An adaptive optimal control for smart structures based on the subspace tracking identification technique

NASA Astrophysics Data System (ADS)

Ripamonti, Francesco; Resta, Ferruccio; Borroni, Massimo; Cazzulani, Gabriele

2014-04-01

A new method for the real-time identification of mechanical system modal parameters is used in order to design different adaptive control logics aiming to reduce the vibrations in a carbon fiber plate smart structure. It is instrumented with three piezoelectric actuators, three accelerometers and three strain gauges. The real-time identification is based on a recursive subspace tracking algorithm whose outputs are elaborated by an ARMA model. A statistical approach is finally applied to choose the modal parameter correct values. These are given in input to model-based control logics such as a gain scheduling and an adaptive LQR control.

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

PubMed Central

Swings, Toon; Weytjens, Bram; Schalck, Thomas; Bonte, Camille; Verstraeten, Natalie; Michiels, Jan

2017-01-01

Abstract Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well. PMID:28961727

Interior Noise Reduction by Adaptive Feedback Vibration Control

NASA Technical Reports Server (NTRS)

Lim, Tae W.

1998-01-01

The objective of this project is to investigate the possible use of adaptive digital filtering techniques in simultaneous, multiple-mode identification of the modal parameters of a vibrating structure in real-time. It is intended that the results obtained from this project will be used for state estimation needed in adaptive structural acoustics control. The work done in this project is basically an extension of the work on real-time single mode identification, which was performed successfully using a digital signal processor (DSP) at NASA, Langley. Initially, in this investigation the single mode identification work was duplicated on a different processor, namely the Texas Instruments TMS32OC40 DSP. The system identification results for the single mode case were very good. Then an algorithm for simultaneous two mode identification was developed and tested using analytical simulation. When it successfully performed the expected tasks, it was implemented in real-time on the DSP system to identify the first two modes of vibration of a cantilever aluminum beam. The results of the simultaneous two mode case were good but some problems were identified related to frequency warping and spurious mode identification. The frequency warping problem was found to be due to the bilinear transformation used in the algorithm to convert the system transfer function from the continuous-time domain to the discrete-time domain. An alternative approach was developed to rectify the problem. The spurious mode identification problem was found to be associated with high sampling rates. Noise in the signal is suspected to be the cause of this problem but further investigation will be needed to clarify the cause. For simultaneous identification of more than two modes, it was found that theoretically an adaptive digital filter can be designed to identify the required number of modes, but the algebra became very complex which made it impossible to implement in the DSP system used in this study. The on-line identification algorithm developed in this research will be useful in constructing a state estimator for feedback vibration control.

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Performance study of LMS based adaptive algorithms for unknown system identification

NASA Astrophysics Data System (ADS)

Javed, Shazia; Ahmad, Noor Atinah

2014-07-01

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

Performance study of LMS based adaptive algorithms for unknown system identification

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

Javed, Shazia; Ahmad, Noor Atinah

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

Self-Tuning Adaptive-Controller Using Online Frequency Identification

NASA Technical Reports Server (NTRS)

Chiang, W. W.; Cannon, R. H., Jr.

1985-01-01

A real time adaptive controller was designed and tested successfully on a fourth order laboratory dynamic system which features very low structural damping and a noncolocated actuator sensor pair. The controller, implemented in a digital minicomputer, consists of a state estimator, a set of state feedback gains, and a frequency locked loop (FLL) for real time parameter identification. The FLL can detect the closed loop natural frequency of the system being controlled, calculate the mismatch between a plant parameter and its counterpart in the state estimator, and correct the estimator parameter in real time. The adaptation algorithm can correct the controller error and stabilize the system for more than 50% variation in the plant natural frequency, compared with a 10% stability margin in frequency variation for a fixed gain controller having the same performance at the nominal plant condition. After it has locked to the correct plant frequency, the adaptive controller works as well as the fixed gain controller does when there is no parameter mismatch. The very rapid convergence of this adaptive system is demonstrated experimentally, and can also be proven with simple root locus methods.

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations.

PubMed

Swings, Toon; Weytjens, Bram; Schalck, Thomas; Bonte, Camille; Verstraeten, Natalie; Michiels, Jan; Marchal, Kathleen

2017-11-01

Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

NASA Technical Reports Server (NTRS)

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach.

PubMed

Li, Nailu; Mu, Anle; Yang, Xiyun; Magar, Kaman T; Liu, Chao

2018-05-01

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A fault-tolerant control architecture for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Drozeski, Graham R.

Research has presented several approaches to achieve varying degrees of fault-tolerance in unmanned aircraft. Approaches in reconfigurable flight control are generally divided into two categories: those which incorporate multiple non-adaptive controllers and switch between them based on the output of a fault detection and identification element, and those that employ a single adaptive controller capable of compensating for a variety of fault modes. Regardless of the approach for reconfigurable flight control, certain fault modes dictate system restructuring in order to prevent a catastrophic failure. System restructuring enables active control of actuation not employed by the nominal system to recover controllability of the aircraft. After system restructuring, continued operation requires the generation of flight paths that adhere to an altered flight envelope. The control architecture developed in this research employs a multi-tiered hierarchy to allow unmanned aircraft to generate and track safe flight paths despite the occurrence of potentially catastrophic faults. The hierarchical architecture increases the level of autonomy of the system by integrating five functionalities with the baseline system: fault detection and identification, active system restructuring, reconfigurable flight control; reconfigurable path planning, and mission adaptation. Fault detection and identification algorithms continually monitor aircraft performance and issue fault declarations. When the severity of a fault exceeds the capability of the baseline flight controller, active system restructuring expands the controllability of the aircraft using unconventional control strategies not exploited by the baseline controller. Each of the reconfigurable flight controllers and the baseline controller employ a proven adaptive neural network control strategy. A reconfigurable path planner employs an adaptive model of the vehicle to re-shape the desired flight path. Generation of the revised flight path is posed as a linear program constrained by the response of the degraded system. Finally, a mission adaptation component estimates limitations on the closed-loop performance of the aircraft and adjusts the aircraft mission accordingly. A combination of simulation and flight test results using two unmanned helicopters validates the utility of the hierarchical architecture.

An adaptive learning control system for large flexible structures

NASA Technical Reports Server (NTRS)

Thau, F. E.

1985-01-01

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

Adaptive control of periodic systems

NASA Astrophysics Data System (ADS)

Tian, Zhiling

2009-12-01

Adaptive control is needed to cope with parametric uncertainty in dynamical systems. The adaptive control of LTI systems in both discrete and continuous time has been studied for four decades and the results are currently used widely in many different fields. In recent years, interest has shifted to the adaptive control of time-varying systems. It is known that the adaptive control of arbitrarily rapidly time-varying systems is in general intractable, but systems with periodically time-varying parameters (LTP systems) which have much more structure, are amenable to mathematical analysis. Further, there is also a need for such control in practical problems which have arisen in industry during the past twenty years. This thesis is the first attempt to deal with the adaptive control of LTP systems. Adaptive Control involves estimation of unknown parameters, adjusting the control parameters based on the estimates, and demonstrating that the overall system is stable. System theoretic properties such as stability, controllability, and observability play an important role both in formulating of the problems, as well as in generating solutions for them. For LTI systems, these properties have been studied since 1960s, and algebraic conditions that have to be satisfied to assure these properties are now well established. In the case of LTP systems, these properties can be expressed only in terms of transition matrices that are much more involved than those for LTI systems. Since adaptive control problems can be formulated only when these properties are well understood, it is not surprising that systematic efforts have not been made thus far for formulating and solving adaptive control problems that arise in LTP systems. Even in the case of LTI systems, it is well recognized that problems related to adaptive discrete-time system are not as difficult as those that arise in the continuous-time systems. This is amply evident in the solutions that were derived in the 1980s and 1990s for all the important problems. These differences are even more amplified in the LTP case; some problems in continuous time cannot even be formulated precisely. This thesis consequently focuses primarily on the adaptive identification and control of discrete-time systems, and derives most of the results that currently exist in the literature for LTI systems. Based on these investigations of discrete-time adaptive systems, attempts are made in the thesis to examine their continuous-time counterparts, and discuss the principal difficulties encountered. The dissertation examines critically the system theoretic properties of LTP systems in Chapter 2, and the mathematical framework provided for their analysis by Floquet theory in Chapter 3. Assuming that adaptive identification and control problems can be formulated precisely, a unified method of developing stable adaptive laws using error models is treated in Chapter 4. Chapter 5 presents a detailed study of the adaptation in SISO discrete-time LTP systems, and represents the core of the thesis. The important problems of identification, stabilization, regulation, and tracking of arbitrary signals are investigated, and practically implementable stable adaptive laws are derived. The dissertation concludes with a discussion of continuous-time adaptive control in Chapter 6 and discrete multivariable systems in Chapter 7. Directions for future research are indicated towards the end of the dissertation.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors.

PubMed

Villaverde, Monica; Perez, David; Moreno, Felix

2015-11-17

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor's infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Online vegetation parameter estimation using passive microwave remote sensing observations

USDA-ARS?s Scientific Manuscript database

In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...

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

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

Systems identification and the adaptive management of waterfowl in the United States

USGS Publications Warehouse

Williams, B.K.; Nichols, J.D.

2001-01-01

Waterfowl management in the United States is one of the more visible conservation success stories in the United States. It is authorized and supported by appropriate legislative authorities, based on large-scale monitoring programs, and widely accepted by the public. The process is one of only a limited number of large-scale examples of effective collaboration between research and management, integrating scientific information with management in a coherent framework for regulatory decision-making. However, harvest management continues to face some serious technical problems, many of which focus on sequential identification of the resource system in a context of optimal decision-making. The objective of this paper is to provide a theoretical foundation of adaptive harvest management, the approach currently in use in the United States for regulatory decision-making. We lay out the legal and institutional framework for adaptive harvest management and provide a formal description of regulatory decision-making in terms of adaptive optimization. We discuss some technical and institutional challenges in applying adaptive harvest management and focus specifically on methods of estimating resource states for linear resource systems.

Identification of Learning Styles Online by Observing Learners Browsing Behaviour through a Neural Network

ERIC Educational Resources Information Center

Lo, Jia-Jiunn; Shu, Pai-Chuan

2005-01-01

Identification of individual learning style is important when developing adaptive educational hypermedia systems. Current systems ask learners to complete questionnaires to identify their learning styles, which might not be appropriate in some contexts. The goal of this research is to identify the learner's learning style by simply observing…

ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control

DOE PAGES

Liu, Hesen; Zhu, Lin; Pan, Zhuohong; ...

2015-09-14

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. our paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-outputmore » (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. Our results demonstrate that the measurement-based model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control.« less

Survey of adaptive control using Liapunov design

NASA Technical Reports Server (NTRS)

Lindorff, D. P.; Carroll, R. L.

1972-01-01

A survey was made of the literature devoted to the synthesis of model-tracking adaptive systems based on application of Liapunov's second method. The basic synthesis procedure is introduced and a critical review of extensions made to the theory since 1966 is made. The extensions relate to design for relative stability, reduction of order techniques, design with disturbance, design with time variable parameters, multivariable systems, identification, and an adaptive observer.

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

PubMed

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

2013-07-01

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

The ALICE-HMPID Detector Control System: Its evolution towards an expert and adaptive system

NASA Astrophysics Data System (ADS)

De Cataldo, G.; Franco, A.; Pastore, C.; Sgura, I.; Volpe, G.

2011-05-01

The High Momentum Particle IDentification (HMPID) detector is a proximity focusing Ring Imaging Cherenkov (RICH) for charged hadron identification. The HMPID is based on liquid C 6F 14 as the radiator medium and on a 10 m 2 CsI coated, pad segmented photocathode of MWPCs for UV Cherenkov photon detection. To ensure full remote control, the HMPID is equipped with a detector control system (DCS) responding to industrial standards for robustness and reliability. It has been implemented using PVSS as Slow Control And Data Acquisition (SCADA) environment, Programmable Logic Controller as control devices and Finite State Machines for modular and automatic command execution. In the perspective of reducing human presence at the experiment site, this paper focuses on DCS evolution towards an expert and adaptive control system, providing, respectively, automatic error recovery and stable detector performance. HAL9000, the first prototype of the HMPID expert system, is then presented. Finally an analysis of the possible application of the adaptive features is provided.

Modeling, Control, and Estimation of Flexible, Aerodynamic Structures

NASA Astrophysics Data System (ADS)

Ray, Cody W.

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

An adaptive tracking observer for failure-detection systems

NASA Technical Reports Server (NTRS)

Sidar, M.

1982-01-01

The design problem of adaptive observers applied to linear, constant and variable parameters, multi-input, multi-output systems, is considered. It is shown that, in order to keep the observer's (or Kalman filter) false-alarm rate (FAR) under a certain specified value, it is necessary to have an acceptable proper matching between the observer (or KF) model and the system parameters. An adaptive observer algorithm is introduced in order to maintain desired system-observer model matching, despite initial mismatching and/or system parameter variations. Only a properly designed adaptive observer is able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability and FAR. Conditions for convergence for the adaptive process were obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors and accurate and fast parameter identification, in both deterministic and stochastic cases.

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.

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

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

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

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification.

PubMed

Dehzangi, Omid; Farooq, Muhamed

2018-01-01

A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. We evaluate subject-specific models against other subjects. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care.

Nonlinear stability and control study of highly maneuverable high performance aircraft, phase 2

NASA Technical Reports Server (NTRS)

Mohler, R. R.

1992-01-01

Research leading to the development of new nonlinear methodologies for the adaptive control and stability analysis of high angle of attack aircraft such as the F-18 is discussed. The emphasis has been on nonlinear adaptive control, but associated model development, system identification, stability analysis, and simulation were studied in some detail as well. Studies indicated that nonlinear adaptive control can outperform linear adaptive control for rapid maneuvers with large changes in angle of attack. Included here are studies on nonlinear model algorithmic controller design and an analysis of nonlinear system stability using robust stability analysis for linear systems.

A configurable sensor network applied to ambient assisted living.

PubMed

Villacorta, Juan J; Jiménez, María I; Del Val, Lara; Izquierdo, Alberto

2011-01-01

The rising older people population has increased the interest in ambient assisted living systems. This article presents a system for monitoring the disabled or older persons developed from an existing surveillance system. The modularity and adaptability characteristics of the system allow an easy adaptation for a different purpose. The proposed system uses a network of sensors capable of motion detection that includes fall warning, identification of persons and a configurable control system which allows its use in different scenarios.

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.

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.

Survey of adaptive control using Liapunov design

NASA Technical Reports Server (NTRS)

Lindorff, D. P.; Carroll, R. L.

1973-01-01

A survey of the literature in which Liapunov's second method is used in determining the control law is presented, with emphasis placed on the model-tracking adaptive control problem. Forty references are listed. Following a brief tutorial exposition of the adaptive control problem, the techniques for treating reduction of order, disturbance and time-varying parameters, multivariable systems, identification, and adaptive observers are discussed. The method is critically evaluated, particularly with respect to possibilities for application.

Health monitoring system for transmission shafts based on adaptive parameter identification

NASA Astrophysics Data System (ADS)

Souflas, I.; Pezouvanis, A.; Ebrahimi, K. M.

2018-05-01

A health monitoring system for a transmission shaft is proposed. The solution is based on the real-time identification of the physical characteristics of the transmission shaft i.e. stiffness and damping coefficients, by using a physical oriented model and linear recursive identification. The efficacy of the suggested condition monitoring system is demonstrated on a prototype transient engine testing facility equipped with a transmission shaft capable of varying its physical properties. Simulation studies reveal that coupling shaft faults can be detected and isolated using the proposed condition monitoring system. Besides, the performance of various recursive identification algorithms is addressed. The results of this work recommend that the health status of engine dynamometer shafts can be monitored using a simple lumped-parameter shaft model and a linear recursive identification algorithm which makes the concept practically viable.

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

PubMed

Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

2006-04-01

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.

Parametric recursive system identification and self-adaptive modeling of the human energy metabolism for adaptive control of fat weight.

PubMed

Őri, Zsolt P

2017-05-01

A mathematical model has been developed to facilitate indirect measurements of difficult to measure variables of the human energy metabolism on a daily basis. The model performs recursive system identification of the parameters of the metabolic model of the human energy metabolism using the law of conservation of energy and principle of indirect calorimetry. Self-adaptive models of the utilized energy intake prediction, macronutrient oxidation rates, and daily body composition changes were created utilizing Kalman filter and the nominal trajectory methods. The accuracy of the models was tested in a simulation study utilizing data from the Minnesota starvation and overfeeding study. With biweekly macronutrient intake measurements, the average prediction error of the utilized carbohydrate intake was -23.2 ± 53.8 kcal/day, fat intake was 11.0 ± 72.3 kcal/day, and protein was 3.7 ± 16.3 kcal/day. The fat and fat-free mass changes were estimated with an error of 0.44 ± 1.16 g/day for fat and -2.6 ± 64.98 g/day for fat-free mass. The daily metabolized macronutrient energy intake and/or daily macronutrient oxidation rate and the daily body composition change from directly measured serial data are optimally predicted with a self-adaptive model with Kalman filter that uses recursive system identification.

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

Wang, Shi-bing, E-mail: wang-shibing@dlut.edu.cn, E-mail: wangxy@dlut.edu.cn; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024; Wang, Xing-yuan, E-mail: wang-shibing@dlut.edu.cn, E-mail: wangxy@dlut.edu.cn

With comprehensive consideration of generalized synchronization, combination synchronization and adaptive control, this paper investigates a novel adaptive generalized combination complex synchronization (AGCCS) scheme for different real and complex nonlinear systems with unknown parameters. On the basis of Lyapunov stability theory and adaptive control, an AGCCS controller and parameter update laws are derived to achieve synchronization and parameter identification of two real drive systems and a complex response system, as well as two complex drive systems and a real response system. Two simulation examples, namely, ACGCS for chaotic real Lorenz and Chen systems driving a hyperchaotic complex Lü system, and hyperchaoticmore » complex Lorenz and Chen systems driving a real chaotic Lü system, are presented to verify the feasibility and effectiveness of the proposed scheme.« less

Tip-tilt disturbance model identification based on non-linear least squares fitting for Linear Quadratic Gaussian control

NASA Astrophysics Data System (ADS)

Yang, Kangjian; Yang, Ping; Wang, Shuai; Dong, Lizhi; Xu, Bing

2018-05-01

We propose a method to identify tip-tilt disturbance model for Linear Quadratic Gaussian control. This identification method based on Levenberg-Marquardt method conducts with a little prior information and no auxiliary system and it is convenient to identify the tip-tilt disturbance model on-line for real-time control. This identification method makes it easy that Linear Quadratic Gaussian control runs efficiently in different adaptive optics systems for vibration mitigation. The validity of the Linear Quadratic Gaussian control associated with this tip-tilt disturbance model identification method is verified by experimental data, which is conducted in replay mode by simulation.

Integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control for Lead-Wing close formation systems

NASA Astrophysics Data System (ADS)

Liu, Chun; Jiang, Bin; Zhang, Ke

2018-03-01

This paper investigates the attitude and position tracking control problem for Lead-Wing close formation systems in the presence of loss of effectiveness and lock-in-place or hardover failure. In close formation flight, Wing unmanned aerial vehicle movements are influenced by vortex effects of the neighbouring Lead unmanned aerial vehicle. This situation allows modelling of aerodynamic coupling vortex-effects and linearisation based on optimal close formation geometry. Linearised Lead-Wing close formation model is transformed into nominal robust H-infinity models with respect to Mach hold, Heading hold, and Altitude hold autopilots; static feedback H-infinity controller is designed to guarantee effective tracking of attitude and position while manoeuvring Lead unmanned aerial vehicle. Based on H-infinity control design, an integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control scheme is developed to guarantee asymptotic stability of close-loop systems, error signal boundedness, and attitude and position tracking properties. Simulation results for Lead-Wing close formation systems validate the efficiency of the proposed integrated multiple-model adaptive control algorithm.

Approach to the problem of the parameters optimization of the shooting system

NASA Astrophysics Data System (ADS)

Demidova, L. A.; Sablina, V. A.; Sokolova, Y. S.

2018-02-01

The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers’ ensembles, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

Input-output identification of controlled discrete manufacturing systems

NASA Astrophysics Data System (ADS)

Estrada-Vargas, Ana Paula; López-Mellado, Ernesto; Lesage, Jean-Jacques

2014-03-01

The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.

On neural networks in identification and control of dynamic systems

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Hyland, David C.

1993-01-01

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

Methods and Apparatus for Reducing Multipath Signal Error Using Deconvolution

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor); Lau, Kenneth H. (Inventor)

1999-01-01

A deconvolution approach to adaptive signal processing has been applied to the elimination of signal multipath errors as embodied in one preferred embodiment in a global positioning system receiver. The method and receiver of the present invention estimates then compensates for multipath effects in a comprehensive manner. Application of deconvolution, along with other adaptive identification and estimation techniques, results in completely novel GPS (Global Positioning System) receiver architecture.

Joint Services Electronics Program.

DTIC Science & Technology

1987-03-31

58 (no previous unit) Unit 18 Adaptive Algorithms for Identification. Filtering. Control. and S ignal P rocessin g...two new faculty. Professors Arun and Wah. Finally. a total of six new faculty in the areas of adaptive and nonlinear systems. communication systems. and...previously), we observed an additional higher binding energy site at 2.6 eV The Sb coverage in the E, site increased ,xith ion dose and a model was developed

A Watershed Modeling System for Fort Benning, GA Using the US EPA BASINS Framework

DTIC Science & Technology

2013-01-01

Benning watersheds. The objective of this project was to identify, adapt , and develop watershed management models for Fort Benning that address impacts on...of Need (SON) (SERDP, 2005) which recognized that military installations needed the identification, adaptation , and development of watershed...capabilities. To accomplish these goals the Strategic Plan for SEMP (2005) notes the need for both fundamental and applied ( adaptive ) research; this need

Software Reviews.

ERIC Educational Resources Information Center

Science and Children, 1988

1988-01-01

Reviews five software packages for use with school age children. Includes "Science Toolkit Module 2: Earthquake Lab"; "Adaptations and Identification"; "Geoworld"; "Body Systems II Series: The Blood System: A Liquid of Life," all for Apple II, and "Science Courseware: Life Science/Biology" for…

Response to ``Comment on `Adaptive Q-S (lag, anticipated, and complete) time-varying synchronization and parameters identification of uncertain delayed neural networks''' [Chaos 17, 038101 (2007)

NASA Astrophysics Data System (ADS)

Yu, Wenwu; Cao, Jinde

2007-09-01

Parameter identification of dynamical systems from time series has received increasing interest due to its wide applications in secure communication, pattern recognition, neural networks, and so on. Given the driving system, parameters can be estimated from the time series by using an adaptive control algorithm. Recently, it has been reported that for some stable systems, in which parameters are difficult to be identified [Li et al., Phys Lett. A 333, 269-270 (2004); Remark 5 in Yu and Cao, Physica A 375, 467-482 (2007); and Li et al., Chaos 17, 038101 (2007)], and in this paper, a brief discussion about whether parameters can be identified from time series is investigated. From some detailed analyses, the problem of why parameters of stable systems can be hardly estimated is discussed. Some interesting examples are drawn to verify the proposed analysis.

The stochastic control of the F-8C aircraft using the Multiple Model Adaptive Control (MMAC) method

NASA Technical Reports Server (NTRS)

Athans, M.; Dunn, K. P.; Greene, E. S.; Lee, W. H.; Sandel, N. R., Jr.

1975-01-01

The purpose of this paper is to summarize results obtained for the adaptive control of the F-8C aircraft using the so-called Multiple Model Adaptive Control method. The discussion includes the selection of the performance criteria for both the lateral and the longitudinal dynamics, the design of the Kalman filters for different flight conditions, the 'identification' aspects of the design using hypothesis testing ideas, and the performance of the closed loop adaptive system.

Identification and Control of Non-Linear Time-Varying Dynamical Systems Using Artificial Neural Networks

DTIC Science & Technology

1992-09-01

finding an inverse plant such as was done by Bertrand [BD91] and by Levin, Gewirtzman and Inbar in a binary type inverse controller [LGI91], to self tuning...gain robust control. 2) Self oscillating adaptive controller. 3) Gain scheduling. 4) Self tuning. 5) Model-reference adaptive systems. Although the...of multidimensional systems (CS881 as well as aircraft [HG90]. The self oscillating method is also a feedback based mechanism, utilizing a relay in the

CRISPR-Cas: Adapting to change.

PubMed

Jackson, Simon A; McKenzie, Rebecca E; Fagerlund, Robert D; Kieper, Sebastian N; Fineran, Peter C; Brouns, Stan J J

2017-04-07

Bacteria and archaea are engaged in a constant arms race to defend against the ever-present threats of viruses and invasion by mobile genetic elements. The most flexible weapons in the prokaryotic defense arsenal are the CRISPR-Cas adaptive immune systems. These systems are capable of selective identification and neutralization of foreign DNA and/or RNA. CRISPR-Cas systems rely on stored genetic memories to facilitate target recognition. Thus, to keep pace with a changing pool of hostile invaders, the CRISPR memory banks must be regularly updated with new information through a process termed CRISPR adaptation. In this Review, we outline the recent advances in our understanding of the molecular mechanisms governing CRISPR adaptation. Specifically, the conserved protein machinery Cas1-Cas2 is the cornerstone of adaptive immunity in a range of diverse CRISPR-Cas systems. Copyright © 2017, American Association for the Advancement of Science.

Recent developments in learning control and system identification for robots and structures

NASA Technical Reports Server (NTRS)

Phan, M.; Juang, J.-N.; Longman, R. W.

1990-01-01

This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

Adaptive Identification of Fluid-Dynamic Systems

DTIC Science & Technology

2001-06-14

Fig. 1. Unknown System Adaptive Filter Σ _ + Input u Filter Output y Desired Output d Error e Fig. 1. Modeling of a SISO system using...2J E e n =   (12) Here [ ]. E is the expectation operator and ( ) ( ) ( ) e n d n y n= − is the error between the desired system output and...B … input vector ( ) ( ) ( ) ( )[ ], , ,1 1 Tn u n u n u n N= − − +U … output and error ( ) ( ) ( ) ( ) ( ) ( ) ( ) T T y n n n e n d n n n

Orthonormal filters for identification in active control systems

NASA Astrophysics Data System (ADS)

Mayer, Dirk

2015-12-01

Many active noise and vibration control systems require models of the control paths. When the controlled system changes slightly over time, adaptive digital filters for the identification of the models are useful. This paper aims at the investigation of a special class of adaptive digital filters: orthonormal filter banks possess the robust and simple adaptation of the widely applied finite impulse response (FIR) filters, but at a lower model order, which is important when considering implementation on embedded systems. However, the filter banks require prior knowledge about the resonance frequencies and damping of the structure. This knowledge can be supposed to be of limited precision, since in many practical systems, uncertainties in the structural parameters exist. In this work, a procedure using a number of training systems to find the fixed parameters for the filter banks is applied. The effect of uncertainties in the prior knowledge on the model error is examined both with a basic example and in an experiment. Furthermore, the possibilities to compensate for the imprecise prior knowledge by a higher filter order are investigated. Also comparisons with FIR filters are implemented in order to assess the possible advantages of the orthonormal filter banks. Numerical and experimental investigations show that significantly lower computational effort can be reached by the filter banks under certain conditions.

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.

Towards the identification of the loci of adaptive evolution

PubMed Central

Pardo-Diaz, Carolina; Salazar, Camilo; Jiggins, Chris D

2015-01-01

1. Establishing the genetic and molecular basis underlying adaptive traits is one of the major goals of evolutionary geneticists in order to understand the connection between genotype and phenotype and elucidate the mechanisms of evolutionary change. Despite considerable effort to address this question, there remain relatively few systems in which the genes shaping adaptations have been identified. 2. Here, we review the experimental tools that have been applied to document the molecular basis underlying evolution in several natural systems, in order to highlight their benefits, limitations and suitability. In most cases, a combination of DNA, RNA and functional methodologies with field experiments will be needed to uncover the genes and mechanisms shaping adaptation in nature. PMID:25937885

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.

Adaptive identifier for uncertain complex nonlinear systems based on continuous neural networks.

PubMed

Alfaro-Ponce, Mariel; Cruz, Amadeo Argüelles; Chairez, Isaac

2014-03-01

This paper presents the design of a complex-valued differential neural network identifier for uncertain nonlinear systems defined in the complex domain. This design includes the construction of an adaptive algorithm to adjust the parameters included in the identifier. The algorithm is obtained based on a special class of controlled Lyapunov functions. The quality of the identification process is characterized using the practical stability framework. Indeed, the region where the identification error converges is derived by the same Lyapunov method. This zone is defined by the power of uncertainties and perturbations affecting the complex-valued uncertain dynamics. Moreover, this convergence zone is reduced to its lowest possible value using ideas related to the so-called ellipsoid methodology. Two simple but informative numerical examples are developed to show how the identifier proposed in this paper can be used to approximate uncertain nonlinear systems valued in the complex domain.

Segmentation of financial seals and its implementation on a DSP-based system

NASA Astrophysics Data System (ADS)

He, Jin; Liu, Tiegen; Guo, Jingjing; Zhang, Hao

2009-11-01

Automatic seal imprint identification is an important part of modern financial security. Accurate segmentation is the basis of correct identification. In this paper, a DSP (digital signal processor) based identification system was designed, and an adaptive algorithm was proposed to extract binary seal images from financial instruments. As the kernel of the identification system, a DSP chip of TMS320DM642 was used to implement image processing, controlling and coordinating works of each system module. The proposed algorithm consisted of three stages, including extraction of grayscale seal image, denoising and binarization. A grayscale seal image was extracted by color transform from a financial instrument image. Adaptive morphological operations were used to highlight details of the extracted grayscale seal image and smooth the background. After median filter for noise elimination, the filtered seal image was binarized by Otsu's method. The algorithm was developed based on the DSP development environment CCS and real-time operation system DSP/BIOS. To simplify the implementation of the proposed algorithm, the calibration of white balance and the coarse positioning of the seal imprint were implemented by TMS320DM642 controlling image acquisition. IMGLIB of TMS320DM642 was used for the efficiency improvement. The experiment result showed that financial seal imprints, even with intricate and dense strokes can be correctly segmented by the proposed algorithm. Adhesion and incompleteness distortions in the segmentation results were reduced, even when the original seal imprint had a poor quality.

Quantifying detection performance of a passive low-frequency RFID system in an environmental preference chamber for laying hens

USDA-ARS?s Scientific Manuscript database

Radio Frequency Identification (RFID) systems have been widely used in production livestock systems for identifying, tracing, and registering animals and improving subsidy management. Adaptations have been made to extend RFID technology to animal behavior and welfare research. An RFID system was imp...

Parametric diagnosis of the adaptive gas path in the automatic control system of the aircraft engine

NASA Astrophysics Data System (ADS)

Kuznetsova, T. A.

2017-01-01

The paper dwells on the adaptive multimode mathematical model of the gas-turbine aircraft engine (GTE) embedded in the automatic control system (ACS). The mathematical model is based on the throttle performances, and is characterized by high accuracy of engine parameters identification in stationary and dynamic modes. The proposed on-board engine model is the state space linearized low-level simulation. The engine health is identified by the influence of the coefficient matrix. The influence coefficient is determined by the GTE high-level mathematical model based on measurements of gas-dynamic parameters. In the automatic control algorithm, the sum of squares of the deviation between the parameters of the mathematical model and real GTE is minimized. The proposed mathematical model is effectively used for gas path defects detecting in on-line GTE health monitoring. The accuracy of the on-board mathematical model embedded in ACS determines the quality of adaptive control and reliability of the engine. To improve the accuracy of identification solutions and sustainability provision, the numerical method of Monte Carlo was used. The parametric diagnostic algorithm based on the LPτ - sequence was developed and tested. Analysis of the results suggests that the application of the developed algorithms allows achieving higher identification accuracy and reliability than similar models used in practice.

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.

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

NASA Technical Reports Server (NTRS)

Momoh, James A.; Wang, Yanchun; Dolce, James L.

1997-01-01

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

Is Integration Always most Adaptive? The Role of Cultural Identity in Academic Achievement and in Psychological Adaptation of Immigrant Students in Germany.

PubMed

Schotte, Kristin; Stanat, Petra; Edele, Aileen

2018-01-01

Immigrant adaptation research views identification with the mainstream context as particularly beneficial for sociocultural adaptation, including academic achievement, and identification with the ethnic context as particularly beneficial for psychological adaptation. A strong identification with both contexts is considered most beneficial for both outcomes (integration hypothesis). However, it is unclear whether the integration hypothesis applies in assimilative contexts, across different outcomes, and across different immigrant groups. This study investigates the association of cultural identity with several indicators of academic achievement and psychological adaptation in immigrant adolescents (N = 3894, 51% female, M age = 16.24, SD age  = 0.71) in Germany. Analyses support the integration hypothesis for aspects of psychological adaptation but not for academic achievement. Moreover, for some outcomes, findings vary across immigrant groups from Turkey (n = 809), the former Soviet Union (n = 712), and heterogeneous other countries (n = 2373). The results indicate that the adaptive potential of identity integration is limited in assimilative contexts, such as Germany, and that it may vary across different outcomes and groups. As each identification is positively associated with at least one outcome, however, both identification dimensions seem to be important for the adaptation of immigrant adolescents.

Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering

NASA Astrophysics Data System (ADS)

Fu, Xiaowei; Xiang, Yuhan; Chen, Li; Xu, Xin; Li, Xi

2015-12-01

High quality microstructure modeling can optimize the design of fuel cells. For three-phase accurate identification of Solid Oxide Fuel Cell (SOFC) microstructure, this paper proposes a novel image segmentation method on YSZ/Ni anode Optical Microscopic (OM) images. According to Quantum Signal Processing (QSP), the proposed approach exploits a quantum-inspired adaptive fuzziness factor to adaptively estimate the energy function in the fuzzy system based on Markov Random Filed (MRF). Before defuzzification, a quantum-inspired probability distribution based on distance and gray correction is proposed, which can adaptively adjust the inaccurate probability estimation of uncertain points caused by noises and edge points. In this study, the proposed method improves accuracy and effectiveness of three-phase identification on the micro-investigation. It provides firm foundation to investigate the microstructural evolution and its related properties.

System identification of jet engines

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

Sugiyama, N.

2000-01-01

System identification plays an important role in advanced control systems for jet engines, in which controls are performed adaptively using data from the actual engine and the identified engine. An identification technique for jet engine using the Constant Gain Extended Kalman Filter (CGEKF) is described. The filter is constructed for a two-spool turbofan engine. The CGEKF filter developed here can recognize parameter change in engine components and estimate unmeasurable variables over whole flight conditions. These capabilities are useful for an advanced Full Authority Digital Electric Control (FADEC). Effects of measurement noise and bias, effects of operating point and unpredicted performancemore » change are discussed. Some experimental results using the actual engine are shown to evaluate the effectiveness of CGEKF filter.« less

Advancements in robust algorithm formulation for speaker identification of whispered speech

NASA Astrophysics Data System (ADS)

Fan, Xing

Whispered speech is an alternative speech production mode from neutral speech, which is used by talkers intentionally in natural conversational scenarios to protect privacy and to avoid certain content from being overheard/made public. Due to the profound differences between whispered and neutral speech in production mechanism and the absence of whispered adaptation data, the performance of speaker identification systems trained with neutral speech degrades significantly. This dissertation therefore focuses on developing a robust closed-set speaker recognition system for whispered speech by using no or limited whispered adaptation data from non-target speakers. This dissertation proposes the concept of "High''/"Low'' performance whispered data for the purpose of speaker identification. A variety of acoustic properties are identified that contribute to the quality of whispered data. An acoustic analysis is also conducted to compare the phoneme/speaker dependency of the differences between whispered and neutral data in the feature domain. The observations from those acoustic analysis are new in this area and also serve as a guidance for developing robust speaker identification systems for whispered speech. This dissertation further proposes two systems for speaker identification of whispered speech. One system focuses on front-end processing. A two-dimensional feature space is proposed to search for "Low''-quality performance based whispered utterances and separate feature mapping functions are applied to vowels and consonants respectively in order to retain the speaker's information shared between whispered and neutral speech. The other system focuses on speech-mode-independent model training. The proposed method generates pseudo whispered features from neutral features by using the statistical information contained in a whispered Universal Background model (UBM) trained from extra collected whispered data from non-target speakers. Four modeling methods are proposed for the transformation estimation in order to generate the pseudo whispered features. Both of the above two systems demonstrate a significant improvement over the baseline system on the evaluation data. This dissertation has therefore contributed to providing a scientific understanding of the differences between whispered and neutral speech as well as improved front-end processing and modeling method for speaker identification of whispered speech. Such advancements will ultimately contribute to improve the robustness of speech processing systems.

Cloud-based adaptive exon prediction for DNA analysis.

PubMed

Putluri, Srinivasareddy; Zia Ur Rahman, Md; Fathima, Shaik Yasmeen

2018-02-01

Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries. DNA sequencing storage costs will be minimised by use of cloud service. In this study, the authors put forward a novel genomic informatics system using Amazon Cloud Services, where genomic sequence information is stored and accessed for processing. True identification of exon regions in a DNA sequence is a key task in bioinformatics, which helps in disease identification and design drugs. Three base periodicity property of exons forms the basis of all exon identification techniques. Adaptive signal processing techniques found to be promising in comparison with several other methods. Several adaptive exon predictors (AEPs) are developed using variable normalised least mean square and its maximum normalised variants to reduce computational complexity. Finally, performance evaluation of various AEPs is done based on measures such as sensitivity, specificity and precision using various standard genomic datasets taken from National Center for Biotechnology Information genomic sequence database.

Optimized design of embedded DSP system hardware supporting complex algorithms

NASA Astrophysics Data System (ADS)

Li, Yanhua; Wang, Xiangjun; Zhou, Xinling

2003-09-01

The paper presents an optimized design method for a flexible and economical embedded DSP system that can implement complex processing algorithms as biometric recognition, real-time image processing, etc. It consists of a floating-point DSP, 512 Kbytes data RAM, 1 Mbytes FLASH program memory, a CPLD for achieving flexible logic control of input channel and a RS-485 transceiver for local network communication. Because of employing a high performance-price ratio DSP TMS320C6712 and a large FLASH in the design, this system permits loading and performing complex algorithms with little algorithm optimization and code reduction. The CPLD provides flexible logic control for the whole DSP board, especially in input channel, and allows convenient interface between different sensors and DSP system. The transceiver circuit can transfer data between DSP and host computer. In the paper, some key technologies are also introduced which make the whole system work efficiently. Because of the characters referred above, the hardware is a perfect flat for multi-channel data collection, image processing, and other signal processing with high performance and adaptability. The application section of this paper presents how this hardware is adapted for the biometric identification system with high identification precision. The result reveals that this hardware is easy to interface with a CMOS imager and is capable of carrying out complex biometric identification algorithms, which require real-time process.

A neural network for the identification of measured helicopter noise

NASA Technical Reports Server (NTRS)

Cabell, R. H.; Fuller, C. R.; O'Brien, W. F.

1991-01-01

The results of a preliminary study of the components of a novel acoustic helicopter identification system are described. The identification system uses the relationship between the amplitudes of the first eight harmonics in the main rotor noise spectrum to distinguish between helicopter types. Two classification algorithms are tested; a statistically optimal Bayes classifier, and a neural network adaptive classifier. The performance of these classifiers is tested using measured noise of three helicopters. The statistical classifier can correctly identify the helicopter an average of 67 percent of the time, while the neural network is correct an average of 65 percent of the time. These results indicate the need for additional study of the envelope of harmonic amplitudes as a component of a helicopter identification system. Issues concerning the implementation of the neural network classifier, such as training time and structure of the network, are discussed.

Advanced driver assistance system: Road sign identification using VIAPIX system and a correlation technique

NASA Astrophysics Data System (ADS)

Ouerhani, Y.; Alfalou, A.; Desthieux, M.; Brosseau, C.

2017-02-01

We present a three-step approach based on the commercial VIAPIX® module for road traffic sign recognition and identification. Firstly, detection in a scene of all objects having characteristics of traffic signs is performed. This is followed by a first-level recognition based on correlation which consists in making a comparison between each detected object with a set of reference images of a database. Finally, a second level of identification allows us to confirm or correct the previous identification. In this study, we perform a correlation-based analysis by combining and adapting the Vander Lugt correlator with the nonlinear joint transformation correlator (JTC). Of particular significance, this approach permits to make a reliable decision on road traffic sign identification. We further discuss a robust scheme allowing us to track a detected road traffic sign in a video sequence for the purpose of increasing the decision performance of our system. This approach can have broad practical applications in the maintenance and rehabilitation of transportation infrastructure, or for drive assistance.

Parameters Identification for Motorcycle Simulator's Platform Characterization

NASA Astrophysics Data System (ADS)

Nehaoua, L.; Arioui, H.

2008-06-01

This paper presents the dynamics modeling and parameters identification of a motorcycle simulator's platform. This model begins with some suppositions which consider that the leg dynamics can be neglected with respect to the mobile platform one. The objectif is to synthesis a simplified control scheme, adapted to driving simulation application, minimising dealys and without loss of tracking performance. Electronic system of platform actuation is described. It's based on a CAN BUS communication which offers a large transmission robustness and error handling. Despite some disadvanteges, we adapted a control solution which overcome these inconvenients and preserve the quality of tracking trajectory. A bref description of the simulator's platform is given and results are shown and justified according to our specifications.

ER fluid applications to vibration control devices and an adaptive neural-net controller

NASA Astrophysics Data System (ADS)

Morishita, Shin; Ura, Tamaki

1993-07-01

Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

Assessment Study of the State of the Art in Adaptive Control and its Applications to Aircraft Control

NASA Technical Reports Server (NTRS)

Kaufman, Howard

1998-01-01

Many papers relevant to reconfigurable flight control have appeared over the past fifteen years. In general these have consisted of theoretical issues, simulation experiments, and in some cases, actual flight tests. Results indicate that reconfiguration of flight controls is certainly feasible for a wide class of failures. However many of the proposed procedures although quite attractive, need further analytical and experimental studies for meaningful validation. Many procedures assume the availability of failure detection and identification logic that will supply adequately fast, the dynamics corresponding to the failed aircraft. This in general implies that the failure detection and fault identification logic must have access to all possible anticipated faults and the corresponding dynamical equations of motion. Unless some sort of explicit on line parameter identification is included, the computational demands could possibly be too excessive. This suggests the need for some form of adaptive control, either by itself as the prime procedure for control reconfiguration or in conjunction with the failure detection logic. If explicit or indirect adaptive control is used, then it is important that the identified models be such that the corresponding computed controls deliver adequate performance to the actual aircraft. Unknown changes in trim should be modelled, and parameter identification needs to be adequately insensitive to noise and at the same time capable of tracking abrupt changes. If however, both failure detection and system parameter identification turn out to be too time consuming in an emergency situation, then the concepts of direct adaptive control should be considered. If direct model reference adaptive control is to be used (on a linear model) with stability assurances, then a positive real or passivity condition needs to be satisfied for all possible configurations. This condition is often satisfied with a feedforward compensator around the plant. This compensator must be robustly designed such that the compensated plant satisfies the required positive real conditions over all expected parameter values. Furthermore, with the feedforward only around the plant, a nonzero (but bounded error) will exist in steady state between the plant and model outputs. This error can be removed by placing the compensator also in the reference model. Design of such a compensator should not be too difficult a problem since for flight control it is generally possible to feedback all the system states.

The link between national security and biometrics

NASA Astrophysics Data System (ADS)

Etter, Delores M.

2005-03-01

National security today requires identification of people, things and activities. Biometrics plays an important role in the identification of people, and indirectly, in the identification of things and activities. Therefore, the development of technology and systems that provide faster and more accurate biometric identification is critical to the defense of our country. In addition, the development of a broad range of biometrics is necessary to provide the range of options needed to address flexible and adaptive adversaries. This paper will discuss the importance of a number of critical areas in the development of an environment to support biometrics, including research and development, biometric education, standards, pilot projects, and privacy assurance.

System Identification for Nonlinear Control Using Neural Networks

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Linse, Dennis J.

1990-01-01

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

Experimental Simulation of Active Control With On-line System Identification on Sound Transmission Through an Elastic Plate

NASA Technical Reports Server (NTRS)

1998-01-01

An adaptive control algorithm with on-line system identification capability has been developed. One of the great advantages of this scheme is that an additional system identification mechanism such as an additional uncorrelated random signal generator as the source of system identification is not required. A time-varying plate-cavity system is used to demonstrate the control performance of this algorithm. The time-varying system consists of a stainless-steel plate which is bolted down on a rigid cavity opening where the cavity depth was changed with respect to time. For a given externally located harmonic sound excitation, the system identification and the control are simultaneously executed to minimize the transmitted sound in the cavity. The control performance of the algorithm is examined for two cases. First, all the water was drained, the external disturbance frequency is swept with 1 Hz/sec. The result shows an excellent frequency tracking capability with cavity internal sound suppression of 40 dB. For the second case, the water level is initially empty and then raised to 3/20 full in 60 seconds while the external sound excitation is fixed with a frequency. Hence, the cavity resonant frequency decreases and passes the external sound excitation frequency. The algorithm shows 40 dB transmitted noise suppression without compromising the system identification tracking capability.

Identification and Classification of Orthogonal Frequency Division Multiple Access (OFDMA) Signals Used in Next Generation Wireless Systems

DTIC Science & Technology

2012-03-01

advanced antenna systems AMC adaptive modulation and coding AWGN additive white Gaussian noise BPSK binary phase shift keying BS base station BTC ...QAM-16, and QAM-64, and coding types include convolutional coding (CC), convolutional turbo coding (CTC), block turbo coding ( BTC ), zero-terminating

Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning

ERIC Educational Resources Information Center

Bouchet, Francois; Azevedo, Roger; Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students…

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.

Cloud-based adaptive exon prediction for DNA analysis

PubMed Central

Putluri, Srinivasareddy; Fathima, Shaik Yasmeen

2018-01-01

Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries. DNA sequencing storage costs will be minimised by use of cloud service. In this study, the authors put forward a novel genomic informatics system using Amazon Cloud Services, where genomic sequence information is stored and accessed for processing. True identification of exon regions in a DNA sequence is a key task in bioinformatics, which helps in disease identification and design drugs. Three base periodicity property of exons forms the basis of all exon identification techniques. Adaptive signal processing techniques found to be promising in comparison with several other methods. Several adaptive exon predictors (AEPs) are developed using variable normalised least mean square and its maximum normalised variants to reduce computational complexity. Finally, performance evaluation of various AEPs is done based on measures such as sensitivity, specificity and precision using various standard genomic datasets taken from National Center for Biotechnology Information genomic sequence database. PMID:29515813

Total Integrated Library Information System. Part 2. A Report on the Specific Design Phase: Identification and Evaluation.

ERIC Educational Resources Information Center

Meyer, R. W.; Alexander, George

This study, conducted to determine which automated system would be the most appropriate to replicate or install at Clemson University to support the users of the library, screened 29 library automation systems to determine those most adaptable to Clemson's needs. In-depth comparisons were made with regard to functions available, features, start up…

Aerodynamic parameter estimation via Fourier modulating function techniques

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1995-01-01

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.

Comparative system identification of flower tracking performance in three hawkmoth species reveals adaptations for dim light vision.

PubMed

Stöckl, Anna L; Kihlström, Klara; Chandler, Steven; Sponberg, Simon

2017-04-05

Flight control in insects is heavily dependent on vision. Thus, in dim light, the decreased reliability of visual signal detection also prompts consequences for insect flight. We have an emerging understanding of the neural mechanisms that different species employ to adapt the visual system to low light. However, much less explored are comparative analyses of how low light affects the flight behaviour of insect species, and the corresponding links between physiological adaptations and behaviour. We investigated whether the flower tracking behaviour of three hawkmoth species with different diel activity patterns revealed luminance-dependent adaptations, using a system identification approach. We found clear luminance-dependent differences in flower tracking in all three species, which were explained by a simple luminance-dependent delay model, which generalized across species. We discuss physiological and anatomical explanations for the variance in tracking responses, which could not be explained by such simple models. Differences between species could not be explained by the simple delay model. However, in several cases, they could be explained through the addition on a second model parameter, a simple scaling term, that captures the responsiveness of each species to flower movements. Thus, we demonstrate here that much of the variance in the luminance-dependent flower tracking responses of hawkmoths with different diel activity patterns can be captured by simple models of neural processing.This article is part of the themed issue 'Vision in dim light'. © 2017 The Author(s).

Application of dynamic recurrent neural networks in nonlinear system identification

NASA Astrophysics Data System (ADS)

Du, Yun; Wu, Xueli; Sun, Huiqin; Zhang, Suying; Tian, Qiang

2006-11-01

An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.

IECON '87: Industrial applications of control and simulation; Proceedings of the 1987 International Conference on Industrial Electronics, Control, and Instrumentation, Cambridge, MA, Nov. 3, 4, 1987

NASA Technical Reports Server (NTRS)

Hartley, Tom T. (Editor)

1987-01-01

Recent advances in control-system design and simulation are discussed in reviews and reports. Among the topics considered are fast algorithms for generating near-optimal binary decision programs, trajectory control of robot manipulators with compensation of load effects via a six-axis force sensor, matrix integrators for real-time simulation, a high-level control language for an autonomous land vehicle, and a practical engineering design method for stable model-reference adaptive systems. Also addressed are the identification and control of flexible-limb robots with unknown loads, adaptive control and robust adaptive control for manipulators with feedforward compensation, adaptive pole-placement controllers with predictive action, variable-structure strategies for motion control, and digital signal-processor-based variable-structure controls.

Development and preliminary testing of an automatic turning movements identification system : final report, February 2010.

DOT National Transportation Integrated Search

2010-02-01

It is important for many applications, such as intersection delay estimation and adaptive signal : control, to obtain vehicle turning movement information at signalized intersections. However, : vehicle turning movement information is very time consu...

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.

Parameter Estimation for a Hybrid Adaptive Flight Controller

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

Boundedness of the solutions for certain classes of fractional differential equations with application to adaptive systems.

