the landing approach to hover transition flight phase.

Partitioning of centralized integrated flight/propulsion control design for decentralized implementation

NASA Technical Reports Server (NTRS)

Garg, Sanjay

1993-01-01

The notion of partitioning a centralized controller into a decentralized, hierarchical structure suitable for integrated flight/propulsion control (IFPC) implementation is discussed. A systematic procedure is developed for determining partitioned airframe and engine subsystem controllers (subcontrollers), with the desired interconnection structure, that approximate the closed-loop performance and robustness characteristics of a given centralized controller. The procedure is demonstrated by application to IFPC design for a short take-off and vertical landing (STOVL) aircraft in the landing-approach-to-hover-transition flight phase.

Numerical realization of the variational method for generating self-trapped beams

NASA Astrophysics Data System (ADS)

Duque, Erick I.; Lopez-Aguayo, Servando; Malomed, Boris A.

2018-03-01

We introduce a numerical variational method based on the Rayleigh-Ritz optimization principle for predicting two-dimensional self-trapped beams in nonlinear media. This technique overcomes the limitation of the traditional variational approximation in performing analytical Lagrangian integration and differentiation. Approximate soliton solutions of a generalized nonlinear Schr\\"odinger equation are obtained, demonstrating robustness of the beams of various types (fundamental, vortices, multipoles, azimuthons) in the course of their propagation. The algorithm offers possibilities to produce more sophisticated soliton profiles in general nonlinear models.

Recent Advances in Fiber Lasers for Nonlinear Microscopy

PubMed Central

Xu, C.; Wise, F. W.

2013-01-01

Nonlinear microscopy techniques developed over the past two decades have provided dramatic new capabilities for biological imaging. The initial demonstrations of nonlinear microscopies coincided with the development of solid-state femtosecond lasers, which continue to dominate applications of nonlinear microscopy. Fiber lasers offer attractive features for biological and biomedical imaging, and recent advances are leading to high-performance sources with the potential for robust, inexpensive, integrated instruments. This article discusses recent advances, and identifies challenges and opportunities for fiber lasers in nonlinear bioimaging. PMID:24416074

Guidance and Control Architecture Design and Demonstration for Low Ballistic Coefficient Atmospheric Entry

NASA Technical Reports Server (NTRS)

Swei, Sean

2014-01-01

We propose to develop a robust guidance and control system for the ADEPT (Adaptable Deployable Entry and Placement Technology) entry vehicle. A control-centric model of ADEPT will be developed to quantify the performance of candidate guidance and control architectures for both aerocapture and precision landing missions. The evaluation will be based on recent breakthroughs in constrained controllability/reachability analysis of control systems and constrained-based energy-minimum trajectory optimization for guidance development operating in complex environments.

Resolving Multi-Stakeholder Robustness Asymmetries in Coupled Agricultural and Urban Systems

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea; Reed, Patrick

2016-04-01

The evolving pressures from a changing climate and society are increasingly motivating decision support frameworks that consider the robustness of management actions across many possible futures. Focusing on robustness is helpful for investigating key vulnerabilities within current water systems and for identifying potential tradeoffs across candidate adaptation responses. To date, most robustness studies assume a social planner perspective by evaluating highly aggregated measures of system performance. This aggregate treatment of stakeholders does not explore the equity or intrinsic multi-stakeholder conflicts implicit to the system-wide measures of performance benefits and costs. The commonly present heterogeneity across complex management interests, however, may produce strong asymmetries for alternative adaptation options, designed to satisfy system-level targets. In this work, we advance traditional robustness decision frameworks by replacing the centralized social planner with a bottom-up, agent-based approach, where stakeholders are modeled as individuals, and represented as potentially self-interested agents. This agent-based model enables a more explicit exploration of the potential inequities and asymmetries in the distribution of the system-wide benefit. The approach is demonstrated by exploring the potential conflicts between urban flooding and agricultural production in the Lake Como system (Italy). Lake Como is a regulated lake that is operated to supply water to the downstream agricultural district (Muzza as the pilot study area in this work) composed of a set of farmers with heterogeneous characteristics in terms of water allocation, cropping patterns, and land properties. Supplying water to farmers increases the risk of floods along the lakeshore and therefore the system is operated based on the tradeoff between these two objectives. We generated an ensemble of co-varying climate and socio-economic conditions and evaluated the robustness of the current Lake Como system management as well as of possible adaptation options (e.g., improved irrigation efficiency or changes in the dam operating rules). Numerical results show that crops prices and costs are the main drivers of the simulated system failures when evaluated in terms of system-level expected profitability. Analysis conducted at the farmer-agent scale highlights alternatively that temperature and inflows are the critical drivers leading to failures. Finally, we show that the robustness of the considered adaptation options varies spatially, strongly influenced by stakeholders' context, the metrics used to define success, and the assumed preferences for reservoir operations in balancing urban flooding and agricultural productivity.

Robust Constrained Optimization Approach to Control Design for International Space Station Centrifuge Rotor Auto Balancing Control System

NASA Technical Reports Server (NTRS)

Postma, Barry Dirk

2005-01-01

This thesis discusses application of a robust constrained optimization approach to control design to develop an Auto Balancing Controller (ABC) for a centrifuge rotor to be implemented on the International Space Station. The design goal is to minimize a performance objective of the system, while guaranteeing stability and proper performance for a range of uncertain plants. The Performance objective is to minimize the translational response of the centrifuge rotor due to a fixed worst-case rotor imbalance. The robustness constraints are posed with respect to parametric uncertainty in the plant. The proposed approach to control design allows for both of these objectives to be handled within the framework of constrained optimization. The resulting controller achieves acceptable performance and robustness characteristics.

Does virtual reality simulation have a role in training trauma and orthopaedic surgeons?

PubMed

Bartlett, J D; Lawrence, J E; Stewart, M E; Nakano, N; Khanduja, V

2018-05-01

Aims The aim of this study was to assess the current evidence relating to the benefits of virtual reality (VR) simulation in orthopaedic surgical training, and to identify areas of future research. Materials and Methods A literature search using the MEDLINE, Embase, and Google Scholar databases was performed. The results' titles, abstracts, and references were examined for relevance. Results A total of 31 articles published between 2004 and 2016 and relating to the objective validity and efficacy of specific virtual reality orthopaedic surgical simulators were identified. We found 18 studies demonstrating the construct validity of 16 different orthopaedic virtual reality simulators by comparing expert and novice performance. Eight studies have demonstrated skill acquisition on a simulator by showing improvements in performance with repeated use. A further five studies have demonstrated measurable improvements in operating theatre performance following a period of virtual reality simulator training. Conclusion The demonstration of 'real-world' benefits from the use of VR simulation in knee and shoulder arthroscopy is promising. However, evidence supporting its utility in other forms of orthopaedic surgery is lacking. Further studies of validity and utility should be combined with robust analyses of the cost efficiency of validated simulators to justify the financial investment required for their use in orthopaedic training. Cite this article: Bone Joint J 2018;100-B:559-65.

Probabilistic performance-based design for high performance control systems

NASA Astrophysics Data System (ADS)

Micheli, Laura; Cao, Liang; Gong, Yongqiang; Cancelli, Alessandro; Laflamme, Simon; Alipour, Alice

2017-04-01

High performance control systems (HPCS) are advanced damping systems capable of high damping performance over a wide frequency bandwidth, ideal for mitigation of multi-hazards. They include active, semi-active, and hybrid damping systems. However, HPCS are more expensive than typical passive mitigation systems, rely on power and hardware (e.g., sensors, actuators) to operate, and require maintenance. In this paper, a life cycle cost analysis (LCA) approach is proposed to estimate the economic benefit these systems over the entire life of the structure. The novelty resides in the life cycle cost analysis in the performance based design (PBD) tailored to multi-level wind hazards. This yields a probabilistic performance-based design approach for HPCS. Numerical simulations are conducted on a building located in Boston, MA. LCA are conducted for passive control systems and HPCS, and the concept of controller robustness is demonstrated. Results highlight the promise of the proposed performance-based design procedure.

Atomically Precise Surface Engineering for Producing Imagers

NASA Technical Reports Server (NTRS)

Nikzad, Shouleh (Inventor); Hoenk, Michael E. (Inventor); Greer, Frank (Inventor); Jones, Todd J. (Inventor)

2015-01-01

High-quality surface coatings, and techniques combining the atomic precision of molecular beam epitaxy and atomic layer deposition, to fabricate such high-quality surface coatings are provided. The coatings made in accordance with the techniques set forth by the invention are shown to be capable of forming silicon CCD detectors that demonstrate world record detector quantum efficiency (>50%) in the near and far ultraviolet (155 nm-300 nm). The surface engineering approaches used demonstrate the robustness of detector performance that is obtained by achieving atomic level precision at all steps in the coating fabrication process. As proof of concept, the characterization, materials, and exemplary devices produced are presented along with a comparison to other approaches.

Adaptive attitude control and momentum management for large-angle spacecraft maneuvers

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Sunkel, John W.

1992-01-01

The fully coupled equations of motion are systematically linearized around an equilibrium point of a gravity gradient stabilized spacecraft, controlled by momentum exchange devices. These equations are then used for attitude control system design of an early Space Station Freedom flight configuration, demonstrating the errors caused by the improper approximation of the spacecraft dynamics. A full state feedback controller, incorporating gain-scheduled adaptation of the attitude gains, is developed for use during spacecraft on-orbit assembly or operations characterized by significant mass properties variations. The feasibility of the gain adaptation is demonstrated via a Space Station Freedom assembly sequence case study. The attitude controller stability robustness and transient performance during gain adaptation appear satisfactory.

Statistical methodologies for the control of dynamic remapping

NASA Technical Reports Server (NTRS)

Saltz, J. H.; Nicol, D. M.

1986-01-01

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust.

Free wake analysis of hover performance using a new influence coefficient method

NASA Technical Reports Server (NTRS)

Quackenbush, Todd R.; Bliss, Donald B.; Ong, Ching Cho; Ching, Cho Ong

1990-01-01

A new approach to the prediction of helicopter rotor performance using a free wake analysis was developed. This new method uses a relaxation process that does not suffer from the convergence problems associated with previous time marching simulations. This wake relaxation procedure was coupled to a vortex-lattice, lifting surface loads analysis to produce a novel, self contained performance prediction code: EHPIC (Evaluation of Helicopter Performance using Influence Coefficients). The major technical features of the EHPIC code are described and a substantial amount of background information on the capabilities and proper operation of the code is supplied. Sample problems were undertaken to demonstrate the robustness and flexibility of the basic approach. Also, a performance correlation study was carried out to establish the breadth of applicability of the code, with very favorable results.

Chip-based quantum key distribution

NASA Astrophysics Data System (ADS)

Sibson, P.; Erven, C.; Godfrey, M.; Miki, S.; Yamashita, T.; Fujiwara, M.; Sasaki, M.; Terai, H.; Tanner, M. G.; Natarajan, C. M.; Hadfield, R. H.; O'Brien, J. L.; Thompson, M. G.

2017-02-01

Improvement in secure transmission of information is an urgent need for governments, corporations and individuals. Quantum key distribution (QKD) promises security based on the laws of physics and has rapidly grown from proof-of-concept to robust demonstrations and deployment of commercial systems. Despite these advances, QKD has not been widely adopted, and large-scale deployment will likely require chip-based devices for improved performance, miniaturization and enhanced functionality. Here we report low error rate, GHz clocked QKD operation of an indium phosphide transmitter chip and a silicon oxynitride receiver chip--monolithically integrated devices using components and manufacturing processes from the telecommunications industry. We use the reconfigurability of these devices to demonstrate three prominent QKD protocols--BB84, Coherent One Way and Differential Phase Shift--with performance comparable to state-of-the-art. These devices, when combined with integrated single photon detectors, pave the way for successfully integrating QKD into future telecommunications networks.

Chip-based quantum key distribution

PubMed Central

Sibson, P.; Erven, C.; Godfrey, M.; Miki, S.; Yamashita, T.; Fujiwara, M.; Sasaki, M.; Terai, H.; Tanner, M. G.; Natarajan, C. M.; Hadfield, R. H.; O'Brien, J. L.; Thompson, M. G.

2017-01-01

Improvement in secure transmission of information is an urgent need for governments, corporations and individuals. Quantum key distribution (QKD) promises security based on the laws of physics and has rapidly grown from proof-of-concept to robust demonstrations and deployment of commercial systems. Despite these advances, QKD has not been widely adopted, and large-scale deployment will likely require chip-based devices for improved performance, miniaturization and enhanced functionality. Here we report low error rate, GHz clocked QKD operation of an indium phosphide transmitter chip and a silicon oxynitride receiver chip—monolithically integrated devices using components and manufacturing processes from the telecommunications industry. We use the reconfigurability of these devices to demonstrate three prominent QKD protocols—BB84, Coherent One Way and Differential Phase Shift—with performance comparable to state-of-the-art. These devices, when combined with integrated single photon detectors, pave the way for successfully integrating QKD into future telecommunications networks. PMID:28181489

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

PubMed

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V; Boahen, Kwabena

2013-06-01

Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

PubMed

Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian

2018-05-12

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.

Integrative evaluation for sustainable decisions of urban wastewater system management under uncertainty

NASA Astrophysics Data System (ADS)

Hadjimichael, A.; Corominas, L.; Comas, J.

2017-12-01

With sustainable development as their overarching goal, urban wastewater system (UWS) managers need to take into account multiple social, economic, technical and environmental facets related to their decisions. In this complex decision-making environment, uncertainty can be formidable. It is present both in the ways the system is interpreted stochastically, but also in its natural ever-shifting behavior. This inherent uncertainty suggests that wiser decisions would be made under an adaptive and iterative decision-making regime. No decision-support framework has been presented in the literature to effectively addresses all these needs. The objective of this work is to describe such a conceptual framework to evaluate and compare alternative solutions for various UWS challenges within an adaptive management structure. Socio-economic aspects such as externalities are taken into account, along with other traditional criteria as necessary. Robustness, reliability and resilience analyses test the performance of the system against present and future variability. A valuation uncertainty analysis incorporates uncertain valuation assumptions in the decision-making process. The framework is demonstrated with an application to a case study presenting a typical problem often faced by managers: poor river water quality, increasing population, and more stringent water quality legislation. The application of the framework made use of: i) a cost-benefit analysis including monetized environmental benefits and damages; ii) a robustness analysis of system performance against future conditions; iii) reliability and resilience analyses of the system given contextual variability; and iv) a valuation uncertainty analysis of model parameters. The results suggest that the installation of bigger volumes would give rise to increased benefits despite larger capital costs, as well as increased robustness and resilience. Population numbers appear to affect the estimated benefits most, followed by electricity prices and climate change projections. The presented framework is expected to be a valuable tool for the next generation of UWS decision-making and the application demonstrates a novel and valuable integration of metrics and methods for UWS analysis.

