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Sample records for adaptive neural-network-based flight

  1. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  2. Design of an adaptive neural network based power system stabilizer.

    PubMed

    Liu, Wenxin; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2003-01-01

    Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness. PMID:12850048

  3. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed a pitch frequency sweep and an automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

  4. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method. PMID:26285223

  5. Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

    NASA Technical Reports Server (NTRS)

    Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

    2006-01-01

    The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

  6. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    NASA Technical Reports Server (NTRS)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  7. A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems

    NASA Technical Reports Server (NTRS)

    Gupta, Pramod; Schumann, Johann

    2004-01-01

    High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.

  8. Neural network based adaptive control of nonlinear plants using random search optimization algorithms

    NASA Technical Reports Server (NTRS)

    Boussalis, Dhemetrios; Wang, Shyh J.

    1992-01-01

    This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.

  9. Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

    PubMed

    Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

    2015-03-01

    This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique. PMID:25720014

  10. Neural Network-Based Adaptive Optimal Controller - A Continuous-Time Formulation

    NASA Astrophysics Data System (ADS)

    Vrabie, Draguna; Lewis, Frank; Levine, Daniel

    We present a new online adaptive control scheme, for partially unknown nonlinear systems, which converges to the optimal state-feedback control solution for affine in the input nonlinear systems. The main features of the algorithm map on the characteristics of the rewards-based decision making process in the mammal brain.

  11. Reducing interferences in wireless communication systems by mobile agents with recurrent neural networks-based adaptive channel equalization

    NASA Astrophysics Data System (ADS)

    Beritelli, Francesco; Capizzi, Giacomo; Lo Sciuto, Grazia; Napoli, Christian; Tramontana, Emiliano; Woźniak, Marcin

    2015-09-01

    Solving channel equalization problem in communication systems is based on adaptive filtering algorithms. Today, Mobile Agents (MAs) with Recurrent Neural Networks (RNNs) can be also adopted for effective interference reduction in modern wireless communication systems (WCSs). In this paper MAs with RNNs are proposed as novel computing algorithms for reducing interferences in WCSs performing an adaptive channel equalization. The method to provide it is so called MAs-RNNs. We perform the implementation of this new paradigm for interferences reduction. Simulations results and evaluations demonstrates the effectiveness of this approach and as better transmission performance in wireless communication network can be achieved by using the MAs-RNNs based adaptive filtering algorithm.

  12. A neural network-based design of an on-off adaptive control for Deep Brain Stimulation in movement disorders.

    PubMed

    Shukla, Pitamber; Basu, Ishita; Graupe, Daniel; Tuninetti, Daniela; Slavin, Konstantin V

    2012-01-01

    The current Food and Drug Administration approved system for the treatment of tremor disorders through Deep Brain Stimulation (DBS) of the area of the brain that controls movement, operates open-loop. It does not automatically adapt to the instantaneous patient's needs or to the progression of the disease. This paper demonstrates an adaptive closed-loop controlled DBS that, after switching off stimulation, tracks few physiological signals to predict the reappearance of tremor before the patient experiences discomfort, at which point it instructs the DBS controller to switch on stimulation again. The core of the proposed approach is a Neural Network (NN) which effectively extracts tremor predictive information from non-invasively recorded surface-electromyogram(sEMG) and accelerometer signals measured at the symptomatic extremities. A simple feed-forward back-propagation NN architecture is shown to successfully predict tremor in 31 out of 33 trials in two Parkinson's Disease patients with an overall accuracy of 75.8% and sensitivity of 92.3%. This work therefore shows that closed-loop DBS control is feasible in the near future and that it can be achieved without modifications of the electrodes implanted in the brain, i.e., is backward compatible with approved DBS systems. PMID:23366839

  13. Verification and Validation of Adaptive and Intelligent Systems with Flight Test Results

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Larson, Richard R.

    2009-01-01

    F-15 IFCS project goals are: a) Demonstrate Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions [A] & [B] failures. b) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs with a Pilot in the Loop. Gen II objectives include; a) Implement and Fly a Direct Adaptive Neural Network Based Flight Controller; b) Demonstrate the Ability of the System to Adapt to Simulated System Failures: 1) Suppress Transients Associated with Failure; 2) Re-Establish Sufficient Control and Handling of Vehicle for Safe Recovery. c) Provide Flight Experience for Development of Verification and Validation Processes for Flight Critical Neural Network Software.

  14. Neural network based architectures for aerospace applications

    NASA Technical Reports Server (NTRS)

    Ricart, Richard

    1987-01-01

    A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.

  15. Blur identification by multilayer neural network based on multivalued neurons.

    PubMed

    Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T

    2008-05-01

    A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones. PMID:18467216

  16. Convolutional Neural Network Based dem Super Resolution

    NASA Astrophysics Data System (ADS)

    Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang

    2016-06-01

    DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

  17. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    PubMed

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model. PMID:19964991

  18. Flight Results of the NF-15B Intelligent Flight Control System (IFCS) Aircraft with Adaptation to a Longitudinally Destabilized Plant

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2008-01-01

    Adaptive flight control systems have the potential to be resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. The goal for the adaptive system is to provide an increase in survivability in the event that these extreme changes occur. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane. The adaptive element was incorporated into a dynamic inversion controller with explicit reference model-following. As a test the system was subjected to an abrupt change in plant stability simulating a destabilizing failure. Flight evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to stabilize the vehicle and reestablish good onboard reference model-following. Flight evaluation with the simulated destabilizing failure and adaptation engaged showed improvement in the vehicle stability margins. The convergent properties of this initial system warrant additional improvement since continued maneuvering caused continued adaptation change. Compared to the non-adaptive system the adaptive system provided better closed-loop behavior with improved matching of the onboard reference model. A detailed discussion of the flight results is presented.

  19. Neural Network Based System for Equipment Startup Surveillance

    1996-12-18

    NEBSESS is a system for equipment surveillance and fault detection which relies on a neural-network based means for diagnosing disturbances during startup and for automatically actuating the Sequential Probability Ratio Test (SPRT) as a signal validation means during steady-state operation.

  20. Neural network-based sensor signal accelerator.

    SciTech Connect

    Vogt, M. C.

    2000-10-16

    A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

  1. Neural Network Based Intelligent Sootblowing System

    SciTech Connect

    Mark Rhode

    2005-04-01

    , particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  2. Design development of a neural network-based telemetry monitor

    NASA Technical Reports Server (NTRS)

    Lembeck, Michael F.

    1992-01-01

    This paper identifies the requirements and describes an architectural framework for an artificial neural network-based system that is capable of fulfilling monitoring and control requirements of future aerospace missions. Incorporated into this framework are a newly developed training algorithm and the concept of cooperative network architectures. The feasibility of such an approach is demonstrated for its ability to identify faults in low frequency waveforms.

  3. Adaptive Structures Flight Experiments

    NASA Technical Reports Server (NTRS)

    Martin, Maurice

    1992-01-01

    The topics are presented in viewgraph form and include the following: adaptive structures flight experiments; enhanced resolution using active vibration suppression; Advanced Controls Technology Experiment (ACTEX); ACTEX program status; ACTEX-2; ACTEX-2 program status; modular control patch; STRV-1b Cryocooler Vibration Suppression Experiment; STRV-1b program status; Precision Optical Bench Experiment (PROBE); Clementine Spacecraft Configuration; TECHSAT all-composite spacecraft; Inexpensive Structures and Materials Flight Experiment (INFLEX); and INFLEX program status.

  4. Adaptive structures flight experiments

    NASA Astrophysics Data System (ADS)

    Martin, Maurice

    The topics are presented in viewgraph form and include the following: adaptive structures flight experiments; enhanced resolution using active vibration suppression; Advanced Controls Technology Experiment (ACTEX); ACTEX program status; ACTEX-2; ACTEX-2 program status; modular control patch; STRV-1b Cryocooler Vibration Suppression Experiment; STRV-1b program status; Precision Optical Bench Experiment (PROBE); Clementine Spacecraft Configuration; TECHSAT all-composite spacecraft; Inexpensive Structures and Materials Flight Experiment (INFLEX); and INFLEX program status.

  5. Neural Network Based Montioring and Control of Fluidized Bed.

    SciTech Connect

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

    The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to

  6. Feature Selection for Neural Network Based Stock Prediction

    NASA Astrophysics Data System (ADS)

    Sugunnasil, Prompong; Somhom, Samerkae

    We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.

  7. Optical-Correlator Neural Network Based On Neocognitron

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

    Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

  8. A unified neural-network-based speaker localization technique.

    PubMed

    Arslan, G; Sakarya, F A

    2000-01-01

    Locating and tracking a speaker in real time using microphone arrays is important in many applications such as hands-free video conferencing, speech processing in large rooms, and acoustic echo cancellation. A speaker can be moving from the far field to the near field of the array, or vice versa. Many neural-network-based localization techniques exist, but they are applicable to either far-field or near-field sources, and are computationally intensive for real-time speaker localization applications because of the wide-band nature of the speech. We propose a unified neural-network-based source localization technique, which is simultaneously applicable to wide-band and narrow-band signal sources that are in the far field or near field of a microphone array. The technique exploits a multilayer perceptron feedforward neural network structure and forms the feature vectors by computing the normalized instantaneous cross-power spectrum samples between adjacent pairs of sensors. Simulation results indicate that our technique is able to locate a source with an absolute error of less than 3.5 degrees at a signal-to-noise ratio of 20 dB and a sampling rate of 8000 Hz at each sensor. PMID:18249826

  9. Analog neural network-based helicopter gearbox health monitoring system.

    PubMed

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage. PMID:8550948

  10. A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

    NASA Technical Reports Server (NTRS)

    Guo, T. H.; Musgrave, J.

    1992-01-01

    In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using

  11. Neural network based feed-forward high density associative memory

    NASA Technical Reports Server (NTRS)

    Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

    1987-01-01

    A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

  12. Neural network based analysis for chemical sensor arrays

    SciTech Connect

    Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1995-04-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. In this paper, we examine the effectiveness of using artificial neural networks for real-time data analysis of a sensor array. Analyzing the sensor data in parallel may allow for rapid identification of contaminants in the field without requiring highly selective individual sensors. We use a prototype sensor array which consists of nine tin-oxide Taguchi-type sensors, a temperature sensor, and a humidity sensor. We illustrate that by using neural network based analysis of the sensor data, the selectivity of the sensor array may be significantly improved, especially when some (or all) the sensors are not highly selective.

  13. Adaptive nonlinear flight control

    NASA Astrophysics Data System (ADS)

    Rysdyk, Rolf Theoduor

    1998-08-01

    Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is capable of converting from stable responsive fixed wing flight to unstable sluggish hover in helicopter configuration. It is desirable to provide the pilot with consistency in handling qualities through a conversion from fixed wing flight to hover. The linear model inversion architecture was adapted by providing frequency separation in the command filter and the error-dynamics, while not exiting the actuator modes. This design of the architecture provides for a model following setup with guaranteed performance. This in turn allowed for convenient implementation of guaranteed handling qualities. A rigorous proof of boundedness is presented making use of compact sets and the LaSalle-Yoshizawa theorem. The analysis allows for the addition of the e-modification which guarantees boundedness of the NN weights in the absence of persistent excitation. The controller is demonstrated on the Generic Tiltrotor Simulator of Bell-Textron and NASA Ames R.C. The model inversion implementation is robustified with respect to unmodeled input dynamics, by adding dynamic nonlinear damping. A proof of boundedness of signals in the system is included. The effectiveness of the robustification is also demonstrated on the XV-15 tiltrotor. The SHL Perceptron NN provides a more powerful application, based on the universal approximation property of this type of NN. The SHL NN based architecture is also robustified with the dynamic nonlinear damping. A proof of boundedness extends the SHL NN augmentation with robustness to unmodeled actuator

  14. A Generic Neural Network-Based Tutorial Supervisor for Computer Aided Instruction

    PubMed Central

    Bergeron, B.P.; Morse, A.N.; Greenes, R.A.

    1990-01-01

    When working with review materials in a self-test mode, student involvement is maximized when the problems presented variably fall within, and occasionally slightly beyond, a student's current level of ability or training. Because of the many difficulties associated with developing generic, fully adaptive systems with rule-based expert system technology, we have focused on using neural network technology as a practical, domain-independent means of optimizing the presentation of multimedia educational programs. The pattern classification capabilities of a neural network-based tutorial supervisor, developed as a series of external commands, have been used to successfully mediate the presentation of image-intensive courseware in cardiac pathophysiology. Research issues include identifying how to extend this approach to dynamically-generated courseware content, e.g., graphic simulations, and determining the educational effectiveness of various control algorithms used to assign students to problem sets of different levels of difficulty.

  15. Implementation of an Adaptive Controller System from Concept to Flight Test

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

    2009-01-01

    The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) is used to test and develop these algorithms. Modifications to this airplane include adding canards and changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals include demonstration of revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions and advancement of neural-network-based flight control technology for new aerospace system designs. This report presents an overview of the processes utilized to develop adaptive controller algorithms during a flight-test program, including a description of initial adaptive controller concepts and a discussion of modeling formulation and performance testing. Design finalization led to integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness; these are also discussed.

  16. Implementation of an Adaptive Controller System from Concept to Flight Test

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.; Burken, John J.; Butler, Bradley S.

    2009-01-01

    The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) was used for these algorithms. This airplane has been modified by the addition of canards and by changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals included demonstration of revolutionary control approaches that can efficiently optimize aircraft performance for both normal and failure conditions, and to advance neural-network-based flight control technology for new aerospace systems designs. Before the NF-15B IFCS airplane was certified for flight test, however, certain processes needed to be completed. This paper presents an overview of these processes, including a description of the initial adaptive controller concepts followed by a discussion of modeling formulation and performance testing. Upon design finalization, the next steps are: integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness.

  17. An artificial neural network based matching metric for iris identification

    NASA Astrophysics Data System (ADS)

    Broussard, Randy P.; Kennell, Lauren R.; Ives, Robert W.; Rakvic, Ryan N.

    2008-02-01

    The iris is currently believed to be the most accurate biometric for human identification. The majority of fielded iris identification systems are based on the highly accurate wavelet-based Daugman algorithm. Another promising recognition algorithm by Ives et al uses Directional Energy features to create the iris template. Both algorithms use Hamming distance to compare a new template to a stored database. Hamming distance is an extremely fast computation, but weights all regions of the iris equally. Work from multiple authors has shown that different regions of the iris contain varying levels of discriminatory information. This research evaluates four post-processing similarity metrics for accuracy impacts on the Directional Energy and wavelets based algorithms. Each metric builds on the Hamming distance method in an attempt to use the template information in a more salient manner. A similarity metric extracted from the output stage of a feed-forward multi-layer perceptron artificial neural network demonstrated the most promise. Accuracy tables and ROC curves of tests performed on the publicly available Chinese Academy of Sciences Institute of Automation database show that the neural network based distance achieves greater accuracy than Hamming distance at every operating point, while adding less than one percent computational overhead.

  18. Ordinal regression neural networks based on concentric hyperspheres.

    PubMed

    Gutiérrez, Pedro Antonio; Tiňo, Peter; Hervás-Martínez, César

    2014-11-01

    Threshold models are one of the most common approaches for ordinal regression, based on projecting patterns to the real line and dividing this real line in consecutive intervals, one interval for each class. However, finding such one-dimensional projection can be too harsh an imposition for some datasets. This paper proposes a multidimensional latent space representation with the purpose of relaxing this projection, where the different classes are arranged based on concentric hyperspheres, each class containing the previous classes in the ordinal scale. The proposal is implemented through a neural network model, each dimension being a linear combination of a common set of basis functions. The model is compared to a nominal neural network, a neural network based on the proportional odds model and to other state-of-the-art ordinal regression methods for a total of 12 datasets. The proposed latent space shows an improvement on the two performance metrics considered, and the model based on the three-dimensional latent space obtains competitive performance when compared to the other methods. PMID:25078110

  19. Neural Network Based Intrusion Detection System for Critical Infrastructures

    SciTech Connect

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  20. Flight Test Results from the NF-15B Intelligent Flight Control System (IFCS) Project with Adaptation to a Simulated Stabilator Failure

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.; Williams-Hayes, Peggy S.

    2007-01-01

    Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented.

  1. Flight Test Results from the NF-15B Intelligent Flight Control System (IFCS) Project with Adaptation to a Simulated Stabilator Failure

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.; Williams-Hayes, Peggy S.

    2010-01-01

    Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented.

  2. Neural Network Based Representation of UH-60A Pilot and Hub Accelerations

    NASA Technical Reports Server (NTRS)

    Kottapalli, Sesi

    2000-01-01

    Neural network relationships between the full-scale, experimental hub accelerations and the corresponding pilot floor vertical vibration are studied. The present physics-based, quantitative effort represents an initial systematic study on the UH-60A Black Hawk hub accelerations. The NASA/Army UH-60A Airloads Program flight test database was used. A 'maneuver-effect-factor (MEF)', derived using the roll-angle and the pitch-rate, was used. Three neural network based representation-cases were considered. The pilot floor vertical vibration was considered in the first case and the hub accelerations were separately considered in the second case. The third case considered both the hub accelerations and the pilot floor vertical vibration. Neither the advance ratio nor the gross weight alone could be used to predict the pilot floor vertical vibration. However, the advance ratio and the gross weight together could be used to predict the pilot floor vertical vibration over the entire flight envelope. The hub accelerations data were modeled and found to be of very acceptable quality. The hub accelerations alone could not be used to predict the pilot floor vertical vibration. Thus, the hub accelerations alone do not drive the pilot floor vertical vibration. However, the hub accelerations, along with either the advance ratio or the gross weight or both, could be used to satisfactorily predict the pilot floor vertical vibration. The hub accelerations are clearly a factor in determining the pilot floor vertical vibration.

  3. A neural network-based exploratory learning and motor planning system for co-robots.

    PubMed

    Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object. PMID:26257640

  4. A neural network-based exploratory learning and motor planning system for co-robots

    PubMed Central

    Galbraith, Byron V.; Guenther, Frank H.; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object. PMID:26257640

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

    NASA Technical Reports Server (NTRS)

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

  6. Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

    NASA Astrophysics Data System (ADS)

    Zhang, Li

    With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman

  7. A neural network-based optimization algorithm for the weapon-target assignment problem

    SciTech Connect

    Wacholder, E.

    1989-02-01

    A neural network-based algorithm was developed for the Weapon-Target Assignment Problem (WTAP) in Ballistic Missile Defense (BMD). An optimal assignment policy is one which allocates targets to weapon platforms such that the total expected leakage value of targets surviving the defense is minimized. This involves the minimization of a non-linear objective function subject to inequality constraints specifying the maximum number of interceptors available to each platform and the maximum number of interceptors allowed to be fired at each target as imposed by the Battle Management/Command Control and Communications (BM/C/sup 3/) system. The algorithm consists of solving a system of ODEs trajectories and variables. Simulations of the algorithm on PC and VAX computers were carried out using a simple numerical scheme. In all the battle instances tested, the algorithm has proven to be stable and to converge to solutions very close to global optima. The time to achieve convergence was consistently less than the time constant of the network's processing elements (neurons). This implies that fast solutions can be realized if the algorithm is implemented in hardware circuits. Three series of battle scenarios are analyzed and discussed in this report. Input data and results are presented in detail. The main advantage of this algorithm is that it can be adapted to either a special-purpose hardware circuit or a general-purpose concurrent machine to yield fast and accurate solutions to difficult decision problems. 40 refs., 8 figs., 8 tabs.

  8. Parallel implementation of high-speed, phase diverse atmospheric turbulence compensation method on a neural network-based architecture

    NASA Astrophysics Data System (ADS)

    Arrasmith, William W.; Sullivan, Sean F.

    2008-04-01

    Phase diversity imaging methods work well in removing atmospheric turbulence and some system effects from predominantly near-field imaging systems. However, phase diversity approaches can be computationally intensive and slow. We present a recently adapted, high-speed phase diversity method using a conventional, software-based neural network paradigm. This phase-diversity method has the advantage of eliminating many time consuming, computationally heavy calculations and directly estimates the optical transfer function from the entrance pupil phases or phase differences. Additionally, this method is more accurate than conventional Zernike-based, phase diversity approaches and lends itself to implementation on parallel software or hardware architectures. We use computer simulation to demonstrate how this high-speed, phase diverse imaging method can be implemented on a parallel, highspeed, neural network-based architecture-specifically the Cellular Neural Network (CNN). The CNN architecture was chosen as a representative, neural network-based processing environment because 1) the CNN can be implemented in 2-D or 3-D processing schemes, 2) it can be implemented in hardware or software, 3) recent 2-D implementations of CNN technology have shown a 3 orders of magnitude superiority in speed, area, or power over equivalent digital representations, and 4) a complete development environment exists. We also provide a short discussion on processing speed.

  9. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    PubMed

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. PMID:25913233

  10. Dynamic neural networks based on-line identification and control of high performance motor drives

    NASA Technical Reports Server (NTRS)

    Rubaai, Ahmed; Kotaru, Raj

    1995-01-01

    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  11. Flight control design using a blend of modern nonlinear adaptive and robust techniques

    NASA Astrophysics Data System (ADS)

    Yang, Xiaolong

    In this dissertation, the modern control techniques of feedback linearization, mu synthesis, and neural network based adaptation are used to design novel control laws for two specific applications: F/A-18 flight control and reusable launch vehicle (an X-33 derivative) entry guidance. For both applications, the performance of the controllers is assessed. As a part of a NASA Dryden program to develop and flight test experimental controllers for an F/A-18 aircraft, a novel method of combining mu synthesis and feedback linearization is developed to design longitudinal and lateral-directional controllers. First of all, the open-loop and closed-loop dynamics of F/A-18 are investigated. The production F/A-18 controller as well as the control distribution mechanism are studied. The open-loop and closed-loop handling qualities of the F/A-18 are evaluated using low order transfer functions. Based on this information, a blend of robust mu synthesis and feedback linearization is used to design controllers for a low dynamic pressure envelope of flight conditions. For both the longitudinal and the lateral-directional axes, a robust linear controller is designed for a trim point in the center of the envelope. Then by including terms to cancel kinematic nonlinearities and variations in the aerodynamic forces and moments over the flight envelope, a complete nonlinear controller is developed. In addition, to compensate for the model uncertainty, linearization error and variations between operating points, neural network based adaptation is added to the designed longitudinal controller. The nonlinear simulations, robustness and handling qualities analysis indicate that the performance is similar to or better than that for the production F/A-18 controllers. When the dynamic pressure is very low, the performance of both the experimental and the production flight controllers is degraded, but Level I handling qualities are still achieved. A new generation of Reusable Launch Vehicles

  12. Neural Networks Based Approach to Enhance Space Hardware Reliability

    NASA Technical Reports Server (NTRS)

    Zebulum, Ricardo S.; Thakoor, Anilkumar; Lu, Thomas; Franco, Lauro; Lin, Tsung Han; McClure, S. S.

    2011-01-01

    This paper demonstrates the use of Neural Networks as a device modeling tool to increase the reliability analysis accuracy of circuits targeted for space applications. The paper tackles a number of case studies of relevance to the design of Flight hardware. The results show that the proposed technique generates more accurate models than the ones regularly used to model circuits.

  13. Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids

    NASA Astrophysics Data System (ADS)

    Cuadra, Lucas; Alexandre, Enrique; Gil-Pita, Roberto; Vicen-Bueno, Raúl; Álvarez, Lorena

    2009-12-01

    Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients), and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.

  14. Space flight and neurovestibular adaptation

    NASA Technical Reports Server (NTRS)

    Reschke, M. F.; Bloomberg, J. J.; Harm, D. L.; Paloski, W. H.

    1994-01-01

    Space flight represents a form of sensory stimulus rearrangement requiring modification of established terrestrial response patterns through central reinterpretation. Evidence of sensory reinterpretation is manifested as postflight modifications of eye/head coordination, locomotor patterns, postural control strategies, and illusory perceptions of self or surround motion in conjunction with head movements. Under normal preflight conditions, the head is stabilized during locomotion, but immediately postflight reduced head stability, coupled with inappropriate eye/head coordination, results in modifications of gait. Postflight postural control exhibits increased dependence on vision which compensates for inappropriate interpretation of otolith and proprioceptive inputs. Eye movements compensatory for perceived self motion, rather than actual head movements have been observed postflight. Overall, the in-flight adaptive modification of head stabilization strategies, changes in head/eye coordination, illusionary motion, and postural control are maladaptive for a return to the terrestrial environment.

  15. Boosting feature selection for Neural Network based regression.

    PubMed

    Bailly, Kevin; Milgram, Maurice

    2009-01-01

    The head pose estimation problem is well known to be a challenging task in computer vision and is a useful tool for several applications involving human-computer interaction. This problem can be stated as a regression one where the input is an image and the output is pan and tilt angles. Finding the optimal regression is a hard problem because of the high dimensionality of the input (number of image pixels) and the large variety of morphologies and illumination. We propose a new method combining a boosting strategy for feature selection and a neural network for the regression. Potential features are a very large set of Haar-like wavelets which are well known to be adapted to face image processing. To achieve the feature selection, a new Fuzzy Functional Criterion (FFC) is introduced which is able to evaluate the link between a feature and the output without any estimation of the joint probability density function as in the Mutual Information. The boosting strategy uses this criterion at each step: features are evaluated by the FFC using weights on examples computed from the error produced by the neural network trained at the previous step. Tests are carried out on the commonly used Pointing 04 database and compared with three state-of-the-art methods. We also evaluate the accuracy of the estimation on FacePix, a database with a high angular resolution. Our method is compared positively to a Convolutional Neural Network, which is well known to incorporate feature extraction in its first layers. PMID:19616404

  16. Neural network based optimal control of HVAC&R systems

    NASA Astrophysics Data System (ADS)

    Ning, Min

    supervisory controller, a set of five adaptive PI (proportional-integral) controllers are designed for each of the five local control loops of the HVAC&R system. The five controllers are used to track optimal set points and zone air temperature set points. Parameters of these PI controllers are tuned online to reduce tracking errors. The updating rules are derived from Lyapunov stability analysis. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions.

  17. Flight Approach to Adaptive Control Research

    NASA Technical Reports Server (NTRS)

    Pavlock, Kate Maureen; Less, James L.; Larson, David Nils

    2011-01-01

    The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The testbed served as a full-scale vehicle to test and validate adaptive flight control research addressing technical challenges involved with reducing risk to enable safe flight in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.

  18. Flight Test Approach to Adaptive Control Research

    NASA Technical Reports Server (NTRS)

    Pavlock, Kate Maureen; Less, James L.; Larson, David Nils

    2011-01-01

    The National Aeronautics and Space Administration s Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The validation of adaptive controls has the potential to enhance safety in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.

  19. Evaluating the performances of statistical and neural network based control charts

    NASA Astrophysics Data System (ADS)

    Teoh, Kok Ban; Ong, Hong Choon

    2015-10-01

    Control chart is used widely in many fields and traditional control chart is no longer adequate in detecting a sudden change in a particular process. So, run rules which are built in into Shewhart X ¯ control chart while Exponential Weighted Moving Average control chart (EWMA), Cumulative Sum control chart (CUSUM) and neural network based control chart are introduced to overcome the limitation regarding to the sensitivity of traditional control chart. In this study, the average run length (ARL) and median run length (MRL) in the shifts in the process mean of control charts mentioned will be computed. We will show that interpretations based only on the ARL can be misleading. Thus, MRL is also used to evaluate the performances of the control charts. From this study, neural network based control chart is found to possess a better performance than run rules of Shewhart X ¯ control chart, EWMA and CUSUM control chart.

  20. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    SciTech Connect

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  1. Improved Crack Type Classification Neural Network based on Square Sub-images of Pavement Surface

    NASA Astrophysics Data System (ADS)

    Lee, Byoung Jik; Lee, Hosin “David”

    The previous neural network based on the proximity values was developed using rectangular pavement images. However, the proximity value derived from the rectangular image was biased towards transverse cracking. By sectioning the rectangular image into a set of square sub-images, the neural network based on the proximity value became more robust and consistent in determining a crack type. This paper presents an improved neural network to determine a crack type from a pavement surface image based on square sub-images over the neural network trained using rectangular pavement images. The advantage of using square sub-image is demonstrated by using sample images of transverse cracking, longitudinal cracking and alligator cracking.

  2. Artificial neural network based on SQUIDs: demonstration of network training and operation

    NASA Astrophysics Data System (ADS)

    Chiarello, F.; Carelli, P.; Castellano, M. G.; Torrioli, G.

    2013-12-01

    We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on.

  3. Neural network-based combustion optimization reduces NOx emissions while improving performance

    SciTech Connect

    Booth, R.C.; Roland, W.B.

    1998-07-01

    This paper presents the benefits of applying an on-line, real-time neural network to several bituminous coal fired utility boilers. The system helps reduce NOx emissions up to 60%, meeting compliance while it improves heat rate up to 2% overall (5% at low load) and reduces LOI as much as 30% through combustion optimization alone. The system can avoid or postpone large capital expenditures for low NOx burners, overfire air boiler modifications, SCRs, and SNCRs. The neural network-based system has been applied on 11 electric utility boilers that represent a wide range of furnace and burner types including units with tangential-, cell-, single wall-, and opposed wall-burner arrangements that have ranged in capacity from 146 to 800 MW in an advisory mode. Several sites have employed the neural network-based system for closed-loop supervisory combustion control. Boiler combustion profiles change continuously due to coal quality, boiler loading, changes in slag/soot deposits, ambient conditions, and the condition of plant equipment. Through on-line retraining, the neural network-based system optimizes the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to reduce NO{sub x} emissions and improve heat rate simultaneously.

  4. Neural network-based combustion optimization reduces NOx emissions while improving performance

    SciTech Connect

    Booth, R.C.; Roland, W.B. Jr.

    1998-12-31

    The NeuSIGHT neural network based system has been applied to units with tangential-, cell-, single wall-, and opposed wall-burner arrangements that have ranged in capacity from 146 to 800 MW in an advisory mode. Several sites have employed the neural network-based system for closed-loop supervisory combustion control. Boiler combustion profiles change continuously due to coal quality, boiler loading, changes in slag/soot deposits, ambient conditions, and the condition of plant equipment. Through on-line retraining, the neural network-based system optimizes the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to reduce NO{sub x} emissions and improve heat rate simultaneously. This paper presents the benefits of applying an on-line, real-time neural network to several commercially operating bituminous coal fired utility boilers. The system helps reduce NO{sub x} emissions up to 60%, meeting compliance while it improves heat rate up to 2% overall (5% at low load) and reduces LOI as much as 30% through combustion optimization alone. The system can avoid or postpone large capital expenditures for low NO{sub x} burners, overfire air boiler modifications, SCRs, and SNCRs.

  5. Hybrid Adaptive Flight Control with Model Inversion Adaptation

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan

    2011-01-01

    This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

  6. Physiological adaptation to space flight

    NASA Technical Reports Server (NTRS)

    Nicogossian, Arnauld E.; Sulzman, Frank M.; Gaiser, Karen K.; Teeter, Ronald C.

    1990-01-01

    In space, adaptive physiological changes have been observed in virtually all body systems, but how far these changes progress with time is not known. Their time course demonstrates variable patterns; some systems show evidence of gradual and progressive change. Biomedical postflight data have shown that a compensatory period of readaptation to one gravity is required after space flight, with longer intervals required for longer missions. Consistent readaptation trends include orthostatic intolerance and neurovestibular difficulties. For the long-duration missions of the exploration era, it is critical to determine the extent to which deleterious changes (e.g., bone loss and possible immunological changes) can be reversed upon return to earth. Radiation protection is another critical enabling element for missions beyond low earth orbit. Radiation exposure guidelines have not been established for exploration missions. Currently our experience is insufficient to prescribe countermeasures for the stay times associated with a lunar base or a mission to Mars. Artificial gravity may provide a solution, but the level and duration of exposure necessary to prevent deconditioning must be determined. Central issues for medical care in remote settings are preventive, diagnostic, and therapeutic care and the minimization of risk.

  7. Rotationally Adaptive Flight Test Surface

    NASA Technical Reports Server (NTRS)

    Barrett, Ron

    1999-01-01

    Research on a new design of flutter exciter vane using adaptive materials was conducted. This novel design is based on all-moving aerodynamic surface technology and consists of a structurally stiff main spar, a series of piezoelectric actuator elements and an aerodynamic shell which is pivoted around the main spar. The work was built upon the current missile-type all-moving surface designs and change them so they are better suited for flutter excitation through the transonic flight regime. The first portion of research will be centered on aerodynamic and structural modeling of the system. USAF DatCom and vortex lattice codes was used to capture the fundamental aerodynamics of the vane. Finite element codes and laminated plate theory and virtual work analyses will be used to structurally model the aerodynamic vane and wing tip. Following the basic modeling, a flutter test vane was designed. Each component within the structure was designed to meet the design loads. After the design loads are met, then the deflections will be maximized and the internal structure will be laid out. In addition to the structure, a basic electrical control network will be designed which will be capable of driving a scaled exciter vane. The third and final stage of main investigation involved the fabrication of a 1/4 scale vane. This scaled vane was used to verify kinematics and structural mechanics theories on all-moving actuation. Following assembly, a series of bench tests was conducted to determine frequency response, electrical characteristics, mechanical and kinematic properties. Test results indicate peak-to-peak deflections of 1.1 deg with a corner frequency of just over 130 Hz.

  8. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    NASA Technical Reports Server (NTRS)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  9. Research in digital adaptive flight controllers

    NASA Technical Reports Server (NTRS)

    Kaufman, H.

    1976-01-01

    A design study of adaptive control logic suitable for implementation in modern airborne digital flight computers was conducted. Both explicit controllers which directly utilize parameter identification and implicit controllers which do not require identification were considered. Extensive analytical and simulation efforts resulted in the recommendation of two explicit digital adaptive flight controllers. Interface weighted least squares estimation procedures with control logic were developed using either optimal regulator theory or with control logic based upon single stage performance indices.

  10. Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Kumar, Anikender; Goyal, P.

    2013-04-01

    Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day's AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000-2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons.

  11. Adaptive Flight Control Research at NASA

    NASA Technical Reports Server (NTRS)

    Motter, Mark A.

    2008-01-01

    A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing.

  12. Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots

    NASA Astrophysics Data System (ADS)

    Altaisky, Mikhail V.; Zolnikova, Nadezhda N.; Kaputkina, Natalia E.; Krylov, Victor A.; Lozovik, Yurii E.; Dattani, Nikesh S.

    2016-02-01

    We present the results of the simulation of a quantum neural network based on quantum dots using numerical method of path integral calculation. In the proposed implementation of the quantum neural network using an array of single-electron quantum dots with dipole-dipole interaction, the coherence is shown to survive up to 0.1 nanosecond in time and up to the liquid nitrogen temperature of 77K.We study the quantum correlations between the quantum dots by means of calculation of the entanglement of formation in a pair of quantum dots on the GaAs based substrate with dot size of 100 ÷ 101 nanometer and interdot distance of 101 ÷ 102 nanometers order.

  13. Back propagation neural network based control for the heating system of a polysilicon reduction furnace

    NASA Astrophysics Data System (ADS)

    Cheng, Yuhua; Chen, Kai; Bai, Libing; Dai, Meizhi

    2013-12-01

    In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal Id, which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal Id the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal Id and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results.

  14. Evaluation of neural network based real time maximum power tracking controller for PV system

    SciTech Connect

    Hiyama, Takashi; Kouzuma, Shinichi; Imakubo, Tomofumi; Ortmeyer, T.H.

    1995-09-01

    This paper presents a neural network based maximum power tracking controller for interconnected PV systems to commercial power sources. The neural network is utilized to identify the optimal operating voltage of the PV system. The controller generates the control signal in real time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV system to the identified optimal one, which yields the maximum power generation. The controller is a PI type one. The proportion an the integral gains are set to their optimal values to achieve the fast response and also to prevent the overshoot and also the undershoot. The continuous measurement is required for the open circuit voltage on the monitoring cell, and also for the terminal voltage of the PV system. Because of the accurate identification of the optimal operating voltage of the PV system, more than 99% power is drawn for the actual maximum power.

  15. Evolution of an artificial neural network based autonomous land vehicle controller.

    PubMed

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks. PMID:18263046

  16. Neural network-based control for the fiber placement composite manufacturing process

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, P. F.

    1993-10-01

    At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine.

  17. Estimation of tool wear during CNC milling using neural network-based sensor fusion

    NASA Astrophysics Data System (ADS)

    Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

    2007-01-01

    Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

  18. A neural network-based power system stabilizer using power flow characteristics

    SciTech Connect

    Park, Y.M.; Choi, M.S.; Lee, K.Y.

    1996-06-01

    A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.

  19. Real-time neural network based camera localization and its extension to mobile robot control.

    PubMed

    Choi, D H; Oh, S Y

    1997-06-01

    The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models. PMID:9427102

  20. Generic mesoscopic neural networks based on statistical mechanics of neocortical interactions

    NASA Astrophysics Data System (ADS)

    Ingber, Lester

    1992-02-01

    A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions, demonstrating its capability in describing large-scale properties of short-term memory and electroencephalographic systematics. This methodology also defines an algorithm to construct a mesoscopic neural network, based on realistic neocortical processes and parameters, to record patterns of brain activity and to compute the evolution of this system. Furthermore, this algorithm is quite generic and can be used to similarly process information in other systems, especially, but not limited to, those amenable to modeling by mathematical physics techniques alternatively described by path-integral Lagrangians, Fokker-Planck equations, or Langevin rate equations. This methodology is made possible and practical by a confluence of techniques drawn from SMNI itself, modern methods of functional stochastic calculus defining nonlinear Lagrangians, very fast simulated reannealing, and parallel-processing computation.

  1. Adaptive Flight Control for Aircraft Safety Enhancements

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan T.; Gregory, Irene M.; Joshi, Suresh M.

    2008-01-01

    This poster presents the current adaptive control research being conducted at NASA ARC and LaRC in support of the Integrated Resilient Aircraft Control (IRAC) project. The technique "Approximate Stability Margin Analysis of Hybrid Direct-Indirect Adaptive Control" has been developed at NASA ARC to address the needs for stability margin metrics for adaptive control that potentially enables future V&V of adaptive systems. The technique "Direct Adaptive Control With Unknown Actuator Failures" is developed at NASA LaRC to deal with unknown actuator failures. The technique "Adaptive Control with Adaptive Pilot Element" is being researched at NASA LaRC to investigate the effects of pilot interactions with adaptive flight control that can have implications of stability and performance.

  2. Development and comparison of neural network based soft sensors for online estimation of cement clinker quality.

    PubMed

    Pani, Ajaya Kumar; Vadlamudi, Vamsi Krishna; Mohanta, Hare Krishna

    2013-01-01

    The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models. PMID:22940135

  3. A neural network based artificial vision system for licence plate recognition.

    PubMed

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%. PMID:9228583

  4. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids. PMID:25532191

  5. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  6. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    PubMed

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking. PMID:24808521

  7. Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

    NASA Astrophysics Data System (ADS)

    Zhao, Lin; Jia, Yingmin

    2016-06-01

    In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

  8. Neural-network-based adaptive UPFC for improving transient stability performance of power system.

    PubMed

    Mishra, Sukumar

    2006-03-01

    This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system. PMID:16566472

  9. Unconventional optical imaging using a high-speed neural network based smart sensor

    NASA Astrophysics Data System (ADS)

    Arrasmith, William W.

    2006-05-01

    The advancement of neural network methods and technologies is finding applications in many fields and disciplines of interest to the defense, intelligence, and homeland security communities. Rapidly reconfigurable sensors for real or near-real time signal or image processing can be used for multi-functional purposes such as image compression, target tracking, image fusion, edge detection, thresholding, pattern recognition, and atmospheric turbulence compensation to name a few. A neural network based smart sensor is described that can accomplish these tasks individually or in combination, in real-time or near real-time. As a computationally intensive example, the case of optical imaging through volume turbulence is addressed. For imaging systems in the visible and near infrared part of the electromagnetic spectrum, the atmosphere is often the dominant factor in reducing the imaging system's resolution and image quality. The neural network approach described in this paper is shown to present a viable means for implementing turbulence compensation techniques for near-field and distributed turbulence scenarios. Representative high-speed neural network hardware is presented. Existing 2-D cellular neural network (CNN) hardware is capable of 3 trillion operations per second with peta-operations per second possible using current 3-D manufacturing processes. This hardware can be used for high-speed applications that require fast convolutions and de-convolutions. Existing 3-D artificial neural network technology is capable of peta-operations per second and can be used for fast array processing operations. Methods for optical imaging through distributed turbulence are discussed, simulation results are presented and computational and performance assessments are provided.

  10. Neural network based supervisory & closed loop controls for NOx emission reductions and heat rate improvement

    SciTech Connect

    Radl, B.J.; Corfman, D.; Kish, B.

    1995-12-31

    This paper discusses the operational experience gained from installing a neural network based supervisor setpoint control system for selected combustion parameters at Penn Power`s New Castle station. The primary goal of the program is to reduce NOx emissions while maintaining or improving heat rate. The program was jointly funded by Ohio Edison, U.S. Department of Energy (DOE) and Pegasus Technologies Corp. The target power station, Penn Power`s New Castle Unit 5, is a 1950`s vintage Babcock & Wilcox wall fired furnace with gross generation capacity of 150 MW. Before installation of the neural network system (NeuSIGHT), NOx averaged 0.75 to 0.80 lbs/mbtu at full load conditions. Previous testing reduced this from 1.0 lbs/mbtu under normal operating conditions. To meet the new Pennsylvania DER limits, which set an absolute tonnage limit on NOx, and operate for a full year, a further NOx reduction of 20% was required. The control system setup interfaced a Unix workstation to a Bailey Controls N90 DCS. The neural network and data collection/processing system resided on the workstation. New setpoints were determined by the neural network periodically. These setpoints were constrained within existing control system limits. The objective was to model the multi-dimensional and non-linear problem of NOx formation in the furnace with a neural network. Once modeled the neural network performed many {open_quote}what if{close_quote} simulations to optimize setpoints for the current operating conditions. To keep up with changes in operating conditions the neural network was set to continually learn from the most recent set of measurements. Conditioning algorithms for the input data and output setpoints were developed to handle the inherently {open_quote}noisy{close_quote} input data and to provide stable output recommendations. Test results and parameters used for combustion optimization are summarized in this paper.

  11. Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    2004-01-01

    A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation

  12. Adaptive Control of Truss Structures for Gossamer Spacecraft

    NASA Technical Reports Server (NTRS)

    Yang Bong-Jun; Calise, anthony J.; Craig, James I.; Whorton, Mark S.

    2007-01-01

    Neural network-based adaptive control is considered for active control of a highly flexible truss structure which may be used to support solar sail membranes. The objective is to suppress unwanted vibrations in SAFE (Solar Array Flight Experiment) boom, a test-bed located at NASA. Compared to previous tests that restrained truss structures in planar motion, full three dimensional motions are tested. Experimental results illustrate the potential of adaptive control in compensating for nonlinear actuation and modeling error, and in rejecting external disturbances.

  13. L(sub 1) Adaptive Flight Control System: Flight Evaluation and Technology Transition

    NASA Technical Reports Server (NTRS)

    Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Isaac; Gregory, Irene M.; Cao, Chengyu

    2010-01-01

    Certification of adaptive control technologies for both manned and unmanned aircraft represent a major challenge for current Verification and Validation techniques. A (missing) key step towards flight certification of adaptive flight control systems is the definition and development of analysis tools and methods to support Verification and Validation for nonlinear systems, similar to the procedures currently used for linear systems. In this paper, we describe and demonstrate the advantages of L(sub l) adaptive control architectures for closing some of the gaps in certification of adaptive flight control systems, which may facilitate the transition of adaptive control into military and commercial aerospace applications. As illustrative examples, we present the results of a piloted simulation evaluation on the NASA AirSTAR flight test vehicle, and results of an extensive flight test program conducted by the Naval Postgraduate School to demonstrate the advantages of L(sub l) adaptive control as a verifiable robust adaptive flight control system.

