A method to predict circulation control noise
NASA Astrophysics Data System (ADS)
Reger, Robert W.
Underwater vehicles suffer from reduced maneuverability with conventional lifting append-\\ ages due to the low velocity of operation. Circulation control offers a method to increase maneuverability independent of vehicle speed. However, with circulation control comes additional noise sources, which are not well understood. To better understand these noise sources, a modal-based prediction method is developed, potentially offering a quantitative connection between flow structures and far-field noise. This method involves estimation of the velocity field, surface pressure field, and far-field noise, using only non-time-resolved velocity fields and time-resolved probe measurements. Proper orthogonal decomposition, linear stochastic estimation and Kalman smoothing are employed to estimate time-resolved velocity fields. Poisson's equation is used to calculate time-resolved pressure fields from velocity. Curle's analogy is then used to propagate the surface pressure forces to the far field. This method is developed on a direct numerical simulation of a two-dimensional cylinder at a low Reynolds number (150). Since each of the fields to be estimated are also known from the simulation, a means of obtaining the error from using the methodology is provided. The velocity estimation and the simulated velocity match well when the simulated additive measurement noise is low. The pressure field suffers due to a small domain size; however, the surface pressures estimates fare much better. The far-field estimation contains similar frequency content with reduced magnitudes, attributed to the exclusion of the viscous forces in Curle's analogy. In the absence of added noise, the estimation procedure performs quite nicely for this model problem. The method is tested experimentally on a 650,000 chord-Reynolds-number flow over a 2-D, 20% thick, elliptic circulation control airfoil. Slot jet momentum coefficients of 0 and 0.10 are investigated. Particle image velocimetry, unsteady
Application of linear gauss pseudospectral method in model predictive control
NASA Astrophysics Data System (ADS)
Yang, Liang; Zhou, Hao; Chen, Wanchun
2014-03-01
This paper presents a model predictive control(MPC) method aimed at solving the nonlinear optimal control problem with hard terminal constraints and quadratic performance index. The method combines the philosophies of the nonlinear approximation model predictive control, linear quadrature optimal control and Gauss Pseudospectral method. The current control is obtained by successively solving linear algebraic equations transferred from the original problem via linearization and the Gauss Pseudospectral method. It is not only of high computational efficiency since it does not need to solve nonlinear programming problem, but also of high accuracy though there are a few discrete points. Therefore, this method is suitable for on-board applications. A design of terminal impact with a specified direction is carried out to evaluate the performance of this method. Augmented PN guidance law in the three-dimensional coordinate system is applied to produce the initial guess. And various cases for target with straight-line movements are employed to demonstrate the applicability in different impact angles. Moreover, performance of the proposed method is also assessed by comparison with other guidance laws. Simulation results indicate that this method is not only of high computational efficiency and accuracy, but also applicable in the framework of guidance design.
Comparison of Predictive Control Methods for High Consumption Industrial Furnace
2013-01-01
We describe several predictive control approaches for high consumption industrial furnace control. These furnaces are major consumers in production industries, and reducing their fuel consumption and optimizing the quality of the products is one of the most important engineer tasks. In order to demonstrate the benefits from implementation of the advanced predictive control algorithms, we have compared several major criteria for furnace control. On the basis of the analysis, some important conclusions have been drawn. PMID:24319354
Comparison of predictive control methods for high consumption industrial furnace.
Stojanovski, Goran; Stankovski, Mile
2013-01-01
We describe several predictive control approaches for high consumption industrial furnace control. These furnaces are major consumers in production industries, and reducing their fuel consumption and optimizing the quality of the products is one of the most important engineer tasks. In order to demonstrate the benefits from implementation of the advanced predictive control algorithms, we have compared several major criteria for furnace control. On the basis of the analysis, some important conclusions have been drawn.
A Novel Method to Predict Circulation Control Noise
2016-03-17
curren~y valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (00-MM-YYYY) 1 2. REPORT TYPE 3. DATES...Circulation Control Noise 5b. GRANT NUMBER N00014-12-1-0544 Sc. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Sd. PROJECT NUMBER Robert Reger, Adam Nickels...Underwater vehicles suffer from reduced maneuverability with conventional lifting appendages. Circulation control (CC) offers a method to increase
Two-Step Design Method of Engine Control System Based on Generalized Predictive Control
NASA Astrophysics Data System (ADS)
Hashimoto, Seiji; Okuda, Hiroyuki; Okada, Yasushi; Adachi, Shuichi; Niwa, Shinji; Kajitani, Mitsunobu
Conservation of the environment has become critical to the automotive industry. Recently, requirements for on-board diagnostic and engine control systems have been strictly enforced. In the present paper, in order to meet the requirements for a low-emissions vehicle, a novel construction method of the air-fuel ratio (A/F) control system is proposed. The construction method of the system is divided into two steps. The first step is to design the A/F control system for the engine based on an open loop design. The second step is to design the A/F control system for the catalyst system. The design method is based on the generalized predictive control in order to satisfy the robustness to open loop control as well as model uncertainty. The effectiveness of the proposed A/F control system is verified through experiments using full-scale products.
NASA Astrophysics Data System (ADS)
Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.
2016-08-01
According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.
NASA Astrophysics Data System (ADS)
Rubin, Daniel; Arogeti, Shai A.
2015-09-01
In this paper, the problem of vehicle yaw control using an active limited-slip differential (ALSD) applied on the rear axle is addressed. The controller objective is to minimise yaw-rate and body slip-angle errors, with respect to target values. A novel model predictive controller is designed, using a linear parameter-varying (LPV) vehicle model, which takes into account the ALSD dynamics and its constraints. The controller is simulated using a 10DOF Matlab/Simulink simulation model and a CarSim model. These simulations exemplify the controller yaw-rate and slip-angle tracking performances, under challenging manoeuvres and road conditions. The model predictive controller performances surpass those of a reference sliding mode controller, and can narrow the loss of performances due to the ALSD's inability to transfer torque regardless of driving conditions.
Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa
2013-04-09
Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.
Validation of engineering methods for predicting hypersonic vehicle controls forces and moments
NASA Technical Reports Server (NTRS)
Maughmer, M.; Straussfogel, D.; Long, L.; Ozoroski, L.
1991-01-01
This work examines the ability of the aerodynamic analysis methods contained in an industry standard conceptual design code, the Aerodynamic Preliminary Analysis System (APAS II), to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds. Predicted control forces and moments generated by various control effectors are compared with previously published wind-tunnel and flight-test data for three vehicles: the North American X-15, a hypersonic research airplane concept, and the Space Shuttle Orbiter. Qualitative summaries of the results are given for each force and moment coefficient and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage.
Validation of engineering methods for predicting hypersonic vehicle controls forces and moments
NASA Technical Reports Server (NTRS)
Maughmer, M.; Straussfogel, D.; Long, L.; Ozoroski, L.
1991-01-01
This work examines the ability of the aerodynamic analysis methods contained in an industry standard conceptual design code, the Aerodynamic Preliminary Analysis System (APAS II), to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds. Predicted control forces and moments generated by various control effectors are compared with previously published wind-tunnel and flight-test data for three vehicles: the North American X-15, a hypersonic research airplane concept, and the Space Shuttle Orbiter. Qualitative summaries of the results are given for each force and moment coefficient and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage.
A web-based noise control prediction model for rooms using the method of images
NASA Astrophysics Data System (ADS)
Dance, Stephen
2002-11-01
Previous simple models could only predict sound levels in untreated rooms. Now, using the method of images, it has become possible to accurately predict the sound level in fitted industrial rooms from any computer on the Internet. Thus, a powerful tool in an acoustician's armory is available to all, while requiring only the minimal amount of input data to construct the model. This is only achievable if the scope of the model is reduced to one or two acoustic parameters. Now, two common noise control techniques have been implemented into the image source model: acoustic barriers and absorptive patches. Predictions using the model with and without noise control techniques will be demonstrated, so the advantages can be clearly seen in typical industrial rooms. The models are now available on the web, running directly inside Netscape or Internet Explorer.
A Numerical Process Control Method for Circular-Tube Hydroforming Prediction
Johnson, Kenneth I.; Nguyen, Ba Nghiep; Davies, Richard W.; Grant, Glenn J.; Khaleel, Mohammad A.
2004-03-01
This paper describes the development of a solution control method that tracks the stresses, strains and mechanical behavior of a tube during hydroforming to estimate the proper axial feed (end-feed) and internal pressure loads through time. The analysis uses the deformation theory of plasticity and Hill?s criterion to describe the plastic flow. Before yielding, the pressure and end-feed increments are estimated based on the initial tube geometry, elastic properties and yield stress. After yielding, the pressure increment is calculated based on the tube geometry at the previous solution increment and the current hoop stress increment. The end-feed increment is computed from the increment of the axial plastic strain. Limiting conditions such as column buckling (of long tubes), local axi-symmetric wrinkling of shorter tubes, and bursting due to localized wall thinning are considered. The process control method has been implemented in the Marc finite element code. Hydroforming simulations using this process control method were conducted to predict the load histories for controlled expansion of 6061-T4 aluminum tubes within a conical die shape and under free hydroforming conditions. The predicted loading paths were transferred to the hydroforming equipment to form the conical and free-formed tube shapes. The model predictions and experimental results are compared for deformed shape, strains and the extent of forming at rupture.
Giovanini, Leonardo L
2003-04-01
In this work a new method for designing predictive controllers for linear single-input/single-output systems is presented. It uses only one prediction of the process output J time intervals ahead to compute the correspondent future error. Then, the predictive feedback controller is defined by introducing a filter which weights the last w predicted errors. In this way, the resulting control action is computed by observing the system future behavior and also by weighting present and past errors. This last feature improves the closed-loop performance to disturbance rejection as shown through simulations of two linear systems and a nonlinear continuous stirred tank reactor.
NASA Technical Reports Server (NTRS)
Maughmer, Mark D.; Ozoroski, L.; Ozoroski, T.; Straussfogel, D.
1990-01-01
Many types of hypersonic aircraft configurations are currently being studied for feasibility of future development. Since the control of the hypersonic configurations throughout the speed range has a major impact on acceptable designs, it must be considered in the conceptual design stage. The ability of the aerodynamic analysis methods contained in an industry standard conceptual design system, APAS II, to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds is considered. Predicted control forces and moments generated by various control effectors are compared with previously published wind tunnel and flight test data for three configurations: the North American X-15, the Space Shuttle Orbiter, and a hypersonic research airplane concept. Qualitative summaries of the results are given for each longitudinal force and moment and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage. Results for most lateral/directional control derivatives are acceptable for conceptual design purposes; however, predictions at supersonic Mach numbers for the change in yawing moment due to aileron deflection and the change in rolling moment due to rudder deflection are found to be unacceptable. Including shielding effects in the analysis is shown to have little effect on lift and pitching moment predictions while improving drag predictions.
A Prediction Method of TV Camera Image for Space Manual-control Rendezvous and Docking
NASA Astrophysics Data System (ADS)
Zhen, Huang; Qing, Yang; Wenrui, Wu
Space manual-control rendezvous and docking (RVD) is a key technology for accomplishing the RVD mission in manned space engineering, especially when automatic control system is out of work. The pilot on chase spacecraft manipulates the hand-stick by the image of target spacecraft captured by TV camera. From the TV image, the relative position and attitude of chase and target spacecrafts can be shown. Therefore, the size, the position, the brightness and the shadow of the target on TV camera are key to guarantee the success of manual-control RVD. A method of predicting the on-orbit TV camera image at different relative positions and light conditions during the process of RVD is discussed. Firstly, the basic principle of capturing the image of cross drone on target spacecraft by TV camera is analyzed theoretically, based which the strategy of manual-control RVD is discussed in detail. Secondly, the relationship between the displayed size or position and the real relative distance of chase and target spacecrafts is presented, the brightness and reflection by the target spacecraft at different light conditions are decribed, the shadow on cross drone caused by the chase or target spacecraft is analyzed. Thirdly, a prediction method of on-orbit TV camera images at certain orbit and light condition is provided, and the characteristics of TV camera image during the RVD is analyzed. Finally, the size, the position, the brightness and the shadow of target spacecraft on TV camera image at typical orbit is simulated. The result, by comparing the simulated images with the real images captured by the TV camera on Shenzhou manned spaceship , shows that the prediction method is reasonable
A swarm intelligence-based tuning method for the Sliding Mode Generalized Predictive Control.
Oliveira, J B; Boaventura-Cunha, J; Moura Oliveira, P B; Freire, H
2014-09-01
This work presents an automatic tuning method for the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Quadratic Programming (QP), thus yielding an online dual optimization scheme. Simulations and performance indexes for common process models in industry, such as nonminimum phase and time delayed systems, result in a better performance, improving robustness and tracking accuracy.
Stable predictive control horizons
NASA Astrophysics Data System (ADS)
Estrada, Raúl; Favela, Antonio; Raimondi, Angelo; Nevado, Antonio; Requena, Ricardo; Beltrán-Carbajal, Francisco
2012-04-01
The stability theory of predictive and adaptive predictive control for processes of linear and stable nature is based on the hypothesis of a physically realisable driving desired trajectory (DDT). The formal theoretical verification of this hypothesis is trivial for processes with a stable inverse, but it is not for processes with an unstable inverse. The extended strategy of predictive control was developed with the purpose of overcoming methodologically this stability problem and it has delivered excellent performance and stability in its industrial applications given a suitable choice of the prediction horizon. From a theoretical point of view, the existence of a prediction horizon capable of ensuring stability for processes with an unstable inverse was proven in the literature. However, no analytical solution has been found for the determination of the prediction horizon values which guarantee stability, in spite of the theoretical and practical interest of this matter. This article presents a new method able to determine the set of prediction horizon values which ensure stability under the extended predictive control strategy formulation and a particular performance criterion for the design of the DDT generically used in many industrial applications. The practical application of this method is illustrated by means of simulation examples.
NASA Astrophysics Data System (ADS)
Kawase, Hiroshi; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi
2016-02-01
An effective solution to the continuous Internet traffic expansion is to offload traffic to lower layers such as the L2 or L1 optical layers. One possible approach is to introduce dynamic optical path operations such as adaptive establishment/tear down according to traffic variation. Path operations cannot be done instantaneously; hence, traffic prediction is essential. Conventional prediction techniques need optimal parameter values to be determined in advance by averaging long-term variations from the past. However, this does not allow adaptation to the ever-changing short-term variations expected to be common in future networks. In this paper, we propose a real-time optical path control method based on a machinelearning technique involving support vector machines (SVMs). A SVM learns the most recent traffic characteristics, and so enables better adaptation to temporal traffic variations than conventional techniques. The difficulty lies in determining how to minimize the time gap between optical path operation and buffer management at the originating points of those paths. The gap makes the required learning data set enormous and the learning process costly. To resolve the problem, we propose the adoption of multiple SVMs running in parallel, trained with non-overlapping subsets of the original data set. The maximum value of the outputs of these SVMs will be the estimated number of necessary paths. Numerical experiments prove that our proposed method outperforms a conventional prediction method, the autoregressive moving average method with optimal parameter values determined by Akaike's information criterion, and reduces the packet-loss ratio by up to 98%.
To predict sufentanil requirement for postoperative pain control using a real-time method
Zhang, Yuhao; Duan, Guangyou; Guo, Shanna; Ying, Ying; Huang, Penghao; Zhang, Mi; Li, Ningbo; Zhang, Xianwei
2016-01-01
Abstract Preoperative identification of individual sensitivity to opioid analgesics could improve the quality of postoperative analgesia. We explored the feasibility and utility of a real-time assessment of sufentanil sensitivity in predicting postoperative analgesic requirement. Our primary study included 111 patients who underwent measurements of pressure and quantitative pricking pain thresholds before and 5 minutes after sufentanil infusion. Pain intensity was assessed during the first 24-hour postsurgery, and patients who reported inadequate levels of analgesia were excluded from the study. The sufentanil requirement for patient-controlled analgesia was recorded, and a subsequent exploratory study of 20 patients facilitated the interpretation of the primary study results. In the primary study, experimental pain thresholds increased (P < 0.001) 5 minutes after sufentanil infusion, and the percent change in pricking pain threshold was positively associated with sufentanil requirement at 12 and 24 hours after surgery (β = 0.318, P = 0.001; and β = 0.335, P = 0.001). A receiver-operating characteristic curve analysis showed that patients with a change in pricking pain threshold >188% were >50% likely to require more sufentanil for postoperative pain control. In the exploratory study, experimental pain thresholds significantly decreased after the operation (P < 0.001), and we observed a positive correlation (P < 0.001) between the percent change in pricking pain threshold before and after surgery. Preoperative detection of individual sensitivity to sufentanil via the above described real-time method was effective in predicting postoperative sufentanil requirement. Thus, percent change in pricking pain threshold might be a feasible predictive marker of postoperative analgesia requirement. PMID:27336880
Buceta, David; Tojo, Concha; Vukmirovic, Miomir B; Deepak, Francis Leonard; López-Quintela, M Arturo
2015-07-14
We present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at subnanometer resolution simply by changing the reactant concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad hoc controlled nanostructures.