Roll-to-roll fabrication of large scale and regular arrays of three-dimensional nanospikes for high efficiency and flexible photovoltaics

PubMed Central

Leung, Siu-Fung; Gu, Leilei; Zhang, Qianpeng; Tsui, Kwong-Hoi; Shieh, Jia-Min; Shen, Chang-Hong; Hsiao, Tzu-Hsuan; Hsu, Chin-Hung; Lu, Linfeng; Li, Dongdong; Lin, Qingfeng; Fan, Zhiyong

2014-01-01

Three-dimensional (3-D) nanostructures have demonstrated enticing potency to boost performance of photovoltaic devices primarily owning to the improved photon capturing capability. Nevertheless, cost-effective and scalable fabrication of regular 3-D nanostructures with decent robustness and flexibility still remains as a challenging task. Meanwhile, establishing rational design guidelines for 3-D nanostructured solar cells with the balanced electrical and optical performance are of paramount importance and in urgent need. Herein, regular arrays of 3-D nanospikes (NSPs) were fabricated on flexible aluminum foil with a roll-to-roll compatible process. The NSPs have precisely controlled geometry and periodicity which allow systematic investigation on geometry dependent optical and electrical performance of the devices with experiments and modeling. Intriguingly, it has been discovered that the efficiency of an amorphous-Si (a-Si) photovoltaic device fabricated on NSPs can be improved by 43%, as compared to its planar counterpart, in an optimal case. Furthermore, large scale flexible NSP solar cell devices have been fabricated and demonstrated. These results not only have shed light on the design rules of high performance nanostructured solar cells, but also demonstrated a highly practical process to fabricate efficient solar panels with 3-D nanostructures, thus may have immediate impact on thin film photovoltaic industry. PMID:24603964

Roll-to-roll fabrication of large scale and regular arrays of three-dimensional nanospikes for high efficiency and flexible photovoltaics.

PubMed

Leung, Siu-Fung; Gu, Leilei; Zhang, Qianpeng; Tsui, Kwong-Hoi; Shieh, Jia-Min; Shen, Chang-Hong; Hsiao, Tzu-Hsuan; Hsu, Chin-Hung; Lu, Linfeng; Li, Dongdong; Lin, Qingfeng; Fan, Zhiyong

2014-03-07

Three-dimensional (3-D) nanostructures have demonstrated enticing potency to boost performance of photovoltaic devices primarily owning to the improved photon capturing capability. Nevertheless, cost-effective and scalable fabrication of regular 3-D nanostructures with decent robustness and flexibility still remains as a challenging task. Meanwhile, establishing rational design guidelines for 3-D nanostructured solar cells with the balanced electrical and optical performance are of paramount importance and in urgent need. Herein, regular arrays of 3-D nanospikes (NSPs) were fabricated on flexible aluminum foil with a roll-to-roll compatible process. The NSPs have precisely controlled geometry and periodicity which allow systematic investigation on geometry dependent optical and electrical performance of the devices with experiments and modeling. Intriguingly, it has been discovered that the efficiency of an amorphous-Si (a-Si) photovoltaic device fabricated on NSPs can be improved by 43%, as compared to its planar counterpart, in an optimal case. Furthermore, large scale flexible NSP solar cell devices have been fabricated and demonstrated. These results not only have shed light on the design rules of high performance nanostructured solar cells, but also demonstrated a highly practical process to fabricate efficient solar panels with 3-D nanostructures, thus may have immediate impact on thin film photovoltaic industry.

Interlaboratory Comparison Test as an Evaluation of Applicability of an Alternative Edible Oil Analysis by 1H NMR Spectroscopy.

PubMed

Zailer, Elina; Holzgrabe, Ulrike; Diehl, Bernd W K

2017-11-01

A proton (1H) NMR spectroscopic method was established for the quality assessment of vegetable oils. To date, several research studies have been published demonstrating the high potential of the NMR technique in lipid analysis. An interlaboratory comparison was organized with the following main objectives: (1) to evaluate an alternative analysis of edible oils by using 1H NMR spectroscopy; and (2) to determine the robustness and reproducibility of the method. Five different edible oil samples were analyzed by evaluating 15 signals (free fatty acids, peroxides, aldehydes, double bonds, and linoleic and linolenic acids) in each spectrum. A total of 21 NMR data sets were obtained from 17 international participant laboratories. The performance of each laboratory was assessed by their z-scores. The test was successfully passed by 90.5% of the participants. Results showed that NMR spectroscopy is a robust alternative method for edible oil analysis.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

PubMed

Liu, Huawei; Li, Baoqing; Yuan, Xiaobing; Zhou, Qianwei; Huang, Jingchang

2018-03-27

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Engineering the quantum states of light in a Kerr-nonlinear resonator by two-photon driving

NASA Astrophysics Data System (ADS)

Puri, Shruti; Boutin, Samuel; Blais, Alexandre

2017-04-01

Photonic cat states stored in high-Q resonators show great promise for hardware efficient universal quantum computing. We propose an approach to efficiently prepare such cat states in a Kerr-nonlinear resonator by the use of a two-photon drive. Significantly, we show that this preparation is robust against single-photon loss. An outcome of this observation is that a two-photon drive can eliminate undesirable phase evolution induced by a Kerr nonlinearity. By exploiting the concept of transitionless quantum driving, we moreover demonstrate how non-adiabatic initialization of cat states is possible. Finally, we present a universal set of quantum logical gates that can be performed on the engineered eigenspace of such a two-photon driven resonator and discuss a possible realization using superconducting circuits. The robustness of the engineered subspace to higher-order circuit nonlinearities makes this implementation favorable for scalable quantum computation.

Clothing Matching for Visually Impaired Persons

PubMed Central

Yuan, Shuai; Tian, YingLi; Arditi, Aries

2012-01-01

Matching clothes is a challenging task for many blind people. In this paper, we present a proof of concept system to solve this problem. The system consists of 1) a camera connected to a computer to perform pattern and color matching process; 2) speech commands for system control and configuration; and 3) audio feedback to provide matching results for both color and patterns of clothes. This system can handle clothes in deficient color without any pattern, as well as clothing with multiple colors and complex patterns to aid both blind and color deficient people. Furthermore, our method is robust to variations of illumination, clothing rotation and wrinkling. To evaluate the proposed prototype, we collect two challenging databases including clothes without any pattern, or with multiple colors and different patterns under different conditions of lighting and rotation. Results reported here demonstrate the robustness and effectiveness of the proposed clothing matching system. PMID:22523465

Decorating fiber nanotip with single perovskite quantum dot and other luminescent nanocrystals synthesized in oil-phase

NASA Astrophysics Data System (ADS)

Qian, Yu; Xing, Xing; Xu, Ya; Lu, Zhenda; Zhang, Weihua

2017-11-01

We report a simple yet robust method for fabricating single perovskite quantum dot (QD) decorated fiber nanotips. In this method, a single QD is directly picked up and subsequently glued on the apex of a specially fabricated cantilever fiber tip with a high success rate (approx. 70%) without using expensive close-loop feedback systems. Thanks to the flexibility and robustness of the fiber tips, no damage of the tips was observed in the process. Moreover, nanocrystal (NC) dispersing technique was developed to avoid undesired aggregations of QDs, and it guarantees that only one QD is glued each time. Finally, we demonstrate that this technique can also be applied to other oil-phase synthesized NCs, including CdSe QDs and upconversion luminescent NCs. It leads to many important applications on probing the local environment using high performance luminescent nanoprobes.

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference

PubMed Central

Campbell, Kieran R.

2016-01-01

Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference. PMID:27870852

Inverse transport calculations in optical imaging with subspace optimization algorithms

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

Ding, Tian, E-mail: tding@math.utexas.edu; Ren, Kui, E-mail: ren@math.utexas.edu

2014-09-15

Inverse boundary value problems for the radiative transport equation play an important role in optics-based medical imaging techniques such as diffuse optical tomography (DOT) and fluorescence optical tomography (FOT). Despite the rapid progress in the mathematical theory and numerical computation of these inverse problems in recent years, developing robust and efficient reconstruction algorithms remains a challenging task and an active research topic. We propose here a robust reconstruction method that is based on subspace minimization techniques. The method splits the unknown transport solution (or a functional of it) into low-frequency and high-frequency components, and uses singular value decomposition to analyticallymore » recover part of low-frequency information. Minimization is then applied to recover part of the high-frequency components of the unknowns. We present some numerical simulations with synthetic data to demonstrate the performance of the proposed algorithm.« less

Circadian Phase Resetting via Single and Multiple Control Targets

PubMed Central

Bagheri, Neda; Stelling, Jörg; Doyle, Francis J.

2008-01-01

Circadian entrainment is necessary for rhythmic physiological functions to be appropriately timed over the 24-hour day. Disruption of circadian rhythms has been associated with sleep and neuro-behavioral impairments as well as cancer. To date, light is widely accepted to be the most powerful circadian synchronizer, motivating its use as a key control input for phase resetting. Through sensitivity analysis, we identify additional control targets whose individual and simultaneous manipulation (via a model predictive control algorithm) out-perform the open-loop light-based phase recovery dynamics by nearly 3-fold. We further demonstrate the robustness of phase resetting by synchronizing short- and long-period mutant phenotypes to the 24-hour environment; the control algorithm is robust in the presence of model mismatch. These studies prove the efficacy and immediate application of model predictive control in experimental studies and medicine. In particular, maintaining proper circadian regulation may significantly decrease the chance of acquiring chronic illness. PMID:18795146

SOM-based nonlinear least squares twin SVM via active contours for noisy image segmentation

NASA Astrophysics Data System (ADS)

Xie, Xiaomin; Wang, Tingting

2017-02-01

In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.

Preparation of superhydrophobic titanium surfaces via electrochemical etching and fluorosilane modification

NASA Astrophysics Data System (ADS)

Lu, Yao; Xu, Wenji; Song, Jinlong; Liu, Xin; Xing, Yingjie; Sun, Jing

2012-12-01

The preparation of superhydrophobic surfaces on hydrophilic metal substrates depends on both surface microstructures and low surface energy modification. In this study, a simple and inexpensive electrochemical method for preparing robust superhydrophobic titanium surfaces is reported. The neutral sodium chloride solution is used as electrolyte. Fluoroalkylsilane (FAS) was used to reduce the surface energy of the electrochemically etched surface. Scanning electron microscopy (SEM) images, energy-dispersive spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR) spectra, and contact angle measurement are performed to characterize the morphological features, chemical composition, and wettability of the titanium surfaces. Stability and friction tests indicate that the prepared titanium surfaces are robust. The analysis of electrolyte, reaction process, and products demonstrates that the electrochemical processing is very inexpensive and environment-friendly. This method is believed to be easily adaptable for use in large-scale industry productions to promote the application of superhydrophobic titanium surfaces in aviation, aerospace, shipbuilding, and the military industry.

Optimal Appearance Model for Visual Tracking

PubMed Central

Wang, Yuru; Jiang, Longkui; Liu, Qiaoyuan; Yin, Minghao

2016-01-01

Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models. PMID:26789639

Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty

PubMed Central

Wu, Shunan; Liu, Yufei; Radice, Gianmarco; Tan, Shujun

2017-01-01

With the development of satellite mobile communications, large antennas are now widely used. The precise pointing of the antenna’s optical axis is essential for many space missions. This paper addresses the challenging problem of high-precision autonomous pointing control of a large satellite antenna. The pointing dynamics are firstly proposed. The proportional–derivative feedback and structural filter to perform pointing maneuvers and suppress antenna vibrations are then presented. An adaptive controller to estimate actual system frequencies in the presence of modal parameters uncertainty is proposed. In order to reduce periodic errors, the modified controllers, which include the proposed adaptive controller and an active disturbance rejection filter, are then developed. The system stability and robustness are analyzed and discussed in the frequency domain. Numerical results are finally provided, and the results have demonstrated that the proposed controllers have good autonomy and robustness. PMID:28287450

Shape analysis modeling for character recognition

NASA Astrophysics Data System (ADS)

Khan, Nadeem A. M.; Hegt, Hans A.

1998-10-01

Optimal shape modeling of character-classes is crucial for achieving high performance on recognition of mixed-font, hand-written or (and) poor quality text. A novel scheme is presented in this regard focusing on constructing such structural models that can be hierarchically examined. These models utilize a certain `well-thought' set of shape primitives. They are simplified enough to ignore the inter- class variations in font-type or writing style yet retaining enough details for discrimination between the samples of the similar classes. Thus the number of models per class required can be kept minimal without sacrificing the recognition accuracy. In this connection a flexible multi- stage matching scheme exploiting the proposed modeling is also described. This leads to a system which is robust against various distortions and degradation including those related to cases of touching and broken characters. Finally, we present some examples and test results as a proof-of- concept demonstrating the validity and the robustness of the approach.

Autonomous atmospheric entry on mars: Performance improvement using a novel adaptive control algorithm

NASA Astrophysics Data System (ADS)

Ulrich, Steve; de Lafontaine, Jean

2007-12-01

Upcoming landing missions to Mars will require on-board guidance and control systems in order to meet the scientific requirement of landing safely within hundreds of meters to the target of interest. More specifically, in the longitudinal plane, the first objective of the entry guidance and control system is to bring the vehicle to its specified velocity at the specified altitude (as required for safe parachute deployment), while the second objective is to reach the target position in the longitudinal plane. This paper proposes an improvement to the robustness of the constant flight path angle guidance law for achieving the first objective. The improvement consists of combining this guidance law with a novel adaptive control scheme, derived from the so-called Simple Adaptive Control (SAC) technique. Monte-Carlo simulation results are shown to demonstrate the accuracy and the robustness of the proposed guidance and adaptive control system.