  14. F-8C adaptive flight control laws

    NASA Technical Reports Server (NTRS)

    Hartmann, G. L.; Harvey, C. A.; Stein, G.; Carlson, D. N.; Hendrick, R. C.

    1977-01-01

    Three candidate digital adaptive control laws were designed for NASA's F-8C digital flyby wire aircraft. Each design used the same control laws but adjusted the gains with a different adaptative algorithm. The three adaptive concepts were: high-gain limit cycle, Liapunov-stable model tracking, and maximum likelihood estimation. Sensors were restricted to conventional inertial instruments (rate gyros and accelerometers) without use of air-data measurements. Performance, growth potential, and computer requirements were used as criteria for selecting the most promising of these candidates for further refinement. The maximum likelihood concept was selected primarily because it offers the greatest potential for identifying several aircraft parameters and hence for improved control performance in future aircraft application. In terms of identification and gain adjustment accuracy, the MLE design is slightly superior to the other two, but this has no significant effects on the control performance achievable with the F-8C aircraft. The maximum likelihood design is recommended for flight test, and several refinements to that design are proposed.

  15. Inverse dynamical photon scattering (IDPS): an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy.

    PubMed

    Jiang, Xiaoming; Van den Broek, Wouter; Koch, Christoph T

    2016-04-01

    Inverse dynamical photon scattering (IDPS), an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy, is introduced. Because the inverse problem entails numerical minimization of an explicit error metric, it becomes possible to freely choose a more robust metric, to introduce regularization of the solution, and to retrieve unknown experimental settings or microscope values, while the starting guess is simply set to zero. The regularization is accomplished through an alternate directions augmented Lagrangian approach, implemented on a graphics processing unit. These improvements are demonstrated on open source experimental data, retrieving three-dimensional amplitude and phase for a thick specimen. PMID:27136994

  16. A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling.

    PubMed

    Cheng, Shuiyuan; Li, Li; Chen, Dongsheng; Li, Jianbing

    2012-12-15

    A neural network based ensemble methodology was presented in this study to improve the accuracy of meteorological input fields for regional air quality modeling. Through nonlinear integration of simulation results from two meteorological models (MM5 and WRF), the ensemble approach focused on the optimization of meteorological variable values (temperature, surface air pressure, and wind field) in the vertical layer near ground. To illustrate the proposed approach, a case study in northern China during two selected air pollution events, in 2006, was conducted. The performances of the MM5, the WRF, and the ensemble approach were assessed using different statistical measures. The results indicated that the ensemble approach had a higher simulation accuracy than the MM5 and the WRF model. Performance was improved by more than 12.9% for temperature, 18.7% for surface air pressure field, and 17.7% for wind field. The atmospheric PM(10) concentrations in the study region were also simulated by coupling the air quality model CMAQ with the MM5 model, the WRF model, and the ensemble model. It was found that the modeling accuracy of the ensemble-CMAQ model was improved by more than 7.0% and 17.8% when compared to the MM5-CMAQ and the WRF-CMAQ models, respectively. The proposed neural network based meteorological modeling approach holds great potential for improving the performance of regional air quality modeling. PMID:23000477

  17. HIDEC F-15 adaptive engine control system flight test results

    NASA Technical Reports Server (NTRS)

    Smolka, James W.

    1987-01-01

    NASA-Ames' Highly Integrated Digital Electronic Control (HIDEC) flight test program aims to develop fully integrated airframe, propulsion, and flight control systems. The HIDEC F-15 adaptive engine control system flight test program has demonstrated that significant performance improvements are obtainable through the retention of stall-free engine operation throughout the aircraft flight and maneuver envelopes. The greatest thrust increase was projected for the medium-to-high altitude flight regime at subsonic speed which is of such importance to air combat. Adaptive engine control systems such as the HIDEC F-15's can be used to upgrade the performance of existing aircraft without resort to expensive reengining programs.

  18. Full-Scale Flight Research Testbeds: Adaptive and Intelligent Control

    NASA Technical Reports Server (NTRS)

    Pahle, Joe W.

    2008-01-01

    This viewgraph presentation describes the adaptive and intelligent control methods used for aircraft survival. The contents include: 1) Motivation for Adaptive Control; 2) Integrated Resilient Aircraft Control Project; 3) Full-scale Flight Assets in Use for IRAC; 4) NASA NF-15B Tail Number 837; 5) Gen II Direct Adaptive Control Architecture; 6) Limited Authority System; and 7) 837 Flight Experiments. A simulated destabilization failure analysis along with experience and lessons learned are also presented.

  19. Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

    NASA Technical Reports Server (NTRS)

    Gupta, Pramod; Loparo, Kenneth; Mackall, Dale; Schumann, Johann; Soares, Fola

    2004-01-01

    Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

  20. Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites

    NASA Astrophysics Data System (ADS)

    Valdes, A.; Khorasani, K.

    The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed "High Level" FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the "High Level" FDI scheme and the DNN-based "Low Level" FDI scheme developed earlier by the authors, an "Integrated" DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.

  1. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    PubMed

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. PMID:22738782

  2. Data systems and computer science: Neural networks base R/T program overview

    NASA Technical Reports Server (NTRS)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  3. Control Reallocation Strategies for Damage Adaptation in Transport Class Aircraft

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen; Krishnakumar, K.; Limes, Greg; Bryant, Don

    2003-01-01

    This paper examines the feasibility, potential benefits and implementation issues associated with retrofitting a neural-adaptive flight control system (NFCS) to existing transport aircraft, including both cable/hydraulic and fly-by-wire configurations. NFCS uses a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and to make the controller easy to apply when implemented on different aircraft. Full-motion piloted simulation studies were performed on two different transport models: the Boeing 747-400 and the Boeing C-17. Subjects included NASA, Air Force and commercial airline pilots. Results demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.

  4. Neural Network Based Modeling and Analysis of LP Control Surface Allocation

    NASA Technical Reports Server (NTRS)

    Langari, Reza; Krishnakumar, Kalmanje; Gundy-Burlet, Karen

    2003-01-01

    This paper presents an approach to interpretive modeling of LP based control allocation in intelligent flight control. The emphasis is placed on a nonlinear interpretation of the LP allocation process as a static map to support analytical study of the resulting closed loop system, albeit in approximate form. The approach makes use of a bi-layer neural network to capture the essential functioning of the LP allocation process. It is further shown via Lyapunov based analysis that under certain relatively mild conditions the resulting closed loop system is stable. Some preliminary conclusions from a study at Ames are stated and directions for further research are given at the conclusion of the paper.

  5. Cellular neural network-based hybrid approach toward automatic image registration

    NASA Astrophysics Data System (ADS)

    Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

    2013-01-01

    Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

  6. Closing the Certification Gaps in Adaptive Flight Control Software

    NASA Technical Reports Server (NTRS)

    Jacklin, Stephen A.

    2008-01-01

    Over the last five decades, extensive research has been performed to design and develop adaptive control systems for aerospace systems and other applications where the capability to change controller behavior at different operating conditions is highly desirable. Although adaptive flight control has been partially implemented through the use of gain-scheduled control, truly adaptive control systems using learning algorithms and on-line system identification methods have not seen commercial deployment. The reason is that the certification process for adaptive flight control software for use in national air space has not yet been decided. The purpose of this paper is to examine the gaps between the state-of-the-art methodologies used to certify conventional (i.e., non-adaptive) flight control system software and what will likely to be needed to satisfy FAA airworthiness requirements. These gaps include the lack of a certification plan or process guide, the need to develop verification and validation tools and methodologies to analyze adaptive controller stability and convergence, as well as the development of metrics to evaluate adaptive controller performance at off-nominal flight conditions. This paper presents the major certification gap areas, a description of the current state of the verification methodologies, and what further research efforts will likely be needed to close the gaps remaining in current certification practices. It is envisioned that closing the gap will require certain advances in simulation methods, comprehensive methods to determine learning algorithm stability and convergence rates, the development of performance metrics for adaptive controllers, the application of formal software assurance methods, the application of on-line software monitoring tools for adaptive controller health assessment, and the development of a certification case for adaptive system safety of flight.

  7. Neural Network-Based Control of Networked Trilateral Teleoperation With Geometrically Unknown Constraints.

    PubMed

    Li, Zhijun; Xia, Yuanqing; Wang, Dehong; Zhai, Di-Hua; Su, Chun-Yi; Zhao, Xingang

    2016-05-01

    Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach. PMID:25956001

  8. Hybrid adaptive ascent flight control for a flexible launch vehicle

    NASA Astrophysics Data System (ADS)

    Lefevre, Brian D.

    For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the

  9. Neural network-based visual body weight estimation for drug dosage finding

    NASA Astrophysics Data System (ADS)

    Pfitzner, Christian; May, Stefan; Nüchter, Andreas

    2016-03-01

    Body weight adapted drug dosages are important for emergency treatments: Inaccuracies in body weight estimation may lead to inaccurate drug dosing. This paper describes an improved approach to estimating the body weight of emergency patients in a trauma room, based on images from an RGB-D and a thermal camera. The improvements are specific to several aspects: Fusion of RGB-D and thermal camera eases filtering and segmentation of the patient's body from the background. Robustness and accuracy is gained by an artificial neural network, which considers geometric features from the sensors as input, e.g. the patient's volume, and shape parameters. Preliminary experiments with 69 patients show an accuracy close to 90 percent, with less than 10 percent relative error and the results are compared with the physician's estimate, the patient's statement and an established anthropometric method.

  10. A fast and scalable recurrent neural network based on stochastic meta descent.

    PubMed

    Liu, Zhenzhen; Elhanany, Itamar

    2008-09-01

    This brief presents an efficient and scalable online learning algorithm for recurrent neural networks (RNNs). The approach is based on the real-time recurrent learning (RTRL) algorithm, whereby the sensitivity set of each neuron is reduced to weights associated with either its input or output links. This yields a reduced storage and computational complexity of O(N(2)). Stochastic meta descent (SMD), an adaptive step size scheme for stochastic gradient-descent problems, is employed as means of incorporating curvature information in order to substantially accelerate the learning process. We also introduce a clustered version of our algorithm to further improve its scalability attributes. Despite the dramatic reduction in resource requirements, it is shown through simulation results that the approach outperforms regular RTRL by almost an order of magnitude. Moreover, the scheme lends itself to parallel hardware realization by virtue of the localized property that is inherent to the learning framework. PMID:18779096

  11. An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students.

    PubMed

    Fernández-Alemán, José Luis; López-González, Laura; González-Sequeros, Ofelia; Jayne, Chrisina; López-Jiménez, Juan José; Carrillo-de-Gea, Juan Manuel; Toval, Ambrosio

    2016-04-01

    This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training. PMID:26815339

  12. Is a Complex Neural Network Based Air Quality Prediction Model Better Than a Simple One? A Bayesian Point of View

    NASA Astrophysics Data System (ADS)

    Hoi, K. I.; Yuen, K. V.; Mok, K. M.

    2010-05-01

    In this study the neural network based air quality prediction model was tested in a typical coastal city, Macau, with Latitude 22° 10'N and Longitude 113° 34'E. By using five years of air quality and meteorological data recorded at an ambient air quality monitoring station between 2001 and 2005, it was found that the performance of the ANN model was generally improved by increasing the number of hidden neurons in the training phase. However, the performance of the ANN model was not sensitive to the change in the number of hidden neurons during the prediction phase. Therefore, the improvement in the error statistics for a complex ANN model in the training phase may be only caused by the overfitting of the data. In addition, the posterior PDF of the parameter vector conditional on the training dataset was investigated for different number of hidden neurons. It was found that the parametric space for a simple ANN model was globally identifiable and the Levenberg-Marquardt backpropagation algorithm was able to locate the optimal parameter vector. However, the parameter vector might contain redundant parameters and the parametric space was not globally identifiable when the model class became complex. In addition, the Levenberg-Marquardt backpropagation algorithm was unable to locate the most optimal parameter vector in this situation. Finally, it was concluded that the a more complex MLP model, that fits the data better, is not necessarily better than a simple one.

  13. Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

    NASA Astrophysics Data System (ADS)

    Liu, Yu; Huang, Hong-Zhong; Ling, Dan

    2013-03-01

    Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices (such as reliability, availability and mean time to failure) before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment (FSA) approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study.

  14. Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems.

    PubMed

    Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong

    2014-12-01

    In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach. PMID:25415951

  15. Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

    NASA Astrophysics Data System (ADS)

    Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

    2010-02-01

    Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

  16. F-15 IFCS: Intelligent Flight Control System

    NASA Technical Reports Server (NTRS)

    Bosworth, John

    2007-01-01

    This viewgraph presentation describes the F-15 Intelligent Flight Control System (IFCS). The goals of this project include: 1) Demonstrate revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions; and 2) Demonstrate advance neural network-based flight control technology for new aerospace systems designs.

  17. Neural network-based motion control of an underactuated wheeled inverted pendulum model.

    PubMed

    Yang, Chenguang; Li, Zhijun; Cui, Rongxin; Xu, Bugong

    2014-11-01

    In this paper, automatic motion control is investigated for one of wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second order subsystem Σa consisting of planar movement of vehicle forward and yaw angular motions, and a nonactuated first order subsystem Σb of pendulum motion. Due to the unknown dynamics of subsystem Σa and the universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σa . The model reference approach has been used whereas the reference model is optimized by the finite time linear quadratic regulation technique. The pendulum motion in the passive subsystem Σb is indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σa , such that satisfactory tracking of a set pendulum tilt angle can be guaranteed. Rigours theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method. PMID:25330424

  18. Neural network based approach for tuning of SNS feedback and feedforward controllers.

    SciTech Connect

    Kwon, S. I.; Prokop, M. S.; Regan, A. H.

    2002-01-01

    The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

  19. Novel neural networks-based fault tolerant control scheme with fault alarm.

    PubMed

    Shen, Qikun; Jiang, Bin; Shi, Peng; Lim, Cheng-Chew

    2014-11-01

    In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques. PMID:25014982

  20. Methodologies for Adaptive Flight Envelope Estimation and Protection

    NASA Technical Reports Server (NTRS)

    Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine

    2009-01-01

    This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.

  1. A Neural-Network-Based Semi-Automated Geospatial Classification Tool

    NASA Astrophysics Data System (ADS)

    Hale, R. G.; Herzfeld, U. C.

    2014-12-01

    North America's largest glacier system, the Bering Bagley Glacier System (BBGS) in Alaska, surged in 2011-2013, as shown by rapid mass transfer, elevation change, and heavy crevassing. Little is known about the physics controlling surge glaciers' semi-cyclic patterns; therefore, it is crucial to collect and analyze as much data as possible so that predictive models can be made. In addition, physical signs frozen in ice in the form of crevasses may help serve as a warning for future surges. The BBGS surge provided an opportunity to develop an automated classification tool for crevasse classification based on imagery collected from small aircraft. The classification allows one to link image classification to geophysical processes associated with ice deformation. The tool uses an approach that employs geostatistical functions and a feed-forward perceptron with error back-propagation. The connectionist-geostatistical approach uses directional experimental (discrete) variograms to parameterize images into a form that the Neural Network (NN) can recognize. In an application to preform analysis on airborne video graphic data from the surge of the BBGS, an NN was able to distinguish 18 different crevasse classes with 95 percent or higher accuracy, for over 3,000 images. Recognizing that each surge wave results in different crevasse types and that environmental conditions affect the appearance in imagery, we designed the tool's semi-automated pre-training algorithm to be adaptable. The tool can be optimized to specific settings and variables of image analysis: (airborne and satellite imagery, different camera types, observation altitude, number and types of classes, and resolution). The generalization of the classification tool brings three important advantages: (1) multiple types of problems in geophysics can be studied, (2) the training process is sufficiently formalized to allow non-experts in neural nets to perform the training process, and (3) the time required to

  2. Adaptive Trajectory Prediction Algorithm for Climbing Flights

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

  3. Gemini Augmented Target Docking Adapter during pre-flight checkout

    NASA Technical Reports Server (NTRS)

    1966-01-01

    The Gemini Augmented Target Docking Adapter (ATDA) during pre-flight checkout in the Kennedy Space Center's Cryogenic Building. The ATDA is being used because the Agena Target Vehicle failed to achieve orbit on May 17th, 1966, causing the postponement of the Gemini 9 mission. The mission (renamed Gemini 9-A) has been rescheduled for May 31st.

  4. Flight Test of L1 Adaptive Control Law: Offset Landings and Large Flight Envelope Modeling Work

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Xargay, Enric; Cao, Chengyu; Hovakimyan, Naira

    2011-01-01

    This paper presents new results of a flight test of the L1 adaptive control architecture designed to directly compensate for significant uncertain cross-coupling in nonlinear systems. The flight test was conducted on the subscale turbine powered Generic Transport Model that is an integral part of the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. The results presented include control law evaluation for piloted offset landing tasks as well as results in support of nonlinear aerodynamic modeling and real-time dynamic modeling of the departure-prone edges of the flight envelope.

  5. Development of a digital adaptive optimal linear regulator flight controller

    NASA Technical Reports Server (NTRS)

    Berry, P.; Kaufman, H.

    1975-01-01

    Digital adaptive controllers have been proposed as a means for retaining uniform handling qualities over the flight envelope of a high-performance aircraft. Towards such an implementation, an explicit adaptive controller, which makes direct use of online parameter identification, has been developed and applied to the linearized lateral equations of motion for a typical fighter aircraft. The system is composed of an online weighted least-squares parameter identifier, a Kalman state filter, and a model following control law designed using optimal linear regulator theory. Simulation experiments with realistic measurement noise indicate that the proposed adaptive system has the potential for onboard implementation.

  6. Flight Test Implementation of a Second Generation Intelligent Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams-Hayes, Peggy S.

    2005-01-01

    The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team was to develop and flight-test control systems that use neural network technology, to optimize the performance of the aircraft under nominal conditions, and to stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. The Intelligent Flight Control System team is currently in the process of implementing a second generation control scheme, collectively known as Generation 2 or Gen 2, for flight testing on the NASA F-15 aircraft. This report describes the Gen 2 system as implemented by the team for flight test evaluation. Simulation results are shown which describe the experiment to be performed in flight and highlight the ways in which the Gen 2 system meets the defined objectives.

  7. Optimizing aircraft performance with adaptive, integrated flight/propulsion control

    NASA Technical Reports Server (NTRS)

    Smith, R. H.; Chisholm, J. D.; Stewart, J. F.

    1991-01-01

    The Performance-Seeking Control (PSC) integrated flight/propulsion adaptive control algorithm presented was developed in order to optimize total aircraft performance during steady-state engine operation. The PSC multimode algorithm minimizes fuel consumption at cruise conditions, while maximizing excess thrust during aircraft accelerations, climbs, and dashes, and simultaneously extending engine service life through reduction of fan-driving turbine inlet temperature upon engagement of the extended-life mode. The engine models incorporated by the PSC are continually upgraded, using a Kalman filter to detect anomalous operations. The PSC algorithm will be flight-demonstrated by an F-15 at NASA-Dryden.

  8. Stability and Performance Metrics for Adaptive Flight Control

    NASA Technical Reports Server (NTRS)

    Stepanyan, Vahram; Krishnakumar, Kalmanje; Nguyen, Nhan; VanEykeren, Luarens

    2009-01-01

    This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the system's characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the system's input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircraft's input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers.

  9. Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

    PubMed Central

    Barnickel, Thorsten; Weston, Jason; Collobert, Ronan; Mewes, Hans-Werner; Stümpflen, Volker

    2009-01-01

    To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach

  10. Adaptive integral dynamic surface control of a hypersonic flight vehicle

    NASA Astrophysics Data System (ADS)

    Aslam Butt, Waseem; Yan, Lin; Amezquita S., Kendrick

    2015-07-01

    In this article, non-linear adaptive dynamic surface air speed and flight path angle control designs are presented for the longitudinal dynamics of a flexible hypersonic flight vehicle. The tracking performance of the control design is enhanced by introducing a novel integral term that caters to avoiding a large initial control signal. To ensure feasibility, the design scheme incorporates magnitude and rate constraints on the actuator commands. The uncertain non-linear functions are approximated by an efficient use of the neural networks to reduce the computational load. A detailed stability analysis shows that all closed-loop signals are uniformly ultimately bounded and the ? tracking performance is guaranteed. The robustness of the design scheme is verified through numerical simulations of the flexible flight vehicle model.

  11. Context-specific adaptation of saccade gain in parabolic flight

    NASA Technical Reports Server (NTRS)

    Shelhamer, Mark; Clendaniel, Richard A.; Roberts, Dale C.

    2002-01-01

    Previous studies established that vestibular reflexes can have two adapted states (e.g., gains) simultaneously, and that a context cue (e.g., vertical eye position) can switch between the two states. Our earlier work demonstrated this phenomenon of context-specific adaptation for saccadic eye movements: we asked for gain decrease in one context state and gain increase in another context state, and then determined if a change in the context state would invoke switching between the adapted states. Horizontal and vertical eye position and head orientation could serve, to varying degrees, as cues for switching between two different saccade gains. In the present study, we asked whether gravity magnitude could serve as a context cue: saccade adaptation was performed during parabolic flight, which provides alternating levels of gravitoinertial force (0 g and 1.8 g). Results were less robust than those from ground experiments, but established that different saccade magnitudes could be associated with different gravity levels.

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

    NASA Technical Reports Server (NTRS)

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

    1977-01-01

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

  13. Integrated Neural Flight and Propulsion Control System

    NASA Technical Reports Server (NTRS)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  14. Neural Flight Control System

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  15. HIDEC adaptive engine control system flight evaluation results

    NASA Technical Reports Server (NTRS)

    Yonke, W. A.; Landy, R. J.; Stewart, J. F.

    1987-01-01

    An integrated flight propulsion control mode, the Adaptive Engine Control System (ADECS), has been developed and flight tested on an F-15 aircraft as part of the NASA Highly Integrated Digital Electronic Control program. The ADECS system realizes additional engine thrust by increasing the engine pressure ratio (EPR) at intermediate and afterburning power, with the amount of EPR uptrim modulated using a predictor scheme for angle-of-attack and sideslip angle. Substantial improvement in aircraft and engine performance was demonstrated, with a 16 percent rate of climb increase, a 14 percent reduction in time to climb, and a 15 percent reduction in time to accelerate. Significant EPR uptrim capability was found with angles-of-attack up to 20 degrees.

  16. Integrated flight/propulsion control - Adaptive engine control system mode

    NASA Technical Reports Server (NTRS)

    Yonke, W. A.; Terrell, L. A.; Meyers, L. P.

    1985-01-01

    The adaptive engine control system mode (ADECS) which is developed and tested on an F-15 aircraft with PW1128 engines, using the NASA sponsored highly integrated digital electronic control program, is examined. The operation of the ADECS mode, as well as the basic control logic, the avionic architecture, and the airframe/engine interface are described. By increasing engine pressure ratio (EPR) additional thrust is obtained at intermediate power and above. To modulate the amount of EPR uptrim and to prevent engine stall, information from the flight control system is used. The performance benefits, anticipated from control integration are shown for a range of flight conditions and power settings. It is found that at higher altitudes, the ADECS mode can increase thrust as much as 12 percent, which is used for improved acceleration, improved turn rate, or sustained turn angle.

  17. Flight Validation of a Metrics Driven L(sub 1) Adaptive Control

    NASA Technical Reports Server (NTRS)

    Dobrokhodov, Vladimir; Kitsios, Ioannis; Kaminer, Isaac; Jones, Kevin D.; Xargay, Enric; Hovakimyan, Naira; Cao, Chengyu; Lizarraga, Mariano I.; Gregory, Irene M.

    2008-01-01

    The paper addresses initial steps involved in the development and flight implementation of new metrics driven L1 adaptive flight control system. The work concentrates on (i) definition of appropriate control driven metrics that account for the control surface failures; (ii) tailoring recently developed L1 adaptive controller to the design of adaptive flight control systems that explicitly address these metrics in the presence of control surface failures and dynamic changes under adverse flight conditions; (iii) development of a flight control system for implementation of the resulting algorithms onboard of small UAV; and (iv) conducting a comprehensive flight test program that demonstrates performance of the developed adaptive control algorithms in the presence of failures. As the initial milestone the paper concentrates on the adaptive flight system setup and initial efforts addressing the ability of a commercial off-the-shelf AP with and without adaptive augmentation to recover from control surface failures.

  18. Adaptive Optimization of Aircraft Engine Performance Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Long, Theresa W.

    1995-01-01

    Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.

  19. Nutrition and human physiological adaptations to space flight

    NASA Technical Reports Server (NTRS)

    Lane, H. W.; LeBlanc, A. D.; Putcha, L.; Whitson, P. A.

    1993-01-01

    Space flight provides a model for the study of healthy individuals undergoing unique stresses. This review focuses on how physiological adaptations to weightlessness may affect nutrient and food requirements in space. These adaptations include reductions in body water and plasma volume, which affect the renal and cardiovascular systems and thereby fluid and electrolyte requirements. Changes in muscle mass and function may affect requirements for energy, protein and amino acids. Changes in bone mass lead to increased urinary calcium concentrations, which may increase the risk of forming renal stones. Space motion sickness may influence putative changes in gastro-intestinal-hepatic function; neurosensory alterations may affect smell and taste. Some or all of these effects may be ameliorated through the use of specially designed dietary countermeasures.

  20. Nutrition and human physiological adaptations to space flight.

    PubMed

    Lane, H W; LeBlanc, A D; Putcha, L; Whitson, P A

    1993-11-01

    Space flight provides a model for the study of healthy individuals undergoing unique stresses. This review focuses on how physiological adaptations to weightlessness may affect nutrient and food requirements in space. These adaptations include reductions in body water and plasma volume, which affect the renal and cardiovascular systems and thereby fluid and electrolyte requirements. Changes in muscle mass and function may affect requirements for energy, protein and amino acids. Changes in bone mass lead to increased urinary calcium concentrations, which may increase the risk of forming renal stones. Space motion sickness may influence putative changes in gastro-intestinal-hepatic function; neurosensory alterations may affect smell and taste. Some or all of these effects may be ameliorated through the use of specially designed dietary countermeasures. PMID:8237860

  1. Application of Adaptive Autopilot Designs for an Unmanned Aerial Vehicle

    NASA Technical Reports Server (NTRS)

    Shin, Yoonghyun; Calise, Anthony J.; Motter, Mark A.

    2005-01-01

    This paper summarizes the application of two adaptive approaches to autopilot design, and presents an evaluation and comparison of the two approaches in simulation for an unmanned aerial vehicle. One approach employs two-stage dynamic inversion and the other employs feedback dynamic inversions based on a command augmentation system. Both are augmented with neural network based adaptive elements. The approaches permit adaptation to both parametric uncertainty and unmodeled dynamics, and incorporate a method that permits adaptation during periods of control saturation. Simulation results for an FQM-117B radio controlled miniature aerial vehicle are presented to illustrate the performance of the neural network based adaptation.

  2. Flight control system development and flight test experience with the F-111 mission adaptive wing aircraft

    NASA Technical Reports Server (NTRS)

    Larson, R. R.

    1986-01-01

    The wing on the NASA F-111 transonic aircraft technology airplane was modified to provide flexible leading and trailing edge flaps. This wing is known as the mission adaptive wing (MAW) because aerodynamic efficiency can be maintained at all speeds. Unlike a conventional wing, the MAW has no spoilers, external flap hinges, or fairings to break the smooth contour. The leading edge flaps and three-segment trailing edge flaps are controlled by a redundant fly-by-wire control system that features a dual digital primary system architecture providing roll and symmetric commands to the MAW control surfaces. A segregated analog backup system is provided in the event of a primary system failure. This paper discusses the design, development, testing, qualification, and flight test experience of the MAW primary and backup flight control systems.

  3. Launch Vehicle Manual Steering with Adaptive Augmenting Control In-flight Evaluations Using a Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt

    2014-01-01

    An adaptive augmenting control algorithm for the Space Launch System has been developed at the Marshall Space Flight Center as part of the launch vehicles baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a proposed manual steering mode were investigated by giving the pilot trajectory deviation cues and pitch rate command authority.

  4. Flight Test of an L(sub 1) Adaptive Controller on the NASA AirSTAR Flight Test Vehicle

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Xargay, Enric; Cao, Chengyu; Hovakimyan, Naira

    2010-01-01

    This paper presents results of a flight test of the L-1 adaptive control architecture designed to directly compensate for significant uncertain cross-coupling in nonlinear systems. The flight test was conducted on the subscale turbine powered Generic Transport Model that is an integral part of the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. The results presented are for piloted tasks performed during the flight test.

  5. Ambiguous Tilt and Translation Motion Cues after Space Flight and Otolith Assessment during Post-Flight Re-Adaptation

    NASA Technical Reports Server (NTRS)

    Wood, Scott J.; Clarke, A. H.; Harm, D. L.; Rupert, A. H.; Clement, G. R.

    2009-01-01

    Adaptive changes during space flight in how the brain integrates vestibular cues with other sensory information can lead to impaired movement coordination, vertigo, spatial disorientation and perceptual illusions following Gtransitions. These studies are designed to examine both the physiological basis and operational implications for disorientation and tilt-translation disturbances following short duration space flights.

  6. 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.

  7. Individual 3D region-of-interest atlas of the human brain: automatic training point extraction for neural-network-based classification of brain tissue types

    NASA Astrophysics Data System (ADS)

    Wagenknecht, Gudrun; Kaiser, Hans-Juergen; Obladen, Thorsten; Sabri, Osama; Buell, Udalrich

    2000-04-01

    Individual region-of-interest atlas extraction consists of two main parts: T1-weighted MRI grayscale images are classified into brain tissues types (gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), scalp/bone (SB), background (BG)), followed by class image analysis to define automatically meaningful ROIs (e.g., cerebellum, cerebral lobes, etc.). The purpose of this algorithm is the automatic detection of training points for neural network-based classification of brain tissue types. One transaxial slice of the patient data set is analyzed. Background separation is done by simple region growing. A random generator extracts spatially uniformly distributed training points of class BG from that region. For WM training point extraction (TPE), the homogeneity operator is the most important. The most homogeneous voxels define the region for WM TPE. They are extracted by analyzing the cumulative histogram of the homogeneity operator response. Assuming a Gaussian gray value distribution in WM, a random number is used as a probabilistic threshold for TPE. Similarly, non-white matter and non-background regions are analyzed for GM and CSF training points. For SB TPE, the distance from the BG region is an additional feature. Simulated and real 3D MRI images are analyzed and error rates for TPE and classification calculated.

  8. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    PubMed

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613

  9. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

    PubMed Central

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613

  10. Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Orr, Jeb S.; Miller, Christopher J.; Hanson, Curtis E.

    2014-01-01

    The NASA Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an Adaptive Augmenting Control (AAC) algorithm for launch vehicles that improves robustness and performance by adapting an otherwise welltuned classical control algorithm to unexpected environments or variations in vehicle dynamics. This AAC algorithm is currently part of the baseline design for the SLS Flight Control System (FCS), but prior to this series of research flights it was the only component of the autopilot design that had not been flight tested. The Space Launch System (SLS) flight software prototype, including the adaptive component, was recently tested on a piloted aircraft at Dryden Flight Research Center (DFRC) which has the capability to achieve a high level of dynamic similarity to a launch vehicle. Scenarios for the flight test campaign were designed specifically to evaluate the AAC algorithm to ensure that it is able to achieve the expected performance improvements with no adverse impacts in nominal or nearnominal scenarios. Having completed the recent series of flight characterization experiments on DFRC's F/A-18, the AAC algorithm's capability, robustness, and reproducibility, have been successfully demonstrated. Thus, the entire SLS control architecture has been successfully flight tested in a relevant environment. This has increased NASA's confidence that the autopilot design is ready to fly on the SLS Block I vehicle and will exceed the performance of previous architectures.

  11. Adaptive aerostructures: the first decade of flight on uninhabited aerial vehicles

    NASA Astrophysics Data System (ADS)

    Barrett, Ronald M.

    2004-07-01

    Although many subscale aircraft regularly fly with adaptive materials in sensors and small components in secondary subsystems, only a handful have flown with adaptive aerostructures as flight critical, enabling components. This paper reviews several families of adaptive aerostructures which have enabled or significantly enhanced flightworthy uninhabited aerial vehicles (UAVs), including rotary and fixed wing aircraft, missiles and munitions. More than 40 adaptive aerostructures programs which have had a direct connection to flight test and/or production UAVs, ranging from hover through hypersonic, sea-level to exo-stratospheric are examined. Adaptive material type, design Mach range, test methods, aircraft configuration and performance of each of the designs are presented. An historical analysis shows the evolution of flightworthy adaptive aerostructures from the earliest staggering flights in 1994 to modern adaptive UAVs supporting live-fire exercises in harsh military environments. Because there are profound differences between bench test, wind tunnel test, flight test and military grade flightworthy adaptive aerostructures, some of the most mature industrial design and fabrication techniques in use today will be outlined. The paper concludes with an example of the useful load and performance expansions which are seen on an industrial, military-grade UAV through the use of properly designed, flight-hardened adaptive aerostructures.

  12. Adaptive control of mobile robots using a neural network.

    PubMed

    de Sousa Júnior, C; Hermerly, E M

    2001-06-01

    A Neural Network - based control approach for mobile robot is proposed. The weight adaptation is made on-line, without previous learning. Several possible situations in robot navigation are considered, including uncertainties in the model and presence of disturbance. Weight adaptation laws are presented as well as simulation results. PMID:11574958

  13. Flight Test of Composite Model Reference Adaptive Control (CMRAC) Augmentation Using NASA AirSTAR Infrastructure

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Gadient, ROss; Lavretsky, Eugene

    2011-01-01

    This paper presents flight test results of a robust linear baseline controller with and without composite adaptive control augmentation. The flight testing was conducted using the NASA Generic Transport Model as part of the Airborne Subscale Transport Aircraft Research system at NASA Langley Research Center.

  14. Flight test experience with an adaptive control system using a maximum likelihood parameter estimation technique

    NASA Technical Reports Server (NTRS)

    Hartmann, G.; Stein, G.; Powers, B.

    1979-01-01

    The flight test performance of an adaptive control system for the F-8 DFBW aircraft is summarized. The adaptive system is based on explicit identification of surface effectiveness parameters which are used for gain scheduling in a command augmentation system. Performance of this control law under various design parameter variations is presented. These include variations in test signal level, sample rate, and identification channel structure. Flight performance closely matches analysis and simulation predictions from previous references.

  15. F-15 837 IFCS Intelligent Flight Control System Project

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2007-01-01

    This viewgraph presentation reviews the use of Intelligent Flight Control System (IFCS) for the F-15. The goals of the project are: (1) Demonstrate Revolutionary Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions (2) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs. The motivation for the development are to reduce the chance and skill required for survival.

  16. A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters

    NASA Astrophysics Data System (ADS)

    Chen, Jun; Cui, Tingwei; Quan, Wenting

    2015-01-01

    this study, a neural network-based four-band model (NNFM) for the global oceanic and coastal waters has been developed in order to retrieve the total absorption coefficients a(λ). The applicability of the quasi-analytical algorithm (QAA) and NNFM models is evaluated by five independent data sets. Based on the comparison of a(λ) predicted by these two models with the field measurements taken from the global oceanic and coastal waters, it was found that both the QAA and NNFM models had good performances in deriving a(λ), but that the NNFM model works better than the QAA model. The results of the QAA model-derived a(λ), especially in highly turbid waters with strong backscattering properties of optical activity, was found to be lower than the field measurements. The QAA and NNFM models-derived a(λ) could be obtained from the MODIS data after atmospheric corrections. When compared with the field measurements, the NNFM model decreased by a 0.86-24.15% uncertainty (root-mean-square relative error) of the estimation from the QAA model in deriving a(λ) from the Bohai, Yellow, and East China seas. Finally, the NNFM model was applied to map the global climatological seasonal mean a(443) for the time range of July 2002 to May 2014. As expected, the a(443) value around the coastal regions was always larger than the open ocean around the equator. Viewed on a global scale, the oceans at a high latitude exhibited higher a(443) values than those at a low latitude.

  17. A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging

    NASA Astrophysics Data System (ADS)

    Agarwal, Smriti; Bisht, Amit Singh; Singh, Dharmendra; Pathak, Nagendra Prasad

    2014-12-01

    Millimetre wave imaging (MMW) is gaining tremendous interest among researchers, which has potential applications for security check, standoff personal screening, automotive collision-avoidance, and lot more. Current state-of-art imaging techniques viz. microwave and X-ray imaging suffers from lower resolution and harmful ionizing radiation, respectively. In contrast, MMW imaging operates at lower power and is non-ionizing, hence, medically safe. Despite these favourable attributes, MMW imaging encounters various challenges as; still it is very less explored area and lacks suitable imaging methodology for extracting complete target information. Keeping in view of these challenges, a MMW active imaging radar system at 60 GHz was designed for standoff imaging application. A C-scan (horizontal and vertical scanning) methodology was developed that provides cross-range resolution of 8.59 mm. The paper further details a suitable target identification and classification methodology. For identification of regular shape targets: mean-standard deviation based segmentation technique was formulated and further validated using a different target shape. For classification: probability density function based target material discrimination methodology was proposed and further validated on different dataset. Lastly, a novel artificial neural network based scale and rotation invariant, image reconstruction methodology has been proposed to counter the distortions in the image caused due to noise, rotation or scale variations. The designed neural network once trained with sample images, automatically takes care of these deformations and successfully reconstructs the corrected image for the test targets. Techniques developed in this paper are tested and validated using four different regular shapes viz. rectangle, square, triangle and circle.

  18. A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient

    NASA Astrophysics Data System (ADS)

    Sauzède, R.; Claustre, H.; Uitz, J.; Jamet, C.; Dall'Olmo, G.; D'Ortenzio, F.; Gentili, B.; Poteau, A.; Schmechtig, C.

    2016-04-01

    The present study proposes a novel method that merges satellite ocean color bio-optical products with Argo temperature-salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (bbp). This neural network-based method (SOCA-BBP for Satellite Ocean-Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite-based surface estimates of bbp and chlorophyll a concentration matched up in space and time with (2) depth-resolved physical properties derived from temperature-salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite-Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature-salinity and bio-optical properties collected by Bio-Argo floats, with concomitant satellite-derived products. The Bio-Argo profiles are representative of the global open-ocean in terms of oceanographic conditions, making the proposed method applicable to most open-ocean environments. SOCA-BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio-Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of bbp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth-resolved climatology of bbp for the global ocean.

  19. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

    PubMed Central

    De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S.

    2012-01-01

    Background While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. Methods In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. Results We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. Conclusions A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal

  20. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects.

    PubMed

    De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S

    2011-08-01

    BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal

  1. Investigation of the Multiple Model Adaptive Control (MMAC) method for flight control systems

    NASA Technical Reports Server (NTRS)

    1975-01-01

    The application was investigated of control theoretic ideas to the design of flight control systems for the F-8 aircraft. The design of an adaptive control system based upon the so-called multiple model adaptive control (MMAC) method is considered. Progress is reported.

  2. Aeroelastic Deformation: Adaptation of Wind Tunnel Measurement Concepts to Full-Scale Vehicle Flight Testing

    NASA Technical Reports Server (NTRS)

    Burner, Alpheus W.; Lokos, William A.; Barrows, Danny A.

    2005-01-01

    The adaptation of a proven wind tunnel test technique, known as Videogrammetry, to flight testing of full-scale vehicles is presented. A description is presented of the technique used at NASA's Dryden Flight Research Center for the measurement of the change in wing twist and deflection of an F/A-18 research aircraft as a function of both time and aerodynamic load. Requirements for in-flight measurements are compared and contrasted with those for wind tunnel testing. The methodology for the flight-testing technique and differences compared to wind tunnel testing are given. Measurement and operational comparisons to an older in-flight system known as the Flight Deflection Measurement System (FDMS) are presented.

  3. A practical scheme for adaptive aircraft flight control systems

    NASA Technical Reports Server (NTRS)

    Athans, M.; Willner, D.

    1974-01-01

    A flight control system design is presented, that can be implemented by analog hardware, to be used to control an aircraft with uncertain parameters. The design is based upon the use of modern control theory. The ideas are illustrated by considering control of STOL longitudinal dynamics.

  4. Steerable Adaptive Bullet (StAB) piezoelectric flight control system

    NASA Astrophysics Data System (ADS)

    Barrett, Ron; Barnhart, Ryan; Bramlette, Richard

    2012-04-01

    This paper outlines a new class of piezoelectric flight control actuators which are specifically intended for use in guided hard-launched munitions from under 5.56mm to 40mm in caliber. In March of 2011, US Pat. 7,898,153 was issued, describing this new class of actuators, how they are mounted, laminated, energized and used to control the flight of a wide variety of munitions. This paper is the technical conference paper companion to the Patent. A Low Net Passive Stiffness (LNPS) Post Buckled Precompressed (PBP) piezoelectric actuator element for a 0.40 caliber body, 0.50 caliber round was built and tested. Aerodynamic modeling of the flight control actuator showed that canard deflections of just +/-1° are more than sufficient to provide full flight control against 99% atmospherics to 2km of range while maintaining just 10cm of dispersion with lethal energy pressure levels upon terminal contact. Supersonic wind tunnel testing was conducted as well as a sweep of axial compression. The LNPS/PBP configuration exhibited an amplification factor of 3.8 while maintaining equivalent corner frequencies in excess of 100 Hz and deflection levels of +/-1°. The paper concludes with a fabrication and assembly cost analysis on a mass production scale.

  5. Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation

    NASA Technical Reports Server (NTRS)

    He, Yuning; Lee, Herbert K. H.; Davies, Misty D.

    2012-01-01

    Traditional validation of flight control systems is based primarily upon empirical testing. Empirical testing is sufficient for simple systems in which a.) the behavior is approximately linear and b.) humans are in-the-loop and responsible for off-nominal flight regimes. A different possible concept of operation is to use adaptive flight control systems with online learning neural networks (OLNNs) in combination with a human pilot for off-nominal flight behavior (such as when a plane has been damaged). Validating these systems is difficult because the controller is changing during the flight in a nonlinear way, and because the pilot and the control system have the potential to co-adapt in adverse ways traditional empirical methods are unlikely to provide any guarantees in this case. Additionally, the time it takes to find unsafe regions within the flight envelope using empirical testing means that the time between adaptive controller design iterations is large. This paper describes a new concept for validating adaptive control systems using methods based on Bayesian statistics. This validation framework allows the analyst to build nonlinear models with modal behavior, and to have an uncertainty estimate for the difference between the behaviors of the model and system under test.