PubMed

Aguila-Camacho, Norelys; Duarte-Mermoud, Manuel A

2016-01-01

This paper presents the analysis of three classes of fractional differential equations appearing in the field of fractional adaptive systems, for the case when the fractional order is in the interval α ∈(0,1] and the Caputo definition for fractional derivatives is used. The boundedness of the solutions is proved for all three cases, and the convergence to zero of the mean value of one of the variables is also proved. Applications of the obtained results to fractional adaptive schemes in the context of identification and control problems are presented at the end of the paper, including numerical simulations which support the analytical results. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

On Complete Control and Synchronization of Zhang Chaotic System with Uncertain Parameters using Adaptive Control Method

NASA Astrophysics Data System (ADS)

Tirandaz, Hamed

2018-03-01

Chaos control and synchronization of chaotic systems is seemingly a challenging problem and has got a lot of attention in recent years due to its numerous applications in science and industry. This paper concentrates on the control and synchronization problem of the three-dimensional (3D) Zhang chaotic system. At first, an adaptive control law and a parameter estimation law are achieved for controlling the behavior of the Zhang chaotic system. Then, non-identical synchronization of Zhang chaotic system is provided with considering the Lü chaotic system as the follower system. The synchronization problem and parameters identification are achieved by introducing an adaptive control law and a parameters estimation law. Stability analysis of the proposed method is proved by the Lyapanov stability theorem. In addition, the convergence of the estimated parameters to their truly unknown values are evaluated. Finally, some numerical simulations are carried out to illustrate and to validate the effectiveness of the suggested method.

Adaptive dynamic programming approach to experience-based systems identification and control.

PubMed

Lendaris, George G

2009-01-01

Humans have the ability to make use of experience while selecting their control actions for distinct and changing situations, and their process speeds up and have enhanced effectiveness as more experience is gained. In contrast, current technological implementations slow down as more knowledge is stored. A novel way of employing Approximate (or Adaptive) Dynamic Programming (ADP) is described that shifts the underlying Adaptive Critic type of Reinforcement Learning method "up a level", away from designing individual (optimal) controllers to that of developing on-line algorithms that efficiently and effectively select designs from a repository of existing controller solutions (perhaps previously developed via application of ADP methods). The resulting approach is called Higher-Level Learning Algorithm. The approach and its rationale are described and some examples of its application are given. The notions of context and context discernment are important to understanding the human abilities noted above. These are first defined, in a manner appropriate to controls and system-identification, and as a foundation relating to the application arena, a historical view of the various phases during development of the controls field is given, organized by how the notion 'context' was, or was not, involved in each phase.

Teacher Report versus Adaptive Behavior Scale in Assessment of Mental Retardation.

ERIC Educational Resources Information Center

Al-Ansari, Ahmed

1993-01-01

This study assessed the degree of agreement between teacher report and an adapted Adaptive Behavior Scale in the identification of mental retardation and associated learning difficulties in 257 young Bahraini school children. Findings indicated that the instrument is sensitive in identification of children with mental retardation and exhibits high…

A Mixture Rasch Model-Based Computerized Adaptive Test for Latent Class Identification

ERIC Educational Resources Information Center

Jiao, Hong; Macready, George; Liu, Junhui; Cho, Youngmi

2012-01-01

This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…

Technology of Synergy Manifestation in the Research of Solution's Stability of Differential Equations System

ERIC Educational Resources Information Center

Dvoryatkina, Svetlana N.; Melnikov, Roman A. M.; Smirnov, Eugeny I.

2017-01-01

Effectiveness of mathematical education as non-linear, composite and open system, formation and development of cognitive abilities of the trainee are wholly defined in the solution of complex tasks by means of modern achievements in science to high school practice adaptation. The possibility of complex tasks solution arises at identification of…

Linear and non-linear systems identification for adaptive control in mechanical applications vibration suppression

NASA Astrophysics Data System (ADS)

Cazzulani, Gabriele; Resta, Ferruccio; Ripamonti, Francesco

2012-04-01

During the last years, more and more mechanical applications saw the introduction of active control strategies. In particular, the need of improving the performances and/or the system health is very often associated to vibration suppression. This goal can be achieved considering both passive and active solutions. In this sense, many active control strategies have been developed, such as the Independent Modal Space Control (IMSC) or the resonant controllers (PPF, IRC, . . .). In all these cases, in order to tune and optimize the control strategy, the knowledge of the system dynamic behaviour is very important and it can be achieved both considering a numerical model of the system or through an experimental identification process. Anyway, dealing with non-linear or time-varying systems, a tool able to online identify the system parameters becomes a key-point for the control logic synthesis. The aim of the present work is the definition of a real-time technique, based on ARMAX models, that estimates the system parameters starting from the measurements of piezoelectric sensors. These parameters are returned to the control logic, that automatically adapts itself to the system dynamics. The problem is numerically investigated considering a carbon-fiber plate model forced through a piezoelectric patch.

Road map to adaptive optimal control. [jet engine control

NASA Technical Reports Server (NTRS)

Boyer, R.

1980-01-01

A building block control structure leading toward adaptive, optimal control for jet engines is developed. This approach simplifies the addition of new features and allows for easier checkout of the control by providing a baseline system for comparison. Also, it is possible to eliminate certain features that do not have payoff by being selective in the addition of new building blocks to be added to the baseline system. The minimum risk approach specifically addresses the need for active identification of the plant to be controlled in real time and real time optimization of the control for the identified plant.

An online ID identification system for liquefied-gas cylinder plant

NASA Astrophysics Data System (ADS)

He, Jin; Ding, Zhenwen; Han, Lei; Zhang, Hao

2017-11-01

An automatic ID identification system for gas cylinders' online production was developed based on the production conditions and requirements of the Technical Committee for Standardization of Gas Cylinders. A cylinder ID image acquisition system was designed to improve the image contrast of ID regions on gas cylinders against the background. Then the ID digits region was located by the CNN template matching algorithm. Following that, an adaptive threshold method based on the analysis of local average grey value and standard deviation was proposed to overcome defects of non-uniform background in the segmentation results. To improve the single digit identification accuracy, two BP neural networks were trained respectively for the identification of all digits and the easily confusable digits. If the single digit was classified as one of confusable digits by the former BP neural network, it was further tested by the later one, and the later result was taken as the final identification result of this single digit. At last, the majority voting was adopted to decide the final identification result for the 6-digit cylinder ID. The developed system was installed on a production line of a liquefied-petroleum-gas cylinder plant and worked in parallel with the existing weighing step on the line. Through the field test, the correct identification rate for single ID digit was 94.73%, and none of the tested 2000 cylinder ID was misclassified through the majority voting.

A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke.

PubMed

Roy, S; Cheng, M; Chang, S; Moore, J; De Luca, G; Nawab, S; De Luca, C

2014-04-23

Remote monitoring of physical activity using bodyworn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data were recorded from 10 hemi paretic patients while they carried out a sequence of 11 activities of daily living (Identification tasks), and 10 activities used to evaluate misclassification errors (non-Identification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the non-Identification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of 4 ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0 %, and a mean specificity of 99.7 % for the identification tasks, and a mean misclassification error of < 10% for the non-Identification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

Duong, N.; Johnson, G. R.; Winn, C. B.

1974-01-01

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Nonlinear neural control with power systems applications

NASA Astrophysics Data System (ADS)

Chen, Dingguo

1998-12-01

Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal cases. The adaptive hierarchical neural control scheme is also proposed to deal with the time-varying nature of loads. Further, adaptive neural control, which is based on the on-line updating of the weights and biases of the neural networks, is studied. Simulations provided on the faulted power systems with unknown loads suggest that the proposed adaptive hierarchical neural control schemes should be useful for practical power applications.

Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

PubMed

Hu, Jin; Zeng, Chunna

2017-02-01

The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Design of adaptive control systems by means of self-adjusting transversal filters

NASA Technical Reports Server (NTRS)

Merhav, S. J.

1986-01-01

The design of closed-loop adaptive control systems based on nonparametric identification was addressed. Implementation is by self-adjusting Least Mean Square (LMS) transversal filters. The design concept is Model Reference Adaptive Control (MRAC). Major issues are to preserve the linearity of the error equations of each LMS filter, and to prevent estimation bias that is due to process or measurement noise, thus providing necessary conditions for the convergence and stability of the control system. The controlled element is assumed to be asymptotically stable and minimum phase. Because of the nonparametric Finite Impulse Response (FIR) estimates provided by the LMS filters, a-priori information on the plant model is needed only in broad terms. Following a survey of control system configurations and filter design considerations, system implementation is shown here in Single Input Single Output (SISO) format which is readily extendable to multivariable forms. In extensive computer simulation studies the controlled element is represented by a second-order system with widely varying damping, natural frequency, and relative degree.

Agricultural produce grading and sorting system using color CCD and new color identification algorithm

NASA Astrophysics Data System (ADS)

Wang, Dongsheng; Zou, Jizuo; Yang, Yunping; Dong, Jianhua; Zhang, Yuanxiang

1996-10-01

A high-speed automatic agricultural produce grading and sorting system using color CCD and new color identification algorithm has been developed. In a typical application, the system can sort almonds into tow output grades according to their color. Almonds ar rich in 18 kinds of amino acids and 13 kinds of micro minerals and vitamins and can be made into almond drink. In order to ensure the drink quality, almonds must be sorted carefully before being made into a drink. Using this system, almonds can be sorted into two grades: up to grade and below grade almonds or foreign materials. A color CCD inspects the almonds passing on a conveyor of rotating rollers, a color identification algorithm grades almonds and distinguishes foreign materials from almonds. Employing an elaborately designed mechanism, the below grade almonds and foreign materials can be removed effectively from the raw almonds. This system can be easily adapted for inspecting and sorting other kinds of agricultural produce such as peanuts, beans tomatoes and so on.

Electronic warfare - The next 15 years

NASA Astrophysics Data System (ADS)

Quirk, T. G.

1985-07-01

On the basis of current trends, it is projected that the EW systems available by the year 2000, including avionics, will be distinguished by their compatibility with stealthy vehicular platforms, high adaptability to combat scenarios, vehicle-conformal containers, and multifunction characteristics. Transmitters and receivers will perhaps be contained within a single IC, and AI techniques may be able to yield such capabilities as instantaneous signal digitalization. Fusion of electronic units will allow a single system to accommodate navigation, identification, communications, countermeasures, and fire control functions. VHSIC and GaAs electronics appear to be the two most fundamental technological bases for the aforementioned developments. The adaptive response of these systems is noted to radically depend on the pace of software development.

A Novel Approach for Adaptive Signal Processing

NASA Technical Reports Server (NTRS)

Chen, Ya-Chin; Juang, Jer-Nan

1998-01-01

Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method.

Novel Concept for Flexible and Resilient Large Power Transformers

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

Upadhyay, Parag; Englebretson, Steven; Ramanan, V. R. R.

This feasibility study investigates a flexible and adaptable LPT design solution which can facilitate long-term replacement in the event of both catastrophic failures as well as scheduled replacements, thereby increasing grid resilience. The scope of this project has been defined based on an initial system study and identification of the transformer requirements from an overall system load flow perspective.

Unbalance vibration suppression for AMBs system using adaptive notch filter

NASA Astrophysics Data System (ADS)

Chen, Qi; Liu, Gang; Han, Bangcheng

2017-09-01

The unbalance of rotor levitated by active magnetic bearings (AMBs) will cause synchronous vibration which greatly degrade the performance at high speeds in the rotating machinery. To suppress the unbalance vibration without angular velocity information, a novel modified adaptive notch filter (ANF) with phase shift in the AMBs system is presented in this study. Firstly, a 4-degree-of-freedom (DOF) radial unbalanced AMB rotor system is described and analyzed, and the solution of rotor vibration displacement is compared with the experimental data to verify the preciseness of the dynamic model. Then the principle and structure of the proposed notch filter used for the frequency estimation and online identification of synchronous component are presented. As well, the convergence property of the algorithm is investigated. In addition, the stability analysis of the closed-loop AMB system with the proposed ANF is conducted. Simulation and experiments on an AMB driveline system demonstrate the effectiveness and the adaptive characteristics of the proposed ANF on the elimination of synchronous controlled current in a widely operating speed range.

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.

PubMed

Deshpande, Sunil; Rivera, Daniel E; Younger, Jarred W; Nandola, Naresh N

2014-09-01

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

REVIEW: Internal models in sensorimotor integration: perspectives from adaptive control theory

NASA Astrophysics Data System (ADS)

Tin, Chung; Poon, Chi-Sang

2005-09-01

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

PubMed

Kazemi, Mahdi; Arefi, Mohammad Mehdi

2017-03-01

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Methods for optimizing solutions when considering group arguments by team of experts

NASA Astrophysics Data System (ADS)

Chernyi, Sergei; Budnik, Vlad

2017-11-01

The article is devoted to methods of expert evaluation. The technology of expert evaluation is presented from the standpoint of precedent structures. In this paper, an aspect of the mathematical basis for constructing a component of decision analysis is considered. In fact, this approach leaves out any identification of their knowledge and skills of simulating organizational and manufacturing situations and taking efficient managerial decisions; it doesn't enable any identification and assessment of their knowledge on the basis of multi-informational and least loss-making methods and information technologies. Hence the problem is to research and develop a methodology for systemic identification of professional problem-focused knowledge acquired by employees operating adaptive automated systems of training management (AASTM operators), which shall also further the theory and practice of the intelligence-related aspects thereof.

Wiener-Hammerstein system identification - an evolutionary approach

NASA Astrophysics Data System (ADS)

Naitali, Abdessamad; Giri, Fouad

2016-01-01

The problem of identifying parametric Wiener-Hammerstein (WH) systems is addressed within the evolutionary optimisation context. Specifically, a hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation. The method enjoys three interesting features: (1) the risk of premature convergence of model parameter estimates to local optima is significantly reduced, due to the constantly maintained diversity of model candidates; (2) no prior knowledge is needed except for upper bounds on the system structure indices; (3) the method is fully autonomous as no interaction is needed with the user during the optimum search process. The performances of the proposed method will be illustrated and compared to alternative methods using a well-established WH benchmark.

On Restructurable Control System Theory

NASA Technical Reports Server (NTRS)

Athans, M.

1983-01-01

The state of stochastic system and control theory as it impacts restructurable control issues is addressed. The multivariable characteristics of the control problem are addressed. The failure detection/identification problem is discussed as a multi-hypothesis testing problem. Control strategy reconfiguration, static multivariable controls, static failure hypothesis testing, dynamic multivariable controls, fault-tolerant control theory, dynamic hypothesis testing, generalized likelihood ratio (GLR) methods, and adaptive control are discussed.

Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis

NASA Astrophysics Data System (ADS)

Pan, Jun; Chen, Jinglong; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

2016-12-01

It is significant to perform condition monitoring and fault diagnosis on rolling mills in steel-making plant to ensure economic benefit. However, timely fault identification of key parts in a complicated industrial system under operating condition is still a challenging task since acquired condition signals are usually multi-modulated and inevitably mixed with strong noise. Therefore, a new data-driven mono-component identification method is proposed in this paper for diagnostic purpose. First, the modified nonlocal means algorithm (NLmeans) is proposed to reduce noise in vibration signals without destroying its original Fourier spectrum structure. During the modified NLmeans, two modifications are investigated and performed to improve denoising effect. Then, the modified empirical wavelet transform (MEWT) is applied on the de-noised signal to adaptively extract empirical mono-component modes. Finally, the modes are analyzed for mechanical fault identification based on Hilbert transform. The results show that the proposed data-driven method owns superior performance during system operation compared with the MEWT method.

Traceability in stem cell research: from participant sample to induced pluripotent stem cell and back.

PubMed

Morrison, Michael; Moraia, Linda Briceño; Steele, Jane C

2016-01-01

This paper describes a traceability system developed for the Stem cells for Biological Assays of Novel drugs and prediCtive toxiCology consortium. The system combines records and labels that to biological material across geographical locations and scientific processes from sample donation to induced pluripotent stem cell line. The labeling system uses a unique identification number to link every aliquot of sample at every stage of the reprogramming pathway back to the original donor. Only staff at the clinical recruitment site can reconnect the unique identification number to the identifying details of a specific donor. This ensures the system meets ethical and legal requirements for protecting privacy while allowing full traceability of biological material. The system can be adapted to other projects and for use with different primary sample types.

Planning assistance for the NASA 30/20 GHz program. Network control architecture study.

NASA Technical Reports Server (NTRS)

Inukai, T.; Bonnelycke, B.; Strickland, S.

1982-01-01

Network Control Architecture for a 30/20 GHz flight experiment system operating in the Time Division Multiple Access (TDMA) was studied. Architecture development, identification of processing functions, and performance requirements for the Master Control Station (MCS), diversity trunking stations, and Customer Premises Service (CPS) stations are covered. Preliminary hardware and software processing requirements as well as budgetary cost estimates for the network control system are given. For the trunking system control, areas covered include on board SS-TDMA switch organization, frame structure, acquisition and synchronization, channel assignment, fade detection and adaptive power control, on board oscillator control, and terrestrial network timing. For the CPS control, they include on board processing and adaptive forward error correction control.

Adaptive convex combination approach for the identification of improper quaternion processes.

PubMed

Ujang, Bukhari Che; Jahanchahi, Cyrus; Took, Clive Cheong; Mandic, Danilo P

2014-01-01

Data-adaptive optimal modeling and identification of real-world vector sensor data is provided by combining the fractional tap-length (FT) approach with model order selection in the quaternion domain. To account rigorously for the generality of such processes, both second-order circular (proper) and noncircular (improper), the proposed approach in this paper combines the FT length optimization with both the strictly linear quaternion least mean square (QLMS) and widely linear QLMS (WL-QLMS). A collaborative approach based on QLMS and WL-QLMS is shown to both identify the type of processes (proper or improper) and to track their optimal parameters in real time. Analysis shows that monitoring the evolution of the convex mixing parameter within the collaborative approach allows us to track the improperness in real time. Further insight into the properties of those algorithms is provided by establishing a relationship between the steady-state error and optimal model order. The approach is supported by simulations on model order selection and identification of both strictly linear and widely linear quaternion-valued systems, such as those routinely used in renewable energy (wind) and human-centered computing (biomechanics).

Adaptive Modal Identification for Flutter Suppression Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Drew, Michael; Swei, Sean S.

2016-01-01

In this paper, we will develop an adaptive modal identification method for identifying the frequencies and damping of a flutter mode based on model-reference adaptive control (MRAC) and least-squares methods. The least-squares parameter estimation will achieve parameter convergence in the presence of persistent excitation whereas the MRAC parameter estimation does not guarantee parameter convergence. Two adaptive flutter suppression control approaches are developed: one based on MRAC and the other based on the least-squares method. The MRAC flutter suppression control is designed as an integral part of the parameter estimation where the feedback signal is used to estimate the modal information. On the other hand, the separation principle of control and estimation is applied to the least-squares method. The least-squares modal identification is used to perform parameter estimation.

Design and Experimental Evaluation of a Robust Position Controller for an Electrohydrostatic Actuator Using Adaptive Antiwindup Sliding Mode Scheme

PubMed Central

Lee, Ji Min; Park, Sung Hwan; Kim, Jong Shik

2013-01-01

A robust control scheme is proposed for the position control of the electrohydrostatic actuator (EHA) when considering hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. To reduce overshoot due to a saturation of electric motor and to realize robustness against load disturbance and lumped system uncertainties such as varying parameters and modeling error, this paper proposes an adaptive antiwindup PID sliding mode scheme as a robust position controller for the EHA system. An optimal PID controller and an optimal anti-windup PID controller are also designed to compare control performance. An EHA prototype is developed, carrying out system modeling and parameter identification in designing the position controller. The simply identified linear model serves as the basis for the design of the position controllers, while the robustness of the control systems is compared by experiments. The adaptive anti-windup PID sliding mode controller has been found to have the desired performance and become robust against hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. PMID:23983640

Adaptive estimation of nonlinear parameters of a nonholonomic spherical robot using a modified fuzzy-based speed gradient algorithm

NASA Astrophysics Data System (ADS)

Roozegar, Mehdi; Mahjoob, Mohammad J.; Ayati, Moosa

2017-05-01

This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton-Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.

Supporting child witnesses during identification lineups: Exploring the effectiveness of registered intermediaries.

PubMed

Wilcock, Rachel; Crane, Laura; Hobson, Zoe; Nash, Gilly; Kirke-Smith, Mimi; Henry, Lucy A

2018-01-01

Performance at identification lineup was assessed in eighty-five 6- to 11-year-old typically developing children. Children viewed a live staged event involving 2 male actors, and were asked to identify the perpetrators from 2 separate lineups (one perpetrator-present lineup and one perpetrator-absent lineup). Half the children took part in lineups adapted by a registered intermediary (an impartial, trained professional who facilitates understanding and communication between vulnerable witnesses and members of the justice system), and half took part in "best-practice" lineups, according to the current guidance for eyewitness identification in England and Wales. Children receiving assistance from a registered intermediary (relative to children who received best-practice lineups) were more accurate in their identifications for perpetrator-present lineups, and there was some evidence that they were also more accurate for perpetrator-absent lineups. This provides the first empirical evidence for the effectiveness of registered intermediary support during identification lineups.

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

NASA Astrophysics Data System (ADS)

Al Azzawi, Dia

Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope. Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope. This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems. Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study. The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight simulator. The abnormal conditions considered in this work include locked actuators (stabilator, aileron, rudder, and throttle), structural damage of the wing, horizontal tail, and vertical tail, malfunctioning sensors, and reduced engine effectiveness. The results of applying the proposed approach to this wide range of abnormal conditions show its high capability in detecting the abnormal conditions with zero false alarms and very high detection rates, correctly identifying the failed subsystem and evaluating the type and severity of the failure. The results also reveal that the post-failure flight envelope can be reasonably predicted within this framework.

A Survey of School Psychologists' Practices for Identifying Mentally Retarded Students.

ERIC Educational Resources Information Center

Wodrich, David L.; Barry, Christine T.

1991-01-01

Surveyed school psychologists regarding identification of mentally retarded students. The Wechsler scales were the most frequently used tests for deriving intelligence quotient scores, which together with adaptive behavior scale scores were rated as most influential in identification-placement decisions. The Vineland Adaptive Behavior Scales were…

The Relation between Intelligence and Adaptive Behavior: A Meta-Analysis

ERIC Educational Resources Information Center

Alexander, Ryan M.

2017-01-01

Intelligence tests and adaptive behavior scales measure vital aspects of the multidimensional nature of human functioning. Assessment of each is a required component in the diagnosis or identification of intellectual disability, and both are frequently used conjointly in the assessment and identification of other developmental disabilities. The…

[The present and future state of minimized extracorporeal circulation].

PubMed

Meng, Fan; Yang, Ming

2013-05-01

Minimized extracorporeal circulation improved in the postoperative side effects of conventional extracorporeal circulation is a kind of new extracorporeal circulation. This paper introduces the principle, characteristics, applications and related research of minimized extracorporeal circulation. For the problems of systemic inflammatory response syndrome and limited assist time, the article proposes three development direction including system miniaturization and integration, pulsatile blood pump and the adaptive control by human parameter identification.

Automated smoother for the numerical decoupling of dynamics models.

PubMed

Vilela, Marco; Borges, Carlos C H; Vinga, Susana; Vasconcelos, Ana Tereza R; Santos, Helena; Voit, Eberhard O; Almeida, Jonas S

2007-08-21

Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.

Performance assessment of MEMS adaptive optics in tactical airborne systems

NASA Astrophysics Data System (ADS)

Tyson, Robert K.

1999-09-01

Tactical airborne electro-optical systems are severely constrained by weight, volume, power, and cost. Micro- electrical-mechanical adaptive optics provide a solution that addresses the engineering realities without compromising spatial and temporal compensation requirements. Through modeling and analysis, we determined that substantial benefits could be gained for laser designators, ladar, countermeasures, and missile seekers. The developments potential exists for improving seeker imagery resolution 20 percent, extending countermeasures keep-out range by a factor of 5, doubling the range for ladar detection and identification, and compensating for supersonic and hypersonic aircraft boundary layers. Innovative concepts are required for atmospheric pat hand boundary layer compensation. We have developed design that perform these tasks using high speed scene-based wavefront sensing, IR aerosol laser guide stars, and extended-object wavefront beacons. We have developed a number of adaptive optics system configurations that met the spatial resolution requirements and we have determined that sensing and signal processing requirements can be met. With the help of micromachined deformable mirrors and sensor, we will be able to integrate the systems into existing airborne pods and missiles as well as next generation electro-optical systems.

Optimal sensor placement for time-domain identification using a wavelet-based genetic algorithm

NASA Astrophysics Data System (ADS)

Mahdavi, Seyed Hossein; Razak, Hashim Abdul

2016-06-01

This paper presents a wavelet-based genetic algorithm strategy for optimal sensor placement (OSP) effective for time-domain structural identification. Initially, the GA-based fitness evaluation is significantly improved by using adaptive wavelet functions. Later, a multi-species decimal GA coding system is modified to be suitable for an efficient search around the local optima. In this regard, a local operation of mutation is introduced in addition with regeneration and reintroduction operators. It is concluded that different characteristics of applied force influence the features of structural responses, and therefore the accuracy of time-domain structural identification is directly affected. Thus, the reliable OSP strategy prior to the time-domain identification will be achieved by those methods dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the force effects. The numerical and experimental verification on the effectiveness of the proposed strategy demonstrates the considerably high computational performance of the proposed OSP strategy, in terms of computational cost and the accuracy of identification. It is deduced that the robustness of the proposed OSP algorithm lies in the precise and fast fitness evaluation at larger sampling rates which result in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution.

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

Identification of Modules in Protein-Protein Interaction Networks

NASA Astrophysics Data System (ADS)

Erten, Sinan; Koyutürk, Mehmet

In biological systems, most processes are carried out through orchestration of multiple interacting molecules. These interactions are often abstracted using network models. A key feature of cellular networks is their modularity, which contributes significantly to the robustness, as well as adaptability of biological systems. Therefore, modularization of cellular networks is likely to be useful in obtaining insights into the working principles of cellular systems, as well as building tractable models of cellular organization and dynamics. A common, high-throughput source of data on molecular interactions is in the form of physical interactions between proteins, which are organized into protein-protein interaction (PPI) networks. This chapter provides an overview on identification and analysis of functional modules in PPI networks, which has been an active area of research in the last decade.

A knowledge-based approach to identification and adaptation in dynamical systems control

NASA Technical Reports Server (NTRS)

Glass, B. J.; Wong, C. M.

1988-01-01

Artificial intelligence techniques are applied to the problems of model form and parameter identification of large-scale dynamic systems. The object-oriented knowledge representation is discussed in the context of causal modeling and qualitative reasoning. Structured sets of rules are used for implementing qualitative component simulations, for catching qualitative discrepancies and quantitative bound violations, and for making reconfiguration and control decisions that affect the physical system. These decisions are executed by backward-chaining through a knowledge base of control action tasks. This approach was implemented for two examples: a triple quadrupole mass spectrometer and a two-phase thermal testbed. Results of tests with both of these systems demonstrate that the software replicates some or most of the functionality of a human operator, thereby reducing the need for a human-in-the-loop in the lower levels of control of these complex systems.

Utilization of volume correlation filters for underwater mine identification in LIDAR imagery

NASA Astrophysics Data System (ADS)

Walls, Bradley

2008-04-01

Underwater mine identification persists as a critical technology pursued aggressively by the Navy for fleet protection. As such, new and improved techniques must continue to be developed in order to provide measurable increases in mine identification performance and noticeable reductions in false alarm rates. In this paper we show how recent advances in the Volume Correlation Filter (VCF) developed for ground based LIDAR systems can be adapted to identify targets in underwater LIDAR imagery. Current automated target recognition (ATR) algorithms for underwater mine identification employ spatial based three-dimensional (3D) shape fitting of models to LIDAR data to identify common mine shapes consisting of the box, cylinder, hemisphere, truncated cone, wedge, and annulus. VCFs provide a promising alternative to these spatial techniques by correlating 3D models against the 3D rendered LIDAR data.

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.

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

NASA Astrophysics Data System (ADS)

Huang, Yuan-Ruey; Kang, Yuan; Chu, Ming-Hui; Chang, Yeon-Pun

A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

Ubiquitous Wireless Smart Sensing and Control

NASA Technical Reports Server (NTRS)

Wagner, Raymond

2013-01-01

Need new technologies to reliably and safely have humans interact within sensored environments (integrated user interfaces, physical and cognitive augmentation, training, and human-systems integration tools). Areas of focus include: radio frequency identification (RFID), motion tracking, wireless communication, wearable computing, adaptive training and decision support systems, and tele-operations. The challenge is developing effective, low cost/mass/volume/power integrated monitoring systems to assess and control system, environmental, and operator health; and accurately determining and controlling the physical, chemical, and biological environments of the areas and associated environmental control systems.

Ubiquitous Wireless Smart Sensing and Control. Pumps and Pipes JSC: Uniquely Houston

NASA Technical Reports Server (NTRS)

Wagner, Raymond

2013-01-01

Need new technologies to reliably and safely have humans interact within sensored environments (integrated user interfaces, physical and cognitive augmentation, training, and human-systems integration tools).Areas of focus include: radio frequency identification (RFID), motion tracking, wireless communication, wearable computing, adaptive training and decision support systems, and tele-operations. The challenge is developing effective, low cost/mass/volume/power integrated monitoring systems to assess and control system, environmental, and operator health; and accurately determining and controlling the physical, chemical, and biological environments of the areas and associated environmental control systems.

Robust time and frequency domain estimation methods in adaptive control

NASA Technical Reports Server (NTRS)

Lamaire, Richard Orville

1987-01-01

A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.

Natural frequency identification of smart washer by using adaptive observer

NASA Astrophysics Data System (ADS)

Ito, Hitoshi; Okugawa, Masayuki

2014-04-01

Bolted joints are used in many machines/structures and some of them have been loosened during long time use, and unluckily these bolt loosening may cause a great accident of machines/structures system. These bolted joint, especially in important places, are main object of maintenance inspection. Maintenance inspection with human- involvement is desired to be improved owing to time-consuming, labor-intensive and high-cost. By remote and full automation monitoring of the bolt loosening, constantly monitoring of bolted joint is achieved. In order to detect loosening of bolted joints without human-involvement, applying a structural health monitoring technique and smart structures/materials concept is the key objective. In this study, a new method of bolt loosening detection by adopting a smart washer has been proposed, and the basic detection principle was discussed with numerical analysis about frequency equation of the system, was confirmed experimentally. The smart washer used in this study is in cantilever type with piezoelectric material, which adds the washer the self-sensing and actuation function. The principle used to detect the loosening of the bolts is a method of a bolt loosening detection noted that the natural frequency of a smart washer system is decreasing by the change of the bolt tightening axial tension. The feature of this proposed method is achieving to identify the natural frequency at current condition on demand by adopting the self-sensing and actuation function and system identification algorithm for varying the natural frequency depending the bolt tightening axial tension. A novel bolt loosening detection method by adopting adaptive observer is proposed in this paper. The numerical simulations are performed to verify the possibility of the adaptive observer-based loosening detection. Improvement of the detection accuracy for a bolt loosening is confirmed by adopting initial parameter and variable adaptive gain by numerical simulation.

Certainty Equivalence M-MRAC for Systems with Unmatched Uncertainties

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

The paper presents a certainty equivalence state feedback indirect adaptive control design method for the systems of any relative degree with unmatched uncertainties. The approach is based on the parameter identification (estimation) model, which is completely separated from the control design and is capable of producing parameter estimates as fast as the computing power allows without generating high frequency oscillations. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters.

Search-based model identification of smart-structure damage

NASA Technical Reports Server (NTRS)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

Identification of cascade water tanks using a PWARX model

NASA Astrophysics Data System (ADS)

Mattsson, Per; Zachariah, Dave; Stoica, Petre

2018-06-01

In this paper we consider the identification of a discrete-time nonlinear dynamical model for a cascade water tank process. The proposed method starts with a nominal linear dynamical model of the system, and proceeds to model its prediction errors using a model that is piecewise affine in the data. As data is observed, the nominal model is refined into a piecewise ARX model which can capture a wide range of nonlinearities, such as the saturation in the cascade tanks. The proposed method uses a likelihood-based methodology which adaptively penalizes model complexity and directly leads to a computationally efficient implementation.

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

An Experimental Framework for Generating Evolvable Chemical Systems in the Laboratory

NASA Astrophysics Data System (ADS)

Baum, David A.; Vetsigian, Kalin

2017-12-01

Most experimental work on the origin of life has focused on either characterizing the chemical synthesis of particular biochemicals and their precursors or on designing simple chemical systems that manifest life-like properties such as self-propagation or adaptive evolution. Here we propose a new class of experiments, analogous to artificial ecosystem selection, where we select for spontaneously forming self-propagating chemical assemblages in the lab and then seek evidence of a response to that selection as a key indicator that life-like chemical systems have arisen. Since surfaces and surface metabolism likely played an important role in the origin of life, a key experimental challenge is to find conditions that foster nucleation and spread of chemical consortia on surfaces. We propose high-throughput screening of a diverse set of conditions in order to identify combinations of "food," energy sources, and mineral surfaces that foster the emergence of surface-associated chemical consortia that are capable of adaptive evolution. Identification of such systems would greatly advance our understanding of the emergence of self-propagating entities and the onset of adaptive evolution during the origin of life.

Nonlinear and Digital Man-machine Control Systems Modeling

NASA Technical Reports Server (NTRS)

Mekel, R.

1972-01-01

An adaptive modeling technique is examined by which controllers can be synthesized to provide corrective dynamics to a human operator's mathematical model in closed loop control systems. The technique utilizes a class of Liapunov functions formulated for this purpose, Liapunov's stability criterion and a model-reference system configuration. The Liapunov function is formulated to posses variable characteristics to take into consideration the identification dynamics. The time derivative of the Liapunov function generate the identification and control laws for the mathematical model system. These laws permit the realization of a controller which updates the human operator's mathematical model parameters so that model and human operator produce the same response when subjected to the same stimulus. A very useful feature is the development of a digital computer program which is easily implemented and modified concurrent with experimentation. The program permits the modeling process to interact with the experimentation process in a mutually beneficial way.

Computer aided design of digital controller for radial active magnetic bearings

NASA Technical Reports Server (NTRS)

Cai, Zhong; Shen, Zupei; Zhang, Zuming; Zhao, Hongbin

1992-01-01

A five degree of freedom Active Magnetic Bearing (AMB) system is developed which is controlled by digital controllers. The model of the radial AMB system is linearized and the state equation is derived. Based on the state variables feedback theory, digital controllers are designed. The performance of the controllers are evaluated according to experimental results. The Computer Aided Design (CAD) method is used to design controllers for magnetic bearings. The controllers are implemented with a digital signal processing (DSP) system. The control algorithms are realized with real-time programs. It is very easy to change the controller by changing or modifying the programs. In order to identify the dynamic parameters of the controlled magnetic system, a special experiment was carried out. Also, the online Recursive Least Squares (RLS) parameter identification method is studied. It can be realized with the digital controllers. Online parameter identification is essential for the realization of an adaptive controller.

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.

Evaluating the Use of an Electronic Death Registration System for Mortality Surveillance During and After Hurricane Sandy: New York City, 2012

PubMed Central

Li, Wenhui; Madsen, Ann M.; Wong, Howard; Das, Tara; Betancourt, Flor M.; Nicaj, Leze; Stayton, Catherine; Matte, Thomas; Begier, Elizabeth M.

2015-01-01

Objectives. We evaluated the use of New York City’s (NYC’s) electronic death registration system (EDRS) to conduct mortality surveillance during and after Hurricane Sandy. Methods. We used Centers for Disease Control and Prevention guidelines for surveillance system evaluation to gather evidence on usefulness, flexibility, stability, timeliness, and quality. We assessed system components, interviewed NYC Health Department staff, and analyzed 2010 to 2012 death records. Results. Despite widespread disruptions, NYC’s EDRS was stable and collected timely mortality data that were adapted to provide storm surveillance with minimal additional resources. Direct-injury fatalities and trends in excess all-cause mortality were rapidly identified, providing useful information for response; however, the time and burden of establishing reports, adapting the system, and identifying indirect deaths limited surveillance. Conclusions. The NYC Health Department successfully adapted its EDRS for near real-time disaster-related mortality surveillance. Retrospective assessment of deaths, advanced methods for case identification and analysis, standardized reports, and system enhancements will further improve surveillance. Local, state, and federal partners would benefit from partnering with vital records to develop EDRSs for surveillance and to promote ongoing evaluation. PMID:26378834

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

PubMed

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

Occupational risk identification using hand-held or laptop computers.

PubMed

Naumanen, Paula; Savolainen, Heikki; Liesivuori, Jyrki

2008-01-01

This paper describes the Work Environment Profile (WEP) program and its use in risk identification by computer. It is installed into a hand-held computer or a laptop to be used in risk identification during work site visits. A 5-category system is used to describe the identified risks in 7 groups, i.e., accidents, biological and physical hazards, ergonomic and psychosocial load, chemicals, and information technology hazards. Each group contains several qualifying factors. These 5 categories are colour-coded at this stage to aid with visualization. Risk identification produces visual summary images the interpretation of which is facilitated by colours. The WEP program is a tool for risk assessment which is easy to learn and to use both by experts and nonprofessionals. It is especially well adapted to be used both in small and in larger enterprises. Considerable time is saved as no paper notes are needed.

Supporting child witnesses during identification lineups: Exploring the effectiveness of registered intermediaries

PubMed Central

Crane, Laura; Hobson, Zoe; Nash, Gilly; Kirke‐Smith, Mimi; Henry, Lucy A.

2018-01-01

Summary Performance at identification lineup was assessed in eighty‐five 6‐ to 11‐year‐old typically developing children. Children viewed a live staged event involving 2 male actors, and were asked to identify the perpetrators from 2 separate lineups (one perpetrator‐present lineup and one perpetrator‐absent lineup). Half the children took part in lineups adapted by a registered intermediary (an impartial, trained professional who facilitates understanding and communication between vulnerable witnesses and members of the justice system), and half took part in “best‐practice” lineups, according to the current guidance for eyewitness identification in England and Wales. Children receiving assistance from a registered intermediary (relative to children who received best‐practice lineups) were more accurate in their identifications for perpetrator‐present lineups, and there was some evidence that they were also more accurate for perpetrator‐absent lineups. This provides the first empirical evidence for the effectiveness of registered intermediary support during identification lineups. PMID:29861545

Modeling, simulation and control for a cryogenic fluid management facility, preliminary report

NASA Technical Reports Server (NTRS)

Turner, Max A.; Vanbuskirk, P. D.

1986-01-01

The synthesis of a control system for a cryogenic fluid management facility was studied. The severe demand for reliability as well as instrumentation and control unique to the Space Station environment are prime considerations. Realizing that the effective control system depends heavily on quantitative description of the facility dynamics, a methodology for process identification and parameter estimation is postulated. A block diagram of the associated control system is also produced. Finally, an on-line adaptive control strategy is developed utilizing optimization of the velocity form control parameters (proportional gains, integration and derivative time constants) in appropriate difference equations for direct digital control. Of special concern are the communications, software and hardware supporting interaction between the ground and orbital systems. It is visualized that specialist in the OSI/ISO utilizing the Ada programming language will influence further development, testing and validation of the simplistic models presented here for adaptation to the actual flight environment.

Engaging stakeholders for adaptive management using structured decision analysis

USGS Publications Warehouse

Irwin, Elise R.; Kathryn, D.; Kennedy, Mickett

2009-01-01

Adaptive management is different from other types of management in that it includes all stakeholders (versus only policy makers) in the process, uses resource optimization techniques to evaluate competing objectives, and recognizes and attempts to reduce uncertainty inherent in natural resource systems. Management actions are negotiated by stakeholders, monitored results are compared to predictions of how the system should respond, and management strategies are adjusted in a “monitor-compare-adjust” iterative routine. Many adaptive management projects fail because of the lack of stakeholder identification, engagement, and continued involvement. Primary reasons for this vary but are usually related to either stakeholders not having ownership (or representation) in decision processes or disenfranchisement of stakeholders after adaptive management begins. We present an example in which stakeholders participated fully in adaptive management of a southeastern regulated river. Structured decision analysis was used to define management objectives and stakeholder values and to determine initial flow prescriptions. The process was transparent, and the visual nature of the modeling software allowed stakeholders to see how their interests and values were represented in the decision process. The development of a stakeholder governance structure and communication mechanism has been critical to the success of the project.

Real-time tracking control of electro-hydraulic force servo systems using offline feedback control and adaptive control.

PubMed

Shen, Gang; Zhu, Zhencai; Zhao, Jinsong; Zhu, Weidong; Tang, Yu; Li, Xiang

2017-03-01

This paper focuses on an application of an electro-hydraulic force tracking controller combined with an offline designed feedback controller (ODFC) and an online adaptive compensator in order to improve force tracking performance of an electro-hydraulic force servo system (EHFS). A proportional-integral controller has been employed and a parameter-based force closed-loop transfer function of the EHFS is identified by a continuous system identification algorithm. By taking the identified system model as a nominal plant model, an H ∞ offline design method is employed to establish an optimized feedback controller with consideration of the performance, control efforts, and robustness of the EHFS. In order to overcome the disadvantage of the offline designed controller and cope with the varying dynamics of the EHFS, an online adaptive compensator with a normalized least-mean-square algorithm is cascaded to the force closed-loop system of the EHFS compensated by the ODFC. Some comparative experiments are carried out on a real-time EHFS using an xPC rapid prototype technology, and the proposed controller yields a better force tracking performance improvement. Copyright © 2016. Published by Elsevier Ltd.

Effects of age and cognition on a cross-cultural paediatric adaptation of the Sniffin' Sticks Identification Test.

PubMed

Bastos, Laís Orrico Donnabella; Guerreiro, Marilisa Mantovani; Lees, Andrew John; Warner, Thomas T; Silveira-Moriyama, Laura

2015-01-01

To study the effects of age and cognition on the performance of children aged 3 to 18 years on a culturally adapted version of the 16 item smell identification test from Sniffin' Sticks (SS16). A series of pilots were conducted on 29 children aged 3 to 18 years old and 23 adults to produce an adapted version of the SS16 suitable for Brazilian children (SS16-Child). A final version was applied to 51 children alongside a picture identification test (PIT-SS16-Child) to access cognitive abilities involved in the smell identification task. In addition 20 adults performed the same tasks as a comparison group. The final adapted SS16-Child was applied to 51 children with a mean age of 9.9 years (range 3-18 years, SD=4.25 years), of which 68.3% were girls. There was an independent effect of age (p<0.05) and PIT-SS16-Child (p<0.001) on the performance on the SS16-Child, and older children reached the ceiling for scoring in the cognitive and olfactory test. Pre-school children had difficulties identifying items of the test. A cross-culturally adapted version of the SS16 can be used to test olfaction in children but interpretation of the results must take age and cognitive abilities into consideration.

Identification of non-technical skills from the resilience engineering perspective: a case study of an electricity distributor.

PubMed

Wachs, Priscila; Righi, Angela Weber; Saurin, Tarcisio Abreu

2012-01-01

Training in non-technical skills (NTS) does not usually question the design of the work system, and thus focuses narrowly on workers as the unit of analysis. This study discusses how the identification of NTS, a major step for developing an NTS training program, might be re-interpreted from the perspective of resilience engineering (RE). This discussion is based on a case study of identifying NTS for electricians who perform emergency maintenance activities in an electricity distribution power line. The results of the case study point out that four data analysis procedures might operationalize the RE perspective: (a) identifying factors that make the work difficult and which could be integrated into NTS training scenarios; (b) identifying recommendations for re-designing the system, in order either to reduce or eliminate the need for NTS; (c) classifying the NTS into pragmatic categories, which are meaningful for workers; and (d) regarding the identification of NTS as an opportunity to give visibility to adaptations carried out by workers.

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

PubMed

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

2016-11-01

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

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Identification and characterization of polyclonal αβ T cells with dendritic cell properties

PubMed Central

Kuka, Mirela; Munitic, Ivana; Ashwell, Jonathan D.

2012-01-01

An efficient immune response requires coordination between innate and adaptive immunity, which act through cells different in origin and function. Here we report the identification of thymus-derived αβ TCR+ cells that express CD11c and MHC class II, and require FLT3L for development (TDC). TDC express genes heretofore found uniquely in T cells or DC, as well as a distinctive signature of cytotoxicity-related genes. Unlike other innate T cell subsets, TDC have a polyclonal TCR repertoire andrespond to cognate antigens. However, they differ from conventional T cells in that they do not require help from antigen-presenting cells, respond to TLR-mediated stimulation by producing IL-12 and process and present antigen. The physiologic relevance of TDC, found in mice and humans, is still under investigation, but the fact that they combine key features of T and DC cells suggests that they provide a bridge between the innate and adaptive immune systems. PMID:23187623

Identification and role of regulatory non-coding RNAs in Listeria monocytogenes.

PubMed

Izar, Benjamin; Mraheil, Mobarak Abu; Hain, Torsten

2011-01-01

Bacterial regulatory non-coding RNAs control numerous mRNA targets that direct a plethora of biological processes, such as the adaption to environmental changes, growth and virulence. Recently developed high-throughput techniques, such as genomic tiling arrays and RNA-Seq have allowed investigating prokaryotic cis- and trans-acting regulatory RNAs, including sRNAs, asRNAs, untranslated regions (UTR) and riboswitches. As a result, we obtained a more comprehensive view on the complexity and plasticity of the prokaryotic genome biology. Listeria monocytogenes was utilized as a model system for intracellular pathogenic bacteria in several studies, which revealed the presence of about 180 regulatory RNAs in the listerial genome. A regulatory role of non-coding RNAs in survival, virulence and adaptation mechanisms of L. monocytogenes was confirmed in subsequent experiments, thus, providing insight into a multifaceted modulatory function of RNA/mRNA interference. In this review, we discuss the identification of regulatory RNAs by high-throughput techniques and in their functional role in L. monocytogenes.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting.

PubMed

Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-09-01

Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics.

Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

NASA Technical Reports Server (NTRS)

Innocenti, M.; Napolitano, M.

2003-01-01

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury.

PubMed

Benoussaad, Mourad; Poignet, Philippe; Hayashibe, Mitsuhiro; Azevedo-Coste, Christine; Fattal, Charles; Guiraud, David

2013-06-01

We investigated the parameter identification of a multi-scale physiological model of skeletal muscle, based on Huxley's formulation. We focused particularly on the knee joint controlled by quadriceps muscles under electrical stimulation (ES) in subjects with a complete spinal cord injury. A noninvasive and in vivo identification protocol was thus applied through surface stimulation in nine subjects and through neural stimulation in one ES-implanted subject. The identification protocol included initial identification steps, which are adaptations of existing identification techniques to estimate most of the parameters of our model. Then we applied an original and safer identification protocol in dynamic conditions, which required resolution of a nonlinear programming (NLP) problem to identify the serial element stiffness of quadriceps. Each identification step and cross validation of the estimated model in dynamic condition were evaluated through a quadratic error criterion. The results highlighted good accuracy, the efficiency of the identification protocol and the ability of the estimated model to predict the subject-specific behavior of the musculoskeletal system. From the comparison of parameter values between subjects, we discussed and explored the inter-subject variability of parameters in order to select parameters that have to be identified in each patient.

A combined sEMG and accelerometer system for monitoring functional activity in stroke.

PubMed

Roy, Serge H; Cheng, M Samuel; Chang, Shey-Sheen; Moore, John; De Luca, Gianluca; Nawab, S Hamid; De Luca, Carlo J

2009-12-01

Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of < 10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

PubMed Central

Ahmad, Noor Atinah

2014-01-01

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

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Visual Recognition Software for Binary Classification and its Application to Pollen Identification

NASA Astrophysics Data System (ADS)

Punyasena, S. W.; Tcheng, D. K.; Nayak, A.

2014-12-01

An underappreciated source of uncertainty in paleoecology is the uncertainty of palynological identifications. The confidence of any given identification is not regularly reported in published results, so cannot be incorporated into subsequent meta-analyses. Automated identifications systems potentially provide a means of objectively measuring the confidence of a given count or single identification, as well as a mechanism for increasing sample sizes and throughput. We developed the software ARLO (Automated Recognition with Layered Optimization) to tackle difficult visual classification problems such as pollen identification. ARLO applies pattern recognition and machine learning to the analysis of pollen images. The features that the system discovers are not the traditional features of pollen morphology. Instead, general purpose image features, such as pixel lines and grids of different dimensions, size, spacing, and resolution, are used. ARLO adapts to a given problem by searching for the most effective combination of feature representation and learning strategy. We present a two phase approach which uses our machine learning process to first segment pollen grains from the background and then classify pollen pixels and report species ratios. We conducted two separate experiments that utilized two distinct sets of algorithms and optimization procedures. The first analysis focused on reconstructing black and white spruce pollen ratios, training and testing our classification model at the slide level. This allowed us to directly compare our automated counts and expert counts to slides of known spruce ratios. Our second analysis focused on maximizing classification accuracy at the individual pollen grain level. Instead of predicting ratios of given slides, we predicted the species represented in a given image window. The resulting analysis was more scalable, as we were able to adapt the most efficient parts of the methodology from our first analysis. ARLO was able to distinguish between the pollen of black and white spruce with an accuracy of ~83.61%. This compared favorably to human expert performance. At the writing of this abstract, we are also experimenting with experimenting with the analysis of higher diversity samples, including modern tropical pollen material collected from ground pollen traps.

System Identification of X-33 Neural Network

NASA Technical Reports Server (NTRS)

Aggarwal, Shiv

2003-01-01

Modern flight control research has improved spacecraft survivability as its goal. To this end we need to have a failure detection system on board. In case the spacecraft is performing imperfectly, reconfiguration of control is needed. For that purpose we need to have parameter identification of spacecraft dynamics. Parameter identification of a system is called system identification. We treat the system as a black box which receives some inputs that lead to some outputs. The question is: what kind of parameters for a particular black box can correlate the observed inputs and outputs? Can these parameters help us to predict the outputs for a new given set of inputs? This is the basic problem of system identification. The X33 was supposed to have the onboard capability of evaluating the current performance and if needed to take the corrective measures to adapt to desired performance. The X33 is comprised of both rocket and aircraft vehicle design characteristics and requires, in general, analytical methods for evaluating its flight performance. Its flight consists of four phases: ascent, transition, entry and TAEM (Terminal Area Energy Management). It spends about 200 seconds in ascent phase, reaching an altitude of about 180,000 feet and a speed of about 10 to 15 Mach. During the transition phase which lasts only about 30 seconds, its altitude may increase to about 190,000 feet but its speed is reduced to about 9 Mach. At the beginning of this phase, the Main Engine is Cut Off (MECO) and the control is reconfigured with the help of aerosurfaces (four elevons, two flaps and two rudders) and reaction control system (RCS). The entry phase brings down the altitude of X33 to about 90,000 feet and its speed to about Mach 3. It spends about 250 seconds in this phase. Main engine is still cut off and the vehicle is controlled by complex maneuvers of aerosurfaces. The last phase TAEM lasts for about 450 seconds and the altitude and speed, both are reduced to zero. The present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.