Robust validation of approximate 1-matrix functionals with few-electron harmonium atoms

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

Cioslowski, Jerzy, E-mail: jerzy@wmf.univ.szczecin.pl; Piris, Mario; Matito, Eduard

2015-12-07

A simple comparison between the exact and approximate correlation components U of the electron-electron repulsion energy of several states of few-electron harmonium atoms with varying confinement strengths provides a stringent validation tool for 1-matrix functionals. The robustness of this tool is clearly demonstrated in a survey of 14 known functionals, which reveals their substandard performance within different electron correlation regimes. Unlike spot-testing that employs dissociation curves of diatomic molecules or more extensive benchmarking against experimental atomization energies of molecules comprising some standard set, the present approach not only uncovers the flaws and patent failures of the functionals but, even moremore » importantly, also allows for pinpointing their root causes. Since the approximate values of U are computed at exact 1-densities, the testing requires minimal programming and thus is particularly suitable for rapid screening of new functionals.« less

High-density carbon ablator ignition path with low-density gas-filled rugby hohlraum

NASA Astrophysics Data System (ADS)

Amendt, Peter; Ho, Darwin D.; Jones, Ogden S.

2015-04-01

A recent low gas-fill density (0.6 mg/cc 4He) cylindrical hohlraum experiment on the National Ignition Facility has shown high laser-coupling efficiency (>96%), reduced phenomenological laser drive corrections, and improved high-density carbon capsule implosion symmetry [Jones et al., Bull. Am. Phys. Soc. 59(15), 66 (2014)]. In this Letter, an ignition design using a large rugby-shaped hohlraum [Amendt et al., Phys. Plasmas 21, 112703 (2014)] for high energetics efficiency and symmetry control with the same low gas-fill density (0.6 mg/cc 4He) is developed as a potentially robust platform for demonstrating thermonuclear burn. The companion high-density carbon capsule for this hohlraum design is driven by an adiabat-shaped [Betti et al., Phys. Plasmas 9, 2277 (2002)] 4-shock drive profile for robust high gain (>10) 1-D ignition performance and large margin to 2-D perturbation growth.

Robust synthesis of epoxy resin-filled microcapsules for application to self-healing materials.

PubMed

Bolimowski, Patryk A; Bond, Ian P; Wass, Duncan F

2016-02-28

Mechanically and thermally robust microcapsules containing diglycidyl ether bisphenol A-based epoxy resin and a high-boiling-point organic solvent were synthesized in high yield using in situ polymerization of urea and formaldehyde in an oil-in-water emulsion. Microcapsules were characterized in terms of their size and size distribution, shell surface morphology and thermal resistance to the curing cycles of commercially used epoxy polymers. The size distribution of the capsules and characteristics such as shell thickness can be controlled by the specific parameters of microencapsulation, including concentrations of reagents, stirrer speed and sonication. Selected microcapsules, and separated core and shell materials, were analysed using thermogravimetric analysis and differential scanning calorimetry. It is demonstrated that capsules lose minimal 2.5 wt% at temperatures no higher than 120°C. These microcapsules can be applied to self-healing carbon fibre composite structural materials, with preliminary results showing promising performance. © 2016 The Author(s).

Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

PubMed

Tong, Shaocheng; Li, Yongming

2017-02-01

This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

A robust anonymous biometric-based authenticated key agreement scheme for multi-server environments

PubMed Central

Huang, Yuanfei; Ma, Fangchao

2017-01-01

In order to improve the security in remote authentication systems, numerous biometric-based authentication schemes using smart cards have been proposed. Recently, Moon et al. presented an authentication scheme to remedy the flaws of Lu et al.’s scheme, and claimed that their improved protocol supports the required security properties. Unfortunately, we found that Moon et al.’s scheme still has weaknesses. In this paper, we show that Moon et al.’s scheme is vulnerable to insider attack, server spoofing attack, user impersonation attack and guessing attack. Furthermore, we propose a robust anonymous multi-server authentication scheme using public key encryption to remove the aforementioned problems. From the subsequent formal and informal security analysis, we demonstrate that our proposed scheme provides strong mutual authentication and satisfies the desirable security requirements. The functional and performance analysis shows that the improved scheme has the best secure functionality and is computational efficient. PMID:29121050

A robust anonymous biometric-based authenticated key agreement scheme for multi-server environments.

PubMed

Guo, Hua; Wang, Pei; Zhang, Xiyong; Huang, Yuanfei; Ma, Fangchao

2017-01-01

In order to improve the security in remote authentication systems, numerous biometric-based authentication schemes using smart cards have been proposed. Recently, Moon et al. presented an authentication scheme to remedy the flaws of Lu et al.'s scheme, and claimed that their improved protocol supports the required security properties. Unfortunately, we found that Moon et al.'s scheme still has weaknesses. In this paper, we show that Moon et al.'s scheme is vulnerable to insider attack, server spoofing attack, user impersonation attack and guessing attack. Furthermore, we propose a robust anonymous multi-server authentication scheme using public key encryption to remove the aforementioned problems. From the subsequent formal and informal security analysis, we demonstrate that our proposed scheme provides strong mutual authentication and satisfies the desirable security requirements. The functional and performance analysis shows that the improved scheme has the best secure functionality and is computational efficient.

Adaptive Sparse Representation for Source Localization with Gain/Phase Errors

PubMed Central

Sun, Ke; Liu, Yimin; Meng, Huadong; Wang, Xiqin

2011-01-01

Sparse representation (SR) algorithms can be implemented for high-resolution direction of arrival (DOA) estimation. Additionally, SR can effectively separate the coherent signal sources because the spectrum estimation is based on the optimization technique, such as the L1 norm minimization, but not on subspace orthogonality. However, in the actual source localization scenario, an unknown gain/phase error between the array sensors is inevitable. Due to this nonideal factor, the predefined overcomplete basis mismatches the actual array manifold so that the estimation performance is degraded in SR. In this paper, an adaptive SR algorithm is proposed to improve the robustness with respect to the gain/phase error, where the overcomplete basis is dynamically adjusted using multiple snapshots and the sparse solution is adaptively acquired to match with the actual scenario. The simulation results demonstrate the estimation robustness to the gain/phase error using the proposed method. PMID:22163875

Clothing Matching for Visually Impaired Persons.

PubMed

Yuan, Shuai; Tian, Yingli; Arditi, Aries

2011-01-01

Matching clothes is a challenging task for many blind people. In this paper, we present a proof of concept system to solve this problem. The system consists of 1) a camera connected to a computer to perform pattern and color matching process; 2) speech commands for system control and configuration; and 3) audio feedback to provide matching results for both color and patterns of clothes. This system can handle clothes in deficient color without any pattern, as well as clothing with multiple colors and complex patterns to aid both blind and color deficient people. Furthermore, our method is robust to variations of illumination, clothing rotation and wrinkling. To evaluate the proposed prototype, we collect two challenging databases including clothes without any pattern, or with multiple colors and different patterns under different conditions of lighting and rotation. Results reported here demonstrate the robustness and effectiveness of the proposed clothing matching system.

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter

PubMed Central

Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance. PMID:28797060

A Robust Design Methodology for Optimal Microscale Secondary Flow Control in Compact Inlet Diffusers

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2001-01-01

It is the purpose of this study to develop an economical Robust design methodology for microscale secondary flow control in compact inlet diffusers. To illustrate the potential of economical Robust Design methodology, two different mission strategies were considered for the subject inlet, namely Maximum Performance and Maximum HCF Life Expectancy. The Maximum Performance mission maximized total pressure recovery while the Maximum HCF Life Expectancy mission minimized the mean of the first five Fourier harmonic amplitudes, i.e., 'collectively' reduced all the harmonic 1/2 amplitudes of engine face distortion. Each of the mission strategies was subject to a low engine face distortion constraint, i.e., DC60<0.10, which is a level acceptable for commercial engines. For each of these missions strategies, an 'Optimal Robust' (open loop control) and an 'Optimal Adaptive' (closed loop control) installation was designed over a twenty degree angle-of-incidence range. The Optimal Robust installation used economical Robust Design methodology to arrive at a single design which operated over the entire angle-of-incident range (open loop control). The Optimal Adaptive installation optimized all the design parameters at each angle-of-incidence. Thus, the Optimal Adaptive installation would require a closed loop control system to sense a proper signal for each effector and modify that effector device, whether mechanical or fluidic, for optimal inlet performance. In general, the performance differences between the Optimal Adaptive and Optimal Robust installation designs were found to be marginal. This suggests, however, that Optimal Robust open loop installation designs can be very competitive with Optimal Adaptive close loop designs. Secondary flow control in inlets is inherently robust, provided it is optimally designed. Therefore, the new methodology presented in this paper, combined array 'Lower Order' approach to Robust DOE, offers the aerodynamicist a very viable and economical way of exploring the concept of Robust inlet design, where the mission variables are brought directly into the inlet design process and insensitivity or robustness to the mission variables becomes a design objective.

Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors.

PubMed

Bu, Xiangwei; Wu, Xiaoyan; Zhu, Fujing; Huang, Jiaqi; Ma, Zhen; Zhang, Rui

2015-11-01

A novel prescribed performance neural controller with unknown initial errors is addressed for the longitudinal dynamic model of a flexible air-breathing hypersonic vehicle (FAHV) subject to parametric uncertainties. Different from traditional prescribed performance control (PPC) requiring that the initial errors have to be known accurately, this paper investigates the tracking control without accurate initial errors via exploiting a new performance function. A combined neural back-stepping and minimal learning parameter (MLP) technology is employed for exploring a prescribed performance controller that provides robust tracking of velocity and altitude reference trajectories. The highlight is that the transient performance of velocity and altitude tracking errors is satisfactory and the computational load of neural approximation is low. Finally, numerical simulation results from a nonlinear FAHV model demonstrate the efficacy of the proposed strategy. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Orion Rendezvous, Proximity Operations, and Docking Design and Analysis

NASA Technical Reports Server (NTRS)

D'Souza, Christopher; Hanak, F. Chad; Spehar, Pete; Clark, Fred D.; Jackson, Mark

2007-01-01

The Orion vehicle will be required to perform rendezvous, proximity operations, and docking with the International Space Station (ISS) and the Earth Departure Stage (EDS)/Lunar Landing Vehicle (LLV) stack in Low Earth Orbit (LEO) as well as with the Lunar Landing Vehicle in Low Lunar Orbit (LLO). The RPOD system, which consists of sensors, actuators, and software is being designed to be flexible and robust enough to perform RPOD with different vehicles in different environments. This paper will describe the design and the analysis which has been performed to date to allow the vehicle to perform its mission. Since the RPOD design touches on many areas such as sensors selection and placement, trajectory design, navigation performance, and effector performance, it is inherently a systems design problem. This paper will address each of these issues in order to demonstrate how the Orion RPOD has been designed to accommodate and meet all the requirements levied on the system.

Mechanically stable ternary heterogeneous electrodes for energy storage and conversion.

PubMed

Gao, Libo; Zhang, Hongti; Surjadi, James Utama; Li, Peifeng; Han, Ying; Sun, Dong; Lu, Yang

2018-02-01

Recently, solid asymmetric supercapacitor (ASC) has been deemed as an emerging portable power storage or backup device for harvesting natural resources. Here we rationally engineered a hierarchical, mechanically stable heterostructured FeCo@NiCo layered double hydroxide (LDH) with superior capacitive performance by a simple two-step electrodeposition route for energy storage and conversion. In situ scanning electron microscope (SEM) nanoindentation and electrochemical tests demonstrated the mechanical robustness and good conductivity of FeCo-LDH. This serves as a reliable backbone for supporting the NiCo-LDH nanosheets. When employed as the positive electrode in the solid ASC, the assembly presents high energy density of 36.6 W h kg -1 at a corresponding power density of 783 W kg -1 and durable cycling stability (87.3% after 5000 cycles) as well as robust mechanical stability without obvious capacitance fading when subjected to bending deformation. To demonstrate its promising capability for practical energy storage applications, the ASC has been employed as a portable energy source to power a commercially available digital watch, mini motor car, or household lamp bulb as well as an energy storage reservoir, coupled with a wind energy harvester to power patterned light-emitting diodes (LEDs).

A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

PubMed Central

Chen, Cheng; Wang, Wei; Ozolek, John A.; Rohde, Gustavo K.

2013-01-01

We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model which captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei. PMID:23568787

Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors.

PubMed

Su, Ruiliang; Chen, Xiang; Cao, Shuai; Zhang, Xu

2016-01-14

Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random forests. This framework was used to recognize Chinese sign language subwords using recordings from a pair of portable devices worn on both arms consisting of accelerometers (ACC) and surface electromyography (sEMG) sensors. The experimental results demonstrated the validity of the proposed random forest-based method for recognition of Chinese sign language (CSL) subwords. With the proposed method, 98.25% average accuracy was obtained for the classification of a list of 121 frequently used CSL subwords. Moreover, the random forests method demonstrated a superior performance in resisting the impact of bad training samples. When the proportion of bad samples in the training set reached 50%, the recognition error rate of the random forest-based method was only 10.67%, while that of a single decision tree adopted in our previous work was almost 27.5%. Our study offers a practical way of realizing a robust and wearable EMG-ACC-based SLR systems.

Turboprop IDEAL: a motion-resistant fat-water separation technique.

PubMed

Huo, Donglai; Li, Zhiqiang; Aboussouan, Eric; Karis, John P; Pipe, James G

2009-01-01

Suppression of the fat signal in MRI is very important for many clinical applications. Multi-point water-fat separation methods, such as IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation), can robustly separate water and fat signal, but inevitably increase scan time, making separated images more easily affected by patient motions. PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) and Turboprop techniques offer an effective approach to correct for motion artifacts. By combining these techniques together, we demonstrate that the new TP-IDEAL method can provide reliable water-fat separation with robust motion correction. The Turboprop sequence was modified to acquire source images, and motion correction algorithms were adjusted to assure the registration between different echo images. Theoretical calculations were performed to predict the optimal shift and spacing of the gradient echoes. Phantom images were acquired, and results were compared with regular FSE-IDEAL. Both T1- and T2-weighted images of the human brain were used to demonstrate the effectiveness of motion correction. TP-IDEAL images were also acquired for pelvis, knee, and foot, showing great potential of this technique for general clinical applications.

A quality-by-design approach to risk reduction and optimization for human embryonic stem cell cryopreservation processes.