  6. Adaptive flight control surfaces, wings, rotors, and active aerodynamics

    NASA Astrophysics Data System (ADS)

    Barrett, Ronald M.; Brozoski, Fred

    1996-05-01

    This study outlines active flight control materials, structural arrangements, and several new active flight control methods for rotorcraft, airplanes and missiles. A system-level comparison shows that flight control actuator systems using materials like piezoceramics have approximately double the mass-specific energy and 4 to 6 times the volume specific energy of conventional actuators. New fabrication techniques centered on the principal of directional attachment allow wings and rotor blades to become twist active. Using these new methods, directionally attached piezoelectric (DAP) actuator elements were built into graphite-epoxy sandwich structures. When compared to conventionally attached piezoelectric (CAP) elements, twist deflections (important for flight control) of DAP plates were an order of magnitude greater. By using such twist-active elements in a torque-plate configuration, an active helicopter rotor was built. This Froude-scaled solid state rotor was whirl-stand tested and showed steady blade pitch deflections in excess of plus or minus 8 degrees with good correlation between theory and experiment rates up to 42 Hz (which corresponded to 2.5/rev) and no degradation in deflection as RPM was increased. DAP elements were also used in high aspect ratio subsonic and supersonic wings, demonstrating static twist deflections of plus or minus 2 degrees and plus or minus 6 degrees respectively, with good correlation between experiment and finite element results. The final section compares all-moving active stabilator structural arrangements and pitch deflections, which range up to plus or minus 12 degrees, generating lift coefficient changes in excess of plus or minus 0.8.

  7. Lockheed L-1011 Test Station installation in support of the Adaptive Performance Optimization flight

    NASA Technical Reports Server (NTRS)

    1997-01-01

    Technicians John Huffman, Phil Gonia and Mike Kerner of NASA's Dryden Flight Research Center, Edwards, California, carefully insert a monitor into the Research Engineering Test Station during installation of equipment for the Adaptive Performance Optimization experiment aboard Orbital Sciences Corporation's Lockheed L-1011 in Bakersfield, California, May, 6, 1997. The Adaptive Performance Optimization project is designed to reduce the aerodynamic drag of large subsonic transport aircraft by varying the camber of the wing through real-time adjustment of flaps or ailerons in response to changing flight conditions. Reducing the drag will improve aircraft efficiency and performance, resulting in signifigant fuel savings for the nation's airlines worth hundreds of millions of dollars annually. Flights for the NASA experiment will occur periodically over the next couple of years on the modified wide-bodied jetliner, with all flights flown out of Bakersfield's Meadows Field. The experiment is part of Dryden's Advanced Subsonic Transport Aircraft Research program.

  8. Lockheed L-1011 TriStar first flight to support Adaptive Performance Optimization study

    NASA Technical Reports Server (NTRS)

    1997-01-01

    Bearing the logos of the National Aeronautics and Space Administration and Orbital Sciences Corporation, Orbital's L-1011 Tristar lifts off the Meadows Field Runway at Bakersfield, California, on its first flight May 21, 1997, in NASA's Adaptive Performance Optimization project. Developed by engineers at NASA's Dryden Flight Research Center, Edwards, California, the experiment seeks to reduce fuel consumption of large jetliners by improving the aerodynamic efficency of their wings at cruise conditions. A research computer employing a sophisticated software program adapts to changing flight conditions by commanding small movements of the L-1011's outboard ailerons to give the wings the most efficient - or optimal - airfoil. Up to a dozen research flights will be flown in the current and follow-on phases of the project over the next couple years.

  9. LaPlace Transform1 Adaptive Control Law in Support of Large Flight Envelope Modeling Work

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Xargay, Enric; Cao, Chengyu; Hovakimyan, Naira

    2011-01-01

    This paper presents results of a flight test of the L1 adaptive control architecture designed to directly compensate for significant uncertain cross-coupling in nonlinear systems. The flight test was conducted on the subscale turbine powered Generic Transport Model that is an integral part of the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. The results presented are in support of nonlinear aerodynamic modeling and instrumentation calibration.

  10. Adaptive support vector regression for UAV flight control.

    PubMed

    Shin, Jongho; Jin Kim, H; Kim, Youdan

    2011-01-01

    This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model. PMID:20970303

  11. Approach for Structurally Clearing an Adaptive Compliant Trailing Edge Flap for Flight

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Lokos, William A.; Cruz, Josue; Crampton, Glen; Stephens, Craig A.; Kota, Sridhar; Ervin, Gregory; Flick, Pete

    2015-01-01

    The Adaptive Compliant Trailing Edge (ACTE) flap was flown on the National Aeronautics and Space Administration (NASA) Gulfstream GIII testbed at the NASA Armstrong Flight Research Center. This smoothly curving flap replaced the existing Fowler flaps creating a seamless control surface. This compliant structure, developed by FlexSys Inc. in partnership with the Air Force Research Laboratory, supported NASA objectives for airframe structural noise reduction, aerodynamic efficiency, and wing weight reduction through gust load alleviation. A thorough structures airworthiness approach was developed to move this project safely to flight. A combination of industry and NASA standard practice require various structural analyses, ground testing, and health monitoring techniques for showing an airworthy structure. This paper provides an overview of compliant structures design, the structural ground testing leading up to flight, and the flight envelope expansion and monitoring strategy. Flight data will be presented, and lessons learned along the way will be highlighted.

  12. Highly integrated digital electronic control: Digital flight control, aircraft model identification, and adaptive engine control

    NASA Technical Reports Server (NTRS)

    Baer-Riedhart, Jennifer L.; Landy, Robert J.

    1987-01-01

    The highly integrated digital electronic control (HIDEC) program at NASA Ames Research Center, Dryden Flight Research Facility is a multiphase flight research program to quantify the benefits of promising integrated control systems. McDonnell Aircraft Company is the prime contractor, with United Technologies Pratt and Whitney Aircraft, and Lear Siegler Incorporated as major subcontractors. The NASA F-15A testbed aircraft was modified by the HIDEC program by installing a digital electronic flight control system (DEFCS) and replacing the standard F100 (Arab 3) engines with F100 engine model derivative (EMD) engines equipped with digital electronic engine controls (DEEC), and integrating the DEEC's and DEFCS. The modified aircraft provides the capability for testing many integrated control modes involving the flight controls, engine controls, and inlet controls. This paper focuses on the first two phases of the HIDEC program, which are the digital flight control system/aircraft model identification (DEFCS/AMI) phase and the adaptive engine control system (ADECS) phase.

  13. Development of countermeasures for use in space missions. [to adaptive response to space flight

    NASA Technical Reports Server (NTRS)

    Nicogossian, A. E. T.; Pool, S.; Huntoon, C. S. L.; Leonard, J. I.

    1985-01-01

    Several measures used to mitigate the inappropriate adaptive responses of space flight are investigated. Weighlessness results in a cephalic fluid shift, which causes a reduction in the circulating blood volume, and removal of weight bearing forces from musculoskeletal systems. The physiological changes that occur from one-g initiated hypovolemia and zero-g initiated fluild shifts are analyzed and compared. The role of barorecptors on the activation of the adrenergic responses that occurs as a result of hypovolemia is studied. The proper selection and administration of in-flight and post flight countermeasures, which include passive and active physical conditioning techniques, drugs, and vitamins are examined.

  14. [ROLE OF THE SYMPATHOADRENOMEDULLARY SYSTEM IN FORMATION OF PILOT'S ADAPTATION TO FLIGHT LOADS].

    PubMed

    Sukhoterin, A F; Pashchenko, P S; Plakhov, N N; Zhuravlev, A G

    2015-01-01

    Purpose of the work was to evaluate the sympathoadrenomedullary functions and associated psychophysiological reactions of pilots as a function of flight hours on highly maneuverable aircraft. Volunteers to the investigation were 78 pilots (41 pilots of maneuverable aircraft and 37 pilots of bombers and transporters). Selected methods were to enable comprehensive evaluation of the body functioning against flight loads. Our results evidence that piloting of high maneuverable aircraft but not of bombing and transporting aircrafts activates the sympathoadrenomedullary system significantly. This is particularly common to young pilots with the total flying time less than 1000 hours. Adaptive changes to flight factors were noted to develop with age and experience. PMID:26738308

  15. Flight envelope protection of aircraft using adaptive neural network and online linearisation

    NASA Astrophysics Data System (ADS)

    Shin, Hohyun; Kim, Youdan

    2016-03-01

    Flight envelope protection algorithm is proposed to improve the safety of an aircraft. Flight envelope protection systems find the control inputs to prevent an aircraft from exceeding structure/aerodynamic limits and maximum control surface deflections. The future values of state variables are predicted using the current states and control inputs based on linearised aircraft model. To apply the envelope protection algorithm for the wide envelope of the aircraft, online linearisation is adopted. Finally, the flight envelope protection system is designed using adaptive neural network and least-squares method. Numerical simulations are conducted to verify the performance of the proposed scheme.

  16. Energetic Metabolism and Biochemical Adaptation: A Bird Flight Muscle Model

    ERIC Educational Resources Information Center

    Rioux, Pierre; Blier, Pierre U.

    2006-01-01

    The main objective of this class experiment is to measure the activity of two metabolic enzymes in crude extract from bird pectoral muscle and to relate the differences to their mode of locomotion and ecology. The laboratory is adapted to stimulate the interest of wildlife management students to biochemistry. The enzymatic activities of cytochrome…

  17. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    NASA Technical Reports Server (NTRS)

    Hageman, Jacob; Smith, Mark; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  18. Design and Flight Tests of an Adaptive Control System Employing Normal-Acceleration Command

    NASA Technical Reports Server (NTRS)

    McNeill, Water E.; McLean, John D.; Hegarty, Daniel M.; Heinle, Donovan R.

    1961-01-01

    An adaptive control system employing normal-acceleration command has been designed with the aid of an analog computer and has been flight tested. The design of the system was based on the concept of using a mathematical model in combination with a high gain and a limiter. The study was undertaken to investigate the application of a system of this type to the task of maintaining nearly constant dynamic longitudinal response of a piloted airplane over the flight envelope without relying on air data measurements for gain adjustment. The range of flight conditions investigated was between Mach numbers of 0.36 and 1.15 and altitudes of 10,000 and 40,000 feet. The final adaptive system configuration was derived from analog computer tests, in which the physical airplane control system and much of the control circuitry were included in the loop. The method employed to generate the feedback signals resulted in a model whose characteristics varied somewhat with changes in flight condition. Flight results showed that the system limited the variation in longitudinal natural frequency of the adaptive airplane to about half that of the basic airplane and that, for the subsonic cases, the damping ratio was maintained between 0.56 and 0.69. The system also automatically compensated for the transonic trim change. Objectionable features of the system were an exaggerated sensitivity of pitch attitude to gust disturbances, abnormally large pitch attitude response for a given pilot input at low speeds, and an initial delay in normal-acceleration response to pilot control at all flight conditions. The adaptive system chatter of +/-0.05 to +/-0.10 of elevon at about 9 cycles per second (resulting in a maximum airplane normal-acceleration response of from +/-0.025 g to +/- 0.035 g) was considered by the pilots to be mildly objectionable but tolerable.

  19. Approach for Structurally Clearing an Adaptive Compliant Trailing Edge Flap for Flight

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Lokos, William A.; Cruz, Josue; Crampton, Glen; Stephens, Craig A.; Kota, Sridhar; Ervin, Gregory; Flick, Pete

    2015-01-01

    The Adaptive Compliant Trailing Edge (ACTE) flap was flown on the NASA Gulfstream GIII test bed at the NASA Armstrong Flight Research Center. This smoothly curving flap replaced the existing Fowler flaps creating a seamless control surface. This compliant structure, developed by FlexSys Inc. in partnership with Air Force Research Laboratory, supported NASA objectives for airframe structural noise reduction, aerodynamic efficiency, and wing weight reduction through gust load alleviation. A thorough structures airworthiness approach was developed to move this project safely to flight.

  20. Launch Vehicle Manual Steering with Adaptive Augmenting Control In-flight Evaluations of Adverse Interactions Using a Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt; Miller, Chris; Wall, John H.; Vanzwieten, Tannen S.; Gilligan, Eric; Orr, Jeb S.

    2015-01-01

    An adaptive augmenting control algorithm for the Space Launch System has been developed at the Marshall Space Flight Center as part of the launch vehicles baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a proposed manual steering mode were investigated by giving the pilot trajectory deviation cues and pitch rate command authority. Two NASA research pilots flew a total of twenty five constant pitch-rate trajectories using a prototype manual steering mode with and without adaptive control.

  1. Preliminary flight results of an adaptive engine control system of an F-15 airplane

    NASA Technical Reports Server (NTRS)

    Myers, Lawrence P.; Walsh, Kevin R.

    1987-01-01

    Results of the flight demonstration of the adaptive engine control system (ADECS), an integrated flight and propulsion control system, are reported. The ADECS system provides additional engine thrust by increasing engine pressure ratio (EPR) at intermediate and afterburning power, with the amount of EPR uptrim modulated in accordance with the maneuver requirements, flight conditions, and engine information. As a result of EPR uptrimming, engine thrust has increased by as much as 10.5 percent, rate of climb has increased by 10 percent, and the time to climb from 10,000 to 40,000 ft has been reduced by 12.5 percent. Increases in acceleration of 9.3 and 13 percent have been obtained at intermediate and maximum power, respectively. No engine anomalies have been detected for EPR increases up to 12 percent.

  2. Human physiological adaptation to extended Space Flight and its implications for Space Station

    NASA Technical Reports Server (NTRS)

    Kutyna, F. A.; Shumate, W. H.

    1985-01-01

    Current work evaluating short-term space flight physiological data on the homeostatic changes due to weightlessness is presented as a means of anticipating Space Station long-term effects. An integrated systems analysis of current data shows a vestibulo-sensory adaptation within days; a loss of body mass, fluids, and electrolytes, stabilizing in a month; and a loss in red cell mass over a month. But bone demineralization which did not level off is seen as the biggest concern. Computer algorithms have been developed to simulate the human adaptation to weightlessness. So far these paradigms have been backed up by flight data and it is hoped that they will provide valuable information for future Space Station design. A series of explanatory schematics is attached.

  3. Hybrid Decompositional Verification for Discovering Failures in Adaptive Flight Control Systems

    NASA Technical Reports Server (NTRS)

    Thompson, Sarah; Davies, Misty D.; Gundy-Burlet, Karen

    2010-01-01

    Adaptive flight control systems hold tremendous promise for maintaining the safety of a damaged aircraft and its passengers. However, most currently proposed adaptive control methodologies rely on online learning neural networks (OLNNs), which necessarily have the property that the controller is changing during the flight. These changes tend to be highly nonlinear, and difficult or impossible to analyze using standard techniques. In this paper, we approach the problem with a variant of compositional verification. The overall system is broken into components. Undesirable behavior is fed backwards through the system. Components which can be solved using formal methods techniques explicitly for the ranges of safe and unsafe input bounds are treated as white box components. The remaining black box components are analyzed with heuristic techniques that try to predict a range of component inputs that may lead to unsafe behavior. The composition of these component inputs throughout the system leads to overall system test vectors that may elucidate the undesirable behavior

  4. [Mechanisms of natural variability at adaptation of human physiological systems to conditions of space flight].

    PubMed

    Larina, I M; Nosovskiĭ, A M; Grigor'ev, A I

    2012-01-01

    This article analyzes the physiological data using the principle of invariant relationships, to reveal the mechanisms of adaptive variability. It was used physical-chemical, biochemical, and hormonal blood parameters of cosmonauts who have committed short-term and long space flights. These results suggest that application of the methods of fractal geometry to quantitative estimates of homeostasis allows to allocate the processes depending on the increase/decrease of adaptive variability and fix the state of stability or instability of certain physiological regulatory subsystems, due to mobility and to reduce the level of stability which remains stable internal structure of relationships throughout the body. PMID:22679800

  5. Peculiarities of transformation of adaptation level of the astronaut in conditions of long-lasting flight

    NASA Astrophysics Data System (ADS)

    Padashulya, H.; Prisnyakova, L.; Prisnyakov, V.

    Prognostication of the development of adverse factors of psychological processes in the personality of the astronaut who time and again feels transformation of internal structure of his personality is one of cardinal problems of the long-lasting flight Adaptation to changing conditions of long-lasting flight is of particular importance because it has an effect on the efficiency of discharged functions and mutual relations in the team The fact of standard psychological changes emerging in the personality being in the state of structural transformations is the precondition for the possibility of prognostication Age-specific gender and temperamental differences in the personality enable to standardize these changes Examination of the process of transformation of adaptation level of the personality in the varied environment depending on the type of temperament and constituents age and gender is chief object of the report In the report it is shown that in the process of transformation of adaptation parameters - attitude to guillemotleft work guillemotright guillemotleft family guillemotright guillemotleft environment guillemotright and guillemotleft ego guillemotright - the changes can go in two directions - in the direction of increase and decline of indexes The trend of increase enables to accumulate them and form potentiality to reduce or increase the level of personality adaptation There is a hypothesis that the dynamics of the process of transformation of adaptation parameter is shown up in the orientation of increase of

  6. Flight test results from a supercritical mission adaptive wing with smooth variable camber

    NASA Technical Reports Server (NTRS)

    Powers, Sheryll G.; Webb, Lannie D.; Friend, Edward L.; Lokos, William A.

    1992-01-01

    Results from the wing surface and boundary layer pressures, buffet studies and flight deflection measurement system for the advanced fighter technology integration F-111 mission adaptive wing program are presented. The different aerodynamic technologies studied on the aircraft, and their relationship with each other are described. The wingtip twist measurements provide an insight as to how dynamic pressures for positive normal accelerations affect the wingtip pressure profiles.

  7. Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

    PubMed

    Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

    2012-08-01

    An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures. PMID:22386784

  8. Flight test results from a supercritical mission adaptive wing with smooth variable camber

    NASA Technical Reports Server (NTRS)

    Powers, Sheryll Goecke; Webb, Lannie D.; Friend, Edward L.; Lokos, William A.

    1992-01-01

    The mission adaptive wing (MAW) consisted of leading- and trailing-edge variable-camber surfaces that could be deflected in flight to provide a near-ideal wing camber shape for any flight condition. These surfaces featured smooth, flexible upper surfaces and fully enclosed lower surfaces, distinguishing them from conventional flaps that have discontinuous surfaces and exposed or semiexposed mechanisms. Camber shape was controlled by either a manual or automatic flight control system. The wing and aircraft were extensively instrumented to evaluate the local flow characteristics and the total aircraft performance. This paper discusses the interrelationships between the wing pressure, buffet, boundary-layer and flight deflection measurement system analyses and describes the flight maneuvers used to obtain the data. The results are for a wing sweep of 26 deg, a Mach number of 0.85, leading and trailing-edge cambers (delta(sub LE/TE)) of 0/2 and 5/10, and angles of attack from 3.0 deg to 14.0 deg. For the well-behaved flow of the delta(sub LE/TE) = 0/2 camber, a typical cruise camber shape, the local and global data are in good agreement with respect to the flow properties of the wing. For the delta(sub LE/TE) = 5/10 camber, a maneuvering camber shape, the local and global data have similar trends and conclusions, but not the clear-cut agreement observed for cruise camber.

  9. Design Process of Flight Vehicle Structures for a Common Bulkhead and an MPCV Spacecraft Adapter

    NASA Technical Reports Server (NTRS)

    Aggarwal, Pravin; Hull, Patrick V.

    2015-01-01

    Design and manufacturing space flight vehicle structures is a skillset that has grown considerably at NASA during that last several years. Beginning with the Ares program and followed by the Space Launch System (SLS); in-house designs were produced for both the Upper Stage and the SLS Multipurpose crew vehicle (MPCV) spacecraft adapter. Specifically, critical design review (CDR) level analysis and flight production drawing were produced for the above mentioned hardware. In particular, the experience of this in-house design work led to increased manufacturing infrastructure for both Marshal Space Flight Center (MSFC) and Michoud Assembly Facility (MAF), improved skillsets in both analysis and design, and hands on experience in building and testing (MSA) full scale hardware. The hardware design and development processes from initiation to CDR and finally flight; resulted in many challenges and experiences that produced valuable lessons. This paper builds on these experiences of NASA in recent years on designing and fabricating flight hardware and examines the design/development processes used, as well as the challenges and lessons learned, i.e. from the initial design, loads estimation and mass constraints to structural optimization/affordability to release of production drawing to hardware manufacturing. While there are many documented design processes which a design engineer can follow, these unique experiences can offer insight into designing hardware in current program environments and present solutions to many of the challenges experienced by the engineering team.

  10. In-flight adaptive performance optimization (APO) control using redundant control effectors of an aircraft

    NASA Technical Reports Server (NTRS)

    Gilyard, Glenn B. (Inventor)

    1999-01-01

    Practical application of real-time (or near real-time) Adaptive Performance Optimization (APO) is provided for a transport aircraft in steady climb, cruise, turn descent or other flight conditions based on measurements and calculations of incremental drag from a forced response maneuver of one or more redundant control effectors defined as those in excess of the minimum set of control effectors required to maintain the steady flight condition in progress. The method comprises the steps of applying excitation in a raised-cosine form over an interval of from 100 to 500 sec. at the rate of 1 to 10 sets/sec of excitation, and data for analysis is gathered in sets of measurements made during the excitation to calculate lift and drag coefficients C.sub.L and C.sub.D from two equations, one for each coefficient. A third equation is an expansion of C.sub.D as a function of parasitic drag, induced drag, Mach and altitude drag effects, and control effector drag, and assumes a quadratic variation of drag with positions .delta..sub.i of redundant control effectors i=1 to n. The third equation is then solved for .delta..sub.iopt the optimal position of redundant control effector i, which is then used to set the control effector i for optimum performance during the remainder of said steady flight or until monitored flight conditions change by some predetermined amount as determined automatically or a predetermined minimum flight time has elapsed.

  11. Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

    NASA Technical Reports Server (NTRS)

    Jacklin, Stephen A.; Schumann, Johann; Guenther, Kurt; Bosworth, John

    2006-01-01

    Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

  12. Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)

    NASA Astrophysics Data System (ADS)

    Wade, Robert L.; Walker, Gregory W.

    1996-05-01

    The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.

  13. Adaptive Failure Compensation for Aircraft Flight Control Using Engine Differentials: Regulation

    NASA Technical Reports Server (NTRS)

    Yu, Liu; Xidong, Tang; Gang, Tao; Joshi, Suresh M.

    2005-01-01

    The problem of using engine thrust differentials to compensate for rudder and aileron failures in aircraft flight control is addressed in this paper in a new framework. A nonlinear aircraft model that incorporates engine di erentials in the dynamic equations is employed and linearized to describe the aircraft s longitudinal and lateral motion. In this model two engine thrusts of an aircraft can be adjusted independently so as to provide the control flexibility for rudder or aileron failure compensation. A direct adaptive compensation scheme for asymptotic regulation is developed to handle uncertain actuator failures in the linearized system. A design condition is specified to characterize the system redundancy needed for failure compensation. The adaptive regulation control scheme is applied to the linearized model of a large transport aircraft in which the longitudinal and lateral motions are coupled as the result of using engine thrust differentials. Simulation results are presented to demonstrate the effectiveness of the adaptive compensation scheme.

  14. Flight Test of an Adaptive Controller and Simulated Failure/Damage on the NASA NF-15B

    NASA Technical Reports Server (NTRS)

    Buschbacher, Mark; Maliska, Heather

    2006-01-01

    The method of flight-testing the Intelligent Flight Control System (IFCS) Second Generation (Gen-2) project on the NASA NF-15B is herein described. The Gen-2 project objective includes flight-testing a dynamic inversion controller augmented by a direct adaptive neural network to demonstrate performance improvements in the presence of simulated failure/damage. The Gen-2 objectives as implemented on the NASA NF-15B created challenges for software design, structural loading limitations, and flight test operations. Simulated failure/damage is introduced by modifying control surface commands, therefore requiring structural loads measurements. Flight-testing began with the validation of a structural loads model. Flight-testing of the Gen-2 controller continued, using test maneuvers designed in a sequenced approach. Success would clear the new controller with respect to dynamic response, simulated failure/damage, and with adaptation on and off. A handling qualities evaluation was conducted on the capability of the Gen-2 controller to restore aircraft response in the presence of a simulated failure/damage. Control room monitoring of loads sensors, flight dynamics, and controller adaptation, in addition to postflight data comparison to the simulation, ensured a safe methodology of buildup testing. Flight-testing continued without major incident to accomplish the project objectives, successfully uncovering strengths and weaknesses of the Gen-2 control approach in flight.

  15. Changes in Jump-Down Performance After Space Flight: Short- and Long-Term Adaptation

    NASA Technical Reports Server (NTRS)

    Kofman, I. S.; Reschke, M. F.; Cerisano, J. M.; Fisher, E. A.; Lawrence, E. L.; Peters, B. T.; Bloomberg, J. J.

    2010-01-01

    results demonstrate astronauts adaptive capabilities and full performance recovery within days after flight.

  16. Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen S.; Orr, Jeb S.; Wall, John H.; Gilligan, Eric T.

    2014-01-01

    This paper summarizes the Adaptive Augmenting Control (AAC) flight characterization experiments performed using an F/A-18 (TN 853). AAC was designed and developed specifically for launch vehicles, and is currently part of the baseline autopilot design for NASA's Space Launch System (SLS). The scope covered here includes a brief overview of the algorithm (covered in more detail elsewhere), motivation and benefits of flight testing, top-level SLS flight test objectives, applicability of the F/A-18 as a platform for testing a launch vehicle control design, test cases designed to fully vet the AAC algorithm, flight test results, and conclusions regarding the functionality of AAC. The AAC algorithm developed at Marshall Space Flight Center is a forward loop gain multiplicative adaptive algorithm that modifies the total attitude control system gain in response to sensed model errors or undesirable parasitic mode resonances. The AAC algorithm provides the capability to improve or decrease performance by balancing attitude tracking with the mitigation of parasitic dynamics, such as control-structure interaction or servo-actuator limit cycles. In the case of the latter, if unmodeled or mismodeled parasitic dynamics are present that would otherwise result in a closed-loop instability or near instability, the adaptive controller decreases the total loop gain to reduce the interaction between these dynamics and the controller. This is in contrast to traditional adaptive control logic, which focuses on improving performance by increasing gain. The computationally simple AAC attitude control algorithm has stability properties that are reconcilable in the context of classical frequency-domain criteria (i.e., gain and phase margin). The algorithm assumes that the baseline attitude control design is well-tuned for a nominal trajectory and is designed to adapt only when necessary. Furthermore, the adaptation is attracted to the nominal design and adapts only on an as-needed basis

  17. Adaptation of a Cascade Impactor to Flight Measurement of Droplet Size in Clouds

    NASA Technical Reports Server (NTRS)

    Levine, Joseph; Kleinknecht, Kenneth S.

    1951-01-01

    A cascade impactor, an instrument for obtaining: the size distribution of droplets borne in a low-velocity air stream, was adapted for flight cloud droplet-size studies. The air containing the droplets was slowed down from flight speed by a diffuser to the inlet-air velocity of the impactor. The droplets that enter the impactor impinge on four slides coated with magnesium oxide. Each slide catches a different size range. The relation between the size of droplet impressions and the droplet size was evaluated so that the droplet-size distributions may be found from these slides. The magnesium oxide coating provides a permanent record. of the droplet impression that is not affected by droplet evaporation after the. droplets have impinged.

  18. Dietary and Flight Energetic Adaptations in a Salivary Gland Transcriptome of an Insectivorous Bat

    PubMed Central

    Phillips, Carleton J.; Phillips, Caleb D.; Goecks, Jeremy; Lessa, Enrique P.; Sotero-Caio, Cibele G.; Tandler, Bernard; Gannon, Michael R.; Baker, Robert J.

    2014-01-01

    We hypothesized that evolution of salivary gland secretory proteome has been important in adaptation to insectivory, the most common dietary strategy among Chiroptera. A submandibular salivary gland (SMG) transcriptome was sequenced for the little brown bat, Myotis lucifugus. The likely secretory proteome of 23 genes included seven (RETNLB, PSAP, CLU, APOE, LCN2, C3, CEL) related to M. lucifugus insectivorous diet and metabolism. Six of the secretory proteins probably are endocrine, whereas one (CEL) most likely is exocrine. The encoded proteins are associated with lipid hydrolysis, regulation of lipid metabolism, lipid transport, and insulin resistance. They are capable of processing exogenous lipids for flight metabolism while foraging. Salivary carboxyl ester lipase (CEL) is thought to hydrolyze insect lipophorins, which probably are absorbed across the gastric mucosa during feeding. The other six proteins are predicted either to maintain these lipids at high blood concentrations or to facilitate transport and uptake by flight muscles. Expression of these seven genes and coordinated secretion from a single organ is novel to this insectivorous bat, and apparently has evolved through instances of gene duplication, gene recruitment, and nucleotide selection. Four of the recruited genes are single-copy in the Myotis genome, whereas three have undergone duplication(s) with two of these genes exhibiting evolutionary ‘bursts’ of duplication resulting in multiple paralogs. Evidence for episodic directional selection was found for six of seven genes, reinforcing the conclusion that the recruited genes have important roles in adaptation to insectivory and the metabolic demands of flight. Intragenic frequencies of mobile- element-like sequences differed from frequencies in the whole M. lucifugus genome. Differences among recruited genes imply separate evolutionary trajectories and that adaptation was not a single, coordinated event. PMID:24454705

  19. Dietary and flight energetic adaptations in a salivary gland transcriptome of an insectivorous bat.

    PubMed

    Phillips, Carleton J; Phillips, Caleb D; Goecks, Jeremy; Lessa, Enrique P; Sotero-Caio, Cibele G; Tandler, Bernard; Gannon, Michael R; Baker, Robert J

    2014-01-01

    We hypothesized that evolution of salivary gland secretory proteome has been important in adaptation to insectivory, the most common dietary strategy among Chiroptera. A submandibular salivary gland (SMG) transcriptome was sequenced for the little brown bat, Myotis lucifugus. The likely secretory proteome of 23 genes included seven (RETNLB, PSAP, CLU, APOE, LCN2, C3, CEL) related to M. lucifugus insectivorous diet and metabolism. Six of the secretory proteins probably are endocrine, whereas one (CEL) most likely is exocrine. The encoded proteins are associated with lipid hydrolysis, regulation of lipid metabolism, lipid transport, and insulin resistance. They are capable of processing exogenous lipids for flight metabolism while foraging. Salivary carboxyl ester lipase (CEL) is thought to hydrolyze insect lipophorins, which probably are absorbed across the gastric mucosa during feeding. The other six proteins are predicted either to maintain these lipids at high blood concentrations or to facilitate transport and uptake by flight muscles. Expression of these seven genes and coordinated secretion from a single organ is novel to this insectivorous bat, and apparently has evolved through instances of gene duplication, gene recruitment, and nucleotide selection. Four of the recruited genes are single-copy in the Myotis genome, whereas three have undergone duplication(s) with two of these genes exhibiting evolutionary 'bursts' of duplication resulting in multiple paralogs. Evidence for episodic directional selection was found for six of seven genes, reinforcing the conclusion that the recruited genes have important roles in adaptation to insectivory and the metabolic demands of flight. Intragenic frequencies of mobile- element-like sequences differed from frequencies in the whole M. lucifugus genome. Differences among recruited genes imply separate evolutionary trajectories and that adaptation was not a single, coordinated event. PMID:24454705

  20. Complexity and Pilot Workload Metrics for the Evaluation of Adaptive Flight Controls on a Full Scale Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt; Schaefer, Jacob; Burken, John J.; Larson, David; Johnson, Marcus

    2014-01-01

    Flight research has shown the effectiveness of adaptive flight controls for improving aircraft safety and performance in the presence of uncertainties. The National Aeronautics and Space Administration's (NASA)'s Integrated Resilient Aircraft Control (IRAC) project designed and conducted a series of flight experiments to study the impact of variations in adaptive controller design complexity on performance and handling qualities. A novel complexity metric was devised to compare the degrees of simplicity achieved in three variations of a model reference adaptive controller (MRAC) for NASA's F-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Full-Scale Advanced Systems Testbed (Gen-2A) aircraft. The complexity measures of these controllers are also compared to that of an earlier MRAC design for NASA's Intelligent Flight Control System (IFCS) project and flown on a highly modified F-15 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois). Pilot comments during the IRAC research flights pointed to the importance of workload on handling qualities ratings for failure and damage scenarios. Modifications to existing pilot aggressiveness and duty cycle metrics are presented and applied to the IRAC controllers. Finally, while adaptive controllers may alleviate the effects of failures or damage on an aircraft's handling qualities, they also have the potential to introduce annoying changes to the flight dynamics or to the operation of aircraft systems. A nuisance rating scale is presented for the categorization of nuisance side-effects of adaptive controllers.

  1. F-8C adaptive flight control extensions. [for maximum likelihood estimation

    NASA Technical Reports Server (NTRS)

    Stein, G.; Hartmann, G. L.

    1977-01-01

    An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated.

  2. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  3. Sensory-Motor Adaptation to Space Flight: Human Balance Control and Artificial Gravity

    NASA Technical Reports Server (NTRS)

    Paloski, William H.

    2004-01-01

    Gravity, which is sensed directly by the otolith organs and indirectly by proprioceptors and exteroceptors, provides the CNS a fundamental reference for estimating spatial orientation and coordinating movements in the terrestrial environment. The sustained absence of gravity during orbital space flight creates a unique environment that cannot be reproduced on Earth. Loss of this fundamental CNS reference upon insertion into orbit triggers neuro-adaptive processes that optimize performance for the microgravity environment, while its reintroduction upon return to Earth triggers neuro-adaptive processes that return performance to terrestrial norms. Five pioneering symposia on The Role of the Vestibular Organs in the Exploration of Space were convened between 1965 and 1970. These innovative meetings brought together the top physicians, physiologists, and engineers in the vestibular field to discuss and debate the challenges associated with human vestibular system adaptation to the then novel environment of space flight. These highly successful symposia addressed the perplexing problem of how to understand and ameliorate the adverse physiological effects on humans resulting from the reduction of gravitational stimulation of the vestibular receptors in space. The series resumed in 2002 with the Sixth Symposium, which focused on the microgravity environment as an essential tool for the study of fundamental vestibular functions. The three day meeting included presentations on historical perspectives, vestibular neurobiology, neurophysiology, neuroanatomy, neurotransmitter systems, theoretical considerations, spatial orientation, psychophysics, motor integration, adaptation, autonomic function, space motion sickness, clinical issues, countermeasures, and rehabilitation. Scientists and clinicians entered into lively exchanges on how to design and perform mutually productive research and countermeasure development projects in the future. The problems posed by long duration

  4. Flight Test of an Adaptive Configuration Optimization System for Transport Aircraft

    NASA Technical Reports Server (NTRS)

    Gilyard, Glenn B.; Georgie, Jennifer; Barnicki, Joseph S.

    1999-01-01

    A NASA Dryden Flight Research Center program explores the practical application of real-time adaptive configuration optimization for enhanced transport performance on an L-1011 aircraft. This approach is based on calculation of incremental drag from forced-response, symmetric, outboard aileron maneuvers. In real-time operation, the symmetric outboard aileron deflection is directly optimized, and the horizontal stabilator and angle of attack are indirectly optimized. A flight experiment has been conducted from an onboard research engineering test station, and flight research results are presented herein. The optimization system has demonstrated the capability of determining the minimum drag configuration of the aircraft in real time. The drag-minimization algorithm is capable of identifying drag to approximately a one-drag-count level. Optimizing the symmetric outboard aileron position realizes a drag reduction of 2-3 drag counts (approximately 1 percent). Algorithm analysis of maneuvers indicate that two-sided raised-cosine maneuvers improve definition of the symmetric outboard aileron drag effect, thereby improving analysis results and consistency. Ramp maneuvers provide a more even distribution of data collection as a function of excitation deflection than raised-cosine maneuvers provide. A commercial operational system would require airdata calculations and normal output of current inertial navigation systems; engine pressure ratio measurements would be optional.

  5. Adaptation of antennal neurons in moths is associated with cessation of pheromone-mediated upwind flight.

    PubMed Central

    Baker, T C; Hansson, B S; Löfstedt, C; Löfqvist, J

    1988-01-01

    A wind-borne plume of sex pheromone from a female moth or a synthetic source has a fine, filamentous structure that creates steep and rapid fluctuations in concentration for a male moth flying up the plume's axis. The firing rates from single antennal neurons on Agrotis segetum antennae decreased to nearly zero within seconds after the antennae were placed in a pheromone plume 70 cm downwind of a high-concentration source known from previous studies to cause in-flight arrestment of upwind progress. In a separate experiment, the fluctuating output from chilled neurons on Grapholita molesta antennae became attenuated in response to repetitive, experimentally delivered pheromone pulses. The attenuation was correlated with a previously reported higher percentage of in-flight arrestment exhibited by moths flying at cooler compared to warmer temperatures. These results indicate that two peripheral processes related to excessive concentration, complete adaptation of antennal neurons, or merely the attenuation of fluctuations in burst frequency, are important determinants of when upwind progress by a moth flying in a pheromone plume stops and changes to station keeping. Also, adaptation and attenuation may affect the sensation of blend quality by preferentially affecting cells sensitive to the most abundant components in airborne pheromone blends. PMID:3200859

  6. Adaptation of antennal neurons in moths is associated with cessation of pheromone-mediated upwind flight.

    PubMed

    Baker, T C; Hansson, B S; Löfstedt, C; Löfqvist, J

    1988-12-01

    A wind-borne plume of sex pheromone from a female moth or a synthetic source has a fine, filamentous structure that creates steep and rapid fluctuations in concentration for a male moth flying up the plume's axis. The firing rates from single antennal neurons on Agrotis segetum antennae decreased to nearly zero within seconds after the antennae were placed in a pheromone plume 70 cm downwind of a high-concentration source known from previous studies to cause in-flight arrestment of upwind progress. In a separate experiment, the fluctuating output from chilled neurons on Grapholita molesta antennae became attenuated in response to repetitive, experimentally delivered pheromone pulses. The attenuation was correlated with a previously reported higher percentage of in-flight arrestment exhibited by moths flying at cooler compared to warmer temperatures. These results indicate that two peripheral processes related to excessive concentration, complete adaptation of antennal neurons, or merely the attenuation of fluctuations in burst frequency, are important determinants of when upwind progress by a moth flying in a pheromone plume stops and changes to station keeping. Also, adaptation and attenuation may affect the sensation of blend quality by preferentially affecting cells sensitive to the most abundant components in airborne pheromone blends. PMID:3200859

  7. Launch Vehicle Manual Steering with Adaptive Augmenting Control:In-Flight Evaluations of Adverse Interactions Using a Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt; Miller, Chris; Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Orr, Jeb S.

    2015-01-01

    An Adaptive Augmenting Control (AAC) algorithm for the Space Launch System (SLS) has been developed at the Marshall Space Flight Center (MSFC) as part of the launch vehicle's baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a potential manual steering mode were also investigated by giving the pilot trajectory deviation cues and pitch rate command authority, which is the subject of this paper. Two NASA research pilots flew a total of 25 constant pitch rate trajectories using a prototype manual steering mode with and without adaptive control, evaluating six different nominal and off-nominal test case scenarios. Pilot comments and PIO ratings were given following each trajectory and correlated with aircraft state data and internal controller signals post-flight.

  8. Analytical Investigation of an Adaptive Flight-Control System Using a Sinusoidal Test Signal

    NASA Technical Reports Server (NTRS)

    Harris, Jack E.

    1961-01-01

    An analytical study was made of an adaptive flight-control system which measures vehicle response to small-amplitude control-surface deflections produced by a sinusoidal test signal. Changes in the response to this signal are related to environmental changes,, and the system is continuously altered to maintain this response equal to a preselected value. The system is suitable for use in high-performance aircraft and missiles and requires only the addition of a signal generator and a logic circuit consisting of a filter-rectifier network and a comparator-integrator network to a basic command-control system. Thus, it presents a relatively simple approach to the problem. The effects on system performance of variation in flight condition, system-gain level, test-signal frequency, and sensor location are included in the analysis. Longitudinal control of a high-performance research aircraft over flight conditions ranging from landing approach to a Mach number of 5.8 at an altitude of 150,000 feet, and longitudinal control of a four-stage solid-fuel missile including the first bending mode over the atmospheric portion of a launch trajectory constituted the basis for the analytical study. Results of an analog-computer study using time-varying coefficients are presented to compare the control obtained with the adaptive system with-that obtained with a fixed-gain system during the atmospheric portion of a missile launch trajectory. The system has demonstrated an ability to maintain satisfactory vehicle control-system stability over wide ranges of environmental change.

  9. Flight Testing of the Space Launch System (SLS) Adaptive Augmenting Control (AAC) Algorithm on an F/A-18

    NASA Technical Reports Server (NTRS)

    Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

    2014-01-01

    The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.

  10. Improving Sensorimotor Adaptation Following Long Duration Space Flight by Enhancing Vestibular Information Transfer

    NASA Technical Reports Server (NTRS)

    Mulavara, A. P.; Kofman, I. S.; De Dios, Y. E; Galvan, R.; Goel, R.; Miller, C.; Peters, B.; Cohen, H. S.; Jeevarajan, J.; Reschke, M.; Wood, S.; Bergquist, F.; Seidler, R. D.; Bloomberg, J. J.

    2014-01-01

    Crewmember adapted to the microgravity state may need to egress the vehicle within a few minutes for safety and operational reasons after gravitational transitions. The transition from one sensorimotor state to another consists of two main mechanisms: strategic and plastic-adaptive and have been demonstrated in astronauts returning after long duration space flight. Strategic modifications represent "early adaptation" - immediate and transitory changes in control that are employed to deal with short-term changes in the environment. If these modifications are prolonged then plastic-adaptive changes are evoked that modify central nervous system function, automating new behavioral responses. More importantly, this longer term adaptive recovery mechanism was significantly associated with their strategic ability to recover on the first day after return to Earth G. We are developing a method based on stochastic resonance to enhance information transfer by improving the brain's ability to detect vestibular signals (Vestibular Stochastic Resonance, VSR) especially when combined with balance training exercises such as sensorimotor adaptability (SA) training for rapid improvement in functional skill, for standing and mobility. This countermeasure to improve detection of vestibular signals is a stimulus delivery system that is wearable/portable providing low imperceptible levels of white noise based binaural bipolar electrical stimulation of the vestibular system (stochastic vestibular stimulation). To determine efficacy of vestibular stimulation on physiological and perceptual responses during otolith-canal conflicts and dynamic perturbations we have conducted a series of studies: We have shown that imperceptible binaural bipolar electrical stimulation of the vestibular system across the mastoids enhances balance performance in the mediolateral (ML) plane while standing on an unstable surface. We have followed up on the previous study showing VSR stimulation improved balance

  11. Initial moments of adaptation to microgravity of human orientation behavior, in parabolic flight conditions

    NASA Astrophysics Data System (ADS)

    Tafforin, Carole

    1996-06-01

    The first ethological studies of astronauts' adaptation to microgravity dealt with the behavioral strategies observed during short-term space missions. No attempts had however been made to consider the initial moments of adaptation dynamics, when the subject is first submitted to conditions allowing body orientations in the full three dimensions of space. The present experimental approach was both longitudinal and transversal. It consisted of analysing, during a goal-directed orientation task in parabolic flight, the orientation behavior of 12 subjects with a past experience of 0, 30 or more than 300 parabolas. During each microgravity phase, the subjects were asked to orientate their bodies and touch, with the dominant hand, four coloured targets arranged inside the aircraft. Results showed that for inexperienced subjects, the time between two target contacts was longer than experienced subjects. They often failed to reach all targets in the series during the first parabolas. They showed right-left confusion and a preference for the "up-down" vertical body orientation. Their performance, described by the efficiency of orientation in all three dimensions, improved over time and according to the level of experience. The results are discussed for the spontaneous, preliminary and integrative stages of adaptation, emphasizing new relationships between the body references and those of the surroundings. Such experiences lead the subject to develop a new mental representation of space.