On identified predictive control
NASA Technical Reports Server (NTRS)
Bialasiewicz, Jan T.
1993-01-01
Self-tuning control algorithms are potential successors to manually tuned PID controllers traditionally used in process control applications. A very attractive design method for self-tuning controllers, which has been developed over recent years, is the long-range predictive control (LRPC). The success of LRPC is due to its effectiveness with plants of unknown order and dead-time which may be simultaneously nonminimum phase and unstable or have multiple lightly damped poles (as in the case of flexible structures or flexible robot arms). LRPC is a receding horizon strategy and can be, in general terms, summarized as follows. Using assumed long-range (or multi-step) cost function the optimal control law is found in terms of unknown parameters of the predictor model of the process, current input-output sequence, and future reference signal sequence. The common approach is to assume that the input-output process model is known or separately identified and then to find the parameters of the predictor model. Once these are known, the optimal control law determines control signal at the current time t which is applied at the process input and the whole procedure is repeated at the next time instant. Most of the recent research in this field is apparently centered around the LRPC formulation developed by Clarke et al., known as generalized predictive control (GPC). GPC uses ARIMAX/CARIMA model of the process in its input-output formulation. In this paper, the GPC formulation is used but the process predictor model is derived from the state space formulation of the ARIMAX model and is directly identified over the receding horizon, i.e., using current input-output sequence. The underlying technique in the design of identified predictive control (IPC) algorithm is the identification algorithm of observer/Kalman filter Markov parameters developed by Juang et al. at NASA Langley Research Center and successfully applied to identification of flexible structures.
Steam turbine start up method based on predictive monitoring and control of thermal stresses
Matsumura, J.; Matsumoto, H.; Niyawara, S.; Urushidani, H.
1985-04-01
A turbine start up program decision and control method based on rotor thermal stresses has been developed. This method featured scheduling punctuality for the start up program, in addition to the start up optimization, and was especially suited to daily start up and shut down (DSS) units. The method was applied to a 375MW DSS unit which verified its effectiveness.
Buceta, David; Tojo, Concha; Vukmirovic, Miomir B.; Deepak, F. Leonard; Lopez-Quintela, M. Arturo
2015-06-02
In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.
Improved nonlinear prediction method
NASA Astrophysics Data System (ADS)
Adenan, Nur Hamiza; Md Noorani, Mohd Salmi
2014-06-01
The analysis and prediction of time series data have been addressed by researchers. Many techniques have been developed to be applied in various areas, such as weather forecasting, financial markets and hydrological phenomena involving data that are contaminated by noise. Therefore, various techniques to improve the method have been introduced to analyze and predict time series data. In respect of the importance of analysis and the accuracy of the prediction result, a study was undertaken to test the effectiveness of the improved nonlinear prediction method for data that contain noise. The improved nonlinear prediction method involves the formation of composite serial data based on the successive differences of the time series. Then, the phase space reconstruction was performed on the composite data (one-dimensional) to reconstruct a number of space dimensions. Finally the local linear approximation method was employed to make a prediction based on the phase space. This improved method was tested with data series Logistics that contain 0%, 5%, 10%, 20% and 30% of noise. The results show that by using the improved method, the predictions were found to be in close agreement with the observed ones. The correlation coefficient was close to one when the improved method was applied on data with up to 10% noise. Thus, an improvement to analyze data with noise without involving any noise reduction method was introduced to predict the time series data.
Burin des Roziers, Thibaut
1999-08-01
The purpose of the work is to test and show how well the numerical method called Optima Prediction works. This method is relatively new and only a few experiment have been made. The authors first did a series of simple tests to see how the method behaves. In order to have a better understanding of the method, they then reproduced one of the main experiment which was done about Optimal Prediction by Kupferman. Once they obtained the same results that Kupferman had, they changed a few parameters to see how dependant the method was on this parameters. In this paper, they will present all the tests they made, the results they obtained and what they concluded about the method. Before talking about the experiments, they have to explain what is the Optimal Prediction method and how does it work. This will be done in the first section of this paper.
NASA Technical Reports Server (NTRS)
Padfield, G. D.; Duval, R. K.
1982-01-01
A set of results on rotorcraft system identification is described. Flight measurements collected on an experimental Puma helicopter are reviewed and some notable characteristics highlighted. Following a brief review of previous work in rotorcraft system identification, the results of state estimation and model structure estimation processes applied to the Puma data are presented. The results, which were obtained using NASA developed software, are compared with theoretical predictions of roll, yaw and pitching moment derivatives for a 6 degree of freedom model structure. Anomalies are reported. The theoretical methods used are described. A framework for reduced order modelling is outlined.
Adjoint Method and Predictive Control for 1-D Flow in NASA Ames 11-Foot Transonic Wind Tunnel
NASA Technical Reports Server (NTRS)
Nguyen, Nhan; Ardema, Mark
2006-01-01
This paper describes a modeling method and a new optimal control approach to investigate a Mach number control problem for the NASA Ames 11-Foot Transonic Wind Tunnel. The flow in the wind tunnel is modeled by the 1-D unsteady Euler equations whose boundary conditions prescribe a controlling action by a compressor. The boundary control inputs to the compressor are in turn controlled by a drive motor system and an inlet guide vane system whose dynamics are modeled by ordinary differential equations. The resulting Euler equations are thus coupled to the ordinary differential equations via the boundary conditions. Optimality conditions are established by an adjoint method and are used to develop a model predictive linear-quadratic optimal control for regulating the Mach number due to a test model disturbance during a continuous pitch
NASA Astrophysics Data System (ADS)
Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.
2014-10-01
In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.
Huang, Y.; Pepe, M. S.
2010-01-01
Summary To assess the value of a continuous marker in predicting the risk of a disease, a graphical tool called the predictiveness curve has been proposed. It characterizes the marker’s predictiveness, or capacity to risk stratify the population by displaying the distribution of risk endowed by the marker. Methods for making inference about the curve and for comparing curves in a general population have been developed. However, knowledge about a marker’s performance in the general population only is not enough. Since a marker’s effect on the risk model and its distribution can both differ across subpopulations, its predictiveness may vary when applied to different subpopulations. Moreover, information about the predictiveness of a marker conditional on baseline covariates is valuable for individual decision making about having the marker measured or not. Therefore, to fully realize the usefulness of a risk prediction marker, it is important to study its performance conditional on covariates. In this article, we propose semiparametric methods for estimating covariate-specific predictiveness curves for a continuous marker. Unmatched and matched case-control study designs are accommodated. We illustrate application of the methodology by evaluating serum creatinine as a predictor of risk of renal artery stenosis. PMID:21562626
Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y
2014-05-01
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.
Deadbeat Predictive Controllers
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Phan, Minh
1997-01-01
Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.
Location predicting methods for UAVs
NASA Astrophysics Data System (ADS)
Peng, Xiaodong; Zhang, Yu
2017-08-01
Location prediction of unmanned aerial vehicle (UAV) is important for fighting with its enemy and ensuring its normal operation. This paper presents the motion model of UAVs and reduces the state space into 7 dimensions. The Bayesian Network, Markov Chain, Curve Fitting and Neural Network are introduced for designing predicting methods. Then Curve Fitting Predicting method, Markov Chain Predicting method, Bayesian Network Predicting method and Neural Network Predicting method are designed for UAVs. The simulation result shows that 1) Neural Network Predicting method has highest predicting accuracy; 2) Markov Chain Predicting method and Bayesian Network Predicting method methods have similar performance and both are better than Bayesian Network Predicting method methods; 3) Neural Network Predicting method is the first choice when predicting the locations of UAVs.
Kerr, Clayton R
2002-04-01
A method for predicting the performance of packed columns that control gaseous air pollutants has been developed that exploits the advances in both computer software and hardware commonly used by practicing engineers. The solution of the simultaneous partial differential equations that describe the absorption process in packed columns that occurs in the presence of chemical reaction is obtained by converting the partial differential equations to systems of ordinary differential equations. These systems of ordinary differential equations are then solved using the method of lines along with a variable step, variable order numerical method. The method is applicable to systems in which there are multiple reactions within the liquid phase. The reactions can be of any order and can be reversible. The programming is simple and the machine running time is minimal. The method is illustrated here with an example.
NASA Astrophysics Data System (ADS)
Na, Xiaoxiang; Cole, David J.
2013-02-01
This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy.
Motor degradation prediction methods
Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.
1996-12-01
Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor`s duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures.
Porsa, Sina; Lin, Yi-Chung; Pandy, Marcus G
2016-08-01
The aim of this study was to compare the computational performances of two direct methods for solving large-scale, nonlinear, optimal control problems in human movement. Direct shooting and direct collocation were implemented on an 8-segment, 48-muscle model of the body (24 muscles on each side) to compute the optimal control solution for maximum-height jumping. Both algorithms were executed on a freely-available musculoskeletal modeling platform called OpenSim. Direct collocation converged to essentially the same optimal solution up to 249 times faster than direct shooting when the same initial guess was assumed (3.4 h of CPU time for direct collocation vs. 35.3 days for direct shooting). The model predictions were in good agreement with the time histories of joint angles, ground reaction forces and muscle activation patterns measured for subjects jumping to their maximum achievable heights. Both methods converged to essentially the same solution when started from the same initial guess, but computation time was sensitive to the initial guess assumed. Direct collocation demonstrates exceptional computational performance and is well suited to performing predictive simulations of movement using large-scale musculoskeletal models.
NASA Astrophysics Data System (ADS)
Niewiadomska-Szynkiewicz, Ewa; Malinowski, Krzysztof; Karbowski, Andrzej
1996-04-01
Predictive methods for real-time flood operation of water systems consisting of reservoirs located in parallel on tributaries to the main river are presented and discussed. The aspect of conflicting individual goals of the local decision units and other objectives important from an overall point of view is taken into account. The particular attention is focused on hierarchical control structure which provides framework for organization of an on-line reservoir management problem. The important factor involved in flood control the uncertainty with respect to future inflows is taken into consideration. A case study of the upper Vistula river basin system in the southern part of Poland is presented. Simulation results based on 11 historical floods are briefly described and discussed.
NASA Astrophysics Data System (ADS)
Bauerheim, M.; Nicoud, F.; Poinsot, T.
2016-02-01
Longitudinal low-frequency thermoacoustic unstable modes in combustion chambers have been intensively studied experimentally, numerically, and theoretically, leading to significant progress in both understanding and controlling these acoustic modes. However, modern annular gas turbines may also exhibit azimuthal modes, which are much less studied and feature specific mode structures and dynamic behaviors, leading to more complex situations. Moreover, dealing with 10-20 burners mounted in the same chamber limits the use of high fidelity simulations or annular experiments to investigate these modes because of their complexity and costs. Consequently, for such circumferential acoustic modes, theoretical tools have been developed to uncover underlying phenomena controlling their stability, nature, and dynamics. This review presents recent progress in this field. First, Galerkin and network models are described with their pros and cons in both the temporal and frequency framework. Then, key features of such acoustic modes are unveiled, focusing on their specificities such as symmetry breaking, non-linear modal coupling, forcing by turbulence. Finally, recent works on uncertainty quantifications, guided by theoretical studies and applied to annular combustors, are presented. The objective is to provide a global view of theoretical research on azimuthal modes to highlight their complexities and potential.
NASA Technical Reports Server (NTRS)
Tibbetts, J. G.
1979-01-01
Methods for predicting noise at any point on an aircraft while the aircraft is in a cruise flight regime are presented. Developed for use in laminar flow control (LFC) noise effects analyses, they can be used in any case where aircraft generated noise needs to be evaluated at a location on an aircraft while under high altitude, high speed conditions. For each noise source applicable to the LFC problem, a noise computational procedure is given in algorithm format, suitable for computerization. Three categories of noise sources are covered: (1) propulsion system, (2) airframe, and (3) LFC suction system. In addition, procedures are given for noise modifications due to source soundproofing and the shielding effects of the aircraft structure wherever needed. Sample cases, for each of the individual noise source procedures, are provided to familiarize the user with typical input and computed data.
Data-Based Predictive Control with Multirate Prediction Step
NASA Technical Reports Server (NTRS)
Barlow, Jonathan S.
2010-01-01
Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.
Predictive Control of Speededness in Adaptive Testing
ERIC Educational Resources Information Center
van der Linden, Wim J.
2009-01-01
An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the…
Predictive Control of Speededness in Adaptive Testing
ERIC Educational Resources Information Center
van der Linden, Wim J.
2009-01-01
An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the…
... Z Health Topics Birth control methods Birth control methods > A-Z Health Topics Birth control methods fact ... Publications email updates Enter email Submit Birth control methods Birth control (contraception) is any method, medicine, or ...
1994-12-31
This conference was held December 4--8, 1994 in Asilomar, California. The purpose of this meeting was to provide a forum for exchange of state-of-the-art information concerning the prediction of protein structure. Attention if focused on the following: comparative modeling; sequence to fold assignment; and ab initio folding.
NASA Technical Reports Server (NTRS)
Jones, R. T.; Sternfield, L.
1976-01-01
A method is suggested for predicting the stability of automatically controlled aircraft by a comparison of calculated frequency-response curves for the aircraft and experimentally determined frequency-response curves for the automatic pilot. The method is applied only to stabilization in roll. The method is expected to be useful as a means of establishing the specifications of the performance required of the automatic control device for pilotless aircraft designed as missiles.
Simulation analysis of adaptive cruise prediction control
NASA Astrophysics Data System (ADS)
Zhang, Li; Cui, Sheng Min
2017-09-01
Predictive control is suitable for multi-variable and multi-constraint system control.In order to discuss the effect of predictive control on the vehicle longitudinal motion, this paper establishes the expected spacing model by combining variable pitch spacing and the of safety distance strategy. The model predictive control theory and the optimization method based on secondary planning are designed to obtain and track the best expected acceleration trajectory quickly. Simulation models are established including predictive and adaptive fuzzy control. Simulation results show that predictive control can realize the basic function of the system while ensuring the safety. The application of predictive and fuzzy adaptive algorithm in cruise condition indicates that the predictive control effect is better.
Carney, Paul R.; Myers, Stephen; Geyer, James D.
2011-01-01
Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. PMID:22078526
Adaptive, predictive controller for optimal process control
Brown, S.K.; Baum, C.C.; Bowling, P.S.; Buescher, K.L.; Hanagandi, V.M.; Hinde, R.F. Jr.; Jones, R.D.; Parkinson, W.J.
1995-12-01
One can derive a model for use in a Model Predictive Controller (MPC) from first principles or from experimental data. Until recently, both methods failed for all but the simplest processes. First principles are almost always incomplete and fitting to experimental data fails for dimensions greater than one as well as for non-linear cases. Several authors have suggested the use of a neural network to fit the experimental data to a multi-dimensional and/or non-linear model. Most networks, however, use simple sigmoid functions and backpropagation for fitting. Training of these networks generally requires large amounts of data and, consequently, very long training times. In 1993 we reported on the tuning and optimization of a negative ion source using a special neural network[2]. One of the properties of this network (CNLSnet), a modified radial basis function network, is that it is able to fit data with few basis functions. Another is that its training is linear resulting in guaranteed convergence and rapid training. We found the training to be rapid enough to support real-time control. This work has been extended to incorporate this network into an MPC using the model built by the network for predictive control. This controller has shown some remarkable capabilities in such non-linear applications as continuous stirred exothermic tank reactors and high-purity fractional distillation columns[3]. The controller is able not only to build an appropriate model from operating data but also to thin the network continuously so that the model adapts to changing plant conditions. The controller is discussed as well as its possible use in various of the difficult control problems that face this community.
Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems
NASA Technical Reports Server (NTRS)
Kelkar, Atul G.
2000-01-01
The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.
A Course in... Model Predictive Control.
ERIC Educational Resources Information Center
Arkun, Yaman; And Others
1988-01-01
Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)
A Course in... Model Predictive Control.
ERIC Educational Resources Information Center
Arkun, Yaman; And Others
1988-01-01
Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)
NASA Astrophysics Data System (ADS)
Chen, B.; Su, J. H.; Guo, L.; Chen, J.