PubMed

Mitchell, Peter D; Ratcliffe, Elizabeth; Hourd, Paul; Williams, David J; Thomas, Robert J

2014-12-01

It is well documented that cryopreservation and resuscitation of human embryonic stem cells (hESCs) is complex and ill-defined, and often suffers poor cell recovery and increased levels of undesirable cell differentiation. In this study we have applied Quality-by-Design (QbD) concepts to the critical processes of slow-freeze cryopreservation and resuscitation of hESC colony cultures. Optimized subprocesses were linked together to deliver a controlled complete process. We have demonstrated a rapid, high-throughput, and stable system for measurement of cell adherence and viability as robust markers of in-process and postrecovery cell state. We observed that measurement of adherence and viability of adhered cells at 1 h postseeding was predictive of cell proliferative ability up to 96 h in this system. Application of factorial design defined the operating spaces for cryopreservation and resuscitation, critically linking the performance of these two processes. Optimization of both processes resulted in enhanced reattachment and post-thaw viability, resulting in substantially greater recovery of cryopreserved, pluripotent cell colonies. This study demonstrates the importance of QbD concepts and tools for rapid, robust, and low-risk process design that can inform manufacturing controls and logistics.

Hemostatic kaolin-polyurethane foam composites for multifunctional wound dressing applications.

PubMed

Lundin, Jeffrey G; McGann, Christopher L; Daniels, Grant C; Streifel, Benjamin C; Wynne, James H

2017-10-01

There are numerous challenges associated with the acute care of traumatic limb injuries in forward military settings. A lack of immediate medical facilities necessitates that the wound dressing perform multiple tasks including exudate control, infection prevention, and physical protection of the wound for extended periods of time. Here, kaolin was incorporated into recently developed robust polyurethane (PU) hydrogel foams at 1-10wt% in an effort to impart hemostatic character. ATR-IR and gel fraction analysis demonstrated that the facile, one-pot synthesis of the PU hydrogel was unaffected by kaolin loading, as well as the use of a non-toxic catalyst, which significantly improved cytocompatibility of the materials. Kaolin was generally well dispersed throughout the PU matrix, though higher loadings exhibited minor evidence of aggregation. Kaolin-PU composites exhibited burst release of ciprofloxacin over 2h, the initial release rates of which increased with kaolin loading. Kaolin loading imparted excellent hemostatic character to the PU foams at relatively low loading levels (5wt%). This work demonstrates the simple and inexpensive synthesis of robust, hemostatic, and absorptive kaolin-PU foams that have promising potential as multifunctional wound dressing materials. Published by Elsevier B.V.

An elementary quantum network using robust nuclear spin qubits in diamond

NASA Astrophysics Data System (ADS)

Kalb, Norbert; Reiserer, Andreas; Humphreys, Peter; Blok, Machiel; van Bemmelen, Koen; Twitchen, Daniel; Markham, Matthew; Taminiau, Tim; Hanson, Ronald

Quantum registers containing multiple robust qubits can form the nodes of future quantum networks for computation and communication. Information storage within such nodes must be resilient to any type of local operation. Here we demonstrate multiple robust memories by employing five nuclear spins adjacent to a nitrogen-vacancy defect centre in diamond. We characterize the storage of quantum superpositions and their resilience to entangling attempts with the electron spin of the defect centre. The storage fidelity is found to be limited by the probabilistic electron spin reset after failed entangling attempts. Control over multiple memories is then utilized to encode states in decoherence protected subspaces with increased robustness. Furthermore we demonstrate memory control in two optically linked network nodes and characterize the storage capabilities of both memories in terms of the process fidelity with the identity. These results pave the way towards multi-qubit quantum algorithms in a remote network setting.

Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

NASA Technical Reports Server (NTRS)

Newman, Perry A.; Putko, Michele M.; Taylor, Arthur C., III

2004-01-01

A robust optimization is demonstrated on a two-dimensional inviscid airfoil problem in subsonic flow. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables), an approximate first-order statistical moment method is employed to represent the Computational Fluid Dynamics (CFD) code outputs as expected values with variances. These output quantities are used to form the objective function and constraints. The constraints are cast in probabilistic terms; that is, the probability that a constraint is satisfied is greater than or equal to some desired target probability. Gradient-based robust optimization of this stochastic problem is accomplished through use of both first and second-order sensitivity derivatives. For each robust optimization, the effect of increasing both input standard deviations and target probability of constraint satisfaction are demonstrated. This method provides a means for incorporating uncertainty when considering small deviations from input mean values.

Robust detection-isolation-accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Weiss, J. L.; Pattipati, K. R.; Willsky, A. S.; Eterno, J. S.; Crawford, J. T.

1985-01-01

The results of a one year study to: (1) develop a theory for Robust Failure Detection and Identification (FDI) in the presence of model uncertainty, (2) develop a design methodology which utilizes the robust FDI ththeory, (3) apply the methodology to a sensor FDI problem for the F-100 jet engine, and (4) demonstrate the application of the theory to the evaluation of alternative FDI schemes are presented. Theoretical results in statistical discrimination are used to evaluate the robustness of residual signals (or parity relations) in terms of their usefulness for FDI. Furthermore, optimally robust parity relations are derived through the optimization of robustness metrics. The result is viewed as decentralization of the FDI process. A general structure for decentralized FDI is proposed and robustness metrics are used for determining various parameters of the algorithm.

Robust synergetic control design under inputs and states constraints

NASA Astrophysics Data System (ADS)

Rastegar, Saeid; Araújo, Rui; Sadati, Jalil

2018-03-01

In this paper, a novel robust-constrained control methodology for discrete-time linear parameter-varying (DT-LPV) systems is proposed based on a synergetic control theory (SCT) approach. It is shown that in DT-LPV systems without uncertainty, and for any unmeasured bounded additive disturbance, the proposed controller accomplishes the goal of stabilising the system by asymptotically driving the error of the controlled variable to a bounded set containing the origin and then maintaining it there. Moreover, given an uncertain DT-LPV system jointly subject to unmeasured and constrained additive disturbances, and constraints in states, input commands and reference signals (set points), then invariant set theory is used to find an appropriate polyhedral robust invariant region in which the proposed control framework is guaranteed to robustly stabilise the closed-loop system. Furthermore, this is achieved even for the case of varying non-zero control set points in such uncertain DT-LPV systems. The controller is characterised to have a simple structure leading to an easy implementation, and a non-complex design process. The effectiveness of the proposed method and the implications of the controller design on feasibility and closed-loop performance are demonstrated through application examples on the temperature control on a continuous-stirred tank reactor plant, on the control of a real-coupled DC motor plant, and on an open-loop unstable system example.

Learning Robust and Discriminative Subspace With Low-Rank Constraints.

PubMed

Li, Sheng; Fu, Yun

2016-11-01

In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classification. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularization-based robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The experimental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations.

A topological quantum optics interface

NASA Astrophysics Data System (ADS)

Barik, Sabyasachi; Karasahin, Aziz; Flower, Christopher; Cai, Tao; Miyake, Hirokazu; DeGottardi, Wade; Hafezi, Mohammad; Waks, Edo

2018-02-01

The application of topology in optics has led to a new paradigm in developing photonic devices with robust properties against disorder. Although considerable progress on topological phenomena has been achieved in the classical domain, the realization of strong light-matter coupling in the quantum domain remains unexplored. We demonstrate a strong interface between single quantum emitters and topological photonic states. Our approach creates robust counterpropagating edge states at the boundary of two distinct topological photonic crystals. We demonstrate the chiral emission of a quantum emitter into these modes and establish their robustness against sharp bends. This approach may enable the development of quantum optics devices with built-in protection, with potential applications in quantum simulation and sensing.

Joint estimation of motion and illumination change in a sequence of images

NASA Astrophysics Data System (ADS)

Koo, Ja-Keoung; Kim, Hyo-Hun; Hong, Byung-Woo

2015-09-01

We present an algorithm that simultaneously computes optical flow and estimates illumination change from an image sequence in a unified framework. We propose an energy functional consisting of conventional optical flow energy based on Horn-Schunck method and an additional constraint that is designed to compensate for illumination changes. Any undesirable illumination change that occurs in the imaging procedure in a sequence while the optical flow is being computed is considered a nuisance factor. In contrast to the conventional optical flow algorithm based on Horn-Schunck functional, which assumes the brightness constancy constraint, our algorithm is shown to be robust with respect to temporal illumination changes in the computation of optical flows. An efficient conjugate gradient descent technique is used in the optimization procedure as a numerical scheme. The experimental results obtained from the Middlebury benchmark dataset demonstrate the robustness and the effectiveness of our algorithm. In addition, comparative analysis of our algorithm and Horn-Schunck algorithm is performed on the additional test dataset that is constructed by applying a variety of synthetic bias fields to the original image sequences in the Middlebury benchmark dataset in order to demonstrate that our algorithm outperforms the Horn-Schunck algorithm. The superior performance of the proposed method is observed in terms of both qualitative visualizations and quantitative accuracy errors when compared to Horn-Schunck optical flow algorithm that easily yields poor results in the presence of small illumination changes leading to violation of the brightness constancy constraint.

Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms.

PubMed

Asgharnia, Amirhossein; Shahnazi, Reza; Jamali, Ali

2018-05-11

The most studied controller for pitch control of wind turbines is proportional-integral-derivative (PID) controller. However, due to uncertainties in wind turbine modeling and wind speed profiles, the need for more effective controllers is inevitable. On the other hand, the parameters of PID controller usually are unknown and should be selected by the designer which is neither a straightforward task nor optimal. To cope with these drawbacks, in this paper, two advanced controllers called fuzzy PID (FPID) and fractional-order fuzzy PID (FOFPID) are proposed to improve the pitch control performance. Meanwhile, to find the parameters of the controllers the chaotic evolutionary optimization methods are used. Using evolutionary optimization methods not only gives us the unknown parameters of the controllers but also guarantees the optimality based on the chosen objective function. To improve the performance of the evolutionary algorithms chaotic maps are used. All the optimization procedures are applied to the 2-mass model of 5-MW wind turbine model. The proposed optimal controllers are validated using simulator FAST developed by NREL. Simulation results demonstrate that the FOFPID controller can reach to better performance and robustness while guaranteeing fewer fatigue damages in different wind speeds in comparison to FPID, fractional-order PID (FOPID) and gain-scheduling PID (GSPID) controllers. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust control for uncertain structures

NASA Technical Reports Server (NTRS)

Douglas, Joel; Athans, Michael

1991-01-01

Viewgraphs on robust control for uncertain structures are presented. Topics covered include: robust linear quadratic regulator (RLQR) formulas; mismatched LQR design; RLQR design; interpretations of RLQR design; disturbance rejection; and performance comparisons: RLQR vs. mismatched LQR.

A High-Performance Sintered Iron Electrode for Rechargeable Alkaline Batteries to Enable Large-Scale Energy Storage

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

Yang, Chenguang; Manohar, Aswin K.; Narayanan, S. R.

Iron-based alkaline rechargeable batteries such as iron-air and nickel-iron batteries are particularly attractive for large-scale energy storage because these batteries can be relatively inexpensive, environment- friendly, and also safe. Therefore, our study has focused on achieving the essential electrical performance and cycling properties needed for the widespread use of iron-based alkaline batteries in stationary and distributed energy storage applications.We have demonstrated for the first time, an advanced sintered iron electrode capable of 3500 cycles of repeated charge and discharge at the 1-hour rate and 100% depth of discharge in each cycle, and an average Coulombic efficiency of over 97%. Suchmore » a robust and efficient rechargeable iron electrode is also capable of continuous discharge at rates as high as 3C with no noticeable loss in utilization. We have shown that the porosity, pore size and thickness of the sintered electrode can be selected rationally to optimize specific capacity, rate capability and robustness. As a result, these advances in the electrical performance and durability of the iron electrode enables iron-based alkaline batteries to be a viable technology solution for meeting the dire need for large-scale electrical energy storage.« less

Training attentional control in older adults

PubMed Central

MacKay-Brandt, Anna

2013-01-01

Recent research has demonstrated benefits for older adults from training attentional control using a variable priority strategy, but the construct validity of the training task and the degree to which benefits of training transfer to other contexts are unclear. The goal of this study was to characterize baseline performance on the training task in a sample of 105 healthy older adults and to test for transfer of training in a subset (n = 21). Training gains after 5 days and extent of transfer was compared to another subset (n = 20) that served as a control group. Baseline performance on the training task was characterized by a two-factor model of working memory and processing speed. Processing speed correlated with the training task. Training gains in speed and accuracy were reliable and robust (ps <.001, η2 = .57 to .90). Transfer to an analogous task was observed (ps <.05, η2 = .10 to .17). The beneficial effect of training did not translate to improved performance on related measures of processing speed. This study highlights the robust effect of training and transfer to a similar context using a variable priority training task. Although processing speed is an important aspect of the training task, training benefit is either related to an untested aspect of the training task or transfer of training is limited to the training context. PMID:21728889

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

A characterization of performance by men and women in a virtual Morris water task: a large and reliable sex difference.

PubMed

Astur, R S; Ortiz, M L; Sutherland, R J

1998-06-01

In many mammalian species, it is known that males and females differ in place learning ability. The performance by men and women is commonly reported to also differ, despite a large amount of variability and ambiguity in measuring spatial abilities. In the non-human literature, the gold standard for measuring place learning ability in mammals is the Morris water task. This task requires subjects to use the spatial arrangement of cues outside of a circular pool to swim to a hidden goal platform located in a fixed location. We used a computerized version of the Morris water task to assess whether this task will generalize into the human domain and to examine whether sex differences exist in this domain of topographical learning and memory. Across three separate experiments, varying in attempts to maximize spatial performance, we consistently found males navigate to the hidden platform better than females across a variety of measures. The effect sizes of these differences are some of the largest ever reported and are robust and replicable across experiments. These results are the first to demonstrate the effectiveness and utility of the virtual Morris water task for humans and show a robust sex difference in virtual place learning.

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

PubMed

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

Multiscale decoding for reliable brain-machine interface performance over time.