  12. [Individual peculiarities of adaptation to long-term space flights: 24-hour heart rhythm monitoring

    NASA Technical Reports Server (NTRS)

    Baevskii, R. M.; Bogomolov, V. V.; Gol'dberger, A. L.; Nikulina, G. A.; Charl'z, D. B.; Goldberger, A. L. (Principal Investigator); Charles, J. B. (Principal Investigator)

    2000-01-01

    Presented are results of studying 24-hr variability of the cardiac rhythm which characterizes individual difference in reactions of two crew members to the same set of stresses during a 115-day MIR mission. Spacelab (USA) cardiorecorders were used. Data of monitoring revealed significantly different baseline health statuses of the cosmonauts. These functional differences were also observed in the mission. In one of the cosmonauts, the cardiac regulation changed over to a more economic functioning with the autonomous balance shifted towards enhanced sympathetic activity. After 2-3 months on mission he had almost recovered pre-launch level of regulation. In the other, the regulatory system was appreciably strained at the beginning of the mission as compared with preflight baseline. Later on, on flight months 2-3, this strain kept growing till a drastic depletion of the functional reserve. On return to Earth, this was manifested by a strong stress reaction with a sharp decline in power of high-frequency and grow in power of very low frequency components of the heart rhythm. The data suggest that adaptation to space flight and reactions in the readaptation period are dependent on initial health status of crew members, and functional reserve.

  13. Development and Flight Testing of an Adaptive Vehicle Health-Monitoring Architecture

    NASA Technical Reports Server (NTRS)

    Woodard, Stanley E.; Coffey, Neil C.; Gonzalez, Guillermo A.; Taylor, B. Douglas; Brett, Rube R.; Woodman, Keith L.; Weathered, Brenton W.; Rollins, Courtney H.

    2002-01-01

    On going development and testing of an adaptable vehicle health-monitoring architecture is presented. The architecture is being developed for a fleet of vehicles. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle, and, a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained expert system. The expert system is parameterized, which makes it adaptable to be trained to both a user's subject reasoning and existing quantitative analytic tools. Communication between all levels is done with wireless radio frequency interfaces. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear. The flight tests were performed to validate the following: the wireless radio frequency communication capabilities of the system, the hardware design, command and control; software operation and, data acquisition, storage and retrieval.

  14. Physiological Observations and Omics to Develop Personalized Sensormotor Adaptability Countermeasures Using Bed Rest and Space Flight Data

    NASA Technical Reports Server (NTRS)

    Mulavara, A. P.; Seidler, R. D.; Feiveson, A.; Oddsson, L.; Zanello, S.; Oman, C. M.; Ploutz-Snyder, L.; Peters, B.; Cohen, H. S.; Reschke, M.; Wood, S.; Bloomberg, J. J.

    2014-01-01

    Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the re-adapation phase following a return to an earth-gravitational environment. These alterations may disrupt the ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from space flight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual space flight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures that include: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; 3) genotype markers for genetic polymorphisms in Catechol-O-Methyl Transferase, Dopamine Receptor D2, Brain-derived neurotrophic factor and genetic polymorphism of alpha2-adrenergic receptor that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration space flight and an analog bed rest environment. We will be conducting a retrospective study leveraging data already collected from relevant

  15. Stability Assessment and Tuning of an Adaptively Augmented Classical Controller for Launch Vehicle Flight Control

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen; Zhu, J. Jim; Adami, Tony; Berry, Kyle; Grammar, Alex; Orr, Jeb S.; Best, Eric A.

    2014-01-01

    Recently, a robust and practical adaptive control scheme for launch vehicles [ [1] has been introduced. It augments a classical controller with a real-time loop-gain adaptation, and it is therefore called Adaptive Augmentation Control (AAC). The loop-gain will be increased from the nominal design when the tracking error between the (filtered) output and the (filtered) command trajectory is large; whereas it will be decreased when excitation of flex or sloshing modes are detected. There is a need to determine the range and rate of the loop-gain adaptation in order to retain (exponential) stability, which is critical in vehicle operation, and to develop some theoretically based heuristic tuning methods for the adaptive law gain parameters. The classical launch vehicle flight controller design technics are based on gain-scheduling, whereby the launch vehicle dynamics model is linearized at selected operating points along the nominal tracking command trajectory, and Linear Time-Invariant (LTI) controller design techniques are employed to ensure asymptotic stability of the tracking error dynamics, typically by meeting some prescribed Gain Margin (GM) and Phase Margin (PM) specifications. The controller gains at the design points are then scheduled, tuned and sometimes interpolated to achieve good performance and stability robustness under external disturbances (e.g. winds) and structural perturbations (e.g. vehicle modeling errors). While the GM does give a bound for loop-gain variation without losing stability, it is for constant dispersions of the loop-gain because the GM is based on frequency-domain analysis, which is applicable only for LTI systems. The real-time adaptive loop-gain variation of the AAC effectively renders the closed-loop system a time-varying system, for which it is well-known that the LTI system stability criterion is neither necessary nor sufficient when applying to a Linear Time-Varying (LTV) system in a frozen-time fashion. Therefore, a

  16. Functional Task Test: 3. Skeletal Muscle Performance Adaptations to Space Flight

    NASA Technical Reports Server (NTRS)

    Ryder, Jeffrey W.; Wickwire, P. J.; Buxton, R. E.; Bloomberg, J. J.; Ploutz-Snyder, L.

    2011-01-01

    The functional task test is a multi-disciplinary study investigating how space-flight induced changes to physiological systems impacts functional task performance. Impairment of neuromuscular function would be expected to negatively affect functional performance of crewmembers following exposure to microgravity. This presentation reports the results for muscle performance testing in crewmembers. Functional task performance will be presented in the abstract "Functional Task Test 1: sensory motor adaptations associated with postflight alternations in astronaut functional task performance." METHODS: Muscle performance measures were obtained in crewmembers before and after short-duration space flight aboard the Space Shuttle and long-duration International Space Station (ISS) missions. The battery of muscle performance tests included leg press and bench press measures of isometric force, isotonic power and total work. Knee extension was used for the measurement of central activation and maximal isometric force. Upper and lower body force steadiness control were measured on the bench press and knee extension machine, respectively. Tests were implemented 60 and 30 days before launch, on landing day (Shuttle crew only), and 6, 10 and 30 days after landing. Seven Space Shuttle crew and four ISS crew have completed the muscle performance testing to date. RESULTS: Preliminary results for Space Shuttle crew reveal significant reductions in the leg press performance metrics of maximal isometric force, power and total work on R+0 (p<0.05). Bench press total work was also significantly impaired, although maximal isometric force and power were not significantly affected. No changes were noted for measurements of central activation or force steadiness. Results for ISS crew were not analyzed due to the current small sample size. DISCUSSION: Significant reductions in lower body muscle performance metrics were observed in returning Shuttle crew and these adaptations are likely

  17. Development and Flight Testing of an Adaptable Vehicle Health-Monitoring Architecture

    NASA Technical Reports Server (NTRS)

    Woodard, Stanley E.; Coffey, Neil C.; Gonzalez, Guillermo A.; Woodman, Keith L.; Weathered, Brenton W.; Rollins, Courtney H.; Taylor, B. Douglas; Brett, Rube R.

    2003-01-01

    Development and testing of an adaptable wireless health-monitoring architecture for a vehicle fleet is presented. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle; and a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained adaptable expert system. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate, and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear.

  18. Adaptive sliding mode control on inner axis for high precision flight motion simulator

    NASA Astrophysics Data System (ADS)

    Fu, Yongling; Niu, Jianjun; Wang, Yan

    2008-10-01

    Discrete adaptive sliding mode control (ASMC) with exponential reaching law is proposed to alleviate the influence of the factors such as the periodical fluctuation torque of motor, nonlinear friction, and other disturbance which will deteriorate the tracking performance of a DC torque motor driven inner axis for a high precision flight motion simulator, considering the limited compensating ability of the ASMC for these uncertainty, an equivalent friction advance compensator based on Stribeck model is also presented for extra-low speed servo of the system. Firstly, the way direct using the available parts of the inner axis itself to ascertain the parameters for Stribeck model is listed. Secondly, adaptive approach is used to overcome the difficulty of choice the key parameter for exponential reaching law, and the stability of the algorithm is analyzed. Lastly, comparable experiments are carried out to verify the valid of the combined approach. The experiments results show with a stable 0.00006°/s speed response, 95% of time the tracking error is within 0.0002°, other servos such as sine wave tracking are also with high precision.

  19. Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

    NASA Astrophysics Data System (ADS)

    Shankar, Praveen

    The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network

  20. A Reusable and Adaptable Software Architecture for Embedded Space Flight System: The Core Flight Software System (CFS)

    NASA Technical Reports Server (NTRS)

    Wilmot, Jonathan

    2005-01-01

    The contents include the following: High availability. Hardware is in harsh environment. Flight processor (constraints) very widely due to power and weight constraints. Software must be remotely modifiable and still operate while changes are being made. Many custom one of kind interfaces for one of a kind missions. Sustaining engineering. Price of failure is high, tens to hundreds of millions of dollars.

  1. Implementation of an Adaptive Controller System from Concept to Flight Test

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

    2009-01-01

    tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

  2. A neuro-adaptive autopilot design for guided munitions

    NASA Astrophysics Data System (ADS)

    Sharma, Manu

    This thesis treats some neural-network-based nonlinear control methodologies. In particular, a neuro-adaptive autopilot guided munitions is developed. The motivation is to reduce both the effort required, and direct cost incurred for autopilot design for these vehicles. Towards this end, a neural network is used to augment an inverting controller. The network compensates for error present in the inverting logic, thereby providing robustness to parametric uncertainty in the mathematical model of the munition. This equates to a reduced need for expensive wind-tunnel testing. Furthermore, the adaptive nature of the autopilot obviates the requirement for gain scheduling. The methodology is demonstrated on the MK-84 variant of the Joint Direct Attack Munition family of precision-guided munitions. The entire design and tuning procedure is first performed using a simulation based entirely on analytical aerodynamic data generated by Missile DATCOM. The autopilot is then tested on a second simulation, which is based on validated wind-tunnel data and tested. This last step may be viewed as a flight test. A method of augmenting existing linear controllers with neural networks is also addressed. The motivation is to introduce the benefits of adaptation without requiring modifications to the existing architecture. A framework that collapses to many classical and modern forms is considered, to which a corrective control signal is added. The corrective signal is generated by a neural network to force the plant to track a high-order response model that describes the ideal closed-loop dynamics. Subsequently, this philosophy is combined with adaptive backstepping to address unmatched uncertainties in a class of systems in strict-feedback form.

  3. L1 Adaptive Control Law for Flexible Space Launch Vehicle and Proposed Plan for Flight Test Validation

    NASA Technical Reports Server (NTRS)

    Kharisov, Evgeny; Gregory, Irene M.; Cao, Chengyu; Hovakimyan, Naira

    2008-01-01

    This paper explores application of the L1 adaptive control architecture to a generic flexible Crew Launch Vehicle (CLV). Adaptive control has the potential to improve performance and enhance safety of space vehicles that often operate in very unforgiving and occasionally highly uncertain environments. NASA s development of the next generation space launch vehicles presents an opportunity for adaptive control to contribute to improved performance of this statically unstable vehicle with low damping and low bending frequency flexible dynamics. In this paper, we consider the L1 adaptive output feedback controller to control the low frequency structural modes and propose steps to validate the adaptive controller performance utilizing one of the experimental test flights for the CLV Ares-I Program.

  4. Neural network based system for equipment surveillance

    DOEpatents

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  5. Neural network based system for equipment surveillance

    DOEpatents

    Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

    1998-01-01

    A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

  6. Evaluation of the Hinge Moment and Normal Force Aerodynamic Loads from a Seamless Adaptive Compliant Trailing Edge Flap in Flight

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Cruz, Josue; Lung, Shun-Fat; Kota, Sridhar; Ervin, Gregory; Lu, Kerr-Jia; Flick, Pete

    2016-01-01

    A seamless adaptive compliant trailing edge (ACTE) flap was demonstrated in flight on a Gulfstream III aircraft at the NASA Armstrong Flight Research Center. The trailing edge flap was deflected between minus 2 deg up and plus 30 deg down in flight. The safety-of-flight parameters for the ACTE flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. The attachment fittings connecting the flap to the aircraft wing rear spar were instrumented with strain gages and calibrated using known loads for measuring hinge moment and normal force loads in flight. The safety-of-flight parameters for the ACTE flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. The attachment fittings connecting the flap to the aircraft wing rear spar were instrumented with strain gages and calibrated using known loads for measuring hinge moment and normal force loads in flight. The interface hardware instrumentation layout and load calibration are discussed. Twenty-one applied calibration test load cases were developed for each individual fitting. The 2-sigma residual errors for the hinge moment was calculated to be 2.4 percent, and for normal force was calculated to be 7.3 percent. The hinge moment and normal force generated by the ACTE flap with a hinge point located at 26-percent wing chord were measured during steady state and symmetric pitch maneuvers. The loads predicted from analysis were compared to the loads observed in flight. The hinge moment loads showed good agreement with the flight loads while the normal force loads calculated from analysis were over-predicted by approximately 20 percent. Normal force and hinge moment loads calculated from the pressure sensors located on the ACTE showed good agreement with the loads calculated from the installed strain gages.

  7. In-flight Fault Detection and Isolation in Aircraft Flight Control Systems

    NASA Technical Reports Server (NTRS)

    Azam, Mohammad; Pattipati, Krishna; Allanach, Jeffrey; Poll, Scott; Patterson-Hine, Ann

    2005-01-01

    In this paper we consider the problem of test design for real-time fault detection and isolation (FDI) in the flight control system of fixed-wing aircraft. We focus on the faults that are manifested in the control surface elements (e.g., aileron, elevator, rudder and stabilizer) of an aircraft. For demonstration purposes, we restrict our focus on the faults belonging to nine basic fault classes. The diagnostic tests are performed on the features extracted from fifty monitored system parameters. The proposed tests are able to uniquely isolate each of the faults at almost all severity levels. A neural network-based flight control simulator, FLTZ(Registered TradeMark), is used for the simulation of various faults in fixed-wing aircraft flight control systems for the purpose of FDI.

  8. The three-dimensional flight of red-footed boobies: adaptations to foraging in a tropical environment?

    PubMed

    Weimerskirch, H; Le Corre, M; Ropert-Coudert, Y; Kato, A; Marsac, F

    2005-01-01

    In seabirds a broad variety of morphologies, flight styles and feeding methods exist as an adaptation to optimal foraging in contrasted marine environments for a wide variety of prey types. Because of the low productivity of tropical waters it is expected that specific flight and foraging techniques have been selected there, but very few data are available. By using five different types of high-precision miniaturized logger (global positioning systems, accelerometers, time depth recorders, activity recorders, altimeters) we studied the way a seabird is foraging over tropical waters. Red-footed boobies are foraging in the day, never foraging at night, probably as a result of predation risks. They make extensive use of wind conditions, flying preferentially with crosswinds at median speed of 38 km h(-1), reaching highest speeds with tail winds. They spent 66% of the foraging trip in flight, using a flap-glide flight, and gliding 68% of the flight. Travelling at low costs was regularly interrupted by extremely active foraging periods where birds are very frequently touching water for landing, plunge diving or surface diving (30 landings h(-1)). Dives were shallow (maximum 2.4 m) but frequent (4.5 dives h(-1)), most being plunge dives. While chasing for very mobile prey like flying fishes, boobies have adopted a very active and specific hunting behaviour, but the use of wind allows them to reduce travelling cost by their extensive use of gliding. During the foraging and travelling phases birds climb regularly to altitudes of 20-50 m to spot prey or congeners. During the final phase of the flight, they climb to high altitudes, up to 500 m, probably to avoid attacks by frigatebirds along the coasts. This study demonstrates the use by boobies of a series of very specific flight and activity patterns that have probably been selected as adaptations to the conditions of tropical waters. PMID:15875570

  9. The three-dimensional flight of red-footed boobies: adaptations to foraging in a tropical environment?

    PubMed Central

    Weimerskirch, H.; Le Corre, M.; Ropert-Coudert, Y.; Kato, A.; Marsac, F.

    2005-01-01

    In seabirds a broad variety of morphologies, flight styles and feeding methods exist as an adaptation to optimal foraging in contrasted marine environments for a wide variety of prey types. Because of the low productivity of tropical waters it is expected that specific flight and foraging techniques have been selected there, but very few data are available. By using five different types of high-precision miniaturized logger (global positioning systems, accelerometers, time depth recorders, activity recorders, altimeters) we studied the way a seabird is foraging over tropical waters. Red-footed boobies are foraging in the day, never foraging at night, probably as a result of predation risks. They make extensive use of wind conditions, flying preferentially with crosswinds at median speed of 38 km h−1, reaching highest speeds with tail winds. They spent 66% of the foraging trip in flight, using a flap–glide flight, and gliding 68% of the flight. Travelling at low costs was regularly interrupted by extremely active foraging periods where birds are very frequently touching water for landing, plunge diving or surface diving (30 landings h−1). Dives were shallow (maximum 2.4 m) but frequent (4.5 dives h−1), most being plunge dives. While chasing for very mobile prey like flying fishes, boobies have adopted a very active and specific hunting behaviour, but the use of wind allows them to reduce travelling cost by their extensive use of gliding. During the foraging and travelling phases birds climb regularly to altitudes of 20–50 m to spot prey or congeners. During the final phase of the flight, they climb to high altitudes, up to 500 m, probably to avoid attacks by frigatebirds along the coasts. This study demonstrates the use by boobies of a series of very specific flight and activity patterns that have probably been selected as adaptations to the conditions of tropical waters. PMID:15875570

  10. Assessing Arboreal Adaptations of Bird Antecedents: Testing the Ecological Setting of the Origin of the Avian Flight Stroke

    PubMed Central

    Dececchi, T. Alexander; Larsson, Hans C. E.

    2011-01-01

    The origin of avian flight is a classic macroevolutionary transition with research spanning over a century. Two competing models explaining this locomotory transition have been discussed for decades: ground up versus trees down. Although it is impossible to directly test either of these theories, it is possible to test one of the requirements for the trees-down model, that of an arboreal paravian. We test for arboreality in non-avian theropods and early birds with comparisons to extant avian, mammalian, and reptilian scansors and climbers using a comprehensive set of morphological characters. Non-avian theropods, including the small, feathered deinonychosaurs, and Archaeopteryx, consistently and significantly cluster with fully terrestrial extant mammals and ground-based birds, such as ratites. Basal birds, more advanced than Archaeopteryx, cluster with extant perching ground-foraging birds. Evolutionary trends immediately prior to the origin of birds indicate skeletal adaptations opposite that expected for arboreal climbers. Results reject an arboreal capacity for the avian stem lineage, thus lending no support for the trees-down model. Support for a fully terrestrial ecology and origin of the avian flight stroke has broad implications for the origin of powered flight for this clade. A terrestrial origin for the avian flight stroke challenges the need for an intermediate gliding phase, presents the best resolved series of the evolution of vertebrate powered flight, and may differ fundamentally from the origin of bat and pterosaur flight, whose antecedents have been postulated to have been arboreal and gliding. PMID:21857918

  11. Aerodynamic Flight-Test Results for the Adaptive Compliant Trailing Edge

    NASA Technical Reports Server (NTRS)

    Cumming, Stephen B.; Smith, Mark S.; Ali, Aliyah N.; Bui, Trong T.; Ellsworth, Joel C.; Garcia, Christian A.

    2016-01-01

    The aerodynamic effects of compliant flaps installed onto a modified Gulfstream III airplane were investigated. Analyses were performed prior to flight to predict the aerodynamic effects of the flap installation. Flight tests were conducted to gather both structural and aerodynamic data. The airplane was instrumented to collect vehicle aerodynamic data and wing pressure data. A leading-edge stagnation detection system was also installed. The data from these flights were analyzed and compared with predictions. The predictive tools compared well with flight data for small flap deflections, but differences between predictions and flight estimates were greater at larger deflections. This paper describes the methods used to examine the aerodynamics data from the flight tests and provides a discussion of the flight-test results in the areas of vehicle aerodynamics, wing sectional pressure coefficient profiles, and air data.

  12. Space physiology II: adaptation of the central nervous system to space flight--past, current, and future studies.

    PubMed

    Clément, Gilles; Ngo-Anh, Jennifer Thu

    2013-07-01

    Experiments performed in orbit on the central nervous system have focused on the control of posture, eye movements, spatial orientation, as well as cognitive processes, such as three-dimensional visual perception and mental representation of space. Brain activity has also been recorded during and immediately after space flight for evaluating the changes in brain structure activation during tasks involving perception, attention, memory, decision, and action. Recent ground-based studies brought evidence that the inputs from the neurovestibular system also participate in orthostatic intolerance. It is, therefore, important to revisit the flight data of neuroscience studies in the light of new models of integrative physiology. The outcomes of this exercise will increase our knowledge on the adaptation of body functions to changing gravitational environment, vestibular disorders, aging, and our approach towards more effective countermeasures during human space flight and planetary exploration. PMID:23053128

  13. A review of adaptive change in musculoskeletal impedance during space flight and associated implications for postflight head movement control

    NASA Technical Reports Server (NTRS)

    McDonald, P. V.; Bloomberg, J. J.; Layne, C. S.

    1997-01-01

    We present a review of converging sources of evidence which suggest that the differences between loading histories experienced in 1-g and weightlessness are sufficient to stimulate adaptation in mechanical impedance of the musculoskeletal system. As a consequence of this adaptive change we argue that we should observe changes in the ability to attenuate force transmission through the musculoskeletal system both during and after space flight. By focusing attention on the relation between human sensorimotor activity and support surfaces, the importance of controlling mechanical energy flow through the musculoskeletal system is demonstrated. The implications of such control are discussed in light of visual-vestibular function in the specific context of head and gaze control during postflight locomotion. Evidence from locomotory biomechanics, visual-vestibular function, ergonomic evaluations of human vibration, and specific investigations of locomotion and head and gaze control after space flight, is considered.

  14. Lockheed L-1011 Test Station on-board in support of the Adaptive Performance Optimization flight res

    NASA Technical Reports Server (NTRS)

    1997-01-01

    This console and its compliment of computers, monitors and commmunications equipment make up the Research Engineering Test Station, the nerve center for a new aerodynamics experiment being conducted by NASA's Dryden Flight Research Center, Edwards, California. The equipment is installed on a modified Lockheed L-1011 Tristar jetliner operated by Orbital Sciences Corp., of Dulles, Va., for Dryden's Adaptive Performance Optimization project. The experiment seeks to improve the efficiency of long-range jetliners by using small movements of the ailerons to improve the aerodynamics of the wing at cruise conditions. About a dozen research flights in the Adaptive Performance Optimization project are planned over the next two to three years. Improving the aerodynamic efficiency should result in equivalent reductions in fuel usage and costs for airlines operating large, wide-bodied jetliners.

  15. Towards a genetics-based adaptive agent to support flight testing

    NASA Astrophysics Data System (ADS)

    Cribbs, Henry Brown, III

    Although the benefits of aircraft simulation have been known since the late 1960s, simulation almost always entails interaction with a human test pilot. This "pilot-in-the-loop" simulation process provides useful evaluative information to the aircraft designer and provides a training tool to the pilot. Emulation of a pilot during the early phases of the aircraft design process might provide designers a useful evaluative tool. Machine learning might emulate a pilot in a simulated aircraft/cockpit setting. Preliminary work in the application of machine learning techniques, such as reinforcement learning, to aircraft maneuvering have shown promise. These studies used simplified interfaces between machine learning agent and the aircraft simulation. The simulations employed low order equivalent system models. High-fidelity aircraft simulations exist, such as the simulations developed by NASA at its Dryden Flight Research Center. To expand the applicational domain of reinforcement learning to aircraft designs, this study presents a series of experiments that examine a reinforcement learning agent in the role of test pilot. The NASA X-31 and F-106 high-fidelity simulations provide realistic aircraft for the agent to maneuver. The approach of the study is to examine an agent possessing a genetic-based, artificial neural network to approximate long-term, expected cost (Bellman value) in a basic maneuvering task. The experiments evaluate different learning methods based on a common feedback function and an identical task. The learning methods evaluated are: Q-learning, Q(lambda)-learning, SARSA learning, and SARSA(lambda) learning. Experimental results indicate that, while prediction error remain quite high, similar, repeatable behaviors occur in both aircraft. Similar behavior exhibits portability of the agent between aircraft with different handling qualities (dynamics). Besides the adaptive behavior aspects of the study, the genetic algorithm used in the agent is shown to

  16. In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

    2015-01-01

    NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

  17. In-Flight Suppression of a Destabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

    NASA Technical Reports Server (NTRS)

    Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

    2015-01-01

    NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

  18. Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.

    2010-01-01

    In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as 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 the 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 robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

  19. [Adaptive process in Vietnamese military pilots during the flights on modern Russian aircraft].

    PubMed

    Ushakov, I V; Pham Xuan, Nihn; Bukhtiaiarov, I V; Ushakov, B N

    2013-04-01

    Study on health status of 156 Vietnamese military pilots on Russian modern jet planes (Su-22, Su-27, Su-30, MiG-21B). The results showed that unprofitable factors in working environment (acceleration, radiation, high temperature, humidity, noise) have an impact on the health of pilots during the flight, leading to deterioration of professional health and physiological functions (cardiovascular, respiratory and nervous system) and obesity of pilots after 35 years old. Basing on the studies, we suggested some measures for health protecting, safety of flight and prolonging flight-activity of pilots (training in decompression chamber, vestibular training) and balance in food ration for prevention of professional diseases. PMID:24000611

  20. Strain Gage Load Calibration of the Wing Interface Fittings for the Adaptive Compliant Trailing Edge Flap Flight Test

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Holguin, Andrew C.; Cruz, Josue; Lokos, William A.

    2014-01-01

    This is the presentation to follow conference paper of the same name. The adaptive compliant trailing edge (ACTE) flap experiment safety of flight requires that the flap to wing interface loads be sensed and monitored in real time to ensure that the wing structural load limits are not exceeded. This paper discusses the strain gage load calibration testing and load equation derivation methodology for the ACTE interface fittings. Both the left and right wing flap interfaces will be monitored and each contains four uniquely designed and instrumented flap interface fittings. The interface hardware design and instrumentation layout are discussed. Twenty one applied test load cases were developed using the predicted in-flight loads for the ACTE experiment.

  1. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  2. L(sub 1) Adaptive Control Design for NASA AirSTAR Flight Test Vehicle

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Cao, Chengyu; Hovakimyan, Naira; Zou, Xiaotian

    2009-01-01

    In this paper we present a new L(sub 1) adaptive control architecture that directly compensates for matched as well as unmatched system uncertainty. To evaluate the L(sub 1) adaptive controller, we take advantage of the flexible research environment with rapid prototyping and testing of control laws in the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. We apply the L(sub 1) adaptive control laws to the subscale turbine powered Generic Transport Model. The presented results are from a full nonlinear simulation of the Generic Transport Model and some preliminary pilot evaluations of the L(sub 1) adaptive control law.

  3. Development of Micro Air Vehicle Technology With In-Flight Adaptive-Wing Structure

    NASA Technical Reports Server (NTRS)

    Waszak, Martin R. (Technical Monitor); Shkarayev, Sergey; Null, William; Wagner, Matthew

    2004-01-01

    This is a final report on the research studies, "Development of Micro Air Vehicle Technology with In-Flight Adaptrive-Wing Structure". This project involved the development of variable-camber technology to achieve efficient design of micro air vehicles. Specifically, it focused on the following topics: 1) Low Reynolds number wind tunnel testing of cambered-plate wings. 2) Theoretical performance analysis of micro air vehicles. 3) Design of a variable-camber MAV actuated by micro servos. 4) Test flights of a variable-camber MAV.

  4. Spatial perception changes associated with space flight: implications for adaptation to altered inertial environments.

    PubMed

    Parker, Donald E

    2003-01-01

    Preparation for extended travel by astronauts within the Solar System, including a possible manned mission to Mars, requires more complete understanding of adaptation to altered inertial environments. Improved understanding is needed to support development and evaluation of interventions to facilitate adaptations during transitions between those environments. Travel to another planet escalates the adaptive challenge because astronauts will experience prolonged exposure to microgravity before encountering a novel gravitational environment. This challenge would have to be met without ground support at the landing site. Evaluation of current adaptive status as well as intervention efficacy can be performed using perceptual, eye movement and postural measures. Due to discrepancies of adaptation magnitude and time-course among these measures, complete understanding of adaptation processes, as well as intervention evaluation, requires examination of all three. Previous research and theory that provide models for comprehending adaptation to altered inertial environments are briefly examined. Reports from astronauts of selected pre- in- and postflight self-motion illusions are described. The currently controversial tilt-translation reinterpretation hypothesis is reviewed and possible resolutions to the controversy are proposed. Finally, based on apparent gaps in our current knowledge, further research is proposed to achieve a more complete understanding of adaptation as well as to develop effective counter-measures. PMID:15096676

  5. Request for Information Response for the Flight Validation of Adaptive Control to Prevent Loss-of-Control Events. Overview of RFI Responses

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2009-01-01

    Adaptive control should be integrated with a baseline controller and only used when necessary (5 responses). Implementation as an emergency system. Immediately re-stabilize and return to controlled flight. Forced perturbation (excitation) for fine-tuning system a) Check margins; b) Develop requirements for amplitude of excitation. Adaptive system can improve performance by eating into margin constraints imposed on the non-adaptive system. Nonlinear effects due to multi-string voting.

  6. Falcon versus grouse: flight adaptations of a predator and its prey

    USGS Publications Warehouse

    Pennycuick, C.J.; Fuller, M.R.; Oar, J.J.; Kirkpatrick, S.J.

    1994-01-01

    Several falcons were trained to fly along a 500 m course to a lure. The air speeds of the more consistent performers averaged about 1.5 times their calculated minimum power speeds, and occasionally reached 2.1 times the minimum power speed. Wing beat frequencies of all the falcons were above those estimated from earlier field observations, and the same was true of wild Sage Grouse Centrocercus urophasianus, a regular falconer's quarry in the study area. Measurements of grouse killed by falcons showed that their wings were short, with broad slotted tips, whereas the falcons' wings were longer in relation to their body mass, and tapered. The short wings of grouse result in fast flight, high power requirements, and reduced capacity for aerobic flight. Calculations indicated that the grouse should fly faster than the falcons, and had the large amount of flight muscle needed to do so, but that the falcons would be capable of prolonged aerobic flight, whereas the grouse probably would not. We surmise that Sage Grouse cannot fly continuously without incurring an oxygen debt, and are therefore not long-distance migrants, although this limitation is partly due to their large size, and would not apply to smaller galliform birds such as ptarmigan Lagopus spp. The wing action seen in video recordings of the falcons was not consistent with the maintenance of constant circulation. We call it 'chase mode' because it appears to be associated with a high level of muscular exertion, without special regard to fuel economy. It shows features in common with the 'bounding' flight of passerines.

  7. Investigation of the Multiple Method Adaptive Control (MMAC) method for flight control systems

    NASA Technical Reports Server (NTRS)

    Athans, M.; Baram, Y.; Castanon, D.; Dunn, K. P.; Green, C. S.; Lee, W. H.; Sandell, N. R., Jr.; Willsky, A. S.

    1979-01-01

    The stochastic adaptive control of the NASA F-8C digital-fly-by-wire aircraft using the multiple model adaptive control (MMAC) method is presented. The selection of the performance criteria for the lateral and the longitudinal dynamics, the design of the Kalman filters for different operating conditions, the identification algorithm associated with the MMAC method, the control system design, and simulation results obtained using the real time simulator of the F-8 aircraft at the NASA Langley Research Center are discussed.

  8. Adaptive switching filter for noise removal in highly corrupted depth maps from Time-of-Flight image sensors

    NASA Astrophysics Data System (ADS)

    Lee, Seunghee; Bae, Kwanghyuk; Kyung, Kyu-min; Kim, Tae-Chan

    2012-03-01

    In this work, we present an adaptive switching filter for noise reduction and sharpness preservation in depth maps provided by Time-of-Flight (ToF) image sensors. Median filter and bilateral filter are commonly used in cost-sensitive applications where low computational complexity is needed. However, median filter blurs fine details and edges in depth map while bilateral filter works poorly with impulse noise present in the image. Since the variance of depth is inversely proportional to amplitude, we suggest an adaptive filter that switches between median filter and bilateral filter based on the level of amplitude. If a region of interest has low amplitude indicating low confidence level of measured depth data, then median filter is applied on the depth at the position while regions with high level of amplitude is processed with bilateral filter using Gaussian kernel with adaptive weights. Results show that the suggested algorithm performs surface smoothing and detail preservation as well as median filter and bilateral filter, respectively. By using the suggested algorithm, significant gain in visual quality is obtained in depth maps while low computational cost is maintained.

  9. Multivariate Dynamic Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts

    NASA Technical Reports Server (NTRS)

    Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah

    2014-01-01

    The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in integrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.

  10. Multivariate Dynamical Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts

    NASA Technical Reports Server (NTRS)

    Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah

    2014-01-01

    The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in intergrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.

  11. Central Adaptation to Repeated Galvanic Vestibular Stimulation: Implications for Pre-Flight Astronaut Training

    PubMed Central

    Dilda, Valentina; Morris, Tiffany R.; Yungher, Don A.; MacDougall, Hamish G.; Moore, Steven T.

    2014-01-01

    Healthy subjects (N = 10) were exposed to 10-min cumulative pseudorandom bilateral bipolar Galvanic vestibular stimulation (GVS) on a weekly basis for 12 weeks (120 min total exposure). During each trial subjects performed computerized dynamic posturography and eye movements were measured using digital video-oculography. Follow up tests were conducted 6 weeks and 6 months after the 12-week adaptation period. Postural performance was significantly impaired during GVS at first exposure, but recovered to baseline over a period of 7–8 weeks (70–80 min GVS exposure). This postural recovery was maintained 6 months after adaptation. In contrast, the roll vestibulo-ocular reflex response to GVS was not attenuated by repeated exposure. This suggests that GVS adaptation did not occur at the vestibular end-organs or involve changes in low-level (brainstem-mediated) vestibulo-ocular or vestibulo-spinal reflexes. Faced with unreliable vestibular input, the cerebellum reweighted sensory input to emphasize veridical extra-vestibular information, such as somatosensation, vision and visceral stretch receptors, to regain postural function. After a period of recovery subjects exhibited dual adaption and the ability to rapidly switch between the perturbed (GVS) and natural vestibular state for up to 6 months. PMID:25409443

  12. Central adaptation to repeated galvanic vestibular stimulation: implications for pre-flight astronaut training.

    PubMed

    Dilda, Valentina; Morris, Tiffany R; Yungher, Don A; MacDougall, Hamish G; Moore, Steven T

    2014-01-01

    Healthy subjects (N = 10) were exposed to 10-min cumulative pseudorandom bilateral bipolar Galvanic vestibular stimulation (GVS) on a weekly basis for 12 weeks (120 min total exposure). During each trial subjects performed computerized dynamic posturography and eye movements were measured using digital video-oculography. Follow up tests were conducted 6 weeks and 6 months after the 12-week adaptation period. Postural performance was significantly impaired during GVS at first exposure, but recovered to baseline over a period of 7-8 weeks (70-80 min GVS exposure). This postural recovery was maintained 6 months after adaptation. In contrast, the roll vestibulo-ocular reflex response to GVS was not attenuated by repeated exposure. This suggests that GVS adaptation did not occur at the vestibular end-organs or involve changes in low-level (brainstem-mediated) vestibulo-ocular or vestibulo-spinal reflexes. Faced with unreliable vestibular input, the cerebellum reweighted sensory input to emphasize veridical extra-vestibular information, such as somatosensation, vision and visceral stretch receptors, to regain postural function. After a period of recovery subjects exhibited dual adaption and the ability to rapidly switch between the perturbed (GVS) and natural vestibular state for up to 6 months. PMID:25409443

  13. In-Flight Suppression of a De-Stabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

    NASA Technical Reports Server (NTRS)

    Wall, John; VanZwieten, Tannen; Giiligan Eric; Miller, Chris; Hanson, Curtis; Orr, Jeb

    2015-01-01

    Adaptive Augmenting Control (AAC) has been developed for NASA's Space Launch System (SLS) family of launch vehicles and implemented as a baseline part of its flight control system (FCS). To raise the technical readiness level of the SLS AAC algorithm, the Launch Vehicle Adaptive Control (LVAC) flight test program was conducted in which the SLS FCS prototype software was employed to control the pitch axis of Dryden's specially outfitted F/A-18, the Full Scale Advanced Systems Test Bed (FAST). This presentation focuses on a set of special test cases which demonstrate the successful mitigation of the unstable coupling of an F/A-18 airframe structural mode with the SLS FCS.

  14. Flight performance using a hyperstereo helmet-mounted display: adaptation to hyperstereopsis

    NASA Astrophysics Data System (ADS)

    Stuart, Geoffrey W.; Jennings, Sion A.; Kalich, Melvyn E.; Rash, Clarence E.; Harding, Thomas H.; Craig, Gregory L.

    2009-05-01

    Modern helmet-mounted night vision devices, such as the Thales TopOwlTM helmet, project imagery from intensifiers mounted on the sides of the helmet onto the helmet visor. This increased effective inter-ocular separation distorts several cues to depth and distance that are grouped under the term "hyperstereopsis". Stereoscopic depth perception, at near to moderate distances (several hundred metres), is subject to magnification of binocular disparities. Absolute distance perception at near distances (a few metres) is affected by increased "differential perspective" as well as an increased requirement for convergence of the eyes to achieve binocular fixation. These distortions result in visual illusions such as the "bowl effect" where the ground appears to rise up near the observer. Previous reports have indicated that pilots can adapt to these distortions after several hours of exposure. The present study was concerned with both the time course and the mechanisms involved in this adaptation. Three test pilots flew five sorties with a hyperstereo night vision device. Initially, pilots reported that they were compensating for the effects of hyperstereopsis, but on the third and subsequent sorties all reported perceptual adaptation, that is, a reduction in illusory perception. Given that this adaptation was the result of intermittent exposure, and did not produce visual aftereffects, it was not due to the recalibration of the relationship between binocular cues and depth/distance. A more likely explanation of the observed visual adaptation is that it results from a discounting of distorted binocular cues in favour of veridical monocular cues, such as familiar size, motion parallax and linear perspective.

  15. Morpho-functional adaptations in the bone tissue under the space flight conditions.

    PubMed

    Rodionova, N V; Oganov, V S

    2001-07-01

    Microgravity in space flight--situation of a maximum deficit of supporting loading on the skeleton and good model for finding-out of osteopenia and osteoporosis development laws, which are wide-spreading now and are "civilization diseases". Most typical for bones in conditions of a microgravitation by changes are: a decrease of intensity growth and osteoplastic processes, osteopenia and osteoporosis, decreasing of a mechanical strength and the risk of breaches arising (Oganov V.S., Schneider V. (1996)). Cytological mechanisms of gravity-dependent reactions in a bone tissue remain in many respects not-clear. By the purpose of our work was the analysis of some ultrastructural changes in bone tissue cells of the monkeys (Macaca mulatta), staying during two weeks onboard the biosatellite BION -11. PMID:12650186

  16. Adaptive Responses in Eye-Head-Hand Coordination Following Exposures to a Virtual Environment as a Possible Space Flight Analog

    NASA Technical Reports Server (NTRS)

    Harm, Deborah L.; Taylor, L. C.; Bloomberg, J. J.

    2007-01-01

    Virtual environments (VE) offer unique training opportunities, particularly for training astronauts and preadapting them to the novel sensory conditions of microgravity. Sensorimotor aftereffects of VEs are often quite similar to adaptive sensorimotor responses observed in astronauts during and/or following space flight. The purpose of this research was to compare disturbances in sensorimotor coordination produced by dome virtual environment display and to examine the effects of exposure duration, and repeated exposures to VR systems. The current study examined disturbances in eye-head-hand (EHH) and eye-head coordination. Preliminary results will be presented. Eleven subjects have participated in the study to date. One training session was completed in order to achieve stable performance on the EHH coordination and VE tasks. Three experimental sessions were performed each separated by one day. Subjects performed a navigation and pick and place task in a dome immersive display VE for 30 or 60 min. The subjects were asked to move objects from one set of 15 pedestals to the other set across a virtual square room through a random pathway as quickly and accurately as possible. EHH coordination was measured before, immediately after, and at 1 hr, 2 hr, 4 hr and 6 hr following exposure to VR. EHH coordination was measured as position errors and reaction time in a pointing task that included multiple horizontal and vertical LED targets. Repeated measures ANOVAs were used to analyze the data. In general, we observed significant increases in position errors for both horizontal and vertical targets. The largest decrements were observed immediately following exposure to VR and showed a fairly rapid recovery across test sessions, but not across days. Subjects generally showed faster RTs across days. Individuals recovered from the detrimental effects of exposure to the VE on position errors within 1-2 hours. The fact that subjects did not significantly improve across days

  17. Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

    SciTech Connect

    Williams, Rube B.

    2004-02-04

    Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

  18. Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

    NASA Astrophysics Data System (ADS)

    Williams, Rube B.

    2004-02-01

    Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

  19. Work, exercise, and space flight. 2: Modification of adaptation by exercise (exercise prescription)

    NASA Technical Reports Server (NTRS)

    Thornton, William

    1989-01-01

    The fundamentals of exercise theory on earth must be rigorously understood and applied to prevent adaptation to long periods of weightlessness. Locomotor activity, not weight, determines the capacity or condition of the largest muscles and bones in the body and usually also determines cardio-respiratory capacity. Absence of this activity results in rapid atrophy of muscle, bone, and cardio-respiratory capacity. Upper body muscle and bone are less affected depending upon the individual's usual, or 1-g, activities. Methodology is available to prevent these changes but space operations demand that it be done in the most efficient fashion, i.e., shortest time. At this point in time we can reasonably select the type of exercise and methods of obtaining it, but additional work in 1-g will be required to optimize the time.

  20. Stability Metrics for Simulation and Flight-Software Assessment and Monitoring of Adaptive Control Assist Compensators

    NASA Technical Reports Server (NTRS)

    Hodel, A. S.; Whorton, Mark; Zhu, J. Jim

    2008-01-01

    Due to a need for improved reliability and performance in aerospace systems, there is increased interest in the use of adaptive control or other nonlinear, time-varying control designs in aerospace vehicles. While such techniques are built on Lyapunov stability theory, they lack an accompanying set of metrics for the assessment of stability margins such as the classical gain and phase margins used in linear time-invariant systems. Such metrics must both be physically meaningful and permit the user to draw conclusions in a straightforward fashion. We present in this paper a roadmap to the development of metrics appropriate to nonlinear, time-varying systems. We also present two case studies in which frozen-time gain and phase margins incorrectly predict stability or instability. We then present a multi-resolution analysis approach that permits on-line real-time stability assessment of nonlinear systems.

  1. Physiological Adaptations and Countermeasures Associated with Long-Duration Space Flights

    NASA Technical Reports Server (NTRS)

    Tipton, C. M.; Hargens, A. R.; Baldwin, K. M.; Schneider, V.; Convertino, V. A.; Kozlovskaya, I.; Wade, Charles E. (Technical Monitor)

    1994-01-01

    On earth, the presence of gravity imposes weight-bearing gradients on tissues which influence the functions of multiple integrative systems. On the other hand, conditions of actual or simulated microgravity can modify and/or nullify these gradients and subsequently alter structure and function. The purpose of this symposium is to discuss the results from short-term Shuttle flights, long term Skylab or Mir missions, or long-term ground based experiments which indicate or suggest that performance has been or could be compromised in space missions of long durations (>one year) or with space tasks (e.g. building space stations) with the goal of identifying countermeasures that could minimize or eliminate the expected anatomical and physiological consequences. After an overview by C. Tipton from the U. Arizona, the countermeasures necessary for the fluid shifts and select functions of the cardiovascular system will be discussed by A. Hargens from NASA Ames Research Center. He will be followed by K. Baldwin of the U. California at Irvine who will discuss the countermeasures needed to prevent the changes that alter the structure, function and control of skeletal muscles. Since changes in bone mass with microgravity are a major concern of NASA, V. Schneider from NASA Headquarters will present data and the countermeasures for bone. Although the results are limited, the changes in the endocrine and immune system deserve mentioning and C. Tipton will assume this responsibility. V. Convertino from the Air Force School of Aerospace Medicine has the challenge of discussing the role, importance, and the specificity of exercise as an effective countermeasure while I. Kozlovskaya from Moscow will elaborate on the Russian experiences with past countermeasures and provide a viewpoint on future ones. After the brief (25 min.) presentations, the speakers will assemble as a panel to discuss the issues raised and the concerns of the audience.