2017-06-01
This paper puts forward a maximum power estimation method based on the photovoltaic array (PVA) model to solve the optimization problems about group control of the PV water pumping systems (PVWPS) at the maximum power point (MPP). This method uses the improved genetic algorithm (GA) for model parameters estimation and identification in view of multi P-V characteristic curves of a PVA model, and then corrects the identification results through least square method. On this basis, the irradiation level and operating temperature under any condition are able to estimate so an accurate PVA model is established and the MPP none-disturbance estimation is achieved. The simulation adopts the proposed GA to determine parameters, and the results verify the accuracy and practicability of the methods.
Predictive fuzzy controller for robotic motion control
Huang, S.J.; Hu, C.F.
1995-12-31
A system output prediction strategy incorporated with a fuzzy controller is proposed to manipulate the robotic motion control. Usually, the current position and velocity errors are used to operate the fuzzy logic controller for picking out a corresponding rule. When the system has fast planning speed or time varying behavior, the required tracking accuracy is difficult to achieve by adjusting the fuzzy rules. In order to improve the position control accuracy and system robustness for the industrial application, the current position error in the fuzzy rules look-up table is substituted by the predictive position error of the next step by using the grey predictive algorithm. This idea is implemented on a five degrees of freedom robot. The experimental results show that this fuzzy controller has effectively improve the system performance and achieved the facilitation of fuzzy controller implementation.
Coupled rotor/airframe vibration prediction methods
NASA Technical Reports Server (NTRS)
Staley, J. A.; Sciarra, J. J.
1974-01-01
The problems of airframe structural dynamic representation and effects of coupled rotor/airframe vibration are discussed. Several finite element computer programs (including NASTRAN) and methods for idealization and computation of airframe natural modes and frequencies and forced response are reviewed. Methods for obtaining a simultaneous rotor and fuselage vibratory response, determining effectiveness of vibration control devices, and energy methods for structural optimization are also discussed. Application of these methods is shown for the vibration prediction of the model 347 helicopter.
Model predictive control: A new approach
NASA Astrophysics Data System (ADS)
Nagy, Endre
2017-01-01
New methods are proposed in this paper for solution of the model predictive control problem. Nonlinear state space design techniques are also treated. For nonlinear state prediction (state evolution computation) a new predictor given with an operator is introduced and tested. Settling the model predictive control problem may be obtained through application of the principle "direct stochastic optimum tracking" with a simple algorithm, which can be derived from a previously developed optimization procedure. The final result is obtained through iterations. Two examples show the applicability and advantages of the method.
Predicting and Controlling School Violence.
ERIC Educational Resources Information Center
Rich, John Martin
1992-01-01
Discusses the extent to which violence can be accurately predicted, suggesting interventions, control, and remediation. The educator's role in reducing violence includes dealing with the school, parents, media, and community. Educators need conflict resolution skills for defusing aggression and establishing better relations. (SM)
Richardson, A.S. Jr.
1983-10-15
This has been a technical review and extension of the Windamper methodology for controlling galloping (ice buildup on windward side). The new material consists of three main parts. First, there is the review and analysis of single conductor by coupled two-degree-of-freedom analysis. Second, there is the extension of that method to the bundled conductor. Third, there is the report of the new Windamper aerodynamic data (Appendix). The analytical work supports the general hypothesis and theory of Den Hartog. The Windamper appears to act to stabilize conductors which would otherwise become unstable when lift slope is negative. When lift slope is positive, a flutter type of gallop may be identified, owing to lee-side location of the damper c.g. In single conductors, the realization of the flutter gallop requires very large positive lift slope, and the torsion component dominates the motion which also would limit gallop amplitude. In bundled conductors, the realization of the flutter gallop requires only small positive lift slope because of the proximity of the two natural frequencies to each other. Very little torsion is present in the bundle motion, and the Windamper aerodynamic drag produces a net flow of energy to damp the motion.
Inhibitory Control Predicts Grammatical Ability
Ibbotson, Paul; Kearvell-White, Jennifer
2015-01-01
We present evidence that individual variation in grammatical ability can be predicted by individual variation in inhibitory control. We tested 81 5-year-olds using two classic tests from linguistics and psychology (Past Tense and the Stroop). Inhibitory control was a better predicator of grammatical ability than either vocabulary or age. Our explanation is that giving the correct response in both tests requires using a common cognitive capacity to inhibit unwanted competition. The implications are that understanding the developmental trajectory of language acquisition can benefit from integrating the developmental trajectory of non-linguistic faculties, such as executive control. PMID:26659926
Model Predictive Control of Fractional Order Systems.
Rhouma, Aymen; Bouani, Faouzi; Bouzouita, Badreddine; Ksouri, Mekki
2014-07-01
This paper provides the model predictive control (MPC) of fractional order systems. The direct method will be used as internal model to predict the future dynamic behavior of the process, which is used to achieve the control law. This method is based on the Grünwald-Letnikov's definition that consists of replacing the noninteger derivation operator of the adopted system representation by a discrete approximation. The performances and the efficiency of this approach are illustrated with practical results on a thermal system and compared to the MPC based on the integer ARX model.
Predictive control and estimation - State space approach
NASA Technical Reports Server (NTRS)
Gawronski, W.
1991-01-01
A modified output prediction procedure and a new controller design based on the predictive control law are presented. A new predictive estimator enhances system performance. The predictive controller was designed and applied to the tracking control of the NASA/JPL 70-m antenna. Simulation results show significant improvement in tracking performance over the linear quadratic controller and estimator presently in use.
Novel hyperspectral prediction method and apparatus
NASA Astrophysics Data System (ADS)
Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf
2009-05-01
Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.
Wilson, David G [Tijeras, NM; Robinett, III, Rush D.
2012-02-21
A control system design method and concomitant control system comprising representing a physical apparatus to be controlled as a Hamiltonian system, determining elements of the Hamiltonian system representation which are power generators, power dissipators, and power storage devices, analyzing stability and performance of the Hamiltonian system based on the results of the determining step and determining necessary and sufficient conditions for stability of the Hamiltonian system, creating a stable control system based on the results of the analyzing step, and employing the resulting control system to control the physical apparatus.
Fast predictive control of networked energy systems
NASA Astrophysics Data System (ADS)
Chuang, Frank Fu-Han
forecasts. We show that the exact control policies can be found efficiently for certain types of robust predictive control problems. The second half of the thesis deals with improving computation speed and memory usage through model reduction and exploitation of symmetries in the predictive control problem. We present a model reduction technique tailored for networked systems which preserves sparsity and thus greatly improves computation speed. We also show how to apply symmetry methods for efficient computation of explicit model predictive controllers to systems which are not perfectly symmetric.
Lindley, C; Mackowiak, J
1985-01-01
Various methods for controlling inventory are described, and the advantages and disadvantages of each are discussed. The open-to-buy (OTB) budget method limits purchases to a specific amount of funds available for purchasing pharmaceuticals during a specified period. The emphasis of the OTB method is financial control of the pharmacy inventory. Although it is useful in monitoring and adjusting the dollar value of the inventory, it should be combined with other methods for a total inventory control system. The primary emphasis of the short-list method is to provide accurate and timely inventory information to the person responsible for order placement. The short list identifies the items that are in short supply. It is the most common feedback and control mechanism in use, but it is best suited for settings where duplicate or reserve stock is maintained and monitored by more rigorous methods. The main objective of the minimum and maximum method is to determine when and how much to order of each item. It also provides limited dollar control. The major disadvantage of this method is the time it requires to establish the minimum and maximum levels and to update them regularly to reflect changes in demand. The stock record card method is used to record information on the movement of goods in and out of the storage area. Stock cards can also be used to monitor inventory levels and facilitate order initiation. It is probably the optimum method to be used alone. The most effective system of inventory control is one employing a combination of these methods tailored to meet the institution's needs and available resources.
Predictive Control of Large Complex Networks
NASA Astrophysics Data System (ADS)
Haber, Aleksandar; Motter, Adilson E.
Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.
Model Predictive Control of Sewer Networks
NASA Astrophysics Data System (ADS)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.
2017-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.
Predictive Thermal Control Technology for Stable Telescope
NASA Astrophysics Data System (ADS)
Stahl, H. Philip
the SOA by developing a predictive control method that uses a thermal 'model in the loop' to control the thermal system. Our goal is to demonstrate a 'system technology solution that enables a thermally stable telescope that keeps the telescope at a constant temperature independent of where it looks on the sky.
Broadband Noise Control Using Predictive Techniques
NASA Technical Reports Server (NTRS)
Eure, Kenneth W.; Juang, Jer-Nan
1997-01-01
Predictive controllers have found applications in a wide range of industrial processes. Two types of such controllers are generalized predictive control and deadbeat control. Recently, deadbeat control has been augmented to include an extended horizon. This modification, named deadbeat predictive control, retains the advantage of guaranteed stability and offers a novel way of control weighting. This paper presents an application of both predictive control techniques to vibration suppression of plate modes. Several system identification routines are presented. Both algorithms are outlined and shown to be useful in the suppression of plate vibrations. Experimental results are given and the algorithms are shown to be applicable to non- minimal phase systems.
Yuan, Jing; Gao, Yanan; Liu, Tiaotiao; Wang, Xinyu; Liu, Hongzhuo; Li, Sanming
2015-01-01
Ion-exchange fiber undergoes a stoichiometric exchange reaction and has large exchange capability, which makes it a promising candidate as a multiple drug carrier. Because combinatorial effects can act synergistically, additively or antagonistically depending on the ratio of the agents being combined, the objective of this study was to learn the dual drug loading of ion-exchange fiber and develop a mathematical method for precisely control of the loading amount. Atenolol and Gatifloxacin, with different loading behaviors into strong cationic ion-exchange fiber ZB-1, were used to build a representative of dual loading. Not suitable pH value of drug solutions could make simultaneous loading fail, while the change of drug solution volume hardly affected the equilibrium. Ion-exchange groups occupied by the drug which owned lower affinity to fiber could be grabbed by the higher affinity drug, indicating the existence of competition between drugs. Thermodynamic model was introduced to guide the loading prediction and a favorable relevance had been shown between determined and predicted data. The release behaviors of each drug from dual drug-fiber complex were similar to those from single drug-fiber complexes.
NASA Astrophysics Data System (ADS)
Matetic, Rudy J.
Over-exposure to noise remains a widespread and serious health hazard in the U.S. mining industries despite 25 years of regulation. Every day, 80% of the nation's miners go to work in an environment where the time weighted average (TWA) noise level exceeds 85 dBA and more than 25% of the miners are exposed to a TWA noise level that exceeds 90 dBA, the permissible exposure limit (PEL). Additionally, MSHA coal noise sample data collected from 2000 to 2002 show that 65% of the equipment whose operators exceeded 100% noise dosage comprise only seven different types of machines; auger miners, bulldozers, continuous miners, front end loaders, roof bolters, shuttle cars (electric), and trucks. In addition, the MSHA data indicate that the roof bolter is third among all the equipment and second among equipment in underground coal whose operators exceed 100% dosage. A research program was implemented to: (1) determine, characterize and to measure sound power levels radiated by a roof bolting machine during differing drilling configurations (thrust, rotational speed, penetration rate, etc.) and utilizing differing types of drilling methods in high compressive strength rock media (>20,000 psi). The research approach characterized the sound power level results from laboratory testing and provided the mining industry with empirical data relative to utilizing differing noise control technologies (drilling configurations and types of drilling methods) in reducing sound power level emissions on a roof bolting machine; (2) distinguish and correlate the empirical data into one, statistically valid, equation, in which, provided the mining industry with a tool to predict overall sound power levels of a roof bolting machine given any type of drilling configuration and drilling method utilized in industry; (3) provided the mining industry with several approaches to predict or determine sound pressure levels in an underground coal mine utilizing laboratory test results from a roof bolting
Predictive sensor method and apparatus
NASA Technical Reports Server (NTRS)
Cambridge, Vivien J.; Koger, Thomas L.
1993-01-01
A microprocessor and electronics package employing predictive methodology was developed to accelerate the response time of slowly responding hydrogen sensors. The system developed improved sensor response time from approximately 90 seconds to 8.5 seconds. The microprocessor works in real-time providing accurate hydrogen concentration corrected for fluctuations in sensor output resulting from changes in atmospheric pressure and temperature. Following the successful development of the hydrogen sensor system, the system and predictive methodology was adapted to a commercial medical thermometer probe. Results of the experiment indicate that, with some customization of hardware and software, response time improvements are possible for medical thermometers as well as other slowly responding sensors.
Predictive sensor method and apparatus
NASA Technical Reports Server (NTRS)
Nail, William L. (Inventor); Koger, Thomas L. (Inventor); Cambridge, Vivien (Inventor)
1990-01-01
A predictive algorithm is used to determine, in near real time, the steady state response of a slow responding sensor such as hydrogen gas sensor of the type which produces an output current proportional to the partial pressure of the hydrogen present. A microprocessor connected to the sensor samples the sensor output at small regular time intervals and predicts the steady state response of the sensor in response to a perturbation in the parameter being sensed, based on the beginning and end samples of the sensor output for the current sample time interval.
Prediction of ozone concentrations using nonlinear prediction method
NASA Astrophysics Data System (ADS)
Abd Hamid, Nor Zila; Md Noorani, Mohd Salmi; Juneng, Liew; Latif, Mohd Talib
2013-04-01
Prediction of ozone (O3) is very important because O3 gives effects on human health, human activities and more. Nonlinear prediction method, a method which was developed based on the idea comes from chaos theory is used to predict the concentrations of O3. There are two steps in the nonlinear prediction method. First is the reconstruction of the observed data from the form of a one-dimensional to multi-dimensional phase space. Second is the prediction of the reconstructed phase space through local linear approximation method. Hourly O3 concentrations observed in Shah Alam city located in the state of Selangor, Malaysia were studied. Predictions found in a close agreement with those observed ones. The value of the correlation coefficient obtained in this study is 0.9097. This demonstrates the suitability of the nonlinear prediction method to predict the hourly concentrations of O3. At the end of this paper, suggestions were made for better prediction in the future.
Predicting Loss-of-Control Boundaries Toward a Piloting Aid
NASA Technical Reports Server (NTRS)
Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje
2012-01-01
This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.
Choosing the appropriate forecasting model for predictive parameter control.
Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars
2014-01-01
All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.
Interim prediction method for jet noise
NASA Technical Reports Server (NTRS)
Stone, J. R.
1974-01-01
A method is provided for predicting jet noise for a wide range of nozzle geometries and operating conditions of interest for aircraft engines. Jet noise theory, data and existing prediction methods was reviewed, and based on this information a interim method of jet noise prediction is proposed. Problem areas are idenified where further research is needed to improve the prediction method. This method predicts only the noise generated by the exhaust jets mixing with the surrounding air and does not include other noises emanating from the engine exhaust, such as combustion and machinery noise generated inside the engine (i.e., core noise). It does, however, include thrust reverser noise. Prediction relations are provided for conical nozzles, plug nozzles, coaxial nozzles and slot nozzles.
Protein Residue Contacts and Prediction Methods.
Adhikari, Badri; Cheng, Jianlin
2016-01-01
In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch.In this chapter, we briefly discuss many elements of protein residue-residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated.
NASA Technical Reports Server (NTRS)
Ho, K. K.; Moody, G. B.; Peng, C. K.; Mietus, J. E.; Larson, M. G.; Levy, D.; Goldberger, A. L.
1997-01-01
BACKGROUND: Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted. METHODS AND RESULTS: We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7+/-8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD (P<.01), LF (P<.01), VLF (P<.05), and TP (P<.01) and the nonlinear measure DFA (P<.05) were predictors of survival over a mean follow-up period of 1.9 years; other measures, including ApEn (P>.3), were not. In multivariable models, DFA was of borderline predictive significance (P=.06) after adjustment for the diagnosis of CHF and SD. CONCLUSIONS: These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.
NASA Technical Reports Server (NTRS)
Ho, K. K.; Moody, G. B.; Peng, C. K.; Mietus, J. E.; Larson, M. G.; Levy, D.; Goldberger, A. L.
1997-01-01
BACKGROUND: Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted. METHODS AND RESULTS: We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7+/-8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD (P<.01), LF (P<.01), VLF (P<.05), and TP (P<.01) and the nonlinear measure DFA (P<.05) were predictors of survival over a mean follow-up period of 1.9 years; other measures, including ApEn (P>.3), were not. In multivariable models, DFA was of borderline predictive significance (P=.06) after adjustment for the diagnosis of CHF and SD. CONCLUSIONS: These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.