PubMed

Han-Lin Hsieh; Wong, Yan T; Pesaran, Bijan; Shanechi, Maryam M

2017-07-01

Recordings from invasive implants can degrade over time, resulting in a loss of spiking activity for some electrodes. For brain-machine interfaces (BMI), such a signal degradation lowers control performance. Achieving reliable performance over time is critical for BMI clinical viability. One approach to improve BMI longevity is to simultaneously use spikes and other recording modalities such as local field potentials (LFP), which are more robust to signal degradation over time. We have developed a multiscale decoder that can simultaneously model the different statistical profiles of multi-scale spike/LFP activity (discrete spikes vs. continuous LFP). This decoder can also run at multiple time-scales (millisecond for spikes vs. tens of milliseconds for LFP). Here, we validate the multiscale decoder for estimating the movement of 7 major upper-arm joint angles in a non-human primate (NHP) during a 3D reach-to-grasp task. The multiscale decoder uses motor cortical spike/LFP recordings as its input. We show that the multiscale decoder can improve decoding accuracy by adding information from LFP to spikes, while running at the fast millisecond time-scale of the spiking activity. Moreover, this improvement is achieved using relatively few LFP channels, demonstrating the robustness of the approach. These results suggest that using multiscale decoders has the potential to improve the reliability and longevity of BMIs.

A High-Performance Sintered Iron Electrode for Rechargeable Alkaline Batteries to Enable Large-Scale Energy Storage

DOE PAGES

Yang, Chenguang; Manohar, Aswin K.; Narayanan, S. R.

2017-01-07

Iron-based alkaline rechargeable batteries such as iron-air and nickel-iron batteries are particularly attractive for large-scale energy storage because these batteries can be relatively inexpensive, environment- friendly, and also safe. Therefore, our study has focused on achieving the essential electrical performance and cycling properties needed for the widespread use of iron-based alkaline batteries in stationary and distributed energy storage applications.We have demonstrated for the first time, an advanced sintered iron electrode capable of 3500 cycles of repeated charge and discharge at the 1-hour rate and 100% depth of discharge in each cycle, and an average Coulombic efficiency of over 97%. Suchmore » a robust and efficient rechargeable iron electrode is also capable of continuous discharge at rates as high as 3C with no noticeable loss in utilization. We have shown that the porosity, pore size and thickness of the sintered electrode can be selected rationally to optimize specific capacity, rate capability and robustness. As a result, these advances in the electrical performance and durability of the iron electrode enables iron-based alkaline batteries to be a viable technology solution for meeting the dire need for large-scale electrical energy storage.« less

Information fusion performance evaluation for motion imagery data using mutual information: initial study

NASA Astrophysics Data System (ADS)

Grieggs, Samuel M.; McLaughlin, Michael J.; Ezekiel, Soundararajan; Blasch, Erik

2015-06-01

As technology and internet use grows at an exponential rate, video and imagery data is becoming increasingly important. Various techniques such as Wide Area Motion imagery (WAMI), Full Motion Video (FMV), and Hyperspectral Imaging (HSI) are used to collect motion data and extract relevant information. Detecting and identifying a particular object in imagery data is an important step in understanding visual imagery, such as content-based image retrieval (CBIR). Imagery data is segmented and automatically analyzed and stored in dynamic and robust database. In our system, we seek utilize image fusion methods which require quality metrics. Many Image Fusion (IF) algorithms have been proposed based on different, but only a few metrics, used to evaluate the performance of these algorithms. In this paper, we seek a robust, objective metric to evaluate the performance of IF algorithms which compares the outcome of a given algorithm to ground truth and reports several types of errors. Given the ground truth of a motion imagery data, it will compute detection failure, false alarm, precision and recall metrics, background and foreground regions statistics, as well as split and merge of foreground regions. Using the Structural Similarity Index (SSIM), Mutual Information (MI), and entropy metrics; experimental results demonstrate the effectiveness of the proposed methodology for object detection, activity exploitation, and CBIR.

Robust Feature Selection Technique using Rank Aggregation.

PubMed

Sarkar, Chandrima; Cooley, Sarah; Srivastava, Jaideep

2014-01-01

Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique which produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases while classifiers exploit different statistical properties of data for evaluation. In numerous situations this can put researchers into dilemma as to which feature selection method and a classifiers to choose from a vast range of choices. In this paper, we propose a technique that aggregates the consensus properties of various feature selection methods to develop a more optimal solution. The ensemble nature of our technique makes it more robust across various classifiers. In other words, it is stable towards achieving similar and ideally higher classification accuracy across a wide variety of classifiers. We quantify this concept of robustness with a measure known as the Robustness Index (RI). We perform an extensive empirical evaluation of our technique on eight data sets with different dimensions including Arrythmia, Lung Cancer, Madelon, mfeat-fourier, internet-ads, Leukemia-3c and Embryonal Tumor and a real world data set namely Acute Myeloid Leukemia (AML). We demonstrate not only that our algorithm is more robust, but also that compared to other techniques our algorithm improves the classification accuracy by approximately 3-4% (in data set with less than 500 features) and by more than 5% (in data set with more than 500 features), across a wide range of classifiers.

GPU-accelerated automatic identification of robust beam setups for proton and carbon-ion radiotherapy

NASA Astrophysics Data System (ADS)

Ammazzalorso, F.; Bednarz, T.; Jelen, U.

2014-03-01

We demonstrate acceleration on graphic processing units (GPU) of automatic identification of robust particle therapy beam setups, minimizing negative dosimetric effects of Bragg peak displacement caused by treatment-time patient positioning errors. Our particle therapy research toolkit, RobuR, was extended with OpenCL support and used to implement calculation on GPU of the Port Homogeneity Index, a metric scoring irradiation port robustness through analysis of tissue density patterns prior to dose optimization and computation. Results were benchmarked against an independent native CPU implementation. Numerical results were in agreement between the GPU implementation and native CPU implementation. For 10 skull base cases, the GPU-accelerated implementation was employed to select beam setups for proton and carbon ion treatment plans, which proved to be dosimetrically robust, when recomputed in presence of various simulated positioning errors. From the point of view of performance, average running time on the GPU decreased by at least one order of magnitude compared to the CPU, rendering the GPU-accelerated analysis a feasible step in a clinical treatment planning interactive session. In conclusion, selection of robust particle therapy beam setups can be effectively accelerated on a GPU and become an unintrusive part of the particle therapy treatment planning workflow. Additionally, the speed gain opens new usage scenarios, like interactive analysis manipulation (e.g. constraining of some setup) and re-execution. Finally, through OpenCL portable parallelism, the new implementation is suitable also for CPU-only use, taking advantage of multiple cores, and can potentially exploit types of accelerators other than GPUs.

Superlinearly scalable noise robustness of redundant coupled dynamical systems.

PubMed

Kohar, Vivek; Kia, Behnam; Lindner, John F; Ditto, William L

2016-03-01

We illustrate through theory and numerical simulations that redundant coupled dynamical systems can be extremely robust against local noise in comparison to uncoupled dynamical systems evolving in the same noisy environment. Previous studies have shown that the noise robustness of redundant coupled dynamical systems is linearly scalable and deviations due to noise can be minimized by increasing the number of coupled units. Here, we demonstrate that the noise robustness can actually be scaled superlinearly if some conditions are met and very high noise robustness can be realized with very few coupled units. We discuss these conditions and show that this superlinear scalability depends on the nonlinearity of the individual dynamical units. The phenomenon is demonstrated in discrete as well as continuous dynamical systems. This superlinear scalability not only provides us an opportunity to exploit the nonlinearity of physical systems without being bogged down by noise but may also help us in understanding the functional role of coupled redundancy found in many biological systems. Moreover, engineers can exploit superlinear noise suppression by starting a coupled system near (not necessarily at) the appropriate initial condition.

A Robust False Matching Points Detection Method for Remote Sensing Image Registration

NASA Astrophysics Data System (ADS)

Shan, X. J.; Tang, P.

2015-04-01

Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

Water resources planning under climate change: Assessing the robustness of real options for the Blue Nile

NASA Astrophysics Data System (ADS)

Jeuland, Marc; Whittington, Dale

2014-03-01

This article presents a methodology for planning new water resources infrastructure investments and operating strategies in a world of climate change uncertainty. It combines a real options (e.g., options to defer, expand, contract, abandon, switch use, or otherwise alter a capital investment) approach with principles drawn from robust decision-making (RDM). RDM comprises a class of methods that are used to identify investment strategies that perform relatively well, compared to the alternatives, across a wide range of plausible future scenarios. Our proposed framework relies on a simulation model that includes linkages between climate change and system hydrology, combined with sensitivity analyses that explore how economic outcomes of investments in new dams vary with forecasts of changing runoff and other uncertainties. To demonstrate the framework, we consider the case of new multipurpose dams along the Blue Nile in Ethiopia. We model flexibility in design and operating decisions—the selection, sizing, and sequencing of new dams, and reservoir operating rules. Results show that there is no single investment plan that performs best across a range of plausible future runoff conditions. The decision-analytic framework is then used to identify dam configurations that are both robust to poor outcomes and sufficiently flexible to capture high upside benefits if favorable future climate and hydrological conditions should arise. The approach could be extended to explore design and operating features of development and adaptation projects other than dams.

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Ancell, B. C.

2017-05-01

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Towards robust specularity detection and inpainting in cardiac images

NASA Astrophysics Data System (ADS)

Alsaleh, Samar M.; Aviles, Angelica I.; Sobrevilla, Pilar; Casals, Alicia; Hahn, James

2016-03-01

Computer-assisted cardiac surgeries had major advances throughout the years and are gaining more popularity over conventional cardiac procedures as they offer many benefits to both patients and surgeons. One obvious advantage is that they enable surgeons to perform delicate tasks on the heart while it is still beating, avoiding the risks associated with cardiac arrest. Consequently, the surgical system needs to accurately compensate the physiological motion of the heart which is a very challenging task in medical robotics since there exist different sources of disturbances. One of which is the bright light reflections, known as specular highlights, that appear on the glossy surface of the heart and partially occlude the field of view. This work is focused on developing a robust approach that accurately detects and removes those highlights to reduce their disturbance to the surgeon and the motion compensation algorithm. As a first step, we exploit both color attributes and Fuzzy edge detector to identify specular regions in each acquired image frame. These two techniques together work as restricted thresholding and are able to accurately identify specular regions. Then, in order to eliminate the specularity artifact and give the surgeon a better perception of the heart, the second part of our solution is dedicated to correct the detected regions using inpainting to propagate and smooth the results. Our experimental results, which we carry out in realistic datasets, reveal how efficient and precise the proposed solution is, as well as demonstrate its robustness and real-time performance.

Automated statistical experimental design approach for rapid separation of coenzyme Q10 and identification of its biotechnological process related impurities using UHPLC and UHPLC-APCI-MS.

PubMed

Talluri, Murali V N Kumar; Kalariya, Pradipbhai D; Dharavath, Shireesha; Shaikh, Naeem; Garg, Prabha; Ramisetti, Nageswara Rao; Ragampeta, Srinivas

2016-09-01

A novel ultra high performance liquid chromatography method development strategy was ameliorated by applying quality by design approach. The developed systematic approach was divided into five steps (i) Analytical Target Profile, (ii) Critical Quality Attributes, (iii) Risk Assessments of Critical parameters using design of experiments (screening and optimization phases), (iv) Generation of design space, and (v) Process Capability Analysis (Cp) for robustness study using Monte Carlo simulation. The complete quality-by-design-based method development was made automated and expedited by employing sub-2 μm particles column with an ultra high performance liquid chromatography system. Successful chromatographic separation of the Coenzyme Q10 from its biotechnological process related impurities was achieved on a Waters Acquity phenyl hexyl (100 mm × 2.1 mm, 1.7 μm) column with gradient elution of 10 mM ammonium acetate buffer (pH 4.0) and a mixture of acetonitrile/2-propanol (1:1) as the mobile phase. Through this study, fast and organized method development workflow was developed and robustness of the method was also demonstrated. The method was validated for specificity, linearity, accuracy, precision, and robustness in compliance to the International Conference on Harmonization, Q2 (R1) guidelines. The impurities were identified by atmospheric pressure chemical ionization-mass spectrometry technique. Further, the in silico toxicity of impurities was analyzed using TOPKAT and DEREK software. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

PubMed Central

Svečko, Rajko

2014-01-01

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

An adaptive discontinuous Galerkin solver for aerodynamic flows

NASA Astrophysics Data System (ADS)

Burgess, Nicholas K.

This work considers the accuracy, efficiency, and robustness of an unstructured high-order accurate discontinuous Galerkin (DG) solver for computational fluid dynamics (CFD). Recently, there has been a drive to reduce the discretization error of CFD simulations using high-order methods on unstructured grids. However, high-order methods are often criticized for lacking robustness and having high computational cost. The goal of this work is to investigate methods that enhance the robustness of high-order discontinuous Galerkin (DG) methods on unstructured meshes, while maintaining low computational cost and high accuracy of the numerical solutions. This work investigates robustness enhancement of high-order methods by examining effective non-linear solvers, shock capturing methods, turbulence model discretizations and adaptive refinement techniques. The goal is to develop an all encompassing solver that can simulate a large range of physical phenomena, where all aspects of the solver work together to achieve a robust, efficient and accurate solution strategy. The components and framework for a robust high-order accurate solver that is capable of solving viscous, Reynolds Averaged Navier-Stokes (RANS) and shocked flows is presented. In particular, this work discusses robust discretizations of the turbulence model equation used to close the RANS equations, as well as stable shock capturing strategies that are applicable across a wide range of discretization orders and applicable to very strong shock waves. Furthermore, refinement techniques are considered as both efficiency and robustness enhancement strategies. Additionally, efficient non-linear solvers based on multigrid and Krylov subspace methods are presented. The accuracy, efficiency, and robustness of the solver is demonstrated using a variety of challenging aerodynamic test problems, which include turbulent high-lift and viscous hypersonic flows. Adaptive mesh refinement was found to play a critical role in obtaining a robust and efficient high-order accurate flow solver. A goal-oriented error estimation technique has been developed to estimate the discretization error of simulation outputs. For high-order discretizations, it is shown that functional output error super-convergence can be obtained, provided the discretization satisfies a property known as dual consistency. The dual consistency of the DG methods developed in this work is shown via mathematical analysis and numerical experimentation. Goal-oriented error estimation is also used to drive an hp-adaptive mesh refinement strategy, where a combination of mesh or h-refinement, and order or p-enrichment, is employed based on the smoothness of the solution. The results demonstrate that the combination of goal-oriented error estimation and hp-adaptation yield superior accuracy, as well as enhanced robustness and efficiency for a variety of aerodynamic flows including flows with strong shock waves. This work demonstrates that DG discretizations can be the basis of an accurate, efficient, and robust CFD solver. Furthermore, enhancing the robustness of DG methods does not adversely impact the accuracy or efficiency of the solver for challenging and complex flow problems. In particular, when considering the computation of shocked flows, this work demonstrates that the available shock capturing techniques are sufficiently accurate and robust, particularly when used in conjunction with adaptive mesh refinement . This work also demonstrates that robust solutions of the Reynolds Averaged Navier-Stokes (RANS) and turbulence model equations can be obtained for complex and challenging aerodynamic flows. In this context, the most robust strategy was determined to be a low-order turbulence model discretization coupled to a high-order discretization of the RANS equations. Although RANS solutions using high-order accurate discretizations of the turbulence model were obtained, the behavior of current-day RANS turbulence models discretized to high-order was found to be problematic, leading to solver robustness issues. This suggests that future work is warranted in the area of turbulence model formulation for use with high-order discretizations. Alternately, the use of Large-Eddy Simulation (LES) subgrid scale models with high-order DG methods offers the potential to leverage the high accuracy of these methods for very high fidelity turbulent simulations. This thesis has developed the algorithmic improvements that will lay the foundation for the development of a three-dimensional high-order flow solution strategy that can be used as the basis for future LES simulations.