  2. Flight Test of an Intelligent Flight-Control System

    NASA Technical Reports Server (NTRS)

    Davidson, Ron; Bosworth, John T.; Jacobson, Steven R.; Thomson, Michael Pl; Jorgensen, Charles C.

    2003-01-01

    inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

  3. Strain Gage Load Calibration of the Wing Interface Fittings for the Adaptive Compliant Trailing Edge Flap Flight Test

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Holguin, Andrew C.; Cruz, Josue; Lokos, William A.

    2014-01-01

    The safety-of-flight parameters for the Adaptive Compliant Trailing Edge (ACTE) flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. This paper discusses the strain gage load calibration testing and load equation derivation methodology for the ACTE interface fittings. Both the left and right wing flap interfaces were monitored; each contained four uniquely designed and instrumented flap interface fittings. The interface hardware design and instrumentation layout are discussed. Twenty-one applied test load cases were developed using the predicted in-flight loads. Pre-test predictions of strain gage responses were produced using finite element method models of the interface fittings. Predicted and measured test strains are presented. A load testing rig and three hydraulic jacks were used to apply combinations of shear, bending, and axial loads to the interface fittings. Hardware deflections under load were measured using photogrammetry and transducers. Due to deflections in the interface fitting hardware and test rig, finite element model techniques were used to calculate the reaction loads throughout the applied load range, taking into account the elastically-deformed geometry. The primary load equations were selected based on multiple calibration metrics. An independent set of validation cases was used to validate each derived equation. The 2-sigma residual errors for the shear loads were less than eight percent of the full-scale calibration load; the 2-sigma residual errors for the bending moment loads were less than three percent of the full-scale calibration load. The derived load equations for shear, bending, and axial loads are presented, with the calculated errors for both the calibration cases and the independent validation load cases.

  4. Adaptive Control Strategies for Flexible Robotic Arm

    NASA Technical Reports Server (NTRS)

    Bialasiewicz, Jan T.

    1996-01-01

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

  5. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  6. Auxiliary probe design adaptable to existing probes for remote detection NMR, MRI, and time-of-flight tracing.

    PubMed

    Han, Songi; Granwehr, Josef; Garcia, Sandra; McDonnell, Erin E; Pines, Alexander

    2006-10-01

    A versatile, detection-only probe design is presented that can be adapted to any existing NMR or MRI probe with the purpose of making the remote detection concept generally applicable. Remote detection suggests freeing the NMR experiment from the confinement of using the same radio frequency (RF) coil and magnetic field for both information encoding and signal detection. Information is stored during the encoding step onto a fluid sensor medium whose magnetization is later measured in a different location. The choice of an RF probe and magnetic field for encoding can be made based solely on the size and characteristics of the sample and the desired information quality without considering detection sensitivity, as this aspect is dealt with by a separate detector. While early experiments required building probes that included two resonant circuits, one for encoding and one for detection, a modular approach with a detection-only probe as presented here can be used along with any existing NMR probe of choice for encoding. The design of two different detection-only probes is presented, one with a saddle coil for milliliter-sized detection volumes, and the other one with a microsolenoid coil for sub-microliter fluid quantities. As example applications, we present time-of-flight (TOF) tracing of hyperpolarized (129)Xe spins in a gas mixture through coiled tubing using the microsolenoid coil detector and TOF flow imaging through a nested glass container where the gas flow changes its direction twice between inlet and outlet using the saddle coil detector. PMID:16875855

  7. Use of neural network based auto-associative memory as a data compressor for pre-processing optical emission spectra in gas thermometry with the help of neural network

    NASA Astrophysics Data System (ADS)

    Dolenko, S. A.; Filippov, A. V.; Pal, A. F.; Persiantsev, I. G.; Serov, A. O.

    2003-04-01

    Determination of temperature from optical emission spectra is an inverse problem that is often very difficult to solve, especially when substantial noise is present. One of the means that can be used to solve such a problem is a neural network trained on the results of modeling of spectra at different temperatures (Dolenko, et al., in: I.C. Parmee (Ed.), Adaptive Computing in Design and Manufacture, Springer, London, 1998, p. 345). Reducing the dimensionality of the input data prior to application of neural network can increase the accuracy and stability of temperature determination. In this study, such pre-processing is performed with another neural network working as an auto-associative memory with a narrow bottleneck in the hidden layer. The improvement in the accuracy and stability of temperature determination in presence of noise is demonstrated on model spectra similar to those recorded in a DC-discharge CVD reactor.

  8. Steering by Hearing: A Bat’s Acoustic Gaze Is Linked to Its Flight Motor Output by a Delayed, Adaptive Linear Law

    PubMed Central

    Ghose, Kaushik; Moss, Cynthia F.

    2012-01-01

    Adaptive behaviors require sensorimotor computations that convert information represented initially in sensory coordinates to commands for action in motor coordinates. Fundamental to these computations is the relationship between the region of the environment sensed by the animal (gaze) and the animal’s locomotor plan. Studies of visually guided animals have revealed an anticipatory relationship between gaze direction and the locomotor plan during target-directed locomotion. Here, we study an acoustically guided animal, an echolocating bat, and relate acoustic gaze (direction of the sonar beam) to flight planning as the bat searches for and intercepts insect prey. We show differences in the relationship between gaze and locomotion as the bat progresses through different phases of insect pursuit. We define acoustic gaze angle, θgaze, to be the angle between the sonar beam axis and the bat’s flight path. We show that there is a strong linear linkage between acoustic gaze angle at time t [θgaze(t)] and flight turn rate at time t + τ into the future [θ̇flight (t + τ)], which can be expressed by the formula θ̇flight (t + τ) = kθgaze(t). The gain, k, of this linkage depends on the bat’s behavioral state, which is indexed by its sonar pulse rate. For high pulse rates, associated with insect attacking behavior, k is twice as high compared with low pulse rates, associated with searching behavior. We suggest that this adjustable linkage between acoustic gaze and motor output in a flying echolocating bat simplifies the transformation of auditory information to flight motor commands. PMID:16467518

  9. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  10. Longitudinal Aerodynamic Modeling of the Adaptive Compliant Trailing Edge Flaps on a GIII Airplane and Comparisons to Flight Data

    NASA Technical Reports Server (NTRS)

    Smith, Mark S.; Bui, Trong T.; Garcia, Christian A.; Cumming, Stephen B.

    2016-01-01

    A pair of compliant trailing edge flaps was flown on a modified GIII airplane. Prior to flight test, multiple analysis tools of various levels of complexity were used to predict the aerodynamic effects of the flaps. Vortex lattice, full potential flow, and full Navier-Stokes aerodynamic analysis software programs were used for prediction, in addition to another program that used empirical data. After the flight-test series, lift and pitching moment coefficient increments due to the flaps were estimated from flight data and compared to the results of the predictive tools. The predicted lift increments matched flight data well for all predictive tools for small flap deflections. All tools over-predicted lift increments for large flap deflections. The potential flow and Navier-Stokes programs predicted pitching moment coefficient increments better than the other tools.

  11. Learning in neural networks based on a generalized fluctuation theorem

    NASA Astrophysics Data System (ADS)

    Hayakawa, Takashi; Aoyagi, Toshio

    2015-11-01

    Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

  12. Neural network based algorithm for automatic identification of cough sounds.

    PubMed

    Swarnkar, V; Abeyratne, U R; Amrulloh, Yusuf; Hukins, Craig; Triasih, Rina; Setyati, Amalia

    2013-01-01

    Cough is the most common symptom of the several respiratory diseases containing diagnostic information. It is the best suitable candidate to develop a simplified screening technique for the management of respiratory diseases in timely manner, both in developing and developed countries, particularly in remote areas where medical facilities are limited. However, major issue hindering the development is the non-availability of reliable technique to automatically identify cough events. Medical practitioners still rely on manual counting, which is laborious and time consuming. In this paper we propose a novel method, based on the neural network to automatically identify cough segments, discarding other sounds such a speech, ambient noise etc. We achieved the accuracy of 98% in classifying 13395 segments into two classes, 'cough' and 'other sounds', with the sensitivity of 93.44% and specificity of 94.52%. Our preliminary results indicate that method can develop into a real-time cough identification technique in continuous cough monitoring systems. PMID:24110049

  13. Neural network-based retrieval from software reuse repositories

    NASA Technical Reports Server (NTRS)

    Eichmann, David A.; Srinivas, Kankanahalli

    1992-01-01

    A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary.

  14. Neural network based diagonal decoupling control of powered wheelchair systems.

    PubMed

    Nguyen, Tuan Nghia; Su, Steven; Nguyen, Hung T

    2014-03-01

    This paper proposes an advanced diagonal decoupling control method for powered wheelchair systems. This control method is based on a combination of the systematic diagonalization technique and the neural network control design. As such, this control method reduces coupling effects on a multivariable system, leading to independent control design procedures. Using an obtained dynamic model, the problem of the plant's Jacobian calculation is eliminated in a neural network control design. The effectiveness of the proposed control method is verified in a real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty effects. PMID:23981543

  15. Neural network based speech synthesizer: A preliminary report

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.; Mcintire, Gary

    1987-01-01

    A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan.

  16. Neural network-based QSAR and insecticide discovery: spinetoram.

    PubMed

    Sparks, Thomas C; Crouse, Gary D; Dripps, James E; Anzeveno, Peter; Martynow, Jacek; Deamicis, Carl V; Gifford, James

    2008-01-01

    Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry. PMID:18344004

  17. Automated neural network-based instrument validation system

    NASA Astrophysics Data System (ADS)

    Xu, Xiao

    2000-10-01

    In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex non-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant-wide. Due to the large number of sensors to be monitored and the limited computational capability of an artificial neural network model, a variable grouping process was developed for dividing the sensor variables into small correlated groups which the neural networks can handle. A modification of a statistical method, called Beta method, as well as a principal component analysis (PCA)-based method of estimating the number of neural network hidden nodes was developed. Another development in this dissertation is the sensor fault detection method. The commonly used Sequential Probability Ratio Test (SPRT) continuously measures the likelihood ratio to statistically determine if there is any significant calibration change. This method requires normally distributed signals for correct operation. In practice, the signals deviate from the normal distribution causing problems for the SPRT. A modified SPRT (MSPRT) was developed to suppress the possible, intermittent alarms initiated by spurious spikes in network prediction errors. These methods were applied to data from the Tennessee Valley Authority (TVA) fossil power plant Unit 9 for testing. The results show that the average detectable drift level is about 2.5% for instruments in the boiler system and about 1% in the turbine system of the Unit 9 system. Approximately 74% of the process instruments can be monitored using the methodologies developed in this dissertation.

  18. Battery Performance Modelling ad Simulation: a Neural Network Based Approach

    NASA Astrophysics Data System (ADS)

    Ottavianelli, Giuseppe; Donati, Alessandro

    2002-01-01

    This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg-Marquardt one. The ANN used is a three-layer one (2-4-1) with four inputs and one output. Having established all the ANN parameters and calculated all the input/target training data the ANN has been trained and validated. Afterwards, various simulations have been performed with BAPER to validate the performance of the software and test new alternative battery cycling strategies. Taking into account the small number of available training data for the ANN, and that the simulations have been carried out over a fairly extensive time frame (i.e. one year) the results obtained from the prototype tool must be considered more than satisfactory. It is found that the deliverable discharge capacity can be maintained circa 20% higher than the one obtained with the nominal cycling strategy if the batteries are left discharged for a longer period of time and the storage temperature is decreased. This ANN model has its limitations when asked to predict the discharge capacity deterioration that would be obtained with extraordinary cycling conditions (e.g. extremely low storage temperatures and continuous cycling). Hence, these results must be considered only approximate, as it is impossible to exactly state whether the ANN turn out to give extremely accurate realistic values or not, failing to extrapolate a correct pattern. One way to overcome the problem would be to do some parallel experiments in the laboratory, using the same battery and similar environment conditions (temperature, charge and discharge cycles) to the ones to be encounter in the spacecraft.

  19. Neural Network Based Sensory Fusion for Landmark Detection

    NASA Technical Reports Server (NTRS)

    Kumbla, Kishan -K.; Akbarzadeh, Mohammad R.

    1997-01-01

    NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

  20. Artificial-neural-network-based failure detection and isolation

    NASA Astrophysics Data System (ADS)

    Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

    1998-03-01

    This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

  1. Quantum neural network based machine translator for Hindi to English.

    PubMed

    Narayan, Ravi; Singh, V P; Chakraverty, S

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. PMID:24977198

  2. Three neural network based sensor systems for environmental monitoring

    SciTech Connect

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field.

  3. Neural Network-Based Sensor Validation for Turboshaft Engines

    NASA Technical Reports Server (NTRS)

    Moller, James C.; Litt, Jonathan S.; Guo, Ten-Huei

    1998-01-01

    Sensor failure detection, isolation, and accommodation using a neural network approach is described. An auto-associative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.

  4. Neural network based satellite tracking for deep space applications

    NASA Technical Reports Server (NTRS)

    Amoozegar, F.; Ruggier, C.

    2003-01-01

    The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

  5. Modeling Tokamak Transport with Neural-Network Based Models

    NASA Astrophysics Data System (ADS)

    Meneghini, O.; Luna, C.; Penna, J.; Smith, S. P.; Lao, L. L.

    2014-10-01

    This work uses neural networks (NNs) as a means to extract information from the massive volume of aggregated data that are available either from experiments or from simulation databases, and distill an accurate transport model for the heat, particle, and momentum transport fluxes as a function of local dimensionless plasma parameters. The resulting model has been benchmarked with over 4000 DIII-D plasmas in different regimes, and it is able to capture the experimental behavior inside of ρ < 0 . 95 with average error <20% for all transport channels. The NN model was embedded into the ONETWO transport code and is now being used to develop time-dependent scenarios in support of DIII-D operations. The simulated temperature, density and rotation profiles closely match the experimental measurements, and a stiff response of the heat fluxes has been observed in the model for increasing source power. The numerical efficiency of the NN approach makes it ideal for real time plasma control and scenario preparation for current experiments and for ITER. Work supported in part by the US DOE under DE-FG02-95ER54309 and DE-FC02-04ER54698.

  6. A neural network based reputation bootstrapping approach for service selection

    NASA Astrophysics Data System (ADS)

    Wu, Quanwang; Zhu, Qingsheng; Li, Peng

    2015-10-01

    With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.

  7. Neural network based expert system for compressor stall monitoring

    NASA Technical Reports Server (NTRS)

    Lo, Ching F.; Shi, G. Z.

    1991-01-01

    This research is designed to apply a new information processing technology, artificial neural networks, to monitoring compressor stall. The outputs of neural networks support the dynamic knowledge data base of an expert system. This is the open-loop mode to avoid compressor stall. The integration of a control system with neural networks is the closed-loop mode in stall avoidance. The feasibility of the concept has been demonstrated for the compressor of 16-foot transonic/supersonic propulsion wind tunnels. The construction of a prototpye expert system has been initiated.

  8. Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

    NASA Astrophysics Data System (ADS)

    Hasan, M. A.; Reaz, M. B. I.

    2012-01-01

    The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.

  9. Quantum Neural Network Based Machine Translator for Hindi to English

    PubMed Central

    Singh, V. P.; Chakraverty, S.

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. PMID:24977198

  10. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    NASA Astrophysics Data System (ADS)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  11. Towards a neural network based therapy for hallucinatory disorders.

    PubMed

    Peláez, J R

    2000-01-01

    Pattern completion in a neural network model of the thalamus and a biologically plausible model of synaptic plasticity are the key concepts used in this paper for analyzing some cognitive disorders that involve hallucinations of several kinds: visual hallucinations in the Charles Bonnet syndrome and psychedelic drugs consumption, somatic hallucination in phantom limbs, cognitive hallucinations in schizophrenia and even in multiple personality disorders. All these types of hallucinations are proposed to be the result of a pattern completion dynamics performed in thalamic deafferented areas. Effective treatments of some of these disorders involve peripheral stimulation jointly with a central inhibition so that the neural circuits generating the disorders are depressed according to the proposed model of synaptic plasticity. PMID:11156194

  12. Integrated Flight/Structural Mode Control for Very Flexible Aircraft Using L1 Adaptive Output Feedback Controller

    NASA Technical Reports Server (NTRS)

    Che, Jiaxing; Cao, Chengyu; Gregory, Irene M.

    2012-01-01

    This paper explores application of adaptive control architecture to a light, high-aspect ratio, flexible aircraft configuration that exhibits strong rigid body/flexible mode coupling. Specifically, an L(sub 1) adaptive output feedback controller is developed for a semi-span wind tunnel model capable of motion. The wind tunnel mount allows the semi-span model to translate vertically and pitch at the wing root, resulting in better simulation of an aircraft s rigid body motion. The control objective is to design a pitch control with altitude hold while suppressing body freedom flutter. The controller is an output feedback nominal controller (LQG) augmented by an L(sub 1) adaptive loop. A modification to the L(sub 1) output feedback is proposed to make it more suitable for flexible structures. The new control law relaxes the required bounds on the unmatched uncertainty and allows dependence on the state as well as time, i.e. a more general unmatched nonlinearity. The paper presents controller development and simulated performance responses. Simulation is conducted by using full state flexible wing models derived from test data at 10 different dynamic pressure conditions. An L(sub 1) adaptive output feedback controller is designed for a single test point and is then applied to all the test cases. The simulation results show that the L(sub 1) augmented controller can stabilize and meet the performance requirements for all 10 test conditions ranging from 30 psf to 130 psf dynamic pressure.

  13. Simulation evaluation of a low-altitude helicopter flight guidance system adapted for a helmet-mounted display

    NASA Technical Reports Server (NTRS)

    Swenson, Harry N.; Zelenka, Richard E.; Hardy, Gordon H.; Dearing, Munro G.

    1992-01-01

    A computer aiding concept for low-altitude helicopter flight was developed and evaluated in a real-time piloted simulation. The concept included an optimal control trajectory-generation algorithm based upon dynamic programming and a helmet-mounted display (HMD) presentation of a pathway-in-the-sky, a phantom aircraft, and flight-path vector/predictor guidance symbology. The trajectory-generation algorithm uses knowledge of the global mission requirements, a digital terrain map, aircraft performance capabilities, and advanced navigation information to determine a trajectory between mission way points that seeks valleys to minimize threat exposure. The pilot evaluation was conducted at NASA ARC moving base Vertical Motion Simulator (VMS) by pilots representing NASA, the U.S. Army, the Air Force, and the helicopter industry. The pilots manually tracked the trajectory generated by the algorithm utilizing the HMD symbology. The pilots were able to satisfactorily perform the tracking tasks while maintaining a high degree of awareness of the outside world.

  14. Findings on American astronauts bearing on the issue of artificial gravity for future manned space vehicles. [adaptation to weightlessness during manned space flight

    NASA Technical Reports Server (NTRS)

    Berry, C. A.

    1973-01-01

    Findings for American astronauts are reviewed that may indicate some alteration in vestibular response related to exposure to zero gravity. Of 25 individuals participating in Apollo missions 7 through 15, nine have experienced symptomatology that could be related to motion sickness. The apparent divergence between these results and those from the Soviet space program, which initially appears great, may reflect the greater emphasis given by Soviet investigators to vestibular aberrations. Presently the incidence of motion sickness, long known as an indicator of vestibular disturbance, seems too low to warrant any positive statement regarding inclusion of an artificial gravity system in future long term space missions. Where motion sickness has occurred, adaptation to weightlessness has always resulted in abatement of symptoms. In the absence of biomedical justification for incorporating artificial gravity systems in long term space flight vehicles, engineering considerations may dictate the manner in which the final ballot is cast.

  15. Flight Wing Surface Pressure and Boundary-Layer Data Report from the F-111 Smooth Variable-Camber Supercritical Mission Adaptive Wing

    NASA Technical Reports Server (NTRS)

    Powers, Sheryll Goecke; Webb, Lannie D.

    1997-01-01

    Flight tests were conducted using the advanced fighter technology integration F-111 (AFTI/F-111) aircraft modified with a variable-sweep supercritical mission adaptive wing (MAW). The MAW leading- and trailing-edge variable-camber surfaces were deflected in flight to provide a near-ideal wing camber shape for the flight condition. The MAW features smooth, flexible upper surfaces and fully enclosed lower surfaces, which distinguishes it from conventional flaps that have discontinuous surfaces and exposed or semi-exposed mechanisms. Upper and lower surface wing pressure distributions were measured along four streamwise rows on the right wing for cruise, maneuvering, and landing configurations. Boundary-layer measurements were obtained near the trailing edge for one of the rows. Cruise and maneuvering wing leading-edge sweeps were 26 deg for Mach numbers less than 1 and 45 deg or 58 deg for Mach numbers greater than 1. The landing wing sweep was 9 deg or 16 deg. Mach numbers ranged from 0.27 to 1.41, angles of attack from 2 deg to 13 deg, and Reynolds number per unit foot from 1.4 x 10(exp 6) to 6.5 x 10(exp 6). Leading-edge cambers ranged from O deg to 20 deg down, and trailing-edge cambers ranged from 1 deg up to 19 deg down. Wing deflection data for a Mach number of 0.85 are shown for three cambers. Wing pressure and boundary-layer data are given. Selected data comparisons are shown. Measured wing coordinates are given for three streamwise semispan locations for cruise camber and one spanwise location for maneuver camber.

  16. Miracle Flights

    MedlinePlus

    ... the perfect solution for your needs. Book A Flight Request a flight now Click on the link ... Now Make your donation today Saving Lives One Flight At A Time Miracle Flights provides free flights ...

  17. Flight control actuation system

    NASA Technical Reports Server (NTRS)

    Wingett, Paul T. (Inventor); Gaines, Louie T. (Inventor); Evans, Paul S. (Inventor); Kern, James I. (Inventor)

    2004-01-01

    A flight control actuation system comprises a controller, electromechanical actuator and a pneumatic actuator. During normal operation, only the electromechanical actuator is needed to operate a flight control surface. When the electromechanical actuator load level exceeds 40 amps positive, the controller activates the pneumatic actuator to offset electromechanical actuator loads to assist the manipulation of flight control surfaces. The assistance from the pneumatic load assist actuator enables the use of an electromechanical actuator that is smaller in size and mass, requires less power, needs less cooling processes, achieves high output forces and adapts to electrical current variations. The flight control actuation system is adapted for aircraft, spacecraft, missiles, and other flight vehicles, especially flight vehicles that are large in size and travel at high velocities.

  18. Adaptive neural control of aeroelastic response

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, Peter F.; Little, Gerald R.; Scott, Robert C.

    1996-05-01

    The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.

  19. Spacelab 3 flight experiment No. 3AFT23: Autogenic-feedback training as a preventive method for space adaptation syndrome

    NASA Technical Reports Server (NTRS)

    Cowings, Patricia S.; Toscano, William B.; Kamiya, Joe; Miller, Neal E.; Sharp, Joseph C.

    1988-01-01

    Space adaptation syndrome is a motion sickness-like disorder which affects up to 50 percent of all people exposed to microgravity in space. This experiment tested a physiological conditioning procedure (Autogenic-Feedback Training, AFT) as an alternative to pharmacological management. Four astronauts participated as subjects in this experiment. Crewmembers A and B served as treatment subjects. Both received preflight training for control of heart rate, respiration rate, peripheral blood volume, and skin conductance. Crewmembers C and D served as controls (i.e., did not receive training). Crewmember A showed reliable control of his own physiological responses, and a significant increase in motion sickness tolerance after training. Crewmember B, however, demonstrated much less control and only a moderate increase in motion sickness tolerance was observed after training. The inflight symptom reports and physiological data recordings revealed that Crewmember A did not experience any severe symptom episodes during the mission, while Crewmember B reported one severe symptom episode. Both control group subjects, C and D (who took antimotion sickness medication), reported multiple symptom episodes on mission day 0. Both inflight data and crew reports indicate that AFT may be an effective countermeasure. Additional data must be obtained inflight (a total of eight treatment and eight control subjects) before final evaluation of this treatment can be made.

  20. Intelligent flight control systems

    NASA Technical Reports Server (NTRS)

    Stengel, Robert F.

    1993-01-01

    The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.

  1. Fighter aircraft flight control technology design requirements

    NASA Technical Reports Server (NTRS)

    Nelson, W. E., Jr.

    1984-01-01

    The evolution of fighter aircraft flight control technology is briefly surveyed. Systems engineering, battle damage considerations for adaptive flutter suppression, in-flight simulation, and artificial intelligence are briefly discussed.

  2. Assessment of individual adaptation to microgravity during long term space flight based on stepwise discriminant analysis of heart rate variability parameters

    NASA Astrophysics Data System (ADS)

    Baevsky, Roman M.; Chernikova, Anna G.; Funtova, Irina I.; Tank, Jens

    2011-12-01

    Optimization of the cardiovascular system under conditions of long term space flight is provided by individual changes of autonomic cardiovascular control. Heart rate variability (HRV) analysis is an easy to use method under these extreme conditions. We tested the hypothesis that individual HRV analysis provides important information for crew health monitoring. HRV data from 14 Russian cosmonauts measured during long term space flights are presented (two times before and after flight, monthly in flight). HRV characteristics in the time and in the frequency domain were calculated. Predefined discriminant function equations obtained in reference groups (L1=-0.112*HR-1.006*SI-0.047*pNN50-0.086*HF; L2=0.140*HR-0.165*SI-1.293*pNN50+0.623*HF) were used to define four functional states. (1) Physiological normal, (2) prenosological, (3) premorbid and (4) pathological. Geometric mean values for the ISS cosmonauts based on L1 and L2 remained within normal ranges. A shift from the physiological normal state to the prenosological functional state during space flight was detected. The functional state assessed by HRV improved during space flight if compared to pre-flight and early post-flight functional states. Analysis of individual cosmonauts showed distinct patterns depending on the pre-flight functional state. Using the developed classification a transition process from the state of physiological normal into a prenosological state or premorbid state during different stages of space flight can be detected for individual Russian cosmonauts. Our approach to an estimation of HR regulatory pattern can be useful for prognostic purposes.

  3. YF-17 in Flight

    NASA Technical Reports Server (NTRS)

    1976-01-01

    The Northrop Aviation YF-17 technology demonstrator aircraft in flight during a 1976 flight research program at NASA's Dryden Flight Research Center, Edwards, California. From May 27 to July 14, 1976, the Dryden Flight Research Center, Edwards, California, flew the Northrop Aviation YF-17 technology demonstrator to test the high-performance U.S. Air Force fighter at transonic speeds. The objectives of the seven-week flight test program included the study of maneuverability of this aircraft at transonic speeds and the collection of in-flight pressure data from around the afterbody of the aircraft to improve wind-tunnel predictions for future fighter aircraft. Also studied were stability and control and buffeting at high angles of attack as well as handling qualities at high load factors. Another objective of this program was to familiarize center pilots with the operation of advanced high-performance fighter aircraft. During the seven-week program, all seven of the center's test pilots were able to fly the aircraft with Gary Krier serving as project pilot. In general the pilots reported no trouble adapting to the aircraft and reported that it was easy to fly. There were no familiarization flights. All 25 research flights were full-data flights. They obtained data on afterbody pressures, vertical-fin dynamic loads, agility, pilot physiology, and infrared signatures. Average flight time was 45 minutes, although two flights involving in-flight refueling lasted approximately one hour longer than usual. Dryden Project Manager Roy Bryant considered the program a success. Center pilots felt that the aircraft was generations ahead of then current active military aircraft. Originally built for the Air Force's lightweight fighter program, the YF-17 Cobra left Dryden to support the Northrop/Navy F-18 Program. The F-18 Hornet evolved from the YF-17.

  4. In-Flight System Identification

    NASA Technical Reports Server (NTRS)

    Morelli, Eugene A.

    1998-01-01

    A method is proposed and studied whereby the system identification cycle consisting of experiment design and data analysis can be repeatedly implemented aboard a test aircraft in real time. This adaptive in-flight system identification scheme has many advantages, including increased flight test efficiency, adaptability to dynamic characteristics that are imperfectly known a priori, in-flight improvement of data quality through iterative input design, and immediate feedback of the quality of flight test results. The technique uses equation error in the frequency domain with a recursive Fourier transform for the real time data analysis, and simple design methods employing square wave input forms to design the test inputs in flight. Simulation examples are used to demonstrate that the technique produces increasingly accurate model parameter estimates resulting from sequentially designed and implemented flight test maneuvers. The method has reasonable computational requirements, and could be implemented aboard an aircraft in real time.

  5. Sensory adaptation of antennae and sex pheromone-mediated flight behavior in male oriental fruit moths (Leptidoptera: Tortricidae) after prolonged exposure to single and tertiary blends of synthetic sex pheromone.

    PubMed

    D'Errico, G; Faraone, N; Rotundo, G; De Cristofaro, A; Trimble, R M

    2013-06-01

    Sensory adaptation has been measured in the antennae of male Grapholita molesta (Busck) after 15 min of exposure to its main pheromone compound (Z)-8-dodecen-1-yl acetate (Z8-12:OAc) at the aerial concentration of 1 ng/m(3) measured in orchards treated with pheromone for mating disruption. Exposing males to this aerial concentration of Z8-12:OAc for 15 min, however, had only a small effect on their ability to orientate by flight to virgin calling females in a flight tunnel. Experiments were undertaken to determine if exposure to the main pheromone compound in combination with the two biologically active minor compounds of this species, (E)-8-dodecen-1-yl acetate (E8-12:OAc) and (Z)-8-dodecen-1-ol (Z8-12:OH) would induce greater levels of sensory adaptation and have a greater effect on male sexual behavior. The exposure of male antennae to 0.5 g/m(3) air of one of the three pheromone compounds induced sensory adaptation to this compound and to the other two pheromone compounds demonstrating cross adaptation. Average percentage sensory adaptation to a pheromone compound was similar after 15 min of exposure to 1 ng/m(3) air of Z8-12:OAc, or to 1 ng/m(3) air of a 1:1:1 or 93:6:1 blend of Z8-12:OAc, E8-12:OAc, and Z8-12:OH. The exposure of males to 1 ng/m(3) air of Z8-12:OAc or the two ratios of Z8-12:OAc, E8-12:OAc, and Z8-12:OH for 15 min had no effect on their ability to orientate to a virgin calling female. The implications of these results for the operative mechanisms of sex pheromone-mediated mating disruption of this species are discussed. PMID:23726064

  6. Experiments on Adaptive Techniques for Host-Based Intrusion Detection

    SciTech Connect

    DRAELOS, TIMOTHY J.; COLLINS, MICHAEL J.; DUGGAN, DAVID P.; THOMAS, EDWARD V.; WUNSCH, DONALD

    2001-09-01

    This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerable preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.

  7. Integrated Resilient Aircraft Control Project Full Scale Flight Validation

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2009-01-01

    Objective: Provide validation of adaptive control law concepts through full scale flight evaluation. Technical Approach: a) Engage failure mode - destabilizing or frozen surface. b) Perform formation flight and air-to-air tracking tasks. Evaluate adaptive algorithm: a) Stability metrics. b) Model following metrics. Full scale flight testing provides an ability to validate different adaptive flight control approaches. Full scale flight testing adds credence to NASA's research efforts. A sustained research effort is required to remove the road blocks and provide adaptive control as a viable design solution for increased aircraft resilience.

  8. Engineering study for pallet adapting the Apollo laser altimeter and photographic camera system for the Lidar Test Experiment on orbital flight tests 2 and 4

    NASA Technical Reports Server (NTRS)

    Kuebert, E. J.

    1977-01-01

    A Laser Altimeter and Mapping Camera System was included in the Apollo Lunar Orbital Experiment Missions. The backup system, never used in the Apollo Program, is available for use in the Lidar Test Experiments on the STS Orbital Flight Tests 2 and 4. Studies were performed to assess the problem associated with installation and operation of the Mapping Camera System in the STS. They were conducted on the photographic capabilities of the Mapping Camera System, its mechanical and electrical interface with the STS, documentation, operation and survivability in the expected environments, ground support equipment, test and field support.

  9. Orion Abort Flight Test

    NASA Technical Reports Server (NTRS)

    Hayes, Peggy Sue

    2010-01-01

    The purpose of NASA's Constellation project is to create the new generation of spacecraft for human flight to the International Space Station in low-earth orbit, the lunar surface, as well as for use in future deep-space exploration. One portion of the Constellation program was the development of the Orion crew exploration vehicle (CEV) to be used in spaceflight. The Orion spacecraft consists of a crew module, service module, space adapter and launch abort system. The crew module was designed to hold as many as six crew members. The Orion crew exploration vehicle is similar in design to the Apollo space capsules, although larger and more massive. The Flight Test Office is the responsible flight test organization for the launch abort system on the Orion crew exploration vehicle. The Flight Test Office originally proposed six tests that would demonstrate the use of the launch abort system. These flight tests were to be performed at the White Sands Missile Range in New Mexico and were similar in nature to the Apollo Little Joe II tests performed in the 1960s. The first flight test of the launch abort system was a pad abort (PA-1), that took place on 6 May 2010 at the White Sands Missile Range in New Mexico. Primary flight test objectives were to demonstrate the capability of the launch abort system to propel the crew module a safe distance away from a launch vehicle during a pad abort, to demonstrate the stability and control characteristics of the vehicle, and to determine the performance of the motors contained within the launch abort system. The focus of the PA-1 flight test was engineering development and data acquisition, not certification. In this presentation, a high level overview of the PA-1 vehicle is given, along with an overview of the Mobile Operations Facility and information on the White Sands tracking sites for radar & optics. Several lessons learned are presented, including detailed information on the lessons learned in the development of wind

  10. Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

    NASA Technical Reports Server (NTRS)

    Bosworth, John

    2006-01-01

    A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

  11. Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models.

    PubMed

    Yang, Chenguang; Li, Zhijun; Li, Jing

    2013-02-01

    In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly "controls" the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller. PMID:22695357

  12. Adaptive output feedback control of flexible systems

    NASA Astrophysics Data System (ADS)

    Yang, Bong-Jun

    Neural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems. Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of the system together with external disturbance process. Within this context, adaptive compensation for external disturbances is addressed. In the first approach, internal model-following control, adaptive elements are designed using feedback inversion. The effect of an actuator limit is treated using control hedging, and the effect of other actuation nonlinearities, such as dead zone and backlash, is mitigated by a disturbance observer-based control design. The effectiveness of the approach is illustrated through simulation and experimental testing with a three-disk torsional system, which is subjected to control voltage limit and stiction. While the internal model-following control is limited to minimum phase systems, the second approach, external model-following control, does not involve feedback linearization and can be applied to non-minimum phase systems. The unstable zero dynamics are assumed to have been modelled in the design of the existing linear controller. The laboratory tests for this method include a three-disk torsional pendulum, an inverted pendulum, and a flexible-base robot manipulator. The external model-following control architecture is further extended in three ways. The first extension is an approach for control of multivariable nonlinear systems. The second extension is a decentralized adaptive control approach for large-scale interconnected systems. The third extension is to make use of an adaptive observer to augment a linear observer-based controller. In this extension, augmenting terms for the adaptive observer can be used to achieve adaptation in

  13. Understanding Flight

    SciTech Connect

    Anderson, David

    2001-01-31

    Through the years the explanation of flight has become mired in misconceptions that have become dogma. Wolfgang Langewiesche, the author of 'Stick and Rudder' (1944) got it right when he wrote: 'Forget Bernoulli's Theorem'. A wing develops lift by diverting (from above) a lot of air. This is the same way that a propeller produces thrust and a helicopter produces lift. Newton's three laws and a phenomenon called the Coanda effect explain most of it. With an understanding of the real physics of flight, many things become clear. Inverted flight, symmetric wings, and the flight of insects are obvious. It is easy to understand the power curve, high-speed stalls, and the effect of load and altitude on the power requirements for lift. The contribution of wing aspect ratio on the efficiency of a wing, and the true explanation of ground effect will also be discussed.

  14. Cardiovascular function in space flight

    NASA Technical Reports Server (NTRS)

    Nicogossian, A. E.; Charles, J. B.; Bungo, M. W.; Leach-Huntoon, C. S.

    1990-01-01

    Postflight orthostatic intolerance and cardiac hemodynamics associated with manned space flight have been investigated on seven STS missions. Orthostatic heart rates appear to be influenced by the mission duration. The rates increase during the first 7-10 days of flight and recover partially after that. Fluid loading is used as a countermeasure to the postflight orthostatic intolerance. The carotid baroreceptor function shows only slight responsiveness to orthostatic stimulation. Plots of the baroreceptor function are presented. It is concluded that an early adaptation to the space flight conditions involves a fluid shift and that the subsequent alterations in the neutral controlling mechanisms contribute to the orthoststic intolerance.

  15. Active Listening in a Bat Cocktail Party: Adaptive Echolocation and Flight Behaviors of Big Brown Bats, Eptesicus fuscus, Foraging in a Cluttered Acoustic Environment.

    PubMed

    Warnecke, Michaela; Chiu, Chen; Engelberg, Jonathan; Moss, Cynthia F

    2015-09-01

    In their natural environment, big brown bats forage for small insects in open spaces, as well as in vegetation and in the presence of acoustic clutter. While searching and hunting for prey, bats experience sonar interference, not only from densely cluttered environments, but also from calls of conspecifics foraging in close proximity. Previous work has shown that when two bats compete for a single prey item in a relatively open environment, one of the bats may go silent for extended periods of time, which can serve to minimize sonar interference between conspecifics. Additionally, pairs of big brown bats have been shown to adjust frequency characteristics of their vocalizations to avoid acoustic interference in echo processing. In this study, we extended previous work by examining how the presence of conspecifics and environmental clutter influence the bat's echolocation behavior. By recording multichannel audio and video data of bats engaged in insect capture in open and cluttered spaces, we quantified the bats' vocal and flight behaviors. Big brown bats flew individually and in pairs in an open and cluttered room, and the results of this study shed light on the different strategies that this species employs to negotiate a complex and dynamic environment. PMID:26398707

  16. Miracle Flights for Kids

    MedlinePlus

    ... today Saving Lives One Flight At A Time Miracle Flights provides free flights to distant specialized care and valuable second opinions. Miracle Flights Through June 2016 Flights Coordinated: 101,862 ...

  17. Flight (Children's Books).

    ERIC Educational Resources Information Center

    Matthews, Susan; Reid, Rebecca; Sylvan, Anne; Woolard, Linda; Freeman, Evelyn B.

    1997-01-01

    Presents brief annotations of 43 children's books, grouped around the theme of flight: flights of imagination, flights across time and around the globe, flights of adventure, and nature's flight. (SR)

  18. Adaptive Filtering Using Recurrent Neural Networks

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

    A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

  19. The IBEX Flight Segment

    NASA Astrophysics Data System (ADS)

    Scherrer, J.; Carrico, J.; Crock, J.; Cross, W.; Delossantos, A.; Dunn, A.; Dunn, G.; Epperly, M.; Fields, B.; Fowler, E.; Gaio, T.; Gerhardus, J.; Grossman, W.; Hanley, J.; Hautamaki, B.; Hawes, D.; Holemans, W.; Kinaman, S.; Kirn, S.; Loeffler, C.; McComas, D. J.; Osovets, A.; Perry, T.; Peterson, M.; Phillips, M.; Pope, S.; Rahal, G.; Tapley, M.; Tyler, R.; Ungar, B.; Walter, E.; Wesley, S.; Wiegand, T.

    2009-08-01

    IBEX provides the observations needed for detailed modeling and in-depth understanding of the interstellar interaction (McComas et al. in Physics of the Outer Heliosphere, Third Annual IGPP Conference, pp. 162-181, 2004; Space Sci. Rev., 2009a, this issue). From mission design to launch and acquisition, this goal drove all flight system development. This paper describes the management, design, testing and integration of IBEX’s flight system, which successfully launched from Kwajalein Atoll on October 19, 2008. The payload is supported by a simple, Sun-pointing, spin-stabilized spacecraft with no deployables. The spacecraft bus consists of the following subsystems: attitude control, command and data handling, electrical power, hydrazine propulsion, RF, thermal, and structures. A novel 3-step orbit approach was employed to put IBEX in its highly elliptical, 8-day final orbit using a Solid Rocket Motor, which provided large delta-V after IBEX separated from the Pegasus launch vehicle; an adapter cone, which interfaced between the SRM and Pegasus; Motorized Lightbands, which performed separation from the Pegasus, ejection of the adapter cone, and separation of the spent SRM from the spacecraft; a ShockRing isolation system to lower expected launch loads; and the onboard Hydrazine Propulsion System. After orbit raising, IBEX transitioned from commissioning to nominal operations and science acquisition. At every phase of development, the Systems Engineering and Mission Assurance teams supervised the design, testing and integration of all IBEX flight elements.

  20. Do birds sleep in flight?

    NASA Astrophysics Data System (ADS)

    Rattenborg, Niels C.

    2006-09-01

    The following review examines the evidence for sleep in flying birds. The daily need to sleep in most animals has led to the common belief that birds, such as the common swift ( Apus apus), which spend the night on the wing, sleep in flight. The electroencephalogram (EEG) recordings required to detect sleep in flight have not been performed, however, rendering the evidence for sleep in flight circumstantial. The neurophysiology of sleep and flight suggests that some types of sleep might be compatible with flight. As in mammals, birds exhibit two types of sleep, slow-wave sleep (SWS) and rapid eye-movement (REM) sleep. Whereas, SWS can occur in one or both brain hemispheres at a time, REM sleep only occurs bihemispherically. During unihemispheric SWS, the eye connected to the awake hemisphere remains open, a state that may allow birds to visually navigate during sleep in flight. Bihemispheric SWS may also be possible during flight when constant visual monitoring of the environment is unnecessary. Nevertheless, the reduction in muscle tone that usually accompanies REM sleep makes it unlikely that birds enter this state in flight. Upon landing, birds may need to recover the components of sleep that are incompatible with flight. Periods of undisturbed postflight recovery sleep may be essential for maintaining adaptive brain function during wakefulness. The recent miniaturization of EEG recording devices now makes it possible to measure brain activity in flight. Determining if and how birds sleep in flight will contribute to our understanding of a largely unexplored aspect of avian behavior and may also provide insight into the function of sleep.

  1. Space Flight Resource Management for ISS Operations

    NASA Technical Reports Server (NTRS)

    Schmidt, Larry; Slack, Kelley; O'Keefe, William; Huning, Therese; Sipes, Walter; Holland, Albert

    2011-01-01

    This slide presentation reviews the International Space Station (ISS) Operations space flight resource management, which was adapted to the ISS from the shuttle processes. It covers crew training and behavior elements.