Model Predictive Control for Nonlinear Parabolic Partial Differential Equations
NASA Astrophysics Data System (ADS)
Hashimoto, Tomoaki; Yoshioka, Yusuke; Ohtsuka, Toshiyuki
In this study, the optimal control problem of nonlinear parabolic partial differential equations (PDEs) is investigated. Optimal control of nonlinear PDEs is an open problem with applications that include fluid, thermal, biological, and chemically-reacting systems. Model predictive control with a fast numerical solution method has been well established to solve the optimal control problem of nonlinear systems described by ordinary differential equations. In this study, we develop a design method of the model predictive control for nonlinear systems described by parabolic PDEs. Our approach is a direct infinite dimensional extension of the model predictive control method for finite-dimensional systems. The objective of this paper is to develop an efficient algorithm for numerically solving the model predictive control problem of nonlinear parabolic PDEs. The effectiveness of the proposed method is verified by numerical simulations.
Drawability Prediction Method using Continuous Texture Evolution Model
NASA Astrophysics Data System (ADS)
Morimoto, Toshiharu; Yanagimoto, Jun
2011-08-01
Drawability is one of steel strip properties which control press forming. Many predicted method for the Lankford value have been proposed. First, we predict recystallization texture based on the idea that total amount of microscopic crystal slips is proportional to accumulated dislocation density in grain boundaries. Next, we can predict Lankford value of ultra low carbon strips and ferritic stainless steel strips using Sachs model. Our method is very practical to use in hot and cold steel rolling industry.
Automatic transmission control method
Hasegawa, H.; Ishiguro, T.
1989-07-04
This patent describes a method of controlling an automatic transmission of an automotive vehicle. The transmission has a gear train which includes a brake for establishing a first lowest speed of the transmission, the brake acting directly on a ring gear which meshes with a pinion, the pinion meshing with a sun gear in a planetary gear train, the ring gear connected with an output member, the sun gear being engageable and disengageable with an input member of the transmission by means of a clutch. The method comprises the steps of: detecting that a shift position of the automatic transmission has been shifted to a neutral range; thereafter introducing hydraulic pressure to the brake if present vehicle velocity is below a predetermined value, whereby the brake is engaged to establish the first lowest speed; and exhausting hydraulic pressure from the brake if present vehicle velocity is higher than a predetermined value, whereby the brake is disengaged.
Protein structure prediction using hybrid AI methods
Guan, X.; Mural, R.J.; Uberbacher, E.C.
1993-11-01
This paper describes a new approach for predicting protein structures based on Artificial Intelligence methods and genetic algorithms. We combine nearest neighbor searching algorithms, neural networks, heuristic rules and genetic algorithms to form an integrated system to predict protein structures from their primary amino acid sequences. First we describe our methods and how they are integrated, and then apply our methods to several protein sequences. The results are very close to the real structures obtained by crystallography. Parallel genetic algorithms are also implemented.
Networked Robust Predictive Control Systems Design with Packet Loss
NASA Astrophysics Data System (ADS)
Nguyen, Quang T.; Veselý, Vojtech; Kozáková, Alena; Pakshin, Pavel
2014-01-01
The paper addresses problem of designing a robust output feedback model predictive control for uncertain linear systems over networks with packet-loss. The packet-loss process is arbitrary and bounded by the control horizon of model predictive control. Networked predictive control systems with packet loss are modeled as switched linear systems. This enables us to apply the theory of switched systems to establish the stability condition. The stabilizing controller design is based on sufficient robust stability conditions formulated as a solution of bilinear matrix inequality. Finally, a benchmark numerical example-double integrator is given to illustrate the effectiveness of the proposed method.
Jongejan, F; Uilenberg, G
1994-12-01
Ticks are the most important ectoparasites of livestock in tropical and sub-tropical areas, and are responsible for severe economic losses both through the direct effects of blood sucking and indirectly as vectors of pathogens and toxins. Feeding by large numbers of ticks causes reduction in live weight gain and anaemia among domestic animals, while tick bites also reduce the quality of hides. However, the major losses caused by ticks are due to the ability to transmit protozoan, rickettsial and viral diseases of livestock, which are of great economic importance world-wide. The authors review general aspects of tick biology, the taxonomy, pathogenic effects and vector role of these species, and methods for the control of ticks. The distribution of ticks is continuously changing, as illustrated by the spread of the African tick Amblyomma variegatum in the Caribbean, where a large-scale eradication campaign is now under way.
Numerical Methods for Explosion Plume Predictions
1993-03-12
AD-A262 343 6 NAVSWC TR 91-718 A -22~4..v~w•T ,,-., I II It ill/111111 ti(. NUMERICAL METHODS FOR EXPLOSION PLUME PREDICTIONS BY W.G. SZYMCZAK AND A...METHODS FOR EXPLOSION PLUME PREDICTIONS BY W. G. SZYMCZAK AND A. B. WARDLAW RESEARCH AND TECHNOLOGY DEPARTMENT 12 MARCH 1993 Approved for public release...2 TABLES Table Page 3-1 SHALLOW DEPTH EXPLOSION BUBBLE INITIAL DATA
Modern Prediction Methods for Turbomachine Performance
1976-01-01
Techniques. In Distortion Induced Engine Instability. AGARD LS-72. October 1974. Paper 5. 107. Erdos , John, Alznor, Ldgar, Kalben, Paul , McNally...subject of Modern Prediction Methods for Turbo- machine Performance, is sponsored by the Propulsion and Energetics Panel of AGARD and implemented by...the Consultan’. and Exechange Programme. Propulsion system development costs may be significantly reduced by improvement of methods tor prediction of
H ∞ predictive control of networked control systems
NASA Astrophysics Data System (ADS)
Xia, Yuanqing; Li, Li; Liu, Guo-Ping; Shi, Peng
2011-06-01
This article is concerned with the problem of H ∞ predictive control of networked control system with random network delay. A new control scheme termed networked predictive control is proposed. This scheme mainly consists of the control prediction generator and network delay compensator. While designing the predictor, the control input to the actuator may be different due to networked induced time-delay and data dropout, and two cases are considered depending on the way that the observer obtains the plant control input u t . The necessary and sufficient conditions are given for the closed-loop networked predictive control system to be stochastically stable for different u t and random network delays in controller to actuator channel (CAC) and sensor to controller channel (SCC). A simulation study shows the effectiveness of the proposed scheme.
Prediction and Control in a Dynamic Environment
Osman, Magda; Speekenbrink, Maarten
2011-01-01
The present study compared the accuracy of cue-outcome knowledge gained during prediction-based and control-based learning in stable and unstable dynamic environments. Participants either learnt to make cue-interventions in order to control an outcome, or learnt to predict the outcome from observing changes to the cue values. Study 1 (N = 60) revealed that in tests of control, after a short period of familiarization, performance of Predictors was equivalent to Controllers. Study 2 (N = 28) showed that Controllers showed equivalent task knowledge when to compared to Predictors. Though both Controllers and Predictors showed good performance at test, overall Controllers showed an advantage. The cue-outcome knowledge acquired during learning was sufficiently flexible to enable successful transfer to tests of control and prediction. PMID:22419913
Hierarchical Ensemble Methods for Protein Function Prediction
2014-01-01
Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954
Hierarchical ensemble methods for protein function prediction.
Valentini, Giorgio
2014-01-01
Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware "flat" prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a "consensus" ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research.
Predicting and Controlling Complex Networks
2015-06-22
networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83...of Physics B 76, 179-183 (2010). 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games Biodiversity is
A Robustly Stabilizing Model Predictive Control Algorithm
NASA Technical Reports Server (NTRS)
Ackmece, A. Behcet; Carson, John M., III
2007-01-01
A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.
Methods for Predicting Submersible Hydrodynamic Characteristics
1978-07-01
TO PREDICT COMPLETE CONFIGURATION CHARACTERISTICS In this section the previously developed methods are combined to determine... characteristics of individual vehicle components (bodies, tails ), and with their mutual interactions when combined into complete configurations . Each method is...approach used successfully in mis- sile aerodynamics , a set of models was built and tested to obtain systematic data over relevant ranges of geometry
NASA Astrophysics Data System (ADS)
Mehrmann, Volker; Xu, Hongguo
2000-11-01
We study classical control problems like pole assignment, stabilization, linear quadratic control and H[infinity] control from a numerical analysis point of view. We present several examples that show the difficulties with classical approaches and suggest reformulations of the problems in a more general framework. We also discuss some new algorithmic approaches.
Plasma Stabilization Based on Model Predictive Control
NASA Astrophysics Data System (ADS)
Sotnikova, Margarita
The nonlinear model predictive control algorithms for plasma current and shape stabilization are proposed. Such algorithms are quite suitable for the situations when the plant to be controlled has essentially nonlinear dynamics. Besides that, predictive model based control algorithms allow to take into account a lot of requirements and constraints involved both on the controlled and manipulated variables. The significant drawback of the algorithms is that they require a lot of time to compute control input at each sampling instant. In this paper the model predictive control algorithms are demonstrated by the example of plasma vertical stabilization for ITER-FEAT tokamak. The tuning of parameters of algorithms is performed in order to decrease computational load.
Linear predictive control with state variable constraints
NASA Astrophysics Data System (ADS)
Bdirina, K.; Djoudi, D.; Lagoun, M.
2012-11-01
While linear model predictive control is popular since the 70s of the past century, the 90s have witnessed a steadily increasing attention from control theoretists as well as control practitioners in the area of model predictive control (MPC). The practical interest is driven by the fact that today's processes need to be operated under tighter performance specifications. At the same time more and more constraints, stemming for example from environmental and safety considerations, need to besatisfied. Often these demands can only be met when process constraints are explicitly considered in the controller. Predictive control with constraints appears to be a well suited approach for this kind of problems. In this paper the basic principle of MPC with constraints is reviewed and some of the theoretical, computational, and implementation aspects of MPC are discussed. Furthermore the MPC with constraints was applied to linear example.
Prediction, Control and the Challenge to Complexity
ERIC Educational Resources Information Center
Radford, Mike
2008-01-01
The dominant discourse in research, management and teaching is one that may loosely be characterised as that of prediction and control. The objective of research is to identify causal correlations within policy, management, teaching strategies and educational outcomes that are sufficiently robust as to be able to predict outcomes and make…
Random vibration ESS adequacy prediction method
NASA Astrophysics Data System (ADS)
Lambert, Ronald G.
Closed form analytical expressions have been derived and are used as part of the proposed method to quantitatively predict the adequacy of the random vibration portion of an Environmental Stress Screen (ESS) to meet its main objective for screening typical avionics electronic assemblies for workmanship defects without consuming excessive useful life. This method is limited to fatigue related defects (including initial damage/Fracture Mechanics effects) and requires defect fatigue and service environment parameter values. Examples are given to illustrate the method.
Birth control - slow release methods
... this page: //medlineplus.gov/ency/article/007555.htm Birth control - slow release methods To use the sharing features on this page, please enable JavaScript. Certain birth control methods contain man-made forms of hormones. These ...
Nonconvex model predictive control for commercial refrigeration
NASA Astrophysics Data System (ADS)
Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John
2013-08-01
We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.
Soft Computing Methods for Disulfide Connectivity Prediction
Márquez-Chamorro, Alfonso E.; Aguilar-Ruiz, Jesús S.
2015-01-01
The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods. PMID:26523116
GECluster: a novel protein complex prediction method.
Su, Lingtao; Liu, Guixia; Wang, Han; Tian, Yuan; Zhou, Zhihui; Han, Liang; Yan, Lun
2014-07-04
Identification of protein complexes is of great importance in the understanding of cellular organization and functions. Traditional computational protein complex prediction methods mainly rely on the topology of protein-protein interaction (PPI) networks but seldom take biological information of proteins (such as Gene Ontology (GO)) into consideration. Meanwhile, the environment relevant analysis of protein complex evolution has been poorly studied, partly due to the lack of high-precision protein complex datasets. In this paper, a combined PPI network is introduced to predict protein complexes which integrate both GO and expression value of relevant protein-coding genes. A novel protein complex prediction method GECluster (Gene Expression Cluster) was proposed based on a seed node expansion strategy, in which a combined PPI network was utilized. GECluster was applied to a training combined PPI network and it predicted more credible complexes than peer methods. The results indicate that using a combined PPI network can efficiently improve protein complex prediction accuracy. In order to study protein complex evolution within cells due to changes in the living environment surrounding cells, GECluster was applied to seven combined PPI networks constructed using the data of a test set including yeast response to stress throughout a wine fermentation process. Our results showed that with the rise of alcohol concentration, protein complexes within yeast cells gradually evolve from one state to another. Besides this, the number of core and attachment proteins within a protein complex both changed significantly.
Coastal Erosion Control Methods
NASA Astrophysics Data System (ADS)
Greene, V.
2016-12-01
Coastal erosion is bad because the ecosystem there will be washed away and the animals could drown or be displaced and have to adapt to a new ecosystem that they are not prepared for. I'm interested in this problem because if there aren't beaches when I grow up I won't be able to do the things I would really like to do. I would like to be a marine biologist. Secondly, I don't want to see beach houses washed away. I would like to see people live in harmony with their environment. So, to study ways in which to preserve beaches I will make and use models that test different erosion controls. Two different ideas for erosion control I tested are using seaweed or a rock berm. I think the rock berm will work better than the model of seaweed because the seaweed is under water and the waves can carry the sand over the seaweed, and the rock berm will work better because the rocks will help break the waves up before they reach the shore and the waves can not carry the sand over the rocks that are above the water. To investigate this I got a container to use to model the Gulf of Mexico coastline. I performed several test runs using sand and water in the container to mimic the beach and waves from the Gulf of Mexico hitting the shoreline. I did three trials for the control (no erosion control), seaweed and a rock berm. Rock berms are a border of a raised area of rock. The model for seaweed that I used was plastic shopping bags cut into strips and glued to the bottom of my container to mimic seaweed. My results were that the control had the most erosion which ranged from 2.75 - 3 inches over 3 trials. The seaweed was a little better than the control but was very variable and ranged from 1.5 - 3 inches over 3 trials. The rock berm worked the best out of all at controlling erosion with erosion ranging from 1.5 - 2 inches. My hypothesis was correct because the rock berm did best to control erosion compared to the control which had no erosion control and the model with seaweed.
Hybrid robust predictive optimization method of power system dispatch
Chandra, Ramu Sharat; Liu, Yan; Bose, Sumit; de Bedout, Juan Manuel
2011-08-02
A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.
On some methods for assessing earthquake predictions
NASA Astrophysics Data System (ADS)
Molchan, G.; Romashkova, L.; Peresan, A.
2017-09-01
A regional approach to the problem of assessing earthquake predictions inevitably faces a deficit of data. We point out some basic limits of assessment methods reported in the literature, considering the practical case of the performance of the CN pattern recognition method in the prediction of large Italian earthquakes. Along with the classical hypothesis testing, a new game approach, the so-called parimutuel gambling (PG) method, is examined. The PG, originally proposed for the evaluation of the probabilistic earthquake forecast, has been recently adapted for the case of 'alarm-based' CN prediction. The PG approach is a non-standard method; therefore it deserves careful examination and theoretical analysis. We show that the PG alarm-based version leads to an almost complete loss of information about predicted earthquakes (even for a large sample). As a result, any conclusions based on the alarm-based PG approach are not to be trusted. We also show that the original probabilistic PG approach does not necessarily identifies the genuine forecast correctly among competing seismicity rate models, even when applied to extensive data.
Mass spectrometry methods for predicting antibiotic resistance.
Charretier, Yannick; Schrenzel, Jacques
2016-10-01
Developing elaborate techniques for clinical applications can be a complicated process. Whole-cell MALDI-TOF MS revolutionized reliable microorganism identification in clinical microbiology laboratories and is now replacing phenotypic microbial identification. This technique is a generic, accurate, rapid, and cost-effective growth-based method. Antibiotic resistance keeps emerging in environmental and clinical microorganisms, leading to clinical therapeutic challenges, especially for Gram-negative bacteria. Antimicrobial susceptibility testing is used to reliably predict antimicrobial success in treating infection, but it is inherently limited by the need to isolate and grow cultures, delaying the application of appropriate therapies. Antibiotic resistance prediction by growth-independent methods is expected to reduce the turnaround time. Recently, the potential of next-generation sequencing and microarrays in predicting microbial resistance has been demonstrated, and this review evaluates the potential of MS in this field. First, technological advances are described, and the possibility of predicting antibiotic resistance by MS is then illustrated for three prototypical human pathogens: Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Clearly, MS methods can identify antimicrobial resistance mediated by horizontal gene transfers or by mutations that affect the quantity of a gene product, whereas antimicrobial resistance mediated by target mutations remains difficult to detect.