Sulfite pretreatment to overcome recalcitrance of lignocellulose (SPORL) for robust enzymatic saccharification of hardwoods

Treesearch

G. S. Wang; X. J. Pan; Junyong Zhu; Roland Gleisner; D. Rockwood

2009-01-01

This study demonstrates sulfite pretreatment to overcome recalcitrance of lignocellulose (SPORL) for robust bioconversion of hardwoods. With only about 4% sodium bisulfite charge on aspen and 30-min pretreatment at temperature 180[...

Robust regression for large-scale neuroimaging studies.

PubMed

Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand

2015-05-01

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies. Copyright © 2015 Elsevier Inc. All rights reserved.

In-situ calibration of nonuniformity in infrared staring and modulated systems

NASA Astrophysics Data System (ADS)

Black, Wiley T.

Infrared cameras can directly measure the apparent temperature of objects, providing thermal imaging. However, the raw output from most infrared cameras suffers from a strong, often limiting noise source called nonuniformity. Manufacturing imperfections in infrared focal planes lead to high pixel-to-pixel sensitivity to electronic bias, focal plane temperature, and other effects. The resulting imagery can only provide useful thermal imaging after a nonuniformity calibration has been performed. Traditionally, these calibrations are performed by momentarily blocking the field of view with a at temperature plate or blackbody cavity. However because the pattern is a coupling of manufactured sensitivities with operational variations, periodic recalibration is required, sometimes on the order of tens of seconds. A class of computational methods called Scene-Based Nonuniformity Correction (SBNUC) has been researched for over 20 years where the nonuniformity calibration is estimated in digital processing by analysis of the video stream in the presence of camera motion. The most sophisticated SBNUC methods can completely and robustly eliminate the high-spatial frequency component of nonuniformity with only an initial reference calibration or potentially no physical calibration. I will demonstrate a novel algorithm that advances these SBNUC techniques to support all spatial frequencies of nonuniformity correction. Long-wave infrared microgrid polarimeters are a class of camera that incorporate a microscale per-pixel wire-grid polarizer directly affixed to each pixel of the focal plane. These cameras have the capability of simultaneously measuring thermal imagery and polarization in a robust integrated package with no moving parts. I will describe the necessary adaptations of my SBNUC method to operate on this class of sensor as well as demonstrate SBNUC performance in LWIR polarimetry video collected on the UA mall.

Denoising the Speaking Brain: Toward a Robust Technique for Correcting Artifact-Contaminated fMRI Data under Severe Motion

PubMed Central

Xu, Yisheng; Tong, Yunxia; Liu, Siyuan; Chow, Ho Ming; AbdulSabur, Nuria Y.; Mattay, Govind S.; Braun, Allen R.

2014-01-01

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity. PMID:25225001

Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.

PubMed

Xu, Yisheng; Tong, Yunxia; Liu, Siyuan; Chow, Ho Ming; AbdulSabur, Nuria Y; Mattay, Govind S; Braun, Allen R

2014-12-01

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; and 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies.

PubMed

Nikolova, Olga; Moser, Russell; Kemp, Christopher; Gönen, Mehmet; Margolin, Adam A

2017-05-01

In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments. We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas. : The code for this work is available at https://github.com/olganikolova/gbgfa. : nikolova@ohsu.edu or margolin@ohsu.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Single sample extraction and HPLC processing for quantification of NAD and NADH levels in Saccharomyces cerevisiae

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

Sporty, J; Kabir, M M; Turteltaub, K

A robust redox extraction protocol for quantitative and reproducible metabolite isolation and recovery has been developed for simultaneous measurement of nicotinamide adenine dinucleotide (NAD) and its reduced form, NADH, from Saccharomyces cerevisiae. Following culture in liquid media, approximately 10{sup 8} yeast cells were harvested by centrifugation and then lysed under non-oxidizing conditions by bead blasting in ice-cold, nitrogen-saturated 50-mM ammonium acetate. To enable protein denaturation, ice cold nitrogen-saturated CH{sub 3}CN + 50-mM ammonium acetate (3:1; v:v) was added to the cell lysates. After sample centrifugation to pellet precipitated proteins, organic solvent removal was performed on supernatants by chloroform extraction. Themore » remaining aqueous phase was dried and resuspended in 50-mM ammonium acetate. NAD and NADH were separated by HPLC and quantified using UV-VIS absorbance detection. Applicability of this procedure for quantifying NAD and NADH levels was evaluated by culturing yeast under normal (2% glucose) and calorie restricted (0.5% glucose) conditions. NAD and NADH contents are similar to previously reported levels in yeast obtained using enzymatic assays performed separately on acid (for NAD) and alkali (for NADH) extracts. Results demonstrate that it is possible to perform a single preparation to reliably and robustly quantitate both NAD and NADH contents in the same sample. Robustness of the protocol suggests it will be (1) applicable to quantification of these metabolites in mammalian and bacterial cell cultures; and (2) amenable to isotope labeling strategies to determine the relative contribution of specific metabolic pathways to total NAD and NADH levels in cell cultures.« less

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

Fong, Erika J.; Huang, Chao; Hamilton, Julie

Here, a major advantage of microfluidic devices is the ability to manipulate small sample volumes, thus reducing reagent waste and preserving precious sample. However, to achieve robust sample manipulation it is necessary to address device integration with the macroscale environment. To realize repeatable, sensitive particle separation with microfluidic devices, this protocol presents a complete automated and integrated microfluidic platform that enables precise processing of 0.15–1.5 ml samples using microfluidic devices. Important aspects of this system include modular device layout and robust fixtures resulting in reliable and flexible world to chip connections, and fully-automated fluid handling which accomplishes closed-loop sample collection,more » system cleaning and priming steps to ensure repeatable operation. Different microfluidic devices can be used interchangeably with this architecture. Here we incorporate an acoustofluidic device, detail its characterization, performance optimization, and demonstrate its use for size-separation of biological samples. By using real-time feedback during separation experiments, sample collection is optimized to conserve and concentrate sample. Although requiring the integration of multiple pieces of equipment, advantages of this architecture include the ability to process unknown samples with no additional system optimization, ease of device replacement, and precise, robust sample processing.« less

Efficient l1 -norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method.

PubMed

Kim, Eunwoo; Lee, Minsik; Choi, Chong-Ho; Kwak, Nojun; Oh, Songhwai

2015-02-01

Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l2 -norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l2 -norm exaggerates the negative effect of outliers. Recently, to overcome this problem, various methods based on the l1 -norm, such as robust PCA methods, have been proposed for low-rank matrix approximation. Despite the robustness of the methods, they require heavy computational effort and substantial memory for high-dimensional data, which is impractical for real-world problems. In this paper, we propose two efficient low-rank factorization methods based on the l1 -norm that find proper projection and coefficient matrices using the alternating rectified gradient method. The proposed methods are applied to a number of low-rank matrix approximation problems to demonstrate their efficiency and robustness. The experimental results show that our proposals are efficient in both execution time and reconstruction performance unlike other state-of-the-art methods.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm.

PubMed

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.

Cognitive Mapping Based on Conjunctive Representations of Space and Movement

PubMed Central

Zeng, Taiping; Si, Bailu

2017-01-01

It is a challenge to build robust simultaneous localization and mapping (SLAM) system in dynamical large-scale environments. Inspired by recent findings in the entorhinal–hippocampal neuronal circuits, we propose a cognitive mapping model that includes continuous attractor networks of head-direction cells and conjunctive grid cells to integrate velocity information by conjunctive encodings of space and movement. Visual inputs from the local view cells in the model provide feedback cues to correct drifting errors of the attractors caused by the noisy velocity inputs. We demonstrate the mapping performance of the proposed cognitive mapping model on an open-source dataset of 66 km car journey in a 3 km × 1.6 km urban area. Experimental results show that the proposed model is robust in building a coherent semi-metric topological map of the entire urban area using a monocular camera, even though the image inputs contain various changes caused by different light conditions and terrains. The results in this study could inspire both neuroscience and robotic research to better understand the neural computational mechanisms of spatial cognition and to build robust robotic navigation systems in large-scale environments. PMID:29213234

Robust image features: concentric contrasting circles and their image extraction

NASA Astrophysics Data System (ADS)

Gatrell, Lance B.; Hoff, William A.; Sklair, Cheryl W.

1992-03-01

Many computer vision tasks can be simplified if special image features are placed on the objects to be recognized. A review of special image features that have been used in the past is given and then a new image feature, the concentric contrasting circle, is presented. The concentric contrasting circle image feature has the advantages of being easily manufactured, easily extracted from the image, robust extraction (true targets are found, while few false targets are found), it is a passive feature, and its centroid is completely invariant to the three translational and one rotational degrees of freedom and nearly invariant to the remaining two rotational degrees of freedom. There are several examples of existing parallel implementations which perform most of the extraction work. Extraction robustness was measured by recording the probability of correct detection and the false alarm rate in a set of images of scenes containing mockups of satellites, fluid couplings, and electrical components. A typical application of concentric contrasting circle features is to place them on modeled objects for monocular pose estimation or object identification. This feature is demonstrated on a visually challenging background of a specular but wrinkled surface similar to a multilayered insulation spacecraft thermal blanket.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

PubMed

Wu, Dongjin; Xia, Linyuan; Geng, Jijun

2018-06-19

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

Robust estimation approach for blind denoising.

PubMed

Rabie, Tamer

2005-11-01

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.

A new way to improve the robustness of complex communication networks by allocating redundancy links

NASA Astrophysics Data System (ADS)

Shi, Chunhui; Peng, Yunfeng; Zhuo, Yue; Tang, Jieying; Long, Keping

2012-03-01

We investigate the robustness of complex communication networks on allocating redundancy links. The protecting key nodes (PKN) strategy is proposed to improve the robustness of complex communication networks against intentional attack. Our numerical simulations show that allocating a few redundant links among key nodes using the PKN strategy will significantly increase the robustness of scale-free complex networks. We have also theoretically proved and demonstrated the effectiveness of the PKN strategy. We expect that our work will help achieve a better understanding of communication networks.

Coherent diffractive imaging using randomly coded masks

DOE PAGES

Seaberg, Matthew H.; d'Aspremont, Alexandre; Turner, Joshua J.

2015-12-07

We experimentally demonstrate an extension to coherent diffractive imaging that encodes additional information through the use of a series of randomly coded masks, removing the need for typical object-domain constraints while guaranteeing a unique solution to the phase retrieval problem. Phase retrieval is performed using a numerical convex relaxation routine known as “PhaseCut,” an iterative algorithm known for its stability and for its ability to find the global solution, which can be found efficiently and which is robust to noise. As a result, the experiment is performed using a laser diode at 532.2 nm, enabling rapid prototyping for future X-raymore » synchrotron and even free electron laser experiments.« less

Network coding multiuser scheme for indoor visible light communications

NASA Astrophysics Data System (ADS)

Zhang, Jiankun; Dang, Anhong

2017-12-01

Visible light communication (VLC) is a unique alternative for indoor data transfer and developing beyond point-to-point. However, for realizing high-capacity networks, VLC is facing challenges including the constrained bandwidth of the optical access point and random occlusion. A network coding scheme for VLC (NC-VLC) is proposed, with increased throughput and system robustness. Based on the Lambertian illumination model, theoretical decoding failure probability of the multiuser NC-VLC system is derived, and the impact of the system parameters on the performance is analyzed. Experiments demonstrate the proposed scheme successfully in the indoor multiuser scenario. These results indicate that the NC-VLC system shows a good performance under the link loss and random occlusion.

Coherent diffractive imaging using randomly coded masks

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

Seaberg, Matthew H., E-mail: seaberg@slac.stanford.edu; Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025; D'Aspremont, Alexandre

2015-12-07

We experimentally demonstrate an extension to coherent diffractive imaging that encodes additional information through the use of a series of randomly coded masks, removing the need for typical object-domain constraints while guaranteeing a unique solution to the phase retrieval problem. Phase retrieval is performed using a numerical convex relaxation routine known as “PhaseCut,” an iterative algorithm known for its stability and for its ability to find the global solution, which can be found efficiently and which is robust to noise. The experiment is performed using a laser diode at 532.2 nm, enabling rapid prototyping for future X-ray synchrotron and even freemore » electron laser experiments.« less

Judgments of the lucky across development and culture

PubMed Central

Olson, Kristina R.; Dunham, Yarrow; Dweck, Carol S.; Spelke, Elizabeth S.; Banaji, Mahzarin R.

2009-01-01

For millennia human beings have believed that it is morally wrong to judge others by the fortuitous or unfortunate events that befall them or by the actions of another person. Rather, an individual’s own intended, deliberate actions should be the basis of his/her evaluation, reward and punishment. In a series of studies we investigate whether such rules guide the judgments of children. The first three studies demonstrate that children view lucky others as more likely than unlucky others to perform intentional good actions. Children similarly assess the siblings of lucky others as more likely to perform intentional good actions than the siblings of unlucky others. The next three studies demonstrate that children as young as 3 years believe that lucky people are nicer than unlucky people. The final two studies find that Japanese children also demonstrate a robust preference for the lucky and their associates. These findings are discussed in relation to Lerner’s just world theory and Piaget’s immanent justice research and in relation to the development of intergroup attitudes. PMID:18444737

All-fiber wavelength swept ring laser based on Fabry-Perot filter for optical frequency domain imaging

PubMed Central

Jun, Changsu; Villiger, Martin; Oh, Wang-Yuhl; Bouma, Brett E.