  2. Influence of environmental information in natural scenes and the effects of motion adaptation on a fly motion-sensitive neuron during simulated flight

    PubMed Central

    Ullrich, Thomas W.; Kern, Roland; Egelhaaf, Martin

    2015-01-01

    ABSTRACT Gaining information about the spatial layout of natural scenes is a challenging task that flies need to solve, especially when moving at high velocities. A group of motion sensitive cells in the lobula plate of flies is supposed to represent information about self-motion as well as the environment. Relevant environmental features might be the nearness of structures, influencing retinal velocity during translational self-motion, and the brightness contrast. We recorded the responses of the H1 cell, an individually identifiable lobula plate tangential cell, during stimulation with image sequences, simulating translational motion through natural sceneries with a variety of differing depth structures. A correlation was found between the average nearness of environmental structures within large parts of the cell's receptive field and its response across a variety of scenes, but no correlation was found between the brightness contrast of the stimuli and the cell response. As a consequence of motion adaptation resulting from repeated translation through the environment, the time-dependent response modulations induced by the spatial structure of the environment were increased relatively to the background activity of the cell. These results support the hypothesis that some lobula plate tangential cells do not only serve as sensors of self-motion, but also as a part of a neural system that processes information about the spatial layout of natural scenes. PMID:25505148

  3. Immune response during space flight.

    PubMed

    Criswell-Hudak, B S

    1991-01-01

    The health status of an astronaut prior to and following space flight has been a prime concern of NASA throughout the Apollo series of lunar landings, Skylab, Apollo-Soyuz Test Projects (ASTP), and the new Spacelab-Shuttle missions. Both humoral and cellular immunity has been studied using classical clinical procedures. Serum proteins show fluctuations that can be explained with adaptation to flight. Conversely, cellular immune responses of lymphocytes appear to be depressed in both in vivo as well as in vitro. If this depression in vivo and in vitro is a result of the same cause, then man's adaptation to outer space living will present interesting challenges in the future. Since the cause may be due to reduced gravity, perhaps the designs of the experiments for space flight will offer insights at the cellular levels that will facilitate development of mechanisms for adaptation. Further, if the aging process is viewed as an adaptational concept or model and not as a disease process then perhaps space flight could very easily interact to supply some information on our biological time clocks. PMID:1915698

  4. Optimum Strategies for Selecting Descent Flight-Path Angles

    NASA Technical Reports Server (NTRS)

    Wu, Minghong G. (Inventor); Green, Steven M. (Inventor)

    2016-01-01

    An information processing system and method for adaptively selecting an aircraft descent flight path for an aircraft, are provided. The system receives flight adaptation parameters, including aircraft flight descent time period, aircraft flight descent airspace region, and aircraft flight descent flyability constraints. The system queries a plurality of flight data sources and retrieves flight information including any of winds and temperatures aloft data, airspace/navigation constraints, airspace traffic demand, and airspace arrival delay model. The system calculates a set of candidate descent profiles, each defined by at least one of a flight path angle and a descent rate, and each including an aggregated total fuel consumption value for the aircraft following a calculated trajectory, and a flyability constraints metric for the calculated trajectory. The system selects a best candidate descent profile having the least fuel consumption value while the fly ability constraints metric remains within aircraft flight descent flyability constraints.

  5. Calcium Kinetics During Space Flight

    NASA Technical Reports Server (NTRS)

    Smith, Scott M.; OBrien, K. O.; Abrams, S. A.; Wastney, M. E.

    2005-01-01

    Bone loss during space flight is one of the most critical challenges to astronaut health on space exploration missions. Defining the time course and mechanism of these changes will aid in developing means to counteract bone loss during space flight, and will have relevance for other clinical situations that impair weight-bearing activity. Bone health is a product of the balance between bone formation and bone resorption. Early space research could not clearly identify which of these was the main process altered in bone loss, but identification of the collagen crosslinks in the 1990s made possible a clear understanding that the impact of space flight was greater on bone resorption, with bone formation being unchanged or only slightly decreased. Calcium kinetics data showed that bone resorption was greater during flight than before flight (668 plus or minus 130 vs. 427 plus or minus 153 mg/d, p less than 0.001), and clearly documented that true intestinal calcium absorption was lower during flight than before flight (233 plus or minus 87 vs. 460 plus or minus 47 mg/d, p less than 0.01). Weightlessness had a detrimental effect on the balance in bone turnover: the difference between daily calcium balance during flight (-234 plus or minus 102 mg/d) and calcium balance before flight (63 plus or minus 75 mg/d) approached 300 mg/d (p less than 0.01). These data demonstrate that the bone loss that occurs during space flight is a consequence of increased bone resorption and decreased intestinal calcium absorption. Examining the changes in bone and calcium homeostasis in the initial days and weeks of space flight, as well as at later times on missions longer than 6 months, is critical to understanding the nature of bone adaptation to weightlessness. To increase knowledge of these changes, we studied bone adaptation to space flight on the 16-day Space Shuttle Columbia (STS-107) mission. When the brave and talented crew of Columbia were lost during reentry on the tragic morning

  6. Space flight rehabilitation.

    PubMed

    Payne, Michael W C; Williams, David R; Trudel, Guy

    2007-07-01

    The weightless environment of space imposes specific physiologic adaptations on healthy astronauts. On return to Earth, these adaptations manifest as physical impairments that necessitate a period of rehabilitation. Physiologic changes result from unloading in microgravity and highly correlate with those seen in relatively immobile terrestrial patient populations such as spinal cord, geriatric, or deconditioned bed-rest patients. Major postflight impairments requiring rehabilitation intervention include orthostatic intolerance, bone demineralization, muscular atrophy, and neurovestibular symptoms. Space agencies are preparing for extended-duration missions, including colonization of the moon and interplanetary exploration of Mars. These longer-duration flights will result in more severe and more prolonged disability, potentially beyond the point of safe return to Earth. This paper will review and discuss existing space rehabilitation plans for major postflight impairments. Evidence-based rehabilitation interventions are imperative not only to facilitate return to Earth but also to extend the safe duration of exposure to a physiologically hostile microgravity environment. PMID:17167347

  7. Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller.

    PubMed

    Ding, Zhixia; Shen, Yi

    2016-04-01

    This paper investigates global projective synchronization of nonidentical fractional-order neural networks (FNNs) based on sliding mode control technique. We firstly construct a fractional-order integral sliding surface. Then, according to the sliding mode control theory, we design a sliding mode controller to guarantee the occurrence of the sliding motion. Based on fractional Lyapunov direct methods, system trajectories are driven to the proposed sliding surface and remain on it evermore, and some novel criteria are obtained to realize global projective synchronization of nonidentical FNNs. As the special cases, some sufficient conditions are given to ensure projective synchronization of identical FNNs, complete synchronization of nonidentical FNNs and anti-synchronization of nonidentical FNNs. Finally, one numerical example is given to demonstrate the effectiveness of the obtained results. PMID:26874968

  8. Neural network based system for damage identification and location in structural and mechanical systems

    SciTech Connect

    Farrar, C.R.; Doebling, S.W.; Prime, M.B.; Cornwell, P.; Kam, M.; Straser, E.G.; Hoerst, B.C.

    1998-11-01

    This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). Recent advances in wireless, remotely monitored data acquisition systems coupled with the development of vibration-based damage detection algorithms make the possibility of self- or remotely-monitored structures and mechanical systems appear to be within the capabilities of current technology. However, before such a system can be relied upon to perform this monitoring, the variability of the vibration properties that are the basis for the damage detection algorithm must be understood and quantified. This understanding is necessary so that the artificial intelligence/expert system that is employed to discriminate when changes in modal properties are indicative of damage will not yield false indications of damage. To this end, this project has focused on developing statistical methods for quantifying variability in identified vibration proper ties of structural and mechanical systems.

  9. Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations

    NASA Technical Reports Server (NTRS)

    Jani, Yashvant

    1992-01-01

    As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.

  10. A Neural Network-Based Optimal Spatial Filter Design Method for Motor Imagery Classification

    PubMed Central

    Yuksel, Ayhan; Olmez, Tamer

    2015-01-01

    In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy. PMID:25933101

  11. A neural network-based optimal spatial filter design method for motor imagery classification.

    PubMed

    Yuksel, Ayhan; Olmez, Tamer

    2015-01-01

    In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy. PMID:25933101

  12. Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

    NASA Astrophysics Data System (ADS)

    Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

    2016-01-01

    In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

  13. Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

    PubMed Central

    Cardoso, L.; Marins, F.; Magalhães, R.; Marins, N.; Oliveira, T.; Vicente, H.; Abelha, A.; Machado, J.; Neves, J.

    2015-01-01

    Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information. PMID:25834836

  14. Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature

    NASA Astrophysics Data System (ADS)

    Mahesh, C.; Prakash, Satya; Sathiyamoorthy, V.; Gairola, R. M.

    2011-11-01

    An Artificial Neural Network (ANN) based technique is proposed for estimating precipitation over Indian land and oceanic regions [30° S - 40° N and 30° E - 120° E] using Scattering Index (SI) and Polarization Corrected Temperature (PCT) derived from Special Sensor Microwave Imager (SSM/I) measurements. This rainfall retrieval algorithm is designed to estimate rainfall using a combination of SSM/I and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measurements. For training the ANN, SI and PCT (which signify rain signatures in a better way) calculated from SSM/I brightness temperature are considered as inputs and Precipitation Radar (PR) rain rate as output. SI is computed using 19.35 GHz, 22.235 GHz and 85.5 GHz Vertical channels and PCT is computed using 85.5 GHz Vertical and Horizontal channels. Once the training is completed, the independent data sets (which were not included in the training) were used to test the performance of the network. Instantaneous precipitation estimates with independent test data sets are validated with PR surface rain rate measurements. The results are compared with precipitation estimated using power law based (i) global algorithm and (ii) regional algorithm. Overall results show that ANN based present algorithm shows better agreement with PR rain rate. This study is aimed at developing a more accurate operational rainfall retrieval algorithm for Indo-French Megha-Tropiques Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) radiometer.

  15. A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature.

    PubMed

    Ferreira, Pedro M; Gomes, João M; Martins, Igor A C; Ruano, António E

    2012-01-01

    Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. PMID:23202230

  16. Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

    NASA Astrophysics Data System (ADS)

    Dhawan, Atam P.; Chitre, Yateen S.; Moskowitz, Myron

    1993-07-01

    Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. It is however, difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of `difficult to diagnose' microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 `difficult-to-diagnose' microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of `difficult-to- diagnose' microcalcifications into benign and malignant categories.

  17. Neural network based visualization of collaborations in a citizen science project

    NASA Astrophysics Data System (ADS)

    Morais, Alessandra M. M.; Santos, Rafael D. C.; Raddick, M. Jordan

    2014-05-01

    Citizen science projects are those in which volunteers are asked to collaborate in scientific projects, usually by volunteering idle computer time for distributed data processing efforts or by actively labeling or classifying information - shapes of galaxies, whale sounds, historical records are all examples of citizen science projects in which users access a data collecting system to label or classify images and sounds. In order to be successful, a citizen science project must captivate users and keep them interested on the project and on the science behind it, increasing therefore the time the users spend collaborating with the project. Understanding behavior of citizen scientists and their interaction with the data collection systems may help increase the involvement of the users, categorize them accordingly to different parameters, facilitate their collaboration with the systems, design better user interfaces, and allow better planning and deployment of similar projects and systems. Users behavior can be actively monitored or derived from their interaction with the data collection systems. Records of the interactions can be analyzed using visualization techniques to identify patterns and outliers. In this paper we present some results on the visualization of more than 80 million interactions of almost 150 thousand users with the Galaxy Zoo I citizen science project. Visualization of the attributes extracted from their behaviors was done with a clustering neural network (the Self-Organizing Map) and a selection of icon- and pixel-based techniques. These techniques allows the visual identification of groups of similar behavior in several different ways.

  18. A radial basis function neural network based on artificial immune systems for remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Yan, Qin; Zhong, Yanfei

    2008-12-01

    The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre-defined number of iterations. The classification results are evaluated by comparing with that of the k-means center selection procedures and other results from the literature using remote sensing imagery. It is shown that aiNet-RBF NN algorithm outperform other algorithms and provides an effective option for remote sensing image classification.

  19. Single-hidden-layer feed-forward quantum neural network based on Grover learning.

    PubMed

    Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

    2013-09-01

    In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. PMID:23545155

  20. Online particle detection with Neural Networks based on topological calorimetry information

    NASA Astrophysics Data System (ADS)

    Ciodaro, T.; Deva, D.; de Seixas, J. M.; Damazio, D.

    2012-06-01

    This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.

  1. Neural network-based inversion algorithms in magnetic flux leakage nondestructive evaluation

    NASA Astrophysics Data System (ADS)

    Ramuhalli, Pradeep; Udpa, Lalita; Udpa, Satish S.

    2003-05-01

    Magnetic flux leakage (MFL) methods are commonly used in the nondestructive evaluation (NDE) of ferromagnetic materials. An important problem in MFL NDE is the determination of flaw parameters such as the flaw length, depth, and shape (profile) from the measured values of the flux density B. Commonly used methods use a forward model in a loop to determine B for a given set of flaw parameters. This approach iteratively adjusts the flaw parameters to minimize the error between the measured and predicted values of B. This article proposes the use of neural networks as forward models. The proposed approach uses two neural networks in feedback configuration—a forward network and an inverse network. The second network is used to predict the profile given the measured value of B, and acts to constrain the solution space. Results of applying these methods to MFL data obtained from a two-dimensional finite-element model, with rectangular flaws of various dimensions, are presented.

  2. Artificial neural network based torque calculation of switched reluctance motor without locking the rotor

    NASA Astrophysics Data System (ADS)

    Kucuk, Fuat; Goto, Hiroki; Guo, Hai-Jiao; Ichinokura, Osamu

    2009-04-01

    Feedback of motor torque is required in most of switched reluctance (SR) motor applications in order to control torque and its ripple. An SR motor shows highly nonlinear property which does not allow calculating torque analytically. Torque can be directly measured by torque sensor, but it inevitably increases the cost and has to be properly mounted on the motor shaft. Instead of torque sensor, finite element analysis (FEA) may be employed for torque calculation. However, motor modeling and calculation takes relatively long time. The results of FEA may also differ from the actual results. The most convenient way seems to calculate torque from the measured values of rotor position, current, and flux linkage while locking the rotor at definite positions. However, this method needs an extra assembly to lock the rotor. In this study, a novel torque calculation based on artificial neural networks (ANNs) is presented. Magnetizing data are collected while a 6/4 SR motor is running. They need to be interpolated for torque calculation. ANN is very strong tool for data interpolation. ANN based torque estimation is verified on the 6/4 SR motor and is compared by FEA based torque estimation to show its validity.

  3. Artificial Neural Network Based Algorithm for Acoustic Impact Based Nondestructive Process Monitoring of Composite Products

    NASA Astrophysics Data System (ADS)

    Srivatsan, V.; Balasubramaniam, Krishnan; Nair, N. V.

    2003-03-01

    Damages like cracks, delaminations, etc., in composite parts have traditionally been evaluated using manual methods like acoustic impact (using measurements in the audio frequencies). This technique is currently used during manufacturing for product quality testing and later for maintenance and assurance of structural integrity. The automation of this technique will significantly improve the reliability of inspection. The signals obtained from the composites are analyzed using signal-processing techniques in the time-frequency domain to build a robust algorithm for detection and identification of defects. A feature vector is constructed using these techniques and then applied to a neural network for defect identification. Comparative studies are conducted to search for the best and most comprehensive feature vector. Results using different signal processing techniques are presented. Similarly comparative results are presented between two different kinds of neural networks (namely Radial Basis functions and MLP) and various architectures in each kind. A low cost data acquisition system has also been developed for acquiring audio signals using the sound card and the microphone in a multi-media PC.

  4. Creation and testing of an artificial neural network based carbonate detector for Mars rovers

    NASA Technical Reports Server (NTRS)

    Bornstein, Benjamin; Castano, Rebecca; Gilmore, Martha S.; Merrill, Matthew; Greenwood, James P.

    2005-01-01

    We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350-2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments.

  5. Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems.

    PubMed

    Xu, Hao; Jagannathan, Sarangapani

    2015-03-01

    The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. PMID:25720004

  6. Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines

    NASA Astrophysics Data System (ADS)

    Karungaru, Stephen; Akashi, Takuya; Nakano, Miyoko; Fukumi, Minoru

    Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.

  7. A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

    PubMed Central

    Ferreira, Pedro M.; Gomes, João M.; Martins, Igor A. C.; Ruano, António E.

    2012-01-01

    Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. PMID:23202230

  8. Neural network-based distributed attitude coordination control for spacecraft formation flying with input saturation.

    PubMed

    Zou, An-Min; Kumar, Krishna Dev

    2012-07-01

    This brief considers the attitude coordination control problem for spacecraft formation flying when only a subset of the group members has access to the common reference attitude. A quaternion-based distributed attitude coordination control scheme is proposed with consideration of the input saturation and with the aid of the sliding-mode observer, separation principle theorem, Chebyshev neural networks, smooth projection algorithm, and robust control technique. Using graph theory and a Lyapunov-based approach, it is shown that the distributed controller can guarantee the attitude of all spacecraft to converge to a common time-varying reference attitude when the reference attitude is available only to a portion of the group of spacecraft. Numerical simulations are presented to demonstrate the performance of the proposed distributed controller. PMID:24807141

  9. A neural network-based local model for prediction of geomagnetic disturbances

    NASA Astrophysics Data System (ADS)

    Gleisner, Hans; Lundstedt, Henrik

    2001-05-01

    This study shows how locally observed geomagnetic disturbances can be predicted from solar wind data with artificial neural network (ANN) techniques. After subtraction of a secularly varying base level, the horizontal components XSq and YSq of the quiet time daily variations are modeled with radial basis function networks taking into account seasonal and solar activity modulations. The remaining horizontal disturbance components ΔX and ΔY are modeled with gated time delay networks taking local time and solar wind data as input. The observed geomagnetic field is not used as input to the networks, which thus constitute explicit nonlinear mappings from the solar wind to the locally observed geomagnetic disturbances. The ANNs are applied to data from Sodankylä Geomagnetic Observatory located near the peak of the auroral zone. It is shown that 73% of the ΔX variance, but only 34% of the ΔY variance, is predicted from a sequence of solar wind data. The corresponding results for prediction of all transient variations XSq+ΔX and YSq+ΔY are 74% and 51%, respectively. The local time modulations of the prediction accuracies are shown, and the qualitative agreement between observed and predicted values are discussed. If driven by real-time data measured upstream in the solar wind, the ANNs here developed can be used for short-term forecasting of the locally observed geomagnetic activity.

  10. Neural network-based prediction of candidate T-cell epitopes.

    PubMed

    Honeyman, M C; Brusic, V; Stone, N L; Harrison, L C

    1998-10-01

    Activation of T cells requires recognition by T-cell receptors of specific peptides bound to major histocompatibility complex (MHC) molecules on the surface of either antigen-presenting or target cells. These peptides, T-cell epitopes, have potential therapeutic applications, such as for use as vaccines. Their identification, however, usually requires that multiple overlapping synthetic peptides encompassing a protein antigen be assayed, which in humans, is limited by volume of donor blood. T-cell epitopes are a subset of peptides that bind to MHC molecules. We use an artificial neural network (ANN) model trained to predict peptides that bind to the MHC class II molecule HLA-DR4(*0401). Binding prediction facilitates identification of T-cell epitopes in tyrosine phosphatase IA-2, an autoantigen in DR4-associated type1 diabetes. Synthetic peptides encompassing IA-2 were tested experimentally for DR4 binding and T-cell proliferation in humans at risk for diabetes. ANN-based binding prediction was sensitive and specific, and reduced the number of peptides required for T-cell assay by more than half, with only a minor loss of epitopes. This strategy could expedite identification of candidate T-cell epitopes in diverse diseases. PMID:9788355

  11. Controlling basins of attraction in a neural network-based telemetry monitor

    NASA Technical Reports Server (NTRS)

    Bell, Benjamin; Eilbert, James L.

    1988-01-01

    The size of the basins of attraction around fixed points in recurrent neural nets (NNs) can be modified by a training process. Controlling these attractive regions by presenting training data with various amount of noise added to the prototype signal vectors is discussed. Application of this technique to signal processing results in a classification system whose sensitivity can be controlled. This new technique is applied to the classification of temporal sequences in telemetry data.

  12. Neural network-based recognition of whistlers on spectrograms detected by satellite

    NASA Astrophysics Data System (ADS)

    Conti, Livio

    2016-04-01

    We present a system to automatically recognize and classify the occurrence of whistler waves on spectrograms of electric field measurements performed by satellite. Whistlers - VLF waves generated by lightning, with a specific spectral dispersion relation - can induce precipitation of trapped Van Allen particles and have a role in the chemistry of some atmospheric components (mainly NOx). Moreover, it has also been suggested that the increase of the number of anomalous whistlers (i.e. whistlers with high value of dispersion constant) could be induced by disturbances in the Earth-ionosphere wave-guide, generated by seismo-electromagnetic emissions. On satellite, the recognition of whistlers asks for analyzing high-resolution spectrograms that cannot be downloaded to Earth, due to the limits of data transmission. For this reason, a real time identification and classification must be performed on satellite, by avoiding downloading all the unprocessed data. The procedure that we have developed is based on a Time Delay Neural Network (TDNN). The TDNN, proposed some years ago for speech recognition, can be fruitfully also applied in real-time analysis of electromagnetic spectrograms in order to detect phenomena characterized by a specific shape/signature such as those of the whistler waves. Some studies have been performed by the RNF experiment on board of the DEMETER satellite and our algorithm could be adopted on board of the satellite CSES (China Seismo-Electromagnetic Satellite), launch scheduled by the end of 2016. Moreover, the procedure can be also adopted to automatic analysis of whistlers detected on ground.

  13. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

    PubMed

    Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

    2014-01-01

    A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

  14. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    PubMed Central

    Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  15. Neural network based automatic limit prediction and avoidance system and method

    NASA Technical Reports Server (NTRS)

    Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)

    2001-01-01

    A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

  16. Identification of continuous-time dynamical systems: Neural network based algorithms and parallel implementation

    SciTech Connect

    Farber, R.M.; Lapedes, A.S.; Rico-Martinez, R.; Kevrekidis, I.G.

    1993-06-01

    Time-delay mappings constructed using neural networks have proven successful performing nonlinear system identification; however, because of their discrete nature, their use in bifurcation analysis of continuous-tune systems is limited. This shortcoming can be avoided by embedding the neural networks in a training algorithm that mimics a numerical integrator. Both explicit and implicit integrators can be used. The former case is based on repeated evaluations of the network in a feedforward implementation; the latter relies on a recurrent network implementation. Here the algorithms and their implementation on parallel machines (SIMD and MIMD architectures) are discussed.

  17. First on-sky results of a neural network based tomographic reconstructor: Carmen on Canary

    NASA Astrophysics Data System (ADS)

    Osborn, J.; Guzman, D.; de Cos Juez, F. J.; Basden, A. G.; Morris, T. J.; Gendron, É.; Butterley, T.; Myers, R. M.; Guesalaga, A.; Sanchez Lasheras, F.; Gomez Victoria, M.; Sánchez Rodríguez, M. L.; Gratadour, D.; Rousset, G.

    2014-07-01

    We present on-sky results obtained with Carmen, an artificial neural network tomographic reconstructor. It was tested during two nights in July 2013 on Canary, an AO demonstrator on the William Hershel Telescope. Carmen is trained during the day on the Canary calibration bench. This training regime ensures that Carmen is entirely flexible in terms of atmospheric turbulence profile, negating any need to re-optimise the reconstructor in changing atmospheric conditions. Carmen was run in short bursts, interlaced with an optimised Learn and Apply reconstructor. We found the performance of Carmen to be approximately 5% lower than that of Learn and Apply.

  18. Identification of continuous-time dynamical systems: Neural network based algorithms and parallel implementation

    SciTech Connect

    Farber, R.M.; Lapedes, A.S. ); Rico-Martinez, R.; Kevrekidis, I.G. . Dept. of Chemical Engineering)

    1993-01-01

    Time-delay mappings constructed using neural networks have proven successful performing nonlinear system identification; however, because of their discrete nature, their use in bifurcation analysis of continuous-tune systems is limited. This shortcoming can be avoided by embedding the neural networks in a training algorithm that mimics a numerical integrator. Both explicit and implicit integrators can be used. The former case is based on repeated evaluations of the network in a feedforward implementation; the latter relies on a recurrent network implementation. Here the algorithms and their implementation on parallel machines (SIMD and MIMD architectures) are discussed.

  19. Empirical study on neural network based predictive techniques for automatic number plate recognition

    NASA Astrophysics Data System (ADS)

    Shashidhara, M. S.; Indrakumar, S. S.

    2011-10-01

    The objective of this study is to provide an easy, accurate and effective technology for the Bangalore city traffic control. This is based on the techniques of image processing and laser beam technology. The core concept chosen here is an image processing technology by the method of automatic number plate recognition system. First number plate is recognized if any vehicle breaks the traffic rules in the signals. The number is fetched from the database of the RTO office by the process of automatic database fetching. Next this sends the notice and penalty related information to the vehicle owner email-id and an SMS sent to vehicle owner. In this paper, we use of cameras with zooming options & laser beams to get accurate pictures further applied image processing techniques such as Edge detection to understand the vehicle, Identifying the location of the number plate, Identifying the number plate for further use, Plain plate number, Number plate with additional information, Number plates in the different fonts. Accessing the database of the vehicle registration office to identify the name and address and other information of the vehicle number. The updates to be made to the database for the recording of the violation and penalty issues. A feed forward artificial neural network is used for OCR. This procedure is particularly important for glyphs that are visually similar such as '8' and '9' and results in training sets of between 25,000 and 40,000 training samples. Over training of the neural network is prevented by Bayesian regularization. The neural network output value is set to 0.05 when the input is not desired glyph, and 0.95 for correct input.

  20. A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application.

    PubMed

    Onumanyi, A J; Onwuka, E N; Aibinu, A M; Ugweje, O C; Salami, M J E

    2014-01-01

    A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application. PMID:27379318

  1. A feedforward artificial neural network based on quantum effect vector-matrix multipliers.

    PubMed

    Levy, H J; McGill, T C

    1993-01-01

    The vector-matrix multiplier is the engine of many artificial neural network implementations because it can simulate the way in which neurons collect weighted input signals from a dendritic arbor. A new technology for building analog weighting elements that is theoretically capable of densities and speeds far beyond anything that conventional VLSI in silicon could ever offer is presented. To illustrate the feasibility of such a technology, a small three-layer feedforward prototype network with five binary neurons and six tri-state synapses was built and used to perform all of the fundamental logic functions: XOR, AND, OR, and NOT. PMID:18267745

  2. Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

    PubMed

    Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. PMID:26356597

  3. Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

    PubMed

    Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

    2016-01-01

    For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme. PMID:27608018

  4. A neural network based error correction method for radio occultation electron density retrieval

    NASA Astrophysics Data System (ADS)

    Pham, Viet-Cuong; Juang, Jyh-Ching

    2015-12-01

    Abel inversion techniques have been widely employed to retrieve electron density profiles (EDPs) from radio occultation (RO) measurements, which are available by observing Global Navigation Satellite System (GNSS) satellites from low-earth-orbit (LEO) satellites. It is well known that the ordinary Abel inversion might introduce errors in the retrieval of EDPs when the spherical symmetry assumption is violated. The error, however, is case-dependent; therefore it is desirable to associate an error index or correction coefficient with respect to each retrieved EDP. Several error indices have been proposed but they only deal with electron density at the F2 peak and suffer from some drawbacks. In this paper we propose an artificial neural network (ANN) based error correction method for EDPs obtained by the ordinary Abel inversion. The ANN is first trained to learn the relationship between vertical total electron content (TEC) measurements and retrieval errors at the F2 peak, 220 km and 110 km altitudes; correction coefficients are then estimated to correct the retrieved EDPs at these three altitudes. Experiments using the NeQuick2 model and real FORMOSAT-3/COSMIC RO geometry show that the proposed method outperforms existing ones. Real incoherent scatter radar (ISR) measurements at the Jicamarca Radio Observatory and the global TEC map provided by the International GNSS Service (IGS) are also used to valid the proposed method.

  5. Ensembles of neural networks based on the alteration of input feature values.

    PubMed

    Akhand, M A H; Murase, K

    2012-02-01

    An ensemble performs well when the component classifiers are diverse yet accurate, so that the failure of one is compensated for by others. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a new technique of training pattern generation. The method alters input feature values of some patterns using the values of other patterns to generate different patterns for different classifiers. The effectiveness of neural network ensemble based on the proposed technique was evaluated using a suite of 25 benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods. Experimental investigation of different input values alteration techniques finds that alteration with pattern values in the same class is better for generalization, although other alteration techniques may offer more diversity. PMID:22262526

  6. Discriminative boosted forest with convolutional neural network-based patch descriptor for object detection

    NASA Astrophysics Data System (ADS)

    Xiang, Tao; Li, Tao; Ye, Mao; Li, Xudong

    2016-01-01

    Object detection with intraclass variations is challenging. The existing methods have not achieved the optimal combinations of classifiers and features, especially features learned by convolutional neural networks (CNNs). To solve this problem, we propose an object-detection method based on improved random forest and local image patches represented by CNN features. First, we compute CNN-based patch descriptors for each sample by modified CNNs. Then, the random forest is built whose split functions are defined by patch selector and linear projection learned by linear support vector machine. To improve the classification accuracy, the split functions in each depth of the forest make up a local classifier, and all local classifiers are assembled in a layer-wise manner by a boosting algorithm. The main contributions of our approach are summarized as follows: (1) We propose a new local patch descriptor based on CNN features. (2) We define a patch-based split function which is optimized with maximum class-label purity and minimum classification error over the samples of the node. (3) Each local classifier is assembled by minimizing the global classification error. We evaluate the method on three well-known challenging datasets: TUD pedestrians, INRIA pedestrians, and UIUC cars. The experiments demonstrate that our method achieves state-of-the-art or competitive performance.

  7. q-state Potts-glass neural network based on pseudoinverse rule

    SciTech Connect

    Xiong Daxing; Zhao Hong

    2010-08-15

    We study the q-state Potts-glass neural network with the pseudoinverse (PI) rule. Its performance is investigated and compared with that of the counterpart network with the Hebbian rule instead. We find that there exists a critical point of q, i.e., q{sub cr}=14, below which the storage capacity and the retrieval quality can be greatly improved by introducing the PI rule. We show that the dynamics of the neural networks constructed with the two learning rules respectively are quite different; but however, regardless of the learning rules, in the q-state Potts-glass neural networks with q{>=}3 there is a common novel dynamical phase in which the spurious memories are completely suppressed. This property has never been noticed in the symmetric feedback neural networks. Free from the spurious memories implies that the multistate Potts-glass neural networks would not be trapped in the metastable states, which is a favorable property for their applications.

  8. Automatic intraductal breast carcinoma classification using a neural network-based recognition system.

    PubMed

    Reigosa, A; Hernández, L; Torrealba, V; Barrios, V; Montilla, G; Bosnjak, A; Araez, M; Turiaf, M; Leon, A

    1998-07-01

    A contour-based automatic recognition system was applied to classify intraductal breast carcinoma into high nuclear grade and low nuclear grade in a digitized histologic image. The image discriminating characteristics were selected by their invariability condition to rotation and translation. They were acquired from cellular contours information. The totally interconnected multilayer perceptron network architecture was selected, and it was trained with the error back propagation algorithm. Forty cases were analyzed by the system and the diagnoses were compared with that of pathologist consensus, obtaining agreement in 97.5% (p < .00001 of cases). The system may become a very useful tool for the pathologist in the definitive classification of intraductal carcinoma. PMID:21223442

  9. Short-term load and wind power forecasting using neural network-based prediction intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time. PMID:24807030

  10. A spiking neural network based cortex-like mechanism and application to facial expression recognition.

    PubMed

    Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

    2012-01-01

    In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism. PMID:23193391

  11. Further Investigation of Receding Horizion-Based Controllers and Neural Network-Based Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.; Haley, Pamela J. (Technical Monitor)

    2000-01-01

    This report provides a comprehensive summary of the research work performed over the entire duration of the co-operative research agreement between NASA Langley Research Center and Kansas State University. This summary briefly lists the findings and also suggests possible future directions for the continuation of the subject research in the area of Generalized Predictive Control (GPC) and Network Based Generalized Predictive Control (NGPC).

  12. Feedforward, high density, programmable read only neural network based memory system

    NASA Technical Reports Server (NTRS)

    Daud, Taher; Moopenn, Alex; Lamb, James; Thakoor, Anil; Khanna, Satish

    1988-01-01

    Neural network-inspired, nonvolatile, programmable associative memory using thin-film technology is demonstrated. The details of the architecture, which uses programmable resistive connection matrices in synaptic arrays and current summing and thresholding amplifiers as neurons, are described. Several synapse configurations for a high-density array of a binary connection matrix are also described. Test circuits are evaluated for operational feasibility and to demonstrate the speed of the read operation. The results are discussed to highlight the potential for a read data rate exceeding 10 megabits/sec.

  13. All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

    PubMed

    Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Klein, Jacques-Olivier; Wang, Yu; Zhao, Weisheng

    2016-08-01

    Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work. PMID:27214913

  14. Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images

    NASA Astrophysics Data System (ADS)

    Blonda, Palma N.; Bennardo, A.; Satalino, Giuseppe; Pasquariello, Guido; De Blasi, Roberto A.; Milella, D.

    1996-06-01

    This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.

  15. Convolutional neural network based sensor fusion for forward looking ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.

  16. A neural networks-based hybrid routing protocol for wireless mesh networks.

    PubMed

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

  17. Large eddy simulation of extinction and reignition with artificial neural networks based chemical kinetics

    SciTech Connect

    Sen, Baris Ali; Menon, Suresh; Hawkes, Evatt R.

    2010-03-15

    Large eddy simulation (LES) of a non-premixed, temporally evolving, syngas/air flame is performed with special emphasis on speeding-up the sub-grid chemistry computations using an artificial neural networks (ANN) approach. The numerical setup for the LES is identical to a previous direct numerical simulation (DNS) study, which reported considerable local extinction and reignition physics, and hence, offers a challenging test case. The chemical kinetics modeling with ANN is based on a recent approach, and replaces the stiff ODE solver (DI) to predict the species reaction rates in the subgrid linear eddy mixing (LEM) model based LES (LEMLES). In order to provide a comprehensive evaluation of the current approach, additional information on conditional statistics of some of the key species and temperature are extracted from the previous DNS study and are compared with the LEMLES using ANN (ANN-LEMLES, hereafter). The results show that the current approach can detect the correct extinction and reignition physics with reasonable accuracy compared to the DNS. The syngas flame structure and the scalar dissipation rate statistics obtained by the current ANN-LEMLES are provided to further probe the flame physics. It is observed that, in contrast to H{sub 2}, CO exhibits a smooth variation within the region enclosed by the stoichiometric mixture fraction. The probability density functions (PDFs) of the scalar dissipation rates calculated based on the mixture fraction and CO demonstrate that the mean value of the PDF is insensitive to extinction and reignition. However, this is not the case for the scalar dissipation rate calculated by the OH mass fraction. Overall, ANN provides considerable computational speed-up and memory saving compared to DI, and can be used to investigate turbulent flames in a computationally affordable manner. (author)

  18. Neural Network Based State of Health Diagnostics for an Automated Radioxenon Sampler/Analyzer

    SciTech Connect

    Keller, Paul E.; Kangas, Lars J.; Hayes, James C.; Schrom, Brian T.; Suarez, Reynold; Hubbard, Charles W.; Heimbigner, Tom R.; McIntyre, Justin I.

    2009-05-13

    Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSA’s complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

  19. Minimum data requirement for neural networks based on power spectral density analysis.

    PubMed

    Deng, Jiamei; Maass, Bastian; Stobart, Richard

    2012-04-01

    One of the most critical challenges ahead for diesel engines is to identify new techniques for fuel economy improvement without compromising emissions regulations. One technique is the precise control of air/fuel ratio, which requires the measurement of instantaneous fuel consumption. Measurement accuracy and repeatability for fuel rate is the key to successfully controlling the air/fuel ratio and real-time measurement of fuel consumption. The volumetric and gravimetric measurement principles are well-known methods for measurement of fuel consumption in internal combustion engines. However, the fuel flow rate measured by these methods is not suitable for either real-time control or real-time measurement purposes because of the intermittent nature of the measurements. This paper describes a technique that can be used to find the minimum data [consisting of data from just 2.5% of the non-road transient cycle (NRTC)] to solve the problem concerning discontinuous data of fuel flow rate measured using an AVL 733S fuel meter for a medium or heavy-duty diesel engine using neural networks. Only torque and speed are used as the input parameters for the fuel flow rate prediction. Power density analysis is used to find the minimum amount of the data. The results show that the nonlinear autoregressive model with exogenous inputs could predict the particulate matter successfully with R(2) above 0.96 using 2.5% NRTC data with only torque and speed as inputs. PMID:24805042

  20. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    PubMed

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. PMID:26081920

  1. Neural network-based diagnosing for optic nerve disease from visual-evoked potential.

    PubMed

    Kara, Sadik; Güven, Ayşegül

    2007-10-01

    In this paper, we purpose a diagnostic procedure to identify the optic nerve disease from visual evoked potential (VEP) signals using an Artificial Neural Network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented. The correct classification rate was 96.87% for subjects having optic nerve disease and 96.66% for healthy subjects. The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis, angiography, VEP and pattern electroretinography. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system. PMID:17918693

  2. Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach

    NASA Astrophysics Data System (ADS)

    Cardelli, E.; Faba, A.; Laudani, A.; Lozito, G. M.; Riganti Fulginei, F.; Salvini, A.

    2016-04-01

    This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.

  3. Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis

    USGS Publications Warehouse

    Hong, Y.-S.T.; Rosen, Michael R.; Bhamidimarri, R.

    2003-01-01

    This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP. ?? 2003 Elsevier Science Ltd. All rights reserved.

  4. Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach

    PubMed Central

    Narain, Renu; Saxena, Sanjai; Goyal, Achal Kumar

    2016-01-01

    Purpose Currently cardiovascular diseases (CVDs) are the main cause of death worldwide. Disease risk estimates can be used as prognostic information and support for treating CVDs. The commonly used Framingham risk score (FRS) for CVD prediction is outdated for the modern population, so FRS may not be accurate enough. In this paper, a novel CVD prediction system based on machine learning is proposed. Methods This study has been conducted with the data of 689 patients showing symptoms of CVD. Furthermore, the dataset of 5,209 CVD patients of the famous Framingham study has been used for validation purposes. Each patient’s parameters have been analyzed by physicians in order to make a diagnosis. The proposed system uses the quantum neural network for machine learning. This system learns and recognizes the pattern of CVD. The proposed system has been experimentally evaluated and compared with FRS. Results During testing, patients’ data in combination with the doctors’ diagnosis (predictions) are used for evaluation and validation. The proposed system achieved 98.57% accuracy in predicting the CVD risk. The CVD risk predictions by the proposed system, using the dataset of the Framingham study, confirmed the potential risk of death, deaths which actually occurred and had been recorded as due to myocardial infarction and coronary heart disease in the dataset of the Framingham study. The accuracy of the proposed system is significantly higher than FRS and other existing approaches. Conclusion The proposed system will serve as an excellent tool for a medical practitioner in predicting the risk of CVD. This system will be serving as an aid to medical practitioners for planning better medication and treatment strategies. An early diagnosis may be effectively made by using this system. An overall accuracy of 98.57% has been achieved in predicting the risk level. The accuracy is considerably higher compared to the other existing approaches. Thus, this system must be used instead of the well-known FRS. PMID:27486312

  5. Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS

    NASA Astrophysics Data System (ADS)

    Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin

    Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.

  6. Neural-network-based state of health diagnostics for an automated radioxenon sampler/analyzer

    NASA Astrophysics Data System (ADS)

    Keller, Paul E.; Kangas, Lars J.; Hayes, James C.; Schrom, Brian T.; Suarez, Reynold; Hubbard, Charles W.; Heimbigner, Tom R.; McIntyre, Justin I.

    2009-05-01

    Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSA's complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

  7. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

    PubMed

    Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  8. Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

    NASA Astrophysics Data System (ADS)

    Li, Zhichen; An, Qiu; Ji, Changying

    The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

  9. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

    PubMed Central

    Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

    2014-01-01

    A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

  10. Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis.

    PubMed

    Hong, Yoon-Seok Timothy; Rosen, Michael R; Bhamidimarri, Rao

    2003-04-01

    This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP. PMID:12600389

  11. A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application

    PubMed Central

    Onumanyi, A. J.; Onwuka, E. N.; Aibinu, A. M.; Ugweje, O. C.; Salami, M. J. E.

    2014-01-01

    A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application. PMID:27379318

  12. A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

    PubMed Central

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

  13. Neural network based neutral particles reconstruction with the E687 hadron calorimeter

    NASA Astrophysics Data System (ADS)

    Arena, V.; Boca, G.; Bonomi, G.; Gérard, G.; Gianini, G.; Marchesotti, M.; Merlo, M.; Ratti, S. P.; Riccardi, C.; Viola, L.; Vitulo, P.; Buchholz, D.; Claes, D.; O'Reilly, B.

    1996-02-01

    We present a neutral particle reconstruction algorithm based on a neural network approach applied to the E687 hadron calorimeter. A measurement of the invariant mass of the Σ± → nπ± is presented to verify the reliability of the reconstruction. The reconstructed invariant mass of the charmed meson D + → K L0π+π+π- is also presented to show the possible application of this technique to charmed particles decaying into a neutral hadron. An example of this would be Λc+ → nK -π+π+.

  14. Predicting the acute neurotoxicity of diverse organic solvents using probabilistic neural networks based QSTR modeling approaches.

    PubMed

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2016-03-01

    Organic solvents are widely used chemicals and the neurotoxic properties of some are well established. In this study, we established nonlinear qualitative and quantitative structure-toxicity relationship (STR) models for predicting neurotoxic classes and neurotoxicity of structurally diverse solvents in rodent test species following OECD guideline principles for model development. Probabilistic neural network (PNN) based qualitative and generalized regression neural network (GRNN) based quantitative STR models were constructed using neurotoxicity data from rat and mouse studies. Further, interspecies correlation based quantitative activity-activity relationship (QAAR) and global QSTR models were also developed using the combined data set of both rodent species for predicting the neurotoxicity of solvents. The constructed models were validated through deriving several statistical coefficients for the test data and the prediction and generalization abilities of these models were evaluated. The qualitative STR models (rat and mouse) yielded classification accuracies of 92.86% in the test data sets, whereas, the quantitative STRs yielded correlation (R(2)) of >0.93 between the measured and model predicted toxicity values in both the test data (rat and mouse). The prediction accuracies of the QAAR (R(2) 0.859) and global STR (R(2) 0.945) models were comparable to those of the independent local STR models. The results suggest the ability of the developed QSTR models to reliably predict binary neurotoxicity classes and the endpoint neurotoxicities of the structurally diverse organic solvents. PMID:26721664

  15. Adaptive neural network/expert system that learns fault diagnosis for different structures

    NASA Astrophysics Data System (ADS)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  16. An adaptive sliding mode backstepping control for the mobile manipulator with nonholonomic constraints

    NASA Astrophysics Data System (ADS)

    Chen, Naijian; Song, Fangzhen; Li, Guoping; Sun, Xuan; Ai, Changsheng

    2013-10-01

    To solve disturbances, nonlinearity, nonholonomic constraints and dynamic coupling between the platform and its mounted robot manipulator, an adaptive sliding mode controller based on the backstepping method applied to the robust trajectory tracking of the wheeled mobile manipulator is described in this article. The control algorithm rests on adopting the backstepping method to improve the global ultimate asymptotic stability and applying the sliding mode control to obtain high response and invariability to uncertainties. According to the Lyapunov stability criterion, the wheeled mobile manipulator is divided into several stabilizing subsystems, and an adaptive law is designed to estimate the general nondeterminacy, which make the controller be capable to drive the trajectory tracking error of the mobile manipulator to converge to zero even in the presence of perturbations and mathematical model errors. We compare our controller with the robust neural network based algorithm in nonholonomic constraints and uncertainties, and simulation results prove the effectivity and feasibility of the proposed method in the trajectory tracking of the wheeled mobile manipulator.