Optimal Tuning for Disturbance Suppression Mechanism for Model Predictive Control
NASA Astrophysics Data System (ADS)
Tange, Yoshio; Nakazawa, Chikashi
Disturbance suppression is one of most required performances in process control. We recently proposed a new disturbance suppression mechanism applicable for model predictive control in order to enhance disturbance suppression performance for ramp-like disturbances. The proposed method utilized the prediction error of controlled values and generates a disturbance compensation signal by a constant gain feedback. In this paper, we propose an improved version of the disturbance suppression mechanism by applying a low-pass filter and parameter tuning methods by which we can make the mechanism more tolerant to various disturbances such as ramp, step, and other supposable ones. We also show numerical simulation results with an oil distillation tower plant.
Predicting abrasive wear with coupled Lagrangian methods
NASA Astrophysics Data System (ADS)
Beck, Florian; Eberhard, Peter
2015-05-01
In this paper, a mesh-less approach for the simulation of a fluid with particle loading and the prediction of abrasive wear is presented. We are using the smoothed particle hydrodynamics (SPH) method for modeling the fluid and the discrete element method (DEM) for the solid particles, which represent the loading of the fluid. These Lagrangian methods are used to describe heavily sloshing fluids with their free surfaces as well as the interface between the fluid and the solid particles accurately. A Reynolds-averaged Navier-Stokes equations model is applied for handling turbulences. We are predicting abrasive wear on the boundary geometry with two different wear models taking cutting and deformation mechanisms into account. The boundary geometry is discretized with special DEM particles. In doing so, it is possible to use the same particle type for both the calculation of the boundary conditions for the SPH method as well as the DEM and for predicting the abrasive wear. After a brief introduction to the SPH method and the DEM, the handling of the boundary and the coupling of the fluid and the solid particles are discussed. Then, the applied wear models are presented and the simulation scenarios are described. The first numerical experiment is the simulation of a fluid with loading which is sloshing inside a tank. The second numerical experiment is the simulation of the impact of a free jet with loading to a simplified pelton bucket. We are especially investigating the wear patterns inside the tank and the bucket.
Robust predictive cruise control for commercial vehicles
NASA Astrophysics Data System (ADS)
Junell, Jaime; Tumer, Kagan
2013-10-01
In this paper we explore learning-based predictive cruise control and the impact of this technology on increasing fuel efficiency for commercial trucks. Traditional cruise control is wasteful when maintaining a constant velocity over rolling hills. Predictive cruise control (PCC) is able to look ahead at future road conditions and solve for a cost-effective course of action. Model- based controllers have been implemented in this field but cannot accommodate many complexities of a dynamic environment which includes changing road and vehicle conditions. In this work, we focus on incorporating a learner into an already successful model- based predictive cruise controller in order to improve its performance. We explore back propagating neural networks to predict future errors then take actions to prevent said errors from occurring. The results show that this approach improves the model based PCC by up to 60% under certain conditions. In addition, we explore the benefits of classifier ensembles to further improve the gains due to intelligent cruise control.
Global stabilization of fixed points using predictive control
NASA Astrophysics Data System (ADS)
Liz, Eduardo; Franco, Daniel
2010-06-01
We analyze the global stability properties of some methods of predictive control. We particularly focus on the optimal control function introduced by de Sousa Vieira and Lichtenberg [Phys. Rev. E 54, 1200 (1996)]. We rigorously prove that it is possible to use this method for the global stabilization of a discrete system xn +1=f(xn) into a positive equilibrium for a class of maps commonly used in population dynamics. Moreover, the controlled system is globally stable for all values of the control parameter for which it is locally asymptotically stable. Our study highlights the difficulty of obtaining global stability results for other methods of predictive control, where higher iterations of f are used in the control scheme.
Switched linear model predictive controllers for periodic exogenous signals
NASA Astrophysics Data System (ADS)
Wang, Liuping; Gawthrop, Peter; Owens, David. H.; Rogers, Eric
2010-04-01
This article develops switched linear controllers for periodic exogenous signals using the framework of a continuous-time model predictive control. In this framework, the control signal is generated by an algorithm that uses receding horizon control principle with an on-line optimisation scheme that permits inclusion of operational constraints. Unlike traditional repetitive controllers, applying this method in the form of switched linear controllers ensures bumpless transfer from one controller to another. Simulation studies are included to demonstrate the efficacy of the design with or without hard constraints.
Artificial neural network intelligent method for prediction
NASA Astrophysics Data System (ADS)
Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi
2017-09-01
Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.
Chemical control methods and tools
Steven Manning; James. Miller
2011-01-01
After determining the best course of action for control of an invasive plant population, it is important to understand the variety of methods available to the integrated pest management professional. A variety of methods are now widely used in managing invasive plants in natural areas, including chemical, mechanical, and cultural control methods. Once the preferred...
Launch ascent guidance by discrete multi-model predictive control
NASA Astrophysics Data System (ADS)
Vachon, Alexandre; Desbiens, André; Gagnon, Eric; Bérard, Caroline
2014-02-01
This paper studies the application of discrete multi-model predictive control as a trajectory tracking guidance law for a space launcher. Two different algorithms are developed, each one based on a different representation of launcher translation dynamics. These representations are based on an interpolation of the linear approximation of nonlinear pseudo-five degrees of freedom equations of translation around an elliptical Earth. The interpolation gives a linear-time-varying representation and a linear-fractional representation. They are used as the predictive model of multi-model predictive controllers. The controlled variables are the orbital parameters, and constraints on a terminal region for the minimal accepted precision are also included. Use of orbital parameters as the controlled variables allows for a partial definition of the trajectory. Constraints can also be included in multi-model predictive control to reduce the number of unknowns of the problem by defining input shaping constraints. The guidance algorithms are tested in nominal conditions and off-nominal conditions with uncertainties on the thrust. The results are compared to those of a similar formulation with a nonlinear model predictive controller and to a guidance method based on the resolution of a simplified version of the two-point boundary value problem. In nominal conditions, the model predictive controllers are more precise and produce a more optimal trajectory but are longer to compute than the two-point boundary solution. Moreover, in presence of uncertainties, developed algorithms exhibit poor robustness properties. The multi-model predictive control algorithms do not reach the desired orbit while the nonlinear model predictive control algorithm still converges but produces larger maneuvers than the other method.
Fuzzy Predictive Control Strategy in the Application of the Industrial Furnace Temperature Control
NASA Astrophysics Data System (ADS)
Dai, Luping; Chen, Xingliang; Chen, Liu; Liu, Xia
Ceramic kiln with large heat capacity, big lag and nonlinear characteristic, this paper proposes a combining fuzzy control and predictive control of the control algorithm, to enhance the tracking and anti-interference ability of the algorithm. The simulation results show, this method compared with the control of PID has the high steady precision and dynamic characteristic.
Advances in Adaptive Control Methods
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2009-01-01
This poster presentation describes recent advances in adaptive control technology developed by NASA. Optimal Control Modification is a novel adaptive law that can improve performance and robustness of adaptive control systems. A new technique has been developed to provide an analytical method for computing time delay stability margin for adaptive control systems.
Constrained incremental predictive controller design for a flexible joint robot.
Ghahramani, Nemat Ollah; Towhidkhah, Farzad
2009-07-01
In this paper, an improved predictive control algorithm for controlling a typical nonlinear flexible-joint robot (FJR) with input constraint is proposed. The receding horizon algorithm, called generalized incremental predictive control (GIPC), utilizes both present and previous states rather than present states only. The GIPC algorithm includes the weighted difference of the current and the previous states and the summation of the control action increments. In order to illustrate the effectiveness of the proposed control strategy, it is implemented to the FJR and the results are compared with those of generalized predictive control (GPC). It is demonstrated that the proposed GIPC algorithm is more robust than the standard GPC method. Furthermore, the constrained GIPC algorithm using the quadratic programming removes instabilities caused by actuator saturation.
Method for controlling brazing
Hosking, F. Michael; Hall, Aaron C.; Givler, Richard C.; Walker, Charles A.
2006-08-01
A method for making a braze joint across a discontinuity in a work piece using alternating current. A filler metal is pre-placed at a location sufficiently close to the discontinuity such that, when an alternating current is applied across a work piece to heat the work piece and melt the filler metal, the filler metal is drawn into the discontinuity. The alternating current is maintained for a set residence time, generally less than 10 seconds and more particularly less than 3 seconds. The alternating current is then altered, generally by reducing the current and/or voltage such that the filler metal can solidify to form a braze joint of desired quality and thickness.
Model predictive formation control of helicopter systems
NASA Astrophysics Data System (ADS)
Saffarian, Mehdi
In this thesis, a robust formation control framework for formation control of a group of helicopters is proposed and designed. The dynamic model of the helicopter has been developed and verified through simulations. The control framework is constructed using two main control schemes for navigation of a helicopter group in three-dimensional (3D) environments. Two schemes are designed to maintain the position of one helicopter with respect to one or two other neighboring members, respectively. The developed parameters can uniquely define the position of the helicopters with respect to each other and can be used for any other aerial and under water vehicles such as airplanes, spacecrafts and submarines. Also, since this approach is modular, it is possible to use it for desired number and form of the group helicopters. Using the defined control parameters, two decentralized controllers are designed based on Nonlinear Model Predictive Control (NMPC) algorithm technique. The framework performance has been tested through simulation of different formation scenarios.
Adaptive method for electron bunch profile prediction
NASA Astrophysics Data System (ADS)
Scheinker, Alexander; Gessner, Spencer
2015-10-01
We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.
Adaptive method for electron bunch profile prediction
Scheinker, Alexander; Gessner, Spencer
2015-10-01
We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET. © 2015 authors. Published by the American Physical Society.
Adaptive method for electron bunch profile prediction
Scheinker, Alexander; Gessner, Spencer
2015-10-15
We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. Thus, the simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrialmore » control system. Finally, the main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.« less
Model predictive control for cooperative control of space robots
NASA Astrophysics Data System (ADS)
Kannan, Somasundar; Alamdari, Seyed Amin Sajadi; Dentler, Jan; Olivares-Mendez, Miguel A.; Voos, Holger
2017-01-01
The problem of Orbital Manipulation of Passive body is discussed here. Two scenarios including passive object rigidly attached to robotic servicers and passive body attached to servicers through manipulators are discussed. The Model Predictive Control (MPC) technique is briefly presented and successfully tested through simulations on two cases of position control of passive body in the orbit.
In silico methods to predict drug toxicity.
Roncaglioni, Alessandra; Toropov, Andrey A; Toropova, Alla P; Benfenati, Emilio
2013-10-01
This review describes in silico methods to characterize the toxicity of pharmaceuticals, including tools which predict toxicity endpoints such as genotoxicity or organ-specific models, tools addressing ADME processes, and methods focusing on protein-ligand docking binding. These in silico tools are rapidly evolving. Nowadays, the interest has shifted from classical studies to support toxicity screening of candidates, toward the use of in silico methods to support the expert. These methods, previously considered useful only to provide a rough, initial estimation, currently have attracted interest as they can assist the expert in investigating toxic potential. They provide the expert with safety perspectives and insights within a weight-of-evidence strategy. This represents a shift of the general philosophy of in silico methodology, and it is likely to further evolve especially exploiting links with system biology. Copyright © 2013 Elsevier Ltd. All rights reserved.
A numerical method for predicting hypersonic flowfields
NASA Technical Reports Server (NTRS)
Maccormack, Robert W.; Candler, Graham V.
1989-01-01
The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to chemically react and reach states in thermal nonequilibrium. The prediction of hypersonic flowfields requires a numerical method capable of solving the conservation equations of fluid flow, the chemical rate equations for specie formation and dissociation, and the transfer of energy relations between translational and vibrational temperature states. Because the number of equations to be solved is large, the numerical method should also be as efficient as possible. The proposed paper presents a fully implicit method that fully couples the solution of the fluid flow equations with the gas physics and chemistry relations. The method flux splits the inviscid flow terms, central differences of the viscous terms, preserves element conservation in the strong chemistry source terms, and solves the resulting block matrix equation by Gauss Seidel line relaxation.
A numerical method for predicting hypersonic flowfields
NASA Technical Reports Server (NTRS)
Maccormack, Robert W.; Candler, Graham V.
1989-01-01
The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to chemically react and reach states in thermal nonequilibrium. The prediction of hypersonic flowfields requires a numerical method capable of solving the conservation equations of fluid flow, the chemical rate equations for specie formation and dissociation, and the transfer of energy relations between translational and vibrational temperature states. Because the number of equations to be solved is large, the numerical method should also be as efficient as possible. The proposed paper presents a fully implicit method that fully couples the solution of the fluid flow equations with the gas physics and chemistry relations. The method flux splits the inviscid flow terms, central differences of the viscous terms, preserves element conservation in the strong chemistry source terms, and solves the resulting block matrix equation by Gauss Seidel line relaxation.
Model predictive torque control with an extended prediction horizon for electrical drive systems
NASA Astrophysics Data System (ADS)
Wang, Fengxiang; Zhang, Zhenbin; Kennel, Ralph; Rodríguez, José
2015-07-01
This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.
Method of controlling gene expression
Peters, Norman K.; Frost, John W.; Long, Sharon R.
1991-12-03
A method of controlling expression of a DNA segment under the control of a nod gene promoter which comprises administering to a host containing a nod gene promoter an amount sufficient to control expression of the DNA segment of a compound of the formula: ##STR1## in which each R is independently H or OH, is described.
Respiratory Pattern Variability Analysis Based on Nonlinear Prediction Methods
2007-11-02
Brobely. All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions . Electroencephalogr. Clin. Neurophisiol...methods. These methods use the volume signals generated by the respiratory system in order to construct a model of its dynamics, and then to estimate the...definition have been considered. The incidence of different prediction depths and embedding dimensions have been analyzed. A group of 12 patients on
Airframe Noise Prediction Using the Sngr Method
NASA Astrophysics Data System (ADS)
Chen, Rongqian; Wu, Yizhao; Xia, Jian
In this paper, the Stochastic Noise Generation and Radiation method (SNGR) is used to predict airframe noise. The SNGR method combines a stochastic model with Computational Fluid Dynamics (CFD), and it can give acceptable noise results while the computation cost is relatively low. In the method, the time-averaged mean flow field is firstly obtained by solving Reynolds Averaged Navier-Stokes equations (RANS), and a stochastic velocity is generated based on the obtained information. Then the turbulent field is used to generate the source for the Acoustic Perturbation Equations (APEs) that simulate the noise propagation. For numerical methods, timeaveraged RANS equations are solved by finite volume method, and the turbulent model is K - ɛ model; APEs are solved by finite difference method, and the numerical scheme is the Dispersion-Relation-Preserving (DRP) scheme, with explicit optimized 5-stage Rung-Kutta scheme time step. In order to test the APE solver, propagation of a Gaussian pulse in a uniform mean flow is firstly simulated and compared with the analytical solution. Then, using the method, the trailing edge noise of NACA0012 airfoil is calculated. The results are compared with reference data, and good agreements are demonstrated.
Computational predictive methods for fracture and fatigue
NASA Technical Reports Server (NTRS)
Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.
1994-01-01
The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.
Computational predictive methods for fracture and fatigue
NASA Astrophysics Data System (ADS)
Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.
1994-09-01
The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.
Composite predictive flight control for airbreathing hypersonic vehicles
NASA Astrophysics Data System (ADS)
Yang, Jun; Zhao, Zhenhua; Li, Shihua; Zheng, Wei Xing
2014-09-01
The robust optimised tracking control problem for a generic airbreathing hypersonic vehicle (AHV) subject to nonvanishing mismatched disturbances/uncertainties is investigated in this paper. A baseline nonlinear model predictive control (MPC) method is firstly introduced for optimised tracking control of the nominal dynamics. A nonlinear-disturbance-observer-based control law is then developed for robustness enhancement in the presence of both external disturbances and uncertainties. Compared with the existing robust tracking control methods for AHVs, the proposed composite nonlinear MPC method obtains not only promising robustness and disturbance rejection performance but also optimised nominal tracking control performance. The merits of the proposed method are validated by implementing simulation studies on the AHV system.
Semiconductor CMP Process Control Predicting Degradation Effect of Consumed Materials
NASA Astrophysics Data System (ADS)
Tamaki, Kenji; Kaneko, Shun'ichi
This paper describes a methodology to build a virtual metrology (VM) model for semiconductor chemical mechanical polishing (CMP) process control. The VM model predicts the polishing rate based on equipment-derived data as soon as allowed, and immediately applies the results to advanced process control (APC). The proposed methodology uses Markov chain Monte Carlo (MCMC) methods to build an analytical model with many parameters for individual consumed materials from historical data in small quantities. The mutual interference of two kinds of consumed materials: dresser and pad are modeled in a form of multilevel predictive model. The methodology uses MCMC methods again to identify the multilevel predictive model taking into account the assumed operation of an actual manufacturing line, for instance, using preliminary test result, learning a model parameter online, and being affected by metrology lag as disturbance. The simulation results show the APC with the proposed VM model is low sensitivity to metrology lag and high precision on polishing amount control.