A survey on the utility of the USEPA CADDIS stressor identification procedure.

PubMed

Harwood, John J; Stroud, Robert Adam

2012-06-01

The Environmental Protection Agency (EPA) has made available on the worldwide web a systematic stream stressor identification procedure, the "Causal Analysis/Diagnosis Decision Information System" or CADDIS. We report here the results of a survey of regulators and scientists in 11 states who use CADDIS or another stressor identification procedure in their work. The 13 survey questions address guidelines as to what impairment scenarios to approach with stressor identification, what information is needed to perform stressor identification, and what the stakeholder role is in performing stressor identification. At the time of this survey (the summer of 2009), the EPA CADDIS website was less commonly used among the state regulators surveyed than the published EPA stressor identification document on which it is based. The respondents generally find the EPA stressor identification procedure useful and capable of being adapted to their individual needs. Survey respondents all use stressor identification in their Total Maximum Daily Load work, but also in a wide variety of other applications. All the "types of evidence" included in the CADDIS stressor identification procedure are used by the practitioners surveyed with the exception of the results of ecological simulation models. While the CADDIS documentation encourages the involvement of stakeholders in stressor identification, most respondents do not assemble stakeholder teams of local officials and citizens to participate in stressor analyses.

Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

NASA Astrophysics Data System (ADS)

Chernick, Julian A.; Perlovsky, Leonid I.; Tye, David M.

1994-06-01

This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

Contextual and interdependent causes of climate change adaptation barriers: Insights from water management institutions in Himachal Pradesh, India.

PubMed

Azhoni, Adani; Holman, Ian; Jude, Simon

2017-01-15

Research on adaptation barriers is increasing as the need for climate change adaptation becomes evident. However, empirical studies regarding the emergence, causes and sustenance of adaptation barriers remain limited. This research identifies key contextual causes of adaptation barriers in water institutions in the mountainous Himalayan state of Himachal Pradesh in northern India. Semi-structured interviews were carried out with representatives from twenty-six key governmental, non-governmental, academic and research institutions in the State with responsibilities spanning domestic water supply, irrigation and hydropower generation, environmental monitoring and research. It identified low knowledge capacity and resources, policy implementation gaps, normative attitudes, and unavailability and inaccessibility of data and information compounded with weak interinstitutional networks as key adaptation barriers. Although these barriers are similar to those reported elsewhere, they have important locally-contextual root causes. For instance, inadequate resources result from fragmented resources allocation due to competing developmental priorities and the desire of the political leadership to please diverse electors, rather than climate scepticism. The identified individual barriers are found to be highly inter-dependent and closely intertwined which enables the identification of leverage points for interventions to maximise barrier removal. For instance, breaking down key barriers hindering accessibility to data and information, which are shaped by systemic bureaucracies and cultural attitudes, will involve attitudinal change through sensitisation to the importance of accurate and accessible data and information and the building trust between different actors, in addition to institutional structural changes through legislation and inter-institutional agreements. Approaching barriers as a system of contextually interconnected cultural, systemic, geographical and political underlying factors enriches the understanding of adaptation enablers, thereby contributing to achieving a better adapted society. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

NASA Astrophysics Data System (ADS)

Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

2011-01-01

This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

Neuromorphic learning of continuous-valued mappings in the presence of noise: Application to real-time adaptive control

NASA Technical Reports Server (NTRS)

Troudet, Terry; Merrill, Walter C.

1989-01-01

The ability of feed-forward neural net architectures to learn continuous-valued mappings in the presence of noise is demonstrated in relation to parameter identification and real-time adaptive control applications. Factors and parameters influencing the learning performance of such nets in the presence of noise are identified. Their effects are discussed through a computer simulation of the Back-Error-Propagation algorithm by taking the example of the cart-pole system controlled by a nonlinear control law. Adequate sampling of the state space is found to be essential for canceling the effect of the statistical fluctuations and allowing learning to take place.

The AcCell series 2000 as a support system for training and evaluation in educational and clinical settings.

PubMed

Greening, S E; Grohs, D H; Guidos, B J

1997-01-01

Providing effective training, retraining and evaluation programs, including proficiency testing programs, for cytoprofessionals is a challenge shared by many academic and clinical educators internationally. In cytopathology the quality of training has immediately transferable and critically important impacts on satisfactory performance in the clinical setting. Well-designed interactive computer-assisted instruction and testing programs have been shown to enhance initial learning and to reinforce factual and conceptual knowledge. Computer systems designed not only to promote diagnostic accuracy but to integrate and streamline work flow in clinical service settings are candidates for educational adaptation. The AcCell 2000 system, designed as a diagnostic screening support system, offers technology that is adaptable to educational needs during basic and in-service training as well as testing of screening proficiency in both locator and identification skills. We describe the considerations, approaches and applications of the AcCell 2000 system in education programs for both training and evaluation of gynecologic diagnostic screening proficiency.

Identification and control of a multizone crystal growth furnace

NASA Technical Reports Server (NTRS)

Batur, C.; Sharpless, R. B.; Duval, W. M. B.; Rosenthal, B. N.; Singh, N. B.

1992-01-01

This paper presents an intelligent adaptive control system for the control of a solid-liquid interface of a crystal while it is growing via directional solidification inside a multizone transparent furnace. The task of the process controller is to establish a user-specified axial temperature profile and to maintain a desirable interface shape. Both single-input-single-output and multi-input-multi-output adaptive pole placement algorithms have been used to control the temperature. Also described is an intelligent measurement system to assess the shape of the crystal while it is growing. A color video imaging system observes the crystal in real time and determines the position and the shape of the interface. This information is used to evaluate the crystal growth rate, and to analyze the effects of translational velocity and temperature profiles on the shape of the interface. Creation of this knowledge base is the first step to incorporate image processing into furnace control.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

[Increase in the effectiveness of identifying peaks and feet of the photoplethysmographic pulse to be reconstructed it using adaptive filtering].

PubMed

Becerra-Luna, Brayans; Martínez-Memije, Raúl; Cartas-Rosado, Raúl; Infante-Vázquez, Oscar

To improve the identification of peaks and feet in photoplethysmographic (PPG) pulses deformed by myokinetic noise, through the implementation of a modified fingertip and applying adaptive filtering. PPG signals were recordedfrom 10 healthy volunteers using two photoplethysmography systems placed on the index finger of each hand. Recordings lasted three minutes andwere done as follows: during the first minute, both handswere at rest, and for the lasting two minutes only the left hand was allowed to make quasi-periodicmovementsin order to add myokinetic noise. Two methodologies were employed to process the signals off-line. One consisted on using an adaptive filter based onthe Least Mean Square (LMS) algorithm, and the other includeda preprocessing stage in addition to the same LMS filter. Both filtering methods were compared and the one with the lowest error was chosen to assess the improvement in the identification of peaks and feet from PPG pulses. Average percentage errorsobtained wereof 22.94% with the first filtering methodology, and 3.72% withthe second one. On identifying peaks and feet from PPG pulsesbefore filtering, error percentages obtained were of 24.26% and 48.39%, respectively, and once filtered error percentageslowered to 2.02% for peaks and 3.77% for feet. The attenuation of myokinetic noise in PPG pulses through LMS filtering, plusa preprocessing stage, allows increasingthe effectiveness onthe identification of peaks and feet from PPG pulses, which are of great importance for medical assessment. Copyright © 2016 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Minimalist identification system based on venous map for security applications

NASA Astrophysics Data System (ADS)

Jacinto G., Edwar; Martínez S., Fredy; Martínez S., Fernando

2015-07-01

This paper proposes a technique and an algorithm used to build a device for people identification through the processing of a low resolution camera image. The infrared channel is the only information needed, sensing the blood reaction with the proper wave length, and getting a preliminary snapshot of the vascular map of the back side of the hand. The software uses this information to extract the characteristics of the user in a limited area (region of interest, ROI), unique for each user, which applicable to biometric access control devices. This kind of recognition prototypes functions are expensive, but in this case (minimalist design), the biometric equipment only used a low cost camera and the matrix of IR emitters adaptation to construct an economic and versatile prototype, without neglecting the high level of effectiveness that characterizes this kind of identification method.

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting

PubMed Central

Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-01-01

Purpose Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Methods Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. Results There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). Conclusions MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics. PMID:28873173

Retrospective Cost Adaptive Control with Concurrent Closed-Loop Identification

NASA Astrophysics Data System (ADS)

Sobolic, Frantisek M.

Retrospective cost adaptive control (RCAC) is a discrete-time direct adaptive control algorithm for stabilization, command following, and disturbance rejection. RCAC is known to work on systems given minimal modeling information which is the leading numerator coefficient and any nonminimum-phase (NMP) zeros of the plant transfer function. This information is normally needed a priori and is key in the development of the filter, also known as the target model, within the retrospective performance variable. A novel approach to alleviate the need for prior modeling of both the leading coefficient of the plant transfer function as well as any NMP zeros is developed. The extension to the RCAC algorithm is the use of concurrent optimization of both the target model and the controller coefficients. Concurrent optimization of the target model and controller coefficients is a quadratic optimization problem in the target model and controller coefficients separately. However, this optimization problem is not convex as a joint function of both variables, and therefore nonconvex optimization methods are needed. Finally, insights within RCAC that include intercalated injection between the controller numerator and the denominator, unveil the workings of RCAC fitting a specific closed-loop transfer function to the target model. We exploit this interpretation by investigating several closed-loop identification architectures in order to extract this information for use in the target model.

Development as adaptation: a paradigm for gravitational and space biology

NASA Technical Reports Server (NTRS)

Alberts, Jeffrey R.; Ronca, April E.

2005-01-01

Adaptation is a central precept of biology; it provides a framework for identifying functional significance. We equate mammalian development with adaptation, by viewing the developmental sequence as a series of adaptations to a stereotyped sequence of habitats. In this way development is adaptation. The Norway rat is used as a mammalian model, and the sequence of habitats that is used to define its adaptive-developmental sequence is (a) the uterus, (b) the mother's body, (c) the huddle, and (d) the coterie of pups as they gain independence. Then, within this framework and in relation to each of the habitats, we consider problems of organismal responses to altered gravitational forces (micro-g to hyper-g), especially those encountered during space flight and centrifugation. This approach enables a clearer identification of simple "effects" and active "responses" with respect to gravity. It focuses our attention on functional systems and brings to the fore the manner in which experience shapes somatic adaptation. We argue that this basic developmental approach is not only central to basic issues in gravitational biology, but that it provides a natural tool for understanding the underlying processes that are vital to astronaut health and well-being during long duration flights that will involve adaptation to space flight conditions and eventual re-adaptation to Earth's gravity.

AUTOMATED BIOCHEMICAL IDENTIFICATION OF BACTERIAL FISH PATHOGENS USING THE ABBOTT QUANTUM II

EPA Science Inventory

The Quantum II, originally designed by Abbott Diagnostics for automated rapid identification of members of Enterobacteriaceae, was adapted for the identification of bacterial fish pathogens. he instrument operates as a spectrophotometer at a wavelength of 492.600 nm. ample cartri...

Adaptive filtering in biological signal processing.

PubMed

Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A

1990-01-01

The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.

Speckle reduction during all-fiber common-path optical coherence tomography of the cavernous nerves

NASA Astrophysics Data System (ADS)

Chitchian, Shahab; Fiddy, Michael; Fried, Nathaniel M.

2009-02-01

Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery, which are responsible for erectile function, may improve nerve preservation and postoperative sexual potency. In this study, we use a rat prostate, ex vivo, to evaluate the feasibility of optical coherence tomography (OCT) as a diagnostic tool for real-time imaging and identification of the cavernous nerves. A novel OCT system based on an all single-mode fiber common-path interferometer-based scanning system is used for this purpose. A wavelet shrinkage denoising technique using Stein's unbiased risk estimator (SURE) algorithm to calculate a data-adaptive threshold is implemented for speckle noise reduction in the OCT image. The signal-to-noise ratio (SNR) was improved by 9 dB and the image quality metrics of the cavernous nerves also improved significantly.

Plant-bacterial pathogen interactions mediated by type III effectors.

PubMed

Feng, Feng; Zhou, Jian-Min

2012-08-01

Effectors secreted by the bacterial type III system play a central role in the interaction between Gram-negative bacterial pathogens and their host plants. Recent advances in the effector studies have helped cementing several key concepts concerning bacterial pathogenesis, plant immunity, and plant-pathogen co-evolution. Type III effectors use a variety of biochemical mechanisms to target specific host proteins or DNA for pathogenesis. The identifications of their host targets led to the identification of novel components of plant innate immune system. Key modules of plant immune signaling pathways such as immune receptor complexes and MAPK cascades have emerged as a major battle ground for host-pathogen adaptation. These modules are attacked by multiple type III effectors, and some components of these modules have evolved to actively sense the effectors and trigger immunity. Copyright © 2012 Elsevier Ltd. All rights reserved.

Design of analytical failure detection using secondary observers

NASA Technical Reports Server (NTRS)

Sisar, M.

1982-01-01

The problem of designing analytical failure-detection systems (FDS) for sensors and actuators, using observers, is addressed. The use of observers in FDS is related to the examination of the n-dimensional observer error vector which carries the necessary information on possible failures. The problem is that in practical systems, in which only some of the components of the state vector are measured, one has access only to the m-dimensional observer-output error vector, with m or = to n. In order to cope with these cases, a secondary observer is synthesized to reconstruct the entire observer-error vector from the observer output error vector. This approach leads toward the design of highly sensitive and reliable FDS, with the possibility of obtaining a unique fingerprint for every possible failure. In order to keep the observer's (or Kalman filter) false-alarm rate under a certain specified value, it is necessary to have an acceptable matching between the observer (or Kalman filter) models and the system parameters. A previously developed adaptive observer algorithm is used to maintain the desired system-observer model matching, despite initial mismatching or system parameter variations. Conditions for convergence for the adaptive process are obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors, while accurate and fast parameter identification, in both deterministic and stochastic cases, is obtained.

Model-based aberration correction in a closed-loop wavefront-sensor-less adaptive optics system.

PubMed

Song, H; Fraanje, R; Schitter, G; Kroese, H; Vdovin, G; Verhaegen, M

2010-11-08

In many scientific and medical applications, such as laser systems and microscopes, wavefront-sensor-less (WFSless) adaptive optics (AO) systems are used to improve the laser beam quality or the image resolution by correcting the wavefront aberration in the optical path. The lack of direct wavefront measurement in WFSless AO systems imposes a challenge to achieve efficient aberration correction. This paper presents an aberration correction approach for WFSlss AO systems based on the model of the WFSless AO system and a small number of intensity measurements, where the model is identified from the input-output data of the WFSless AO system by black-box identification. This approach is validated in an experimental setup with 20 static aberrations having Kolmogorov spatial distributions. By correcting N=9 Zernike modes (N is the number of aberration modes), an intensity improvement from 49% of the maximum value to 89% has been achieved in average based on N+5=14 intensity measurements. With the worst initial intensity, an improvement from 17% of the maximum value to 86% has been achieved based on N+4=13 intensity measurements.

Adaptive precompensators for flexible-link manipulator control

NASA Technical Reports Server (NTRS)

Tzes, Anthony P.; Yurkovich, Stephen

1989-01-01

The application of input precompensators to flexible manipulators is considered. Frequency domain compensators color the input around the flexible mode locations, resulting in a bandstop or notch filter in cascade with the system. Time domain compensators apply a sequence of impulses at prespecified times related to the modal frequencies. The resulting control corresponds to a feedforward term that convolves in real-time the desired reference input with a sequence of impulses and produces a vibration-free output. An adaptive precompensator can be implemented by combining a frequency domain identification scheme which is used to estimate online the modal frequencies and subsequently update the bandstop interval or the spacing between the impulses. The combined adaptive input preshaping scheme provides the most rapid slew that results in a vibration-free output. Experimental results are presented to verify the results.

System identification and sensorimotor determinants of flight maneuvers in an insect

NASA Astrophysics Data System (ADS)

Sponberg, Simon; Hall, Robert; Roth, Eatai

Locomotor maneuvers are inherently closed-loop processes. They are generally characterized by the integration of multiple sensory inputs and adaptation or learning over time. To probe sensorimotor processing we take a system identification approach treating the underlying physiological systems as dynamic processes and altering the feedback topology in experiment and analysis. As a model system, we use agile hawk moths (Manduca sexta), which feed from real and robotic flowers while hovering in mid air. Moths rely on vision and mechanosensation to track floral targets and can do so at exceptionally low luminance levels despite hovering being a mechanically unstable behavior that requires neural feedback to stabilize. By altering the sensory environment and placing mechanical and visual signals in conflict we show a surprisingly simple linear summation of visual and mechanosensation produces a generative prediction of behavior to novel stimuli. Tracking performance is also limited more by the mechanics of flight than the magnitude of the sensory cue. A feedback systems approach to locomotor control results in new insights into how behavior emerges from the interaction of nonlinear physiological systems.

A New Concept Map Model for E-Learning Environments

NASA Astrophysics Data System (ADS)

Dattolo, Antonina; Luccio, Flaminia L.

Web-based education enables learners and teachers to access a wide quantity of continuously updated educational sources. In order to support the learning process, a system has to provide some fundamental features, such as simple mechanisms for the identification of the collection of “interesting” documents, adequate structures for storing, organizing and visualizing these documents, and appropriate mechanisms for creating personalized adaptive paths and views for learners.

Biomimetics: determining engineering opportunities from nature

NASA Astrophysics Data System (ADS)

Fish, Frank E.

2009-08-01

The biomimetic approach seeks to incorporate designs based on biological organisms into engineered technologies. Biomimetics can be used to engineer machines that emulate the performance of organisms, particularly in instances where the organism's performance exceeds current mechanical technology or provides new directions to solve existing problems. For biologists, an adaptationist program has allowed for the identification of novel features of organisms based on engineering principles; whereas for engineers, identification of such novel features is necessary to exploit them for biomimetic development. Adaptations (leading edge tubercles to passively modify flow and high efficiency oscillatory propulsive systems) from marine animals demonstrate potential utility in the development of biomimetic products. Nature retains a store of untouched knowledge, which would be beneficial in advancing technology.

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

PubMed Central

Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose; Moore, Jason H.

2014-01-01

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data. PMID:25431544

A Methodological Study of Family Cohesion and Adaptability.

ERIC Educational Resources Information Center

Russell, Candyce S.

1980-01-01

Assessed the validity of four separate instruments: SIMFAM; an adaptation of the Bowerman and Bahr Identification Scale; the Moos Family Environment Scale; and the Kvebaek Family Sculpture Test. Data support the Family Sculpture Test as a useful tool for measuring family cohesion but not adaptability. (Author)

Nonlinear system identification of smart structures under high impact loads

NASA Astrophysics Data System (ADS)

Sarp Arsava, Kemal; Kim, Yeesock; El-Korchi, Tahar; Park, Hyo Seon

2013-05-01

The main purpose of this paper is to develop numerical models for the prediction and analysis of the highly nonlinear behavior of integrated structure control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling of the complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validation of the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the displacement and acceleration responses from the structure-MR damper system are used as the output signals. The benchmark adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. Comparisons of the trained TANFIS models with experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure-MR damper systems under high impact loading. In addition, the performance of the TANFIS model is much better than that of ANFIS in both the training and the validation processes.

Nonlinear stability and control study of highly maneuverable high performance aircraft, phase 2

NASA Technical Reports Server (NTRS)

Mohler, R. R.

1992-01-01

This research should lead to the development of new nonlinear methodologies for the adaptive control and stability analysis of high angle-of-attack aircraft such as the F18 (HARV). The emphasis has been on nonlinear adaptive control, but associated model development, system identification, stability analysis and simulation is performed in some detail as well. Various models under investigation for different purposes are summarized in tabular form. Models and simulation for the longitudinal dynamics have been developed for all types except the nonlinear ordinary differential equation model. Briefly, studies completed indicate that nonlinear adaptive control can outperform linear adaptive control for rapid maneuvers with large changes in alpha. The transient responses are compared where the desired alpha varies from 5 degrees to 60 degrees to 30 degrees and back to 5 degrees in all about 16 sec. Here, the horizontal stabilator is the only control used with an assumed first-order linear actuator with a 1/30 sec time constant.

Adaptive Enhancement of X-Band Marine Radar Imagery to Detect Oil Spill Segments

PubMed Central

Liu, Peng; Li, Ying; Xu, Jin; Zhu, Xueyuan

2017-01-01

Oil spills generate a large cost in environmental and economic terms. Their identification plays an important role in oil-spill response. We propose an oil spill detection method with improved adaptive enhancement on X-band marine radar systems. The radar images used in this paper were acquired on 21 July 2010, from the teaching-training ship “YUKUN” of the Dalian Maritime University. According to the shape characteristic of co-channel interference, two convolutional filters are used to detect the location of the interference, followed by a mean filter to erase the interference. Small objects, such as bright speckles, are taken as a mask in the radar image and improved by the Fields-of-Experts model. The region marked by strong reflected signals from the sea’s surface is selected to identify oil spills. The selected region is subject to improved adaptive enhancement designed based on features of radar images. With the proposed adaptive enhancement technique, calculated oil spill detection is comparable to visual interpretation in accuracy. PMID:29036892

Dual adaptive control: Design principles and applications

NASA Technical Reports Server (NTRS)

Mookerjee, Purusottam

1988-01-01

The design of an actively adaptive dual controller based on an approximation of the stochastic dynamic programming equation for a multi-step horizon is presented. A dual controller that can enhance identification of the system while controlling it at the same time is derived for multi-dimensional problems. This dual controller uses sensitivity functions of the expected future cost with respect to the parameter uncertainties. A passively adaptive cautious controller and the actively adaptive dual controller are examined. In many instances, the cautious controller is seen to turn off while the latter avoids the turn-off of the control and the slow convergence of the parameter estimates, characteristic of the cautious controller. The algorithms have been applied to a multi-variable static model which represents a simplified linear version of the relationship between the vibration output and the higher harmonic control input for a helicopter. Monte Carlo comparisons based on parametric and nonparametric statistical analysis indicate the superiority of the dual controller over the baseline controller.

Unsupervised real-time speaker identification for daily movies

NASA Astrophysics Data System (ADS)

Li, Ying; Kuo, C.-C. Jay

2002-07-01

The problem of identifying speakers for movie content analysis is addressed in this paper. While most previous work on speaker identification was carried out in a supervised mode using pure audio data, more robust results can be obtained in real-time by integrating knowledge from multiple media sources in an unsupervised mode. In this work, both audio and visual cues will be employed and subsequently combined in a probabilistic framework to identify speakers. Particularly, audio information is used to identify speakers with a maximum likelihood (ML)-based approach while visual information is adopted to distinguish speakers by detecting and recognizing their talking faces based on face detection/recognition and mouth tracking techniques. Moreover, to accommodate for speakers' acoustic variations along time, we update their models on the fly by adapting to their newly contributed speech data. Encouraging results have been achieved through extensive experiments, which shows a promising future of the proposed audiovisual-based unsupervised speaker identification system.

A continually online-trained neural network controller for brushless DC motor drives

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

Rubaai, A.; Kotaru, R.; Kankam, M.D.

2000-04-01

In this paper, a high-performance controller with simultaneous online identification and control is designed for brushless dc motor drives. The dynamics of the motor/load are modeled online, and controlled using two different neural network based identification and control schemes, as the system is in operation. In the first scheme, an attempt is made to control the rotor angular speed, utilizing a single three-hidden-layer network. The second scheme attempts to control the stator currents, using a predetermined control law as a function of the estimated states. This schemes incorporates three multilayered feedforward neural networks that are online trained, using the Levenburg-Marquadtmore » training algorithm. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of trajectories after relatively short online training periods. The control strategy adapts to the uncertainties of the motor/load dynamics and, in addition, learns their inherent nonlinearities. Simulation results illustrated that a neurocontroller used in conjunction with adaptive control schemes can result in a flexible control device which may be utilized in a wide range of environments.« less

Variable input observer for structural health monitoring of high-rate systems

NASA Astrophysics Data System (ADS)

Hong, Jonathan; Laflamme, Simon; Cao, Liang; Dodson, Jacob

2017-02-01

The development of high-rate structural health monitoring methods is intended to provide damage detection on timescales of 10 µs -10ms where speed of detection is critical to maintain structural integrity. Here, a novel Variable Input Observer (VIO) coupled with an adaptive observer is proposed as a potential solution for complex high-rate problems. The VIO is designed to adapt its input space based on real-time identification of the system's essential dynamics. By selecting appropriate time-delayed coordinates defined by both a time delay and an embedding dimension, the proper input space is chosen which allows more accurate estimations of the current state and a reduction of the convergence rate. The optimal time-delay is estimated based on mutual information, and the embedding dimension is based on false nearest neighbors. A simulation of the VIO is conducted on a two degree-of-freedom system with simulated damage. Results are compared with an adaptive Luenberger observer, a fixed time-delay observer, and a Kalman Filter. Under its preliminary design, the VIO converges significantly faster than the Luenberger and fixed observer. It performed similarly to the Kalman Filter in terms of convergence, but with greater accuracy.

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

NASA Astrophysics Data System (ADS)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

A tunable algorithm for collective decision-making.

PubMed

Pratt, Stephen C; Sumpter, David J T

2006-10-24

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

Adaptation Pathway of Low Impact Development Planning under Climate Change for a Sustainable Rural Community

NASA Astrophysics Data System (ADS)

Chen, P. Y.; Tung, C. P.

2016-12-01

The study focuses on developing the methodology of adaptation pathway for storm water management in a community scale. Following previous results on adaptation procedures including problem and goal setup, current risk assessment and analysis, future risk assessment and analysis, and adaptation options identification and evaluation, the study aims at analyzing adaptation pathway planning and implementation, namely the fifth step, for applying low impact development (LID). Based on the efficacy analyses of the feasible adaptation options, an adaptation pathway map can be build. Each pathway is a combination of the adaptation measures arranged in certain order. The developed adaptation pathway map visualizes the relative effectiveness and the connection of the adaptation measures. In addition, the tipping points of the system can be clearly identified and the triggers can be defined accordingly. There are multiple choices of pathways in an adaptation pathway map, which can be referred as pathway candidates. To ensure the applicability and operability, the methodology of adaptation pathway analysis is applied to a case study. Required information for developing an adaptation pathway map includes the scores of the adaptation options on the criteria, namely the effects, costs, immediacy, and side effect. Feasible adaptation options for the design case are dredging, pipeline expansion, pumping station, LID and detention pond. By ranking the options according to the criteria, LID is found dominating dredging and pumping station in this case. The information of the pathway candidates can be further used by the stakeholders to select the most suitable and promising pathway.

Radar network communication through sensing of frequency hopping

DOEpatents

Dowla, Farid; Nekoogar, Faranak

2013-05-28

In one embodiment, a radar communication system includes a plurality of radars having a communication range and being capable of operating at a sensing frequency and a reporting frequency, wherein the reporting frequency is different than the sensing frequency, each radar is adapted for operating at the sensing frequency until an event is detected, each radar in the plurality of radars has an identification/location frequency for reporting information different from the sensing frequency, a first radar of the radars which senses the event sends a reporting frequency corresponding to its identification/location frequency when the event is detected, and all other radars in the plurality of radars switch their reporting frequencies to match the reporting frequency of the first radar upon detecting the reporting frequency switch of a radar within the communication range. In another embodiment, a method is presented for communicating information in a radar system.

Application of Two-Parameter Stabilizing Functions in Solving a Convolution-Type Integral Equation by Regularization Method

NASA Astrophysics Data System (ADS)

Maslakov, M. L.

2018-04-01

This paper examines the solution of convolution-type integral equations of the first kind by applying the Tikhonov regularization method with two-parameter stabilizing functions. The class of stabilizing functions is expanded in order to improve the accuracy of the resulting solution. The features of the problem formulation for identification and adaptive signal correction are described. A method for choosing regularization parameters in problems of identification and adaptive signal correction is suggested.

Social and Behavioral Science: Monitoring Social Foraging Behavior in a Biological Model System

DTIC Science & Technology

2016-10-12

SECURITY CLASSIFICATION OF: The aim of this project was to establish instrumentation to record honey bee foraging behavior through a Radio- Frequency...Identification (RFID) monitoring and to train students in the use of this technology and in the science underlying honey bee behavior. This enables...basic scientific advances in how honey bees adapt behaviorally to different stressors. Most notably, it will examine how early life stress and

Identification of a motor to auditory pathway important for vocal learning

PubMed Central

Roberts, Todd F.; Hisey, Erin; Tanaka, Masashi; Kearney, Matthew; Chattree, Gaurav; Yang, Cindy F.; Shah, Nirao M.; Mooney, Richard

2017-01-01

Summary Learning to vocalize depends on the ability to adaptively modify the temporal and spectral features of vocal elements. Neurons that convey motor-related signals to the auditory system are theorized to facilitate vocal learning, but the identity and function of such neurons remain unknown. Here we identify a previously unknown neuron type in the songbird brain that transmits vocal motor signals to the auditory cortex. Genetically ablating these neurons in juveniles disrupted their ability to imitate features of an adult tutor’s song. Ablating these neurons in adults had little effect on previously learned songs, but interfered with their ability to adaptively modify the duration of vocal elements and largely prevented the degradation of song’s temporal features normally caused by deafening. These findings identify a motor to auditory circuit essential to vocal imitation and to the adaptive modification of vocal timing. PMID:28504672

Characterization of Pump-Induced Acoustics in Space Launch System Main Propulsion System Liquid Hydrogen Feedline Using Airflow Test Data

NASA Technical Reports Server (NTRS)

Eberhart, C. J.; Snellgrove, L. M.; Zoladz, T. F.

2015-01-01

High intensity acoustic edgetones located upstream of the RS-25 Low Pressure Fuel Turbo Pump (LPFTP) were previously observed during Space Launch System (STS) airflow testing of a model Main Propulsion System (MPS) liquid hydrogen (LH2) feedline mated to a modified LPFTP. MPS hardware has been adapted to mitigate the problematic edgetones as part of the Space Launch System (SLS) program. A follow-on airflow test campaign has subjected the adapted hardware to tests mimicking STS-era airflow conditions, and this manuscript describes acoustic environment identification and characterization born from the latest test results. Fluid dynamics responsible for driving discrete excitations were well reproduced using legacy hardware. The modified design was found insensitive to high intensity edgetone-like discretes over the bandwidth of interest to SLS MPS unsteady environments. Rather, the natural acoustics of the test article were observed to respond in a narrowband-random/mixed discrete manner to broadband noise thought generated by the flow field. The intensity of these responses were several orders of magnitude reduced from those driven by edgetones.

System identification of smart structures using a wavelet neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

2012-11-01

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

The Burmese python genome reveals the molecular basis for extreme adaptation in snakes

PubMed Central

Castoe, Todd A.; de Koning, A. P. Jason; Hall, Kathryn T.; Card, Daren C.; Schield, Drew R.; Fujita, Matthew K.; Ruggiero, Robert P.; Degner, Jack F.; Daza, Juan M.; Gu, Wanjun; Reyes-Velasco, Jacobo; Shaney, Kyle J.; Castoe, Jill M.; Fox, Samuel E.; Poole, Alex W.; Polanco, Daniel; Dobry, Jason; Vandewege, Michael W.; Li, Qing; Schott, Ryan K.; Kapusta, Aurélie; Minx, Patrick; Feschotte, Cédric; Uetz, Peter; Ray, David A.; Hoffmann, Federico G.; Bogden, Robert; Smith, Eric N.; Chang, Belinda S. W.; Vonk, Freek J.; Casewell, Nicholas R.; Henkel, Christiaan V.; Richardson, Michael K.; Mackessy, Stephen P.; Bronikowski, Anne M.; Yandell, Mark; Warren, Wesley C.; Secor, Stephen M.; Pollock, David D.

2013-01-01

Snakes possess many extreme morphological and physiological adaptations. Identification of the molecular basis of these traits can provide novel understanding for vertebrate biology and medicine. Here, we study snake biology using the genome sequence of the Burmese python (Python molurus bivittatus), a model of extreme physiological and metabolic adaptation. We compare the python and king cobra genomes along with genomic samples from other snakes and perform transcriptome analysis to gain insights into the extreme phenotypes of the python. We discovered rapid and massive transcriptional responses in multiple organ systems that occur on feeding and coordinate major changes in organ size and function. Intriguingly, the homologs of these genes in humans are associated with metabolism, development, and pathology. We also found that many snake metabolic genes have undergone positive selection, which together with the rapid evolution of mitochondrial proteins, provides evidence for extensive adaptive redesign of snake metabolic pathways. Additional evidence for molecular adaptation and gene family expansions and contractions is associated with major physiological and phenotypic adaptations in snakes; genes involved are related to cell cycle, development, lungs, eyes, heart, intestine, and skeletal structure, including GRB2-associated binding protein 1, SSH, WNT16, and bone morphogenetic protein 7. Finally, changes in repetitive DNA content, guanine-cytosine isochore structure, and nucleotide substitution rates indicate major shifts in the structure and evolution of snake genomes compared with other amniotes. Phenotypic and physiological novelty in snakes seems to be driven by system-wide coordination of protein adaptation, gene expression, and changes in the structure of the genome. PMID:24297902

The Burmese python genome reveals the molecular basis for extreme adaptation in snakes.

PubMed

Castoe, Todd A; de Koning, A P Jason; Hall, Kathryn T; Card, Daren C; Schield, Drew R; Fujita, Matthew K; Ruggiero, Robert P; Degner, Jack F; Daza, Juan M; Gu, Wanjun; Reyes-Velasco, Jacobo; Shaney, Kyle J; Castoe, Jill M; Fox, Samuel E; Poole, Alex W; Polanco, Daniel; Dobry, Jason; Vandewege, Michael W; Li, Qing; Schott, Ryan K; Kapusta, Aurélie; Minx, Patrick; Feschotte, Cédric; Uetz, Peter; Ray, David A; Hoffmann, Federico G; Bogden, Robert; Smith, Eric N; Chang, Belinda S W; Vonk, Freek J; Casewell, Nicholas R; Henkel, Christiaan V; Richardson, Michael K; Mackessy, Stephen P; Bronikowski, Anne M; Bronikowsi, Anne M; Yandell, Mark; Warren, Wesley C; Secor, Stephen M; Pollock, David D

2013-12-17

Snakes possess many extreme morphological and physiological adaptations. Identification of the molecular basis of these traits can provide novel understanding for vertebrate biology and medicine. Here, we study snake biology using the genome sequence of the Burmese python (Python molurus bivittatus), a model of extreme physiological and metabolic adaptation. We compare the python and king cobra genomes along with genomic samples from other snakes and perform transcriptome analysis to gain insights into the extreme phenotypes of the python. We discovered rapid and massive transcriptional responses in multiple organ systems that occur on feeding and coordinate major changes in organ size and function. Intriguingly, the homologs of these genes in humans are associated with metabolism, development, and pathology. We also found that many snake metabolic genes have undergone positive selection, which together with the rapid evolution of mitochondrial proteins, provides evidence for extensive adaptive redesign of snake metabolic pathways. Additional evidence for molecular adaptation and gene family expansions and contractions is associated with major physiological and phenotypic adaptations in snakes; genes involved are related to cell cycle, development, lungs, eyes, heart, intestine, and skeletal structure, including GRB2-associated binding protein 1, SSH, WNT16, and bone morphogenetic protein 7. Finally, changes in repetitive DNA content, guanine-cytosine isochore structure, and nucleotide substitution rates indicate major shifts in the structure and evolution of snake genomes compared with other amniotes. Phenotypic and physiological novelty in snakes seems to be driven by system-wide coordination of protein adaptation, gene expression, and changes in the structure of the genome.

Performing Comparative Peptidomics Analyses of Salmonella from Different Growth Conditions

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

Adkins, Joshua N.; Mottaz, Heather; Metz, Thomas O.

2010-01-08

Host–pathogen interactions are complex competitions during which both the host and the pathogen adapt rapidly to each other in order for one or the other to survive. Salmonella enterica serovar Typhimurium is a pathogen with a broad host range that causes a typhoid fever-like disease in mice and severe food poisoning in humans. The murine typhoid fever is a systemic infection in which S.typhimurium evades part of the immune system by replicating inside macrophages and other cells. The transition from a foodborne contaminant to an intracellular pathogen must occur rapidly in multiple,ordered steps in order for S. typhimurium to thrivemore » within its host environment. Using S. typhimurium isolated from rich culture conditions and from conditions that mimic the hostile intracellular environment of the host cell, a native low molecular weight protein fraction, or peptidome, was enriched from cell lysates by precipitation with organic solvents. The enriched peptidome was analyzed by both LC–MS/MS and LC–MS-based methods, although several other methods are possible. Pre-fractionation of peptides allowed identification of small proteins and protein degradation products that would normally be overlooked. Comparison of peptides present in lysates prepared from Salmonella grown under different conditions provided a unique insight into cellular degradation processes as well as identification of novel peptides encoded in the genome but not annotated. The overall approach is detailed here as applied to Salmonella and is adaptable to a broad range of biological systems.« less

Adaptive PIF Control for Permanent Magnet Synchronous Motors Based on GPC

PubMed Central

Lu, Shaowu; Tang, Xiaoqi; Song, Bao

2013-01-01

To enhance the control performance of permanent magnet synchronous motors (PMSMs), a generalized predictive control (GPC)-based proportional integral feedforward (PIF) controller is proposed for the speed control system. In this new approach, firstly, based on the online identification of controlled model parameters, a simplified GPC law supplies the PIF controller with suitable control parameters according to the uncertainties in the operating conditions. Secondly, the speed reference curve for PMSMs is usually required to be continuous and continuously differentiable according to the general servo system design requirements, so the adaptation of the speed reference is discussed in details in this paper. Hence, the performance of the speed control system using a GPC-based PIF controller is improved for tracking some specified signals. The main motivation of this paper is the extension of GPC law to replace the traditional PI or PIF controllers in industrial applications. The efficacy and usefulness of the proposed controller are verified through experimental results. PMID:23262481

Adaptive PIF control for permanent magnet synchronous motors based on GPC.

PubMed

Lu, Shaowu; Tang, Xiaoqi; Song, Bao

2012-12-24

To enhance the control performance of permanent magnet synchronous motors (PMSMs), a generalized predictive control (GPC)-based proportional integral feedforward (PIF) controller is proposed for the speed control system. In this new approach, firstly, based on the online identification of controlled model parameters, a simplified GPC law supplies the PIF controller with suitable control parameters according to the uncertainties in the operating conditions. Secondly, the speed reference curve for PMSMs is usually required to be continuous and continuously differentiable according to the general servo system design requirements, so the adaptation of the speed reference is discussed in details in this paper. Hence, the performance of the speed control system using a GPC-based PIF controller is improved for tracking some specified signals. The main motivation of this paper is the extension of GPC law to replace the traditional PI or PIF controllers in industrial applications. The efficacy and usefulness of the proposed controller are verified through experimental results.

Real-Time Stability and Control Derivative Extraction From F-15 Flight Data

NASA Technical Reports Server (NTRS)

Smith, Mark S.; Moes, Timothy R.; Morelli, Eugene A.

2003-01-01

A real-time, frequency-domain, equation-error parameter identification (PID) technique was used to estimate stability and control derivatives from flight data. This technique is being studied to support adaptive control system concepts currently being developed by NASA (National Aeronautics and Space Administration), academia, and industry. This report describes the basic real-time algorithm used for this study and implementation issues for onboard usage as part of an indirect-adaptive control system. A confidence measures system for automated evaluation of PID results is discussed. Results calculated using flight data from a modified F-15 aircraft are presented. Test maneuvers included pilot input doublets and automated inputs at several flight conditions. Estimated derivatives are compared to aerodynamic model predictions. Data indicate that the real-time PID used for this study performs well enough to be used for onboard parameter estimation. For suitable test inputs, the parameter estimates converged rapidly to sufficient levels of accuracy. The devised confidence measures used were moderately successful.

DyNAvectors: dynamic constitutional vectors for adaptive DNA transfection.

PubMed

Clima, Lilia; Peptanariu, Dragos; Pinteala, Mariana; Salic, Adrian; Barboiu, Mihail

2015-12-25

Dynamic constitutional frameworks, based on squalene, PEG and PEI components, reversibly connected to core centers, allow the efficient identification of adaptive vectors for good DNA transfection efficiency and are well tolerated by mammalian cells.

Cost-constrained optimal sampling for system identification in pharmacokinetics applications with population priors and nuisance parameters.

PubMed

Sorzano, Carlos Oscars S; Pérez-De-La-Cruz Moreno, Maria Angeles; Burguet-Castell, Jordi; Montejo, Consuelo; Ros, Antonio Aguilar

2015-06-01

Pharmacokinetics (PK) applications can be seen as a special case of nonlinear, causal systems with memory. There are cases in which prior knowledge exists about the distribution of the system parameters in a population. However, for a specific patient in a clinical setting, we need to determine her system parameters so that the therapy can be personalized. This system identification is performed many times by measuring drug concentrations in plasma. The objective of this work is to provide an irregular sampling strategy that minimizes the uncertainty about the system parameters with a fixed amount of samples (cost constrained). We use Monte Carlo simulations to estimate the average Fisher's information matrix associated to the PK problem, and then estimate the sampling points that minimize the maximum uncertainty associated to system parameters (a minimax criterion). The minimization is performed employing a genetic algorithm. We show that such a sampling scheme can be designed in a way that is adapted to a particular patient and that it can accommodate any dosing regimen as well as it allows flexible therapeutic strategies. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Enriched Imperialist Competitive Algorithm for system identification of magneto-rheological dampers

NASA Astrophysics Data System (ADS)

Talatahari, Siamak; Rahbari, Nima Mohajer

2015-10-01

In the current research, the imperialist competitive algorithm is dramatically enhanced and a new optimization method dubbed as Enriched Imperialist Competitive Algorithm (EICA) is effectively introduced to deal with high non-linear optimization problems. To conduct a close examination of its functionality and efficacy, the proposed metaheuristic optimization approach is actively employed to sort out the parameter identification of two different types of hysteretic Bouc-Wen models which are simulating the non-linear behavior of MR dampers. Two types of experimental data are used for the optimization problems to minutely examine the robustness of the proposed EICA. The obtained results self-evidently demonstrate the high adaptability of EICA to suitably get to the bottom of such non-linear and hysteretic problems.

Robust uncertainty evaluation for system identification on distributed wireless platforms

NASA Astrophysics Data System (ADS)

Crinière, Antoine; Döhler, Michael; Le Cam, Vincent; Mevel, Laurent

2016-04-01

Health monitoring of civil structures by system identification procedures from automatic control is now accepted as a valid approach. These methods provide frequencies and modeshapes from the structure over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. The underlying algorithms are usually running under Matlab under the assumption of large memory pool and considerable computational power. Even under these premises, computational and memory usage are heavy and not realistic for being embedded in on-site sensor platforms such as the PEGASE platform. Moreover, the current push for distributed wireless systems calls for algorithmic adaptation for lowering data exchanges and maximizing local processing. Finally, the recent breakthrough in system identification allows us to process both frequency information and its related uncertainty together from one and only one data sequence, at the expense of computational and memory explosion that require even more careful attention than before. The current approach will focus on presenting a system identification procedure called multi-setup subspace identification that allows to process both frequencies and their related variances from a set of interconnected wireless systems with all computation running locally within the limited memory pool of each system before being merged on a host supervisor. Careful attention will be given to data exchanges and I/O satisfying OGC standards, as well as minimizing memory footprints and maximizing computational efficiency. Those systems are built in a way of autonomous operations on field and could be later included in a wide distributed architecture such as the Cloud2SM project. The usefulness of these strategies is illustrated on data from a progressive damage action on a prestressed concrete bridge. References [1] E. Carden and P. Fanning. Vibration based condition monitoring: a review. Structural Health Monitoring, 3(4):355-377, 2004. [2] M. Döhler and L. Mevel. Efficient multi-order uncertainty computation for stochastic subspace identification. Mechanical Systems and Signal Processing, 38(2):346-366, 2013. [3] M.Döhler, L. Mevel. Modular subspace-based system identification from multi-setup measurements. IEEE Transactions on Automatic Control, 57(11):2951-2956, 2012. [4] M. Döhler, X.-B. Lam, and L. Mevel. Uncertainty quantification for modal parameters from stochastic subspace identification on multi-setup measurements. MechanicalSystems and Signal Processing, 36(2):562-581, 2013. [5] A Crinière, J Dumoulin, L Mevel, G Andrade-Barosso, M Simonin. The Cloud2SM Project.European Geosciences Union General Assembly (EGU2015), Apr 2015, Vienne, Austria. 2015.

Recognition of the Component Odors in Mixtures

PubMed Central

Fletcher, Dane B; Hettinger, Thomas P

2017-01-01

Abstract Natural olfactory stimuli are volatile-chemical mixtures in which relative perceptual saliencies determine which odor-components are identified. Odor identification also depends on rapid selective adaptation, as shown for 4 odor stimuli in an earlier experimental simulation of natural conditions. Adapt-test pairs of mixtures of water-soluble, distinct odor stimuli with chemical features in common were studied. Identification decreased for adapted components but increased for unadapted mixture-suppressed components, showing compound identities were retained, not degraded to individual molecular features. Four additional odor stimuli, 1 with 2 perceptible odor notes, and an added “water-adapted” control tested whether this finding would generalize to other 4-compound sets. Selective adaptation of mixtures of the compounds (odors): 3 mM benzaldehyde (cherry), 5 mM maltol (caramel), 1 mM guaiacol (smoke), and 4 mM methyl anthranilate (grape-smoke) again reciprocally unmasked odors of mixture-suppressed components in 2-, 3-, and 4-component mixtures with 2 exceptions. The cherry note of “benzaldehyde” (itself) and the shared note of “methyl anthranilate and guaiacol” (together) were more readily identified. The pervasive mixture-component dominance and dynamic perceptual salience may be mediated through peripheral adaptation and central mutual inhibition of neural responses. Originating in individual olfactory receptor variants, it limits odor identification and provides analytic properties for momentary recognition of a few remaining mixture-components. PMID:28641388

New techniques for the analysis of manual control systems. [mathematical models of human operator behavior

NASA Technical Reports Server (NTRS)

Bekey, G. A.

1971-01-01

Studies are summarized on the application of advanced analytical and computational methods to the development of mathematical models of human controllers in multiaxis manual control systems. Specific accomplishments include the following: (1) The development of analytical and computer methods for the measurement of random parameters in linear models of human operators. (2) Discrete models of human operator behavior in a multiple display situation were developed. (3) Sensitivity techniques were developed which make possible the identification of unknown sampling intervals in linear systems. (4) The adaptive behavior of human operators following particular classes of vehicle failures was studied and a model structure proposed.

A practical guide for the identification of membrane and plasma membrane proteins in human embryonic stem cells and human embryonal carcinoma cells.

PubMed

Dormeyer, Wilma; van Hoof, Dennis; Mummery, Christine L; Krijgsveld, Jeroen; Heck, Albert J R

2008-10-01

The identification of (plasma) membrane proteins in cells can provide valuable insights into the regulation of their biological processes. Pluripotent cells such as human embryonic stem cells and embryonal carcinoma cells are capable of unlimited self-renewal and share many of the biological mechanisms that regulate proliferation and differentiation. The comparison of their membrane proteomes will help unravel the biological principles of pluripotency, and the identification of biomarker proteins in their plasma membranes is considered a crucial step to fully exploit pluripotent cells for therapeutic purposes. For these tasks, membrane proteomics is the method of choice, but as indicated by the scarce identification of membrane and plasma membrane proteins in global proteomic surveys it is not an easy task. In this minireview, we first describe the general challenges of membrane proteomics. We then review current sample preparation steps and discuss protocols that we found particularly beneficial for the identification of large numbers of (plasma) membrane proteins in human tumour- and embryo-derived stem cells. Our optimized assembled protocol led to the identification of a large number of membrane proteins. However, as the composition of cells and membranes is highly variable we still recommend adapting the sample preparation protocol for each individual system.

Use of in vivo Expression Technology for the Identification of Putative Host Adaptation Factors of the Lyme Disease Spirochete.

PubMed

Casselli, Timothy; Bankhead, Troy

2015-01-01

The causative agent of Lyme disease, Borrelia burgdorferi, is an obligate parasite that requires either a tick vector or a mammalian host for survival. Identification of the bacterial genes that are specifically expressed during infection of the mammalian host could provide targets for novel therapeutics and vaccines. In vivo expression technology (IVET) is a reporter-based promoter trap system that utilizes selectable markers to identify promoters of bacterial host-specific genes. Using previously characterized genes for in vivo and in vitro selection, this study utilized an IVET system that allows for selection of B. burgdorferi sequences that act as active promoters only during murine infection. This promoter trap system was able to successfully distinguish active promoter sequences both in vivo and in vitro from control sequences and a library of cloned B. burgdorferi genomic fragments. However, a bottleneck effect during the experimental mouse infection limited the utility for genome-wide promoter screening. Overall, IVET was demonstrated as a tool for the identification of in vivo-induced promoter elements of B. burgdorferi, and the observed infection bottleneck apparent using a polyclonal infection pool provides insight into the dynamics of experimental infection with B. burgdorferi. © 2015 S. Karger AG, Basel.

Multiple-target tracking implementation in the ebCMOS camera system: the LUSIPHER prototype

NASA Astrophysics Data System (ADS)

Doan, Quang Tuyen; Barbier, Remi; Dominjon, Agnes; Cajgfinger, Thomas; Guerin, Cyrille

2012-06-01

The domain of the low light imaging systems progresses very fast, thanks to detection and electronic multiplication technology evolution, such as the emCCD (electron multiplying CCD) or the ebCMOS (electron bombarded CMOS). We present an ebCMOS camera system that is able to track every 2 ms more than 2000 targets with a mean number of photons per target lower than two. The point light sources (targets) are spots generated by a microlens array (Shack-Hartmann) used in adaptive optics. The Multiple-Target-Tracking designed and implemented on a rugged workstation is described. The results and the performances of the system on the identification and tracking are presented and discussed.

Risk Identification in a Smart Monitoring System Used to Preserve Artefacts Based on Textile Materials

NASA Astrophysics Data System (ADS)

Diaconescu, V. D.; Scripcariu, L.; Mătăsaru, P. D.; Diaconescu, M. R.; Ignat, C. A.