2014-01-01

Innovations in laser engineering have yielded several novel configurations for high repetition rate, broad sweep range, and long coherence length wavelength swept lasers. Although these lasers have enabled high performance frequency-domain optical coherence tomography, they are typically complicated and costly and many require access to proprietary materials or devices. Here, we demonstrate a simplified ring resonator configuration that is straightforward to construct from readily available materials at a low total cost. It was enabled by an insight regarding the significance of isolation against bidirectional operation and by configuring the sweep range of the intracavity filter to exceed its free spectral range. The design can easily be optimized to meet a range of operating specifications while yielding robust and stable performance. As an example, we demonstrate 240 kHz operation with 125 nm sweep range and >70 mW of average output power and demonstrate high quality frequency domain OCT imaging. The complete component list and directions for assembly of the laser are posted on-line at www.octresearch.org. PMID:25401614

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

PubMed

Van Valen, David A; Kudo, Takamasa; Lane, Keara M; Macklin, Derek N; Quach, Nicolas T; DeFelice, Mialy M; Maayan, Inbal; Tanouchi, Yu; Ashley, Euan A; Covert, Markus W

2016-11-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Assessing the internal consistency of the event-related potential: An example analysis.

PubMed

Thigpen, Nina N; Kappenman, Emily S; Keil, Andreas

2017-01-01

ERPs are widely and increasingly used to address questions in psychophysiological research. As discussed in this special issue, a renewed focus on questions of reliability and stability marks the need for intuitive, quantitative descriptors that allow researchers to communicate the robustness of ERP measures used in a given study. This report argues that well-established indices of internal consistency and effect size meet this need and can be easily extracted from most ERP datasets, as demonstrated with example analyses using a representative dataset from a feature-based visual selective attention task. We demonstrate how to measure the internal consistency of three aspects commonly considered in ERP studies: voltage measurements for specific time ranges at selected sensors, voltage dynamics across all time points of the ERP waveform, and the distribution of voltages across the scalp. We illustrate methods for quantifying the robustness of experimental condition differences, by calculating effect size for different indices derived from the ERP. The number of trials contributing to the ERP waveform was manipulated to examine the relationship between signal-to-noise ratio (SNR), internal consistency, and effect size. In the present example dataset, satisfactory consistency (Cronbach's alpha > 0.7) of individual voltage measurements was reached at lower trial counts than were required to reach satisfactory effect sizes for differences between experimental conditions. Comparing different metrics of robustness, we conclude that the internal consistency and effect size of ERP findings greatly depend on the quantification strategy, the comparisons and analyses performed, and the SNR. © 2016 Society for Psychophysiological Research.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

DOE PAGES

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.; ...

2016-11-04

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less

Assessing the internal consistency of the event-related potential: An example analysis

PubMed Central

Thigpen, Nina; Kappenman, Emily; Keil, Andreas

2017-01-01

Event-related potentials (ERPs) are widely and increasingly used to address questions in Psychophysiological research. As discussed in this special issue, a renewed focus on questions of reliability and stability marks the need for intuitive, quantitative descriptors that allow researchers to communicate the robustness of ERP measures used in a given study. This report argues that well-established indices of internal consistency and effect size meet this need and can be easily extracted from most ERP data sets, as demonstrated with example analyses using a representative data set from a feature-based visual selective attention task. We demonstrate how to measure the internal consistency of three aspects commonly considered in ERP studies: Voltage measurements for specific time ranges at selected sensors, voltage dynamics across all time points of the ERP waveform, and the distribution of voltages across the scalp. We illustrate methods for quantifying the robustness of experimental condition differences, by calculating effect size for different indices derived from the ERP. The number of trials contributing to the ERP waveform was manipulated to examine the relationship between signal-to-noise ratio, internal consistency, and effect size. In the present example data set, satisfactory consistency (Cronbach’s alpha > 0.7) of individual voltage measurements was reached at lower trial counts than were required to reach satisfactory effect sizes for differences between experimental conditions. Comparing different metrics of robustness, we conclude that the SNR, internal consistency, and effect size of ERP findings greatly depend on the quantification strategy, the comparisons and analyses performed, and the signal-to-noise ratio. PMID:28000264

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

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

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

PubMed Central

Van Valen, David A.; Lane, Keara M.; Quach, Nicolas T.; Maayan, Inbal

2016-01-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems. PMID:27814364

Robust and Recyclable Substrate Template with an Ultrathin Nanoporous Counter Electrode for Organic-Hole-Conductor-Free Monolithic Perovskite Solar Cells.

PubMed

Li, Ming-Hsien; Yang, Yu-Syuan; Wang, Kuo-Chin; Chiang, Yu-Hsien; Shen, Po-Shen; Lai, Wei-Chih; Guo, Tzung-Fang; Chen, Peter

2017-12-06

A robust and recyclable monolithic substrate applying all-inorganic metal-oxide selective contact with a nanoporous (np) Au:NiO x counter electrode is successfully demonstrated for efficient perovskite solar cells, of which the perovskite active layer is deposited in the final step for device fabrication. Through annealing of the Ni/Au bilayer, the nanoporous NiO/Au electrode is formed in virtue of interconnected Au network embedded in oxidized Ni. By optimizing the annealing parameters and tuning the mesoscopic layer thickness (mp-TiO 2 and mp-Al 2 O 3 ), a decent power conversion efficiency (PCE) of 10.25% is delivered. With mp-TiO 2 /mp-Al 2 O 3 /np-Au:NiO x as a template, the original perovskite solar cell with 8.52% PCE can be rejuvenated by rinsing off the perovskite material with dimethylformamide and refilling with newly deposited perovskite. A renewed device using the recycled substrate once and twice, respectively, achieved a PCE of 8.17 and 7.72% that are comparable to original performance. This demonstrates that the novel device architecture is possible to recycle the expensive transparent conducting glass substrates together with all the electrode constituents. Deposition of stable multicomponent perovskite materials in the template also achieves an efficiency of 8.54%, which shows its versatility for various perovskite materials. The application of such a novel NiO/Au nanoporous electrode has promising potential for commercializing cost-effective, large scale, and robust perovskite solar cells.

Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots.

PubMed

Park, Gyeong-Moon; Yoo, Yong-Ho; Kim, Deok-Hwa; Kim, Jong-Hwan; Gyeong-Moon Park; Yong-Ho Yoo; Deok-Hwa Kim; Jong-Hwan Kim; Yoo, Yong-Ho; Park, Gyeong-Moon; Kim, Jong-Hwan; Kim, Deok-Hwa

2018-06-01

Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations.

PubMed

Rogers, Emily; Murrugarra, David; Heitsch, Christine

2017-07-25

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be "ill conditioned" in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling's improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones. Copyright © 2017. Published by Elsevier Inc.

The first PANDA tests

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

Dreier, J.; Huggenberger, M.; Aubert, C.

1996-08-01

The PANDA test facility at PSI in Switzerland is used to study the long-term Simplified Boiling Water Reactor (SBWR) Passive Containment Cooling System (PCCS) performance. The PANDA tests demonstrate performance on a larger scale than previous tests and examine the effects of any non-uniform spatial distributions of steam and non-condensables in the system. The PANDA facility has a 1:1 vertical scale, and 1:25 ``system`` scale (volume, power, etc.). Steady-state PCCS condenser performance tests and extensive facility characterization tests have been completed. Transient system behavior tests were conducted late in 1995; results from the first three transient tests (M3 series) aremore » reviewed. The first PANDA tests showed that the overall global behavior of the SBWR containment was globally repeatable and very favorable; the system exhibited great ``robustness.``« less

Normalised subband adaptive filtering with extended adaptiveness on degree of subband filters

NASA Astrophysics Data System (ADS)

Samuyelu, Bommu; Rajesh Kumar, Pullakura

2017-12-01

This paper proposes an adaptive normalised subband adaptive filtering (NSAF) to accomplish the betterment of NSAF performance. In the proposed NSAF, an extended adaptiveness is introduced from its variants in two ways. In the first way, the step-size is set adaptive, and in the second way, the selection of subbands is set adaptive. Hence, the proposed NSAF is termed here as variable step-size-based NSAF with selected subbands (VS-SNSAF). Experimental investigations are carried out to demonstrate the performance (in terms of convergence) of the VS-SNSAF against the conventional NSAF and its state-of-the-art adaptive variants. The results report the superior performance of VS-SNSAF over the traditional NSAF and its variants. It is also proved for its stability, robustness against noise and substantial computing complexity.

Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996

Flexible body control using neural networks

NASA Technical Reports Server (NTRS)

Mccullough, Claire L.

1992-01-01

Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

Evaluation of biasing and protection circuitry components for cryogenic MMIC low-noise amplifiers

NASA Astrophysics Data System (ADS)

Lamb, James W.

2014-05-01

Millimeter-wave integrated circuits with gate lengths as short as 35 nm are demonstrating extremely low-noise performance, especially when cooled to cryogenic temperatures. These operate at low voltages and are susceptible to damage from electrostatic discharge and improper biasing, as well as being sensitive to low-level interference. Designing a protection circuit for low voltages and temperatures is challenging because there is very little data available on components that may be suitable. Extensive testing at low temperatures yielded a set of components and a circuit topology that demonstrates the required level of protection for critical MMICs and similar devices. We present a circuit that provides robust protection for low voltage devices from room temperature down to 4 K.

Effect of metal coating in all-fiber acousto-optic tunable filter using torsional wave.

PubMed

Song, Du-Ri; Jun, Chang Su; Do Lim, Sun; Kim, Byoung Yoon

2014-12-15

Torsional mode acousto-optic tunable filter (AOTF) is demonstrated using a metal-coated birefringent optical fiber for an improved robustness. The changes in acoustic and optical properties of a metal-coated birefringent optical fiber induced by the thin metal coating were analyzed experimentally and theoretically. The filter wavelength shift is successfully explained as a result of combined effect of acoustic wavelength change and optical birefringence change. We also demonstrated a small form-factor configuration by coiling the fiber with 6 cm diameter without performance degradation. The center wavelength of the filter can be tuned >35 nm by changing the applied frequency, and the coupling efficiency is higher than 92% with <5 nm 3-dB bandwidth.

Experimental demonstration of MIMO-OFDM underwater wireless optical communication

NASA Astrophysics Data System (ADS)

Song, Yuhang; Lu, Weichao; Sun, Bin; Hong, Yang; Qu, Fengzhong; Han, Jun; Zhang, Wei; Xu, Jing

2017-11-01

In this paper, we propose and experimentally demonstrate a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) underwater wireless optical communication (UWOC) system, with a gross bit rate of 33.691 Mb/s over a 2-m water channel using low-cost blue light-emitting-diodes (LEDs) and 10-MHz PIN photodiodes. The system is capable of realizing robust data transmission within a relatively large reception area, leading to relaxed alignment requirement for UWOC. In addition, we have compared the system performance of repetition coding OFDM (RC-OFDM), Alamouti-OFDM and multiple-input single-output OFDM (MISO-OFDM) in turbid water. Results show that the Alamouti-OFDM UWOC is more resistant to delay than the RC-OFDM-based system.

Synthesis of arbitrary pulse waveforms in QCL-seeded ns-pulse CO₂ laser for optimization of an LPP EUV source.

PubMed

Nowak, Krzysztof M; Kurosawa, Yoshiaki; Suganuma, Takashi; Kawasuji, Yasufumi; Nakarai, Hiroaki; Saito, Takashi; Fujimoto, Junichi; Mizoguchi, Hakaru

2016-07-01

One of the unique features of the quantum-cascade-laser-seeded, nanosecond-pulse CO₂ laser, invented for the purpose of generation of extreme UV by laser-produced-plasma, is a robust synthesis of arbitrary pulse waveforms. In the present Letter we report on experimental results that are, to our best knowledge, the first demonstration of such functionality obtainable from nanosecond-pulse CO₂ laser technology. An online pulse duration adjustment within 10-40 ns was demonstrated, and a few exemplary pulse waveforms were synthesized, such as "tophat," "tailspike," and "leadspike" shapes. Such output characteristics may be useful to optimize the performance of LPP EUV source.

Robust linear discriminant models to solve financial crisis in banking sectors

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Idris, Faoziah; Ali, Hazlina; Omar, Zurni

2014-12-01

Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories. However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA.

Integration of the Response Surface Methodology with the Compromise Decision Support Problem in Developing a General Robust Design Procedure

NASA Technical Reports Server (NTRS)

Chen, Wei; Tsui, Kwok-Leung; Allen, Janet K.; Mistree, Farrokh

1994-01-01

In this paper we introduce a comprehensive and rigorous robust design procedure to overcome some limitations of the current approaches. A comprehensive approach is general enough to model the two major types of robust design applications, namely, robust design associated with the minimization of the deviation of performance caused by the deviation of noise factors (uncontrollable parameters), and robust design due to the minimization of the deviation of performance caused by the deviation of control factors (design variables). We achieve mathematical rigor by using, as a foundation, principles from the design of experiments and optimization. Specifically, we integrate the Response Surface Method (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example. Our focus in this paper is on illustrating our approach rather than on the results per se.

An integrated biotechnology platform for developing sustainable chemical processes.

PubMed

Barton, Nelson R; Burgard, Anthony P; Burk, Mark J; Crater, Jason S; Osterhout, Robin E; Pharkya, Priti; Steer, Brian A; Sun, Jun; Trawick, John D; Van Dien, Stephen J; Yang, Tae Hoon; Yim, Harry

2015-03-01

Genomatica has established an integrated computational/experimental metabolic engineering platform to design, create, and optimize novel high performance organisms and bioprocesses. Here we present our platform and its use to develop E. coli strains for production of the industrial chemical 1,4-butanediol (BDO) from sugars. A series of examples are given to demonstrate how a rational approach to strain engineering, including carefully designed diagnostic experiments, provided critical insights about pathway bottlenecks, byproducts, expression balancing, and commercial robustness, leading to a superior BDO production strain and process.

Oceanic eddy detection and lifetime forecast using machine learning methods

NASA Astrophysics Data System (ADS)

Ashkezari, Mohammad D.; Hill, Christopher N.; Follett, Christopher N.; Forget, Gaël.; Follows, Michael J.