Seminal quality prediction using data mining methods.
Sahoo, Anoop J; Kumar, Yugal
2014-01-01
Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of
NASA Technical Reports Server (NTRS)
Phillips, William H; Brown, B Porter; Matthews, James T , Jr
1953-01-01
A number of examples are presented of control difficulties not completely covered by existing handling-qualities requirements. 520/3:*:These control difficulties appear to result from a tendency for dynamic instability of the combination of pilot, control system, and airplane. The unsatisfactory characteristics involved have been encountered most frequently with hydraulic-power control systems. The nature of the difficulties may range from a slight interference with the ability of the pilot to hold precisely straight and level flight to a dangerous tendency toward divergent short-period oscillations which require constant attention of the pilot to control. Tests of a bomber and a fighter airplane with experimental power control systems have been made to study this problem further. The results of the investigation show that control difficulties of the type considered have always been associated with a marked phase difference between the pilot's control force and the associated control-surface deflection. The presence of static friction in the control valves of hydraulic-power control systems was found to be the explanation for unsatisfactory characteristics in several airplanes equipped with such systems. The valve friction may cause a phase lag between the pilot's control force and the associated control-surface deflection approaching 180 degrees at small control deflections. Definite limits or simple rules for the tolerable amount of valve friction appear to be difficult to establish because of the large number of variables which may influence the problem.The control characteristics of the airplanes tested were strongly influenced by minor design details of the power control systems.
Model predictive control of MSMPR crystallizers
NASA Astrophysics Data System (ADS)
Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc
2005-02-01
A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.
A method of predicting anomalous flashovers
Shindo, Takatoshi; Suzuki, Toshio
1995-07-01
When a long air gap or an insulator string is tested with a switching impulse voltage, flashovers sometimes occur at a gap which is longer than the test specimen. This phenomenon has been called anomalous flashover and the results of several experiments have been already reported. Although the mechanism of this anomalous flashover phenomena is important in coordinating insulation of high voltage transmission systems, especially for a UHV transmission system, almost no studies have been conducted on it. The authors analyze anomalous flashover phenomena statistically and propose a calculation method to predict the probability of anomalous flashovers occurs. This calculation method is then used to estimate the safety clearance needed for a UHV substation.
Drug permeability prediction using PMF method.
Meng, Fancui; Xu, Weiren
2013-03-01
Drug permeability determines the oral availability of drugs via cellular membranes. Poor permeability makes a drug unsuitable for further development. The permeability may be estimated as the free energy change that the drug should overcome through crossing membrane. In this paper the drug permeability was simulated using molecular dynamics method and the potential energy profile was calculated with potential of mean force (PMF) method. The membrane was simulated using DPPC bilayer and three drugs with different permeability were tested. PMF studies on these three drugs show that doxorubicin (low permeability) should pass higher free energy barrier from water to DPPC bilayer center while ibuprofen (high permeability) has a lower energy barrier. Our calculation indicates that the simulation model we built is suitable to predict drug permeability.
Aircraft digital control design methods
NASA Technical Reports Server (NTRS)
Powell, J. D.; Parsons, E.; Tashker, M. G.
1976-01-01
Variations in design methods for aircraft digital flight control are evaluated and compared. The methods fall into two categories; those where the design is done in the continuous domain (or s plane) and those where the design is done in the discrete domain (or z plane). Design method fidelity is evaluated by examining closed loop root movement and the frequency response of the discretely controlled continuous aircraft. It was found that all methods provided acceptable performance for sample rates greater than 10 cps except the uncompensated s plane design method which was acceptable above 20 cps. A design procedure based on optimal control methods was proposed that provided the best fidelity at very slow sample rates and required no design iterations for changing sample rates.
Robust model predictive control of Wiener systems
NASA Astrophysics Data System (ADS)
Biagiola, S. I.; Figueroa, J. L.
2011-03-01
Block-oriented models (BOMs) have shown to be appealing and efficient as nonlinear representations for many applications. They are at the same time valid and simple models in a more extensive region than time-invariant linear models. In this work, Wiener models are considered. They are one of the most diffused BOMs, and their structure consists in a linear dynamics in cascade with a nonlinear static block. Particularly, the problem of control of these systems in the presence of uncertainty is treated. The proposed methodology makes use of a robust identification procedure in order to obtain a robust model to represent the uncertain system. This model is then employed to design a model predictive controller. The mathematical problem involved in the controller design is formulated in the context of the existing linear matrix inequalities (LMI) theory. The main feature of this approach is that it takes advantage of the static nature of the nonlinearity, which allows to solve the control problem by focusing only in the linear dynamics. This formulation results in a simplified design procedure, because the original nonlinear model predictive control (MPC) problem turns into a linear one.
A Disturbance Rejection for Model Predictive Control Using a Multivariable Disturbance Observer
NASA Astrophysics Data System (ADS)
Tange, Yoshio; Matsui, Tetsuro; Matsumoto, Koji; Nishida, Hideyuki
Model predictive control has been widely used in industrial applications. And more efficient and more precise control is being required to meet growing demands such as energy savings and fewer emissions in industrial plants. In this paper, we focus on step response model based predictive control, which is one of most applied predictive control methods, and propose a new disturbance rejection method to overcome control performance degradation caused by unmeasured ramp-like disturbances.
Phase analysis method for burst onset prediction
NASA Astrophysics Data System (ADS)
Stellino, Flavio; Mazzoni, Alberto; Storace, Marco
2017-02-01
The response of bursting neurons to fluctuating inputs is usually hard to predict, due to their strong nonlinearity. For the same reason, decoding the injected stimulus from the activity of a bursting neuron is generally difficult. In this paper we propose a method describing (for neuron models) a mechanism of phase coding relating the burst onsets with the phase profile of the input current. This relation suggests that burst onset may provide a way for postsynaptic neurons to track the input phase. Moreover, we define a method of phase decoding to solve the inverse problem and estimate the likelihood of burst onset given the input state. Both methods are presented here in a unified framework, describing a complete coding-decoding procedure. This procedure is tested by using different neuron models, stimulated with different inputs (stochastic, sinusoidal, up, and down states). The results obtained show the efficacy and broad range of application of the proposed methods. Possible applications range from the study of sensory information processing, in which phase-of-firing codes are known to play a crucial role, to clinical applications such as deep brain stimulation, helping to design stimuli in order to trigger or prevent neural bursting.
Nonlinear model predictive control of managed pressure drilling.
Nandan, Anirudh; Imtiaz, Syed
2017-07-01
A new design of nonlinear model predictive controller (NMPC) is proposed for managed pressure drilling (MPD) system. The NMPC is based on output feedback control architecture and employs offset-free formulation proposed in [1]. NMPC uses active set method for computing control inputs. The controller implements an automatic switching from constant bottom hole pressure (CBHP) regulation to flow control mode in the event of a reservoir kick. In the flow control mode the controller automatically raises the bottom hole pressure setpoint, and thereby keeps the reservoir fluid flow to the surface within a tunable threshold. This is achieved by exploiting constraint handling capability of NMPC. In addition to kick mitigation the controller demonstrated good performance in containing the bottom hole pressure (BHP) during the pipe connection sequence. The controller also delivered satisfactory performance in the presence of measurement noise and uncertainty in the system. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Voltage control in pulsed system by predict-ahead control
Payne, A.N.; Watson, J.A.; Sampayan, S.E.
1994-09-13
A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load. 4 figs.
Voltage control in pulsed system by predict-ahead control
Payne, Anthony N.; Watson, James A.; Sampayan, Stephen E.
1994-01-01
A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load.
Wind farms production: Control and prediction
NASA Astrophysics Data System (ADS)
El-Fouly, Tarek Hussein Mostafa
Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect
Real-time Adaptive Control Using Neural Generalized Predictive Control
NASA Technical Reports Server (NTRS)
Haley, Pam; Soloway, Don; Gold, Brian
1999-01-01
The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.
A nozzle internal performance prediction method
NASA Technical Reports Server (NTRS)
Carlson, John R.
1992-01-01
A prediction method was written and incorporated into a three-dimensional Navier-Stokes code (PAB3D) for the calculation of nozzle internal performance. The following quantities are calculated: (1) discharge coefficient; (2) normal, side, and axial thrust ratios; (3) rolling, pitching, and yawing moments; and (4) effective pitch and yaw vector angles. Four different case studies are presented to confirm the applicability of the methodology. Internal and, in most situations, external flow-field regions are required to be modeled. The computed nozzle discharge coefficient matches both the level and the trend of the experimental data within quoted experimental data accuracy (0.5 percent). Moment and force ratios are generally within 1 to 2 percent of the absolute level of experimental data, with the trends of data matched accurately.
Systems and methods for predicting materials properties
Ceder, Gerbrand; Fischer, Chris; Tibbetts, Kevin; Morgan, Dane; Curtarolo, Stefano
2007-11-06
Systems and methods for predicting features of materials of interest. Reference data are analyzed to deduce relationships between the input data sets and output data sets. Reference data includes measured values and/or computed values. The deduced relationships can be specified as equations, correspondences, and/or algorithmic processes that produce appropriate output data when suitable input data is used. In some instances, the output data set is a subset of the input data set, and computational results may be refined by optionally iterating the computational procedure. To deduce features of a new material of interest, a computed or measured input property of the material is provided to an equation, correspondence, or algorithmic procedure previously deduced, and an output is obtained. In some instances, the output is iteratively refined. In some instances, new features deduced for the material of interest are added to a database of input and output data for known materials.
Security control methods for CEDR
Rotem, D.
1990-09-01
The purpose of this document is to summarize the findings of recent studies on the security problem in statistical databases and examine their applicability to the specific needs of CEDR. The document is organized as follows: In Section 2 we describe some general control methods which are available on most commercial database software. In Section 3 we provide a classification of statistical security methods. In Section 4 we analyze the type of users of CEDR and the security control methods which may be applied to each type. In Section 5 we summarize the findings of this study and recommend possible solutions.
NASA Technical Reports Server (NTRS)
Mollo, Christopher G.; Bernhard, Robert J.
1987-01-01
An optimal active noise controller is formulated and analyzed for three different active noise control problems. The first problem formulated is the active control of enclosed or partially enclosed harmonic sound fields where the noise source strengths and enclosure boundary description are known. The enclosure boundary is described by either pressure, velocity, or impedance boundary conditions. The second problem formulated is the active control of the free field power radiated from a distributed noise source with a known time harmonic surface velocity. The third problem formulated is the active control of enclosed or partially enclosed harmonic sound field where the noise source strengths of enclosure boundary description may not be known. All three formulations are derived using an indirect boundary element technique. Formulation and verification of an indirect boundary element method is presented. The active noise controller formulations for enclosures are capable of analyzing systems with generalized enclosure shapes, point noise sources, and/or locally reacting impedance boundary conditions. For each formulation, representative results of optimal active noise controller case studies are presented, and some general conclusions are drawn.
[Validation of a prediction method for indoor air pollution].
Qu, Hong-juan; Qin, Hua-peng; Yao, Ting-ting; Luan, Sheng-ji
2008-02-01
The validation study of the prediction method for indoor air pollution was carried out by comparing the results of emission models based on data obtained in a large and a small emission chamber, with actual measured concentrations. A new decorated room was studied as a case. Emissions of complicated objects and simple surface layer materials were studied respectively in the large and small chamber and emission models were developed. Those models were based on the assumptions regarding mass conservation of substances and the hypothesis that pollutants were well mixed. The emissions of formaldehyde and TVOC (total volatile organic compounds) in the studied room were predicted by the method. The predicted concentration trend of pollutants was in accordance with the measured trend when some air exchange (0.03 ACH, air change per hour) was taken into account. The normalized standard errors of formaldehyde and TVOC pollution prediction were respectively 2.8% and 1.6%. Modeling analysis shows that the contribution to total formaldehyde pollution of the studied room was: furniture > paint > floor; the contribution to total TVOC pollution was: paint > floor > furniture. The results lead to the conclusion that this prediction method can well describe the pollution trend, can assess the contribution of different sources, can guide the choice of building materials and is an effective tool for indoor air pollution assessment and control.
Liu, Xudong; Zhang, Chenghui; Li, Ke; Zhang, Qi
2017-09-06
This paper addresses the current control of permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and disturbances. A generalized predictive current control method combined with sliding mode disturbance compensation is proposed to satisfy the requirement of fast response and strong robustness. Firstly, according to the generalized predictive control (GPC) theory based on the continuous time model, a predictive current control method is presented without considering the disturbance, which is convenient to be realized in the digital controller. In fact, it's difficult to derive the exact motor model and parameters in the practical system. Thus, a sliding mode disturbance compensation controller is studied to improve the adaptiveness and robustness of the control system. The designed controller attempts to combine the merits of both predictive control and sliding mode control, meanwhile, the controller parameters are easy to be adjusted. Lastly, the proposed controller is tested on an interior PMSM by simulation and experiment, and the results indicate that it has good performance in both current tracking and disturbance rejection. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Comparison of Predictive Modeling Methods of Aircraft Landing Speed
NASA Technical Reports Server (NTRS)
Diallo, Ousmane H.
2012-01-01
Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.
Transformation method and wave control
NASA Astrophysics Data System (ADS)
Chang, Zheng; Hu, Jin; Hu, Geng-Kai
2010-12-01
Transformation method provides an efficient way to control wave propagation by materials. The transformed relations for field and material during a transformation are essential to fulfill this method. We propose a systematic method to derive the transformed relations for a general physic process, the constraint conditions are obtained by considering geometrical and physical constraint during a mapping. The proposed method is applied to Navier's equation for elastodynamics, Helmholtz's equation for acoustic wave and Maxwell's equation for electromagnetic wave, the corresponding transformed relations are derived, which can be used in the framework of transformation method for wave control. We show that contrary to electromagnetic wave, the transformed relations are not uniquely determined for elastic wave and acoustic wave, so we have a freedom to choose them differently. Using the obtained transformed relations, we also provide some examples for device design, a concentrator for elastic wave, devices for illusion acoustic and illusion optics are conceived and validated by numerical simulations.
Methods of Predicting Solid Waste Characteristics.
ERIC Educational Resources Information Center
Boyd, Gail B.; Hawkins, Myron B.
The project summarized by this report involved a preliminary design of a model for estimating and predicting the quantity and composition of solid waste and a determination of its feasibility. The novelty of the prediction model is that it estimates and predicts on the basis of knowledge of materials and quantities before they become a part of the…
Controlling motion prediction errors in radiotherapy with relevance vector machines.
Dürichen, Robert; Wissel, Tobias; Schweikard, Achim
2015-04-01
Robotic radiotherapy can precisely ablate moving tumors when time latencies have been compensated. Recently, relevance vector machines (RVM), a probabilistic regression technique, outperformed six other prediction algorithms for respiratory compensation. The method has the distinct advantage that each predicted point is assumed to be drawn from a normal distribution. Second-order statistics, the predicted variance, were used to control RVM prediction error during a treatment and to construct hybrid prediction algorithms. First, the duty cycle and the precision were correlated to the variance by interrupting the treatment if the variance exceeds a threshold. Second, two hybrid algorithms based on the variance were developed, one consisting of multiple RVMs (HYB(RVM)) and the other of a combination between a wavelet-based least mean square algorithm (wLMS) and a RVM (HYB(wLMS-RVM)). The variance for different motion traces was analyzed to reveal a characteristic variance pattern which gives insight in what kind of prediction errors can be controlled by the variance. Limiting the variance by a threshold resulted in an increased precision with a decreased duty cycle. All hybrid algorithms showed an increased prediction accuracy compared to using only their individual algorithms. The best hybrid algorithm, HYB(RVM), can decrease the mean RMSE over all 304 motion traces from 0.18 mm for a linear RVM to 0.17 mm. The predicted variance was shown to be an efficient metric to control prediction errors, resulting in a more robust radiotherapy treatment. The hybrid algorithm HYB(RVM) could be translated to clinical practice. It does not require further parameters, can be completely parallelised and easily further extended.
Design of an adaptive predictive controller for steam generators
NASA Astrophysics Data System (ADS)
Na, Man Gyun; Sim, Young Rok; Lee, Yoon Joon
2003-02-01
The water level control of a nuclear steam generator is very important to secure the sufficient cooling inventory for the nuclear reactor and, at the same time, to prevent the damage of turbine blades. The dynamics of steam generators is very different according to power levels and changes as time goes on. The generalized predictive control method is used to solve an optimization problem for the finite future time steps at current time and to implement only the first control input among the solved optimal control inputs of several time steps. A recursive parameter estimation algorithm estimates on-line the mathematical model of steam generator every time step to generate the linear controller design model. In this work, by combining these generalized predictive control method and recursive parameter estimation algorithm, a new controller is designed to control the water level of nuclear steam generators. It is shown through application to a linear model and a nonlinear model of steam generators that the proposed controller has good performance.