2018-06-01

Exhibited textile-materials-based artefacts can be affected by the environmental conditions. A smart monitoring system that commands an adaptive automatic environment control system is proposed for indoor exhibition spaces containing various textile artefacts. All exhibited objects are monitored by many multi-sensor nodes containing temperature, relative humidity and light sensors. Data collected periodically from the entire sensor network is stored in a database and statistically processed in order to identify and classify the environment risk. Risk consequences are analyzed depending on the risk class and the smart system commands different control measures in order to stabilize the indoor environment conditions to the recommended values and prevent material degradation.

Identification of water-deficit responsive genes in maritime pine (Pinus pinaster Ait.) roots.

PubMed

Dubos, Christian; Plomion, Christophe

2003-01-01

Root adaptation to soil environmental factors is very important to maritime pine, the main conifer species used for reforestation in France. The range of climates in the sites where this species is established varies from flooded in winter to drought-prone in summer. No studies have yet focused on the morphological, physiological or molecular variability of the root system to adapt its growth to such an environment. We developed a strategy to isolate drought-responsive genes in the root tissue in order to identify the molecular mechanisms that trees have evolved to cope with drought (the main problem affecting wood productivity), and to exploit this information to improve drought stress tolerance. In order to provide easy access to the root system, seedlings were raised in hydroponic solution. Polyethylene glycol was used as an osmoticum to induce water deficit. Using the cDNA-AFLP technique, we screened more than 2500 transcript derived fragments, of which 33 (1.2%) showed clear variation in presence/absence between non stressed and stressed medium. The relative abundance of these transcripts was then analysed by reverse northern. Only two out of these 33 genes showed significant opposite behaviour between both techniques. The identification and characterization of water-deficit responsive genes in roots provide the emergence of physiological understanding of the patterns of gene expression and regulation involved in the drought stress response of maritime pine.

Model-based safety analysis of human-robot interactions: the MIRAS walking assistance robot.

PubMed

Guiochet, Jérémie; Hoang, Quynh Anh Do; Kaaniche, Mohamed; Powell, David

2013-06-01

Robotic systems have to cope with various execution environments while guaranteeing safety, and in particular when they interact with humans during rehabilitation tasks. These systems are often critical since their failure can lead to human injury or even death. However, such systems are difficult to validate due to their high complexity and the fact that they operate within complex, variable and uncertain environments (including users), in which it is difficult to foresee all possible system behaviors. Because of the complexity of human-robot interactions, rigorous and systematic approaches are needed to assist the developers in the identification of significant threats and the implementation of efficient protection mechanisms, and in the elaboration of a sound argumentation to justify the level of safety that can be achieved by the system. For threat identification, we propose a method called HAZOP-UML based on a risk analysis technique adapted to system description models, focusing on human-robot interaction models. The output of this step is then injected in a structured safety argumentation using the GSN graphical notation. Those approaches have been successfully applied to the development of a walking assistant robot which is now in clinical validation.

Stable modeling based control methods using a new RBF network.

PubMed

Beyhan, Selami; Alci, Musa

2010-10-01

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

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

Pierre, John W.; Wies, Richard; Trudnowski, Daniel

Time-synchronized measurements provide rich information for estimating a power-system's electromechanical modal properties via advanced signal processing. This information is becoming critical for the improved operational reliability of interconnected grids. A given mode's properties are described by its frequency, damping, and shape. Modal frequencies and damping are useful indicators of power-system stress, usually declining with increased load or reduced grid capacity. Mode shape provides critical information for operational control actions. This project investigated many advanced techniques for power system identification from measured data focusing on mode frequency and damping ratio estimation. Investigators from the three universities coordinated their effort with Pacificmore » Northwest National Laboratory (PNNL). Significant progress was made on developing appropriate techniques for system identification with confidence intervals and testing those techniques on field measured data and through simulation. Experimental data from the western area power system was provided by PNNL and Bonneville Power Administration (BPA) for both ambient conditions and for signal injection tests. Three large-scale tests were conducted for the western area in 2005 and 2006. Measured field PMU (Phasor Measurement Unit) data was provided to the three universities. A 19-machine simulation model was enhanced for testing the system identification algorithms. Extensive simulations were run with this model to test the performance of the algorithms. University of Wyoming researchers participated in four primary activities: (1) Block and adaptive processing techniques for mode estimation from ambient signals and probing signals, (2) confidence interval estimation, (3) probing signal design and injection method analysis, and (4) performance assessment and validation from simulated and field measured data. Subspace based methods have been use to improve previous results from block processing techniques. Bootstrap techniques have been developed to estimate confidence intervals for the electromechanical modes from field measured data. Results were obtained using injected signal data provided by BPA. A new probing signal was designed that puts more strength into the signal for a given maximum peak to peak swing. Further simulations were conducted on a model based on measured data and with the modifications of the 19-machine simulation model. Montana Tech researchers participated in two primary activities: (1) continued development of the 19-machine simulation test system to include a DC line; and (2) extensive simulation analysis of the various system identification algorithms and bootstrap techniques using the 19 machine model. Researchers at the University of Alaska-Fairbanks focused on the development and testing of adaptive filter algorithms for mode estimation using data generated from simulation models and on data provided in collaboration with BPA and PNNL. There efforts consist of pre-processing field data, testing and refining adaptive filter techniques (specifically the Least Mean Squares (LMS), the Adaptive Step-size LMS (ASLMS), and Error Tracking (ET) algorithms). They also improved convergence of the adaptive algorithms by using an initial estimate from block processing AR method to initialize the weight vector for LMS. Extensive testing was performed on simulated data from the 19 machine model. This project was also extensively involved in the WECC (Western Electricity Coordinating Council) system wide tests carried out in 2005 and 2006. These tests involved injecting known probing signals into the western power grid. One of the primary goals of these tests was the reliable estimation of electromechanical mode properties from measured PMU data. Applied to the system were three types of probing inputs: (1) activation of the Chief Joseph Dynamic Brake, (2) mid-level probing at the Pacific DC Intertie (PDCI), and (3) low-level probing on the PDCI. The Chief Joseph Dynamic Brake is a 1400 MW disturbance to the system and is injected for a half of a second. For the mid and low-level probing, the Celilo terminal of the PDCI is modulated with a known probing signal. Similar but less extensive tests were conducted in June of 2000. The low-level probing signals were designed at the University of Wyoming. A number of important design factors are considered. The designed low-level probing signal used in the tests is a multi-sine signal. Its frequency content is focused in the range of the inter-area electromechanical modes. The most frequently used of these low-level multi-sine signals had a period of over two minutes, a root-mean-square (rms) value of 14 MW, and a peak magnitude of 20 MW. Up to 15 cycles of this probing signal were injected into the system resulting in a processing gain of 15. The resulting measured response at points throughout the system was not much larger than the ambient noise present in the measurements.« less

Roogle: an information retrieval engine for clinical data warehouse.

PubMed

Cuggia, Marc; Garcelon, Nicolas; Campillo-Gimenez, Boris; Bernicot, Thomas; Laurent, Jean-François; Garin, Etienne; Happe, André; Duvauferrier, Régis

2011-01-01

High amount of relevant information is contained in reports stored in the electronic patient records and associated metadata. R-oogle is a project aiming at developing information retrieval engines adapted to these reports and designed for clinicians. The system consists in a data warehouse (full-text reports and structured data) imported from two different hospital information systems. Information retrieval is performed using metadata-based semantic and full-text search methods (as Google). Applications may be biomarkers identification in a translational approach, search of specific cases, and constitution of cohorts, professional practice evaluation, and quality control assessment.

Methodology to explore interactions between the water system and society in order to identify adaptation strategies

NASA Astrophysics Data System (ADS)

Offermans, A. G. E.; Haasnoot, M.

2009-04-01

Development of sustainable water management strategies involves analysing current and future vulnerability, identification of adaptation possibilities, effect analysis and evaluation of the strategies under different possible futures. Recent studies on water management often followed the pressure-effect chain and compared the state of social, economic and ecological functions of the water systems in one or two future situations with the current situation. The future is, however, more complex and dynamic. Water management faces major challenges to cope with future uncertainties in both the water system as well as the social system. Uncertainties in our water system relate to (changes in) drivers and pressures and their effects on the state, like the effects of climate change on discharges. Uncertainties in the social world relate to changing of perceptions, objectives and demands concerning water (management), which are often related with the aforementioned changes in the physical environment. The methodology presented here comprises the 'Perspectives method', derived from the Cultural Theory, a method on analyzing and classifying social response to social and natural states and pressures. The method will be used for scenario analysis and to identify social responses including changes in perspectives and management strategies. The scenarios and responses will be integrated within a rapid assessment tool. The purpose of the tool is to provide users with insight about the interaction of the social and physical system and to identify robust water management strategies by analysing the effectiveness under different possible futures on the physical, social and socio-economic system. This method allows for a mutual interaction between the physical and social system. We will present the theoretical background of the perspectives method as well as a historical overview of perspective changes in the Dutch Meuse area to show how social and physical systems interrelate. We will also show how the integration of both can contribute to the identification of robust water management strategies.

Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks.

PubMed

Zhao, Yubin; Li, Xiaofan; Zhang, Sha; Meng, Tianhui; Zhang, Yiwen

2016-08-23

In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér-Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for the indoor localization systems and serves as an indicator for practical system evaluation.

A unique charge-coupled device/xenon arc lamp based imaging system for the accurate detection and quantitation of multicolour fluorescence.

PubMed

Spibey, C A; Jackson, P; Herick, K

2001-03-01

In recent years the use of fluorescent dyes in biological applications has dramatically increased. The continual improvement in the capabilities of these fluorescent dyes demands increasingly sensitive detection systems that provide accurate quantitation over a wide linear dynamic range. In the field of proteomics, the detection, quantitation and identification of very low abundance proteins are of extreme importance in understanding cellular processes. Therefore, the instrumentation used to acquire an image of such samples, for spot picking and identification by mass spectrometry, must be sensitive enough to be able, not only, to maximise the sensitivity and dynamic range of the staining dyes but, as importantly, adapt to the ever changing portfolio of fluorescent dyes as they become available. Just as the available fluorescent probes are improving and evolving so are the users application requirements. Therefore, the instrumentation chosen must be flexible to address and adapt to those changing needs. As a result, a highly competitive market for the supply and production of such dyes and the instrumentation for their detection and quantitation have emerged. The instrumentation currently available is based on either laser/photomultiplier tube (PMT) scanning or lamp/charge-coupled device (CCD) based mechanisms. This review briefly discusses the advantages and disadvantages of both System types for fluorescence imaging, gives a technical overview of CCD technology and describes in detail a unique xenon/are lamp CCD based instrument, from PerkinElmer Life Sciences. The Wallac-1442 ARTHUR is unique in its ability to scan both large areas at high resolution and give accurate selectable excitation over the whole of the UV/visible range. It operates by filtering both the excitation and emission wavelengths, providing optimal and accurate measurement and quantitation of virtually any available dye and allows excellent spectral resolution between different fluorophores. This flexibility and excitation accuracy is key to multicolour applications and future adaptation of the instrument to address the application requirements and newly emerging dyes.

A study of helicopter gust response alleviation by automatic control

NASA Technical Reports Server (NTRS)

Saito, S.

1983-01-01

Two control schemes designed to alleviate gust-induced vibration are analytically investigated for a helicopter with four articulated blades. One is an individual blade pitch control scheme. The other is an adaptive blade pitch control algorithm based on linear optimal control theory. In both controllers, control inputs to alleviate gust response are superimposed on the conventional control inputs required to maintain the trim condition. A sinusoidal vertical gust model and a step gust model are used. The individual blade pitch control, in this research, is composed of sensors and a pitch control actuator for each blade. Each sensor can detect flapwise (or lead-lag or torsionwise) deflection of the respective blade. The acturator controls the blade pitch angle for gust alleviation. Theoretical calculations to predict the performance of this feedback system have been conducted by means of the harmonic method. The adaptive blade pitch control system is composed of a set of measurements (oscillatory hub forces and moments), an identification system using a Kalman filter, and a control system based on the minimization of the quadratic performance function.

Wearable sensors for human health monitoring

NASA Astrophysics Data System (ADS)

Asada, H. Harry; Reisner, Andrew

2006-03-01

Wearable sensors for continuous monitoring of vital signs for extended periods of weeks or months are expected to revolutionize healthcare services in the home and workplace as well as in hospitals and nursing homes. This invited paper describes recent research progress in wearable health monitoring technology and its clinical applications, with emphasis on blood pressure and circulatory monitoring. First, a finger ring-type wearable blood pressure sensor based on photo plethysmogram is presented. Technical issues, including motion artifact reduction, power saving, and wearability enhancement, will be addressed. Second, sensor fusion and sensor networking for integrating multiple sensors with diverse modalities will be discussed for comprehensive monitoring and diagnosis of health status. Unlike traditional snap-shot measurements, continuous monitoring with wearable sensors opens up the possibility to treat the physiological system as a dynamical process. This allows us to apply powerful system dynamics and control methodologies, such as adaptive filtering, single- and multi-channel system identification, active noise cancellation, and adaptive control, to the monitoring and treatment of highly complex physiological systems. A few clinical trials illustrate the potentials of the wearable sensor technology for future heath care services.

SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate

Treesearch

Gretchen H. Roffler; Stephen J. Amish; Seth Smith; Ted Cosart; Marty Kardos; Michael K. Schwartz; Gordon Luikart

2016-01-01

Identification of genes underlying genomic signatures of natural selection is key to understanding adaptation to local conditions. We used targeted resequencing to identify SNP markers in 5321 candidate adaptive genes associated with known immunological, metabolic and growth functions in ovids and other ungulates. We selectively targeted 8161 exons in protein-coding...

Adaptation of cardiovascular system stent implants.

PubMed

Ostasevicius, Vytautas; Tretsyakou-Savich, Yahor; Venslauskas, Mantas; Bertasiene, Agne; Minchenya, Vladimir; Chernoglaz, Pavel

2018-06-27

Time-consuming design and manufacturing processes are a serious disadvantage when adapting human cardiovascular implants as they cause unacceptable delays after the decision to intervene surgically has been made. An ideal cardiovascular implant should have a broad range of characteristics such as strength, viscoelasticity and blood compatibility. The present research proposes the sequence of the geometrical adaptation procedures and presents their results. The adaptation starts from the identification of a person's current health status while performing abdominal aortic aneurysm (AAA) imaging, which is a point of departure for the mathematical model of a cardiovascular implant. The computerized tomography scan shows the patient-specific geometry parameters of AAA and helps to create a model using COMSOL Multiphysics software. The initial parameters for flow simulation are taken from the results of a patient survey. The simulation results allow choosing the available shape of an implant which ensures a non-turbulent flow. These parameters are essential for the design and manufacturing of an implant prototype which should be tested experimentally for the assurance that the mathematical model is adequate to a physical one. The article gives a focused description of competences and means that are necessary to achieve the shortest possible preparation of the adapted cardiovascular implant for the surgery.

Novel Insights into the Organization of Laticifer Cells: A Cell Comprising a Unified Whole System1

PubMed Central

Castelblanque, Lourdes; Balaguer, Begoña; Rodríguez, Juan José; Orozco, Marianela; Vera, Pablo

2016-01-01

Laticifer cells are specialized plant cells that synthesize and accumulate latex. Studies on laticifers have lagged behind in recent years, and data regarding the functional role of laticifers and their fitness benefit still remain elusive. Laticifer differentiation and its impact on plant growth and development also remain to be investigated. Here, cellular, molecular, and genetic tools were developed to examine the distribution, differentiation, ontogeny, and other characteristic features, as well as the potential developmental role of laticifer cells in the latex-bearing plant Euphorbia lathyris. The organization of the laticiferous system within the E. lathyris plant body is reported, emerging as a single elongated and branched coenocytic cell, constituting the largest cell type existing in plants. We also report the ontogeny and organization of laticifer cells in the embryo and the identification of a laticifer-associated gene expression pattern. Moreover, the identification of laticifer- and latex-deficient mutants (pil mutants) allowed for the identification of distinct loci regulating laticifer differentiation, growth, and metabolic activity. Additionally, pil mutants revealed that laticifer cells appear nonessential for plant growth and development, thus pointing toward their importance, instead, for specific ecophysiological adaptations of latex-bearing plants in natural environments. PMID:27468995

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

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

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

Improved CEEMDAN-wavelet transform de-noising method and its application in well logging noise reduction

NASA Astrophysics Data System (ADS)

Zhang, Jingxia; Guo, Yinghai; Shen, Yulin; Zhao, Difei; Li, Mi

2018-06-01

The use of geophysical logging data to identify lithology is an important groundwork in logging interpretation. Inevitably, noise is mixed in during data collection due to the equipment and other external factors and this will affect the further lithological identification and other logging interpretation. Therefore, to get a more accurate lithological identification it is necessary to adopt de-noising methods. In this study, a new de-noising method, namely improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-wavelet transform, is proposed, which integrates the superiorities of improved CEEMDAN and wavelet transform. Improved CEEMDAN, an effective self-adaptive multi-scale analysis method, is used to decompose non-stationary signals as the logging data to obtain the intrinsic mode function (IMF) of N different scales and one residual. Moreover, one self-adaptive scale selection method is used to determine the reconstruction scale k. Simultaneously, given the possible frequency aliasing problem between adjacent IMFs, a wavelet transform threshold de-noising method is used to reduce the noise of the (k-1)th IMF. Subsequently, the de-noised logging data are reconstructed by the de-noised (k-1)th IMF and the remaining low-frequency IMFs and the residual. Finally, empirical mode decomposition, improved CEEMDAN, wavelet transform and the proposed method are applied for analysis of the simulation and the actual data. Results show diverse performance of these de-noising methods with regard to accuracy for lithological identification. Compared with the other methods, the proposed method has the best self-adaptability and accuracy in lithological identification.

Adaptive Control and Parameter Identification of a Doubly-Fed Induction Generator for Wind Power

DTIC Science & Technology

2011-09-01

Computer Controlled Systems, Theory and Design, Third Edition, Prentice Hall, New Jersey, 1997. [27] R. G. Brown and P. Y.C. Hwang , Introduction to...V n y iT iT , (0.0) with Ts as the sampling interval. From [26], the recursive estimate can be interpreted as a Kalman Filter for the process...by substituting t with n. The recursive equations for the RLS can then be derived from the Kalman filter equations used in [27]: 29 $ $ $ 1 1

MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System

DTIC Science & Technology

2015-08-02

AUTHORS 7. PERFORMING ORGANIZATION NAMES AND ADDRESSES 15. SUBJECT TERMS b. ABSTRACT 2 . REPORT TYPE 17. LIMITATION OF ABSTRACT 15. NUMBER OF...fuzzy logic can deal with the uncertainty of human cognition [ 2 ]. ANFIS offers an alternative to rules’ identification. While Mamdani [3] and Sugeno [4...dimensional vector with elements xpjk corresponding to features, i.e., Bp =  xp11 xp12 . . . xp1D xp21 xp22 . . . xp2D ... ... . . . ... xpMp1

SisPorto 4.0 - computer analysis following the 2015 FIGO Guidelines for intrapartum fetal monitoring.

PubMed

Ayres-de-Campos, Diogo; Rei, Mariana; Nunes, Inês; Sousa, Paulo; Bernardes, João

2017-01-01

SisPorto 4.0 is the most recent version of a program for the computer analysis of cardiotocographic (CTG) signals and ST events, which has been adapted to the 2015 International Federation of Gynaecology and Obstetrics (FIGO) guidelines for intrapartum foetal monitoring. This paper provides a detailed description of the analysis performed by the system, including the signal-processing algorithms involved in identification of basic CTG features and the resulting real-time alerts.

Analytical techniques for the study of some parameters of multispectral scanner systems for remote sensing

NASA Technical Reports Server (NTRS)

Wiswell, E. R.; Cooper, G. R. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The concept of average mutual information in the received spectral random process about the spectral scene was developed. Techniques amenable to implementation on a digital computer were also developed to make the required average mutual information calculations. These techniques required identification of models for the spectral response process of scenes. Stochastic modeling techniques were adapted for use. These techniques were demonstrated on empirical data from wheat and vegetation scenes.

Adaptive control of large space structures using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Goglia, G. L.

1985-01-01

The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2018-05-11

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. We herein introduce an adaptive framework in which optimal input design is integrated with Square root Cubature Kalman Filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 msec in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications. © 2018 IOP Publishing Ltd.

ID’ing Innate and Innate-like Lymphoid Cells

PubMed Central

Verykokakis, Mihalis; Zook, Erin C.; Kee, Barbara L.

2014-01-01

Summary The immune system can be divided into innate and adaptive components that differ in their rate and mode of cellular activation, with innate immune cells being the first responders to invading pathogens. Recent advances in the identification and characterization of innate lymphoid cells have revealed reiterative developmental programs that result in cells with effector fates that parallel those of adaptive lymphoid cells and are tailored to effectively eliminate a broad spectrum of pathogenic challenges. However, activation of these cells can also be associated with pathologies such as autoimmune disease. One major distinction between innate and adaptive immune system cells is the constitutive expression of ID proteins in the former and inducible expression in the latter. ID proteins function as antagonists of the E protein transcription factors that play critical roles in lymphoid specification as well as B and T-lymphocyte development. In this review, we examine the transcriptional mechanisms controlling the development of innate lymphocytes, including natural killer cells and the recently identified innate lymphoid cells (ILC1, ILC2, and ILC3), and innate-like lymphocytes, including natural killer T cells, with an emphasis on the known requirements for the ID proteins. PMID:25123285

ID'ing innate and innate-like lymphoid cells.

PubMed

Verykokakis, Mihalis; Zook, Erin C; Kee, Barbara L

2014-09-01

The immune system can be divided into innate and adaptive components that differ in their rate and mode of cellular activation, with innate immune cells being the first responders to invading pathogens. Recent advances in the identification and characterization of innate lymphoid cells have revealed reiterative developmental programs that result in cells with effector fates that parallel those of adaptive lymphoid cells and are tailored to effectively eliminate a broad spectrum of pathogenic challenges. However, activation of these cells can also be associated with pathologies such as autoimmune disease. One major distinction between innate and adaptive immune system cells is the constitutive expression of ID proteins in the former and inducible expression in the latter. ID proteins function as antagonists of the E protein transcription factors that play critical roles in lymphoid specification as well as B- and T-lymphocyte development. In this review, we examine the transcriptional mechanisms controlling the development of innate lymphocytes, including natural killer cells and the recently identified innate lymphoid cells (ILC1, ILC2, and ILC3), and innate-like lymphocytes, including natural killer T cells, with an emphasis on the known requirements for the ID proteins. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Automated Segmentation of High-Resolution Photospheric Images of Active Regions

NASA Astrophysics Data System (ADS)

Yang, Meng; Tian, Yu; Rao, Changhui

2018-02-01

Due to the development of ground-based, large-aperture solar telescopes with adaptive optics (AO) resulting in increasing resolving ability, more accurate sunspot identifications and characterizations are required. In this article, we have developed a set of automated segmentation methods for high-resolution solar photospheric images. Firstly, a local-intensity-clustering level-set method is applied to roughly separate solar granulation and sunspots. Then reinitialization-free level-set evolution is adopted to adjust the boundaries of the photospheric patch; an adaptive intensity threshold is used to discriminate between umbra and penumbra; light bridges are selected according to their regional properties from candidates produced by morphological operations. The proposed method is applied to the solar high-resolution TiO 705.7-nm images taken by the 151-element AO system and Ground-Layer Adaptive Optics prototype system at the 1-m New Vacuum Solar Telescope of the Yunnan Observatory. Experimental results show that the method achieves satisfactory robustness and efficiency with low computational cost on high-resolution images. The method could also be applied to full-disk images, and the calculated sunspot areas correlate well with the data given by the National Oceanic and Atmospheric Administration (NOAA).

Organization Development. Symposium.

ERIC Educational Resources Information Center

2002

This document contains four papers on organization development and human resources. "Identification of Key Predictors of Rapid Change Adaptation in a Service Organization" (Constantine Kontoghiorghes, Carol Hansen) reports on the results of an exploratory study, which suggests that rapid change adaptation will be more likely to occur in…

Building the framework for climate change adaptation in the urban areas using participatory approach: the Czech Republic experience

NASA Astrophysics Data System (ADS)

Emmer, Adam; Hubatová, Marie; Lupač, Miroslav; Pondělíček, Michael; Šafařík, Miroslav; Šilhánková, Vladimíra; Vačkář, David

2016-04-01

The Czech Republic has experienced numerous extreme hydrometeorological / climatological events such as floods (significant ones in 1997, 2002, 2010, 2013), droughts (2013, 2015), heat waves (2015) and windstorms (2007) during past decades. These events are generally attributed to the ongoing climate change and caused loss of lives and significant material damages (up to several % of GDP in some years), especially in urban areas. To initiate the adaptation process of urban areas, the main objective was to prepare a framework for creating climate change adaptation strategies of individual cities reflecting physical-geographical and socioeconomical conditions of the Czech Republic. Three pilot cities (Hradec Králové, Žďár nad Sázavou, Dobru\\vska) were used to optimize entire procedure. Two sets of participatory seminars were organised in order to involve all key stakeholders (the city council, department of the environment, department of the crisis management, hydrometeorological institute, local experts, ...) into the process of creation of the adaptation strategy from its early stage. Lesson learned for the framework were related especially to its applicability on a local level, which is largely a matter of the understandability of the concept. Finally, this illustrative and widely applicable framework (so called 'road map to adaptation strategy') includes five steps: (i) analysis of existing strategies and plans on national, regional and local levels; (ii) analysing climate-change related hazards and key vulnerabilities; (iii) identification of adaptation needs, evaluation of existing adaptation capacity and formulation of future adaptation priorities; (iv) identification of limits and barriers for the adaptation (economical, environmental, ...); and (v) selection of specific types of adaptation measures reflecting identified adaptation needs and formulated adaptation priorities. Keywords: climate change adaptation (CCA); urban areas; participatory approach; road map

Aircraft Flight Envelope Determination using Upset Detection and Physical Modeling Methods

NASA Technical Reports Server (NTRS)

Keller, Jeffrey D.; McKillip, Robert M. Jr.; Kim, Singwan

2009-01-01

The development of flight control systems to enhance aircraft safety during periods of vehicle impairment or degraded operations has been the focus of extensive work in recent years. Conditions adversely affecting aircraft flight operations and safety may result from a number of causes, including environmental disturbances, degraded flight operations, and aerodynamic upsets. To enhance the effectiveness of adaptive and envelope limiting controls systems, it is desirable to examine methods for identifying the occurrence of anomalous conditions and for assessing the impact of these conditions on the aircraft operational limits. This paper describes initial work performed toward this end, examining the use of fault detection methods applied to the aircraft for aerodynamic performance degradation identification and model-based methods for envelope prediction. Results are presented in which a model-based fault detection filter is applied to the identification of aircraft control surface and stall departure failures/upsets. This application is supported by a distributed loading aerodynamics formulation for the flight dynamics system reference model. Extensions for estimating the flight envelope due to generalized aerodynamic performance degradation are also described.

Biological complexity and adaptability of simple mammalian olfactory memory systems.

PubMed

Brennan, P; Keverne, E B

2015-03-01

Chemosensory systems play vital roles in the lives of most mammals, including the detection and identification of predators, as well as sex and reproductive status and the identification of individual conspecifics. All of these capabilities require a process of recognition involving a combination of innate (kairomonal/pheromonal) and learned responses. Across very different phylogenies, the mechanisms for pheromonal and odour learning have much in common. They are frequently associated with plasticity of GABA-ergic feedback at the initial level of processing the chemosensory information, which enhances its pattern separation capability. Association of odourant features into an odour object primarily involves anterior piriform cortex for non-social odours. However, the medial amygdala appears to be involved in both the recognition of social odours and their association with chemosensory information sensed by the vomeronasal system. Unusually not only the sensory neurons themselves, but also the GABA-ergic interneurons in the olfactory bulb are continually being replaced, with implications for the induction and maintenance of learned chemosensory responses. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Analysis of the 3’ untranslated regions of α-tubulin and S-crystallin mRNA and the identification of CPEB in dark- and light-adapted octopus retinas

PubMed Central

Kelly, Shannan; Yamamoto, Hideki

2008-01-01

Purpose We previously reported the differential expression and translation of mRNA and protein in dark- and light-adapted octopus retinas, which may result from cytoplasmic polyadenylation element (CPE)–dependent mRNA masking and unmasking. Here we investigate the presence of CPEs in α-tubulin and S-crystallin mRNA and report the identification of cytoplasmic polyadenylation element binding protein (CPEB) in light- and dark-adapted octopus retinas. Methods 3’-RACE and sequencing were used to isolate and analyze the 3’-UTRs of α-tubulin and S-crystallin mRNA. Total retinal protein isolated from light- and dark-adapted octopus retinas was subjected to western blot analysis followed by CPEB antibody detection, PEP-171 inhibition of CPEB, and dephosphorylation of CPEB. Results The following CPE-like sequence was detected in the 3’-UTR of isolated long S-crystallin mRNA variants: UUUAACA. No CPE or CPE-like sequences were detected in the 3’-UTRs of α-tubulin mRNA or of the short S-crystallin mRNA variants. Western blot analysis detected CPEB as two putative bands migrating between 60-80 kDa, while a third band migrated below 30 kDa in dark- and light-adapted retinas. Conclusions The detection of CPEB and the identification of the putative CPE-like sequences in the S-crystallin 3’-UTR suggest that CPEB may be involved in the activation of masked S-crystallin mRNA, but not in the regulation of α-tubulin mRNA, resulting in increased S-crystallin protein synthesis in dark-adapted octopus retinas. PMID:18682811

An adaptive, object oriented strategy for base calling in DNA sequence analysis.

PubMed Central

Giddings, M C; Brumley, R L; Haker, M; Smith, L M

1993-01-01

An algorithm has been developed for the determination of nucleotide sequence from data produced in fluorescence-based automated DNA sequencing instruments employing the four-color strategy. This algorithm takes advantage of object oriented programming techniques for modularity and extensibility. The algorithm is adaptive in that data sets from a wide variety of instruments and sequencing conditions can be used with good results. Confidence values are provided on the base calls as an estimate of accuracy. The algorithm iteratively employs confidence determinations from several different modules, each of which examines a different feature of the data for accurate peak identification. Modules within this system can be added or removed for increased performance or for application to a different task. In comparisons with commercial software, the algorithm performed well. Images PMID:8233787

Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

NASA Astrophysics Data System (ADS)

Rustamov, Samir; Mustafayev, Elshan; Clements, Mark A.

2018-04-01

The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

2012-01-01

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

Observations to support adaptation: Principles, scales and decision-making

NASA Astrophysics Data System (ADS)

Pulwarty, R. S.

2012-12-01

As has been long noted, a comprehensive, coordinated observing system is the backbone of any Earth information system. Demands are increasingly placed on earth observation and prediction systems and attendant services to address the needs of economically and environmentally vulnerable sectors and investments, including energy, water, human health, transportation, agriculture, fisheries, tourism, biodiversity, and national security. Climate services include building capacity to interpret information and recognize standards and limitations of data in the promotion of social and economic development in a changing climate. This includes improving the understanding of climate in the context of a variety of temporal and spatial scales (including the influence of decadal scale forcings and land surface feedbacks on seasonal forecast reliability). Climate data and information are central for developing decision options that are sensitive to climate-related uncertainties and the design of flexible adaptation pathways. Ideally monitoring should be action oriented to support climate risk assessment and adaptation including informing robust decision making to multiple risks over the long term. Based on the experience of global observations programs and empirical research we outline- Challenges in developing effective monitoring and climate information systems to support adaptation. The types of observations of critical importance needed for sector planning to enhance food, water and energy security, and to improve early warning for disaster risk reduction Observations needed for ecosystem-based adaptation including the identification of thresholds, maintenance of biological diversity and land degradation The benefits and limits of linking regional model output to local observations including analogs and verification for adaptation planning To support these goals a robust systems of integrated observations are needed to characterize the uncertainty surrounding emergent risks including overcoming unrealistically precise information demands. While monitoring systems design and operation should be guided by the standards and requirements of management, those who provide information to the system (e.g. hydromet services) should also derive benefits. Drawing on identified information needs to support climate risk management (in drought, water resources and other areas) we outline principles of effective monitoring and develop preliminary strategic guidance for information systems being developed through the GEO, GCOS and Global and national frameworks for climate services. The efficacy of such services are improved by a problem-solving orientation, participatory planning, extension management and improvements in the use and value of existing data to legitimize new investments.

Johnny was here: From airmanship to airlineship.

PubMed

Haavik, Torgeir K; Kongsvik, Trond; Bye, Rolf Johan; Dalseth Røyrvik, Jens Olgard; Almklov, Petter Grytten

2017-03-01

In this article we explore the phenomenon of airmanship in commercial passenger flights, in a context of increasing standardisation of procedures and technologies. Through observation studies in cockpits and interviews we have studied pilots' practices and how they relate to the larger system of procedures and the technical environment. We find that practices are to a large extent guided by standard operating procedures, and that interchangeability of pilots and aircrafts is both a prerequisite for and enabled by this standardised regime. However, since sociotechnical systems in general - and operation of aircrafts is no exception - are inherently underspecified, the pilots' exercise of discretion in their context-sensitive adaptation of the procedures and technical environments is another prerequisite for well-functioning systems. Mastering these adaptations - and recognising the absolute delimitations of adaptations - is a central aspect of airmanship. Outside this space of manoeuvre for the pilots, the aircrafts are managed by what we call airlineship: The inter-organisational efforts to create predictability and safe practices through de-identification and interchangeability of personnel and aircrafts. Pilots are actors in sociotechnical systems that are not demarcated by the cockpits. To understand pilots' work, studies must account also for the wider sociotechnical context of organisational, regulative and techno-material structures. The article is a contribution to the a generic attempt in the field of ergonomics to contribute with models and theories that portray individuals, groups, organisations and systems in ways that keep sight of the individuals in the systems and the systems in the individuals at the same time. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

The Variable Vector Countermeasure Suit (V2Suit) for space habitation and exploration.

PubMed

Duda, Kevin R; Vasquez, Rebecca A; Middleton, Akil J; Hansberry, Mitchell L; Newman, Dava J; Jacobs, Shane E; West, John J

2015-01-01

The "Variable Vector Countermeasure Suit (V2Suit) for Space Habitation and Exploration" is a novel system concept that provides a platform for integrating sensors and actuators with daily astronaut intravehicular activities to improve health and performance, while reducing the mass and volume of the physiologic adaptation countermeasure systems, as well as the required exercise time during long-duration space exploration missions. The V2Suit system leverages wearable kinematic monitoring technology and uses inertial measurement units (IMUs) and control moment gyroscopes (CMGs) within miniaturized modules placed on body segments to provide a "viscous resistance" during movements against a specified direction of "down"-initially as a countermeasure to the sensorimotor adaptation performance decrements that manifest themselves while living and working in microgravity and during gravitational transitions during long-duration spaceflight, including post-flight recovery and rehabilitation. Several aspects of the V2Suit system concept were explored and simulated prior to developing a brassboard prototype for technology demonstration. This included a system architecture for identifying the key components and their interconnects, initial identification of key human-system integration challenges, development of a simulation architecture for CMG selection and parameter sizing, and the detailed mechanical design and fabrication of a module. The brassboard prototype demonstrates closed-loop control from "down" initialization through CMG actuation, and provides a research platform for human performance evaluations to mitigate sensorimotor adaptation, as well as a tool for determining the performance requirements when used as a musculoskeletal deconditioning countermeasure. This type of countermeasure system also has Earth benefits, particularly in gait or movement stabilization and rehabilitation.

Coping with expanding nursing practice, knowledge, and technology.

PubMed

Gaudinski, M A

1979-10-01

Nurses utilize transcultural, transactional, systems, primary, and interdisciplinary approaches to physiological and psychosocial components of patient care. Expanded roles, as well as advances in knowledge and technology have prepared nurses for critical, specialized, primary, aerospace, and independent nursing practice. Exciting as they are, nursing's expanded roles and practices frequently contribute to the burnout and distress phenomena increasingly observed in practicing health care professionals. Causes and symptoms of the burnout distress phenomena are many and varied. Selye, Shubin, Maslach, and others adeptly identified and wrote on the phenomena as it specifically relates to nurses and the many facets of nursing practice. Rather than utilizing crisis intervention coping techniques, preventive strategies and adaptations are suggested. This paper reviews and discusses: 1. Factors associated with burnout-distress phenomena identified in professional literature; 2. Identification of factors associated with expanded roles and practice which contribute to burnout stress; 3. Identification of factors in military and civilian air ambulance and aeromedical evacuation systems which contribute to burnout stress; 4. Recommendations for strategies to prevent and cope with burnout distress factors.

Virtual Sensor for Failure Detection, Identification and Recovery in the Transition Phase of a Morphing Aircraft

PubMed Central

Heredia, Guillermo; Ollero, Aníbal

2010-01-01

The Helicopter Adaptive Aircraft (HADA) is a morphing aircraft which is able to take-off as a helicopter and, when in forward flight, unfold the wings that are hidden under the fuselage, and transfer the power from the main rotor to a propeller, thus morphing from a helicopter to an airplane. In this process, the reliable folding and unfolding of the wings is critical, since a failure may determine the ability to perform a mission, and may even be catastrophic. This paper proposes a virtual sensor based Fault Detection, Identification and Recovery (FDIR) system to increase the reliability of the HADA aircraft. The virtual sensor is able to capture the nonlinear interaction between the folding/unfolding wings aerodynamics and the HADA airframe using the navigation sensor measurements. The proposed FDIR system has been validated using a simulation model of the HADA aircraft, which includes real phenomena as sensor noise and sampling characteristics and turbulence and wind perturbations. PMID:22294922

Virtual sensor for failure detection, identification and recovery in the transition phase of a morphing aircraft.

PubMed

Heredia, Guillermo; Ollero, Aníbal

2010-01-01

The Helicopter Adaptive Aircraft (HADA) is a morphing aircraft which is able to take-off as a helicopter and, when in forward flight, unfold the wings that are hidden under the fuselage, and transfer the power from the main rotor to a propeller, thus morphing from a helicopter to an airplane. In this process, the reliable folding and unfolding of the wings is critical, since a failure may determine the ability to perform a mission, and may even be catastrophic. This paper proposes a virtual sensor based Fault Detection, Identification and Recovery (FDIR) system to increase the reliability of the HADA aircraft. The virtual sensor is able to capture the nonlinear interaction between the folding/unfolding wings aerodynamics and the HADA airframe using the navigation sensor measurements. The proposed FDIR system has been validated using a simulation model of the HADA aircraft, which includes real phenomena as sensor noise and sampling characteristics and turbulence and wind perturbations.

Assessing sound exposure from shipping in coastal waters using a single hydrophone and Automatic Identification System (AIS) data.

PubMed

Merchant, Nathan D; Witt, Matthew J; Blondel, Philippe; Godley, Brendan J; Smith, George H

2012-07-01

Underwater noise from shipping is a growing presence throughout the world's oceans, and may be subjecting marine fauna to chronic noise exposure with potentially severe long-term consequences. The coincidence of dense shipping activity and sensitive marine ecosystems in coastal environments is of particular concern, and noise assessment methodologies which describe the high temporal variability of sound exposure in these areas are needed. We present a method of characterising sound exposure from shipping using continuous passive acoustic monitoring combined with Automatic Identification System (AIS) shipping data. The method is applied to data recorded in Falmouth Bay, UK. Absolute and relative levels of intermittent ship noise contributions to the 24-h sound exposure level are determined using an adaptive threshold, and the spatial distribution of potential ship sources is then analysed using AIS data. This technique can be used to prioritize shipping noise mitigation strategies in coastal marine environments. Copyright © 2012 Elsevier Ltd. All rights reserved.

Data-driven adaptive fractional order PI control for PMSM servo system with measurement noise and data dropouts.

PubMed

Xie, Yuanlong; Tang, Xiaoqi; Song, Bao; Zhou, Xiangdong; Guo, Yixuan

2018-04-01

In this paper, data-driven adaptive fractional order proportional integral (AFOPI) control is presented for permanent magnet synchronous motor (PMSM) servo system perturbed by measurement noise and data dropouts. The proposed method directly exploits the closed-loop process data for the AFOPI controller design under unknown noise distribution and data missing probability. Firstly, the proposed method constructs the AFOPI controller tuning problem as a parameter identification problem using the modified l p norm virtual reference feedback tuning (VRFT). Then, iteratively reweighted least squares is integrated into the l p norm VRFT to give a consistent compensation solution for the AFOPI controller. The measurement noise and data dropouts are estimated and eliminated by feedback compensation periodically, so that the AFOPI controller is updated online to accommodate the time-varying operating conditions. Moreover, the convergence and stability are guaranteed by mathematical analysis. Finally, the effectiveness of the proposed method is demonstrated both on simulations and experiments implemented on a practical PMSM servo system. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Implementation and adaptation in Colombia of the Communities That Care.

PubMed

Mejía-Trujillo, Juliana; Pérez-Gómez, Augusto; Reyes-Rodríguez, María Fernanda

2015-12-15

For more than two years, Corporación Nuevos Rumbos (Colombia) has been carrying out, in eight Colombian communities, a preventive system called Comunidades Que se Cuidan (CQC), an adaptation of Communities That Care (CTC), created at the University of Washington (Seattle), developed for more than 25 years in the United States of America and implemented in eight countries of America, Oceania, and Europe. The system is based on the public health approach, and the social development strategy for community empowerment. The core idea is to teach communities how to make decisions based on data regarding drugs and alcohol consumption and the identification of protective and risk factors, on the basis of the original survey validated in Colombia: these will allow communities to choose the best preventive interventions, tailored for each of them according to their needs. This paper describes the process of implementation of CQC in Colombia, its differences with CTC, the creation of Colombian cut-points, the main difficulties and how these were solved. CQC seems to be a preventive system with a wide potential applicability in other Latin American countries.

Systemic lupus erythematosus biomarkers: the challenging quest

PubMed Central

Wren, Jonathan D.; Munroe, Melissa E.; Mohan, Chandra

2017-01-01

Abstract SLE, a multisystem heterogeneous disease, is characterized by production of antibodies to cellular components, with activation of both the innate and the adaptive immune system. Decades of investigation of blood biomarkers has resulted in incremental improvements in the understanding of SLE. Owing to the heterogeneity of immune dysregulation, no single biomarker has emerged as a surrogate for disease activity or prediction of disease. Beyond identification of surrogate biomarkers, a multitude of clinical trials have sought to inhibit elevated SLE biomarkers for therapeutic benefit. Armed with new -omics technologies, the necessary yet daunting quest to identify better surrogate biomarkers and successful therapeutics for SLE continues with tenacity. PMID:28013203

Immunotherapy for pancreatic cancer: present and future.

PubMed

Aroldi, Francesca; Zaniboni, Alberto

2017-06-01

Despite the identification of some efficient drugs for the treatment of metastatic pancreatic cancer, this tumor remains one of the most lethal cancers and is characterized by a strong resistance to therapies. Pancreatic cancer has some unique features including the presence of a microenvironment filled with immunosuppressive mediators and a dense stroma, which is both a physical barrier to drug penetration and a dynamic entity involved in immune system control. Therefore, the immune system has been hypothesized to play an important role in pancreatic cancer. Thus, therapies acting on innate or adaptive immunity are being investigated. Here, we review the literature, report the most interesting results and hypothesize future treatment directions.

Topological classification of periodic orbits in the Kuramoto-Sivashinsky equation

NASA Astrophysics Data System (ADS)

Dong, Chengwei

2018-05-01

In this paper, we systematically research periodic orbits of the Kuramoto-Sivashinsky equation (KSe). In order to overcome the difficulties in the establishment of one-dimensional symbolic dynamics in the nonlinear system, two basic periodic orbits can be used as basic building blocks to initialize cycle searching, and we use the variational method to numerically determine all the periodic orbits under parameter ν = 0.02991. The symbolic dynamics based on trajectory topology are very successful for classifying all short periodic orbits in the KSe. The current research can be conveniently adapted to the identification and classification of periodic orbits in other chaotic systems.

Computerized decision support system for mass identification in breast using digital mammogram: a study on GA-based neuro-fuzzy approaches.

PubMed

Das, Arpita; Bhattacharya, Mahua

2011-01-01

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input.

PubMed

Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali

2017-09-01

In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs' weights, identification error, and system states is guaranteed using Lyapunov's direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Advances in the genetically complex autoinflammatory diseases.

PubMed

Ombrello, Michael J

2015-07-01

Monogenic diseases usually demonstrate Mendelian inheritance and are caused by highly penetrant genetic variants of a single gene. In contrast, genetically complex diseases arise from a combination of multiple genetic and environmental factors. The concept of autoinflammation originally emerged from the identification of individual, activating lesions of the innate immune system as the molecular basis of the hereditary periodic fever syndromes. In addition to these rare, monogenic forms of autoinflammation, genetically complex autoinflammatory diseases like the periodic fever, aphthous stomatitis, pharyngitis, and cervical adenitis (PFAPA) syndrome, chronic recurrent multifocal osteomyelitis (CRMO), Behçet's disease, and systemic arthritis also fulfill the definition of autoinflammatory diseases-namely, the development of apparently unprovoked episodes of inflammation without identifiable exogenous triggers and in the absence of autoimmunity. Interestingly, investigations of these genetically complex autoinflammatory diseases have implicated both innate and adaptive immune abnormalities, blurring the line between autoinflammation and autoimmunity. This reinforces the paradigm of concerted innate and adaptive immune dysfunction leading to genetically complex autoinflammatory phenotypes.

Aeroservoelastic Model Validation and Test Data Analysis of the F/A-18 Active Aeroelastic Wing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Prazenica, Richard J.

2003-01-01

Model validation and flight test data analysis require careful consideration of the effects of uncertainty, noise, and nonlinearity. Uncertainty prevails in the data analysis techniques and results in a composite model uncertainty from unmodeled dynamics, assumptions and mechanics of the estimation procedures, noise, and nonlinearity. A fundamental requirement for reliable and robust model development is an attempt to account for each of these sources of error, in particular, for model validation, robust stability prediction, and flight control system development. This paper is concerned with data processing procedures for uncertainty reduction in model validation for stability estimation and nonlinear identification. F/A-18 Active Aeroelastic Wing (AAW) aircraft data is used to demonstrate signal representation effects on uncertain model development, stability estimation, and nonlinear identification. Data is decomposed using adaptive orthonormal best-basis and wavelet-basis signal decompositions for signal denoising into linear and nonlinear identification algorithms. Nonlinear identification from a wavelet-based Volterra kernel procedure is used to extract nonlinear dynamics from aeroelastic responses, and to assist model development and uncertainty reduction for model validation and stability prediction by removing a class of nonlinearity from the uncertainty.

Detection, Identification, Location, and Remote Sensing Using SAW RFID Sensor Tags

NASA Technical Reports Server (NTRS)

Barton, Richard J.; Kennedy, Timothy F.; Williams, Robert M.; Fink, Patrick W.; Ngo, Phong H.

2009-01-01

The Electromagnetic Systems Branch (EV4) of the Avionic Systems Division at NASA Johnson Space Center in Houston, TX is studying the utility of surface acoustic wave (SAW) radiofrequency identification (RFID) tags for multiple wireless applications including detection, identification, tracking, and remote sensing of objects on the lunar surface, monitoring of environmental test facilities, structural shape and health monitoring, and nondestructive test and evaluation of assets. For all of these applications, it is anticipated that the system utilized to interrogate the SAW RFID tags may need to operate at fairly long range and in the presence of considerable multipath and multiple-access interference. Towards that end, EV4 is developing a prototype SAW RFID wireless interrogation system for use in such environments called the Passive Adaptive RFID Sensor Equipment (PARSED) system. The system utilizes a digitally beam-formed planar receiving antenna array to extend range and provide direction-of-arrival information coupled with an approximate maximum-likelihood signal processing algorithm to provide near-optimal estimation of both range and temperature. The system is capable of forming a large number of beams within the field of view and resolving the information from several tags within each beam. The combination of both spatial and waveform discrimination provides the capability to track and monitor telemetry from a large number of objects appearing simultaneously within the field of view of the receiving array. In this paper, we will consider the application of the PARSEQ system to the problem of simultaneous detection, identification, localization, and temperature estimation for multiple objects. We will summarize the overall design of the PARSEQ system and present a detailed description of the design and performance of the signal detection and estimation algorithms incorporated in the system. The system is currently configured only to measure temperature (jointly with range and tag ID), but future versions will be revised to measure parameters other than temperature as SAW tags capable of interfacing with external sensors become available. It is anticipated that the estimation of arbitrary parameters measured using SAW-based sensors will be based on techniques very similar to the joint range and temperature estimation techniques described in this paper.

Identification of coal seam strata from geophysical logs of borehole using Adaptive Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Yegireddi, Satyanarayana; Uday Bhaskar, G.

2009-01-01

Different parameters obtained through well-logging geophysical sensors such as SP, resistivity, gamma-gamma, neutron, natural gamma and acoustic, help in identification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular stratigraphy formation, are function of its composition, physical properties and help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify or assess the type of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the type of stratigraphy from borehole geophysical log data using a combined approach of neural networks and fuzzy logic, known as Adaptive Neuro-Fuzzy Inference System. A model is built based on a few data sets (geophysical logs) of known stratigraphy of in coal areas of Kothagudem, Godavari basin and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. The results are very encouraging and the model is able to decipher even thin cola seams and other strata from borehole geophysical logs. The model can be further modified to assess the physical properties of the strata, if the corresponding ground truth is made available for simulation.

Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and experimental results.

PubMed

Jeffrey Yang, Y; Haught, Roy C; Goodrich, James A

2009-06-01

Accurate detection and identification of natural or intentional contamination events in a drinking water pipe is critical to drinking water supply security and health risk management. To use conventional water quality sensors for the purpose, we have explored a real-time event adaptive detection, identification and warning (READiw) methodology and examined it using pilot-scale pipe flow experiments of 11 chemical and biological contaminants each at three concentration levels. The tested contaminants include pesticide and herbicides (aldicarb, glyphosate and dicamba), alkaloids (nicotine and colchicine), E. coli in terrific broth, biological growth media (nutrient broth, terrific broth, tryptic soy broth), and inorganic chemical compounds (mercuric chloride and potassium ferricyanide). First, through adaptive transformation of the sensor outputs, contaminant signals were enhanced and background noise was reduced in time-series plots leading to detection and identification of all simulated contamination events. The improved sensor detection threshold was 0.1% of the background for pH and oxidation-reduction potential (ORP), 0.9% for free chlorine, 1.6% for total chlorine, and 0.9% for chloride. Second, the relative changes calculated from adaptively transformed residual chlorine measurements were quantitatively related to contaminant-chlorine reactivity in drinking water. We have shown that based on these kinetic and chemical differences, the tested contaminants were distinguishable in forensic discrimination diagrams made of adaptively transformed sensor measurements.