  17. AFTI F-111 in flight

    NASA Technical Reports Server (NTRS)

    1986-01-01

    This NASA Ames-Dryden Flight Research Facility photograph shows a modified General Dynamics AFTI/F-111A Aardvark with supercritical mission adaptive wings (MAW) installed. The Aircraft is in a banking turn towards Rogers Dry Lake and Edwards Air Force Base, California. With the phasing out of the TACT program came a renewed effort by the Air Force Flight Dynamics Laboratory to extend supercritical wing technology to a higher level of performance. In the early 1980s the supercritical wing on the F-111A aircraft was replaced with a wing built by Boeing Aircraft Company System called a 'mission adaptive wing' (MAW), and a joint NASA and Air Force program called Advanced Fighter Technology Integration (AFTI) was born.

  18. AFTI F-111 in flight

    NASA Technical Reports Server (NTRS)

    1986-01-01

    This NASA Ames-Dryden Flight Research Facility photograph shows a modified General Dynamics AFTI/F-111A Aardvark with supercritical mission adaptive wings (MAW) installed. In this photograph the AFTI/F111A is seen banking towards Rodgers Dry Lake and Edwards Air Force Base. With the phasing out of the TACT program came a renewed effort by the Air Force Flight Dynamics Laboratory to extend supercritical wing technology to a higher level of performance. In the early 1980s the supercritical wing on the F-111A aircraft was replaced with a wing built by Boeing Aircraft Company System called a 'mission adaptive wing' (MAW), and a joint NASA and Air Force program called Advanced Fighter Technology Integration (AFTI) was born.

  19. Adaptive fuzzy approach to modeling of operational space for autonomous mobile robots

    NASA Astrophysics Data System (ADS)

    Musilek, Petr; Gupta, Madan M.

    1998-10-01

    Robots operating in an unstructured environment need high level of modeling of their operational space in order to plan a suitable path from an initial position to a desired goal. From this perspective, operational space modeling seems to be crucial to ensure a sufficient level of autonomy. In order to compile the information from various sources, we propose a fuzzy approach to evaluate each unit region on a grid map by a certain value of transition cost. This value expresses the cost of movement over the unit region: the higher the value, the more expensive the movement through the region in terms of energy, time, danger, etc. The approach for modeling, proposed in this paper, employs fuzzy granulation of information on various terrain features and their combination based on a fuzzy neural network. In order to adapt to the changing environmental conditions, and to improve the validity of constructed cost maps on-line, the system can be endowed with learning abilities. The learning subsystem would change parameters of the fuzzy neural network based decision system by reinforcements derived from comparisons of the actual cost of transition with the cost obtained from the model.

  20. An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

    PubMed

    Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

    2016-05-01

    Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. PMID:26920088

  1. Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

    PubMed Central

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models

  2. Green Flight Challenge

    NASA Video Gallery

    The CAFE Green Flight Challenge sponsored by Google will be held at the CAFE Foundation Flight Test Center at Charles M. Schulz Sonoma County Airport in Santa Rosa, Calif. The Green Flight Challeng...

  3. Cibola flight experiment

    SciTech Connect

    Roussel-Dupre, D.; Caffrey, M. P.

    2004-01-01

    Los Alamos National Laboratory is building the Cibola Flight Experiment (CFE), a reconfigurable processor payload intended for a Low Earth Orbit system. It will survey portions of the VHF and UHF radio spectra. The experiment uses networks of reprogrammable, Field Programmable Gate Arrays (FPGAs) to process the received signals for ionospheric and lightning studies. The objective is to validate the on-orbit use of commercial, reconfigurable FPGA technology utilizing several different single-event upset mitigation schemes. It will also detect and measure impulsive events that occur in a complex background. Surrey Satellite Technology, Ltd (SSTL) is building the small host satellite, CFESat, based upon SSTL's disaster monitoring constellation (DMC) and Topsat mission satellite designs. The CFESat satellite will be launched by the Space Test Program in September 2006 on the US Air Force Evolved Expendable Launch Vehicle (EELV) using the EELV's Secondary Payload Adapter (ESPA) that allows up to six small satellites to be launched as 'piggyback' passengers with larger spacecraft.

  4. Adaptive optimization and control using neural networks

    SciTech Connect

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  5. Method and system for determining induction motor speed

    DOEpatents

    Parlos, Alexander G.; Bharadwaj, Raj M.

    2004-03-30

    A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.

  6. NASA's Flight Opportunities Program

    NASA Video Gallery

    NASA's Flight Opportunities Program is facilitating low-cost access to suborbital space, where researchers can test technologies using commercially developed vehicles. Suborbital flights can quickl...

  7. Digital adaptive control laws for VTOL aircraft

    NASA Technical Reports Server (NTRS)

    Hartmann, G. L.; Stein, G.

    1979-01-01

    Honeywell has designed a digital self-adaptive flight control system for flight test in the VALT Research Aircraft (a modified CH-47). The final design resulted from a comparison of two different adaptive concepts: one based on explicit parameter estimates from a real-time maximum likelihood estimation algorithm and the other based on an implicit model reference adaptive system. The two designs are compared on the basis of performance and complexity.

  8. Aircraft flight characteristics in icing conditions

    NASA Astrophysics Data System (ADS)

    Cao, Yihua; Wu, Zhenlong; Su, Yuan; Xu, Zhongda

    2015-04-01

    Aircraft flight dynamic characteristics can be greatly changed by ice accretion, which has been considered a considerable threat to aircraft flight safety for a long time. An overview of the studies on several ice accretion effects on aircraft flight dynamics is presented here. Special attention is paid to the following areas: ways to obtain the aerodynamic data of iced aircraft, flight dynamic modeling and simulation for iced aircraft, effects of ice accretion on aircraft stability and control as well as on flight performance and aircraft icing envelope protection and control adaption. Finally based on the progress of existing research in these areas, some key issues which deserve more attention for researchers to resolve are addressed, including obtaining aerodynamic data of iced aircraft through numerical simulation method, consummating the existing calculation models about effects of ice accretion on aircraft aerodynamic derivatives and enhancing the investigation on problems of tailplane ice accretion.

  9. Cardiovascular adaptation to spaceflight

    NASA Technical Reports Server (NTRS)

    Hargens, A. R.; Watenpaugh, D. E.

    1996-01-01

    This article reviews recent flight and ground-based studies of cardiovascular adaptation to spaceflight. Prominent features of microgravity exposure include loss of gravitational pressures, relatively low venous pressures, headward fluid shifts, plasma volume loss, and postflight orthostatic intolerance and reduced exercise capacity. Many of these short-term responses to microgravity extend themselves during long-duration microgravity exposure and may be explained by altered pressures (blood and tissue) and fluid balance in local tissues nourished by the cardiovascular system. In this regard, it is particularly noteworthy that tissues of the lower body (e.g., foot) are well adapted to local hypertension on Earth, whereas tissues of the upper body (e.g., head) are not as well adapted to increase in local blood pressure. For these and other reasons, countermeasures for long-duration flight should include reestablishment of higher, Earth-like blood pressures in the lower body.

  10. Flight Test Series 3: Flight Test Report

    NASA Technical Reports Server (NTRS)

    Marston, Mike; Sternberg, Daniel; Valkov, Steffi

    2015-01-01

    This document is a flight test report from the Operational perspective for Flight Test Series 3, a subpart of the Unmanned Aircraft System (UAS) Integration in the National Airspace System (NAS) project. Flight Test Series 3 testing began on June 15, 2015, and concluded on August 12, 2015. Participants included NASA Ames Research Center, NASA Armstrong Flight Research Center, NASA Glenn Research Center, NASA Langley Research center, General Atomics Aeronautical Systems, Inc., and Honeywell. Key stakeholders analyzed their System Under Test (SUT) in two distinct configurations. Configuration 1, known as Pairwise Encounters, was subdivided into two parts: 1a, involving a low-speed UAS ownship and intruder(s), and 1b, involving a high-speed surrogate ownship and intruder. Configuration 2, known as Full Mission, involved a surrogate ownship, live intruder(s), and integrated virtual traffic. Table 1 is a summary of flights for each configuration, with data collection flights highlighted in green. Section 2 and 3 of this report give an in-depth description of the flight test period, aircraft involved, flight crew, and mission team. Overall, Flight Test 3 gathered excellent data for each SUT. We attribute this successful outcome in large part from the experience that was acquired from the ACAS Xu SS flight test flown in December 2014. Configuration 1 was a tremendous success, thanks to the training, member participation, integration/testing, and in-depth analysis of the flight points. Although Configuration 2 flights were cancelled after 3 data collection flights due to various problems, the lessons learned from this will help the UAS in the NAS project move forward successfully in future flight phases.

  11. Overview of Pre-Flight Physical Training, In-Flight Exercise Countermeasures and the Post-Flight Reconditioning Program for International Space Station Astronauts

    NASA Technical Reports Server (NTRS)

    Kerstman, Eric

    2011-01-01

    International Space Station (ISS) astronauts receive supervised physical training pre-flight, utilize exercise countermeasures in-flight, and participate in a structured reconditioning program post-flight. Despite recent advances in exercise hardware and prescribed exercise countermeasures, ISS crewmembers are still found to have variable levels of deconditioning post-flight. This presentation provides an overview of the astronaut medical certification requirements, pre-flight physical training, in-flight exercise countermeasures, and the post-flight reconditioning program. Astronauts must meet medical certification requirements on selection, annually, and prior to ISS missions. In addition, extensive physical fitness testing and standardized medical assessments are performed on long duration crewmembers pre-flight. Limited physical fitness assessments and medical examinations are performed in-flight to develop exercise countermeasure prescriptions, ensure that the crewmembers are physically capable of performing mission tasks, and monitor astronaut health. Upon mission completion, long duration astronauts must re-adapt to the 1 G environment, and be certified as fit to return to space flight training and active duty. A structured, supervised postflight reconditioning program has been developed to prevent injuries, facilitate re-adaptation to the 1 G environment, and subsequently return astronauts to training and space flight. The NASA reconditioning program is implemented by the Astronaut Strength, Conditioning, and Rehabilitation (ASCR) team and supervised by NASA flight surgeons. This program has evolved over the past 10 years of the International Space Station (ISS) program and has been successful in ensuring that long duration astronauts safely re-adapt to the 1 g environment and return to active duty. Lessons learned from this approach to managing deconditioning can be applied to terrestrial medicine and future exploration space flight missions.

  12. Cardiovascular function in space flight

    NASA Technical Reports Server (NTRS)

    Nicogossian, A. E.; Charles, J. B.; Bungo, M. W.; Leach-Huntoon, C. S.; Nicgossian, A. E.

    1991-01-01

    Changes in orthostatic heart rate have been noted universally in Soviet and U.S. crewmembers post space flight. The magnitude of these changes appears to be influenced by mission duration, with increasing orthostatic intolerance for the first 7-10 days of flight and then a partial recovery in the orthostatic heart rate response. Fluid loading has been used as a countermeasure to this postflight orthostatic intolerance. Previous reports have documented the effectiveness of this technique, but it has also been noted that the effectiveness of volume expansion diminishes as flight duration exceeds one week. The response of carotid baroreceptor function was investigated utilizing a commercially available neck collar which could apply positive and negative pressure to effect receptor stimulation. Bedrest studies had validated the usefulness and validity of the device. In these studies it was shown that carotid baroreceptor function curves demonstrated less responsiveness to orthostatic stimulation than control individuals. Twelve Space Shuttle crewmembers were examined pre- and postflight from flights lasting from 4-5 days. Plots of baroreceptor function were constructed and plotted as change in R-R interval vs. carotid distending pressure (an orthostatic stimulus). Typical sigmoidal curves were obtained. Postflight the resting heart rate was higher (smaller R-R interval) and the range of R-R value and the slope of the carotid sigmoidal response were both depressed. These changes were not significant immediately postflight (L + O), but did become significant by the second day postflight (L + 2), and remained suppressed for several days thereafter. It is hypothesized that the early adaptation to space flight involves a central fluid shift during the initial days of flight, but subsequent alterations in neural controlling mechanisms (such as carotid baroreceptor function) contribute to orthostatic intolerance.

  13. 14 CFR 121.493 - Flight time limitations: Flight engineers and flight navigators.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false Flight time limitations: Flight engineers... Limitations: Flag Operations § 121.493 Flight time limitations: Flight engineers and flight navigators. (a) In any operation in which one flight engineer or flight navigator is required, the flight...

  14. Application of neural network technology to fly-by-light control systems

    NASA Astrophysics Data System (ADS)

    Urnes, James M., Sr.

    1995-05-01

    McDonnell Douglas Aerospace (MDA) is developing a Neural Network based Intelligent Flight Control System that utilizes fly-by-light data transmission combined with high capacity flight processors to implement control advancements and fault monitoring processes. In this control system design, high speed data transfer and electromagnetic interference protection obtained through fiber optic technology is linked with Neural Network based flight control hardware processors that are programmed with damage adaptive control capability, thus providing maximum survivability for fighter aircraft. This system also provides enhanced component fault diagnostics that can identify subsystem failures during flight, thus providing reduced life cycle cost through efficient maintenance action and less downtime of the aircraft. The Intelligent Flight Control products apply to fighter and transport aircraft. This program is Task 2C of the ARPA Fly-by-Light Advanced Systems Hardware (FLASH) Technology Reinvestment Program. The principal partner with MDA for the Task 2C Intelligent Flight Control development is Martin Marietta Control Systems. The program will mature the system hardware and software for laboratory demonstrations of component fault diagnostics and highly adaptive flight control performance.

  15. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    NASA Technical Reports Server (NTRS)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-01-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  16. IRAC Full-Scale Flight Testbed Capabilities

    NASA Technical Reports Server (NTRS)

    Lee, James A.; Pahle, Joseph; Cogan, Bruce R.; Hanson, Curtis E.; Bosworth, John T.

    2009-01-01

    Overview: Provide validation of adaptive control law concepts through full scale flight evaluation in a representative avionics architecture. Develop an understanding of aircraft dynamics of current vehicles in damaged and upset conditions Real-world conditions include: a) Turbulence, sensor noise, feedback biases; and b) Coupling between pilot and adaptive system. Simulated damage includes 1) "B" matrix (surface) failures; and 2) "A" matrix failures. Evaluate robustness of control systems to anticipated and unanticipated failures.

  17. Analysis and modification of Volterra/Wiener neural networks for the adaptive identification of non-linear hysteretic dynamic systems

    NASA Astrophysics Data System (ADS)

    Pei, J.-S.; Smyth, A. W.; Kosmatopoulos, E. B.

    2004-08-01

    This study attempts to demystify a powerful neural network approach for modelling non-linear hysteretic systems and in turn to streamline its architecture to achieve better computational efficiency. The recently developed neural network modelling approach, the Volterra/Wiener neural network (VWNN), demonstrated its usefulness in identifying the restoring forces for hysteretic systems in an off-line or even in an adaptive (on-line) mode, however, the mechanism of how and why it works has not been thoroughly explored especially in terms of a physical interpretation. Artificial neural network are often treated as "black box" modelling tools, in contrast, here the authors carry out a detailed analysis in terms of problem formulation and network architecture to explore the inner workings of this neural network. Based on the understanding of the dynamics of hysteretic systems, some simplifications and modifications are made to the original VWNN in predicting accelerations of hysteretic systems under arbitrary force excitations. Through further examination of the algorithm related to the VWNN applications, the efficiency of the previously published approach is improved by reducing the number of the hidden nodes without affecting the modelling accuracy of the network. One training example is presented to illustrate the application of the VWNN; and another is provided to demonstrate that the VWNN is able to yield a unique set of weights when the values of the controlling design parameters are fixed. The practical issue of how to choose the values of these important parameters is discussed to aid engineering applications.

  18. Poor flight performance in deep-diving cormorants.

    PubMed

    Watanabe, Yuuki Y; Takahashi, Akinori; Sato, Katsufumi; Viviant, Morgane; Bost, Charles-André

    2011-02-01

    Aerial flight and breath-hold diving present conflicting morphological and physiological demands, and hence diving seabirds capable of flight are expected to face evolutionary trade-offs regarding locomotory performances. We tested whether Kerguelen shags Phalacrocorax verrucosus, which are remarkable divers, have poor flight capability using newly developed tags that recorded their flight air speed (the first direct measurement for wild birds) with propeller sensors, flight duration, GPS position and depth during foraging trips. Flight air speed (mean 12.7 m s(-1)) was close to the speed that minimizes power requirement, rather than energy expenditure per distance, when existing aerodynamic models were applied. Flights were short (mean 92 s), with a mean summed duration of only 24 min day(-1). Shags sometimes stayed at the sea surface without diving between flights, even on the way back to the colony, and surface durations increased with the preceding flight durations; these observations suggest that shags rested after flights. Our results indicate that their flight performance is physiologically limited, presumably compromised by their great diving capability (max. depth 94 m, duration 306 s) through their morphological adaptations for diving, including large body mass (enabling a large oxygen store), small flight muscles (to allow for large leg muscles for underwater propulsion) and short wings (to decrease air volume in the feathers and hence buoyancy). The compromise between flight and diving, as well as the local bathymetry, shape the three-dimensional foraging range (<26 km horizontally, <94 m vertically) in this bottom-feeding cormorant. PMID:21228200

  19. F-111 TACT Flight Over the Mojave Desert

    NASA Video Gallery

    Transonic aircraft technology (TACT/F-111A) added an highly efficient supercritical wing and later the third phase applied advanced wing (Mission Adaptive Wing-MAW) flight control technologies and ...

  20. Adaptive Controller Effects on Pilot Behavior

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

    Adaptive control provides robustness and resilience for highly uncertain, and potentially unpredictable, flight dynamics characteristic. Some of the recent flight experiences of pilot-in-the-loop with an adaptive controller have exhibited unpredicted interactions. In retrospect, this is not surprising once it is realized that there are now two adaptive controllers interacting, the software adaptive control system and the pilot. An experiment was conducted to categorize these interactions on the pilot with an adaptive controller during control surface failures. One of the objectives of this experiment was to determine how the adaptation time of the controller affects pilots. The pitch and roll errors, and stick input increased for increasing adaptation time and during the segment when the adaptive controller was adapting. Not surprisingly, altitude, cross track and angle deviations, and vertical velocity also increase during the failure and then slowly return to pre-failure levels. Subjects may change their behavior even as an adaptive controller is adapting with additional stick inputs. Therefore, the adaptive controller should adapt as fast as possible to minimize flight track errors. This will minimize undesirable interactions between the pilot and the adaptive controller and maintain maneuvering precision.

  1. Flight Test Engineering

    NASA Technical Reports Server (NTRS)

    Pavlock, Kate Maureen

    2013-01-01

    Although the scope of flight test engineering efforts may vary among organizations, all point to a common theme: flight test engineering is an interdisciplinary effort to test an asset in its operational flight environment. Upfront planning where design, implementation, and test efforts are clearly aligned with the flight test objective are keys to success. This chapter provides a top level perspective of flight test engineering for the non-expert. Additional research and reading on the topic is encouraged to develop a deeper understanding of specific considerations involved in each phase of flight test engineering.

  2. Operational efficiency: Automatic ascent flight design

    NASA Technical Reports Server (NTRS)

    1990-01-01

    Major objectives, milestones, key contacts, major accomplishments, technology issues, and candidate programs of the automatic ascent flight design are outlined. Topics discussed include: advanced avionics concepts; advanced training concepts; telerobotics/telepresence; integrated command and control; advanced software integration; atmospheric adaptive guidance; and health status and monitoring concept. This presentation is represented by viewgraphs only.

  3. Planning Flight Paths of Autonomous Aerobots

    NASA Technical Reports Server (NTRS)

    Kulczycki, Eric; Elfes, Alberto; Sharma, Shivanjli

    2009-01-01

    Algorithms for planning flight paths of autonomous aerobots (robotic blimps) to be deployed in scientific exploration of remote planets are undergoing development. These algorithms are also adaptable to terrestrial applications involving robotic submarines as well as aerobots and other autonomous aircraft used to acquire scientific data or to perform surveying or monitoring functions.

  4. In-flight aeroelastic measurement technique development

    NASA Astrophysics Data System (ADS)

    Burner, Alpheus W.; Lokos, William A.; Barrows, Danny A.

    2003-11-01

    The initial concept and development of a low-cost, adaptable method for the measurement of static and dynamic aeroelastic deformation of aircraft during flight testing is presented. The method is adapted from a proven technique used in wind tunnel testing to measure model deformation, often referred to as the videogrammetric model deformation (or VMD) technique. The requirements for in-flight measurements are compared and contrasted with those for wind tunnel testing. The methodology for the proposed measurements and differences compared with that used for wind tunnel testing is given. Several error sources and their effects are identified. Measurement examples using the new technique, including change in wing twist and deflection as a function of time, from an F/A-18 research aircraft at NASA's Dryden Flight Research Center are presented.

  5. Flight experience with flight control redundancy management

    NASA Technical Reports Server (NTRS)

    Szalai, K. J.; Larson, R. R.; Glover, R. D.

    1980-01-01

    Flight experience with both current and advanced redundancy management schemes was gained in recent flight research programs using the F-8 digital fly by wire aircraft. The flight performance of fault detection, isolation, and reconfiguration (FDIR) methods for sensors, computers, and actuators is reviewed. Results of induced failures as well as of actual random failures are discussed. Deficiencies in modeling and implementation techniques are also discussed. The paper also presents comparison off multisensor tracking in smooth air, in turbulence, during large maneuvers, and during maneuvers typical of those of large commercial transport aircraft. The results of flight tests of an advanced analytic redundancy management algorithm are compared with the performance of a contemporary algorithm in terms of time to detection, false alarms, and missed alarms. The performance of computer redundancy management in both iron bird and flight tests is also presented.

  6. Integrating Space Flight Resource Management Skills into Technical Lessons for International Space Station Flight Controller Training

    NASA Technical Reports Server (NTRS)

    Baldwin, Evelyn

    2008-01-01

    The Johnson Space Center s (JSC) International Space Station (ISS) Space Flight Resource Management (SFRM) training program is designed to teach the team skills required to be an effective flight controller. It was adapted from the SFRM training given to Shuttle flight controllers to fit the needs of a "24 hours a day/365 days a year" flight controller. More recently, the length reduction of technical training flows for ISS flight controllers impacted the number of opportunities for fully integrated team scenario based training, where most SFRM training occurred. Thus, the ISS SFRM training program is evolving yet again, using a new approach of teaching and evaluating SFRM alongside of technical materials. Because there are very few models in other industries that have successfully tied team and technical skills together, challenges are arising. Despite this, the Mission Operations Directorate of NASA s JSC is committed to implementing this integrated training approach because of the anticipated benefits.

  7. 'Mighty Eagle' Takes Flight

    NASA Video Gallery

    The "Mighty Eagle," a NASA robotic prototype lander, had a successful first untethered flight Aug. 8 at the Marshall Center. During the 34-second flight, the Mighty Eagle soared and hovered at 30 f...

  8. Autonomous Soaring Flight Results

    NASA Technical Reports Server (NTRS)

    Allen, Michael J.

    2006-01-01

    A viewgraph presentation on autonomous soaring flight results for Unmanned Aerial Vehicles (UAV)'s is shown. The topics include: 1) Background; 2) Thermal Soaring Flight Results; 3) Autonomous Dolphin Soaring; and 4) Future Plans.

  9. Surface tension dominates insect flight on fluid interfaces.

    PubMed

    Mukundarajan, Haripriya; Bardon, Thibaut C; Kim, Dong Hyun; Prakash, Manu

    2016-03-01

    Flight on the 2D air-water interface, with body weight supported by surface tension, is a unique locomotion strategy well adapted for the environmental niche on the surface of water. Although previously described in aquatic insects like stoneflies, the biomechanics of interfacial flight has never been analysed. Here, we report interfacial flight as an adapted behaviour in waterlily beetles (Galerucella nymphaeae) which are also dexterous airborne fliers. We present the first quantitative biomechanical model of interfacial flight in insects, uncovering an intricate interplay of capillary, aerodynamic and neuromuscular forces. We show that waterlily beetles use their tarsal claws to attach themselves to the interface, via a fluid contact line pinned at the claw. We investigate the kinematics of interfacial flight trajectories using high-speed imaging and construct a mathematical model describing the flight dynamics. Our results show that non-linear surface tension forces make interfacial flight energetically expensive compared with airborne flight at the relatively high speeds characteristic of waterlily beetles, and cause chaotic dynamics to arise naturally in these regimes. We identify the crucial roles of capillary-gravity wave drag and oscillatory surface tension forces which dominate interfacial flight, showing that the air-water interface presents a radically modified force landscape for flapping wing flight compared with air. PMID:26936640

  10. Surface tension dominates insect flight on fluid interfaces

    PubMed Central

    Mukundarajan, Haripriya; Bardon, Thibaut C.; Kim, Dong Hyun; Prakash, Manu

    2016-01-01

    ABSTRACT Flight on the 2D air–water interface, with body weight supported by surface tension, is a unique locomotion strategy well adapted for the environmental niche on the surface of water. Although previously described in aquatic insects like stoneflies, the biomechanics of interfacial flight has never been analysed. Here, we report interfacial flight as an adapted behaviour in waterlily beetles (Galerucella nymphaeae) which are also dexterous airborne fliers. We present the first quantitative biomechanical model of interfacial flight in insects, uncovering an intricate interplay of capillary, aerodynamic and neuromuscular forces. We show that waterlily beetles use their tarsal claws to attach themselves to the interface, via a fluid contact line pinned at the claw. We investigate the kinematics of interfacial flight trajectories using high-speed imaging and construct a mathematical model describing the flight dynamics. Our results show that non-linear surface tension forces make interfacial flight energetically expensive compared with airborne flight at the relatively high speeds characteristic of waterlily beetles, and cause chaotic dynamics to arise naturally in these regimes. We identify the crucial roles of capillary–gravity wave drag and oscillatory surface tension forces which dominate interfacial flight, showing that the air–water interface presents a radically modified force landscape for flapping wing flight compared with air. PMID:26936640

  11. Flight control experiences

    NASA Technical Reports Server (NTRS)

    Musgrave, F. S.

    1977-01-01

    A multidisciplinary medical-management team at mission control provided Skylab crew support by monitoring health, retrieving and compiling experimental data, assisting in the development of flight plans, and by contributing to in-flight procedures and checklists. Real time computers assisted the flight crews in performing medical and other experiments.

  12. In Flight, Online

    ERIC Educational Resources Information Center

    Lucking, Robert A.; Wighting, Mervyn J.; Christmann, Edwin P.

    2005-01-01

    The concept of flight for human beings has always been closely tied to imagination. To fly like a bird requires a mind that also soars. Therefore, good teachers who want to teach the scientific principles of flight recognize that it is helpful to share stories of their search for the keys to flight. The authors share some of these with the reader,…

  13. Free Flight Rotorcraft Flight Test Vehicle Technology Development

    NASA Technical Reports Server (NTRS)

    Hodges, W. Todd; Walker, Gregory W.

    1994-01-01

    A rotary wing, unmanned air vehicle (UAV) is being developed as a research tool at the NASA Langley Research Center by the U.S. Army and NASA. This development program is intended to provide the rotorcraft research community an intermediate step between rotorcraft wind tunnel testing and full scale manned flight testing. The technologies under development for this vehicle are: adaptive electronic flight control systems incorporating artificial intelligence (AI) techniques, small-light weight sophisticated sensors, advanced telepresence-telerobotics systems and rotary wing UAV operational procedures. This paper briefly describes the system's requirements and the techniques used to integrate the various technologies to meet these requirements. The paper also discusses the status of the development effort. In addition to the original aeromechanics research mission, the technology development effort has generated a great deal of interest in the UAV community for related spin-off applications, as briefly described at the end of the paper. In some cases the technologies under development in the free flight program are critical to the ability to perform some applications.

  14. X-37 Flight Demonstrator: X-40A Flight Test Approach

    NASA Technical Reports Server (NTRS)

    Mitchell, Dan

    2004-01-01

    The flight test objectives are: Evaluate calculated air data system (CADS) experiment. Evaluate Honeywell SIGI (GPS/INS) under flight conditions. Flight operation control center (FOCC) site integration and flight test operations. Flight test and tune GN&C algorithms. Conduct PID maneuvers to improve the X-37 aero database. Develop computer air date system (CADS) flight data to support X-37 system design.

  15. Ariane flight testing

    NASA Astrophysics Data System (ADS)

    Vedrenne, M.

    1983-11-01

    The object of this paper is to present the way in which the flight development tests of the Ariane launch vehicle have enabled the definition to be frozen and its qualification to be demonstrated before the beginning of the operational phase. A first part is devoted to the in-flight measurement facilities, the acquisition and evaluation systems, and to the organization of the in-flight results evaluation. The following part consists of the comparison between ground predictions and flight results for the main parameters as classified by system (stages, trajectory, propulsion, flight mechanics, auto pilot and guidance). The corrective actions required are then identified and the corresponding results shown.

  16. Adaptive Controller Adaptation Time and Available Control Authority Effects on Piloting

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna; Gregory, Irene

    2013-01-01

    Adaptive control is considered for highly uncertain, and potentially unpredictable, flight dynamics characteristic of adverse conditions. This experiment looked at how adaptive controller adaptation time to recover nominal aircraft dynamics affects pilots and how pilots want information about available control authority transmitted. Results indicate that an adaptive controller that takes three seconds to adapt helped pilots when looking at lateral and longitudinal errors. The controllability ratings improved with the adaptive controller, again the most for the three seconds adaptation time while workload decreased with the adaptive controller. The effects of the displays showing the percentage amount of available safe flight envelope used in the maneuver were dominated by the adaptation time. With the displays, the altitude error increased, controllability slightly decreased, and mental demand increased. Therefore, the displays did require some of the subjects resources but these negatives may be outweighed by pilots having more situation awareness of their aircraft.

  17. Biomechanics of bird flight.

    PubMed

    Tobalske, Bret W

    2007-09-01

    Power output is a unifying theme for bird flight and considerable progress has been accomplished recently in measuring muscular, metabolic and aerodynamic power in birds. The primary flight muscles of birds, the pectoralis and supracoracoideus, are designed for work and power output, with large stress (force per unit cross-sectional area) and strain (relative length change) per contraction. U-shaped curves describe how mechanical power output varies with flight speed, but the specific shapes and characteristic speeds of these curves differ according to morphology and flight style. New measures of induced, profile and parasite power should help to update existing mathematical models of flight. In turn, these improved models may serve to test behavioral and ecological processes. Unlike terrestrial locomotion that is generally characterized by discrete gaits, changes in wing kinematics and aerodynamics across flight speeds are gradual. Take-off flight performance scales with body size, but fully revealing the mechanisms responsible for this pattern awaits new study. Intermittent flight appears to reduce the power cost for flight, as some species flap-glide at slow speeds and flap-bound at fast speeds. It is vital to test the metabolic costs of intermittent flight to understand why some birds use intermittent bounds during slow flight. Maneuvering and stability are critical for flying birds, and design for maneuvering may impinge upon other aspects of flight performance. The tail contributes to lift and drag; it is also integral to maneuvering and stability. Recent studies have revealed that maneuvers are typically initiated during downstroke and involve bilateral asymmetry of force production in the pectoralis. Future study of maneuvering and stability should measure inertial and aerodynamic forces. It is critical for continued progress into the biomechanics of bird flight that experimental designs are developed in an ecological and evolutionary context. PMID:17766290

  18. 14 CFR 121.493 - Flight time limitations: Flight engineers and flight navigators.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... 14 Aeronautics and Space 3 2013-01-01 2013-01-01 false Flight time limitations: Flight engineers... AND OPERATIONS OPERATING REQUIREMENTS: DOMESTIC, FLAG, AND SUPPLEMENTAL OPERATIONS Flight Time Limitations: Flag Operations § 121.493 Flight time limitations: Flight engineers and flight navigators. (a)...

  19. 14 CFR 121.493 - Flight time limitations: Flight engineers and flight navigators.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false Flight time limitations: Flight engineers... AND OPERATIONS OPERATING REQUIREMENTS: DOMESTIC, FLAG, AND SUPPLEMENTAL OPERATIONS Flight Time Limitations: Flag Operations § 121.493 Flight time limitations: Flight engineers and flight navigators. (a)...

  20. 14 CFR 121.493 - Flight time limitations: Flight engineers and flight navigators.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false Flight time limitations: Flight engineers... AND OPERATIONS OPERATING REQUIREMENTS: DOMESTIC, FLAG, AND SUPPLEMENTAL OPERATIONS Flight Time Limitations: Flag Operations § 121.493 Flight time limitations: Flight engineers and flight navigators. (a)...

  1. The NASA F-15 Intelligent Flight Control Systems: Generation II

    NASA Technical Reports Server (NTRS)

    Buschbacher, Mark; Bosworth, John

    2006-01-01

    The Second Generation (Gen II) control system for the F-15 Intelligent Flight Control System (IFCS) program implements direct adaptive neural networks to demonstrate robust tolerance to faults and failures. The direct adaptive tracking controller integrates learning neural networks (NNs) with a dynamic inversion control law. The term direct adaptive is used because the error between the reference model and the aircraft response is being compensated or directly adapted to minimize error without regard to knowing the cause of the error. No parameter estimation is needed for this direct adaptive control system. In the Gen II design, the feedback errors are regulated with a proportional-plus-integral (PI) compensator. This basic compensator is augmented with an online NN that changes the system gains via an error-based adaptation law to improve aircraft performance at all times, including normal flight, system failures, mispredicted behavior, or changes in behavior resulting from damage.

  2. Flight code validation simulator

    SciTech Connect

    Sims, B.A.

    1995-08-01

    An End-To-End Simulation capability for software development and validation of missile flight software on the actual embedded computer has been developed utilizing a 486 PC, i860 DSP coprocessor, embedded flight computer and custom dual port memory interface hardware. This system allows real-time interrupt driven embedded flight software development and checkout. The flight software runs in a Sandia Digital Airborne Computer (SANDAC) and reads and writes actual hardware sensor locations in which IMU (Inertial Measurements Unit) data resides. The simulator provides six degree of freedom real-time dynamic simulation, accurate real-time discrete sensor data and acts on commands and discretes from the flight computer. This system was utilized in the development and validation of the successful premier flight of the Digital Miniature Attitude Reference System (DMARS) in January 1995 at the White Sands Missile Range on a two stage attitude controlled sounding rocket.

  3. Pilot's Desk Flight Station

    NASA Technical Reports Server (NTRS)

    Sexton, G. A.

    1984-01-01

    Aircraft flight station designs have generally evolved through the incorporation of improved or modernized controls and displays. In connection with a continuing increase in the amount of information displayed, this process has produced a complex and cluttered conglomeration of knobs, switches, and electromechanical displays. The result was often high crew workload, missed signals, and misinterpreted information. Advances in electronic technology have now, however, led to new concepts in flight station design. An American aerospace company in cooperation with NASA has utilized these concepts to develop a candidate conceptual design for a 1995 flight station. The obtained Pilot's Desk Flight Station is a unique design which resembles more an operator's console than today's cockpit. Attention is given to configuration, primary flight controllers, front panel displays, flight/navigation display, approach charts and weather display, head-up display, and voice command and response systems.

  4. Flight Checklists And Interruptions

    NASA Technical Reports Server (NTRS)

    Linde, C.; Goguen, J.

    1991-01-01

    Report examines relation between performances of flight checklists and interruptions. Based on study of simulated flights of Boeing 707 Airplane. During each flight series of overlapping problems introduced. Study investigated patterns of communication that in carrying out checklists, may contribute to accidents. Showed good crews had high continuity in following checklists and it is not number of interruptions but rather duration of interruptions associated with quality of performance. Suggests greater burden placed on memory by one long interruption than by several short ones.

  5. Autonomous Flight Safety System

    NASA Technical Reports Server (NTRS)

    Simpson, James

    2010-01-01

    The Autonomous Flight Safety System (AFSS) is an independent self-contained subsystem mounted onboard a launch vehicle. AFSS has been developed by and is owned by the US Government. Autonomously makes flight termination/destruct decisions using configurable software-based rules implemented on redundant flight processors using data from redundant GPS/IMU navigation sensors. AFSS implements rules determined by the appropriate Range Safety officials.

  6. Unified powered flight guidance

    NASA Technical Reports Server (NTRS)

    Brand, T. J.; Brown, D. W.; Higgins, J. P.

    1973-01-01

    A complete revision of the orbiter powered flight guidance scheme is presented. A unified approach to powered flight guidance was taken to accommodate all phases of exo-atmospheric orbiter powered flight, from ascent through deorbit. The guidance scheme was changed from the previous modified version of the Lambert Aim Point Maneuver Mode used in Apollo to one that employs linear tangent guidance concepts. This document replaces the previous ascent phase equation document.

  7. Bat flight: aerodynamics, kinematics and flight morphology.

    PubMed

    Hedenström, Anders; Johansson, L Christoffer

    2015-03-01

    Bats evolved the ability of powered flight more than 50 million years ago. The modern bat is an efficient flyer and recent research on bat flight has revealed many intriguing facts. By using particle image velocimetry to visualize wake vortices, both the magnitude and time-history of aerodynamic forces can be estimated. At most speeds the downstroke generates both lift and thrust, whereas the function of the upstroke changes with forward flight speed. At hovering and slow speed bats use a leading edge vortex to enhance the lift beyond that allowed by steady aerodynamics and an inverted wing during the upstroke to further aid weight support. The bat wing and its skeleton exhibit many features and control mechanisms that are presumed to improve flight performance. Whereas bats appear aerodynamically less efficient than birds when it comes to cruising flight, they have the edge over birds when it comes to manoeuvring. There is a direct relationship between kinematics and the aerodynamic performance, but there is still a lack of knowledge about how (and if) the bat controls the movements and shape (planform and camber) of the wing. Considering the relatively few bat species whose aerodynamic tracks have been characterized, there is scope for new discoveries and a need to study species representing more extreme positions in the bat morphospace. PMID:25740899

  8. Development of flying qualities criteria for single pilot instrument flight operations

    NASA Technical Reports Server (NTRS)

    Bar-Gill, A.; Nixon, W. B.; Miller, G. E.

    1982-01-01

    Flying qualities criteria for Single Pilot Instrument Flight Rule (SPIFR) operations were investigated. The ARA aircraft was modified and adapted for SPIFR operations. Aircraft configurations to be flight-tested were chosen and matched on the ARA in-flight simulator, implementing modern control theory algorithms. Mission planning and experimental matrix design were completed. Microprocessor software for the onboard data acquisition system was debugged and flight-tested. Flight-path reconstruction procedure and the associated FORTRAN program were developed. Algorithms associated with the statistical analysis of flight test results and the SPIFR flying qualities criteria deduction are discussed.

  9. Design of a Computerised Flight Mill Device to Measure the Flight Potential of Different Insects.

    PubMed

    Martí-Campoy, Antonio; Ávalos, Juan Antonio; Soto, Antonia; Rodríguez-Ballester, Francisco; Martínez-Blay, Victoria; Malumbres, Manuel Pérez

    2016-01-01

    Several insect species pose a serious threat to different plant species, sometimes becoming a pest that produces significant damage to the landscape, biodiversity, and/or the economy. This is the case of Rhynchophorus ferrugineus Olivier (Coleoptera: Dryophthoridae), Semanotus laurasii Lucas (Coleoptera: Cerambycidae), and Monochamus galloprovincialis Olivier (Coleoptera: Cerambycidae), which have become serious threats to ornamental and productive trees all over the world such as palm trees, cypresses, and pines. Knowledge about their flight potential is very important for designing and applying measures targeted to reduce the negative effects from these pests. Studying the flight capability and behaviour of some insects is difficult due to their small size and the large area wherein they can fly, so we wondered how we could obtain information about their flight capabilities in a controlled environment. The answer came with the design of flight mills. Relevant data about the flight potential of these insects may be recorded and analysed by means of a flight mill. Once an insect is attached to the flight mill, it is able to fly in a circular direction without hitting walls or objects. By adding sensors to the flight mill, it is possible to record the number of revolutions and flight time. This paper presents a full description of a computer monitored flight mill. The description covers both the mechanical and the electronic parts in detail. The mill was designed to easily adapt to the anatomy of different insects and was successfully tested with individuals from three species R. ferrugineus, S. laurasii, and M. galloprovincialis. PMID:27070600

  10. Design of a Computerised Flight Mill Device to Measure the Flight Potential of Different Insects

    PubMed Central

    Martí-Campoy, Antonio; Ávalos, Juan Antonio; Soto, Antonia; Rodríguez-Ballester, Francisco; Martínez-Blay, Victoria; Malumbres, Manuel Pérez

    2016-01-01

    Several insect species pose a serious threat to different plant species, sometimes becoming a pest that produces significant damage to the landscape, biodiversity, and/or the economy. This is the case of Rhynchophorus ferrugineus Olivier (Coleoptera: Dryophthoridae), Semanotus laurasii Lucas (Coleoptera: Cerambycidae), and Monochamus galloprovincialis Olivier (Coleoptera: Cerambycidae), which have become serious threats to ornamental and productive trees all over the world such as palm trees, cypresses, and pines. Knowledge about their flight potential is very important for designing and applying measures targeted to reduce the negative effects from these pests. Studying the flight capability and behaviour of some insects is difficult due to their small size and the large area wherein they can fly, so we wondered how we could obtain information about their flight capabilities in a controlled environment. The answer came with the design of flight mills. Relevant data about the flight potential of these insects may be recorded and analysed by means of a flight mill. Once an insect is attached to the flight mill, it is able to fly in a circular direction without hitting walls or objects. By adding sensors to the flight mill, it is possible to record the number of revolutions and flight time. This paper presents a full description of a computer monitored flight mill. The description covers both the mechanical and the electronic parts in detail. The mill was designed to easily adapt to the anatomy of different insects and was successfully tested with individuals from three species R. ferrugineus, S. laurasii, and M. galloprovincialis. PMID:27070600

  11. Technology review of flight crucial flight controls

    NASA Technical Reports Server (NTRS)

    Rediess, H. A.; Buckley, E. C.

    1984-01-01

    The results of a technology survey in flight crucial flight controls conducted as a data base for planning future research and technology programs are provided. Free world countries were surveyed with primary emphasis on the United States and Western Europe because that is where the most advanced technology resides. The survey includes major contemporary systems on operational aircraft, R&D flight programs, advanced aircraft developments, and major research and technology programs. The survey was not intended to be an in-depth treatment of the technology elements, but rather a study of major trends in systems level technology. The information was collected from open literature, personal communications and a tour of several companies, government organizations and research laboratories in the United States, United Kingdom, France, and the Federal Republic of Germany.

  12. Somatosensory Substrates of Flight Control in Bats

    PubMed Central

    Marshall, Kara L.; Chadha, Mohit; deSouza, Laura A.; Sterbing-D’Angelo, Susanne J.; Moss, Cynthia F.; Lumpkin, Ellen A.