Zygarlicke, C.J.; Mann, M.D.; Laudal, D.L.; Miller, S.J.
1994-01-01
The Energy & Environmental Research Center (EERC) has been developing advanced indices that rank coals according to their fouling and slagging propensity in utility boilers. The indices are based on sophisticated analytical techniques for identifying and quantifying coal inorganics and are useful in predicting the effects of proposed operational changes on ash deposition in coal-fired boilers. These indices are intended to provide an economical way to reduce the amount of full-scale testing needed to determine the best means of minimizing ash-related problems. The successful design and operation of the fluidized-bed combustor requires the ability to control and mitigate ash-related problems. The major ash-related problems in FBC are agglomeration of bed material, ash deposition on heat-transfer surfaces, ash deposition on refractory and uncooled surfaces, corrosion, and erosion. The focus of the Development of Methods to Predict Agglomeration and Deposition in FBCs is on the agglomeration and deposition problems in atmospheric bubbling and circulating beds. The 1990 Clean Air Act Amendments require study of air toxic emissions from coal combustion systems. Since most of the toxic metals present in coal will be in particulate form, a high level of fine-particle control appears to be the best approach to achieving a high level of air toxics control. However, over 50% of mercury and a portion of selenium emissions are in vapor form and are not typically collected in particulate control devices. Therefore, the goal of this project is to develop methods that capture the vapor-phase metals while simultaneously achieving ultrahigh collection efficiency of particulate air toxics.
Optimal Control of Distributed Energy Resources using Model Predictive Control
Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.; Zhang, Wei; Lu, Shuai; Samaan, Nader A.; Butler-Purry, Karen
2012-07-22
In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.
Prediction Methods in Solar Sunspots Cycles
Ng, Kim Kwee
2016-01-01
An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle. PMID:26868269
Prediction Methods in Solar Sunspots Cycles.
Ng, Kim Kwee
2016-02-12
An understanding of the Ohl's Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun's electromagnetic dipole is moving toward the Sun's Equator during a solar cycle.
Prediction Methods in Solar Sunspots Cycles
NASA Astrophysics Data System (ADS)
Ng, Kim Kwee
2016-02-01
An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle.
The predictive protective control of the heat exchanger
NASA Astrophysics Data System (ADS)
Nevriva, Pavel; Filipova, Blanka; Vilimec, Ladislav
2016-06-01
The paper deals with the predictive control applied to flexible cogeneration energy system FES. FES was designed and developed by the VITKOVICE POWER ENGINEERING joint-stock company and represents a new solution of decentralized cogeneration energy sources. In FES, the heating medium is flue gas generated by combustion of a solid fuel. The heated medium is power gas, which is a gas mixture of air and water steam. Power gas is superheated in the main heat exchanger and led to gas turbines. To protect the main heat exchanger against damage by overheating, the novel predictive protective control based on the mathematical model of exchanger was developed. The paper describes the principle, the design and the simulation of the predictive protective method applied to main heat exchanger of FES.
Predictive onboard flow control for packet switching satellites
NASA Technical Reports Server (NTRS)
Bobinsky, Eric A.
1992-01-01
We outline two alternate approaches to predicting the onset of congestion in a packet switching satellite, and argue that predictive, rather than reactive, flow control is necessary for the efficient operation of such a system. The first method discussed is based on standard, statistical techniques which are used to periodically calculate a probability of near-term congestion based on arrival rate statistics. If this probability exceeds a present threshold, the satellite would transmit a rate-reduction signal to all active ground stations. The second method discussed would utilize a neural network to periodically predict the occurrence of buffer overflow based on input data which would include, in addition to arrival rates, the distributions of packet lengths, source addresses, and destination addresses.
Predictive onboard flow control in packet switching satellites
NASA Technical Reports Server (NTRS)
Bobinsky, E. A.
1992-01-01
We outline two alternate approaches to predicting the onset of congestion in a packet switching satellite, and argue that predictive, rather than reactive, flow control is necessary for the efficient operation of such a system. The first method discussed is based on standard, statistical techniques which are used to periodically calculate a probability of near-term congestion based on arrival rate statistics. If this probability exceeds a present threshold, the satellite would transmit a rate-reduction signal to all active ground stations. The second method discussed would utilize a neural network to periodically predict the occurrence of buffer overflow based on input data which would include, in addition to arrival rates, the distributions of packet lengths, source addresses, and destination addresses.
Adaptive model predictive process control using neural networks
Buescher, K.L.; Baum, C.C.; Jones, R.D.
1997-08-19
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Adaptive model predictive process control using neural networks
Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.
1997-01-01
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.
Predictive control of crystal size distribution in protein crystallization.
Shi, Dan; Mhaskar, Prashant; El-Farra, Nael H; Christofides, Panagiotis D
2005-07-01
This work focuses on the modelling, simulation and control of a batch protein crystallization process that is used to produce the crystals of tetragonal hen egg-white (HEW) lysozyme. First, a model is presented that describes the formation of protein crystals via nucleation and growth. Existing experimental data are used to develop empirical models of the nucleation and growth mechanisms of the tetragonal HEW lysozyme crystal. The developed growth and nucleation rate expressions are used within a population balance model to simulate the batch crystallization process. Then, model reduction techniques are used to derive a reduced-order moments model for the purpose of controller design. Online measurements of the solute concentration and reactor temperature are assumed to be available, and a Luenberger-type observer is used to estimate the moments of the crystal size distribution based on the available measurements. A predictive controller, which uses the available state estimates, is designed to achieve the objective of maximizing the volume-averaged crystal size while respecting constraints on the manipulated input variables (which reflect physical limitations of control actuators) and on the process state variables (which reflect performance considerations). Simulation results demonstrate that the proposed predictive controller is able to increase the volume-averaged crystal size by 30% and 8.5% compared to constant temperature control (CTC) and constant supersaturation control (CSC) strategies, respectively, while reducing the number of fine crystals produced. Furthermore, a comparison of the crystal size distributions (CSDs) indicates that the product achieved by the proposed predictive control strategy has larger total volume and lower polydispersity compared to the CTC and CSC strategies. Finally, the robustness of the proposed method (achieved due to the presence of feedback) with respect to plant-model mismatch is demonstrated. The proposed method is
Cascade generalized predictive control strategy for boiler drum level.
Xu, Min; Li, Shaoyuan; Cai, Wenjian
2005-07-01
This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.
Method for controlling powertrain pumps
Sime, Karl Andrew; Spohn, Brian L; Demirovic, Besim; Martini, Ryan D; Miller, Jean Marie
2013-10-22
A method of controlling a pump supplying a fluid to a transmission includes sensing a requested power and an excess power for a powertrain. The requested power substantially meets the needs of the powertrain, while the excess power is not part of the requested power. The method includes sensing a triggering condition in response to the ability to convert the excess power into heat in the transmission, and determining that an operating temperature of the transmission is below a maximum. The method also includes determining a calibrated baseline and a dissipation command for the pump. The calibrated baseline command is configured to supply the fluid based upon the requested power, and the dissipation command is configured to supply additional fluid and consume the excess power with the pump. The method operates the pump at a combined command, which is equal to the calibrated baseline command plus the dissipation command.
Flutter prediction for a wing with active aileron control
NASA Technical Reports Server (NTRS)
Penning, K.; Sandlin, D. R.
1983-01-01
A method for predicting the vibrational stability of an aircraft with an analog active aileron flutter suppression system (FSS) is expained. Active aileron refers to the use of an active control system connected to the aileron to damp vibrations. Wing vibrations are sensed by accelerometers and the information is used to deflect the aileron. Aerodynamic force caused by the aileron deflection oppose wing vibrations and effectively add additional damping to the system.
Nonlinear Methods Applied to Atmospheric Prediction
2007-11-02
states being a minimum and the spatial correlation being a maximum to determine the best analogs. They also started exploring the value of finding...the best analog of each of the trajectories in the ensemble of numerical predictions from the start of the prediction to the verification time. These...Benard convection, with the fluid layer heated below and cooled above, cellular convection occurs with cells of width very nearly equal to their
Earthquake prediction: Simple methods for complex phenomena
NASA Astrophysics Data System (ADS)
Luen, Bradley
2010-09-01
Earthquake predictions are often either based on stochastic models, or tested using stochastic models. Tests of predictions often tacitly assume predictions do not depend on past seismicity, which is false. We construct a naive predictor that, following each large earthquake, predicts another large earthquake will occur nearby soon. Because this "automatic alarm" strategy exploits clustering, it succeeds beyond "chance" according to a test that holds the predictions _xed. Some researchers try to remove clustering from earthquake catalogs and model the remaining events. There have been claims that the declustered catalogs are Poisson on the basis of statistical tests we show to be weak. Better tests show that declustered catalogs are not Poisson. In fact, there is evidence that events in declustered catalogs do not have exchangeable times given the locations, a necessary condition for the Poisson. If seismicity followed a stochastic process, an optimal predictor would turn on an alarm when the conditional intensity is high. The Epidemic-Type Aftershock (ETAS) model is a popular point process model that includes clustering. It has many parameters, but is still a simpli_cation of seismicity. Estimating the model is di_cult, and estimated parameters often give a non-stationary model. Even if the model is ETAS, temporal predictions based on the ETAS conditional intensity are not much better than those of magnitude-dependent automatic (MDA) alarms, a much simpler strategy with only one parameter instead of _ve. For a catalog of Southern Californian seismicity, ETAS predictions again o_er only slight improvement over MDA alarms
Potential Method of Predicting Coronal Mass Ejection
NASA Astrophysics Data System (ADS)
Imholt, Timothy
2001-10-01
Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communication. The early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occurs allows us to have earlier warnings as to when they will affect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME’s could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.
Potential Method of Predicting Coronal Mass Ejection
NASA Astrophysics Data System (ADS)
Imholt, Timothy; Roberts, J. A.; Scott, J. B.; University Of North Texas Team
2000-10-01
Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communications. The importance of an early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occur allows us to have earlier warnings as to when they will effect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME's could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.
Predictive Control of Networked Multiagent Systems via Cloud Computing.
Liu, Guo-Ping
2017-01-18
This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.
Predictive controller and estimator for NASA Deep Space Network antennas
NASA Technical Reports Server (NTRS)
Gawronski, W.
1992-01-01
A new design procedure is presented for a predictive controller that significantly improves antenna tracking performance. The predictive controller uses future values of the stored output command to generate the control signal. For antennas tracking stars or spacecraft, these values are known in advance, hence the predictive control scheme is easily implemented in this case. The predictive controller is designed for tracking control of the the NASA/JPL 70-m antenna. On-axis tracking is considered, where the output is taken on the encoder, or tachometer. Simulation results show a significant improvement in performance over the LQ controller.
Integrated method for combustion stability prediction
NASA Astrophysics Data System (ADS)
Yu, Y. C.; O'Hara, L.; Smith, R. J.; Anderson, W. E.; Merkle, C. L.
2011-10-01
Major obstacles in overcoming combustion instability include the absence of a mechanistic and a priori prediction capability, and the difficulty in studying instability in the laboratory due to the perceived need for testing at the full-scale pressure and geometry to ensure that important processes are maintained. A hierarchal approach toward combustion instability is described that comprises experiment, analysis, and highfidelity computation to develop combustion response submodels that can be used in engineering-level design analysis. The paper provides an illustrative example of how these elements are used to develop a prediction for growth rates in model rocket combustors that generate spontaneous longitudinal combustion instabilities.
Control method for prosthetic devices
NASA Technical Reports Server (NTRS)
Bozeman, Richard J., Jr. (Inventor)
1995-01-01
A control system and method for prosthetic devices is provided. The control system comprises a transducer for receiving movement from a body part for generating a sensing signal associated with that movement. The sensing signal is processed by a linearizer for linearizing the sensing signal to be a linear function of the magnitude of the distance moved by the body part. The linearized sensing signal is normalized to be a function of the entire range of body part movement from the no-shrug position of the moveable body part. The normalized signal is divided into a plurality of discrete command signals. The discrete command signals are used by typical converter devices which are in operational association with the prosthetic device. The converter device uses the discrete command signals for driving the moveable portions of the prosthetic device and its sub-prosthesis. The method for controlling a prosthetic device associated with the present invention comprises the steps of receiving the movement from the body part, generating a sensing signal in association with the movement of the body part, linearizing the sensing signal to be a linear function of the magnitude of the distance moved by the body part, normalizing the linear signal to be a function of the entire range of the body part movement, dividing the normalized signal into a plurality of discrete command signals, and implementing the plurality of discrete command signals for driving the respective moveable prosthesis device and its sub-prosthesis.
Predictions for rapid methods and automation in food microbiology.
Fung, Daniel Y C
2002-01-01
A discussion is presented on the present status of rapid methods and automation in microbiology. Predictions are also presented for development in the following areas: viable cell counts; real-time monitoring of hygiene; polymerase chain reaction, ribotyping, and genetic tests in food laboratories; automated enzyme-linked immunosorbent assay and immunotests; rapid dipstick technology; biosensors for Hazard Analysis Critical Control Point programs; instant detection of target pathogens by computer-generated matrix; effective separation and concentration for rapid identification of target cells; microbiological alert systems in food packages; and rapid alert kits for detecting pathogens at home.
Experimental results of a predictive neural network HVAC controller
Jeannette, E.; Assawamartbunlue, K.; Kreider, J.F.; Curtiss, P.S.
1998-12-31
Proportional, integral, and derivative (PID) control is widely used in many HVAC control processes and requires constant attention for optimal control. Artificial neural networks offer the potential for improved control of processes through predictive techniques. This paper introduces and shows experimental results of a predictive neural network (PNN) controller applied to an unstable hot water system in an air-handling unit. Actual laboratory testing of the PNN and PID controllers show favorable results for the PNN controller.
A Novel Method for Satellite Maneuver Prediction
NASA Astrophysics Data System (ADS)
Shabarekh, C.; Kent-Bryant, J.; Keselman, G.; Mitidis, A.
2016-09-01
A space operations tradecraft consisting of detect-track-characterize-catalog is insufficient for maintaining Space Situational Awareness (SSA) as space becomes increasingly congested and contested. In this paper, we apply analytical methodology from the Geospatial-Intelligence (GEOINT) community to a key challenge in SSA: predicting where and when a satellite may maneuver in the future. We developed a machine learning approach to probabilistically characterize Patterns of Life (PoL) for geosynchronous (GEO) satellites. PoL are repeatable, predictable behaviors that an object exhibits within a context and is driven by spatio-temporal, relational, environmental and physical constraints. An example of PoL are station-keeping maneuvers in GEO which become generally predictable as the satellite re-positions itself to account for orbital perturbations. In an earlier publication, we demonstrated the ability to probabilistically predict maneuvers of the Galaxy 15 (NORAD ID: 28884) satellite with high confidence eight days in advance of the actual maneuver. Additionally, we were able to detect deviations from expected PoL within hours of the predicted maneuver [6]. This was done with a custom unsupervised machine learning algorithm, the Interval Similarity Model (ISM), which learns repeating intervals of maneuver patterns from unlabeled historical observations and then predicts future maneuvers. In this paper, we introduce a supervised machine learning algorithm that works in conjunction with the ISM to produce a probabilistic distribution of when future maneuvers will occur. The supervised approach uses a Support Vector Machine (SVM) to process the orbit state whereas the ISM processes the temporal intervals between maneuvers and the physics-based characteristics of the maneuvers. This multiple model approach capitalizes on the mathematical strengths of each respective algorithm while incorporating multiple features and inputs. Initial findings indicate that the combined
Nonlinear Model Predictive Control for Cooperative Control and Estimation
NASA Astrophysics Data System (ADS)
Ru, Pengkai
Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation. Linear MPC has been well researched and its stability can be guaranteed in the majority of its application scenarios. However, one issue that still remains with linear MPC is that it completely ignores the system's inherent nonlinearities thus giving a sub-optimal solution. On the other hand, if achievable, nonlinear MPC, would naturally yield a globally optimal solution and take into account all the innate nonlinear characteristics. While an exact solution to a nonlinear MPC problem remains extremely computationally intensive, if not impossible, one might wonder if there is a middle ground between the two. We tried to strike a balance in this dissertation by employing a state representation technique, namely, the state dependent coefficient (SDC) representation. This new technique would render an improved performance in terms of optimality compared to linear MPC while still keeping the problem tractable. In fact, the computational power required is bounded only by a constant factor of the completely linearized MPC. The purpose of this research is to provide a theoretical framework for the design of a specific kind of nonlinear MPC controller and its extension into a general cooperative scheme. The controller is designed and implemented on quadcopter systems.