A Pilot Program in Adapted Physical Education: Hillsborough High School.

ERIC Educational Resources Information Center

Thompson, Vince

The instructor of an adapted physical education program describes his experiences and suggests guidelines for implementing other programs. Reviewed are such aspects as program orientation, class procedures, identification of student participants, and grading procedures. Objectives, lesson plans and evaluations are presented for the following units…

Assessment of vulnerability of forest ecosystems to climate change and adaptation planning in Nepal

NASA Astrophysics Data System (ADS)

Matin, M. A.; Chitale, V. S.

2016-12-01

Understanding ecosystem level vulnerability of forests and dependence of local communities on these ecosystems is a first step towards developing effective adaptation strategies. As forests are important components of livelihoods system for a large percentage of the population in the Himalayan region, they offer an important basis for creating and safeguarding more climate-resilient communities. Increased frequency, duration, and/or severity of drought and heat stress, changes in winter ecology, and pest and fire outbreaksunder climate change scenarios could fundamentally alter the composition, productivity and biogeography of forests affecting the potential ecosystem services offered and forest-based livelihoods. Hence, forest ecosystem vulnerability assessment to climate change and the development of a knowledgebase to identify and support relevant adaptation strategies is identified as an urgent need. Climate change vulnerability is measured as a function of exposure, sensitivity and the adaptive capacity of the system towards climate variability and extreme events. Effective adaptation to climate change depends on the availability of two important prerequisites: a) information on what, where, and how to adapt, and b) availability of resources to implement the adaptation measures. In the present study, we introduce the concept of two way multitier approach, which can support effective identification and implementation of adaptation measures in Nepal and the framework can be replicated in other countries in the HKH region. The assessment of overall vulnerability of forests comprises of two components: 1) understanding the relationship between exposure and sensitivity and positive feedback from adaptive capacity of forests; 2) quantifying the dependence of local communities on these ecosystems. We use climate datasets from Bioclim and biophysical products from MODIS, alongwith field datasets. We report that most of the forests along the high altitude areas and few patches in midhills and terai (plains) in Central Nepal depict moderate to high vulnerability of forests, while the forests from most of the other areas experience low vulnerability. Based on the matrix of vulnerability and dependence we suggest adaptation footprints for prioritization of adaptation measures on the ground.

High-temperature protein G is essential for activity of the Escherichia coli clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system.

PubMed

Yosef, Ido; Goren, Moran G; Kiro, Ruth; Edgar, Rotem; Qimron, Udi

2011-12-13

Prokaryotic DNA arrays arranged as clustered regularly interspaced short palindromic repeats (CRISPR), along with their associated proteins, provide prokaryotes with adaptive immunity by RNA-mediated targeting of alien DNA or RNA matching the sequences between the repeats. Here, we present a thorough screening system for the identification of bacterial proteins participating in immunity conferred by the Escherichia coli CRISPR system. We describe the identification of one such protein, high-temperature protein G (HtpG), a homolog of the eukaryotic chaperone heat-shock protein 90. We demonstrate that in the absence of htpG, the E. coli CRISPR system loses its suicidal activity against λ prophage and its ability to provide immunity from lysogenization. Transcomplementation of htpG restores CRISPR activity. We further show that inactivity of the CRISPR system attributable to htpG deficiency can be suppressed by expression of Cas3, a protein that is essential for its activity. Accordingly, we also find that the steady-state level of overexpressed Cas3 is significantly enhanced following HtpG expression. We conclude that HtpG is a newly identified positive modulator of the CRISPR system that is essential for maintaining functional levels of Cas3.

High-temperature protein G is essential for activity of the Escherichia coli clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system

PubMed Central

Yosef, Ido; Goren, Moran G.; Kiro, Ruth; Edgar, Rotem; Qimron, Udi

2011-01-01

Prokaryotic DNA arrays arranged as clustered regularly interspaced short palindromic repeats (CRISPR), along with their associated proteins, provide prokaryotes with adaptive immunity by RNA-mediated targeting of alien DNA or RNA matching the sequences between the repeats. Here, we present a thorough screening system for the identification of bacterial proteins participating in immunity conferred by the Escherichia coli CRISPR system. We describe the identification of one such protein, high-temperature protein G (HtpG), a homolog of the eukaryotic chaperone heat-shock protein 90. We demonstrate that in the absence of htpG, the E. coli CRISPR system loses its suicidal activity against λ prophage and its ability to provide immunity from lysogenization. Transcomplementation of htpG restores CRISPR activity. We further show that inactivity of the CRISPR system attributable to htpG deficiency can be suppressed by expression of Cas3, a protein that is essential for its activity. Accordingly, we also find that the steady-state level of overexpressed Cas3 is significantly enhanced following HtpG expression. We conclude that HtpG is a newly identified positive modulator of the CRISPR system that is essential for maintaining functional levels of Cas3. PMID:22114197

Study of aircraft in intraurban transportation systems, volume 1

NASA Technical Reports Server (NTRS)

Stout, E. G.; Kesling, P. H.; Matteson, H. C.; Sherwood, D. E.; Tuck, W. R., Jr.; Vaughn, L. A.

1971-01-01

An analysis of an effective short range, high density computer transportation system for intraurban systems is presented. The seven county Detroit, Michigan, metropolitan area, was chosen as the scenario for the analysis. The study consisted of an analysis and forecast of the Detroit market through 1985, a parametric analysis of appropriate short haul aircraft concepts and associated ground systems, and a preliminary overall economic analysis of a simplified total system designed to evaluate the candidate vehicles and select the most promising VTOL and STOL aircraft. Data are also included on the impact of advanced technology on the system, the sensitivity of mission performance to changes in aircraft characteristics and system operations, and identification of key problem areas that may be improved by additional research. The approach, logic, and computer models used are adaptable to other intraurban or interurban areas.

Ethnolinguistic Identification and Adaptation of Repatriates in Polycultural Kazakhstan

ERIC Educational Resources Information Center

Bokayev, Baurzhan; Zharkynbekova, Sholpan; Nurseitova, Khalida; Bokayeva, Ainash; Akzhigitova, Assel; Nurgalieva, Saniya

2012-01-01

The issues of social, cultural, and language adjustment and the integration of repatriates into the Kazakhstani society are crucial factors in maintaining a stable society. The complicated process of self-identification of ethnic Kazakhs is a major aspect of their sociolinguistic "penetration" into Kazakh society. In this work we…

A Botanical Treasure Hunt: A Fun and Educational Tree Identification Exercise.

ERIC Educational Resources Information Center

Fox, Marty; Gaynor, John J.; Cribben, Larry

1998-01-01

Shares an approach to tree identification that can be adapted to use with all levels from middle school through college. Stresses student involvement and cooperation in a botanical scavenger hunt. Describes the development of the treasure map and how to use the guide sheet. (DDR)

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.

PubMed

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-01-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS

NASA Astrophysics Data System (ADS)

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-10-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Functional Identification of Dendritic Cells in the Teleost Model, Rainbow Trout (Oncorhynchus mykiss)

PubMed Central

Bassity, Elizabeth; Clark, Theodore G.

2012-01-01

Dendritic cells are specialized antigen presenting cells that bridge innate and adaptive immunity in mammals. This link between the ancient innate immune system and the more evolutionarily recent adaptive immune system is of particular interest in fish, the oldest vertebrates to have both innate and adaptive immunity. It is unknown whether dendritic cells co-evolved with the adaptive response, or if the connection between innate and adaptive immunity relied on a fundamentally different cell type early in evolution. We approached this question using the teleost model organism, rainbow trout (Oncorhynchus mykiss), with the aim of identifying dendritic cells based on their ability to stimulate naïve T cells. Adapting mammalian protocols for the generation of dendritic cells, we established a method of culturing highly motile, non-adherent cells from trout hematopoietic tissue that had irregular membrane processes and expressed surface MHCII. When side-by-side mixed leukocyte reactions were performed, these cells stimulated greater proliferation than B cells or macrophages, demonstrating their specialized ability to present antigen and therefore their functional homology to mammalian dendritic cells. Trout dendritic cells were then further analyzed to determine if they exhibited other features of mammalian dendritic cells. Trout dendritic cells were found to have many of the hallmarks of mammalian DCs including tree-like morphology, the expression of dendritic cell markers, the ability to phagocytose small particles, activation by toll-like receptor-ligands, and the ability to migrate in vivo. As in mammals, trout dendritic cells could be isolated directly from the spleen, or larger numbers could be derived from hematopoietic tissue and peripheral blood mononuclear cells in vitro. PMID:22427987

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

PubMed

Huang, X N; Ren, H P

2016-05-13

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

Unravelling evolutionary strategies of yeast for improving galactose utilization through integrated systems level analysis.

PubMed

Hong, Kuk-Ki; Vongsangnak, Wanwipa; Vemuri, Goutham N; Nielsen, Jens

2011-07-19

Identification of the underlying molecular mechanisms for a derived phenotype by adaptive evolution is difficult. Here, we performed a systems-level inquiry into the metabolic changes occurring in the yeast Saccharomyces cerevisiae as a result of its adaptive evolution to increase its specific growth rate on galactose and related these changes to the acquired phenotypic properties. Three evolved mutants (62A, 62B, and 62C) with higher specific growth rates and faster specific galactose uptake were isolated. The evolved mutants were compared with a reference strain and two engineered strains, SO16 and PGM2, which also showed higher galactose uptake rate in previous studies. The profile of intermediates in galactose metabolism was similar in evolved and engineered mutants, whereas reserve carbohydrates metabolism was specifically elevated in the evolved mutants and one evolved strain showed changes in ergosterol biosynthesis. Mutations were identified in proteins involved in the global carbon sensing Ras/PKA pathway, which is known to regulate the reserve carbohydrates metabolism. We evaluated one of the identified mutations, RAS2(Tyr112), and this mutation resulted in an increased specific growth rate on galactose. These results show that adaptive evolution results in the utilization of unpredicted routes to accommodate increased galactose flux in contrast to rationally engineered strains. Our study demonstrates that adaptive evolution represents a valuable alternative to rational design in bioengineering of improved strains and, that through systems biology, it is possible to identify mutations in evolved strain that can serve as unforeseen metabolic engineering targets for improving microbial strains for production of biofuels and chemicals.

Identification and tracking of particular speaker in noisy environment

NASA Astrophysics Data System (ADS)

Sawada, Hideyuki; Ohkado, Minoru

2004-10-01

Human is able to exchange information smoothly using voice under different situations such as noisy environment in a crowd and with the existence of plural speakers. We are able to detect the position of a source sound in 3D space, extract a particular sound from mixed sounds, and recognize who is talking. By realizing this mechanism with a computer, new applications will be presented for recording a sound with high quality by reducing noise, presenting a clarified sound, and realizing a microphone-free speech recognition by extracting particular sound. The paper will introduce a realtime detection and identification of particular speaker in noisy environment using a microphone array based on the location of a speaker and the individual voice characteristics. The study will be applied to develop an adaptive auditory system of a mobile robot which collaborates with a factory worker.

Provision of Adaptive Instruction: Implementation and Effects.

ERIC Educational Resources Information Center

Wang, Margaret C.

Despite increased interest in and acceptance of the concept and mandate of providing adaptive instruction to ensure schooling success for each student, a sizable gap exists between identification of specific educational practices and application of such practices in schools. The work described in this paper is aimed at examining the feasibility…

Novel Insights into the Organization of Laticifer Cells: A Cell Comprising a Unified Whole System.

PubMed

Castelblanque, Lourdes; Balaguer, Begoña; Martí, Cristina; Rodríguez, Juan José; Orozco, Marianela; Vera, Pablo

2016-10-01

Laticifer cells are specialized plant cells that synthesize and accumulate latex. Studies on laticifers have lagged behind in recent years, and data regarding the functional role of laticifers and their fitness benefit still remain elusive. Laticifer differentiation and its impact on plant growth and development also remain to be investigated. Here, cellular, molecular, and genetic tools were developed to examine the distribution, differentiation, ontogeny, and other characteristic features, as well as the potential developmental role of laticifer cells in the latex-bearing plant Euphorbia lathyris. The organization of the laticiferous system within the E. lathyris plant body is reported, emerging as a single elongated and branched coenocytic cell, constituting the largest cell type existing in plants. We also report the ontogeny and organization of laticifer cells in the embryo and the identification of a laticifer-associated gene expression pattern. Moreover, the identification of laticifer- and latex-deficient mutants (pil mutants) allowed for the identification of distinct loci regulating laticifer differentiation, growth, and metabolic activity. Additionally, pil mutants revealed that laticifer cells appear nonessential for plant growth and development, thus pointing toward their importance, instead, for specific ecophysiological adaptations of latex-bearing plants in natural environments. © 2016 American Society of Plant Biologists. All Rights Reserved.

Vulnerability and risk of deltaic social-ecological systems exposed to multiple hazards.

PubMed

Hagenlocher, Michael; Renaud, Fabrice G; Haas, Susanne; Sebesvari, Zita

2018-08-01

Coastal river deltas are hotspots of global change impacts. Sustainable delta futures are increasingly threatened due to rising hazard exposure combined with high vulnerabilities of deltaic social-ecological systems. While the need for integrated multi-hazard approaches has been clearly articulated, studies on vulnerability and risk in deltas either focus on local case studies or single hazards and do not apply a social-ecological systems perspective. As a result, vulnerabilities and risks in areas with strong social and ecological coupling, such as coastal deltas, are not fully understood and the identification of risk reduction and adaptation strategies are often based on incomplete assumptions. To overcome these limitations, we propose an innovative modular indicator library-based approach for the assessment of multi-hazard risk of social-ecological systems across and within coastal deltas globally, and apply it to the Amazon, Ganges-Brahmaputra-Meghna (GBM), and Mekong deltas. Results show that multi-hazard risk is highest in the GBM delta and lowest in the Amazon delta. The analysis reveals major differences between social and environmental vulnerability across the three deltas, notably in the Mekong and the GBM deltas where environmental vulnerability is significantly higher than social vulnerability. Hotspots and drivers of risk vary spatially, thus calling for spatially targeted risk reduction and adaptation strategies within the deltas. Ecosystems have been identified as both an important element at risk as well as an entry point for risk reduction and adaptation strategies. Copyright © 2018. Published by Elsevier B.V.

Automatic vasculature identification in coronary angiograms by adaptive geometrical tracking.

PubMed

Xiao, Ruoxiu; Yang, Jian; Goyal, Mahima; Liu, Yue; Wang, Yongtian

2013-01-01

As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.

Identification of Arabidopsis mutants with altered freezing tolerance.

PubMed

Perea-Resa, Carlos; Salinas, Julio

2014-01-01

Low temperature is an important determinant in the configuration of natural plant communities and defines the range of distribution and growth of important crops. Some plants, including Arabidopsis, have evolved sophisticated adaptive mechanisms to tolerate low and freezing temperatures. Central to this adaptation is the process of cold acclimation. By means of this process, many plants from temperate regions are able to develop or increase their freezing tolerance in response to low, nonfreezing temperatures. The identification and characterization of factors involved in freezing tolerance are crucial to understand the molecular mechanisms underlying the cold acclimation response and have a potential interest to improve crop tolerance to freezing temperatures. Many genes implicated in cold acclimation have been identified in numerous plant species by using molecular approaches followed by reverse genetic analysis. Remarkably, however, direct genetic analyses have not been conveniently exploited in their capacity for identifying genes with pivotal roles in that adaptive response. In this chapter, we describe a protocol for evaluating the freezing tolerance of both non-acclimated and cold-acclimated Arabidopsis plants. This protocol allows the accurate and simple screening of mutant collections for the identification of novel factors involved in freezing tolerance and cold acclimation.

BP network identification technology of infrared polarization based on fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Zeng, Haifang; Gu, Guohua; He, Weiji; Chen, Qian; Yang, Wei

2011-08-01

Infrared detection system is frequently employed on surveillance operations and reconnaissance mission to detect particular targets of interest in both civilian and military communities. By incorporating the polarization of light as supplementary information, the target discrimination performance could be enhanced. So this paper proposed an infrared target identification method which is based on fuzzy theory and neural network with polarization properties of targets. The paper utilizes polarization degree and light intensity to advance the unsupervised KFCM (kernel fuzzy C-Means) clustering method. And establish different material pol1arization properties database. In the built network, the system can feedback output corresponding material types of probability distribution toward any input polarized degree such as 10° 15°, 20°, 25°, 30°. KFCM, which has stronger robustness and accuracy than FCM, introduces kernel idea and gives the noise points and invalid value different but intuitively reasonable weights. Because of differences in characterization of material properties, there will be some conflicts in classification results. And D - S evidence theory was used in the combination of the polarization and intensity information. Related results show KFCM clustering precision and operation rate are higher than that of the FCM clustering method. The artificial neural network method realizes material identification, which reasonable solved the problems of complexity in environmental information of infrared polarization, and improperness of background knowledge and inference rule. This method of polarization identification is fast in speed, good in self-adaption and high in resolution.

A numerical study of sensory-guided multiple views for improved object identification

NASA Astrophysics Data System (ADS)

Blakeslee, B. A.; Zelnio, E. G.; Koditschek, D. E.

2014-06-01

We explore the potential on-line adjustment of sensory controls for improved object identification and discrimination in the context of a simulated high resolution camera system carried onboard a maneuverable robotic platform that can actively choose its observational position and pose. Our early numerical studies suggest the significant efficacy and enhanced performance achieved by even very simple feedback-driven iteration of the view in contrast to identification from a fixed pose, uninformed by any active adaptation. Specifically, we contrast the discriminative performance of the same conventional classification system when informed by: a random glance at a vehicle; two random glances at a vehicle; or a random glance followed by a guided second look. After each glance, edge detection algorithms isolate the most salient features of the image and template matching is performed through the use of the Hausdor↵ distance, comparing the simulated sensed images with reference images of the vehicles. We present initial simulation statistics that overwhelmingly favor the third scenario. We conclude with a sketch of our near-future steps in this study that will entail: the incorporation of more sophisticated image processing and template matching algorithms; more complex discrimination tasks such as distinguishing between two similar vehicles or vehicles in motion; more realistic models of the observers mobility including platform dynamics and eventually environmental constraints; and expanding the sensing task beyond the identification of a specified object selected from a pre-defined library of alternatives.

AFLPs and Mitochondrial Haplotypes Reveal Local Adaptation to Extreme Thermal Environments in a Freshwater Gastropod

PubMed Central

Quintela, María; Johansson, Magnus P.; Kristjánsson, Bjarni K.; Barreiro, Rodolfo; Laurila, Anssi

2014-01-01

The way environmental variation shapes neutral and adaptive genetic variation in natural populations is a key issue in evolutionary biology. Genome scans allow the identification of the genetic basis of local adaptation without previous knowledge of genetic variation or traits under selection. Candidate loci for divergent adaptation are expected to show higher FST than neutral loci influenced solely by random genetic drift, migration and mutation. The comparison of spatial patterns of neutral markers and loci under selection may help disentangle the effects of gene flow, genetic drift and selection among populations living in contrasting environments. Using the gastropod Radix balthica as a system, we analyzed 376 AFLP markers and 25 mtDNA COI haplotypes for candidate loci and associations with local adaptation among contrasting thermal environments in Lake Mývatn, a volcanic lake in northern Iceland. We found that 2% of the analysed AFLP markers were under directional selection and 12% of the mitochondrial haplotypes correlated with differing thermal habitats. The genetic networks were concordant for AFLP markers and mitochondrial haplotypes, depicting distinct topologies at neutral and candidate loci. Neutral topologies were characterized by intense gene flow revealed by dense nets with edges connecting contrasting thermal habitats, whereas the connections at candidate loci were mostly restricted to populations within each thermal habitat and the number of edges decreased with temperature. Our results suggest microgeographic adaptation within Lake Mývatn and highlight the utility of genome scans in detecting adaptive divergence. PMID:25007329

Using recurrent neural networks for adaptive communication channel equalization.

PubMed

Kechriotis, G; Zervas, E; Manolakos, E S

1994-01-01

Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive recurrent neural network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and nonlinear channel equalization cases.

Adaptive-filtering of trisomy 21: risk of Down syndrome depends on family size and age of previous child

NASA Astrophysics Data System (ADS)

Neuhäuser, Markus; Krackow, Sven

2007-02-01

The neonatal incidence rate of Down syndrome (DS) is well-known to accelerate strongly with maternal age. This non-linearity renders mere accumulation of defects at recombination during prolonged first meiotic prophase implausible as an explanation for DS rate increase with maternal age, but might be anticipated from chromosomal drive (CD) for trisomy 21. Alternatively, as there is selection against genetically disadvantaged embryos, the screening system that eliminates embryos with trisomy 21 might decay with maternal age. In this paper, we provide the first evidence for relaxed filtering stringency (RFS) to represent an adaptive maternal response that could explain accelerating DS rates with maternal age. Using historical data, we show that the proportion of aberrant live births decrease with increased family size in older mothers, that inter-birth intervals are longer before affected neonates than before normal ones, and that primiparae exhibit elevated levels of DS incidence at higher age. These findings are predicted by adaptive RFS but cannot be explained by the currently available alternative non-adaptive hypotheses, including CD. The identification of the relaxation control mechanism and therapeutic restoration of a stringent screen may have considerable medical implications.

Cultural variation in the focus on goals versus processes of actions.

PubMed

Miyamoto, Yuri; Knoepfler, Christopher A; Ishii, Keiko; Ji, Li-Jun

2013-06-01

Everyday actions (e.g., riding a bike) can be described in ways that emphasize either the goals of the action by adapting a higher level identification (e.g., getting exercise) or the processes of the action by adapting a lower level identification (e.g., pedaling). In Studies 1 and 2, we demonstrate cultural differences in focusing on the process or goal of actions at the individual level: Americans are more likely than Japanese to focus on the goal (rather than the process) of actions. Study 3 recruited Chinese participants in addition to American and Japanese participants and found that cultural differences in action identification are partly explained by cultural differences in self-consistency. Study 4 further showed cultural differences at the collective level: American media presents more goal-oriented information and less process-oriented information than does Japanese media. These findings highlight the role of culture in shaping how people attend to different aspects of actions.

A Variable Step-Size Proportionate Affine Projection Algorithm for Identification of Sparse Impulse Response

NASA Astrophysics Data System (ADS)

Liu, Ligang; Fukumoto, Masahiro; Saiki, Sachio; Zhang, Shiyong

2009-12-01

Proportionate adaptive algorithms have been proposed recently to accelerate convergence for the identification of sparse impulse response. When the excitation signal is colored, especially the speech, the convergence performance of proportionate NLMS algorithms demonstrate slow convergence speed. The proportionate affine projection algorithm (PAPA) is expected to solve this problem by using more information in the input signals. However, its steady-state performance is limited by the constant step-size parameter. In this article we propose a variable step-size PAPA by canceling the a posteriori estimation error. This can result in high convergence speed using a large step size when the identification error is large, and can then considerably decrease the steady-state misalignment using a small step size after the adaptive filter has converged. Simulation results show that the proposed approach can greatly improve the steady-state misalignment without sacrificing the fast convergence of PAPA.

Genetic and epigenetic control of gene expression by CRISPR–Cas systems

PubMed Central

Lo, Albert; Qi, Lei

2017-01-01

The discovery and adaption of bacterial clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR-associated (Cas) systems has revolutionized the way researchers edit genomes. Engineering of catalytically inactivated Cas variants (nuclease-deficient or nuclease-deactivated [dCas]) combined with transcriptional repressors, activators, or epigenetic modifiers enable sequence-specific regulation of gene expression and chromatin state. These CRISPR–Cas-based technologies have contributed to the rapid development of disease models and functional genomics screening approaches, which can facilitate genetic target identification and drug discovery. In this short review, we will cover recent advances of CRISPR–dCas9 systems and their use for transcriptional repression and activation, epigenome editing, and engineered synthetic circuits for complex control of the mammalian genome. PMID:28649363

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

Sandbox University: estimating influence of institutional action.

PubMed

Forsman, Jonas; Mann, Richard P; Linder, Cedric; van den Bogaard, Maartje

2014-01-01

The approach presented in this article represents a generalizable and adaptable methodology for identifying complex interactions in educational systems and for investigating how manipulation of these systems may affect educational outcomes of interest. Multilayer Minimum Spanning Tree and Monte-Carlo methods are used. A virtual Sandbox University is created in order to facilitate effective identification of successful and stable initiatives within higher education, which can affect students' credits and student retention - something that has been lacking up until now. The results highlight the importance of teacher feedback and teacher-student rapport, which is congruent with current educational findings, illustrating the methodology's potential to provide a new basis for further empirical studies of issues in higher education from a complex systems perspective.

Sandbox University: Estimating Influence of Institutional Action

PubMed Central

Forsman, Jonas; Mann, Richard P.; Linder, Cedric; van den Bogaard, Maartje

2014-01-01

The approach presented in this article represents a generalizable and adaptable methodology for identifying complex interactions in educational systems and for investigating how manipulation of these systems may affect educational outcomes of interest. Multilayer Minimum Spanning Tree and Monte-Carlo methods are used. A virtual Sandbox University is created in order to facilitate effective identification of successful and stable initiatives within higher education, which can affect students' credits and student retention – something that has been lacking up until now. The results highlight the importance of teacher feedback and teacher-student rapport, which is congruent with current educational findings, illustrating the methodology's potential to provide a new basis for further empirical studies of issues in higher education from a complex systems perspective. PMID:25054313

Study of the Time Response of a Simulated Hydroelectric System

NASA Astrophysics Data System (ADS)

Simani, S.; Alvisi, S.; Venturini, M.

2014-12-01

This paper addresses the design of an advanced control strategy for a typical hydroelectric dynamic process, performed in the Matlab and Simulink environments. The hydraulic system consists of a high water head and a long penstock with upstream and downstream surge tanks, and is equipped with a Francis turbine. The nonlinear characteristics of hydraulic turbine and the inelastic water hammer effects were considered to calculate and simulate the hydraulic transients. With reference to the control solution, the proposed methodology relies on an adaptive control designed by means of the on-line identification of the system model under monitoring. Extensive simulations and comparison with respect to a classic hydraulic turbine speed PID regulator show the effectiveness of the proposed modelling and control tools.

Communication constraints, indexical countermeasures, and crew configuration effects in simulated space-dwelling groups

NASA Astrophysics Data System (ADS)

Hienz, Robert D.; Brady, Joseph V.; Hursh, Steven R.; Banner, Michele J.; Gasior, Eric D.; Spence, Kevin R.

2007-02-01

Previous research with groups of individually isolated crews communicating and problem-solving in a distributed interactive simulation environment has shown that the functional interchangeability of available communication channels can serve as an effective countermeasure to communication constraints. The present report extends these findings by investigating crew performance effects and psychosocial adaptation following: (1) the loss of all communication channels, and (2) changes in crew configuration. Three-person crews participated in a simulated planetary exploration mission that required identification, collection, and analysis of geologic samples. Results showed that crews developed and employed discrete navigation system operations that served as functionally effective communication signals (i.e., “indexical” or “deictic” cues) in generating appropriate crewmember responses and maintaining performance effectiveness in the absence of normal communication channels. Additionally, changes in crew configuration impacted both performance effectiveness and psychosocial adaptation.

Spatiotemporal Phase Synchronization in Adaptive Reconfiguration from Action Observation Network to Mentalizing Network for Understanding Other's Action Intention.

PubMed

Zhang, Li; Gan, John Q; Zheng, Wenming; Wang, Haixian

2018-05-01

In action intention understanding, the mirror system is involved in perception-action matching process and the mentalizing system underlies higher-level intention inference. By analyzing the dynamic functional connectivity in α (8-12 Hz) and β (12-30 Hz) frequency bands over a "hand-cup interaction" observation task, this study investigates the topological transition from the action observation network (AON) to the mentalizing network (MZN), and estimates their functional relevance for intention identification from other's different action kinematics. Sequential brain microstates were extracted based on event-related potentials (ERPs), in which significantly differing neuronal responses were found in N170-P200 related to perceptually matching kinematic profiles and P400-700 involved in goal inference. Inter-electrode weighted phase lag index analysis on the ERP microstates revealed a shift of hub centrality salient in α frequency band, from the AON dominated by left-lateral frontal-premotor-temporal and temporal-parietooccipital synchronizations to the MZN consisting of more bilateral frontal-parietal and temporal-parietal synchronizations. As compared with usual actions, intention identification of unintelligible actions induces weaker synchronizations in the AON but dramatically increased connectivity in right frontal-temporal-parietal regions of the MZN, indicating a spatiotemporally complementary effect between the functional network configurations involved in mirror and mentalizing processes. Perceptual processing in observing usual/unintelligible actions decreases/increases requirements for intention inference, which would induce less/greater functional network reorganization on the way to mentalization. From the comparison, our study suggests that the adaptive topological changes from the AON to the MZN indicate implicit causal association between the mirror and mentalizing systems for decoding others' intentionality.

4-D COMMON OPERATIONAL PICTURE (COP) FOR MISSION ASSURANCE (4D COP) Task Order 0001: Air Force Research Laboratory (AFRL) Autonomy Collaboration in Intelligence, Surveillance, and Reconnaissance (ISR), Electronic Warfare (EW)/Cyber and Combat Identification (CID)

DTIC Science & Technology

2016-10-27

Domain C2, Adaptive Domain Control, Global Integrated ISR, Rapid Global Mobility , and Global Precision Strike, orgnanized within a framework of...mission needs. (Among the dozen implications) A more transparent, networked infrastructure that integrates ubiquitous sensors, automated systems...Conclusion 5.1 Common Technical Trajectory One of the most significant opportunities for AFRL is to develop and mobilize the qualitative roadmap

Web-based healthcare hand drawing management system.

PubMed

Hsieh, Sheau-Ling; Weng, Yung-Ching; Chen, Chi-Huang; Hsu, Kai-Ping; Lin, Jeng-Wei; Lai, Feipei

2010-01-01

The paper addresses Medical Hand Drawing Management System architecture and implementation. In the system, we developed four modules: hand drawing management module; patient medical records query module; hand drawing editing and upload module; hand drawing query module. The system adapts windows-based applications and encompasses web pages by ASP.NET hosting mechanism under web services platforms. The hand drawings implemented as files are stored in a FTP server. The file names with associated data, e.g. patient identification, drawing physician, access rights, etc. are reposited in a database. The modules can be conveniently embedded, integrated into any system. Therefore, the system possesses the hand drawing features to support daily medical operations, effectively improve healthcare qualities as well. Moreover, the system includes the printing capability to achieve a complete, computerized medical document process. In summary, the system allows web-based applications to facilitate the graphic processes for healthcare operations.

The Variable Vector Countermeasure Suit (V2Suit) for space habitation and exploration

PubMed Central

Duda, Kevin R.; Vasquez, Rebecca A.; Middleton, Akil J.; Hansberry, Mitchell L.; Newman, Dava J.; Jacobs, Shane E.; West, John J.

2015-01-01

The “Variable Vector Countermeasure Suit (V2Suit) for Space Habitation and Exploration” is a novel system concept that provides a platform for integrating sensors and actuators with daily astronaut intravehicular activities to improve health and performance, while reducing the mass and volume of the physiologic adaptation countermeasure systems, as well as the required exercise time during long-duration space exploration missions. The V2Suit system leverages wearable kinematic monitoring technology and uses inertial measurement units (IMUs) and control moment gyroscopes (CMGs) within miniaturized modules placed on body segments to provide a “viscous resistance” during movements against a specified direction of “down”—initially as a countermeasure to the sensorimotor adaptation performance decrements that manifest themselves while living and working in microgravity and during gravitational transitions during long-duration spaceflight, including post-flight recovery and rehabilitation. Several aspects of the V2Suit system concept were explored and simulated prior to developing a brassboard prototype for technology demonstration. This included a system architecture for identifying the key components and their interconnects, initial identification of key human-system integration challenges, development of a simulation architecture for CMG selection and parameter sizing, and the detailed mechanical design and fabrication of a module. The brassboard prototype demonstrates closed-loop control from “down” initialization through CMG actuation, and provides a research platform for human performance evaluations to mitigate sensorimotor adaptation, as well as a tool for determining the performance requirements when used as a musculoskeletal deconditioning countermeasure. This type of countermeasure system also has Earth benefits, particularly in gait or movement stabilization and rehabilitation. PMID:25914631

White noise analysis of Phycomyces light growth response system. I. Normal intensity range.

PubMed Central

Lipson, E D

1975-01-01

The Wiener-Lee-Schetzen method for the identification of a nonlinear system through white gaussian noise stimulation was applied to the transient light growth response of the sporangiophore of Phycomyces. In order to cover a moderate dynamic range of light intensity I, the imput variable was defined to be log I. The experiments were performed in the normal range of light intensity, centered about I0 = 10(-6) W/cm2. The kernels of the Wierner functionals were computed up to second order. Within the range of a few decades the system is reasonably linear with log I. The main nonlinear feature of the second-order kernel corresponds to the property of rectification. Power spectral analysis reveals that the slow dynamics of the system are of at least fifth order. The system can be represented approximately by a linear transfer function, including a first-order high-pass (adaptation) filter with a 4 min time constant and an underdamped fourth-order low-pass filter. Accordingly a linear electronic circuit was constructed to simulate the small scale response characteristics. In terms of the adaptation model of Delbrück and Reichardt (1956, in Cellular Mechanisms in Differentiation and Growth, Princeton University Press), kernels were deduced for the dynamic dependence of the growth velocity (output) on the "subjective intensity", a presumed internal variable. Finally the linear electronic simulator above was generalized to accommodate the large scale nonlinearity of the adaptation model and to serve as a tool for deeper test of the model. PMID:1203444

Integrating adaptive management and ecosystem services concepts to improve natural resource management: Challenges and opportunities

USGS Publications Warehouse

Epanchin-Niell, Rebecca S.; Boyd, James W.; Macauley, Molly K.; Scarlett, Lynn; Shapiro, Carl D.; Williams, Byron K.

2018-05-07

Executive Summary—OverviewNatural resource managers must make decisions that affect broad-scale ecosystem processes involving large spatial areas, complex biophysical interactions, numerous competing stakeholder interests, and highly uncertain outcomes. Natural and social science information and analyses are widely recognized as important for informing effective management. Chief among the systematic approaches for improving the integration of science into natural resource management are two emergent science concepts, adaptive management and ecosystem services. Adaptive management (also referred to as “adaptive decision making”) is a deliberate process of learning by doing that focuses on reducing uncertainties about management outcomes and system responses to improve management over time. Ecosystem services is a conceptual framework that refers to the attributes and outputs of ecosystems (and their components and functions) that have value for humans.This report explores how ecosystem services can be moved from concept into practice through connection to a decision framework—adaptive management—that accounts for inherent uncertainties. Simultaneously, the report examines the value of incorporating ecosystem services framing and concepts into adaptive management efforts.Adaptive management and ecosystem services analyses have not typically been used jointly in decision making. However, as frameworks, they have a natural—but to date underexplored—affinity. Both are policy and decision oriented in that they attempt to represent the consequences of resource management choices on outcomes of interest to stakeholders. Both adaptive management and ecosystem services analysis take an empirical approach to the analysis of ecological systems. This systems orientation is a byproduct of the fact that natural resource actions affect ecosystems—and corresponding societal outcomes—often across large geographic scales. Moreover, because both frameworks focus on resource systems, both must confront the analytical challenges of systems modeling—in terms of complexity, dynamics, and uncertainty.Given this affinity, the integration of ecosystem services analysis and adaptive management poses few conceptual hurdles. In this report, we synthesize discussions from two workshops that considered ways in which adaptive management approaches and ecosystem service concepts may be complementary, such that integrating them into a common framework may lead to improved natural resource management outcomes. Although the literature on adaptive management and ecosystem services is vast and growing, the report focuses specifically on the integration of these two concepts rather than aiming to provide new definitions or an indepth review or primer of the concepts individually.Key issues considered include the bidirectional links between adaptive decision making and ecosystem services, as well as the potential benefits and inevitable challenges arising in the development and use of an integrated framework. Specifically, the workshops addressed the following questions:How can application of ecosystem service analysis within an adaptive decision process improve the outcomes of management and advance understanding of ecosystem service identification, production, and valuation?How can these concepts be integrated in concept and practice?What are the constraints and challenges to integrating adaptive management and ecosystem services?And, should the integration of these concepts be moved forward to wider application—and if so, how?

Youths’ Imitation and De-identification from Parents: A Process Associated with Parent–Youth Cultural Incongruence in Mexican-American Families

PubMed Central

Updegraff, Kimberly A.; Umaña-Taylor, Adriana J.

2016-01-01

Cultural adaptation and parent–youth cultural incongruence have strong implications for individuals’ social adaptation and family dynamics. This study highlighted adolescents’ active role in parent–youth cultural incongruence through their decision to imitate or de-identify from parents, parent–youth warmth, and demographic similarities. Longitudinal data, spanning 8 years, from 246 Mexican-American families (mothers, fathers, and an early adolescent child), were used to address two study goals. The first goal was to link parent–youth relationship qualities and demographic similarities (i.e., gender, immigration status) at Wave 1 to adolescents’ imitation and de-identification from parents at Wave 2. Findings revealed that adolescents who reported more parent–youth warmth reported more imitation and less de-identification. Also, adolescents who belonged to U.S.-raised dyads reported less de-identification. The second goal tested adolescents’ reports of imitation and de-identification as predictors of parent–youth cultural incongruence in Mexican and Anglo cultural orientations at Wave 3. Results indicated that more imitation was associated with less mother–youth Anglo incongruence and that more de-identification was associated with more father–youth Anglo and Mexican incongruence. The unique relationship dynamics of mother– youth and father–youth dyads and the implications for intervention programming focused on reducing cultural incongruence and increasing family cohesion are discussed. PMID:24531867

Efficient integration of spectral features for vehicle tracking utilizing an adaptive sensor

NASA Astrophysics Data System (ADS)

Uzkent, Burak; Hoffman, Matthew J.; Vodacek, Anthony

2015-03-01

Object tracking in urban environments is an important and challenging problem that is traditionally tackled using visible and near infrared wavelengths. By inserting extended data such as spectral features of the objects one can improve the reliability of the identification process. However, huge increase in data created by hyperspectral imaging is usually prohibitive. To overcome the complexity problem, we propose a persistent air-to-ground target tracking system inspired by a state-of-the-art, adaptive, multi-modal sensor. The adaptive sensor is capable of providing panchromatic images as well as the spectra of desired pixels. This addresses the data challenge of hyperspectral tracking by only recording spectral data as needed. Spectral likelihoods are integrated into a data association algorithm in a Bayesian fashion to minimize the likelihood of misidentification. A framework for controlling spectral data collection is developed by incorporating motion segmentation information and prior information from a Gaussian Sum filter (GSF) movement predictions from a multi-model forecasting set. An intersection mask of the surveillance area is extracted from OpenStreetMap source and incorporated into the tracking algorithm to perform online refinement of multiple model set. The proposed system is tested using challenging and realistic scenarios generated in an adverse environment.

A finite-element toolbox for the stationary Gross-Pitaevskii equation with rotation

NASA Astrophysics Data System (ADS)

Vergez, Guillaume; Danaila, Ionut; Auliac, Sylvain; Hecht, Frédéric

2016-12-01

We present a new numerical system using classical finite elements with mesh adaptivity for computing stationary solutions of the Gross-Pitaevskii equation. The programs are written as a toolbox for FreeFem++ (www.freefem.org), a free finite-element software available for all existing operating systems. This offers the advantage to hide all technical issues related to the implementation of the finite element method, allowing to easily code various numerical algorithms. Two robust and optimized numerical methods were implemented to minimize the Gross-Pitaevskii energy: a steepest descent method based on Sobolev gradients and a minimization algorithm based on the state-of-the-art optimization library Ipopt. For both methods, mesh adaptivity strategies are used to reduce the computational time and increase the local spatial accuracy when vortices are present. Different run cases are made available for 2D and 3D configurations of Bose-Einstein condensates in rotation. An optional graphical user interface is also provided, allowing to easily run predefined cases or with user-defined parameter files. We also provide several post-processing tools (like the identification of quantized vortices) that could help in extracting physical features from the simulations. The toolbox is extremely versatile and can be easily adapted to deal with different physical models.

Biometric and Emotion Identification: An ECG Compression Based Method.

PubMed

Brás, Susana; Ferreira, Jacqueline H T; Soares, Sandra C; Pinho, Armando J

2018-01-01

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

Biometric and Emotion Identification: An ECG Compression Based Method

PubMed Central

Brás, Susana; Ferreira, Jacqueline H. T.; Soares, Sandra C.; Pinho, Armando J.

2018-01-01

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model. PMID:29670564

A System for Identifying Named Entities in Biomedical Text: how Results From two Evaluations Reflect on Both the System and the Evaluations

PubMed Central

Dingare, Shipra; Nissim, Malvina; Finkel, Jenny; Grover, Claire

2005-01-01

We present a maximum entropy-based system for identifying named entities (NEs) in biomedical abstracts and present its performance in the only two biomedical named entity recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match F-score of 83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss our system in detail, including its rich use of local features, attention to correct boundary identification, innovative use of external knowledge resources, including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than optimal. PMID:18629295

A Systematic Review and Psychometric Evaluation of Adaptive Behavior Scales and Recommendations for Practice

ERIC Educational Resources Information Center

Floyd, Randy G.; Shands, Elizabeth I.; Alfonso, Vincent C.; Phillips, Jessica F.; Autry, Beth K.; Mosteller, Jessica A.; Skinner, Mary; Irby, Sarah

2015-01-01

Adaptive behavior scales are vital in assessing children and adolescents who experience a range of disabling conditions in school settings. This article presents the results of an evaluation of the design characteristics, norming, scale characteristics, reliability and validity evidence, and bias identification studies supporting 14…

MALDI-TOF-mass spectrometry applications in clinical microbiology.

PubMed

Seng, Piseth; Rolain, Jean-Marc; Fournier, Pierre Edouard; La Scola, Bernard; Drancourt, Michel; Raoult, Didier

2010-11-01

MALDI-TOF-mass spectrometry (MS) has been successfully adapted for the routine identification of microorganisms in clinical microbiology laboratories in the past 10 years. This revolutionary technique allows for easier and faster diagnosis of human pathogens than conventional phenotypic and molecular identification methods, with unquestionable reliability and cost-effectiveness. This article will review the application of MALDI-TOF-MS tools in routine clinical diagnosis, including the identification of bacteria at the species, subspecies, strain and lineage levels, and the identification of bacterial toxins and antibiotic-resistance type. We will also discuss the application of MALDI-TOF-MS tools in the identification of Archaea, eukaryotes and viruses. Pathogenic identification from colony-cultured, blood-cultured, urine and environmental samples is also reviewed.

Assembling evidence for identifying reservoirs of infection

PubMed Central

Viana, Mafalda; Mancy, Rebecca; Biek, Roman; Cleaveland, Sarah; Cross, Paul C.; Lloyd-Smith, James O.; Haydon, Daniel T.

2014-01-01

Many pathogens persist in multihost systems, making the identification of infection reservoirs crucial for devising effective interventions. Here, we present a conceptual framework for classifying patterns of incidence and prevalence, and review recent scientific advances that allow us to study and manage reservoirs simultaneously. We argue that interventions can have a crucial role in enriching our mechanistic understanding of how reservoirs function and should be embedded as quasi-experimental studies in adaptive management frameworks. Single approaches to the study of reservoirs are unlikely to generate conclusive insights whereas the formal integration of data and methodologies, involving interventions, pathogen genetics, and contemporary surveillance techniques, promises to open up new opportunities to advance understanding of complex multihost systems. PMID:24726345

Assembling evidence for identifying reservoirs of infection

USGS Publications Warehouse

Mafalda, Viana; Rebecca, Mancy; Roman, Biek; Sarah, Cleaveland; Cross, Paul C.; James O, Lloyd-Smith; Daniel T, Haydon

2014-01-01

Many pathogens persist in multihost systems, making the identification of infection reservoirs crucial for devising effective interventions. Here, we present a conceptual framework for classifying patterns of incidence and prevalence, and review recent scientific advances that allow us to study and manage reservoirs simultaneously. We argue that interventions can have a crucial role in enriching our mechanistic understanding of how reservoirs function and should be embedded as quasi-experimental studies in adaptive management frameworks. Single approaches to the study of reservoirs are unlikely to generate conclusive insights whereas the formal integration of data and methodologies, involving interventions, pathogen genetics, and contemporary surveillance techniques, promises to open up new opportunities to advance understanding of complex multihost systems.

Control - Demands mushroom as station grows

NASA Technical Reports Server (NTRS)

Szirmay, S. Z.; Blair, J.

1983-01-01

The NASA space station, which is presently in the planning stage, is to be composed of both rigid and nonrigid modules, rotating elements, and flexible appendages subjected to environmental disturbances from the earth's atmospheric gravity gradient, and magnetic field, as well as solar radiation and self-generated disturbances. Control functions, which will originally include attitude control, docking and berthing control, and system monitoring and management, will with evolving mission objectives come to encompass such control functions as articulation control, autonomous navigation, space traffic control, and large space structure control. Attention is given to the advancements in modular, distributed, and adaptive control methods, as well as system identification and hardware fault tolerance techniques, which will be required.

Laser Ranging for Effective and Accurate Tracking of Space Debris in Low Earth Orbits

NASA Astrophysics Data System (ADS)

Blanchet, Guillaume; Haag, Herve; Hennegrave, Laurent; Assemat, Francois; Vial, Sophie; Samain, Etienne

2013-08-01

The paper presents the results of preliminary design options for an operational laser ranging system adapted to the measurement of the distance of space debris. Thorough analysis of the operational parameters is provided with identification of performance drivers and assessment of enabling design options. Results from performance simulation demonstrate how the range measurement enables improvement of the orbit determination when combined with astrometry. Besides, experimental results on rocket-stage class debris in LEO were obtained by Astrium beginning of 2012, in collaboration with the Observatoire de la Côte d'Azur (OCA), by operating an experimental laser ranging system supported by the MéO (Métrologie Optique) telescope.

Adaptive management for a turbulent future

USGS Publications Warehouse

Allen, Craig R.; Fontaine, J.J.; Pope, K.L.; Garmestani, A.S.

2011-01-01

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management. ?? 2010 .

Adaptive Management for a Turbulent Future

USGS Publications Warehouse

Allen, Craig R.; Fontaine, Joseph J.; Pope, Kevin L.; Garmestani, Ahjond S.

2011-01-01

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management. Published by Elsevier Ltd.

An RFID-based intelligent vehicle speed controller using active traffic signals.

PubMed

Pérez, Joshué; Seco, Fernando; Milanés, Vicente; Jiménez, Antonio; Díaz, Julio C; de Pedro, Teresa

2010-01-01

These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS). One prime example of ITS is vehicle Cruise Control (CC), which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver's attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V) communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID) technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results.

An RFID-Based Intelligent Vehicle Speed Controller Using Active Traffic Signals

PubMed Central

Pérez, Joshué; Seco, Fernando; Milanés, Vicente; Jiménez, Antonio; Díaz, Julio C.; de Pedro, Teresa

2010-01-01

These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS). One prime example of ITS is vehicle Cruise Control (CC), which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver’s attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V) communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID) technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results. PMID:22219692

Circadian organization in hemimetabolous insects.

PubMed

Tomioka, Kenji; Abdelsalam, Salaheldin

2004-12-01

The circadian system of hemimetabolous insects is reviewed in respect to the locus of the circadian clock and multioscillatory organization. Because of relatively easy access to the nervous system, the neuronal organization of the clock system in hemimetabolous insects has been studied, yielding identification of the compound eye as the major photoreceptor for entrainment and the optic lobe for the circadian clock locus. The clock site within the optic lobe is inconsistent among reported species; in cockroaches the lobula was previously thought to be a most likely clock locus but accessory medulla is recently stressed to be a clock center, while more distal part of the optic lobe including the lamina and the outer medulla area for the cricket. Identification of the clock cells needs further critical studies. Although each optic lobe clock seems functionally identical, in respect to photic entrainment and generation of the rhythm, the bilaterally paired clocks form a functional unit. They interact to produce a stable time structure within individual insects by exchanging photic and temporal information through neural pathways, in which serotonin and pigment-dispersing factor (PDF) are involved as chemical messengers. The mutual interaction also plays an important role in seasonal adaptation of the rhythm.

The Development of the Alpha-Omega Completed Sentence Form (AOCSF): An Instrument to Aid in the Measurement, Identification, and Assessment of an Individual's "Adaptational Approach(es)" to the Stressful Event of Death and Related Issues.

ERIC Educational Resources Information Center

Klein, Ronald; And Others

The Alpha Omega Completed Sentence Form (AOCSF) was developed to identify and measure a person's adaptational approaches to information concerning their own death or the possible death of a significant other. In contrast to the Kubler-Ross stage theory, the adaptational approach recognizes a person's capacity to assimilate new information which…

Development of a Mathematical Ability Test: A Validity and Reliability Study

ERIC Educational Resources Information Center

Dündar, Sefa; Temel, Hasan; Gündüz, Nazan

2016-01-01

The identification of talented students accurately at an early age and the adaptation of the education provided to the students depending on their abilities are of great importance for the future of the countries. In this regard, this study aims to develop a mathematical ability test for the identification of the mathematical abilities of students…

Recovery Based Nanowire Field-Effect Transistor Detection of Pathogenic Avian Influenza DNA

NASA Astrophysics Data System (ADS)

Lin, Chih-Heng; Chu, Chia-Jung; Teng, Kang-Ning; Su, Yi-Jr; Chen, Chii-Dong; Tsai, Li-Chu; Yang, Yuh-Shyong

2012-02-01

Fast and accurate diagnosis is critical in infectious disease surveillance and management. We proposed a DNA recovery system that can easily be adapted to DNA chip or DNA biosensor for fast identification and confirmation of target DNA. This method was based on the re-hybridization of DNA target with a recovery DNA to free the DNA probe. Functionalized silicon nanowire field-effect transistor (SiNW FET) was demonstrated to monitor such specific DNA-DNA interaction using high pathogenic strain virus hemagglutinin 1 (H1) DNA of avian influenza (AI) as target. Specific electric changes were observed in real-time for AI virus DNA sensing and device recovery when nanowire surface of SiNW FET was modified with complementary captured DNA probe. The recovery based SiNW FET biosensor can be further developed for fast identification and further confirmation of a variety of influenza virus strains and other infectious diseases.