    2015-01-01

    Summary Flight maneuvers require rapid sensory integration to generate adaptive motor output. Bats achieve remarkable agility with modified forelimbs that serve as airfoils while retaining capacity for object manipulation. Wing sensory inputs provide behaviorally relevant information to guide flight; however, components of wing sensory-motor circuits have not been analyzed. Here, we elucidate the organization of wing innervation in an insectivore, the big brown bat, Eptesicus fuscus. We demonstrate that wing sensory innervation differs from other vertebrate forelimbs, revealing a peripheral basis for the atypical topographic organization reported for bat somatosensory nuclei. Furthermore, the wing is innervated by an unusual complement of sensory neurons poised to report airflow and touch. Finally, we report that cortical neurons encode tactile and airflow inputs with sparse activity patterns. Together, our findings identify neural substrates of somatosensation in the bat wing and imply that evolutionary pressures giving rise to mammalian flight led to unusual sensorimotor projections. PMID:25937277

  13. SLS-1 flight experiments preliminary significant results

    NASA Astrophysics Data System (ADS)

    1992-01-01

    Spacelab Life Sciences-1 (SLS-1) is the first of a series of dedicated life sciences Spacelab missions designed to investigate the mechanisms involved in the physiological adaptation to weightlessness and the subsequent readaptation to 1 gravity (1 G). Hypotheses generated from the physiological effects observed during earlier missions led to the formulation of several integrated experiments to determine the underlying mechanisms responsible for the observed phenomena. The 18 experiments selected for flight on SLS-1 investigated the cardiovascular, cardiopulmonary, regulatory physiology, musculoskeletal, and neuroscience disciplines in both human and rodent subjects. The SLS-1 preliminary results gave insight to the mechanisms involved in the adaptation to the microgravity environment and readaptation when returning to Earth. The experimental results will be used to promote health and safety for future long duration space flights and, as in the past, will be applied to many biomedical problems encountered here on Earth.

  14. Nuclear Shuttle in Flight

    NASA Technical Reports Server (NTRS)

    1970-01-01

    This 1970 artist's concept shows a Nuclear Shuttle in flight. As envisioned by Marshall Space Flight Center Program Development engineers, the Nuclear Shuttle would deliver payloads to lunar orbit or other destinations then return to Earth orbit for refueling and additional missions.

  15. Electromechanical flight control actuator

    NASA Technical Reports Server (NTRS)

    1979-01-01

    The feasibility of using an electromechanical actuator (EMA) as the primary flight control equipment in aerospace flight is examined. The EMA motor design is presented utilizing improved permanent magnet materials. The necessary equipment to complete a single channel EMA using the single channel power electronics breadboard is reported. The design and development of an improved rotor position sensor/tachometer is investigated.

  16. Space Flight. Teacher Resources.

    ERIC Educational Resources Information Center

    2001

    This teacher's guide contains information, lesson plans, and diverse student learning activities focusing on space flight. The guide is divided into seven sections: (1) "Drawing Activities" (Future Flight; Space Fun; Mission: Draw); (2) "Geography" (Space Places); (3) "History" (Space and Time); (4) "Information" (Space Transportation System;…

  17. Java for flight software

    NASA Technical Reports Server (NTRS)

    Benowitz, E.; Niessner, A.

    2003-01-01

    This work involves developing representative mission-critical spacecraft software using the Real-Time Specification for Java (RTSJ). This work currently leverages actual flight software used in the design of actual flight software in the NASA's Deep Space 1 (DSI), which flew in 1998.

  18. Exploring flight crew behaviour

    NASA Technical Reports Server (NTRS)

    Helmreich, R. L.

    1987-01-01

    A programme of research into the determinants of flight crew performance in commercial and military aviation is described, along with limitations and advantages associated with the conduct of research in such settings. Preliminary results indicate significant relationships among personality factors, attitudes regarding flight operations, and crew performance. The potential theoretical and applied utility of the research and directions for further research are discussed.

  19. Autonomous Flight Safety System

    NASA Technical Reports Server (NTRS)

    Ferrell, Bob; Santuro, Steve; Simpson, James; Zoerner, Roger; Bull, Barton; Lanzi, Jim

    2004-01-01

    Autonomous Flight Safety System (AFSS) is an independent flight safety system designed for small to medium sized expendable launch vehicles launching from or needing range safety protection while overlying relatively remote locations. AFSS replaces the need for a man-in-the-loop to make decisions for flight termination. AFSS could also serve as the prototype for an autonomous manned flight crew escape advisory system. AFSS utilizes onboard sensors and processors to emulate the human decision-making process using rule-based software logic and can dramatically reduce safety response time during critical launch phases. The Range Safety flight path nominal trajectory, its deviation allowances, limit zones and other flight safety rules are stored in the onboard computers. Position, velocity and attitude data obtained from onboard global positioning system (GPS) and inertial navigation system (INS) sensors are compared with these rules to determine the appropriate action to ensure that people and property are not jeopardized. The final system will be fully redundant and independent with multiple processors, sensors, and dead man switches to prevent inadvertent flight termination. AFSS is currently in Phase III which includes updated algorithms, integrated GPS/INS sensors, large scale simulation testing and initial aircraft flight testing.

  20. Perspectives on Highly Adaptive or Morphing Aircraft

    NASA Technical Reports Server (NTRS)

    McGowan, Anna-Maria R.; Vicroy, Dan D.; Busan, Ronald C.; Hahn, Andrew S.

    2009-01-01

    The ability to adapt to different flight conditions has been fundamental to aircraft design since the Wright Brothers first flight. Over a hundred years later, unconventional aircraft adaptability, often called aircraft morphing has become a topic of considerable renewed interest. In the past two decades, this interest has been largely fuelled by advancements in multi-functional or smart materials and structures. However, highly adaptive or morphing aircraft is certainly a cross-discipline challenge that stimulates a wide range of design possibilities. This paper will review some of the history of morphing aircraft including recent research programs and discuss some perspectives on this work.

  1. Disruption of postural readaptation by inertial stimuli following space flight

    NASA Technical Reports Server (NTRS)

    Black, F. O.; Paloski, W. H.; Reschke, M. F.; Igarashi, M.; Guedry, F.; Anderson, D. J.

    1999-01-01

    Postural instability (relative to pre-flight) has been observed in all shuttle astronauts studied upon return from orbital missions. Postural stability was more closely examined in four shuttle astronaut subjects before and after an 8 day orbital mission. Results of the pre- and post-flight postural stability studies were compared with a larger (n = 34) study of astronauts returning from shuttle missions of similar duration. Results from both studies indicated that inadequate vestibular feedback was the most significant sensory deficit contributing to the postural instability observed post flight. For two of the four IML-1 astronauts, post-flight postural instability and rate of recovery toward their earth-normal performance matched the performance of the larger sample. However, post-flight postural control in one returning astronaut was substantially below mean performance. This individual, who was within normal limits with respect to postural control before the mission, indicated that recovery to pre-flight postural stability was also interrupted by a post-flight pitch plane rotation test. A similar, though less extreme departure from the mean recovery trajectory was present in another astronaut following the same post-flight rotation test. The pitch plane rotation stimuli included otolith stimuli in the form of both transient tangential and constant centripetal linear acceleration components. We inferred from these findings that adaptation on orbit and re-adaptation on earth involved a change in sensorimotor integration of vestibular signals most likely from the otolith organs.

  2. Space flight and changes in spatial orientation

    NASA Technical Reports Server (NTRS)

    Reschke, Millard F.; Bloomberg, Jacob J.; Harm, Deborah L.; Paloski, William H.

    1992-01-01

    From a sensory point of view, space flight represents a form of stimulus rearrangement requiring modification of established terrestrial response patterns through central reinterpretation. Evidence of sensory reinterpretation is manifested as postflight modifications of eye/head coordination, locomotor patterns, postural control strategies, and illusory perceptions of self or surround motion in conjunction with head movements. Under normal preflight conditions, the head is stabilized during locomotion, but immediately postflight reduced head stability, coupled with inappropriate eye/head coordination, results in modifications of gait. Postflight postural control exhibits increased dependence on vision which compensates for inappropriate interpretation of otolith and proprioceptive inputs. Eye movements compensatory for perceived self motion, rather than actual head movements have been observed postflight. Overall, the in-flight adaptive modification of head stabilization strategies, changes in head/eye coordination, illusionary motion, and postural control are maladaptive for a return to the terrestrial environment. Appropriate countermeasures for long-duration flights will rely on preflight adaptation and in-flight training.

  3. Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results

    NASA Technical Reports Server (NTRS)

    Jani, Yashvant

    1992-01-01

    The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.

  4. [Multi-layer perceptron neural network based algorithm for simultaneous retrieving temperature and emissivity from hyperspectral FTIR data].

    PubMed

    Cheng, Jie; Xiao, Qing; Li, Xiao-Wen; Liu, Qin-Huo; Du, Yong-Ming

    2008-04-01

    The present paper firstly points out the defect of typical temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data: the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference between the ground-leaving radiance and object's blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur rence near 714 and 1 250 cm(-1) in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simultaneous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library were used to produce the training data, the soil emissivity spectra from the MODIS spectral library were used to produce the test data, and the result of network test shows the MLP is robust. Meanwhile, the ISSTES algorithm was used to retrieve the temperature and emissivity form the test data. By comparing the results of MLP and ISSTES, we found the MLP can overcome the disadvantage of typical temperature and emisivity separation, although the rmse of derived emissivity using MLP is lower than the ISSTES as a whole. Hence, the MLP can be regarded as a beneficial complementarity of the typical temperature and emissivity separation. PMID:18619297

  5. A neural-network based estimator to search for primordial non-Gaussianity in Planck CMB maps

    NASA Astrophysics Data System (ADS)

    Novaes, C. P.; Bernui, A.; Ferreira, I. S.; Wuensche, C. A.

    2015-09-01

    We present an upgraded combined estimator, based on Minkowski Functionals and Neural Networks, with excellent performance in detecting primordial non-Gaussianity in simulated maps that also contain a weighted mixture of Galactic contaminations, besides real pixel's noise from Planck cosmic microwave background radiation data. We rigorously test the efficiency of our estimator considering several plausible scenarios for residual non-Gaussianities in the foreground-cleaned Planck maps, with the intuition to optimize the training procedure of the Neural Network to discriminate between contaminations with primordial and secondary non-Gaussian signatures. We look for constraints of primordial local non-Gaussianity at large angular scales in the foreground-cleaned Planck maps. For the SMICA map we found fNL = 33 ± 23, at 1σ confidence level, in excellent agreement with the WMAP-9yr and Planck results. In addition, for the other three Planck maps we obtain similar constraints with values in the interval fNL in [33, 41], concomitant with the fact that these maps manifest distinct features in reported analyses, like having different pixel's noise intensities.

  6. Neural network based online simultaneous policy update algorithm for solving the HJI equation in nonlinear H∞ control.

    PubMed

    Wu, Huai-Ning; Luo, Biao

    2012-12-01

    It is well known that the nonlinear H∞ state feedback control problem relies on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that has proven to be impossible to solve analytically. In this paper, a neural network (NN)-based online simultaneous policy update algorithm (SPUA) is developed to solve the HJI equation, in which knowledge of internal system dynamics is not required. First, we propose an online SPUA which can be viewed as a reinforcement learning technique for two players to learn their optimal actions in an unknown environment. The proposed online SPUA updates control and disturbance policies simultaneously; thus, only one iterative loop is needed. Second, the convergence of the online SPUA is established by proving that it is mathematically equivalent to Newton's method for finding a fixed point in a Banach space. Third, we develop an actor-critic structure for the implementation of the online SPUA, in which only one critic NN is needed for approximating the cost function, and a least-square method is given for estimating the NN weight parameters. Finally, simulation studies are provided to demonstrate the effectiveness of the proposed algorithm. PMID:24808144

  7. Neural network-based finite-horizon optimal control of uncertain affine nonlinear discrete-time systems.

    PubMed

    Zhao, Qiming; Xu, Hao; Jagannathan, Sarangapani

    2015-03-01

    In this paper, the finite-horizon optimal control design for nonlinear discrete-time systems in affine form is presented. In contrast with the traditional approximate dynamic programming methodology, which requires at least partial knowledge of the system dynamics, in this paper, the complete system dynamics are relaxed utilizing a neural network (NN)-based identifier to learn the control coefficient matrix. The identifier is then used together with the actor-critic-based scheme to learn the time-varying solution, referred to as the value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward-in-time manner. Since the solution of HJB is time-varying, NNs with constant weights and time-varying activation functions are considered. To properly satisfy the terminal constraint, an additional error term is incorporated in the novel update law such that the terminal constraint error is also minimized over time. Policy and/or value iterations are not needed and the NN weights are updated once a sampling instant. The uniform ultimate boundedness of the closed-loop system is verified by standard Lyapunov stability theory under nonautonomous analysis. Numerical examples are provided to illustrate the effectiveness of the proposed method. PMID:25720005

  8. Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

    NASA Astrophysics Data System (ADS)

    Jafari, Mehdi; Kasaei, Shohreh

    2012-01-01

    Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

  9. Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

    NASA Astrophysics Data System (ADS)

    Jafari, Mehdi; Kasaei, Shohreh

    2011-12-01

    Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

  10. Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results

    NASA Technical Reports Server (NTRS)

    Jani, Yashvant

    1992-01-01

    As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

  11. An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

    NASA Astrophysics Data System (ADS)

    Yaşar, Hüseyin; Ceylan, Murat

    2015-03-01

    Breast cancer is one of the types of cancer which is most commonly seen in women. Density of breast is an important indicator for the risk of cancer. In addition, densities of tissue may harden the diagnosis by hiding the abnormalities occurring on the breast. For this reason, during the process of diagnosis, the process of automatic classification of breast density has a significant importance. In this study, a new system with the base of Artificial Neural Network (ANN) and multiple resolution analysis is suggested. Wavelet and curvelet analyses having the most common use have been used as multi resolution analysis. 4 pieces of statistics which are minimum value, maximum value, mean value and standard deviation have been extracted from the images which have been eluted to their sub-bands via multi resolution analysis. For the purpose of testing the success of the system, 322 pieces of images which are in MIAS database have been used. The obtained results for different backgrounds are so satisfying; and the highest classification values have been obtained as 97.16 % with Wavelet transform and ANN for fatty background and 79.80 % with Wavelet transform and ANN for fatty-glanduar background. The same results have been obtained using Wavelet transform and ANN and Curvelet transform and ANN for dense background and accuracy rate of 84.82 % have been reached. The results of mean classification have been obtained, for three pieces of tissue types (fatty, fatty-glanduar, dense), in sequence as 84.47 % with the use of ANN, 85.71 % with the use of curvelet analysis and ANN; and 87.26 % with the use of wavelet analysis and ANN.

  12. Neural network based pattern matching and spike detection tools and services--in the CARMEN neuroinformatics project.

    PubMed

    Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim

    2008-10-01

    In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets. PMID:18674883

  13. Artificial neural network based calibrations for the prediction of galactic [N II] λ6584 and Hα line luminosities

    NASA Astrophysics Data System (ADS)

    Teimoorinia, Hossein; Ellison, Sara L.

    2014-04-01

    The artificial neural network (ANN) is a well-established mathematical technique for data prediction, based on the identification of correlations and pattern recognition in input training sets. We present the application of ANNs to predict the emission line luminosities of Hα and [N II] λ6584 in galaxies. These important spectral diagnostics are used for metallicities, active galactic nuclei (AGN) classification and star formation rates, yet are shifted into the infrared for galaxies above z ˜ 0.5, or may not be covered in spectra with limited wavelength coverage. The ANN is trained with a large sample of emission line galaxies selected from the Sloan Digital Sky Survey (SDSS) using various combinations of emission lines and stellar mass. The ANN is tested for galaxies dominated by both star formation and AGN; in both cases the Hα and [N II] λ6584 line luminosities can be predicted with a scatter σ < 0.1 dex. We also show that the performance of the ANN does not depend significantly on the covering fraction, mass or metallicity of the data. Polynomial functions are derived that allow easy application of the ANN predictions to determine Hα and [N II] λ6584 line luminosities. An ANN calibration for the Balmer decrement (Hα/Hβ) based on line equivalent widths and colours is also presented. The effectiveness of the ANN calibration is demonstrated with an independent data set (the Galaxy Mass and Assembly Survey). We demonstrate the application of our line luminosities to the determination of gas-phase metallicities and AGN classification. The ANN technique yields a significant improvement in the measurement of metallicities that require [N II] and Hα when compared with the function-based conversions of Kewley & Ellison. The AGN classification is successful for 86 per cent of SDSS galaxies.

  14. Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China

    NASA Astrophysics Data System (ADS)

    Zou, Ling; Wang, Lunche; Lin, Aiwen; Zhu, Hongji; Peng, Yuling; Zhao, Zhenzhen

    2016-08-01

    Solar radiation plays important roles in energy application, vegetation growth and climate change. Empirical relations and machine-learning methods have been widely used to estimate global solar radiation (GSR) in recent years. An artificial neural network (ANN) based on spatial interpolation is developed to estimate GSR in southeast China. The improved Bristow-Campbell (IBC) model and the improved Ångström-Prescott (IA-P) model are compared with the ANN model to explore the best model in solar radiation modeling. Daily meteorological parameters, such as sunshine duration hours, mean temperature, maximum temperature, minimum temperature, relative humidity, precipitation, air pressure, water vapor pressure, and wind speed, along with station-measured GSR and a daily surface GSR dataset over China obtained from the Data Assimilation and Modeling Center for Tibetan Multi-spheres (DAM), are used to predict GSR and to validate the models in this work. The ANN model with the network of 9-17-1 provides better accuracy than the two improved empirical models in GSR estimation. The root-mean-square error (RMSE), mean bias error (MBE), and determination coefficient (R2) are 2.65 MJ m-2, -0.94 MJ m-2, and 0.68 in the IA-P model; 2.19 MJ m-2, 1.11 MJ m-2, and 0.83 in the IBC model; 1.34 MJ m-2, -0.11 MJ m-2, and 0.91 in the ANN model, respectively. The regional monthly mean GSR in the measured dataset, DAM dataset, and ANN model is analyzed. The RMSE (RMSE %) is 1.07 MJ m-2 (8.91%) and the MBE (MBE %) is -0.62 MJ m-2 (-5.21%) between the measured and ANN-estimated GSR. The statistical errors of RMSE (RMSE %) are 0.91 MJ m-2 (7.28%) and those of MBE (MBE %) are -0.15 MJ m-2 (-1.20%) between DAM and ANN-modeled GSR. The correlation coefficients and R2 are larger than 0.95. The regional mean GSR is 12.58 MJ m-2. The lowest GSR is observed in the northwest area, and it increases from northwest to southeast. The annual mean GSR decreases by 0.02 MJ m-2 decade-1 over the entire southeast China. The GSR in 52 stations experiences a decreasing trend, and 21% of the stations are significant at the 95% level.

  15. Prediction of Student's Mood during an Online Test Using Formula-based and Neural Network-based Method

    ERIC Educational Resources Information Center

    Moridis, Christos N.; Economides, Anastasios A.

    2009-01-01

    Building computerized mechanisms that will accurately, immediately and continually recognize a learner's affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence…

  16. A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems.

    PubMed

    Cen, Zhaohui; Wei, Jiaolong; Jiang, Rui

    2013-12-01

    A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority. PMID:24156668

  17. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

    PubMed Central

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K.

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process. PMID:26989410

  18. Prediction of auto-ignition temperatures of hydrocarbons by neural network based on atom-type electrotopological-state indices.

    PubMed

    Pan, Yong; Jiang, Juncheng; Wang, Rui; Cao, Hongyin; Zhao, Jinbo

    2008-09-15

    A quantitative structure-property relationship (QSPR) model was constructed to predict the auto-ignition temperature (AIT) of 118 hydrocarbons by means of artificial neural network (ANN). Atom-type electrotopological-state indices were used as molecular structure descriptors which combined together both electronic and topological characteristics of the analyzed molecules. The typical back-propagation (BP) neural network was employed for fitting the possible non-linear relationship existed between the atom-type electrotopological-state indices and AIT. The dataset of 118 hydrocarbons was randomly divided into a training set (60), a validation set (16) and a testing set (42). The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [16-8-1], the results show that most of the predicted AIT values are in good agreement with the experimental data, with the average absolute error being 21.6 degrees C, and the root mean square error (RMS) being 31.09 for the testing set, which are superior to those obtained by multiple linear regression analysis and traditional group contribution method. The model proposed can be used not only to reveal the quantitative relation between AIT and molecular structures of hydrocarbons, but also to predict the AIT values of hydrocarbons for chemical engineering. PMID:18280036

  19. A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink

    NASA Astrophysics Data System (ADS)

    Landschützer, P.; Gruber, N.; Bakker, D. C. E.; Schuster, U.; Nakaoka, S.; Payne, M. R.; Sasse, T. P.; Zeng, J.

    2013-11-01

    The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO2), but this sink has been shown to vary substantially in time. Here we use surface ocean CO2 observations to estimate this sink and the temporal variability from 1998 through 2007 in the Atlantic Ocean. We benefit from (i) a continuous improvement of the observations, i.e. the Surface Ocean CO2 Atlas (SOCAT) v1.5 database and (ii) a newly developed technique to interpolate the observations in space and time. In particular, we use a two-step neural network approach to reconstruct basin-wide monthly maps of the sea surface partial pressure of CO2 (pCO2) at a resolution of 1° × 1°. From those, we compute the air-sea CO2 flux maps using a standard gas exchange parameterization and high-resolution wind speeds. The neural networks fit the observed pCO2 data with a root mean square error (RMSE) of about 10 μatm and with almost no bias. A check against independent time-series data and new data from SOCAT v2 reveals a larger RMSE of 22.8 μatm for the entire Atlantic Ocean, which decreases to 16.3 μatm for data south of 40° N. We estimate a decadal mean uptake flux of -0.45 ± 0.15 Pg C yr-1 for the Atlantic between 44° S and 79° N, representing the sum of a strong uptake north of 18° N (-0.39 ± 0.10 Pg C yr-1), outgassing in the tropics (18° S-18° N, 0.11 ± 0.07 Pg C yr-1), and uptake in the subtropical/temperate South Atlantic south of 18° S (-0.16 ± 0.06 Pg C yr-1), consistent with recent studies. The strongest seasonal variability of the CO2 flux occurs in the temperature-driven subtropical North Atlantic, with uptake in winter and outgassing in summer. The seasonal cycle is antiphased in the subpolar latitudes relative to the subtropics largely as a result of the biologically driven winter-to-summer drawdown of CO2. Over the 10 yr analysis period (1998 through 2007), sea surface pCO2 increased faster than that of the atmosphere in large areas poleward of 40° N, while in other regions of the North Atlantic the sea surface pCO2 increased at a slower rate, resulting in a barely changing Atlantic carbon sink north of the Equator (-0.01 ± 0.02 Pg C yr-1 decade-1). Surface ocean pCO2 increased at a slower rate relative to atmospheric CO2 over most of the Atlantic south of the Equator, leading to a substantial trend toward a stronger CO2 sink for the entire South Atlantic (-0.14 ± 0.02 Pg C yr-1 decade-1). In contrast to the 10 yr trends, the Atlantic Ocean carbon sink varies relatively little on inter-annual timescales (±0.04 Pg C yr-1; 1 σ).

  20. A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink

    NASA Astrophysics Data System (ADS)

    Landschützer, P.; Gruber, N.; Bakker, D. C. E.; Schuster, U.; Nakaoka, S.; Payne, M. R.; Sasse, T.; Zeng, J.

    2013-05-01

    The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO2), but this sink is known to vary substantially in time. Here we use surface ocean CO2 observations to estimate this sink and the temporal variability from 1998 to 2007 in the Atlantic Ocean. We benefit from (i) a continuous improvement of the observations, i.e., the Surface Ocean CO2 Atlas (SOCAT) v1.5 database and (ii) a newly developed technique to interpolate the observations in space and time. In particular, we use a 2 step neural network approach to reconstruct basin-wide monthly maps of the sea surface partial pressure of CO2 (pCO2) at a resolution of 1° × 1°. From those, we compute the air-sea CO2 flux maps using a standard gas exchange parameterization and high-resolution wind speeds. The neural networks fit the observed pCO2 data with a root mean square error (RMSE) of about 10 μatm and with almost no bias. A check against independent time series data reveals a larger RMSE of about 17 μatm. We estimate a decadal mean uptake flux of -0.45 ± 0.15 Pg C yr-1 for the Atlantic between 44° S and 79° N, representing the sum of a strong uptake north of 18° N (-0.39 ± 0.10 Pg C yr-1), outgassing in the tropics (18° S-18° N, 0.11 ± 0.07 Pg C yr-1), and uptake in the subtropical/temperate South Atlantic south of 18° S (-0.16 ± 0.06 Pg C yr-1), consistent with recent studies. We find the strongest seasonal variability of the CO2 flux in the temperature driven subtropical North Atlantic, with uptake in winter and outgassing in summer. The seasonal cycle is antiphased in the subpolar latitudes relative to the subtropics largely as a result of the biologically driven winter-to-summer drawdown of CO2. Over the analysis period (1998 to 2007) sea surface pCO2 increased faster than that of the atmosphere in large areas poleward of 40° N, but many other parts of the North Atlantic increased more slowly, resulting in a barely changing Atlantic carbon sink north of the equator (-0.007 Pg C yr-1 decade-1). Surface ocean pCO2 was also increasing less than that of the atmosphere over most of the Atlantic south of the equator, leading to a substantial trend toward a stronger CO2 sink for the entire South Atlantic (-0.14 Pg C yr-1 decade-1). The Atlantic carbon sink varies relatively little on inter-annual time-scales (±0.04 Pg C yr-1; 1σ).

  1. Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor.

    PubMed

    Nair, Vijay V; Dhar, Hiya; Kumar, Sunil; Thalla, Arun Kumar; Mukherjee, Somnath; Wong, Jonathan W C

    2016-10-01

    The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120kgVS/m(3) was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65LCH4/kgVS added. A target of 60-70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment. PMID:27005793

  2. Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection.

    PubMed

    Carrieri, Arthur H; Copper, Jack; Owens, David J; Roese, Erik S; Bottiger, Jerold R; Everly, Robert D; Hung, Kevin C

    2010-01-20

    An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates gamma-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO(2) laser beams spanning 9.1-12.0 microm wavelengths (lambda). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this "fingerprint" middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements {M(ij)(lambda)/M(11)(lambda)}, which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance. PMID:20090802

  3. A neural network-based method for spectral distortion correction in photon counting x-ray CT.

    PubMed

    Touch, Mengheng; Clark, Darin P; Barber, William; Badea, Cristian T

    2016-08-21

    Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables both 4 energy bins acquisition, as well as full-spectrum mode in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical effects in the detector and can be very noisy due to photon starvation in narrow energy bins. To address spectral distortions, we propose and demonstrate a novel artificial neural network (ANN)-based spectral distortion correction mechanism, which learns to undo the distortion in spectral CT, resulting in improved material decomposition accuracy. To address noise, post-reconstruction denoising based on bilateral filtration, which jointly enforces intensity gradient sparsity between spectral samples, is used to further improve the robustness of ANN training and material decomposition accuracy. Our ANN-based distortion correction method is calibrated using 3D-printed phantoms and a model of our spectral CT system. To enable realistic simulations and validation of our method, we first modeled the spectral distortions using experimental data acquired from (109)Cd and (133)Ba radioactive sources measured with our PCXD. Next, we trained an ANN to learn the relationship between the distorted spectral CT projections and the ideal, distortion-free projections in a calibration step. This required knowledge of the ground truth, distortion-free spectral CT projections, which were obtained by simulating a spectral CT scan of the digital version of a 3D-printed phantom. Once the training was completed, the trained ANN was used to perform distortion correction on any subsequent scans of the same system with the same parameters. We used joint bilateral filtration to perform noise reduction by jointly enforcing intensity gradient sparsity between the reconstructed images for each energy bin. Following reconstruction and denoising, the CT data was spectrally decomposed using the photoelectric effect, Compton scattering, and a K-edge material (i.e. iodine). The ANN-based distortion correction approach was tested using both simulations and experimental data acquired in phantoms and a mouse with our PCXD-based micro-CT system for 4 bins and full-spectrum acquisition modes. The iodine detectability and decomposition accuracy were assessed using the contrast-to-noise ratio and relative error in iodine concentration estimation metrics in images with and without distortion correction. In simulation, the material decomposition accuracy in the reconstructed data was vastly improved following distortion correction and denoising, with 50% and 20% reductions in material concentration measurement error in full-spectrum and 4 energy bins cases, respectively. Overall, experimental data confirms that full-spectrum mode provides superior results to 4-energy mode when the distortion corrections are applied. The material decomposition accuracy in the reconstructed data was vastly improved following distortion correction and denoising, with as much as a 41% reduction in material concentration measurement error for full-spectrum mode, while also bringing the iodine detectability to 4-6 mg ml(-1). Distortion correction also improved the 4 bins mode data, but to a lesser extent. The results demonstrate the experimental feasibility and potential advantages of ANN-based distortion correction and joint bilateral filtration-based denoising for accurate K-edge imaging with a PCXD. Given the computational efficiency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines. PMID:27469292

  4. Classifying Membrane Proteins in the Proteome by Using Artificial Neural Networks Based on the Preferential Parameters of Amino Acids

    NASA Astrophysics Data System (ADS)

    Bose, Subrata K.; Browne, Antony; Kazemian, Hassan; White, Kenneth

    Membrane proteins (MPs) are large set of biological macromolecules that play a fundamental role in physiology and pathophysiology for survival. From a pharma-economical perspective, though it is the fact that MPs constitute ˜75% of possible targets for novel drugs but MPs are one of the most understudied groups of proteins in biochemical research. This is mainly because of the technical difficulties of obtaining structural information about trans-membrane regions (these are small sequences that crossways the bilayer lipid membrane). It is quite useful to predict the location of transmembrane segments down the sequence, since these are the elementary structural building blocks defining their topology. There have been several attempts over the last 20 years to develop tools for predicting membrane-spanning regions but current tools are far away from achieving a considerable reliability in prediction. This study aims to exploit the knowledge and current understanding in the field of artificial neural networks (ANNs) in particular data representation through the development of a system to identify and predict membrane-spanning regions by analysing primary amino acids sequence. In this paper we present a novel neural network (NNs) architecture and algorithms for predicting membrane spanning regions from primary amino acids sequences by using their preference parameters.

  5. Late rectal bleeding after 3D-CRT for prostate cancer: development of a neural-network-based predictive model

    NASA Astrophysics Data System (ADS)

    Tomatis, S.; Rancati, T.; Fiorino, C.; Vavassori, V.; Fellin, G.; Cagna, E.; Mauro, F. A.; Girelli, G.; Monti, A.; Baccolini, M.; Naldi, G.; Bianchi, C.; Menegotti, L.; Pasquino, M.; Stasi, M.; Valdagni, R.

    2012-03-01

    The aim of this study was to develop a model exploiting artificial neural networks (ANNs) to correlate dosimetric and clinical variables with late rectal bleeding in prostate cancer patients undergoing radical radiotherapy and to compare the ANN results with those of a standard logistic regression (LR) analysis. 718 men included in the AIROPROS 0102 trial were analyzed. This multicenter protocol was characterized by the prospective evaluation of rectal toxicity, with a minimum follow-up of 36 months. Radiotherapy doses were between 70 and 80 Gy. Information was recorded for comorbidity, previous abdominal surgery, use of drugs and hormonal therapy. For each patient, a rectal dose-volume histogram (DVH) of the whole treatment was recorded and the equivalent uniform dose (EUD) evaluated as an effective descriptor of the whole DVH. Late rectal bleeding of grade ≥ 2 was considered to define positive events in this study (52 of 718 patients). The overall population was split into training and verification sets, both of which were involved in model instruction, and a test set, used to evaluate the predictive power of the model with independent data. Fourfold cross-validation was also used to provide realistic results for the full dataset. The LR was performed on the same data. Five variables were selected to predict late rectal bleeding: EUD, abdominal surgery, presence of hemorrhoids, use of anticoagulants and androgen deprivation. Following a receiver operating characteristic analysis of the independent test set, the areas under the curves (AUCs) were 0.704 and 0.655 for ANN and LR, respectively. When evaluated with cross-validation, the AUC was 0.714 for ANN and 0.636 for LR, which differed at a significance level of p = 0.03. When a practical discrimination threshold was selected, ANN could classify data with sensitivity and specificity both equal to 68.0%, whereas these values were 61.5% for LR. These data provide reasonable evidence that results obtained with ANNs are superior to those achieved with LR when predicting late radiotherapy-related rectal bleeding. The future introduction of patient-related personal characteristics, such as gene expression profiles, might improve the predictive power of statistical classifiers. More refined morphological aspects of the dose distribution, such as dose surface mapping, might also enhance the overall performance of ANN-based predictive models.

  6. Neural Network-Based Landmark Recognition and Navigation with IAMRs. Understanding the Principles of Thought and Behavior.

    ERIC Educational Resources Information Center

    Doty, Keith L.

    1999-01-01

    Research on neural networks and hippocampal function demonstrating how mammals construct mental maps and develop navigation strategies is being used to create Intelligent Autonomous Mobile Robots (IAMRs). Such robots are able to recognize landmarks and navigate without "vision." (SK)

  7. Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for Baseline Removal.

    PubMed

    Carvajal, Roberto C; Arias, Luis E; Garces, Hugo O; Sbarbaro, Daniel G

    2016-04-01

    This work presents a non-parametric method based on a principal component analysis (PCA) and a parametric one based on artificial neural networks (ANN) to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix. The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing 100 synthetic composed spectra at different signal-to-baseline ratio (SBR), signal-to-noise ratio (SNR), and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used. Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity. PMID:26917856

  8. Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles.

    PubMed

    Tóth-Nagy, Csaba; Conley, John J; Jarrett, Ronald P; Clark, Nigel N

    2006-07-01

    With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased. PMID:16878583

  9. An expert system with radial basis function neural network based on decision trees for predicting sediment transport in sewers.

    PubMed

    Ebtehaj, Isa; Bonakdari, Hossein; Zaji, Amir Hossein

    2016-01-01

    In this study, an expert system with a radial basis function neural network (RBF-NN) based on decision trees (DT) is designed to predict sediment transport in sewer pipes at the limit of deposition. First, sensitivity analysis is carried out to investigate the effect of each parameter on predicting the densimetric Froude number (Fr). The results indicate that utilizing the ratio of the median particle diameter to pipe diameter (d/D), ratio of median particle diameter to hydraulic radius (d/R) and volumetric sediment concentration (C(V)) as the input combination leads to the best Fr prediction. Subsequently, the new hybrid DT-RBF method is presented. The results of DT-RBF are compared with RBF and RBF-particle swarm optimization (PSO), which uses PSO for RBF training. It appears that DT-RBF is more accurate (R(2) = 0.934, MARE = 0.103, RMSE = 0.527, SI = 0.13, BIAS = -0.071) than the two other RBF methods. Moreover, the proposed DT-RBF model offers explicit expressions for use by practicing engineers. PMID:27386995

  10. Calibration of an instrumented treadmill using a precision-controlled device with artificial neural network-based error corrections.

    PubMed

    Hsieh, Hong-Jung; Lin, Hsiu-Chen; Lu, Hsuan-Lun; Chen, Ting-Yi; Lu, Tung-Wu

    2016-03-01

    Instrumented treadmills (ITs) are used to measure reaction forces (RF) and center of pressure (COP) movements for gait and balance assessment. Regular in situ calibration is essential to ensure their accuracy and to identify conditions when a factory re-calibration is needed. The current study aimed to develop and calibrate in situ an IT using a portable, precision-controlled calibration device with an artificial neural network (ANN)-based correction method. The calibration device was used to apply static and dynamic calibrating loads to the surface of the IT at 189 and 25 grid-points, respectively, at four belt speeds (0, 4, 6 and 8 km/h) without the need of a preset template. Part of the applied and measured RF and COP were used to train a threelayered, back-propagation ANN model while the rest of the data were used to evaluate the performance of the ANN. The percent errors of Fz and errors of the Px and Py were significantly decreased from a maximum of -1.15%, -1.64 mm and -0.73 mm to 0.02%, 0.02 mm and 0.03 mm during static calibration, respectively. During dynamic calibration, the corresponding values were decreasing from -3.65%, 2.58 mm and -4.92 mm to 0.30%, -0.14 mm and -0.47 mm, respectively. The results suggest that the calibration device and associated ANN will be useful for correcting measurement errors in vertical loads and COP for ITs. PMID:26979909

  11. A neural network-based method for spectral distortion correction in photon counting x-ray CT

    NASA Astrophysics Data System (ADS)

    Touch, Mengheng; Clark, Darin P.; Barber, William; Badea, Cristian T.

    2016-08-01

    Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables both 4 energy bins acquisition, as well as full-spectrum mode in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical effects in the detector and can be very noisy due to photon starvation in narrow energy bins. To address spectral distortions, we propose and demonstrate a novel artificial neural network (ANN)-based spectral distortion correction mechanism, which learns to undo the distortion in spectral CT, resulting in improved material decomposition accuracy. To address noise, post-reconstruction denoising based on bilateral filtration, which jointly enforces intensity gradient sparsity between spectral samples, is used to further improve the robustness of ANN training and material decomposition accuracy. Our ANN-based distortion correction method is calibrated using 3D-printed phantoms and a model of our spectral CT system. To enable realistic simulations and validation of our method, we first modeled the spectral distortions using experimental data acquired from 109Cd and 133Ba radioactive sources measured with our PCXD. Next, we trained an ANN to learn the relationship between the distorted spectral CT projections and the ideal, distortion-free projections in a calibration step. This required knowledge of the ground truth, distortion-free spectral CT projections, which were obtained by simulating a spectral CT scan of the digital version of a 3D-printed phantom. Once the training was completed, the trained ANN was used to perform distortion correction on any subsequent scans of the same system with the same parameters. We used joint bilateral filtration to perform noise reduction by jointly enforcing intensity gradient sparsity between the reconstructed images for each energy bin. Following reconstruction and denoising, the CT data was spectrally decomposed using the photoelectric effect, Compton scattering, and a K-edge material (i.e. iodine). The ANN-based distortion correction approach was tested using both simulations and experimental data acquired in phantoms and a mouse with our PCXD-based micro-CT system for 4 bins and full-spectrum acquisition modes. The iodine detectability and decomposition accuracy were assessed using the contrast-to-noise ratio and relative error in iodine concentration estimation metrics in images with and without distortion correction. In simulation, the material decomposition accuracy in the reconstructed data was vastly improved following distortion correction and denoising, with 50% and 20% reductions in material concentration measurement error in full-spectrum and 4 energy bins cases, respectively. Overall, experimental data confirms that full-spectrum mode provides superior results to 4-energy mode when the distortion corrections are applied. The material decomposition accuracy in the reconstructed data was vastly improved following distortion correction and denoising, with as much as a 41% reduction in material concentration measurement error for full-spectrum mode, while also bringing the iodine detectability to 4–6 mg ml‑1. Distortion correction also improved the 4 bins mode data, but to a lesser extent. The results demonstrate the experimental feasibility and potential advantages of ANN-based distortion correction and joint bilateral filtration-based denoising for accurate K-edge imaging with a PCXD. Given the computational efficiency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

  12. Object reconstruction in multilayer neural network based profilometry using grating structure comprising two regions with different spatial periods

    NASA Astrophysics Data System (ADS)

    Ganotra, Dinesh; Joseph, Joby; Singh, Kehar

    2004-08-01

    Feed-forward backpropagation neural network has been used in fringe projection profilometry for reconstruction of a three-dimensional (3D) object. A grating structure comprising two regions of different spatial periods is projected on the reference surface over which the object is placed. The shorter spatial period part of the grating is projected over the object, whereas the longer spatial period part is projected on the reference surface only. 3D object shape is reconstructed with the help of neural networks using images of the projected grating. During training phase of the network, the shorter spatial period grating along with the longer spatial period grating is used. Experimental results are presented for a diffuse object, showing that the 3D shape of the object is recovered using the above-mentioned method. However, the phases wrapping takes place in Fourier transform profilometry by using only one grating of shorter spatial period.

  13. Fuzzy-neural network based short term peak and average load forecasting (STPA LF) system with network security

    SciTech Connect

    Mandal, S.K.; Agrawal, A.

    1997-12-31

    In this paper an attempt is made to forecast load using fuzzy neural network (FNN) for an integrated power system. Here, the proposed system uses a two stage FNN for a short term peak and average load forecasting (STPALF). The first stage FNN deals with the load forecasting and the second stage algorithm can be worked independently for network security. This technique is used to forecast load accurately on week days as well as holidays, weekends and some special occasions considering historical data of load and weather information and also take necessary control action for network security.

  14. A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities.

    PubMed

    Guarneri, Paolo; Rocca, Gianpiero; Gobbi, Massimiliano

    2008-09-01

    This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy. PMID:18779087

  15. Fast Prediction of HCCI and PCCI Combustion with an Artificial Neural Network-Based Chemical Kinetic Model

    SciTech Connect

    Piggott, W T; Aceves, S M; Flowers, D L; Chen, J Y

    2007-09-26

    We have added the capability to look at in-cylinder fuel distributions using a previously developed ignition model within a fluid mechanics code (KIVA3V) that uses an artificial neural network (ANN) to predict ignition (The combined code: KIVA3V-ANN). KIVA3V-ANN was originally developed and validated for analysis of Homogeneous Charge Compression Ignition (HCCI) combustion, but it is also applicable to the more difficult problem of Premixed Charge Compression Ignition (PCCI) combustion. PCCI combustion refers to cases where combustion occurs as a nonmixing controlled, chemical kinetics dominated, autoignition process, where the fuel, air, and residual gas mixtures are not necessarily as homogeneous as in HCCI combustion. This paper analyzes the effects of introducing charge non-uniformity into a KIVA3V-ANN simulation. The results are compared to experimental results, as well as simulation results using a more physically representative and computationally intensive code (KIVA3V-MPI-MZ), which links a fluid mechanics code to a multi-zone detailed chemical kinetics solver. The results indicate that KIVA3V-ANN produces reasonable approximations to the more accurate KIVA3V-MPI-MZ at a much reduced computational cost.

  16. A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

    PubMed Central

    Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub

    2015-01-01

    An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases. PMID:26528986

  17. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived prediction interval for a selected hydrograph in the validation data set is presented in Fig 1. It is noted that most of the observed flows lie within the constructed prediction interval, and therefore provides information about the uncertainty of the prediction. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs. Fig. 1 Prediction Interval for selected hydrograph

  18. Artificial neural network--based analysis of high-throughput screening data for improved prediction of active compounds.

    PubMed

    Chakrabarti, Swapan; Svojanovsky, Stan R; Slavik, Romana; Georg, Gunda I; Wilson, George S; Smith, Peter G

    2009-12-01

    Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to nonactive compounds, R(A/N), was 0.0321. Back-propagation ANNs were trained and validated using principal components derived from the physicochemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a 3-fold gain in R(A/N) value. Further gains in R(A/N) values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was used to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a 10-fold gain of the original R(A/N) value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets. PMID:19940083

  19. Implementation of a cellular neural network-based segmentation algorithm on the bio-inspired vision system

    NASA Astrophysics Data System (ADS)

    Karabiber, Fethullah; Grassi, Giuseppe; Vecchio, Pietro; Arik, Sabri; Yalcin, M. Erhan

    2011-01-01

    Based on the cellular neural network (CNN) paradigm, the bio-inspired (bi-i) cellular vision system is a computing platform consisting of state-of-the-art sensing, cellular sensing-processing and digital signal processing. This paper presents the implementation of a novel CNN-based segmentation algorithm onto the bi-i system. The experimental results, carried out for different benchmark video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frame/sec. Comparisons with existing CNN-based methods show that, even though these methods are from two to six times faster than the proposed one, the conceived approach is more accurate and, consequently, represents a satisfying trade-off between real-time requirements and accuracy.

  20. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    NASA Astrophysics Data System (ADS)

    Haddadnia, Javad; Ahmadi, Majid; Faez, Karim

    2003-12-01

    This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.