2016-12-01

We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.

A low power, on demand electrothermal valve for wireless drug delivery applications

PubMed Central

Li, Po-Ying; Givrad, Tina K.; Sheybani, Roya; Holschneider, Daniel P.; Maarek, Jean-Michel I.

2014-01-01

We present a low power, on demand Parylene MEMS electrothermal valve. A novel Ω-shaped thermal resistive element requires low power (~mW) and enables rapid valve opening (~ms). Using both finite element analysis and valve opening experiments, a robust resistive element design for improved valve opening performance in water was obtained. In addition, a thermistor, as an inrush current limiter, was added into the valve circuit to provide variable current ramping. Wireless activation of the valve using RF inductive power transfer was demonstrated. PMID:20024057

Numerical realization of the variational method for generating self-trapped beams.

PubMed

Duque, Erick I; Lopez-Aguayo, Servando; Malomed, Boris A

2018-03-19

We introduce a numerical variational method based on the Rayleigh-Ritz optimization principle for predicting two-dimensional self-trapped beams in nonlinear media. This technique overcomes the limitation of the traditional variational approximation in performing analytical Lagrangian integration and differentiation. Approximate soliton solutions of a generalized nonlinear Schrödinger equation are obtained, demonstrating robustness of the beams of various types (fundamental, vortices, multipoles, azimuthons) in the course of their propagation. The algorithm offers possibilities to produce more sophisticated soliton profiles in general nonlinear models.

Performance characteristics of an adaptive controller based on least-mean-square filters

NASA Technical Reports Server (NTRS)

Mehta, Rajiv S.; Merhav, Shmuel J.

1986-01-01

A closed loop, adaptive control scheme that uses a least mean square filter as the controller model is presented, along with simulation results that demonstrate the excellent robustness of this scheme. It is shown that the scheme adapts very well to unknown plants, even those that are marginally stable, responds appropriately to changes in plant parameters, and is not unduly affected by additive noise. A heuristic argument for the conditions necessary for convergence is presented. Potential applications and extensions of the scheme are also discussed.

Adding flexibility to the search for robust portfolios in non-linear water resource planning

NASA Astrophysics Data System (ADS)

Tomlinson, James; Harou, Julien

2017-04-01

To date robust optimisation of water supply systems has sought to find portfolios or strategies that are robust to a range of uncertainties or scenarios. The search for a single portfolio that is robust in all scenarios is necessarily suboptimal compared to portfolios optimised for a single scenario deterministic future. By contrast establishing a separate portfolio for each future scenario is unhelpful to the planner who must make a single decision today under deep uncertainty. In this work we show that a middle ground is possible by allowing a small number of different portfolios to be found that are each robust to a different subset of the global scenarios. We use evolutionary algorithms and a simple water resource system model to demonstrate this approach. The primary contribution is to demonstrate that flexibility can be added to the search for portfolios, in complex non-linear systems, at the expense of complete robustness across all future scenarios. In this context we define flexibility as the ability to design a portfolio in which some decisions are delayed, but those decisions that are not delayed are themselves shown to be robust to the future. We recognise that some decisions in our portfolio are more important than others. An adaptive portfolio is found by allowing no flexibility for these near-term "important" decisions, but maintaining flexibility in the remaining longer term decisions. In this sense we create an effective 2-stage decision process for a non-linear water resource supply system. We show how this reduces a measure of regret versus the inflexible robust solution for the same system.

Design and development of a robust ATP subsystem for the Altair UAV-to-Ground lasercomm 2.5 Gbps demonstration

NASA Technical Reports Server (NTRS)

Ortiz, G. G.; Lee, S.; Monacos, S.; Wright, M.; Biswas, A.

2003-01-01

A robust acquisition, tracking and pointing (ATP) subsystem is being developed for the 2.5 Gigabit per second (Gbps) Unmanned-Aerial-Vehicle (UAV) to ground free-space optical communications link project.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

2013-06-01

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

MPI-AMRVAC 2.0 for Solar and Astrophysical Applications

NASA Astrophysics Data System (ADS)

Xia, C.; Teunissen, J.; El Mellah, I.; Chané, E.; Keppens, R.

2018-02-01

We report on the development of MPI-AMRVAC version 2.0, which is an open-source framework for parallel, grid-adaptive simulations of hydrodynamic and magnetohydrodynamic (MHD) astrophysical applications. The framework now supports radial grid stretching in combination with adaptive mesh refinement (AMR). The advantages of this combined approach are demonstrated with one-dimensional, two-dimensional, and three-dimensional examples of spherically symmetric Bondi accretion, steady planar Bondi–Hoyle–Lyttleton flows, and wind accretion in supergiant X-ray binaries. Another improvement is support for the generic splitting of any background magnetic field. We present several tests relevant for solar physics applications to demonstrate the advantages of field splitting on accuracy and robustness in extremely low-plasma β environments: a static magnetic flux rope, a magnetic null-point, and magnetic reconnection in a current sheet with either uniform or anomalous resistivity. Our implementation for treating anisotropic thermal conduction in multi-dimensional MHD applications is also described, which generalizes the original slope-limited symmetric scheme from two to three dimensions. We perform ring diffusion tests that demonstrate its accuracy and robustness, and show that it prevents the unphysical thermal flux present in traditional schemes. The improved parallel scaling of the code is demonstrated with three-dimensional AMR simulations of solar coronal rain, which show satisfactory strong scaling up to 2000 cores. Other framework improvements are also reported: the modernization and reorganization into a library, the handling of automatic regression tests, the use of inline/online Doxygen documentation, and a new future-proof data format for input/output.

Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

NASA Astrophysics Data System (ADS)

Jiang, Yulian; Liu, Jianchang; Tan, Shubin; Ming, Pingsong

2014-09-01

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.

An efficient fully-implicit multislope MUSCL method for multiphase flow with gravity in discrete fractured media

NASA Astrophysics Data System (ADS)

Jiang, Jiamin; Younis, Rami M.

2017-06-01

The first-order methods commonly employed in reservoir simulation for computing the convective fluxes introduce excessive numerical diffusion leading to severe smoothing of displacement fronts. We present a fully-implicit cell-centered finite-volume (CCFV) framework that can achieve second-order spatial accuracy on smooth solutions, while at the same time maintain robustness and nonlinear convergence performance. A novel multislope MUSCL method is proposed to construct the required values at edge centroids in a straightforward and effective way by taking advantage of the triangular mesh geometry. In contrast to the monoslope methods in which a unique limited gradient is used, the multislope concept constructs specific scalar slopes for the interpolations on each edge of a given element. Through the edge centroids, the numerical diffusion caused by mesh skewness is reduced, and optimal second order accuracy can be achieved. Moreover, an improved smooth flux-limiter is introduced to ensure monotonicity on non-uniform meshes. The flux-limiter provides high accuracy without degrading nonlinear convergence performance. The CCFV framework is adapted to accommodate a lower-dimensional discrete fracture-matrix (DFM) model. Several numerical tests with discrete fractured system are carried out to demonstrate the efficiency and robustness of the numerical model.

Purple L1 Milestone Review Panel TotalView Debugger Functionality and Performance for ASC Purple

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

Wolfe, M

2006-12-12

ASC code teams require a robust software debugging tool to help developers quickly find bugs in their codes and get their codes running. Development debugging commonly runs up to 512 processes. Production jobs run up to full ASC Purple scale, and at times require introspection while running. Developers want a debugger that runs on all their development and production platforms and that works with all compilers and runtimes used with ASC codes. The TotalView Multiprocess Debugger made by Etnus was specified for ASC Purple to address this needed capability. The ASC Purple environment builds on the environment seen by TotalViewmore » on ASCI White. The debugger must now operate with the Power5 CPU, Federation switch, AIX 5.3 operating system including large pages, IBM compilers 7 and 9, POE 4.2 parallel environment, and rs6000 SLURM resource manager. Users require robust, basic debugger functionality with acceptable performance at development debugging scale. A TotalView installation must be provided at the beginning of the early user access period that meets these requirements. A functional enhancement, fast conditional data watchpoints, and a scalability enhancement, capability up to 8192 processes, are to be demonstrated.« less

Optimum Design of Forging Process Parameters and Preform Shape under Uncertainties

NASA Astrophysics Data System (ADS)

Repalle, Jalaja; Grandhi, Ramana V.

2004-06-01

Forging is a highly complex non-linear process that is vulnerable to various uncertainties, such as variations in billet geometry, die temperature, material properties, workpiece and forging equipment positional errors and process parameters. A combination of these uncertainties could induce heavy manufacturing losses through premature die failure, final part geometric distortion and production risk. Identifying the sources of uncertainties, quantifying and controlling them will reduce risk in the manufacturing environment, which will minimize the overall cost of production. In this paper, various uncertainties that affect forging tool life and preform design are identified, and their cumulative effect on the forging process is evaluated. Since the forging process simulation is computationally intensive, the response surface approach is used to reduce time by establishing a relationship between the system performance and the critical process design parameters. Variability in system performance due to randomness in the parameters is computed by applying Monte Carlo Simulations (MCS) on generated Response Surface Models (RSM). Finally, a Robust Methodology is developed to optimize forging process parameters and preform shape. The developed method is demonstrated by applying it to an axisymmetric H-cross section disk forging to improve the product quality and robustness.

Dynamics of a single-atom electron pump.

PubMed

van der Heijden, J; Tettamanzi, G C; Rogge, S

2017-03-15

Single-electron pumps based on isolated impurity atoms have recently been experimentally demonstrated. In these devices the Coulomb potential of an atom creates a localised electron state with a large charging energy and considerable orbital level spacings, enabling robust charge capturing processes. In contrast to the frequently used gate-defined quantum dot pumps, which experience a strongly time-dependent potential, the confinement potential in these single-atom pumps is hardly affected by the periodic driving of the system. Here we describe the behaviour and performance of an atomic, single parameter, electron pump. This is done by considering the loading, isolating and unloading of one electron at the time, on a phosphorous atom embedded in a silicon double gate transistor. The most important feature of the atom pump is its very isolated ground state, which is populated through the fast loading of much higher lying excited states and a subsequent fast relaxation process. This leads to a substantial increase in pumping accuracy, and is opposed to the adverse role of excited states observed for quantum dot pumps due to non-adiabatic excitations. The pumping performance is investigated as a function of dopant position, revealing a pumping behaviour robust against the expected variability in atomic position.

Dynamics of a single-atom electron pump

PubMed Central

van der Heijden, J.; Tettamanzi, G. C.; Rogge, S.

2017-01-01

Single-electron pumps based on isolated impurity atoms have recently been experimentally demonstrated. In these devices the Coulomb potential of an atom creates a localised electron state with a large charging energy and considerable orbital level spacings, enabling robust charge capturing processes. In contrast to the frequently used gate-defined quantum dot pumps, which experience a strongly time-dependent potential, the confinement potential in these single-atom pumps is hardly affected by the periodic driving of the system. Here we describe the behaviour and performance of an atomic, single parameter, electron pump. This is done by considering the loading, isolating and unloading of one electron at the time, on a phosphorous atom embedded in a silicon double gate transistor. The most important feature of the atom pump is its very isolated ground state, which is populated through the fast loading of much higher lying excited states and a subsequent fast relaxation process. This leads to a substantial increase in pumping accuracy, and is opposed to the adverse role of excited states observed for quantum dot pumps due to non-adiabatic excitations. The pumping performance is investigated as a function of dopant position, revealing a pumping behaviour robust against the expected variability in atomic position. PMID:28295055

Identify High-Quality Protein Structural Models by Enhanced K-Means.

PubMed

Wu, Hongjie; Li, Haiou; Jiang, Min; Chen, Cheng; Lv, Qiang; Wu, Chuang

2017-01-01

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K -means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K -means clustering ( SK -means), whereas the other employs squared distance to optimize the initial centroids ( K -means++). Our results showed that SK -means and K -means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K -means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK -means and K -means++ demonstrated substantial improvements relative to results from SPICKER and classical K -means.

Identify High-Quality Protein Structural Models by Enhanced K-Means

PubMed Central

Li, Haiou; Chen, Cheng; Lv, Qiang; Wu, Chuang

2017-01-01

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means. PMID:28421198

LENSED: a code for the forward reconstruction of lenses and sources from strong lensing observations

NASA Astrophysics Data System (ADS)

Tessore, Nicolas; Bellagamba, Fabio; Metcalf, R. Benton

2016-12-01

Robust modelling of strong lensing systems is fundamental to exploit the information they contain about the distribution of matter in galaxies and clusters. In this work, we present LENSED, a new code which performs forward parametric modelling of strong lenses. LENSED takes advantage of a massively parallel ray-tracing kernel to perform the necessary calculations on a modern graphics processing unit (GPU). This makes the precise rendering of the background lensed sources much faster, and allows the simultaneous optimization of tens of parameters for the selected model. With a single run, the code is able to obtain the full posterior probability distribution for the lens light, the mass distribution and the background source at the same time. LENSED is first tested on mock images which reproduce realistic space-based observations of lensing systems. In this way, we show that it is able to recover unbiased estimates of the lens parameters, even when the sources do not follow exactly the assumed model. Then, we apply it to a subsample of the Sloan Lens ACS Survey lenses, in order to demonstrate its use on real data. The results generally agree with the literature, and highlight the flexibility and robustness of the algorithm.

Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

PubMed Central

Mei, Xiaoguang; Ma, Yong; Li, Chang; Fan, Fan; Huang, Jun; Ma, Jiayi

2015-01-01

The state-of-the-art ultra-spectral sensor technology brings new hope for high precision applications due to its high spectral resolution. However, it also comes with new challenges, such as the high data dimension and noise problems. In this paper, we propose a real-time method for infrared ultra-spectral signature classification via spatial pyramid matching (SPM), which includes two aspects. First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method. Second, we propose the classification method with reference spectral libraries, which utilizes the SPM-based similarity for the real-time infrared ultra-spectral signature classification with robustness performance. Specifically, instead of matching with each spectrum in the spectral library, our method is based on feature matching, which includes a feature library-generating phase. We calculate the SPM-based similarity between the feature of the spectrum and that of each spectrum of the reference feature library, then take the class index of the corresponding spectrum having the maximum similarity as the final result. Experimental comparisons on two publicly-available datasets demonstrate that the proposed method effectively improves the real-time classification performance and robustness to noise. PMID:26205263