Multimodal adaptive optics for depth-enhanced high-resolution ophthalmic imaging

NASA Astrophysics Data System (ADS)

Hammer, Daniel X.; Mujat, Mircea; Iftimia, Nicusor V.; Lue, Niyom; Ferguson, R. Daniel

2010-02-01

We developed a multimodal adaptive optics (AO) retinal imager for diagnosis of retinal diseases, including glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD), and retinitis pigmentosa (RP). The development represents the first ever high performance AO system constructed that combines AO-corrected scanning laser ophthalmoscopy (SLO) and swept source Fourier domain optical coherence tomography (SSOCT) imaging modes in a single compact clinical prototype platform. The SSOCT channel operates at a wavelength of 1 μm for increased penetration and visualization of the choriocapillaris and choroid, sites of major disease activity for DR and wet AMD. The system is designed to operate on a broad clinical population with a dual deformable mirror (DM) configuration that allows simultaneous low- and high-order aberration correction. The system also includes a wide field line scanning ophthalmoscope (LSO) for initial screening, target identification, and global orientation; an integrated retinal tracker (RT) to stabilize the SLO, OCT, and LSO imaging fields in the presence of rotational eye motion; and a high-resolution LCD-based fixation target for presentation to the subject of stimuli and other visual cues. The system was tested in a limited number of human subjects without retinal disease for performance optimization and validation. The system was able to resolve and quantify cone photoreceptors across the macula to within ~0.5 deg (~100-150 μm) of the fovea, image and delineate ten retinal layers, and penetrate to resolve targets deep into the choroid. In addition to instrument hardware development, analysis algorithms were developed for efficient information extraction from clinical imaging sessions, with functionality including automated image registration, photoreceptor counting, strip and montage stitching, and segmentation. The system provides clinicians and researchers with high-resolution, high performance adaptive optics imaging to help guide therapies, develop new drugs, and improve patient outcomes.

System identification of the Arabidopsis plant circadian system

NASA Astrophysics Data System (ADS)

Foo, Mathias; Somers, David E.; Kim, Pan-Jun

2015-02-01

The circadian system generates an endogenous oscillatory rhythm that governs the daily activities of organisms in nature. It offers adaptive advantages to organisms through a coordination of their biological functions with the optimal time of day. In this paper, a model of the circadian system in the plant Arabidopsis (species thaliana) is built by using system identification techniques. Prior knowledge about the physical interactions of the genes and the proteins in the plant circadian system is incorporated in the model building exercise. The model is built by using primarily experimentally-verified direct interactions between the genes and the proteins with the available data on mRNA and protein abundances from the circadian system. Our analysis reveals a great performance of the model in predicting the dynamics of the plant circadian system through the effect of diverse internal and external perturbations (gene knockouts and day-length changes). Furthermore, we found that the circadian oscillatory rhythm is robust and does not vary much with the biochemical parameters except those of a light-sensitive protein P and a transcription factor TOC1. In other words, the circadian rhythmic profile is largely a consequence of the network's architecture rather than its particular parameters. Our work suggests that the current experimental knowledge of the gene-to-protein interactions in the plant Arabidopsis, without considering any additional hypothetical interactions, seems to suffice for system-level modeling of the circadian system of this plant and to present an exemplary platform for the control of network dynamics in complex living organisms.

Application of Protein Microarrays for Multiplexed Detection of Antibodies to Tumor Antigens in Breast Cancer

PubMed Central

Anderson, Karen S.; Ramachandran, Niroshan; Wong, Jessica; Raphael, Jacob V.; Hainsworth, Eugenie; Demirkan, Gokhan; Cramer, Daniel; Aronzon, Diana; Hodi, F. Stephen; Harris, Lyndsay; Logvinenko, Tanya; LaBaer, Joshua

2012-01-01

There is strong preclinical evidence that cancer, including breast cancer, undergoes immune surveillance. This continual monitoring, by both the innate and the adaptive immune systems, recognizes changes in protein expression, mutation, folding, glycosylation, and degradation. Local immune responses to tumor antigens are amplified in draining lymph nodes, and then enter the systemic circulation. The antibody response to tumor antigens, such as p53 protein, are robust, stable, and easily detected in serum, may exist in greater concentrations than their cognate antigens, and are potential highly specific biomarkers for cancer. However, antibodies have limited sensitivities as single analytes, and differences in protein purification and assay characteristics have limited their clinical application. For example, p53 autoantibodies in the sera are highly specific for cancer patients, but are only detected in the sera of 10-20% of patients with breast cancer. Detection of p53 autoantibodies is dependent on tumor burden, p53 mutation, rapidly decreases with effective therapy, but is relatively independent of breast cancer subtype. Although antibodies to hundreds of other tumor antigens have been identified in the sera of breast cancer patients, very little is known about the specificity and clinical impact of the antibody immune repertoire to breast cancer. Recent advances in proteomic technologies have the potential for rapid identification of immune response signatures for breast cancer diagnosis and monitoring. We have adapted programmable protein microarrays for the specific detection of autoantibodies in breast cancer. Here, we present the first demonstration of the application of programmable protein microarray ELISAs for the rapid identification of breast cancer autoantibodies. PMID:18311903

Global Stress Classification System for Materials Used in Solar Energy Applications

NASA Astrophysics Data System (ADS)

Slamova, Karolina; Schill, Christian; Herrmann, Jan; Datta, Pawan; Chih Wang, Chien

2016-08-01

Depending on the geographical location, the individual or combined impact of environmental stress factors and corresponding performance losses for solar applications varies significantly. Therefore, as a strategy to reduce investment risks and operating and maintenance costs, it is necessary to adapt the materials and components of solar energy systems specifically to regional environmental conditions. The project «GloBe Solar» supports this strategy by focusing on the development of a global stress classification system for materials in solar energy applications. The aim of this classification system is to assist in the identification of the individual stress conditions for every location on the earth's surface. The stress classification system could serve as a decision support tool for the industry (manufacturers, investors, lenders and project developers) and help to improve knowledge and services that can provide higher confidence to solar power systems.

Development of an autofluorescence spectral database for the identification and classification of microbial extremophiles

NASA Astrophysics Data System (ADS)

Davis, Justin; Howard, Hillari; Hoover, Richard B.; Sabanayagam, Chandran R.

2010-09-01

Extremophiles are microorganisms that have adapted to severe conditions that were once considered devoid of life. The extreme settings in which these organisms flourish on Earth resemble many extraterrestrial environments. Identification and classification of extremophiles in situ (without the requirement for excessive handling and processing) can provide a basis for designing remotely operated instruments for extraterrestrial life exploration. An important consideration when designing such experiments is to prevent contamination of the environments. We are developing a reference spectral database of autofluorescence from microbial extremophiles using long-UV excitation (408 nm). Aromatic compounds are essential components of living systems, and biological molecules such as aromatic amino acids, nucleotides, porphyrins and vitamins can also exhibit fluorescence under long-UV excitation conditions. Autofluorescence spectra were obtained from a light microscope that additionally allowed observations of microbial geometry and motility. It was observed that all extremophiles studied displayed an autofluorescence peak at around 470 nm, followed by a long decay that was species specific. The autofluorescence database can potentially be used as a reference to identify and classify past or present microbial life in our solar system.

Development of an Autofluorescence Spectral Database for the Identification and Classification of Microbial Extremophiles

NASA Technical Reports Server (NTRS)

Sabanayagam, Chandran; Howard, Hillari; Hoover, Richard B.

2010-01-01

Extremophiles are microorganisms that have adapted to severe conditions that were once considered devoid of life. The extreme settings in which these organisms flourish on earth resemble many extraterrestrial environments. Identification and classification of extremophiles in situ (without the requirement for excessive handling and processing) can provide a basis for designing remotely operated instruments for extraterrestrial life exploration. An important consideration when designing such experiments is to prevent contamination of the environments. We are developing a reference spectral database of autofluorescence from microbial extremophiles using long-UV excitation (405 nm). Aromatic compounds are essential components of living systems, and biological molecules such as aromatic amino acids, nucleotides, porphyrins and vitamins can also exhibit fluorescence under long-UV excitation conditions. Autofluorescence spectra were obtained from a confocal microscope that additionally allowed observations of microbial geometry and motility. It was observed that all extremophiles studied displayed an autofluorescence peak at around 470 nm, followed by a long decay that was species specific. The autofluorescence database can potentially be used as a reference to identify and classify past or present microbial life in our solar system.

Hysteresis modeling and identification of a dielectric electro-active polymer actuator using an APSO-based nonlinear Preisach NARX fuzzy model

NASA Astrophysics Data System (ADS)

Truong, Bui Ngoc Minh; Nam, Doan Ngoc Chi; Ahn, Kyoung Kwan

2013-09-01

Dielectric electro-active polymer (DEAP) materials are attractive since they are low cost, lightweight and have a large deformation capability. They have no operating noise, very low electric power consumption and higher performance and efficiency than competing technologies. However, DEAP materials generally have strong hysteresis as well as uncertain and nonlinear characteristics. These disadvantages can limit the efficiency in the use of DEAP materials. To address these limitations, this research will present the combination of the Preisach model and the dynamic nonlinear autoregressive exogenous (NARX) fuzzy model-based adaptive particle swarm optimization (APSO) identification algorithm for modeling and identification of the nonlinear behavior of one typical type of DEAP actuator. Firstly, open loop input signals are applied to obtain nonlinear features and to investigate the responses of the DEAP actuator system. Then, a Preisach model can be combined with a dynamic NARX fuzzy structure to estimate the tip displacement of a DEAP actuator. To optimize all unknown parameters of the designed combination, an identification scheme based on a least squares method and an APSO algorithm is carried out. Finally, experimental validation research is carefully completed, and the effectiveness of the proposed model is evaluated by employing various input signals.

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

PubMed

Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou

2011-09-01

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

Identification of high performance and component technology for space electrical power systems for use beyond the year 2000

NASA Technical Reports Server (NTRS)

Maisel, James E.

1988-01-01

Addressed are some of the space electrical power system technologies that should be developed for the U.S. space program to remain competitive in the 21st century. A brief historical overview of some U.S. manned/unmanned spacecraft power systems is discussed to establish the fact that electrical systems are and will continue to become more sophisticated as the power levels appoach those on the ground. Adaptive/Expert power systems that can function in an extraterrestrial environment will be required to take an appropriate action during electrical faults so that the impact is minimal. Manhours can be reduced significantly by relinquishing tedious routine system component maintenance to the adaptive/expert system. By cataloging component signatures over time this system can set a flag for a premature component failure and thus possibly avoid a major fault. High frequency operation is important if the electrical power system mass is to be cut significantly. High power semiconductor or vacuum switching components will be required to meet future power demands. System mass tradeoffs have been investigated in terms of operating at high temperature, efficiency, voltage regulation, and system reliability. High temperature semiconductors will be required. Silicon carbide materials will operate at a temperature around 1000 K and the diamond material up to 1300 K. The driver for elevated temperature operation is that radiator mass is reduced significantly because of inverse temperature to the fourth power.

Analysis of energy dispersive x-ray diffraction profiles for material identification, imaging and system control

NASA Astrophysics Data System (ADS)

Cook, Emily Jane

2008-12-01

This thesis presents the analysis of low angle X-ray scatter measurements taken with an energy dispersive system for substance identification, imaging and system control. Diffraction measurements were made on illicit drugs, which have pseudo- crystalline structures and thus produce diffraction patterns comprising a se ries of sharp peaks. Though the diffraction profiles of each drug are visually characteristic, automated detection systems require a substance identification algorithm, and multivariate analysis was selected as suitable. The software was trained with measured diffraction data from 60 samples covering 7 illicit drugs and 5 common cutting agents, collected with a range of statistical qual ities and used to predict the content of 7 unknown samples. In all cases the constituents were identified correctly and the contents predicted to within 15%. Soft tissues exhibit broad peaks in their diffraction patterns. Diffraction data were collected from formalin fixed breast tissue samples and used to gen erate images. Maximum contrast between healthy and suspicious regions was achieved using momentum transfer windows 1.04-1.10 and 1.84-1.90 nm_1. The resulting images had an average contrast of 24.6% and 38.9% compared to the corresponding transmission X-ray images (18.3%). The data was used to simulate the feedback for an adaptive imaging system and the ratio of the aforementioned momentum transfer regions found to be an excellent pa rameter. Investigation into the effects of formalin fixation on human breast tissue and animal tissue equivalents indicated that fixation in standard 10% buffered formalin does not alter the diffraction profiles of tissue in the mo mentum transfer regions examined, though 100% unbuffered formalin affects the profile of porcine muscle tissue (a substitute for glandular and tumourous tissue), though fat is unaffected.

A Cross-Cultural Adaptation of the Sniffin' Sticks Olfactory Identification Test for US children.

PubMed

Cavazzana, Annachiara; Wesarg, Christiane; Schriever, Valentin A; Hummel, Thomas; Lundström, Johan N; Parma, Valentina

2017-02-01

Disorders associated with smell loss are common in adolescents. However, current odor identification tests focus on children from age 6 and older and no cross-cultural test has to date been validated and fully implemented. Here, we aimed to investigate how 3-to-11-year-old US children performed to an adapted and shortened (11 odors instead of 14) version of a European odor identification test-the Sniffin' Kids (Schriever VA, Mori E, Petters W, Boerner C, Smitka M, Hummel T. 2014. The "Sniffin'Kids" test: a 14-item odor identification test for children. Plos One. 9:e101086.). Results confirmed that cued odor identification performance increases with age and revealed little to no differences between girls and boys. Scores below 3 and below 6 may raise hyposmia concerns in US children aged 3-7 years and 8-10 years, respectively. Even though the completion rate of the task reached the 88%, suggesting that children below age 5 were able to finish the test, their performance was relatively poor. In comparing the overall identification performance of US children with that of German children, for whom the test was specifically developed, significant differences emerged, with higher scores obtained by the German sample. Analysis of errors indicated that a lack of semantic knowledge for the olfactory-presented objects may be at the root of poor identification skills in US children and therefore constitutes a problem in the development of an odor identification test for younger children valid across cultures. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Persons with Multiple Disabilities Use Orientation Technology to Find Room Entrances during Indoor Traveling

ERIC Educational Resources Information Center

Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Alberti, Gloria; Scigliuzzo, Francesca; Signorino, Mario; Oliva, Doretta; Smaldone, Angela; La Martire, Maria L.

2010-01-01

These two studies assessed adapted orientation technology for promoting correct direction and room identification during indoor traveling by persons with multiple (e.g., sensory, motor and intellectual/adaptive) disabilities. In Study I, two adults were included who had severe visual impairment or total blindness and deafness and used a wheelchair…

Lessons Learned in Designing and Implementing a Computer-Adaptive Test for English

ERIC Educational Resources Information Center

Burston, Jack; Neophytou, Maro

2014-01-01

This paper describes the lessons learned in designing and implementing a computer-adaptive test (CAT) for English. The early identification of students with weak L2 English proficiency is of critical importance in university settings that have compulsory English language course graduation requirements. The most efficient means of diagnosing the L2…

A Strategy to Join Adaptive and Reputation-Based Social-Collaborative E-Learning, through the Zone of Proximal Development

ERIC Educational Resources Information Center

De Marsico, Maria; Sterbini, Andrea; Temperini, Marco

2013-01-01

The educational concept of "Zone of Proximal Development", introduced by Vygotskij, stems from the identification of a strong need for adaptation of the learning activities, both traditional classroom and modern e-learning ones, to the present state of learner's knowledge and abilities. Furthermore, Vygotskij's educational…

Essential Neuroscience in Immunology

PubMed Central

Chavan, Sangeeta S.; Tracey, Kevin J.

2017-01-01

The field of immunology is principally focused on the molecular mechanisms by which hematopoetic cells initiate and maintain innate and adaptive immunity. That cornerstone of attention has been expanded by recent discoveries that neuronal signals occupy a critical regulatory niche in immunity. The discovery is that neuronal circuits operating reflexively regulate innate and adaptive immunity. One particularly well-characterized circuit regulating innate immunity, the inflammatory reflex, is dependent upon action potentials transmitted to the reticuloendothelial system via the vagus and splenic nerves. This field has grown significantly with identification of several other reflexes regulating discrete immune functions. As reviewed here, the delineation of these mechanisms revealed a new understanding of immunity, enabled a first in class clinical trial using bioelectronic devices to inhibit cytokines and inflammation in rheumatoid arthritis patients, and provided a mosaic view of immunity as the integration of hematopoetic and neural responses to infection and injury. PMID:28416717

Essential Neuroscience in Immunology.

PubMed

Chavan, Sangeeta S; Tracey, Kevin J

2017-05-01

The field of immunology is principally focused on the molecular mechanisms by which hematopoietic cells initiate and maintain innate and adaptive immunity. That cornerstone of attention has been expanded by recent discoveries that neuronal signals occupy a critical regulatory niche in immunity. The discovery is that neuronal circuits operating reflexively regulate innate and adaptive immunity. One particularly well-characterized circuit regulating innate immunity, the inflammatory reflex, is dependent upon action potentials transmitted to the reticuloendothelial system via the vagus and splenic nerves. This field has grown significantly with the identification of several other reflexes regulating discrete immune functions. As outlined in this review, the delineation of these mechanisms revealed a new understanding of immunity, enabled a first-in-class clinical trial using bioelectronic devices to inhibit cytokines and inflammation in rheumatoid arthritis patients, and provided a mosaic view of immunity as the integration of hematopoietic and neural responses to infection and injury. Copyright © 2017 by The American Association of Immunologists, Inc.

Speckle statistics in adaptive optics images at visible wavelengths

NASA Astrophysics Data System (ADS)

Stangalini, Marco; Pedichini, Fernando; Ambrosino, Filippo; Centrone, Mauro; Del Moro, Dario

2016-07-01

Residual speckles in adaptive optics (AO) images represent a well known limitation to the achievement of the contrast needed for faint stellar companions detection. Speckles in AO imagery can be the result of either residual atmospheric aberrations, not corrected by the AO, or slowly evolving aberrations induced by the optical system. In this work we take advantage of new high temporal cadence (1 ms) data acquired by the SHARK forerunner experiment at the Large Binocular Telescope (LBT), to characterize the AO residual speckles at visible waveleghts. By means of an automatic identification of speckles, we study the main statistical properties of AO residuals. In addition, we also study the memory of the process, and thus the clearance time of the atmospheric aberrations, by using information Theory. These information are useful for increasing the realism of numerical simulations aimed at assessing the instrumental performances, and for the application of post-processing techniques on AO imagery.

Adaptation and diversity along an altitudinal gradient in Ethiopian barley (Hordeum vulgare L.) landraces revealed by molecular analysis

PubMed Central

2010-01-01

Background Among the cereal crops, barley is the species with the greatest adaptability to a wide range of environments. To determine the level and structure of genetic diversity in barley (Hordeum vulgare L.) landraces from the central highlands of Ethiopia, we have examined the molecular variation at seven nuclear microsatellite loci. Results A total of 106 landrace populations were sampled in the two growing seasons (Meher and Belg; the long and short rainy seasons, respectively), across three districts (Ankober, Mojanawadera and Tarmaber), and within each district along an altitudinal gradient (from 1,798 to 3,324 m a.s.l). Overall, although significant, the divergence (e.g. FST) is very low between seasons and geographical districts, while it is high between different classes of altitude. Selection for adaptation to different altitudes appears to be the main factor that has determined the observed clinal variation, along with population-size effects. Conclusions Our data show that barley landraces from Ethiopia are constituted by highly variable local populations (farmer's fields) that have large within-population diversity. These landraces are also shown to be locally adapted, with the major driving force that has shaped their population structure being consistent with selection for adaptation along an altitudinal gradient. Overall, our study highlights the potential of such landraces as a source of useful alleles. Furthermore, these landraces also represent an ideal system to study the processes of adaptation and for the identification of genes and genomic regions that have adaptive roles in crop species. PMID:20565982

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

PubMed

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin; Choo, Kim-Kwang Raymond

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

PubMed Central

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). PMID:27611312

Specialization and Universals in the Development of Reading Skill: How Chinese Research Informs a Universal Science of Reading

PubMed Central

Perfetti, Charles; Cao, Fan; Booth, James

2014-01-01

Understanding Chinese reading is important for identifying the universal aspects of reading, separated from those aspects that are specific to alphabetic writing or to English in particular. Chinese and alphabetic writing make different demands on reading and learning to read, despite reading procedures and their supporting brain networks that are partly universal. Learning to read accommodates the demands of a writing system through the specialization of brain networks that support word identification. This specialization increases with reading development, leading to differences in the brain networks for alphabetic and Chinese reading. We suggest that beyond reading procedures that are partly universal and partly writing-system specific, functional reading universals arise across writing systems in their adaptation to human cognitive abilities. PMID:24744605

CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins.

PubMed

Couvin, David; Bernheim, Aude; Toffano-Nioche, Claire; Touchon, Marie; Michalik, Juraj; Néron, Bertrand; C Rocha, Eduardo P; Vergnaud, Gilles; Gautheret, Daniel; Pourcel, Christine

2018-05-22

CRISPR (clustered regularly interspaced short palindromic repeats) arrays and their associated (Cas) proteins confer bacteria and archaea adaptive immunity against exogenous mobile genetic elements, such as phages or plasmids. CRISPRCasFinder allows the identification of both CRISPR arrays and Cas proteins. The program includes: (i) an improved CRISPR array detection tool facilitating expert validation based on a rating system, (ii) prediction of CRISPR orientation and (iii) a Cas protein detection and typing tool updated to match the latest classification scheme of these systems. CRISPRCasFinder can either be used online or as a standalone tool compatible with Linux operating system. All third-party software packages employed by the program are freely available. CRISPRCasFinder is available at https://crisprcas.i2bc.paris-saclay.fr.

Identification and Evaluation of Medical Translator Mobile Applications Using an Adapted APPLICATIONS Scoring System.

PubMed

Khander, Amrin; Farag, Sara; Chen, Katherine T

2017-12-22

With an increasing number of patients requiring translator services, many providers are turning to mobile applications (apps) for assistance. However, there have been no published reviews of medical translator apps. To identify and evaluate medical translator mobile apps using an adapted APPLICATIONS scoring system. A list of apps was identified from the Apple iTunes and Google Play stores, using the search term, "medical translator." Apps not found on two different searches, not in an English-based platform, not used for translation, or not functional after purchase, were excluded. The remaining apps were evaluated using an adapted APPLICATIONS scoring system, which included both objective and subjective criteria. App comprehensiveness was a weighted score defined by the number of non-English languages included in each app relative to the proportion of non-English speakers in the United States. The Apple iTunes and Google Play stores. Medical translator apps identified using the search term "medical translator." Main Outcomes and Measures: Compilation of medical translator apps for provider usage. A total of 524 apps were initially found. After applying the exclusion criteria, 20 (8.2%) apps from the Google Play store and 26 (9.2%) apps from the Apple iTunes store remained for evaluation. The highest scoring apps, Canopy Medical Translator, Universal Doctor Speaker, and Vocre Translate, scored 13.5 out of 18.7 possible points. A large proportion of apps initially found did not function as medical translator apps. Using the APPLICATIONS scoring system, we have identified and evaluated medical translator apps for providers who care for non-English speaking patients.

De-identification of health records using Anonym: effectiveness and robustness across datasets.

PubMed

Zuccon, Guido; Kotzur, Daniel; Nguyen, Anthony; Bergheim, Anton

2014-07-01

Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Adaptive management: Chapter 1

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.; Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Adaptive management

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Eigenstructure Assignment for Fault Tolerant Flight Control Design

NASA Technical Reports Server (NTRS)

Sobel, Kenneth; Joshi, Suresh (Technical Monitor)

2002-01-01

In recent years, fault tolerant flight control systems have gained an increased interest for high performance military aircraft as well as civil aircraft. Fault tolerant control systems can be described as either active or passive. An active fault tolerant control system has to either reconfigure or adapt the controller in response to a failure. One approach is to reconfigure the controller based upon detection and identification of the failure. Another approach is to use direct adaptive control to adjust the controller without explicitly identifying the failure. In contrast, a passive fault tolerant control system uses a fixed controller which achieves acceptable performance for a presumed set of failures. We have obtained a passive fault tolerant flight control law for the F/A-18 aircraft which achieves acceptable handling qualities for a class of control surface failures. The class of failures includes the symmetric failure of any one control surface being stuck at its trim value. A comparison was made of an eigenstructure assignment gain designed for the unfailed aircraft with a fault tolerant multiobjective optimization gain. We have shown that time responses for the unfailed aircraft using the eigenstructure assignment gain and the fault tolerant gain are identical. Furthermore, the fault tolerant gain achieves MIL-F-8785C specifications for all failure conditions.

Perceptions of political leaders.

PubMed

David Schmitz, J; Murray, Gregg R

2017-01-01

Partisan identification is a fundamental force in individual and mass political behavior around the world. Informed by scholarship on human sociality, coalitional psychology, and group behavior, this research argues that partisan identification, like many other group-based behaviors, is influenced by forces of evolution. If correct, then party identifiers should exhibit adaptive behaviors when making group-related political decisions. The authors test this assertion with citizen assessments of the relative physical formidability of competing leaders, an important adaptive factor in leader evaluations. Using original and novel data collected during the contextually different 2008 and 2012 U.S. presidential elections, as well as two distinct measures obtained during both elections, this article presents evidence that partisans overestimate the physical stature of the presidential candidate of their own party compared with the stature of the candidate of the opposition party. These findings suggest that the power of party identification on political behavior may be attributable to the fact that modern political parties address problems similar to the problems groups faced in human ancestral times.

Modeling and parameter identification of impulse response matrix of mechanical systems

NASA Astrophysics Data System (ADS)

Bordatchev, Evgueni V.

1998-12-01

A method for studying the problem of modeling, identification and analysis of mechanical system dynamic characteristic in view of the impulse response matrix for the purpose of adaptive control is developed here. Two types of the impulse response matrices are considered: (i) on displacement, which describes the space-coupled relationship between vectors of the force and simulated displacement, which describes the space-coupled relationship between vectors of the force and simulated displacement and (ii) on acceleration, which also describes the space-coupled relationship between the vectors of the force and measured acceleration. The idea of identification consists of: (a) the practical obtaining of the impulse response matrix on acceleration by 'impact-response' technique; (b) the modeling and parameter estimation of the each impulse response function on acceleration through the fundamental representation of the impulse response function on displacement as a sum of the damped sine curves applying linear and non-linear least square methods; (c) simulating the impulse provides the additional possibility to calculate masses, damper and spring constants. The damped natural frequencies are used as a priori information and are found through the standard FFT analysis. The problem of double numerical integration is avoided by taking two derivations of the fundamental dynamic model of a mechanical system as linear combination of the mass-damper-spring subsystems. The identified impulse response matrix on displacement represents the dynamic properties of the mechanical system. From the engineering point of view, this matrix can be also understood as a 'dynamic passport' of the mechanical system and can be used for dynamic certification and analysis of the dynamic quality. In addition, the suggested approach mathematically reproduces amplitude-frequency response matrix in a low-frequency band and on zero frequency. This allows the possibility of determining the matrix of the static stiffness due to dynamic testing over the time of 10- 15 minutes. As a practical example, the dynamic properties in view of the impulse and frequency response matrices of the lathe spindle are obtained, identified and investigated. The developed approach for modeling and parameter identification appears promising for a wide range o industrial applications; for example, rotary systems.

Staphylococcus aureus genomics and the impact of horizontal gene transfer.

PubMed

Lindsay, Jodi A

2014-03-01

Whole genome sequencing and microarrays have revealed the population structure of Staphylococcus aureus, and identified epidemiological shifts, transmission routes, and adaptation of major clones. S. aureus genomes are highly diverse. This is partly due to a population structure of conserved lineages, each with unique combinations of genes encoding surface proteins, regulators, immune evasion and virulence pathways. Even more variable are the mobile genetic elements (MGE), which encode key proteins for antibiotic resistance, virulence and host-adaptation. MGEs can transfer at high frequency between isolates of the same lineage by horizontal gene transfer (HGT). There is increasing evidence that HGT is key to bacterial adaptation and success. Recent studies have shed light on new mechanisms of DNA transfer such as transformation, the identification of receptors for transduction, on integration of DNA pathways, mechanisms blocking transfer including CRISPR and new restriction systems, strategies for evasion of restriction barriers, as well as factors influencing MGE selection and stability. These studies have also lead to new tools enabling construction of genetically modified clinical S. aureus isolates. This review will focus on HGT mechanisms and their importance in shaping the evolution of new clones adapted to antibiotic resistance, healthcare, communities and livestock. Copyright © 2013 Elsevier GmbH. All rights reserved.

Special issue on Military Operations Research Society (MORS) Symposium (80th): Expanding the Boundaries of National Security Analysis (Phalanx: The Bulletin of Military Operations Research. Volume 45, Number 2, June 2012)

DTIC Science & Technology

2012-06-01

brianmccue@alum.mit.edu Letters to the Editor, John Willis, Augustine Consulting, Inc., jwillis@aciedge.com Modeling and Simulation , James N. Bexfield, FS, OSD...concepts that are now being applied to modern analytical thinking. The tuto- rials are free to MORS members and $75 for the day for nonmembers. The...Overview of Agent- based Modeling and Simulation and Complex Adaptive Systems •  Visual Data Analysis •  Analyzing Combat Identification •  Guidelines for

Progress in understanding the immunopathogenesis of psoriasis

PubMed Central

Mak, R.K.H.; Hundhausen, C.; Nestle, F.O.

2010-01-01

This review emphasizes how translation from bench research to clinical knowledge and vice versa has resulted in considerable progress in understanding the immunopathogenesis of psoriasis. First, the journey in understanding the pathogenic mechanisms behind psoriasis is described. The roles of different components of the adaptive and innate immune systems involved in driving the inflammatory response are explained. Discovery of new immune pathways i.e. the IL23/Th17 axis and its subsequent impact on the development of novel biological therapies is highlighted. Identification of potential targets warranting further research for future therapeutic development are also discussed. PMID:20096156

Odour Identification in Frontotemporal Lobar Degeneration

PubMed Central

Rami, Lorena; Loy, Clement T.; Hailstone, Julia; Warren, Jason D.

2008-01-01

Little information is available concerning olfactory processing in frontotemporal lobar degeneration (FTLD). We undertook a case-control study of olfactory processing in three male patients fulfilling clinical criteria for FTLD. Odour identification (semantic analysis) and odour discrimination (perceptual analysis) were investigated using tests adapted from the University of Pennsylvania Smell Identification Test. General neuropsychometry and structural volumetric brain magnetic resonance imaging (MRI) were also performed. The three patients with FTLD exhibited a disorder of olfactory processing with the characteristics of a predominantly semantic (odour identification) deficit. This olfactory deficit was more prominent in patients with greater involvement of the temporal lobes on MRI. Central deficits of odour identification may be more common in FTLD than previously recognised, and these deficits may assist in clinical characterisation. PMID:17380245

A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference

PubMed Central

Zenil, Hector; Kiani, Narsis A.; Ball, Gordon; Gomez-Cabrero, David

2016-01-01

Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’. PMID:27698038

Quantitative analysis of terahertz spectra for illicit drugs using adaptive-range micro-genetic algorithm

NASA Astrophysics Data System (ADS)

Chen, Yi; Ma, Yong; Lu, Zheng; Peng, Bei; Chen, Qin

2011-08-01

In the field of anti-illicit drug applications, many suspicious mixture samples might consist of various drug components—for example, a mixture of methamphetamine, heroin, and amoxicillin—which makes spectral identification very difficult. A terahertz spectroscopic quantitative analysis method using an adaptive range micro-genetic algorithm with a variable internal population (ARVIPɛμGA) has been proposed. Five mixture cases are discussed using ARVIPɛμGA driven quantitative terahertz spectroscopic analysis in this paper. The devised simulation results show agreement with the previous experimental results, which suggested that the proposed technique has potential applications for terahertz spectral identifications of drug mixture components. The results show agreement with the results obtained using other experimental and numerical techniques.

Adaptive identification of vessel's added moments of inertia with program motion

NASA Astrophysics Data System (ADS)

Alyshev, A. S.; Melnikov, V. G.

2018-05-01

In this paper, we propose a new experimental method for determining the moments of inertia of the ship model. The paper gives a brief review of existing methods, a description of the proposed method and experimental stand, test procedures and calculation formulas and experimental results. The proposed method is based on the energy approach with special program motions. The ship model is fixed in a special rack consisting of a torsion element and a set of additional servo drives with flywheels (reactive wheels), which correct the motion. The servo drives with an adaptive controller provide the symmetry of the motion, which is necessary for the proposed identification procedure. The effectiveness of the proposed approach is confirmed by experimental results.

Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations

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

Alsenas, Gabriel; Dalgleish, Fraser; Ouyang, Bing

Final Report for project DE-EE0006787: Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations. This project developed and tested an optical monitoring system prototype that will be suitable for marine and hydrokinetic (MHK) full project lifecycle observation (baseline, commissioning, and decommissioning), with automated real-time classification of marine animals. This system can be deployed to collect pre-installation baseline species observations at a proposed deployment site with minimal post-processing overhead. To satisfy deployed MHK project species of concern (e.g. Endangered Species Act-listed) monitoring requirements, the system provides automated tracking and notification of the presence of managed animals withinmore » established perimeters of MHK equipment and provides high resolution imagery of their behavior through a wide range of conditions. During a project’s decommissioning stage, the system can remain installed to provide resource managers with post-installation data. Our technology, known as an Unobtrusive Multi-static Serial LiDAR Imager (UMSLI), is a technology transfer of underwater distributed LiDAR imaging technology that preserves the advantages of traditional optical and acoustic solutions while overcoming associated disadvantages for MHK environmental monitoring applications. This new approach is a purposefully-designed, reconfigurable adaptation of an existing technology that can be easily mounted on or around different classes of MHK equipment. The system uses low average power red (638nm) laser illumination to be invisible and eye-safe to marine animals and is compact and cost effective. The equipment is designed for long term, maintenance-free operations, to inherently generate a sparse primary dataset that only includes detected anomalies (animal presence information), and to allow robust real-time automated animal classification/identification with a low data bandwidth requirement. Advantages of the technology over others currently being used or being considered for MHK monitoring include: Unlike a conventional camera, the depth of field is near-infinite and limited by attenuation (approximately 5-8 m) rather than focal properties of a lens; Operation in an adaptive mode which can project a sparse grid of pulses with higher peak power for longer range detection (>10 meters) and track animals within a zone of interest with high resolution imagery for identification of marine life at closer range (<5m); System detection limit and Signal-to-Noise-Ratio is superior to a camera, due to rejection of both backscattering component and ambient solar background; Multiple wide-angle pulsed laser illuminators and bucket detectors can be flexibly configured to cover a 4pi steradian (i.e. omnidirectional) scene volume, while also retrieving 3D features of animal targets from timing information; Process and classification framework centered around a novel active learning and incremental classification classifier that enables accurate identification of a variety of marine animals automatically; A two-tiered monitoring architecture and invisible watermarking-based data archiving and retrieving approach ensures significant data reduction while preserving high fidelity monitoring. A methodology to train and optimize the classifier for target species of concern to optimize site monitoring effectiveness. This technological innovation addresses a high priority regulatory requirement to observe marine life interaction near MHK projects. Our solution improves resource manager confidence that any interactions between marine animals and equipment are observed in a cost-effective and automated manner. Without EERE funding, this novel application of multi-static LiDAR would not have been available to the MHK community for environmental monitoring.« less

Gender Positioning in Education: A Critical Image Analysis of ESL Texts.

ERIC Educational Resources Information Center

Giaschi, Peter

2000-01-01

This article is adapted from a project report prepared for the author's Master's degree in Education. The objective is to report the use of an adapted analytical technique for examining the images contained in contemporary English-as-a-Second-Language (ESL) textbooks. The point of departure for the study was the identification of the trend in mass…

A Universal Model of Giftedness--An Adaptation of the Munich Model

ERIC Educational Resources Information Center

Jessurun, J. H.; Shearer, C. B.; Weggeman, M. C. D. P.

2016-01-01

The Munich Model of Giftedness (MMG) by Heller and his colleagues, developed for the identification of gifted children, is adapted and expanded, with the aim of making it more universally usable as a model for the pathway from talents to performance. On the side of the talent-factors, the concept of multiple intelligences is introduced, and the…

Matrix-assisted laser desorption ionization-time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology.

PubMed

Clark, Andrew E; Kaleta, Erin J; Arora, Amit; Wolk, Donna M

2013-07-01

Within the past decade, clinical microbiology laboratories experienced revolutionary changes in the way in which microorganisms are identified, moving away from slow, traditional microbial identification algorithms toward rapid molecular methods and mass spectrometry (MS). Historically, MS was clinically utilized as a high-complexity method adapted for protein-centered analysis of samples in chemistry and hematology laboratories. Today, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) MS is adapted for use in microbiology laboratories, where it serves as a paradigm-shifting, rapid, and robust method for accurate microbial identification. Multiple instrument platforms, marketed by well-established manufacturers, are beginning to displace automated phenotypic identification instruments and in some cases genetic sequence-based identification practices. This review summarizes the current position of MALDI-TOF MS in clinical research and in diagnostic clinical microbiology laboratories and serves as a primer to examine the "nuts and bolts" of MALDI-TOF MS, highlighting research associated with sample preparation, spectral analysis, and accuracy. Currently available MALDI-TOF MS hardware and software platforms that support the use of MALDI-TOF with direct and precultured specimens and integration of the technology into the laboratory workflow are also discussed. Finally, this review closes with a prospective view of the future of MALDI-TOF MS in the clinical microbiology laboratory to accelerate diagnosis and microbial identification to improve patient care.

Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry: a Fundamental Shift in the Routine Practice of Clinical Microbiology

PubMed Central

Clark, Andrew E.; Kaleta, Erin J.; Arora, Amit

2013-01-01

SUMMARY Within the past decade, clinical microbiology laboratories experienced revolutionary changes in the way in which microorganisms are identified, moving away from slow, traditional microbial identification algorithms toward rapid molecular methods and mass spectrometry (MS). Historically, MS was clinically utilized as a high-complexity method adapted for protein-centered analysis of samples in chemistry and hematology laboratories. Today, matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) MS is adapted for use in microbiology laboratories, where it serves as a paradigm-shifting, rapid, and robust method for accurate microbial identification. Multiple instrument platforms, marketed by well-established manufacturers, are beginning to displace automated phenotypic identification instruments and in some cases genetic sequence-based identification practices. This review summarizes the current position of MALDI-TOF MS in clinical research and in diagnostic clinical microbiology laboratories and serves as a primer to examine the “nuts and bolts” of MALDI-TOF MS, highlighting research associated with sample preparation, spectral analysis, and accuracy. Currently available MALDI-TOF MS hardware and software platforms that support the use of MALDI-TOF with direct and precultured specimens and integration of the technology into the laboratory workflow are also discussed. Finally, this review closes with a prospective view of the future of MALDI-TOF MS in the clinical microbiology laboratory to accelerate diagnosis and microbial identification to improve patient care. PMID:23824373

A Novel Fluorescence Resonance Energy Transfer-Based Screen in High-Throughput Format To Identify Inhibitors of Malarial and Human Glucose Transporters.

PubMed

Kraft, Thomas E; Heitmeier, Monique R; Putanko, Marina; Edwards, Rachel L; Ilagan, Ma Xenia G; Payne, Maria A; Autry, Joseph M; Thomas, David D; Odom, Audrey R; Hruz, Paul W

2016-12-01

The glucose transporter PfHT is essential to the survival of the malaria parasite Plasmodium falciparum and has been shown to be a druggable target with high potential for pharmacological intervention. Identification of compounds against novel drug targets is crucial to combating resistance against current therapeutics. Here, we describe the development of a cell-based assay system readily adaptable to high-throughput screening that directly measures compound effects on PfHT-mediated glucose transport. Intracellular glucose concentrations are detected using a genetically encoded fluorescence resonance energy transfer (FRET)-based glucose sensor. This allows assessment of the ability of small molecules to inhibit glucose uptake with high accuracy (Z' factor of >0.8), thereby eliminating the need for radiolabeled substrates. Furthermore, we have adapted this assay to counterscreen PfHT hits against the human orthologues GLUT1, -2, -3, and -4. We report the identification of several hits after screening the Medicines for Malaria Venture (MMV) Malaria Box, a library of 400 compounds known to inhibit erythrocytic development of P. falciparum Hit compounds were characterized by determining the half-maximal inhibitory concentration (IC 50 ) for the uptake of radiolabeled glucose into isolated P. falciparum parasites. One of our hits, compound MMV009085, shows high potency and orthologue selectivity, thereby successfully validating our assay for antimalarial screening. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Dysfunction of Rapid Neural Adaptation in Dyslexia.

PubMed

Perrachione, Tyler K; Del Tufo, Stephanie N; Winter, Rebecca; Murtagh, Jack; Cyr, Abigail; Chang, Patricia; Halverson, Kelly; Ghosh, Satrajit S; Christodoulou, Joanna A; Gabrieli, John D E

2016-12-21

Identification of specific neurophysiological dysfunctions resulting in selective reading difficulty (dyslexia) has remained elusive. In addition to impaired reading development, individuals with dyslexia frequently exhibit behavioral deficits in perceptual adaptation. Here, we assessed neurophysiological adaptation to stimulus repetition in adults and children with dyslexia for a wide variety of stimuli, spoken words, written words, visual objects, and faces. For every stimulus type, individuals with dyslexia exhibited significantly diminished neural adaptation compared to controls in stimulus-specific cortical areas. Better reading skills in adults and children with dyslexia were associated with greater repetition-induced neural adaptation. These results highlight a dysfunction of rapid neural adaptation as a core neurophysiological difference in dyslexia that may underlie impaired reading development. Reduced neurophysiological adaptation may relate to prior reports of reduced behavioral adaptation in dyslexia and may reveal a difference in brain functions that ultimately results in a specific reading impairment. Copyright © 2016 Elsevier Inc. All rights reserved.

An adaptive deep learning approach for PPG-based identification.

PubMed

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

Assembling evidence for identifying reservoirs of infection.

PubMed

Viana, Mafalda; Mancy, Rebecca; Biek, Roman; Cleaveland, Sarah; Cross, Paul C; Lloyd-Smith, James O; Haydon, Daniel T

2014-05-01

Many pathogens persist in multihost systems, making the identification of infection reservoirs crucial for devising effective interventions. Here, we present a conceptual framework for classifying patterns of incidence and prevalence, and review recent scientific advances that allow us to study and manage reservoirs simultaneously. We argue that interventions can have a crucial role in enriching our mechanistic understanding of how reservoirs function and should be embedded as quasi-experimental studies in adaptive management frameworks. Single approaches to the study of reservoirs are unlikely to generate conclusive insights whereas the formal integration of data and methodologies, involving interventions, pathogen genetics, and contemporary surveillance techniques, promises to open up new opportunities to advance understanding of complex multihost systems. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Autonomous Component Health Management with Failed Component Detection, Identification, and Avoidance

NASA Technical Reports Server (NTRS)

Davis, Robert N.; Polites, Michael E.; Trevino, Luis C.

2004-01-01

This paper details a novel scheme for autonomous component health management (ACHM) with failed actuator detection and failed sensor detection, identification, and avoidance. This new scheme has features that far exceed the performance of systems with triple-redundant sensing and voting, yet requires fewer sensors and could be applied to any system with redundant sensing. Relevant background to the ACHM scheme is provided, and the simulation results for the application of that scheme to a single-axis spacecraft attitude control system with a 3rd order plant and dual-redundant measurement of system states are presented. ACHM fulfills key functions needed by an integrated vehicle health monitoring (IVHM) system. It is: autonomous; adaptive; works in realtime; provides optimal state estimation; identifies failed components; avoids failed components; reconfigures for multiple failures; reconfigures for intermittent failures; works for hard-over, soft, and zero-output failures; and works for both open- and closed-loop systems. The ACHM scheme combines a prefilter that generates preliminary state estimates, detects and identifies failed sensors and actuators, and avoids the use of failed sensors in state estimation with a fixed-gain Kalman filter that generates optimal state estimates and provides model-based state estimates that comprise an integral part of the failure detection logic. The results show that ACHM successfully isolates multiple persistent and intermittent hard-over, soft, and zero-output failures. It is now ready to be tested on a computer model of an actual system.

Remembering forward: Neural correlates of memory and prediction in human motor adaptation

PubMed Central

Scheidt, Robert A; Zimbelman, Janice L; Salowitz, Nicole M G; Suminski, Aaron J; Mosier, Kristine M; Houk, James; Simo, Lucia

2011-01-01

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions - including prefrontal, parietal and hippocampal cortices - exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancellation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures. PMID:21840405

Development and Preliminary Evaluation of an Adaptation of Word Identification Fluency for Beginning Readers in New Zealand

ERIC Educational Resources Information Center

Schaughency, Elizabeth; McLennan, Kathryn M.; McDowall, Philippa S.

2015-01-01

A New Zealand (NZ) version of Word Identification Fluency (NZWIF) was administered to 120 children in their second school year at the beginning, middle, and end of the year, along with a curriculum-based measure of oral passage reading fluency at mid- and end-year. Outcome measures included standardized and high-stakes school-used indicators of…

Improving the healthcare response to domestic violence and abuse in sexual health clinics: feasibility study of a training, support and referral intervention

PubMed Central

Sohal, Alex Hardip; Pathak, Neha; Blake, Sarah; Apea, Vanessa; Berry, Judith; Bailey, Jayne; Griffiths, Chris; Feder, Gene

2018-01-01

Objectives Sexual health and gynaecological problems are the most consistent and largest physical health differences between abused and non-abused female populations. Sexual health services are well placed to identify and support patients experiencing domestic violence and abuse (DVA). Most sexual health professionals have had minimal DVA training despite English National Institute for Health and Care Excellence recommendations. We sought to determine the feasibility of an evidence-based complex DVA training intervention in female sexual health walk-in services (IRIS ADViSE: Identification and Referral to Improve Safety whilst Assessing Domestic Violence in Sexual Health Environments). Methods An adaptive mixed method pilot study in the female walk-in service of two sexual health clinics. Following implementation and evaluation at site 1, the intervention was refined before implementation at site 2. The intervention comprised electronic prompts, multidisciplinary training sessions, clinic materials and simple referral pathways to IRIS ADViSE advocate-educators (AEs). The pilot lasted 7 weeks at site 1 and 12 weeks at site 2. Feasibility outcomes were to assign a supportive DVA clinical lead, an IRIS ADViSE AE employed by a local DVA service provider, adapt electronic records, develop local referral pathways, assess whether enquiry, identification and referral rates were measurable. Results Both sites achieved all feasibility outcomes: appointing a supportive DVA clinical lead and IRIS ADViSE AE, establishing links with a local DVA provider, adapting electronic records, developing local referral pathways and rates of enquiry, identification and referral were found to be measurable. Site 1: 10% enquiry rate (n=267), 4% identification rate (n=16) and eight AE referrals. Site 2: 61% enquiry rate (n=1090), a 7% identification rate (n=79) and eight AE referrals. Conclusions IRIS ADViSE can be successfully developed and implemented in sexual health clinics. It fulfils the unmet need for DVA training. Longer-term evaluation is recommended. PMID:28724743

Portable source identification device

NASA Astrophysics Data System (ADS)

Andersen, Eric S.; Samuel, Todd J.; Gervais, Kevin L.

2005-05-01

U.S. Customs and Border Protection (CBP) is the primary enforcement agency protecting the nation"s ports of entry. CBP is enhancing its capability to interdict the illicit import of nuclear and radiological materials and devices that may be used by terrorists. Pacific Northwest National Laboratory (PNNL) is providing scientific and technical support to CBP in their goal to enable rapid deployment of nuclear and radiation detection systems at U. S. ports of entry to monitor 100% of the incoming international traffic and cargo while not adversely impacting the operations or throughput of the ports. As the deployment of radiation detection systems proceeds, there is a need to adapt the baseline radiation portal monitor (RPM) system technology to operations at these diverse ports of entry. When screening produces an alarm in the primary inspection RPM, the alarming vehicle is removed from the flow of commerce and the alarm is typically confirmed in a secondary inspection RPM. The portable source identification device (PSID) is a radiation sensor panel (RSP), based on thallium-doped sodium iodide (NaI(Tl)) scintillation detector and gamma spectroscopic analysis hardware and software, mounted on a scissor lift on a small truck. The lift supports a box containing a commercial off-the-shelf (COTS) sodium iodide detector that provides real-time isotopic identification, including neutron detectors to interdict Weapons of Mass Destruction (WMD) and radiation dispersion devices (RDD). The scissor lift will lower the detectors to within a foot off the ground and raise them to approximately 24 feet (7.3 m) in the air, allowing a wide vertical scanning range.

Pneumococcal Capsules and Their Types: Past, Present, and Future

PubMed Central

Geno, K. Aaron; Gilbert, Gwendolyn L.; Song, Joon Young; Skovsted, Ian C.; Klugman, Keith P.; Jones, Christopher; Konradsen, Helle B.

2015-01-01

SUMMARY Streptococcus pneumoniae (the pneumococcus) is an important human pathogen. Its virulence is largely due to its polysaccharide capsule, which shields it from the host immune system, and because of this, the capsule has been extensively studied. Studies of the capsule led to the identification of DNA as the genetic material, identification of many different capsular serotypes, and identification of the serotype-specific nature of protection by adaptive immunity. Recent studies have led to the determination of capsular polysaccharide structures for many serotypes using advanced analytical technologies, complete elucidation of genetic basis for the capsular types, and the development of highly effective pneumococcal conjugate vaccines. Conjugate vaccine use has altered the serotype distribution by either serotype replacement or switching, and this has increased the need to serotype pneumococci. Due to great advances in molecular technologies and our understanding of the pneumococcal genome, molecular approaches have become powerful tools to predict pneumococcal serotypes. In addition, more-precise and -efficient serotyping methods that directly detect polysaccharide structures are emerging. These improvements in our capabilities will greatly enhance future investigations of pneumococcal epidemiology and diseases and the biology of colonization and innate immunity to pneumococcal capsules. PMID:26085553

Pneumococcal Capsules and Their Types: Past, Present, and Future.

PubMed

Geno, K Aaron; Gilbert, Gwendolyn L; Song, Joon Young; Skovsted, Ian C; Klugman, Keith P; Jones, Christopher; Konradsen, Helle B; Nahm, Moon H

2015-07-01

Streptococcus pneumoniae (the pneumococcus) is an important human pathogen. Its virulence is largely due to its polysaccharide capsule, which shields it from the host immune system, and because of this, the capsule has been extensively studied. Studies of the capsule led to the identification of DNA as the genetic material, identification of many different capsular serotypes, and identification of the serotype-specific nature of protection by adaptive immunity. Recent studies have led to the determination of capsular polysaccharide structures for many serotypes using advanced analytical technologies, complete elucidation of genetic basis for the capsular types, and the development of highly effective pneumococcal conjugate vaccines. Conjugate vaccine use has altered the serotype distribution by either serotype replacement or switching, and this has increased the need to serotype pneumococci. Due to great advances in molecular technologies and our understanding of the pneumococcal genome, molecular approaches have become powerful tools to predict pneumococcal serotypes. In addition, more-precise and -efficient serotyping methods that directly detect polysaccharide structures are emerging. These improvements in our capabilities will greatly enhance future investigations of pneumococcal epidemiology and diseases and the biology of colonization and innate immunity to pneumococcal capsules. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Systems and methods for biometric identification using the acoustic properties of the ear canal

DOEpatents

Bouchard, Ann Marie; Osbourn, Gordon Cecil

1998-01-01

The present invention teaches systems and methods for verifying or recognizing a person's identity based on measurements of the acoustic response of the individual's ear canal. The system comprises an acoustic emission device, which emits an acoustic source signal s(t), designated by a computer, into the ear canal of an individual, and an acoustic response detection device, which detects the acoustic response signal f(t). A computer digitizes the response (detected) signal f(t) and stores the data. Computer-implemented algorithms analyze the response signal f(t) to produce ear-canal feature data. The ear-canal feature data obtained during enrollment is stored on the computer, or some other recording medium, to compare the enrollment data with ear-canal feature data produced in a subsequent access attempt, to determine if the individual has previously been enrolled. The system can also be adapted for remote access applications.

Systems and methods for biometric identification using the acoustic properties of the ear canal

DOEpatents

Bouchard, A.M.; Osbourn, G.C.

1998-07-28

The present invention teaches systems and methods for verifying or recognizing a person`s identity based on measurements of the acoustic response of the individual`s ear canal. The system comprises an acoustic emission device, which emits an acoustic source signal s(t), designated by a computer, into the ear canal of an individual, and an acoustic response detection device, which detects the acoustic response signal f(t). A computer digitizes the response (detected) signal f(t) and stores the data. Computer-implemented algorithms analyze the response signal f(t) to produce ear-canal feature data. The ear-canal feature data obtained during enrollment is stored on the computer, or some other recording medium, to compare the enrollment data with ear-canal feature data produced in a subsequent access attempt, to determine if the individual has previously been enrolled. The system can also be adapted for remote access applications. 5 figs.

Detection of no-model input-output pairs in closed-loop systems.

PubMed

Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio

2017-11-01

The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Guidelines for the naming of genes, gene products, and mutants in the opportunistic protists.

PubMed

Limper, Andrew H; Weiss, Louis M

2011-01-01

The opportunistic protists encompass a wide diversity of organisms including Pneumocystis, Toxoplasma, cryptosporidia, microsporidia, and related genera. Recent advances in the molecular biology and cellular biochemistry of these organisms have led to the identification of an ever growing numbers of key genes and their cognate proteins. Until now, these molecules have not been designated using any consistent nomenclature system, leading to considerable confusion. The participants of the 11th International Workshop on Opportunistic Protists met on August 3, 2010 to reach consensus of a nomenclature system for genes, gene products, and mutants in the opportunistic protists. The following summary reports the consensus agreement to move toward a unified nomenclature system for these organisms. The system is adapted from that used for Saccharomyces cerevisiae. © 2011 The Author(s). Journal of Eukaryotic Microbiology © 2011 International Society of Protistologists.

Estimating parameters with pre-specified accuracies in distributed parameter systems using optimal experiment design

NASA Astrophysics Data System (ADS)

Potters, M. G.; Bombois, X.; Mansoori, M.; Hof, Paul M. J. Van den

2016-08-01

Estimation of physical parameters in dynamical systems driven by linear partial differential equations is an important problem. In this paper, we introduce the least costly experiment design framework for these systems. It enables parameter estimation with an accuracy that is specified by the experimenter prior to the identification experiment, while at the same time minimising the cost of the experiment. We show how to adapt the classical framework for these systems and take into account scaling and stability issues. We also introduce a progressive subdivision algorithm that further generalises the experiment design framework in the sense that it returns the lowest cost by finding the optimal input signal, and optimal sensor and actuator locations. Our methodology is then applied to a relevant problem in heat transfer studies: estimation of conductivity and diffusivity parameters in front-face experiments. We find good correspondence between numerical and theoretical results.

Limiting vibration in systems with constant amplitude actuators through command preshaping. M.S Thesis - MIT

NASA Technical Reports Server (NTRS)

Rogers, Keith Eric

1994-01-01

The basic concepts of command preshaping were taken and adapted to the framework of systems with constant amplitude (on-off) actuators. In this context, pulse sequences were developed which help to attenuate vibration in flexible systems with high robustness to errors in frequency identification. Sequences containing impulses of different magnitudes were approximated by sequences containing pulses of different durations. The effects of variation in pulse width on this approximation were examined. Sequences capable of minimizing loads induced in flexible systems during execution of commands were also investigated. The usefulness of these techniques in real-world situations was verified by application to a high fidelity simulation of the space shuttle. Results showed that constant amplitude preshaping techniques offer a substantial improvement in vibration reduction over both the standard and upgraded shuttle control methods and may be mission enabling for use of the shuttle with extremely massive payloads.

Identification de lois constitutives et de lois de frottement adaptées aux grandes vitesses de sollicitation

NASA Astrophysics Data System (ADS)

Dalverny, O.; Capéraa, S.; Pantalé, O.; Sattouf, C.

2002-12-01

Cet article présente une méthodologie d'identification de lois constitutives et de lois de contact adaptées aux matériaux métalliques sous chargement dynamique à grande vitesse de déformation. Les essais sont effectués à partir de montages expérimentaux adaptés à un lanceur à gaz permettant d'obtenir une vitesse de projectile de l'ordre de 350m/s pour une masse totale de 30gr. Le premier essai consiste en un impact de Taylor correspondant à un chargement mécanique de type compression. Le second essai de type “extrusion conique" permet la détermination des lois de frottement à grande vitesse. La procédure générale d'identification des lois de comportement à partir d'essais dynamiques se fait au moyen d'une analyse post-mortem des échantillons et de la corrélation entre ces résultats expérimentaux et un modèle numérique des essais. Pour les deux cas précédemment cités, nous présentons la configuration optimale d'essai ainsi que les résultats obtenus à partir d'un algorithme d'optimisation de type Levenberg-Marquard.

The evolution of clinical trials for infant acute lymphoblastic leukemia

PubMed Central

Kotecha, R S; Gottardo, N G; Kees, U R; Cole, C H

2014-01-01

Acute lymphoblastic leukemia (ALL) in infants has a significantly inferior outcome in comparison with older children. Despite initial improvements in survival of infants with ALL since establishment of the first pediatric cooperative group ALL trials, the poor outcome has plateaued in recent years. Historically, infants were treated on risk-adapted childhood ALL protocols. These studies were pivotal in identifying the need for infant-specific protocols, delineating prognostic categories and the requirement for a more unified approach between study groups to overcome limitations in accrual because of low incidence. This subsequently led to the development of collaborative infant-specific studies. Landmark outcomes have included the elimination of cranial radiotherapy following the discovery of intrathecal and high-dose systemic therapy as a superior and effective treatment strategy for central nervous system disease prophylaxis, with improved neurodevelopmental outcome. Universal prospective identification of independent adverse prognostic factors, including presence of a mixed lineage leukemia rearrangement and young age, has established the basis for risk stratification within current trials. The infant-specific trials have defined limits to which conventional chemotherapeutic agents can be intensified to optimize the balance between treatment efficacy and toxicity. Despite variations in therapeutic intensity, there has been no recent improvement in survival due to the equilibrium between relapse and toxicity. Ultimately, to improve the outcome for infants with ALL, key areas still to be addressed include identification and adaptation of novel prognostic markers and innovative therapies, establishing the role of hematopoietic stem cell transplantation in first complete remission, treatment strategies for relapsed/refractory disease and monitoring and timely intervention of late effects in survivors. This would be best achieved through a single unified international trial. PMID:24727996

1978 Archeological Investigations at ELK City Lake, Kansas,

DTIC Science & Technology

1978-01-01

Kansas continued to be hunters and gatherers. Their populations began to increase suggesting further adaptation to the environment. This era is...bone (Marshall 1972:229). By approximately A.D. 1000, cultural changes through adaptation and diffusion brought about a pcpulation group which is...identification has not been made. A large number of gastropods were recovered from the surface and test excavations at 14MY1310. Archeologists are

Adaptation of multijoint coordination during standing balance in healthy young and healthy old individuals

PubMed Central

Pasma, J. H.; Schouten, A. C.; Aarts, R. G. K. M.; Meskers, C. G. M.; Maier, A. B.; van der Kooij, H.

2015-01-01

Standing balance requires multijoint coordination between the ankles and hips. We investigated how humans adapt their multijoint coordination to adjust to various conditions and whether the adaptation differed between healthy young participants and healthy elderly. Balance was disturbed by push/pull rods, applying two continuous and independent force disturbances at the level of the hip and between the shoulder blades. In addition, external force fields were applied, represented by an external stiffness at the hip, either stabilizing or destabilizing the participants' balance. Multivariate closed-loop system-identification techniques were used to describe the neuromuscular control mechanisms by quantifying the corrective joint torques as a response to body sway, represented by frequency response functions (FRFs). Model fits on the FRFs resulted in an estimation of time delays, intrinsic stiffness, reflexive stiffness, and reflexive damping of both the ankle and hip joint. The elderly generated similar corrective joint torques but had reduced body sway compared with the young participants, corresponding to the increased FRF magnitude with age. When a stabilizing or destabilizing external force field was applied at the hip, both young and elderly participants adapted their multijoint coordination by lowering or respectively increasing their neuromuscular control actions around the ankles, expressed in a change of FRF magnitude. However, the elderly adapted less compared with the young participants. Model fits on the FRFs showed that elderly had higher intrinsic and reflexive stiffness of the ankle, together with higher time delays of the hip. Furthermore, the elderly adapted their reflexive stiffness around the ankle joint less compared with young participants. These results imply that elderly were stiffer and were less able to adapt to external force fields. PMID:26719084

Universal characteristics of evolution and development are inherent in fetal autonomic brain maturation.

PubMed

Schmidt, Alexander; Schukat-Talamazzini, Ernst G; Zöllkau, Janine; Pytlik, Adelina; Leibl, Sophia; Kumm, Kathrin; Bode, Franziska; Kynass, Isabelle; Witte, Otto W; Schleussner, Ekkehard; Schneider, Uwe; Hoyer, Dirk

2018-07-01

Adverse prenatal environmental influences to the developing fetus are associated with mental and cardiovascular disease in later life. Universal developmental characteristics such as self-organization, pattern formation, and adaptation in the growing information processing system have not yet been sufficiently analyzed with respect to description of normal fetal development and identification of developmental disturbances. Fetal heart rate patterns are the only non-invasive order parameter of the developing autonomic brain available with respect to the developing complex organ system. The objective of the present study was to investigate whether universal indices, known from evolution and phylogeny, describe the ontogenetic fetal development from 20 weeks of gestation onwards. By means of a 10-fold cross-validated data-driven multivariate regression modeling procedure, relevant indices of heart rate variability (HRV) were explored using 552 fetal heart rate recordings, each lasting over 30 min. We found that models which included HRV indices of increasing fluctuation amplitude, complexity and fractal long-range dependencies largely estimated the maturation age (coefficients of determination 0.61-0.66). Consideration of these characteristics in prenatal care may not only have implications for early identification of developmental disturbances, but also for the development of system-theory-based therapeutic strategies. Copyright © 2018 Elsevier B.V. All rights reserved.

An identification method of orbit responses rooting in vibration analysis of rotor during touchdowns of active magnetic bearings

NASA Astrophysics Data System (ADS)

Liu, Tao; Lyu, Mindong; Wang, Zixi; Yan, Shaoze

2018-02-01

Identification of orbit responses can make the active protection operation more easily realize for active magnetic bearings (AMB) in case of touchdowns. This paper presents an identification method of the orbit responses rooting on signal processing of rotor displacements during touchdowns. The recognition method consists of two major steps. Firstly, the combined rub and bouncing is distinguished from the other orbit responses by the mathematical expectation of axis displacements of the rotor. Because when the combined rub and bouncing occurs, the rotor of AMB will not be always close to the touchdown bearings (TDB). Secondly, we recognize the pendulum vibration and the full rub by the Fourier spectrum of displacement in horizontal direction, as the frequency characteristics of the two responses are different. The principle of the whole identification algorithm is illustrated by two sets of signal generated by a dynamic model of the specific rotor-TDB system. The universality of the method is validated by other four sets of signal. Besides, the adaptability of noise is also tested by adding white noises with different strengths, and the result is promising. As the mathematical expectation and Discrete Fourier transform are major calculations of the algorithm, the calculation quantity of the algorithm is low, so it is fast, easily realized and embedded in the AMB controller, which has an important engineering value for the protection of AMBs during touchdowns.

Driving profile modeling and recognition based on soft computing approach.

PubMed

Wahab, Abdul; Quek, Chai; Tan, Chin Keong; Takeda, Kazuya

2009-04-01

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

A novel method of robot location using RFID and stereo vision

NASA Astrophysics Data System (ADS)

Chen, Diansheng; Zhang, Guanxin; Li, Zhen

2012-04-01

This paper proposed a new global localization method for mobile robot based on RFID (Radio Frequency Identification Devices) and stereo vision, which makes the robot obtain global coordinates with good accuracy when quickly adapting to unfamiliar and new environment. This method uses RFID tags as artificial landmarks, the 3D coordinate of the tags under the global coordinate system is written in the IC memory. The robot can read it through RFID reader; meanwhile, using stereo vision, the 3D coordinate of the tags under the robot coordinate system is measured. Combined with the robot's attitude coordinate system transformation matrix from the pose measuring system, the translation of the robot coordinate system to the global coordinate system is obtained, which is also the coordinate of the robot's current location under the global coordinate system. The average error of our method is 0.11m in experience conducted in a 7m×7m lobby, the result is much more accurate than other location method.

Preserving anonymity in e-voting system using voter non-repudiation oriented scheme

NASA Astrophysics Data System (ADS)

Hamid, Isredza Rahmi A.; Radzi, Siti Nafishah Md; Rahman, Nurul Hidayah Ab; Wen, Chuah Chai; Abdullah, Nurul Azma

2017-10-01

The voting system has been developed from traditional paper ballot to electronic voting (e-voting). The e-voting system has high potential to be widely used in election event. However, the e-voting system still does not meet the most important security properties which are voter's authenticity and non-repudiation. This is because voters can simply vote again by entering other people's identification number. In this project, an electronic voting using voter non-repudiation oriented scheme will be developed. This system contains ten modules which are log in, vote session, voter, candidate, open session, voting results, user account, initial score, logs and reset vote count. In order to ensure there would be no non-repudiation issue, a voter non-repudiation oriented scheme concept will be adapted and implemented in the system. This system will be built using Microsoft Visual Studio 2013 which only can be accessed using personal computers at the voting center. This project will be beneficial for future use in order to overcome non-repudiation issue.

Genome-environment associations in sorghum landraces predict adaptive traits

PubMed Central

Lasky, Jesse R.; Upadhyaya, Hari D.; Ramu, Punna; Deshpande, Santosh; Hash, C. Tom; Bonnette, Jason; Juenger, Thomas E.; Hyma, Katie; Acharya, Charlotte; Mitchell, Sharon E.; Buckler, Edward S.; Brenton, Zachary; Kresovich, Stephen; Morris, Geoffrey P.

2015-01-01

Improving environmental adaptation in crops is essential for food security under global change, but phenotyping adaptive traits remains a major bottleneck. If associations between single-nucleotide polymorphism (SNP) alleles and environment of origin in crop landraces reflect adaptation, then these could be used to predict phenotypic variation for adaptive traits. We tested this proposition in the global food crop Sorghum bicolor, characterizing 1943 georeferenced landraces at 404,627 SNPs and quantifying allelic associations with bioclimatic and soil gradients. Environment explained a substantial portion of SNP variation, independent of geographical distance, and genic SNPs were enriched for environmental associations. Further, environment-associated SNPs predicted genotype-by-environment interactions under experimental drought stress and aluminum toxicity. Our results suggest that genomic signatures of environmental adaptation may be useful for crop improvement, enhancing germplasm identification and marker-assisted selection. Together, genome-environment associations and phenotypic analyses may reveal the basis of environmental adaptation. PMID:26601206

Applying Critical Race Theory to Group Model Building Methods to Address Community Violence.

PubMed

Frerichs, Leah; Lich, Kristen Hassmiller; Funchess, Melanie; Burrell, Marcus; Cerulli, Catherine; Bedell, Precious; White, Ann Marie

2016-01-01

Group model building (GMB) is an approach to building qualitative and quantitative models with stakeholders to learn about the interrelationships among multilevel factors causing complex public health problems over time. Scant literature exists on adapting this method to address public health issues that involve racial dynamics. This study's objectives are to (1) introduce GMB methods, (2) present a framework for adapting GMB to enhance cultural responsiveness, and (3) describe outcomes of adapting GMB to incorporate differences in racial socialization during a community project seeking to understand key determinants of community violence transmission. An academic-community partnership planned a 1-day session with diverse stakeholders to explore the issue of violence using GMB. We documented key questions inspired by critical race theory (CRT) and adaptations to established GMB "scripts" (i.e., published facilitation instructions). The theory's emphasis on experiential knowledge led to a narrative-based facilitation guide from which participants created causal loop diagrams. These early diagrams depict how violence is transmitted and how communities respond, based on participants' lived experiences and mental models of causation that grew to include factors associated with race. Participants found these methods useful for advancing difficult discussion. The resulting diagrams can be tested and expanded in future research, and will form the foundation for collaborative identification of solutions to build community resilience. GMB is a promising strategy that community partnerships should consider when addressing complex health issues; our experience adapting methods based on CRT is promising in its acceptability and early system insights.

Adaptive emotional memory: the key hippocampal-amygdalar interaction.

PubMed

Desmedt, Aline; Marighetto, Aline; Richter-Levin, Gal; Calandreau, Ludovic

2015-01-01

For centuries philosophical and clinical studies have emphasized a fundamental dichotomy between emotion and cognition, as, for instance, between behavioral/emotional memory and explicit/representative memory. However, the last few decades cognitive neuroscience have highlighted data indicating that emotion and cognition, as well as their underlying neural networks, are in fact in close interaction. First, it turns out that emotion can serve cognition, as exemplified by its critical contribution to decision-making or to the enhancement of episodic memory. Second, it is also observed that reciprocally cognitive processes as reasoning, conscious appraisal or explicit representation of events can modulate emotional responses, like promoting or reducing fear. Third, neurobiological data indicate that reciprocal amygdalar-hippocampal influences underlie such mutual regulation of emotion and cognition. While supporting this view, the present review discusses experimental data, obtained in rodents, indicating that the hippocampal and amygdalar systems not only regulate each other and their functional outcomes, but also qualify specific emotional memory representations through specific activations and interactions. Specifically, we review consistent behavioral, electrophysiological, pharmacological, biochemical and imaging data unveiling a direct contribution of both the amygdala and hippocampal-septal system to the identification of the predictor of a threat in different situations of fear conditioning. Our suggestion is that these two brain systems and their interplay determine the selection of relevant emotional stimuli, thereby contributing to the adaptive value of emotional memory. Hence, beyond the mutual quantitative regulation of these two brain systems described so far, we develop the idea that different activations of the hippocampus and amygdala, leading to specific configurations of neural activity, qualitatively impact the formation of emotional memory representations, thereby producing either adaptive or maladaptive fear memories.

On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

NASA Astrophysics Data System (ADS)

Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe

2014-09-01

Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored. These include: battery state of charge (SoC), battery state of health (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.

Rapid group-, serotype-, and vaccine strain-specific identification of poliovirus isolates by real-time reverse transcription-PCR using degenerate primers and probes containing deoxyinosine residues.

PubMed

Kilpatrick, David R; Yang, Chen-Fu; Ching, Karen; Vincent, Annelet; Iber, Jane; Campagnoli, Ray; Mandelbaum, Mark; De, Lina; Yang, Su-Ju; Nix, Allan; Kew, Olen M

2009-06-01

We have adapted our previously described poliovirus diagnostic reverse transcription-PCR (RT-PCR) assays to a real-time RT-PCR (rRT-PCR) format. Our highly specific assays and rRT-PCR reagents are designed for use in the WHO Global Polio Laboratory Network for rapid and large-scale identification of poliovirus field isolates.

Electronic p-Chip-Based System for Identification of Glass Slides and Tissue Cassettes in Histopathology Laboratories.

PubMed

Mandecki, Wlodek; Qian, Jay; Gedzberg, Katie; Gruda, Maryanne; Rodriguez, Efrain Frank; Nesbitt, Leslie; Riben, Michael

2018-01-01

The tagging system is based on a small, electronic, wireless, laser-light-activated microtransponder named "p-Chip." The p-Chip is a silicon integrated circuit, the size of which is 600 μm × 600 μm × 100 μm. Each p-Chip contains a unique identification code stored within its electronic memory that can be retrieved with a custom reader. These features allow the p-Chip to be used as an unobtrusive and scarcely noticeable ID tag on glass slides and tissue cassettes. The system is comprised of p-Chip-tagged sample carriers, a dedicated benchtop p-Chip ID reader that can accommodate both objects, and an additional reader (the Wand), with an adapter for reading IDs of glass slides stored vertically in drawers. On slides, p-Chips are attached with adhesive to the center of the short edge, and on cassettes - embedded directly into the plastic. ID readout is performed by bringing the reader to the proximity of the chip. Standard histopathology laboratory protocols were used for testing. Very good ID reading efficiency was observed for both glass slides and cassettes. When processed slides are stored in vertical filing drawers, p-Chips remain readable without the need to remove them from the storage location, thereby improving the speed of searches in collections. On the cassettes, the ID continues to be readable through a thin layer of paraffin. Both slides and tissue cassettes can be read with the same reader, reducing the need for redundant equipment. The p-Chip is stable to all chemical challenges commonly used in the histopathology laboratory, tolerates temperature extremes, and remains durable in long-term storage. The technology is compatible with laboratory information management systems software systems. The p-Chip system is very well suited for identification of glass slides and cassettes in the histopathology laboratory.

Electronic p-Chip-Based System for Identification of Glass Slides and Tissue Cassettes in Histopathology Laboratories

PubMed Central

Mandecki, Wlodek; Qian, Jay; Gedzberg, Katie; Gruda, Maryanne; Rodriguez, Efrain “Frank”; Nesbitt, Leslie; Riben, Michael

2018-01-01

Background: The tagging system is based on a small, electronic, wireless, laser-light-activated microtransponder named “p-Chip.” The p-Chip is a silicon integrated circuit, the size of which is 600 μm × 600 μm × 100 μm. Each p-Chip contains a unique identification code stored within its electronic memory that can be retrieved with a custom reader. These features allow the p-Chip to be used as an unobtrusive and scarcely noticeable ID tag on glass slides and tissue cassettes. Methods: The system is comprised of p-Chip-tagged sample carriers, a dedicated benchtop p-Chip ID reader that can accommodate both objects, and an additional reader (the Wand), with an adapter for reading IDs of glass slides stored vertically in drawers. On slides, p-Chips are attached with adhesive to the center of the short edge, and on cassettes – embedded directly into the plastic. ID readout is performed by bringing the reader to the proximity of the chip. Standard histopathology laboratory protocols were used for testing. Results: Very good ID reading efficiency was observed for both glass slides and cassettes. When processed slides are stored in vertical filing drawers, p-Chips remain readable without the need to remove them from the storage location, thereby improving the speed of searches in collections. On the cassettes, the ID continues to be readable through a thin layer of paraffin. Both slides and tissue cassettes can be read with the same reader, reducing the need for redundant equipment. Conclusions: The p-Chip is stable to all chemical challenges commonly used in the histopathology laboratory, tolerates temperature extremes, and remains durable in long-term storage. The technology is compatible with laboratory information management systems software systems. The p-Chip system is very well suited for identification of glass slides and cassettes in the histopathology laboratory. PMID:29692946

Adaptation of genetically monomorphic bacteria: evolution of copper resistance through multiple horizontal gene transfers of complex and versatile mobile genetic elements.

PubMed

Richard, D; Ravigné, V; Rieux, A; Facon, B; Boyer, C; Boyer, K; Grygiel, P; Javegny, S; Terville, M; Canteros, B I; Robène, I; Vernière, C; Chabirand, A; Pruvost, O; Lefeuvre, P

2017-04-01

Copper-based antimicrobial compounds are widely used to control plant bacterial pathogens. Pathogens have adapted in response to this selective pressure. Xanthomonas citri pv. citri, a major citrus pathogen causing Asiatic citrus canker, was first reported to carry plasmid-encoded copper resistance in Argentina. This phenotype was conferred by the copLAB gene system. The emergence of resistant strains has since been reported in Réunion and Martinique. Using microsatellite-based genotyping and copLAB PCR, we demonstrated that the genetic structure of the copper-resistant strains from these three regions was made up of two distant clusters and varied for the detection of copLAB amplicons. In order to investigate this pattern more closely, we sequenced six copper-resistant X. citri pv. citri strains from Argentina, Martinique and Réunion, together with reference copper-resistant Xanthomonas and Stenotrophomonas strains using long-read sequencing technology. Genes involved in copper resistance were found to be strain dependent with the novel identification in X. citri pv. citri of copABCD and a cus heavy metal efflux resistance-nodulation-division system. The genes providing the adaptive trait were part of a mobile genetic element similar to Tn3-like transposons and included in a conjugative plasmid. This indicates the system's great versatility. The mining of all available bacterial genomes suggested that, within the bacterial community, the spread of copper resistance associated with mobile elements and their plasmid environments was primarily restricted to the Xanthomonadaceae family. © 2017 John Wiley & Sons Ltd.

High resolution crop growth simulation for identification of potential adaptation strategies under climate change

NASA Astrophysics Data System (ADS)

Kim, K. S.; Yoo, B. H.

2016-12-01

Impact assessment of climate change on crop production would facilitate planning of adaptation strategies. Because socio-environmental conditions would differ by local areas, it would be advantageous to assess potential adaptation measures at a specific area. The objectives of this study was to develop a crop growth simulation system at a very high spatial resolution, e.g., 30 m, and to assess different adaptation options including shift of planting date and use of different cultivars. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to predict yields of soybean and maize in Korea. Gridded data for climate and soil were used to prepare input data for the DSSAT model. Weather input data were prepared at the resolution of 30 m using bilinear interpolation from gridded climate scenario data. Those climate data were obtained from Korean Meteorology Administration. Spatial resolution of temperature and precipitation was 1 km whereas that of solar radiation was 12.5 km. Soil series data at the 30 m resolution were obtained from the soil database operated by Rural Development Administration, Korea. The SOL file, which is a soil input file for the DSSAT model was prepared using physical and chemical properties of a given soil series, which were available from the soil database. Crop yields were predicted by potential adaptation options based on planting date and cultivar. For example, 10 planting dates and three cultivars were used to identify ideal management options for climate change adaptation. In prediction of maize yield, combination of 20 planting dates and two cultivars was used as management options. Predicted crop yields differed by site even within a relatively small region. For example, the maximum of average yields for 2001-2010 seasons differed by sites In a county of which areas is 520 km2 (Fig. 1). There was also spatial variation in the ideal management option in the region (Fig. 2). These results suggested that local assessment of climate change impact on crop production would be useful for planning adaptation options.

Real-time Adaptive EEG Source Separation using Online Recursive Independent Component Analysis

PubMed Central

Hsu, Sheng-Hsiou; Mullen, Tim; Jung, Tzyy-Ping; Cauwenberghs, Gert

2016-01-01

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: (a) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; (b) capability to detect and adapt to non-stationarity in 64-ch simulated EEG data; and (c) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces. PMID:26685257

Experiments on vibration control of a piezoelectric laminated paraboloidal shell

NASA Astrophysics Data System (ADS)

Yue, Honghao; Lu, Yifan; Deng, Zongquan; Tzou, Hornsen

2017-01-01

A paraboloidal shell plays a key role in aerospace and optical structural systems applied to large optical reflector, communications antenna, rocket fairing, missile radome, etc. Due to the complexity of analytical procedures, an experimental study of active vibration control of a piezoelectric laminated paraboloidal shell by positive position feedback is carried out. Sixteen PVDF patches are laminated inside and outside of the shell, in which eight of them are used as sensors and eight as actuators to control the vibration of the first two natural modes. Lower natural frequencies and vibration modes of the paraboloidal shell are obtained via the frequency response function analysis by Modal VIEW software. A mathematical model of the control system is formulated by means of parameter identification. The first shell mode is controlled as well as coupled the first and second modes based on the positive position feedback (PPF) algorithm. To minimize the control energy consumption in orbit, an adaptive modal control method is developed in this study by using the PPF in laboratory experiments. The control system collects vibration signals from the piezoelectric sensors to identify location(s) of the largest vibration amplitudes and then select the best two from eight PVDF actuators to apply control forces so that the modal vibration suppression could be accomplished adaptively and effectively.

Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture.

PubMed

Li, Cai; Lowe, Robert; Ziemke, Tom

2013-01-01

The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value.

Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture

PubMed Central

Li, Cai; Lowe, Robert; Ziemke, Tom

2013-01-01

The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value. PMID:23675345

Refinement and evaluation of helicopter real-time self-adaptive active vibration controller algorithms

NASA Technical Reports Server (NTRS)

Davis, M. W.

1984-01-01

A Real-Time Self-Adaptive (RTSA) active vibration controller was used as the framework in developing a computer program for a generic controller that can be used to alleviate helicopter vibration. Based upon on-line identification of system parameters, the generic controller minimizes vibration in the fuselage by closed-loop implementation of higher harmonic control in the main rotor system. The new generic controller incorporates a set of improved algorithms that gives the capability to readily define many different configurations by selecting one of three different controller types (deterministic, cautious, and dual), one of two linear system models (local and global), and one or more of several methods of applying limits on control inputs (external and/or internal limits on higher harmonic pitch amplitude and rate). A helicopter rotor simulation analysis was used to evaluate the algorithms associated with the alternative controller types as applied to the four-bladed H-34 rotor mounted on the NASA Ames Rotor Test Apparatus (RTA) which represents the fuselage. After proper tuning all three controllers provide more effective vibration reduction and converge more quickly and smoothly with smaller control inputs than the initial RTSA controller (deterministic with external pitch-rate limiting). It is demonstrated that internal limiting of the control inputs a significantly improves the overall performance of the deterministic controller.

Identification of Leishmania by Matrix-Assisted Laser Desorption Ionization-Time of Flight (MALDI-TOF) Mass Spectrometry Using a Free Web-Based Application and a Dedicated Mass-Spectral Library.

PubMed

Lachaud, Laurence; Fernández-Arévalo, Anna; Normand, Anne-Cécile; Lami, Patrick; Nabet, Cécile; Donnadieu, Jean Luc; Piarroux, Martine; Djenad, Farid; Cassagne, Carole; Ravel, Christophe; Tebar, Silvia; Llovet, Teresa; Blanchet, Denis; Demar, Magalie; Harrat, Zoubir; Aoun, Karim; Bastien, Patrick; Muñoz, Carmen; Gállego, Montserrat; Piarroux, Renaud

2017-10-01

Human leishmaniases are widespread diseases with different clinical forms caused by about 20 species within the Leishmania genus. Leishmania species identification is relevant for therapeutic management and prognosis, especially for cutaneous and mucocutaneous forms. Several methods are available to identify Leishmania species from culture, but they have not been standardized for the majority of the currently described species, with the exception of multilocus enzyme electrophoresis. Moreover, these techniques are expensive, time-consuming, and not available in all laboratories. Within the last decade, mass spectrometry (MS) has been adapted for the identification of microorganisms, including Leishmania However, no commercial reference mass-spectral database is available. In this study, a reference mass-spectral library (MSL) for Leishmania isolates, accessible through a free Web-based application (mass-spectral identification [MSI]), was constructed and tested. It includes mass-spectral data for 33 different Leishmania species, including species that infect humans, animals, and phlebotomine vectors. Four laboratories on two continents evaluated the performance of MSI using 268 samples, 231 of which were Leishmania strains. All Leishmania strains, but one, were correctly identified at least to the complex level. A risk of species misidentification within the Leishmania donovani , L. guyanensis , and L. braziliensis complexes was observed, as previously reported for other techniques. The tested application was reliable, with identification results being comparable to those obtained with reference methods but with a more favorable cost-efficiency ratio. This free online identification system relies on a scalable database and can be implemented directly in users' computers. Copyright © 2017 American Society for Microbiology.

Optical fundamentals of an adaptive substance-on-surface chemical recognizer

NASA Astrophysics Data System (ADS)

Fauconier, Richard; Ndoye, Mandoye; Montlouis, Webert

2017-10-01

The objective is to identify the chemical composition of (isotropic and homogeneous) thin liquid and gel films on various surfaces by their infrared reflectance spectra. A bistatic optical sensing concept is proposed here in which a multi-wavelength laser source and a detector are physically displaced from each other. With the aid of the concept apparatus proposed, key optical variables can be measured in real time. The variables in question (substance thickness, refractive index, etc.) are those whose un-observability causes many types of monostatic sensor (in use today) to give ambiguous identifications. Knowledge of the aforementioned key optical variables would allow an adaptive signal-processing algorithm to make unambiguous identifications of the unknown chemicals by their infrared spectra, despite their variable presentations. The proposed bistatic sensor system consists of an optical transmitter and an optical receiver. The whole system can be mounted on a stable platform. Both the optical transmitter subsystem and the optical receiver subsystem contain auxiliary sensors to determine their relative spatial positions and orientations. For each subsystem, these auxiliary sensors include an orientation sensor, and rotational sensors for absolute angular position. A profilometer-and-machine-vision subsystem is also included. An important aspect of determining the necessary optical variables is an aperture that limits the interrogatory beams to a coherent pair, rejecting those resulting from successive multiple reflections. A set of equations is developed to characterize the propagation of a coherent pair of frequency-modulated thin beams through the system. It is also shown that frequency modulation can produce easily measurable beat frequencies for determination of sample thicknesses on the order of microns to millimeters. Also shown is how the apparatus's polarization features allow it to measure the refractive index of any isotropic, homogeneous dielectric surface on which the unknown substance can sit. Concave, convex and flat supporting surfaces and menisci are discussed.

Complete-proteome mapping of human influenza A adaptive mutations: implications for human transmissibility of zoonotic strains.

PubMed

Miotto, Olivo; Heiny, A T; Albrecht, Randy; García-Sastre, Adolfo; Tan, Tin Wee; August, J Thomas; Brusic, Vladimir

2010-02-03

There is widespread concern that H5N1 avian influenza A viruses will emerge as a pandemic threat, if they become capable of human-to-human (H2H) transmission. Avian strains lack this capability, which suggests that it requires important adaptive mutations. We performed a large-scale comparative analysis of proteins from avian and human strains, to produce a catalogue of mutations associated with H2H transmissibility, and to detect their presence in avian isolates. We constructed a dataset of influenza A protein sequences from 92,343 public database records. Human and avian sequence subsets were compared, using a method based on mutual information, to identify characteristic sites where human isolates present conserved mutations. The resulting catalogue comprises 68 characteristic sites in eight internal proteins. Subtype variability prevented the identification of adaptive mutations in the hemagglutinin and neuraminidase proteins. The high number of sites in the ribonucleoprotein complex suggests interdependence between mutations in multiple proteins. Characteristic sites are often clustered within known functional regions, suggesting their functional roles in cellular processes. By isolating and concatenating characteristic site residues, we defined adaptation signatures, which summarize the adaptive potential of specific isolates. Most adaptive mutations emerged within three decades after the 1918 pandemic, and have remained remarkably stable thereafter. Two lineages with stable internal protein constellations have circulated among humans without reassorting. On the contrary, H5N1 avian and swine viruses reassort frequently, causing both gains and losses of adaptive mutations. Human host adaptation appears to be complex and systemic, involving nearly all influenza proteins. Adaptation signatures suggest that the ability of H5N1 strains to infect humans is related to the presence of an unusually high number of adaptive mutations. However, these mutations appear unstable, suggesting low pandemic potential of H5N1 in its current form. In addition, adaptation signatures indicate that pandemic H1N1/09 strain possesses multiple human-transmissibility mutations, though not an unusually high number with respect to swine strains that infected humans in the past. Adaptation signatures provide a novel tool for identifying zoonotic strains with the potential to infect humans.

A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

PubMed

Quan, Wei; Fang, Jiancheng

2010-01-01

A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.

Neural Network Target Identification System for False Alarm Reduction

NASA Technical Reports Server (NTRS)

Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

Spatial and Temporal Flood Risk Assessment for Decision Making Approach

NASA Astrophysics Data System (ADS)

Azizat, Nazirah; Omar, Wan-Mohd-Sabki Wan

2018-03-01

Heavy rainfall, adversely impacting inundation areas, depends on the magnitude of the flood. Significantly, location of settlements, infrastructure and facilities in floodplains result in many regions facing flooding risks. A problem faced by the decision maker in an assessment of flood vulnerability and evaluation of adaptation measures is recurrent flooding in the same areas. Identification of recurrent flooding areas and frequency of floods should be priorities for flood risk management. However, spatial and temporal variability become major factors of uncertainty in flood risk management. Therefore, dynamic and spatial characteristics of these changes in flood impact assessment are important in making decisions about the future of infrastructure development and community life. System dynamics (SD) simulation and hydrodynamic modelling are presented as tools for modelling the dynamic characteristics of flood risk and spatial variability. This paper discusses the integration between spatial and temporal information that is required by the decision maker for the identification of multi-criteria decision problems involving multiple stakeholders.

Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators

PubMed Central

Ruano, Susana; Cuevas, Carlos; Gallego, Guillermo; García, Narciso

2017-01-01

Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other displays information about the mission plan and information coming from other sensors. To avoid the burden of fusing information displayed in the two screens, an Augmented Reality (AR) tool is proposed in this paper. The AR system has two functionalities for Medium-Altitude Long-Endurance (MALE) UAVs: route orientation and target identification. Route orientation allows the operator to identify the upcoming waypoints and the path that the UAV is going to follow. Target identification allows a fast target localization, even in the presence of occlusions. The AR tool is implemented following the North Atlantic Treaty Organization (NATO) standards so that it can be used in different GCSs. The experiments show how the AR tool improves significantly the situational awareness of the UAV operators. PMID:28178189

Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis

PubMed Central

Chauhan, Rinki; Ravi, Janani; Datta, Pratik; Chen, Tianlong; Schnappinger, Dirk; Bassler, Kevin E.; Balázsi, Gábor; Gennaro, Maria Laura

2016-01-01

Accessory sigma factors, which reprogram RNA polymerase to transcribe specific gene sets, activate bacterial adaptive responses to noxious environments. Here we reconstruct the complete sigma factor regulatory network of the human pathogen Mycobacterium tuberculosis by an integrated approach. The approach combines identification of direct regulatory interactions between M. tuberculosis sigma factors in an E. coli model system, validation of selected links in M. tuberculosis, and extensive literature review. The resulting network comprises 41 direct interactions among all 13 sigma factors. Analysis of network topology reveals (i) a three-tiered hierarchy initiating at master regulators, (ii) high connectivity and (iii) distinct communities containing multiple sigma factors. These topological features are likely associated with multi-layer signal processing and specialized stress responses involving multiple sigma factors. Moreover, the identification of overrepresented network motifs, such as autoregulation and coregulation of sigma and anti-sigma factor pairs, provides structural information that is relevant for studies of network dynamics. PMID:27029515

In situ identification of the synthrophic protein fermentative Coprothermobacter spp. involved in the thermophilic anaerobic digestion process.

PubMed

Gagliano, Maria Cristina; Braguglia, Camilla Maria; Rossetti, Simona

2014-09-01

Thermophilic bacteria have recently attracted great attention because of their potential application in improving different biochemical processes such as anaerobic digestion of various substrates, wastewater treatment or hydrogen production. In this study we report on the design of a specific 16S rRNA-targeted oligonucleotide probe for detecting members of Coprothermobacter genus characterized by a strong protease activity to degrade proteins and peptides. The newly designed CTH485 probe and helper probes hCTH429 and hCTH439 were optimized for use in fluorescence in situ hybridization (FISH) on thermophilic anaerobic sludge samples. In situ probing revealed that thermo-adaptive mechanisms shaping the 16S rRNA gene may affect the identification of thermophilic microorganisms. The novel developed FISH probe extends the possibility to study the widespread thermophilic syntrophic interaction of Coprothermobacter spp. with hydrogenotrophic methanogenic archaea, whose establishment is a great benefit for the whole anaerobic system. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators.

PubMed

Ruano, Susana; Cuevas, Carlos; Gallego, Guillermo; García, Narciso

2017-02-06

Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other displays information about the mission plan and information coming from other sensors. To avoid the burden of fusing information displayed in the two screens, an Augmented Reality (AR) tool is proposed in this paper. The AR system has two functionalities for Medium-Altitude Long-Endurance (MALE) UAVs: route orientation and target identification. Route orientation allows the operator to identify the upcoming waypoints and the path that the UAV is going to follow. Target identification allows a fast target localization, even in the presence of occlusions. The AR tool is implemented following the North Atlantic Treaty Organization (NATO) standards so that it can be used in different GCSs. The experiments show how the AR tool improves significantly the situational awareness of the UAV operators.

DNN-state identification of 2D distributed parameter systems

NASA Astrophysics Data System (ADS)

Chairez, I.; Fuentes, R.; Poznyak, A.; Poznyak, T.; Escudero, M.; Viana, L.

2012-02-01

There are many examples in science and engineering which are reduced to a set of partial differential equations (PDEs) through a process of mathematical modelling. Nevertheless there exist many sources of uncertainties around the aforementioned mathematical representation. Moreover, to find exact solutions of those PDEs is not a trivial task especially if the PDE is described in two or more dimensions. It is well known that neural networks can approximate a large set of continuous functions defined on a compact set to an arbitrary accuracy. In this article, a strategy based on the differential neural network (DNN) for the non-parametric identification of a mathematical model described by a class of two-dimensional (2D) PDEs is proposed. The adaptive laws for weights ensure the 'practical stability' of the DNN-trajectories to the parabolic 2D-PDE states. To verify the qualitative behaviour of the suggested methodology, here a non-parametric modelling problem for a distributed parameter plant is analysed.

Candidate Medical Countermeasures Targeting Ebola Virus Cell Entry

DTIC Science & Technology

2017-04-03

interface, rather than as expected to the more exposed surface of the 134 GP1,2 trimer [16]. Importantly, KZ52 protected guinea pigs (Cavia porcellus...from death after 135 inoculation with guinea pig -adapted EBOV [64], but failed to have a beneficial effect on EBOV-136 exposed rhesus monkeys... guinea pigs infected with 145 rodent-adapted EBOV or its antigenically distant relative, Sudan virus (SUDV) [68]. 146 Identification of ebolavirus

Candidate Medical Countermeasures Targeting Ebola Virus Cell Entry

DTIC Science & Technology

2017-03-31

interface, rather than as expected to the more exposed surface of the 134 GP1,2 trimer [16]. Importantly, KZ52 protected guinea pigs (Cavia porcellus...from death after 135 inoculation with guinea pig -adapted EBOV [64], but failed to have a beneficial effect on EBOV-136 exposed rhesus monkeys... guinea pigs infected with 145 rodent-adapted EBOV or its antigenically distant relative, Sudan virus (SUDV) [68]. 146 Identification of ebolavirus

New insights into the pathogenesis and management of lupus in children.

PubMed

Midgley, A; Watson, L; Beresford, M W

2014-06-01

Systemic lupus erythematosus (SLE) is the archetypal systemic autoimmune disease, characterised by inflammation causing a wide spectrum of major clinical manifestations that may affect any organ. Childhood-onset SLE (cSLE) is more severe with greater damage and drug burden than adult-onset SLE. Understanding the pathogenesis of cSLE is a key step in directing medical management. The dysregulated immune system, that in health is usually vital in protecting the body from infection, contributes significantly to the disease process. Improved knowledge of disease mechanism will help to identify potential targets for novel agents and the identification of new biomarkers of disease activity. This review will present current knowledge of the innate and adaptive immune responses in cSLE and the optimal patient management that aims to control the disease. Innate immune dysregulation includes the overexpression of interferon-α, dendritic cell activation, neutrophil extracellular traps and phagocyte abnormalities. The classical adaptive immune system is over activated in lupus with excessive autoantibody production due to abnormalities in B and T cell regulation. Novel biologic medications are being developed to specifically target these areas with the ultimate aim of improving the long-term outlook and quality of life for children living with Lupus. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Micromachined actuators/sensors for intratubular positioning/steering

DOEpatents

Lee, Abraham P.; Krulevitch, Peter A.; Northrup, M. Allen; Trevino, Jimmy C.

1998-01-01

Micromachined thin film cantilever actuators having means for individually controlling the deflection of the cantilevers, valve members, and rudders for steering same through blood vessels, or positioning same within a blood vessel, for example. Such cantilever actuators include tactile sensor arrays mounted on a catheter or guide wire tip for navigation and tissues identification, shape-memory alloy film based catheter/guide wire steering mechanisms, and rudder-based steering devices that allow the selective actuation of rudders that use the flowing blood itself to help direct the catheter direction through the blood vessel. While particularly adapted for medical applications, these cantilever actuators can be used for steering through piping and tubing systems.

Microvalve

DOEpatents

Lee, A.P.; Krulevitch, P.A.; Northrup, M.A.; Trevino, J.C.

1998-10-13

Micromachined thin film cantilever actuators having means for individually controlling the deflection of the cantilevers, valve members, and rudders for steering same through blood vessels, or positioning same within a blood vessel, for example. Such cantilever actuators include tactile sensor arrays mounted on a catheter or guide wire tip for navigation and tissues identification, shape-memory alloy film based catheter/guide wire steering mechanisms, and rudder-based steering devices that allow the selective actuation of rudders that use the flowing blood itself to help direct the catheter direction through the blood vessel. While particularly adapted for medical applications, these cantilever actuators can be used for steering through piping and tubing systems. 14 figs.

Microvalve

DOEpatents

Lee, Abraham P.; Krulevitch, Peter A.; Northrup, M. Allen; Trevino, Jimmy C.

1998-01-01

Micromachined thin film cantilever actuators having means for individually controlling the deflection of the cantilevers, valve members, and rudders for steering same through blood vessels, or positioning same within a blood vessel, for example. Such cantilever actuators include tactile sensor arrays mounted on a catheter or guide wire tip for navigation and tissues identification, shape-memory alloy film based catheter/guide wire steering mechanisms, and rudder-based steering devices that allow the selective actuation of rudders that use the flowing blood itself to help direct the catheter direction through the blood vessel. While particularly adapted for medical applications, these cantilever actuators can be used for steering through piping and tubing systems.

Evidence-Based Prevention for Adolescent Substance Use.

PubMed

Harrop, Erin; Catalano, Richard F

2016-07-01

Due to the significant consequences of adolescent substance use behaviors, researchers have increasingly focused on prevention approaches. The field of prevention science is based on the identification of predictors of problem behaviors, and the development and testing of prevention programs that seek to change these predictors. As the field of prevention science moves forward, there are many opportunities for growth, including the integration of prevention programs into service systems and primary care, an expansion of program adaptations to fit the needs of local populations, and a greater emphasis on the development of programs targeted at young adult populations. Copyright © 2016 Elsevier Inc. All rights reserved.

Threshold Values for Identification of Contamination Predicted by Reduced-Order Models

DOE PAGES

Last, George V.; Murray, Christopher J.; Bott, Yi-Ju; ...

2014-12-31

The U.S. Department of Energy’s (DOE’s) National Risk Assessment Partnership (NRAP) Project is developing reduced-order models to evaluate potential impacts on underground sources of drinking water (USDWs) if CO2 or brine leaks from deep CO2 storage reservoirs. Threshold values, below which there would be no predicted impacts, were determined for portions of two aquifer systems. These threshold values were calculated using an interwell approach for determining background groundwater concentrations that is an adaptation of methods described in the U.S. Environmental Protection Agency’s Unified Guidance for Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities.

Complex Patterns of Local Adaptation in Teosinte

PubMed Central

Pyhäjärvi, Tanja; Hufford, Matthew B.; Mezmouk, Sofiane; Ross-Ibarra, Jeffrey

2013-01-01

Populations of widely distributed species encounter and must adapt to local environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and an understanding of population structure and potential selection pressures. Here, we used single-nucleotide polymorphism genotyping and data on numerous environmental variables to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found complex hierarchical genetic structure created by altitude, dispersal events, and admixture among subspecies, which complicated identification of locally beneficial alleles. Patterns of linkage disequilibrium revealed four large putative inversion polymorphisms showing clinal patterns of frequency. Population differentiation and environmental correlations suggest that both inversions and intergenic polymorphisms are involved in local adaptation. PMID:23902747

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

NASA Astrophysics Data System (ADS)

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

1993-08-01

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

Cultural Adaptation of the Portuguese Version of the “Sniffin’ Sticks” Smell Test: Reliability, Validity, and Normative Data

PubMed Central

Ribeiro, João Carlos; Simões, João; Silva, Filipe; Silva, Eduardo D.; Hummel, Cornelia; Hummel, Thomas; Paiva, António

2016-01-01

The cross-cultural adaptation and validation of the Sniffin`Sticks test for the Portuguese population is described. Over 270 people participated in four experiments. In Experiment 1, 67 participants rated the familiarity of presented odors and seven descriptors of the original test were adapted to a Portuguese context. In Experiment 2, the Portuguese version of Sniffin`Sticks test was administered to 203 healthy participants. Older age, male gender and active smoking status were confirmed as confounding factors. The third experiment showed the validity of the Portuguese version of Sniffin`Sticks test in discriminating healthy controls from patients with olfactory dysfunction. In Experiment 4, the test-retest reliability for both the composite score (r71 = 0.86) and the identification test (r71 = 0.62) was established (p<0.001). Normative data for the Portuguese version of Sniffin`Sticks test is provided, showing good validity and reliability and effectively distinguishing patients from healthy controls with high sensitivity and specificity. The Portuguese version of Sniffin`Sticks test identification test is a clinically suitable screening tool in routine outpatient Portuguese settings. PMID:26863023

Development of a Design Methodology for Reconfigurable Flight Control Systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.; McLean, C.

2000-01-01

A methodology is presented for the design of flight control systems that exhibit stability and performance-robustness in the presence of actuator failures. The design is based upon two elements. The first element consists of a control law that will ensure at least stability in the presence of a class of actuator failures. This law is created by inner-loop, reduced-order, linear dynamic inversion, and outer-loop compensation based upon Quantitative Feedback Theory. The second element consists of adaptive compensators obtained from simple and approximate time-domain identification of the dynamics of the 'effective vehicle' with failed actuator(s). An example involving the lateral-directional control of a fighter aircraft is employed both to introduce the proposed methodology and to demonstrate its effectiveness and limitations.

Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm.

PubMed

Ren, Fulong; Cao, Peng; Li, Wei; Zhao, Dazhe; Zaiane, Osmar

2017-01-01

Diabetic retinopathy (DR) is a progressive disease, and its detection at an early stage is crucial for saving a patient's vision. An automated screening system for DR can help in reduce the chances of complete blindness due to DR along with lowering the work load on ophthalmologists. Among the earliest signs of DR are microaneurysms (MAs). However, current schemes for MA detection appear to report many false positives because detection algorithms have high sensitivity. Inevitably some non-MAs structures are labeled as MAs in the initial MAs identification step. This is a typical "class imbalance problem". Class imbalanced data has detrimental effects on the performance of conventional classifiers. In this work, we propose an ensemble based adaptive over-sampling algorithm for overcoming the class imbalance problem in the false positive reduction, and we use Boosting, Bagging, Random subspace as the ensemble framework to improve microaneurysm detection. The ensemble based over-sampling methods we proposed combine the strength of adaptive over-sampling and ensemble. The objective of the amalgamation of ensemble and adaptive over-sampling is to reduce the induction biases introduced from imbalanced data and to enhance the generalization classification performance of extreme learning machines (ELM). Experimental results show that our ASOBoost method has higher area under the ROC curve (AUC) and G-mean values than many existing class imbalance learning methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring

NASA Astrophysics Data System (ADS)

Zhou, Cong; Chase, J. Geoffrey; Rodgers, Geoffrey W.; Xu, Chao

2017-02-01

The model-free hysteresis loop analysis (HLA) method for structural health monitoring (SHM) has significant advantages over the traditional model-based SHM methods that require a suitable baseline model to represent the actual system response. This paper provides a unique validation against both an experimental reinforced concrete (RC) building and a calibrated numerical model to delineate the capability of the model-free HLA method and the adaptive least mean squares (LMS) model-based method in detecting, localizing and quantifying damage that may not be visible, observable in overall structural response. Results clearly show the model-free HLA method is capable of adapting to changes in how structures transfer load or demand across structural elements over time and multiple events of different size. However, the adaptive LMS model-based method presented an image of greater spread of lesser damage over time and story when the baseline model is not well defined. Finally, the two algorithms are tested over a simpler hysteretic behaviour typical steel structure to quantify the impact of model mismatch between the baseline model used for identification and the actual response. The overall results highlight the need for model-based methods to have an appropriate model that can capture the observed response, in order to yield accurate results, even in small events where the structure remains linear.

Natural products that reduce rotavirus infectivity identified by a cell-based moderate-throughput screening assay.

PubMed

Shaneyfelt, Mark E; Burke, Anna D; Graff, Joel W; Jutila, Mark A; Hardy, Michele E

2006-09-01

There is widespread interest in the use of innate immune modulators as a defense strategy against infectious pathogens. Using rotavirus as a model system, we developed a cell-based, moderate-throughput screening (MTS) assay to identify compounds that reduce rotavirus infectivity in vitro, toward a long-term goal of discovering immunomodulatory agents that enhance innate responses to viral infection. A natural product library consisting of 280 compounds was screened in the assay and 15 compounds that significantly reduced infectivity without cytotoxicity were identified. Time course analysis of four compounds with previously characterized effects on inflammatory gene expression inhibited replication with pre-treatment times as minimal as 2 hours. Two of these four compounds, alpha-mangostin and 18-beta-glycyrrhetinic acid, activated NFkappaB and induced IL-8 secretion. The assay is adaptable to other virus systems, and amenable to full automation and adaptation to a high-throughput format. Identification of several compounds with known effects on inflammatory and antiviral gene expression that confer resistance to rotavirus infection in vitro suggests the assay is an appropriate platform for discovery of compounds with potential to amplify innate antiviral responses.

Adaptive model reduction for continuous systems via recursive rational interpolation

NASA Technical Reports Server (NTRS)

Lilly, John H.

1994-01-01

A method for adaptive identification of reduced-order models for continuous stable SISO and MIMO plants is presented. The method recursively finds a model whose transfer function (matrix) matches that of the plant on a set of frequencies chosen by the designer. The algorithm utilizes the Moving Discrete Fourier Transform (MDFT) to continuously monitor the frequency-domain profile of the system input and output signals. The MDFT is an efficient method of monitoring discrete points in the frequency domain of an evolving function of time. The model parameters are estimated from MDFT data using standard recursive parameter estimation techniques. The algorithm has been shown in simulations to be quite robust to additive noise in the inputs and outputs. A significant advantage of the method is that it enables a type of on-line model validation. This is accomplished by simultaneously identifying a number of models and comparing each with the plant in the frequency domain. Simulations of the method applied to an 8th-order SISO plant and a 10-state 2-input 2-output plant are presented. An example of on-line model validation applied to the SISO plant is also presented.

Rapid Detection & Identification of Bacillus Species using MALDI-TOF/TOF and Biomarker Database

DTIC Science & Technology

2006-06-01

rRNA sequence analysis. Multilocus enzyme electrophoresis ( MEE ) and comparative DNA sequence analysis suggest that they may represent a single species...adaptation of the MEE method [63] but with greater discrimination [64]. All of these new PCR-based subtyping methods are certainly superior and more...Demirev, P.A., Lin, J.S., Pineda , F.J., and Fenselau, C. (2001). Bioinformatics and mass spectrometry for microorganism identification: proteome-wide

Low cost audiovisual playback and recording triggered by radio frequency identification using Raspberry Pi.

PubMed

Lendvai, Ádám Z; Akçay, Çağlar; Weiss, Talia; Haussmann, Mark F; Moore, Ignacio T; Bonier, Frances

2015-01-01

Playbacks of visual or audio stimuli to wild animals is a widely used experimental tool in behavioral ecology. In many cases, however, playback experiments are constrained by observer limitations such as the time observers can be present, or the accuracy of observation. These problems are particularly apparent when playbacks are triggered by specific events, such as performing a specific behavior, or are targeted to specific individuals. We developed a low-cost automated playback/recording system, using two field-deployable devices: radio-frequency identification (RFID) readers and Raspberry Pi micro-computers. This system detects a specific passive integrated transponder (PIT) tag attached to an individual, and subsequently plays back the stimuli, or records audio or visual information. To demonstrate the utility of this system and to test one of its possible applications, we tagged female and male tree swallows (Tachycineta bicolor) from two box-nesting populations with PIT tags and carried out playbacks of nestling begging calls every time focal females entered the nestbox over a six-hour period. We show that the RFID-Raspberry Pi system presents a versatile, low-cost, field-deployable system that can be adapted for many audio and visual playback purposes. In addition, the set-up does not require programming knowledge, and it easily customized to many other applications, depending on the research questions. Here, we discuss the possible applications and limitations of the system. The low cost and the small learning curve of the RFID-Raspberry Pi system provides a powerful new tool to field biologists.

Low cost audiovisual playback and recording triggered by radio frequency identification using Raspberry Pi

PubMed Central

Akçay, Çağlar; Weiss, Talia; Haussmann, Mark F.; Moore, Ignacio T.; Bonier, Frances

2015-01-01

Playbacks of visual or audio stimuli to wild animals is a widely used experimental tool in behavioral ecology. In many cases, however, playback experiments are constrained by observer limitations such as the time observers can be present, or the accuracy of observation. These problems are particularly apparent when playbacks are triggered by specific events, such as performing a specific behavior, or are targeted to specific individuals. We developed a low-cost automated playback/recording system, using two field-deployable devices: radio-frequency identification (RFID) readers and Raspberry Pi micro-computers. This system detects a specific passive integrated transponder (PIT) tag attached to an individual, and subsequently plays back the stimuli, or records audio or visual information. To demonstrate the utility of this system and to test one of its possible applications, we tagged female and male tree swallows (Tachycineta bicolor) from two box-nesting populations with PIT tags and carried out playbacks of nestling begging calls every time focal females entered the nestbox over a six-hour period. We show that the RFID-Raspberry Pi system presents a versatile, low-cost, field-deployable system that can be adapted for many audio and visual playback purposes. In addition, the set-up does not require programming knowledge, and it easily customized to many other applications, depending on the research questions. Here, we discuss the possible applications and limitations of the system. The low cost and the small learning curve of the RFID-Raspberry Pi system provides a powerful new tool to field biologists. PMID:25870771

Science-society collaboration for robust adaptation planning in water management - The Maipo River Basin in Chile

NASA Astrophysics Data System (ADS)

Ocampo Melgar, Anahí; Vicuña, Sebastián; Gironás, Jorge

2015-04-01

The Metropolitan Region (M.R.) in Chile is populated by over 6 million people and supplied by the Maipo River and its large number of irrigation channels. Potential environmental alterations caused by global change will extremely affect managers and users of water resources in this semi-arid basin. These hydro-climatological impacts combined with demographic and economic changes will be particularly complex in the city of Santiago, due to the diverse, counterpoised and equally important existing activities and demands. These challenges and complexities request the implementation of flexible plans and actions to adapt policies, institutions, infrastructure and behaviors to a new future with climate change. Due to the inherent uncertainties in the future, a recent research project entitled MAPA (Maipo Adaptation Plan for its initials in Spanish) has formed a collaborative science-society platform to generate insights into the vulnerabilities, challenges and possible mitigation measures that would be necessary to deal with the potential changes in the M.R. This large stakeholder platform conformed by around 30 public, private and civil society organizations, both at the local and regional level and guided by a Robust Decision Making Framework (RDMF) has identified vulnerabilities, future scenarios, performance indicators and mitigation measures for the Maipo River basin. The RDMF used in this project is the XLRM framework (Lempert et al. 2006) that incorporates policy levers (L), exogenous uncertainties (X), measures of performance standards (M) and relationships (R) in an interlinked process. Both stakeholders' expertise and computational capabilities have been used to create hydrological models for the urban, rural and highland sectors supported also by the Water Evaluation and Planning system software (WEAP). The identification of uncertainties and land use transition trends was used to develop future development scenarios to explore possible water management challenges. Finally a collaborative process guided by the Water Security concept resulted in the identification of local-based performance indicators that will be used to evaluate scenarios and the need for adaptation measures. This collaborative approach has allowed capturing the general aspirations of different water users in this basin and identifying the main challenges and possible adaptation measures that will be necessary to explore if some of these scenarios become real. Furthermore, this science-society effort has formed the basis for a more extended and long-term collaboration for the implementation of adaptation measures to other unavoidable land-based changes in the Maipo river basin.

Adaptive introgression across species boundaries in Heliconius butterflies.

PubMed

Pardo-Diaz, Carolina; Salazar, Camilo; Baxter, Simon W; Merot, Claire; Figueiredo-Ready, Wilsea; Joron, Mathieu; McMillan, W Owen; Jiggins, Chris D

2012-01-01

It is widely documented that hybridisation occurs between many closely related species, but the importance of introgression in adaptive evolution remains unclear, especially in animals. Here, we have examined the role of introgressive hybridisation in transferring adaptations between mimetic Heliconius butterflies, taking advantage of the recent identification of a gene regulating red wing patterns in this genus. By sequencing regions both linked and unlinked to the red colour locus, we found a region that displays an almost perfect genotype by phenotype association across four species, H. melpomene, H. cydno, H. timareta, and H. heurippa. This particular segment is located 70 kb downstream of the red colour specification gene optix, and coalescent analysis indicates repeated introgression of adaptive alleles from H. melpomene into the H. cydno species clade. Our analytical methods complement recent genome scale data for the same region and suggest adaptive introgression has a crucial role in generating adaptive wing colour diversity in this group of butterflies.

Molecular specificity, convergence and constraint shape adaptive evolution in nutrient-poor environments.

PubMed

Hong, Jungeui; Gresham, David

2014-01-01

One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increase the predictability of adaptive evolution.

Behavioral assessment of adaptive feedback equalization in a digital hearing aid.

PubMed

French-St George, M; Wood, D J; Engebretson, A M

1993-01-01

An evaluation was made of the efficacy of a digital feedback equalization algorithm employed by the Central Institute for the Deaf Wearable Adaptive Digital Hearing Aid. Three questions were addressed: 1) Does acoustic feedback limit gain adjustments made by hearing aid users? 2) Does feedback equalization permit users with hearing-impairment to select more gain without feedback? and, 3) If more gain is used when feedback equalization is active, does word identification performance improve? Nine subjects with hearing impairment participated in the study. Results suggest that listeners with hearing impairment are indeed limited by acoustic feedback when listening to soft speech (55 dB A) in quiet. The average listener used an additional 4 dB gain when feedback equalization was active. This additional gain resulted in an average 10 rationalized arcsine units (RAU) improvement in word identification score.

A Targeted Attack For Enhancing Resiliency of Intelligent Intrusion Detection Modules in Energy Cyber Physical Systems

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

Youssef, Tarek; El Hariri, Mohammad; Habib, Hani

Abstract— Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time- critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware tomore » trigger false positives in the neural network’s response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network’s ability to learn and adapt to these attacks.« less

When the ends outweigh the means: mood and level of identification in depression.

PubMed

Watkins, Edward R; Moberly, Nicholas J; Moulds, Michelle L

2011-11-01

Research in healthy controls has found that mood influences cognitive processing via level of action identification: happy moods are associated with global and abstract processing; sad moods are associated with local and concrete processing. However, this pattern seems inconsistent with the high level of abstract processing observed in depressed patients, leading Watkins (2008, 2010) to hypothesise that the association between mood and level of goal/action identification is impaired in depression. We tested this hypothesis by measuring level of identification on the Behavioural Identification Form after happy and sad mood inductions in never-depressed controls and currently depressed patients. Participants used increasingly concrete action identifications as they became sadder and less happy, but this effect was moderated by depression status. Consistent with Watkins' (2008) hypothesis, increases in sad mood and decreases in happiness were associated with shifts towards the use of more concrete action identifications in never-depressed individuals, but not in depressed patients. These findings suggest that the putatively adaptive association between mood and level of identification is impaired in major depression.

When the ends outweigh the means: Mood and level of identification in depression

PubMed Central

Watkins, Edward R.; Moberly, Nicholas J.; Moulds, Michelle L.

2011-01-01

Research in healthy controls has found that mood influences cognitive processing via level of action identification: happy moods are associated with global and abstract processing; sad moods are associated with local and concrete processing. However, this pattern seems inconsistent with the high level of abstract processing observed in depressed patients, leading Watkins (2008, 2010) to hypothesise that the association between mood and level of goal/action identification is impaired in depression. We tested this hypothesis by measuring level of identification on the Behavioural Identification Form after happy and sad mood inductions in never-depressed controls and currently depressed patients. Participants used increasingly concrete action identifications as they became sadder and less happy, but this effect was moderated by depression status. Consistent with Watkins' (2008) hypothesis, increases in sad mood and decreases in happiness were associated with shifts towards the use of more concrete action identifications in never-depressed individuals, but not in depressed patients. These findings suggest that the putatively adaptive association between mood and level of identification is impaired in major depression. PMID:22017614

System identification and model reduction using modulating function techniques

NASA Technical Reports Server (NTRS)

Shen, Yan

1993-01-01

Weighted least squares (WLS) and adaptive weighted least squares (AWLS) algorithms are initiated for continuous-time system identification using Fourier type modulating function techniques. Two stochastic signal models are examined using the mean square properties of the stochastic calculus: an equation error signal model with white noise residuals, and a more realistic white measurement noise signal model. The covariance matrices in each model are shown to be banded and sparse, and a joint likelihood cost function is developed which links the real and imaginary parts of the modulated quantities. The superior performance of above algorithms is demonstrated by comparing them with the LS/MFT and popular predicting error method (PEM) through 200 Monte Carlo simulations. A model reduction problem is formulated with the AWLS/MFT algorithm, and comparisons are made via six examples with a variety of model reduction techniques, including the well-known balanced realization method. Here the AWLS/MFT algorithm manifests higher accuracy in almost all cases, and exhibits its unique flexibility and versatility. Armed with this model reduction, the AWLS/MFT algorithm is extended into MIMO transfer function system identification problems. The impact due to the discrepancy in bandwidths and gains among subsystem is explored through five examples. Finally, as a comprehensive application, the stability derivatives of the longitudinal and lateral dynamics of an F-18 aircraft are identified using physical flight data provided by NASA. A pole-constrained SIMO and MIMO AWLS/MFT algorithm is devised and analyzed. Monte Carlo simulations illustrate its high-noise rejecting properties. Utilizing the flight data, comparisons among different MFT algorithms are tabulated and the AWLS is found to be strongly favored in almost all facets.

DSP-Based dual-polarity mass spectrum pattern recognition for bio-detection

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

Riot, V; Coffee, K; Gard, E

2006-04-21

The Bio-Aerosol Mass Spectrometry (BAMS) instrument analyzes single aerosol particles using a dual-polarity time-of-flight mass spectrometer recording simultaneously spectra of thirty to a hundred thousand points on each polarity. We describe here a real-time pattern recognition algorithm developed at Lawrence Livermore National Laboratory that has been implemented on a nine Digital Signal Processor (DSP) system from Signatec Incorporated. The algorithm first preprocesses independently the raw time-of-flight data through an adaptive baseline removal routine. The next step consists of a polarity dependent calibration to a mass-to-charge representation, reducing the data to about five hundred to a thousand channels per polarity. Themore » last step is the identification step using a pattern recognition algorithm based on a library of known particle signatures including threat agents and background particles. The identification step includes integrating the two polarities for a final identification determination using a score-based rule tree. This algorithm, operating on multiple channels per-polarity and multiple polarities, is well suited for parallel real-time processing. It has been implemented on the PMP8A from Signatec Incorporated, which is a computer based board that can interface directly to the two one-Giga-Sample digitizers (PDA1000 from Signatec Incorporated) used to record the two polarities of time-of-flight data. By using optimized data separation, pipelining, and parallel processing across the nine DSPs it is possible to achieve a processing speed of up to a thousand particles per seconds, while maintaining the recognition rate observed on a non-real time implementation. This embedded system has allowed the BAMS technology to improve its throughput and therefore its sensitivity while maintaining a large dynamic range (number of channels and two polarities) thus maintaining the systems specificity for bio-detection.« less

Arduino control of a pulsatile flow rig.

PubMed

Drost, S; de Kruif, B J; Newport, D

2018-01-01

This note describes the design and testing of a programmable pulsatile flow pump using an Arduino micro-controller. The goal of this work is to build a compact and affordable system that can relatively easily be programmed to generate physiological waveforms. The system described here was designed to be used in an in-vitro set-up for vascular access hemodynamics research, and hence incorporates a gear pump that delivers a mean flow of 900 ml/min in a test flow loop, and a peak flow of 1106 ml/min. After a number of simple identification experiments to assess the dynamic behaviour of the system, a feed-forward control routine was implemented. The resulting system was shown to be able to produce the targeted representative waveform with less than 3.6% error. Finally, we outline how to further increase the accuracy of the system, and how to adapt it to specific user needs. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Molecular determinants of enzyme cold adaptation: comparative structural and computational studies of cold- and warm-adapted enzymes.

PubMed

Papaleo, Elena; Tiberti, Matteo; Invernizzi, Gaetano; Pasi, Marco; Ranzani, Valeria

2011-11-01

The identification of molecular mechanisms underlying enzyme cold adaptation is a hot-topic both for fundamental research and industrial applications. In the present contribution, we review the last decades of structural computational investigations on cold-adapted enzymes in comparison to their warm-adapted counterparts. Comparative sequence and structural studies allow the definition of a multitude of adaptation strategies. Different enzymes carried out diverse mechanisms to adapt to low temperatures, so that a general theory for enzyme cold adaptation cannot be formulated. However, some common features can be traced in dynamic and flexibility properties of these enzymes, as well as in their intra- and inter-molecular interaction networks. Interestingly, the current data suggest that a family-centered point of view is necessary in the comparative analyses of cold- and warm-adapted enzymes. In fact, enzymes belonging to the same family or superfamily, thus sharing at least the three-dimensional fold and common features of the functional sites, have evolved similar structural and dynamic patterns to overcome the detrimental effects of low temperatures.

Adaptive and neuroadaptive control for nonnegative and compartmental dynamical systems

NASA Astrophysics Data System (ADS)

Volyanskyy, Kostyantyn Y.

Neural networks have been extensively used for adaptive system identification as well as adaptive and neuroadaptive control of highly uncertain systems. The goal of adaptive and neuroadaptive control is to achieve system performance without excessive reliance on system models. To improve robustness and the speed of adaptation of adaptive and neuroadaptive controllers several controller architectures have been proposed in the literature. In this dissertation, we develop a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture with additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty. Linear and nonlinear parameterizations of the system uncertainty are considered and state and output feedback neuroadaptive controllers are developed. Furthermore, we extend the developed framework to discrete-time dynamical systems. To illustrate the efficacy of the proposed approach we apply our results to an aircraft model with wing rock dynamics, a spacecraft model with unknown moment of inertia, and an unmanned combat aerial vehicle undergoing actuator failures, and compare our results with standard neuroadaptive control methods. Nonnegative systems are essential in capturing the behavior of a wide range of dynamical systems involving dynamic states whose values are nonnegative. A sub-class of nonnegative dynamical systems are compartmental systems. These systems are derived from mass and energy balance considerations and are comprised of homogeneous interconnected microscopic subsystems or compartments which exchange variable quantities of material via intercompartmental flow laws. In this dissertation, we develop direct adaptive and neuroadaptive control framework for stabilization, disturbance rejection and noise suppression for nonnegative and compartmental dynamical systems with noise and exogenous system disturbances. We then use the developed framework to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of continuing hemorrhage and hemodilution. Critical care patients, whether undergoing surgery or recovering in intensive care units, require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. In this dissertation, we develop a neuroadaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs and noisy measurements. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the developed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of noisy electroencephalographic (EEG) measurements. Clinical trials demonstrate excellent regulation of unconsciousness allowing for a safe and effective administration of the anesthetic agent propofol. Furthermore, a neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the imposed amplitude and integral input constraints are satisfied and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and a drug dosing constraint over a specified period. In addition, the aforementioned control architecture is used to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure- and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multi-compartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patient's physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. The effect of spontaneous breathing is incorporated within the lung model and the control framework. Finally, a neural network hybrid adaptive control framework for nonlinear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov-based and guarantees partial asymptotic stability of the closed-loop hybrid system; that is, asymptotic stability with respect to part of the closed-loop system states associated with the hybrid plant states. A numerical example is provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach.

ISYMOD: a knowledge warehouse for the identification, assembly and analysis of bacterial integrated systems.

PubMed

Chabalier, Julie; Capponi, Cécile; Quentin, Yves; Fichant, Gwennaele

2005-04-01

Complex biological functions emerge from interactions between proteins in stable supra-molecular assemblies and/or through transitory contacts. Most of the time protein partners of the assemblies are composed of one or several domains which exhibit different biochemical functions. Thus the study of cellular process requires the identification of different functional units and their integration in an interaction network; such complexes are referred to as integrated systems. In order to exploit with optimum efficiency the increased release of data, automated bioinformatics strategies are needed to identify, reconstruct and model such systems. For that purpose, we have developed a knowledge warehouse dedicated to the representation and acquisition of bacterial integrated systems involved in the exchange of the bacterial cell with its environment. ISYMOD is a knowledge warehouse that consistently integrates in the same environment the data and the methods used for their acquisition. This is achieved through the construction of (1) a domain knowledge base (DKB) devoted to the storage of the knowledge about the systems, their functional specificities, their partners and how they are related and (2) a methodological knowledge base (MKB) which depicts the task layout used to identify and reconstruct functional integrated systems. Instantiation of the DKB is obtained by solving the tasks of the MKB, whereas some tasks need instances of the DKB to be solved. AROM, an object-based knowledge representation system, has been used to design the DKB, and its task manager, AROMTasks, for developing the MKB. In this study two integrated systems, ABC transporters and two component systems, both involved in adaptation processes of a bacterial cell to its biotope, have been used to evaluate the feasibility of the approach.

Multisource information fusion applied to ship identification for the recognized maritime picture

NASA Astrophysics Data System (ADS)

Simard, Marc-Alain; Lefebvre, Eric; Helleur, Christopher

2000-04-01

The Recognized Maritime Picture (RMP) is defined as a composite picture of activity over a maritime area of interest. In simplistic terms, building an RAMP comes down to finding if an object of interest, a ship in our case, is there or not, determining what it is, determining what it is doing and determining if some type of follow-on action is required. The Canadian Department of National Defence currently has access to or may, in the near future, have access to a number of civilians, military and allied information or sensor systems to accomplish these purposes. These systems include automatic self-reporting positional systems, air patrol surveillance systems, high frequency surface radars, electronic intelligence systems, radar space systems and high frequency direction finding sensors. The ability to make full use of these systems is limited by the existing capability to fuse data from all sources in a timely, accurate and complete manner. This paper presents an information fusion systems under development that correlates and fuses these information and sensor data sources. This fusion system, named Adaptive Fuzzy Logic Correlator, correlates the information in batch but fuses and constructs ship tracks sequentially. It applies standard Kalman filter techniques and fuzzy logic correlation techniques. We propose a set of recommendations that should improve the ship identification process. Particularly it is proposed to utilize as many non-redundant sources of information as possible that address specific vessel attributes. Another important recommendation states that the information fusion and data association techniques should be capable of dealing with incomplete and imprecise information. Some fuzzy logic techniques capable of tolerating imprecise and dissimilar data are proposed.

Development of an antigen-based rapid diagnostic test for the identification of blowfly (Calliphoridae) species of forensic significance.

PubMed

McDonagh, Laura; Thornton, Chris; Wallman, James F; Stevens, Jamie R

2009-06-01

In this study we examine the limitations of currently used sequence-based approaches to blowfly (Calliphoridae) identification and evaluate the utility of an immunological approach to discriminate between blowfly species of forensic importance. By investigating antigenic similarity and dissimilarity between the first instar larval stages of four forensically important blowfly species, we have been able to identify immunoreactive proteins of potential use in the development of species-specific immuno-diagnostic tests. Here we outline our protein-based approach to species determination, and describe how it may be adapted to develop rapid diagnostic assays for the 'on-site' identification of blowfly species.

Epigenomics and human adaptation to high altitude.

PubMed

Julian, Colleen G

2017-11-01

Over the past decade, major technological and analytical advancements have propelled efforts toward identifying the molecular mechanisms that govern human adaptation to high altitude. Despite remarkable progress with respect to the identification of adaptive genomic signals that are strongly associated with the "hypoxia-tolerant" physiological characteristics of high-altitude populations, many questions regarding the fundamental biological processes underlying human adaptation remain unanswered. Vital to address these enduring questions will be determining the role of epigenetic processes, or non-sequence-based features of the genome, that are not only critical for the regulation of transcriptional responses to hypoxia but heritable across generations. This review proposes that epigenomic processes are involved in shaping patterns of adaptation to high altitude by influencing adaptive potential and phenotypic variability under conditions of limited oxygen supply. Improved understanding of the interaction between genetic, epigenetic, and environmental factors holds great promise to provide deeper insight into the mechanisms underlying human adaptive potential, and clarify its implications for biomedical research. Copyright © 2017 the American Physiological Society.

Personality Patterns Among Correctional Officer Applicants

ERIC Educational Resources Information Center

Holland, Terrill R.; And Others

1976-01-01

The MMPI profiles of 359 correctional officer applicants were cluster analyzed, which resulted in the identification of five relatively homogeneous subgroups. The implications of the findings for occupationally adaptive and maladaptive correctional officer behavior were discussed. (Editor)

Improving the healthcare response to domestic violence and abuse in sexual health clinics: feasibility study of a training, support and referral intervention.

PubMed

Sohal, Alex Hardip; Pathak, Neha; Blake, Sarah; Apea, Vanessa; Berry, Judith; Bailey, Jayne; Griffiths, Chris; Feder, Gene

2018-03-01

Sexual health and gynaecological problems are the most consistent and largest physical health differences between abused and non-abused female populations. Sexual health services are well placed to identify and support patients experiencing domestic violence and abuse (DVA). Most sexual health professionals have had minimal DVA training despite English National Institute for Health and Care Excellence recommendations. We sought to determine the feasibility of an evidence-based complex DVA training intervention in female sexual health walk-in services (IRIS ADViSE: Identification and Referral to Improve Safety whilst Assessing Domestic Violence in Sexual Health Environments). An adaptive mixed method pilot study in the female walk-in service of two sexual health clinics. Following implementation and evaluation at site 1, the intervention was refined before implementation at site 2. The intervention comprised electronic prompts, multidisciplinary training sessions, clinic materials and simple referral pathways to IRIS ADViSE advocate-educators (AEs). The pilot lasted 7 weeks at site 1 and 12 weeks at site 2. Feasibility outcomes were to assign a supportive DVA clinical lead, an IRIS ADViSE AE employed by a local DVA service provider, adapt electronic records, develop local referral pathways, assess whether enquiry, identification and referral rates were measurable. Both sites achieved all feasibility outcomes: appointing a supportive DVA clinical lead and IRIS ADViSE AE, establishing links with a local DVA provider, adapting electronic records, developing local referral pathways and rates of enquiry, identification and referral were found to be measurable. Site 1: 10% enquiry rate (n=267), 4% identification rate (n=16) and eight AE referrals. Site 2: 61% enquiry rate (n=1090), a 7% identification rate (n=79) and eight AE referrals. IRIS ADViSE can be successfully developed and implemented in sexual health clinics. It fulfils the unmet need for DVA training. Longer-term evaluation is recommended. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Identification of inhibitors using a cell-based assay for monitoring Golgi-resident protease activity.

PubMed

Coppola, Julia M; Hamilton, Christin A; Bhojani, Mahaveer S; Larsen, Martha J; Ross, Brian D; Rehemtulla, Alnawaz

2007-05-01

Noninvasive real-time quantification of cellular protease activity allows monitoring of enzymatic activity and identification of activity modulators within the protease's natural milieu. We developed a protease activity assay based on differential localization of a recombinant reporter consisting of a Golgi retention signal and a protease cleavage sequence fused to alkaline phosphatase (AP). When expressed in mammalian cells, this protein localizes to Golgi bodies and, on protease-mediated cleavage, AP translocates to the extracellular medium where its activity is measured. We used this system to monitor the Golgi-associated protease furin, a pluripotent enzyme with a key role in tumorigenesis, viral propagation of avian influenza, ebola, and HIV as well as in activation of anthrax, pseudomonas, and diphtheria toxins. This technology was adapted for high-throughput screening of 39,000-compound small molecule libraries, leading to identification of furin inhibitors. Furthermore, this strategy was used to identify inhibitors of another Golgi protease, the beta-site amyloid precursor protein (APP)-cleaving enzyme (BACE). BACE cleavage of the APP leads to formation of the Abeta peptide, a key event that leads to Alzheimer's disease. In conclusion, we describe a customizable noninvasive technology for real-time assessment of Golgi protease activity used to identify inhibitors of furin and BACE.

A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO4 battery

NASA Astrophysics Data System (ADS)

Li, Xiaoyu; Pan, Ke; Fan, Guodong; Lu, Rengui; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

2017-11-01

State of energy (SOE) is an important index for the electrochemical energy storage system in electric vehicles. In this paper, a robust state of energy estimation method in combination with a physical model parameter identification method is proposed to achieve accurate battery state estimation at different operating conditions and different aging stages. A physics-based fractional order model with variable solid-state diffusivity (FOM-VSSD) is used to characterize the dynamic performance of a LiFePO4/graphite battery. In order to update the model parameter automatically at different aging stages, a multi-step model parameter identification method based on the lexicographic optimization is especially designed for the electric vehicle operating conditions. As the battery available energy changes with different applied load current profiles, the relationship between the remaining energy loss and the state of charge, the average current as well as the average squared current is modeled. The SOE with different operating conditions and different aging stages are estimated based on an adaptive fractional order extended Kalman filter (AFEKF). Validation results show that the overall SOE estimation error is within ±5%. The proposed method is suitable for the electric vehicle online applications.

Identification of inhibitors using a cell based assay for monitoring golgi-resident protease activity

PubMed Central

Coppola, Julia M.; Hamilton, Christin A.; Bhojani, Mahaveer S.; Larsen, Martha J.; Ross, Brian D.; Rehemtulla, Alnawaz

2007-01-01

Non-invasive real time quantification of cellular protease activity allows monitoring of enzymatic activity and identification of activity modulators within the protease’s natural milieu. We developed a protease-activity assay based on differential localization of a recombinant reporter consisting of a Golgi retention signal and a protease cleavage sequence fused to alkaline phosphatase (AP). When expressed in mammalian cells, this protein localizes to Golgi bodies and, upon protease mediated cleavage, AP translocates to the extracellular medium where its activity is measured. We used this system to monitor the Golgi-associated protease furin, a pluripotent enzyme with a key role in tumorigenesis, viral propagation of avian influenza, ebola, and HIV, and in activation of anthrax, pseudomonas, and diphtheria toxins. This technology was adapted for high throughput screening of 30,000 compound small molecule libraries, leading to identification of furin inhibitors. Further, this strategy was utilized to identify inhibitors of another Golgi protease, the β-site APP-cleaving enzyme (BACE). BACE cleavage of the amyloid precursor protein leads to formation of the Aβ peptide, a key event that leads to Alzheimer’s disease. In conclusion, we describe a customizable, non-invasive technology for real time assessment of Golgi protease activity used to identify inhibitors of furin and BACE. PMID:17316541

Method, system and apparatus for monitoring and adjusting the quality of indoor air

DOEpatents

Hartenstein, Steven D.; Tremblay, Paul L.; Fryer, Michael O.; Hohorst, Frederick A.

2004-03-23

A system, method and apparatus is provided for monitoring and adjusting the quality of indoor air. A sensor array senses an air sample from the indoor air and analyzes the air sample to obtain signatures representative of contaminants in the air sample. When the level or type of contaminant poses a threat or hazard to the occupants, the present invention takes corrective actions which may include introducing additional fresh air. The corrective actions taken are intended to promote overall health of personnel, prevent personnel from being overexposed to hazardous contaminants and minimize the cost of operating the HVAC system. The identification of the contaminants is performed by comparing the signatures provided by the sensor array with a database of known signatures. Upon identification, the system takes corrective actions based on the level of contaminant present. The present invention is capable of learning the identity of previously unknown contaminants, which increases its ability to identify contaminants in the future. Indoor air quality is assured by monitoring the contaminants not only in the indoor air, but also in the outdoor air and the air which is to be recirculated. The present invention is easily adaptable to new and existing HVAC systems. In sum, the present invention is able to monitor and adjust the quality of indoor air in real time by sensing the level and type of contaminants present in indoor air, outdoor and recirculated air, providing an intelligent decision about the quality of the air, and minimizing the cost of operating an HVAC system.

WiFi RFID demonstration for resource tracking in a statewide disaster drill.

PubMed

Cole, Stacey L; Siddiqui, Javeed; Harry, David J; Sandrock, Christian E

2011-01-01

To investigate the capabilities of Radio Frequency Identification (RFID) tracking of patients and medical equipment during a simulated disaster response scenario. RFID infrastructure was deployed at two small rural hospitals, in one large academic medical center and in two vehicles. Several item types from the mutual aid equipment list were selected for tracking during the demonstration. A central database server was installed at the UC Davis Medical Center (UCDMC) that collected RFID information from all constituent sites. The system was tested during a statewide disaster drill. During the drill, volunteers at UCDMC were selected to locate assets using the traditional method of locating resources and then using the RFID system. This study demonstrated the effectiveness of RFID infrastructure in real-time resource identification and tracking. Volunteers at UCDMC were able to locate assets substantially faster using RFID, demonstrating that real-time geolocation can be substantially more efficient and accurate than traditional manual methods. A mobile, Global Positioning System (GPS)-enabled RFID system was installed in a pediatric ambulance and connected to the central RFID database via secure cellular communication. This system is unique in that it provides for seamless region-wide tracking that adaptively uses and seamlessly integrates both outdoor cellular-based mobile tracking and indoor WiFi-based tracking. RFID tracking can provide a real-time picture of the medical situation across medical facilities and other critical locations, leading to a more coordinated deployment of resources. The RFID system deployed during this study demonstrated the potential to improve the ability to locate and track victims, healthcare professionals, and medical equipment during a region-wide disaster.

Adaptation of Rhizobium leguminosarum to pea, alfalfa and sugar beet rhizospheres investigated by comparative transcriptomics

PubMed Central

2011-01-01

Background The rhizosphere is the microbe-rich zone around plant roots and is a key determinant of the biosphere's productivity. Comparative transcriptomics was used to investigate general and plant-specific adaptations during rhizosphere colonization. Rhizobium leguminosarum biovar viciae was grown in the rhizospheres of pea (its legume nodulation host), alfalfa (a non-host legume) and sugar beet (non-legume). Gene expression data were compared to metabolic and transportome maps to understand adaptation to the rhizosphere. Results Carbon metabolism was dominated by organic acids, with a strong bias towards aromatic amino acids, C1 and C2 compounds. This was confirmed by induction of the glyoxylate cycle required for C2 metabolism and gluconeogenesis in all rhizospheres. Gluconeogenesis is repressed in R. leguminosarum by sugars, suggesting that although numerous sugar and putative complex carbohydrate transport systems are induced in the rhizosphere, they are less important carbon sources than organic acids. A common core of rhizosphere-induced genes was identified, of which 66% are of unknown function. Many genes were induced in the rhizosphere of the legumes, but not sugar beet, and several were plant specific. The plasmid pRL8 can be considered pea rhizosphere specific, enabling adaptation of R. leguminosarum to its host. Mutation of many of the up-regulated genes reduced competitiveness for pea rhizosphere colonization, while two genes specifically up-regulated in the pea rhizosphere reduced colonization of the pea but not alfalfa rhizosphere. Conclusions Comparative transcriptome analysis has enabled differentiation between factors conserved across plants for rhizosphere colonization as well as identification of exquisite specific adaptation to host plants. PMID:22018401

Application of RNAMlet to surface defect identification of steels

NASA Astrophysics Data System (ADS)

Xu, Ke; Xu, Yang; Zhou, Peng; Wang, Lei

2018-06-01

As three main production lines of steels, continuous casting slabs, hot rolled steel plates and cold rolled steel strips have different surface appearances and are produced at different speeds of their production lines. Therefore, the algorithms for the surface defect identifications of the three steel products have different requirements for real-time and anti-interference. The existing algorithms cannot be adaptively applied to surface defect identification of the three steel products. A new method of adaptive multi-scale geometric analysis named RNAMlet was proposed. The idea of RNAMlet came from the non-symmetry anti-packing pattern representation model (NAM). The image is decomposed into a set of rectangular blocks asymmetrically according to gray value changes of image pixels. Then two-dimensional Haar wavelet transform is applied to all blocks. If the image background is complex, the number of blocks is large, and more details of the image are utilized. If the image background is simple, the number of blocks is small, and less computation time is needed. RNAMlet was tested with image samples of the three steel products, and compared with three classical methods of multi-scale geometric analysis, including Contourlet, Shearlet and Tetrolet. For the image samples with complicated backgrounds, such as continuous casting slabs and hot rolled steel plates, the defect identification rate obtained by RNAMlet was 1% higher than other three methods. For the image samples with simple backgrounds, such as cold rolled steel strips, the computation time of RNAMlet was one-tenth of the other three MGA methods, while the defect identification rates obtained by RNAMlet were higher than the other three methods.

[Measures to prevent patient identification errors in blood collection/physiological function testing utilizing a laboratory information system].

PubMed

Shimazu, Chisato; Hoshino, Satoshi; Furukawa, Taiji

2013-08-01

We constructed an integrated personal identification workflow chart using both bar code reading and an all in-one laboratory information system. The information system not only handles test data but also the information needed for patient guidance in the laboratory department. The reception terminals at the entrance, displays for patient guidance and patient identification tools at blood-sampling booths are all controlled by the information system. The number of patient identification errors was greatly reduced by the system. However, identification errors have not been abolished in the ultrasound department. After re-evaluation of the patient identification process in this department, we recognized that the major reason for the errors came from excessive identification workflow. Ordinarily, an ultrasound test requires patient identification 3 times, because 3 different systems are required during the entire test process, i.e. ultrasound modality system, laboratory information system and a system for producing reports. We are trying to connect the 3 different systems to develop a one-time identification workflow, but it is not a simple task and has not been completed yet. Utilization of the laboratory information system is effective, but is not yet perfect for patient identification. The most fundamental procedure for patient identification is to ask a person's name even today. Everyday checks in the ordinary workflow and everyone's participation in safety-management activity are important for the prevention of patient identification errors.

Biomarkers for Uranium Risk Assessment for the Development of the CURE (Concerted Uranium Research in Europe) Molecular Epidemiological Protocol.

PubMed

Guéguen, Yann; Roy, Laurence; Hornhardt, Sabine; Badie, Christophe; Hall, Janet; Baatout, Sarah; Pernot, Eileen; Tomasek, Ladislav; Laurent, Olivier; Ebrahimian, Teni; Ibanez, Chrystelle; Grison, Stephane; Kabacik, Sylwia; Laurier, Dominique; Gomolka, Maria

2017-01-01

Despite substantial experimental and epidemiological research, there is limited knowledge of the uranium-induce health effects after chronic low-dose exposures in humans. Biological markers can objectively characterize pathological processes or environmental responses to uranium and confounding agents. The integration of such biological markers into a molecular epidemiological study would be a useful approach to improve and refine estimations of uranium-induced health risks. To initiate such a study, Concerted Uranium Research in Europe (CURE) was established, and involves biologists, epidemiologists and dosimetrists. The aims of the biological work package of CURE were: 1. To identify biomarkers and biological specimens relevant to uranium exposure; 2. To define standard operating procedures (SOPs); and 3. To set up a common protocol (logistic, questionnaire, ethical aspects) to perform a large-scale molecular epidemiologic study in uranium-exposed cohorts. An intensive literature review was performed and led to the identification of biomarkers related to: 1. retention organs (lungs, kidneys and bone); 2. other systems/organs with suspected effects (cardiovascular system, central nervous system and lympho-hematopoietic system); 3. target molecules (DNA damage, genomic instability); and 4. high-throughput methods for the identification of new biomarkers. To obtain high-quality biological materials, SOPs were established for the sampling and storage of different biospecimens. A questionnaire was developed to assess potential confounding factors. The proposed strategy can be adapted to other internal exposures and should improve the characterization of the biological and health effects that are relevant for risk assessment.

Intelligent Control for the BEES Flyer

NASA Technical Reports Server (NTRS)

Krishnakumar, K.; Gundy-Burlet, Karen; Aftosmis, Mike; Nemec, Marian; Limes, Greg; Berry, Misty; Logan, Michael

2004-01-01

This paper describes the effort to provide a preliminary capability analysis and a neural network based adaptive flight control system for the JPL-led BEES aircraft project. The BEES flyer was envisioned to be a small, autonomous platform with sensing and control systems mimicking those of biological systems for the purpose of scientific exploration on the surface of Mars. The platform is physically tightly constrained by the necessity of efficient packing within rockets for the trip to Mars. Given the physical constraints, the system is not an ideal configuration for aerodynamics or stability and control. The objectives of this effort are to evaluate the aerodynamics characteristics of the existing design, to make recommendaaons as to potential improvements and to provide a control system that stabilizes the existing aircraft for nominal flight and damaged conditions. Towards this several questions are raised and analyses are presented to arrive at answers to some of the questions raised. CART3D, a high-fidelity inviscid analysis package for conceptual and preliminary aerodynamic design, was used to compute a parametric set of solutions over the expected flight domain. Stability and control derivatives were extracted from the database and integrated with the neural flight control system. The Integrated Vehicle Modeling Environment (IVME) was also used for estimating aircraft geometric, inertial, and aerodynamic characteristics. A generic neural flight control system is used to provide adaptive control without the requirement for extensive gain scheduling or explicit system identification. The neural flight control system uses reference models to specify desired handling qualities in the roll, pitch, and yaw axes, and incorporates both pre-trained and on-line learning neural networks in the inverse model portion of the controller. Results are presented for the BEES aircraft in the subsonic regime for terrestrial and Martian environments.

Compact storage of medical images with patient information.

PubMed

Acharya, R; Anand, D; Bhat, S; Niranjan, U C

2001-12-01

Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images to reduce storage and transmission overheads. The text data are encrypted before interleaving with images to ensure greater security. The graphical signals are compressed and subsequently interleaved with the image. Differential pulse-code-modulation and adaptive-delta-modulation techniques are employed for data compression, and encryption and results are tabulated for a specific example.

Screening of antifungal azole drugs and agrochemicals with an adapted alamarBlue-based assay demonstrates antibacterial activity of croconazole against Mycobacterium ulcerans.

PubMed

Scherr, Nicole; Röltgen, Katharina; Witschel, Matthias; Pluschke, Gerd

2012-12-01

An alamarBlue-based growth inhibition assay has been adapted for the thermosensitive and slow-growing pathogen Mycobacterium ulcerans. The standardized test procedure enables medium-throughput screening of preselected compound libraries. Testing of a set of 48 azoles with known antifungal activity led to the identification of an imidazole antifungal displaying an inhibitory dose (ID) of 9 μM for M. ulcerans.

Screening of Antifungal Azole Drugs and Agrochemicals with an Adapted alamarBlue-Based Assay Demonstrates Antibacterial Activity of Croconazole against Mycobacterium ulcerans

PubMed Central

Röltgen, Katharina; Witschel, Matthias; Pluschke, Gerd

2012-01-01

An alamarBlue-based growth inhibition assay has been adapted for the thermosensitive and slow-growing pathogen Mycobacterium ulcerans. The standardized test procedure enables medium-throughput screening of preselected compound libraries. Testing of a set of 48 azoles with known antifungal activity led to the identification of an imidazole antifungal displaying an inhibitory dose (ID) of 9 μM for M. ulcerans. PMID:23006761

Proteomics-based identification of differentially abundant proteins reveals adaptation mechanisms of Xanthomonas citri subsp. citri during Citrus sinensis infection.

PubMed

Moreira, Leandro M; Soares, Márcia R; Facincani, Agda P; Ferreira, Cristiano B; Ferreira, Rafael M; Ferro, Maria I T; Gozzo, Fábio C; Felestrino, Érica B; Assis, Renata A B; Garcia, Camila Carrião M; Setubal, João C; Ferro, Jesus A; de Oliveira, Julio C F

2017-07-11

Xanthomonas citri subsp. citri (Xac) is the causal agent of citrus canker. A proteomic analysis under in planta infectious and non-infectious conditions was conducted in order to increase our knowledge about the adaptive process of Xac during infection. For that, a 2D-based proteomic analysis of Xac at 1, 3 and 5 days after inoculation, in comparison to Xac growth in NB media was carried out and followed by MALDI-TOF-TOF identification of 124 unique differentially abundant proteins. Among them, 79 correspond to up-regulated proteins in at least one of the three stages of infection. Our results indicate an important role of proteins related to biofilm synthesis, lipopolysaccharides biosynthesis, and iron uptake and metabolism as possible modulators of plant innate immunity, and revealed an intricate network of proteins involved in reactive oxygen species adaptation during Plants` Oxidative Burst response. We also identified proteins previously unknown to be involved in Xac-Citrus interaction, including the hypothetical protein XAC3981. A mutant strain for this gene has proved to be non-pathogenic in respect to classical symptoms of citrus canker induced in compatible plants. This is the first time that a protein repertoire is shown to be active and working in an integrated manner during the infection process in a compatible host, pointing to an elaborate mechanism for adaptation of Xac once inside the plant.

Combining Genotype, Phenotype, and Environment to Infer Potential Candidate Genes.

PubMed

Talbot, Benoit; Chen, Ting-Wen; Zimmerman, Shawna; Joost, Stéphane; Eckert, Andrew J; Crow, Taylor M; Semizer-Cuming, Devrim; Seshadri, Chitra; Manel, Stéphanie

2017-03-01

Population genomic analysis can be an important tool in understanding local adaptation. Identification of potential adaptive loci in such analyses is usually based on the survey of a large genomic dataset in combination with environmental variables. Phenotypic data are less commonly incorporated into such studies, although combining a genome scan analysis with a phenotypic trait analysis can greatly improve the insights obtained from each analysis individually. Here, we aimed to identify loci potentially involved in adaptation to climate in 283 Loblolly pine (Pinus taeda) samples from throughout the species' range in the southeastern United States. We analyzed associations between phenotypic, molecular, and environmental variables from datasets of 3082 single nucleotide polymorphism (SNP) loci and 3 categories of phenotypic traits (gene expression, metabolites, and whole-plant traits). We found only 6 SNP loci that displayed potential signals of local adaptation. Five of the 6 identified SNPs are linked to gene expression traits for lignin development, and 1 is linked with whole-plant traits. We subsequently compared the 6 candidate genes with environmental variables and found a high correlation in only 3 of them (R2 > 0.2). Our study highlights the need for a combination of genotypes, phenotypes, and environmental variables, and for an appropriate sampling scheme and study design, to improve confidence in the identification of potential candidate genes. © The American Genetic Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Identification of propulsion systems

NASA Technical Reports Server (NTRS)

Merrill, Walter; Guo, Ten-Huei; Duyar, Ahmet

1991-01-01

This paper presents a tutorial on the use of model identification techniques for the identification of propulsion system models. These models are important for control design, simulation, parameter estimation, and fault detection. Propulsion system identification is defined in the context of the classical description of identification as a four step process that is unique because of special considerations of data and error sources. Propulsion system models are described along with the dependence of system operation on the environment. Propulsion system simulation approaches are discussed as well as approaches to propulsion system identification with examples for both air breathing and rocket systems.

49 CFR 1544.231 - Airport-approved and exclusive area personnel identification systems.

Code of Federal Regulations, 2010 CFR

2010-10-01

... carry out a personnel identification system for identification media that are airport-approved, or identification media that are issued for use in an exclusive area. The system must include the following: (1) Personnel identification media that— (i) Convey a full face image, full name, employer, and identification...