Novel method for controlled oxidation
Benjamin, B.M.; Raaen, V.F.
1981-01-01
The purpose of this paper is to describe a novel method for the oxidative degradation of coal or other organic material. The procedure is potentially useful for structure determination. As originally conceived, this method was intended for use with aqueous potassium permanganate as oxidant, but it is equally applicable with other oxidizing agents. Sodium hyprochlorite can be substituted for KMnO/sub 4/ except that controlling the pH and monitoring the end pilot become more difficult. Results with potassium permanganate only are described here but sodium hypochlorite was tried. An advantageous feature of the method is the simultaneous removal of soluble products from further contact with oxidizing agent as the oxidizing agent attacks the substrate. In principle, the experimental approach resembles that of column chromatography. Any oxidative degradation of a natural product for structure determination is of little use if carried out too far; for example, to the smallest, most oxidation-resistant materials such as carbon dioxide, acetic acid, and benzoic acid. Potassium permanganate oxidations of reactive species such as coal and kerogen are particularly difficult to control. Partially oxidized fragments which go into solution can be attacked more effectively than the solid starting phase, a situation which results in loss of structural information. Another difficulty is that phenolic materials can undergo coupling reactions thus generating larger molecules and giving misleading results due to a larger number of substituents. The procedure used is described.
A survey of aftbody flow prediction methods
NASA Technical Reports Server (NTRS)
Putnam, L. E.; Mace, J.
1981-01-01
A survey of computational methods used in the calculation of nozzle aftbody flows is presented. One class of methods reviewed are those which patch together solutions for the inviscid, boundary layer, and plume flow regions. The second class of methods reviewed are those which computationally solve the Navier Stokes equations over nozzle aftbodies with jet exhaust flow. Computed results from the methods are compared with experiment. Advantages and disadvantages of the various methods are discussed along with opportunities for further development of these methods.
Methanol tailgas combustor control method
Hart-Predmore, David J.; Pettit, William H.
2002-01-01
A method for controlling the power and temperature and fuel source of a combustor in a fuel cell apparatus to supply heat to a fuel processor where the combustor has dual fuel inlet streams including a first fuel stream, and a second fuel stream of anode effluent from the fuel cell and reformate from the fuel processor. In all operating modes, an enthalpy balance is determined by regulating the amount of the first and/or second fuel streams and the quantity of the first air flow stream to support fuel processor power requirements.
[Lice and methods of control].
Franc, M
1994-12-01
The morphology and biology of sucking lice (Anoplura) and biting lice (Mallophaga) are described. A table shows the main species for given hosts and provides simplified keys for identification. Lice have a direct pathogenic effect (damage to skin and cutaneous appendages, fall in productivity) and an indirect effect (transmission of Rickettsia prowazeki, R. quintana and Borrelia recurrentis in human beings; African and classical swine fever virus, equine infectious anaemia virus and Dipylidium caninum in animals). Control methods, current active insecticides (and those being tested) and appropriate formulations are outlined.
Predictive Thermal Control Applied to HabEx
NASA Technical Reports Server (NTRS)
Brooks, Thomas E.
2017-01-01
Exoplanet science can be accomplished with a telescope that has an internal coronagraph or with an external starshade. An internal coronagraph architecture requires extreme wavefront stability (10 pm change/10 minutes for 10(exp -10) contrast), so every source of wavefront error (WFE) must be controlled. Analysis has been done to estimate the thermal stability required to meet the wavefront stability requirement. This paper illustrates the potential of a new thermal control method called predictive thermal control (PTC) to achieve the required thermal stability. A simple development test using PTC indicates that PTC may meet the thermal stability requirements. Further testing of the PTC method in flight-like environments will be conducted in the X-ray and Cryogenic Facility (XRCF) at Marshall Space Flight Center (MSFC).
Precise flight-path control using a predictive algorithm
NASA Technical Reports Server (NTRS)
Hess, R. A.; Jung, Y. C.
1991-01-01
Generalized predictive control describes an algorithm for the control of dynamic systems in which a control input is generated that minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. A design technique is discussed for applying the single-input/single-output generalized predictive control algorithm to a problem of longitudinal/vertical terrain-following flight of a rotorcraft. By using the generalized predictive control technique to provide inputs to a classically designed stability and control augmentation system, it is demonstrated that a robust flight-path control system can be created that exhibits excellent tracking performance.
Gene structure prediction by linguistic methods
Dong, S.; Searls, D.B.
1994-10-01
The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and to assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters of the grammar rules are optimized for several different species, and mixing experiments are performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction. 24 refs., 5 figs., 3 tabs.
Predictive and Neural Predictive Control of Uncertain Systems
NASA Technical Reports Server (NTRS)
Kelkar, Atul G.
2000-01-01
Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.
Evaluating Prediction Markets for Internal Control Applications
2016-05-01
0 1. Introduction Since the beginning of prediction market research , various applications have been discussed . From the predictions of political...While the use 2 of corporate prediction markets for research purposes tends to lack transparency and reproducibility, the experi ment was...Econontic Research 1.60 Lecturer #3 Responsible Project Management 0.67 Lecturer #5 Macroeconomics 2. 14 Lecturer #6 Market and State 2.59
Applying new optimization algorithms to more predictive control
Wright, S.J.
1996-03-01
The connections between optimization and control theory have been explored by many researchers and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.
Method for controlling protein crystallization
NASA Technical Reports Server (NTRS)
Noever, David A. (Inventor)
1993-01-01
A method and apparatus for controlling the crystallization of protein by solvent evaporation including placing a drop of protein solution between and in contact with a pair of parallel plates and driving one of the plates toward and away from the other plate in a controlled manner to adjust the spacing between the plates is presented. The drop of solution forms a liquid cylinder having a height dependent upon the plate spacing thereby effecting the surface area available for solvent evaporation. When the spacing is close, evaporation is slow. Evaporation is increased by increasing the spacing between the plates until the breaking point of the liquid cylinder. One plate is mounted upon a fixed post while the other plate is carried by a receptacle movable relative to the post and driven by a belt driven screw drive. The temperature and humidity of the drop of protein solution are controlled by sealing the drop within the receptacle and mounting a heater and dessicant within the receptacle.
Sequential Prediction for Information Fusion and Control
2013-10-14
paradigms , and external feedback mechanisms. Online prediction and targeted collection of information is an emerging paradigm at the intersection of...unknown, environmental dynamics, potentially stemming from an adversary who reacts to sensing actions, active sensing paradigms , and external feedback mech...anisms. Online prediction and targeted collection of information is an emerging paradigm at the inter- section of optimization, machine learning and
Gaussian mixture models as flux prediction method for central receivers
NASA Astrophysics Data System (ADS)
Grobler, Annemarie; Gauché, Paul; Smit, Willie
2016-05-01
Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.
Sann: solvent accessibility prediction of proteins by nearest neighbor method.
Joo, Keehyoung; Lee, Sung Jong; Lee, Jooyoung
2012-07-01
We present a method to predict the solvent accessibility of proteins which is based on a nearest neighbor method applied to the sequence profiles. Using the method, continuous real-value prediction as well as two-state and three-state discrete predictions can be obtained. The method utilizes the z-score value of the distance measure in the feature vector space to estimate the relative contribution among the k-nearest neighbors for prediction of the discrete and continuous solvent accessibility. The Solvent accessibility database is constructed from 5717 proteins extracted from PISCES culling server with the cutoff of 25% sequence identities. Using optimal parameters, the prediction accuracies (for discrete predictions) of 78.38% (two-state prediction with the threshold of 25%), 65.1% (three-state prediction with the thresholds of 9 and 36%), and the Pearson correlation coefficient (between the predicted and true RSA's for continuous prediction) of 0.676 are achieved An independent benchmark test was performed with the CASP8 targets where we find that the proposed method outperforms existing methods. The prediction accuracies are 80.89% (for two state prediction with the threshold of 25%), 67.58% (three-state prediction), and the Pearson correlation coefficient of 0.727 (for continuous prediction) with mean absolute error of 0.148. We have also investigated the effect of increasing database sizes on the prediction accuracy, where additional improvement in the accuracy is observed as the database size increases. The SANN web server is available at http://lee.kias.re.kr/~newton/sann/. Copyright © 2012 Wiley Periodicals, Inc.
NASA Astrophysics Data System (ADS)
Ali, Zeeshan; Popov, Atanas A.; Charles, Guy
2013-06-01
A vehicle following control law, based on the model predictive control method, to perform transition manoeuvres (TMs) for a nonlinear adaptive cruise control (ACC) vehicle is presented in this paper. The TM controller ultimately establishes a steady-state following distance behind a preceding vehicle to avoid collision, keeping account of acceleration limits, safe distance, and state constraints. The vehicle dynamics model is for continuous-time domain and captures the real dynamics of the sub-vehicle models for steady-state and transient operations. The ACC vehicle can execute the TM successfully and achieves a steady-state in the presence of complex dynamics within the constraint boundaries.
Quality Control Analytical Methods: Method Validation.
Klang, Mark G; Williams, LaVonn A
2016-01-01
To properly determine the accuracy of a pharmaceutical product or compounded preparation, tests must be designed specifically for that evaluation. The procedures selected must be verified through a process referred to as method validation, an integral part of any good analytical practice. The results from a method validation procedure can be used to judge the quality, reliability, and consistency of analytical results. The purpose of this article is to deliver the message of the importance of validation of a pharmaceutical product or compounded preparation and to briefly discuss the results of a lack of such validation. Copyright© by International Journal of Pharmaceutical Compounding, Inc.
A survey of the broadband shock associated noise prediction methods
NASA Technical Reports Server (NTRS)
Kim, Chan M.; Krejsa, Eugene A.; Khavaran, Abbas
1992-01-01
Several different prediction methods to estimate the broadband shock associated noise of a supersonic jet are introduced and compared with experimental data at various test conditions. The nozzle geometries considered for comparison include a convergent and a convergent-divergent nozzle, both axisymmetric. Capabilities and limitations of prediction methods in incorporating the two nozzle geometries, flight effect, and temperature effect are discussed. Predicted noise field shows the best agreement for a convergent nozzle geometry under static conditions. Predicted results for nozzles in flight show larger discrepancies from data and more dependable flight data are required for further comparison. Qualitative effects of jet temperature, as observed in experiment, are reproduced in predicted results.
Protein beta-turn prediction using nearest-neighbor method.
Kim, Saejoon
2004-01-01
With the emerging success of protein secondary structure prediction through the applications of various statistical and machine learning techniques, similar techniques have been applied to protein beta-turn prediction. In this study, we perform protein beta-turn prediction using a k-nearest neighbor method, which is combined with a filter that uses predicted protein secondary structure information. Traditional beta-turn prediction from k-nearest neighbor method is modified to account for the unbalanced ratio of the natural occurrence of beta-turns and non-beta-turns. Our prediction scheme is tested on a set of 426 non-homologous protein sequences. The prediction scheme consists of two stages: k-nearest neighbor method stage and filtering stage. Variations of the k-nearest neighbor method were used to take property of beta-turns into consideration. Our filtering method uses beta-turn/non-beta-turn estimates from the k-nearest neighbor method stage and predicted protein secondary structure information from PSI-PRED in order to get new beta-turn/non-beta-turn estimate. Our result is compared with the previously best known beta-turn prediction method on the dataset of 426 non-homologous protein sequences and is shown to give slightly superior performance at significantly lower computational complexity. Contact the author for information on the source code of the programs used.
Method for Predicting Which Customers' Time Deposit Balances Will Increase
NASA Astrophysics Data System (ADS)
Ono, Toshiyuki; Yoshikawa, Hiroshi; Morita, Masahiro; Komoda, Norihisa
This paper proposes a method of predicting which customers' account balances will increase by using data mining to effectively and efficiently promote sales. Prediction by mining all the data in a business is difficult because of much time required to collect, process, and calculate it. The selection of which features are used for prediction is a critical issue. We propose a method of selecting features to improve the accuracy of prediction within practical time limits. It consists of three parts: (1) converting collected features into financial behavior features that reflect customer actions, (2) extracting features affecting increases in account balances from these collected and financial behavior features, and (3) predicting customers whose account balances will increase based on the extracted features. We found the accuracy of prediction in an experiment with our method to be higher than with other conventional methods.
Improved method for predicting protein fold patterns with ensemble classifiers.
Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C
2012-01-27
Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.
Efficient Methods to Compute Genomic Predictions
USDA-ARS?s Scientific Manuscript database
Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and simultaneously estimate thousands of marker effects. Algorithms were derived and computer programs tested on simulated data for 50,000 markers and 2,967 bulls. Accurate estimates of ...
Developing Control System of Electrical Devices with Operational Expense Prediction
NASA Astrophysics Data System (ADS)
Sendari, Siti; Wahyu Herwanto, Heru; Rahmawati, Yuni; Mukti Putranto, Dendi; Fitri, Shofiana
2017-04-01
The purpose of this research is to develop a system that can monitor and record home electrical device’s electricity usage. This system has an ability to control electrical devices in distance and predict the operational expense. The system was developed using micro-controllers and WiFi modules connected to PC server. The communication between modules is arranged by server via WiFi. Beside of reading home electrical devices electricity usage, the unique point of the proposed-system is the ability of micro-controllers to send electricity data to server for recording the usage of electrical devices. The testing of this research was done by Black-box method to test the functionality of system. Testing system run well with 0% error.
Statistical energy analysis response prediction methods for structural systems
NASA Technical Reports Server (NTRS)
Davis, R. F.
1979-01-01
The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.
Adaptive Current Control Method for Hybrid Active Power Filter
NASA Astrophysics Data System (ADS)
Chau, Minh Thuyen
2016-09-01
This paper proposes an adaptive current control method for Hybrid Active Power Filter (HAPF). It consists of a fuzzy-neural controller, identification and prediction model and cost function. The fuzzy-neural controller parameters are adjusted according to the cost function minimum criteria. For this reason, the proposed control method has a capability on-line control clings to variation of the load harmonic currents. Compared to the single fuzzy logic control method, the proposed control method shows the advantages of better dynamic response, compensation error in steady-state is smaller, able to online control is better and harmonics cancelling is more effective. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.
Predictive powertrain control using powertrain history and GPS data
Weslati, Feisel; Krupadanam, Ashish A
2015-03-03
A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.
A Versatile Nonlinear Method for Predictive Modeling
NASA Technical Reports Server (NTRS)
Liou, Meng-Sing; Yao, Weigang
2015-01-01
As computational fluid dynamics techniques and tools become widely accepted for realworld practice today, it is intriguing to ask: what areas can it be utilized to its potential in the future. Some promising areas include design optimization and exploration of fluid dynamics phenomena (the concept of numerical wind tunnel), in which both have the common feature where some parameters are varied repeatedly and the computation can be costly. We are especially interested in the need for an accurate and efficient approach for handling these applications: (1) capturing complex nonlinear dynamics inherent in a system under consideration and (2) versatility (robustness) to encompass a range of parametric variations. In our previous paper, we proposed to use first-order Taylor expansion collected at numerous sampling points along a trajectory and assembled together via nonlinear weighting functions. The validity and performance of this approach was demonstrated for a number of problems with a vastly different input functions. In this study, we are especially interested in enhancing the method's accuracy; we extend it to include the second-orer Taylor expansion, which however requires a complicated evaluation of Hessian matrices for a system of equations, like in fluid dynamics. We propose a method to avoid these Hessian matrices, while maintaining the accuracy. Results based on the method are presented to confirm its validity.
An evidential link prediction method and link predictability based on Shannon entropy
NASA Astrophysics Data System (ADS)
Yin, Likang; Zheng, Haoyang; Bian, Tian; Deng, Yong
2017-09-01
Predicting missing links is of both theoretical value and practical interest in network science. In this paper, we empirically investigate a new link prediction method base on similarity and compare nine well-known local similarity measures on nine real networks. Most of the previous studies focus on the accuracy, however, it is crucial to consider the link predictability as an initial property of networks itself. Hence, this paper has proposed a new link prediction approach called evidential measure (EM) based on Dempster-Shafer theory. Moreover, this paper proposed a new method to measure link predictability via local information and Shannon entropy.
New Methods for Estimating Seasonal Potential Climate Predictability
NASA Astrophysics Data System (ADS)
Feng, Xia
This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that
A numerical method for predicting hypersonic flowfields
NASA Technical Reports Server (NTRS)
Maccormack, Robert W.; Candler, Graham V.
1988-01-01
The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to react chemically and reach states in thermal nonequilibrium. In this paper, a new procedure based on Gauss-Seidel line relaxation is shown to solve the equations of hypersonic flow fields containing finite reaction rate chemistry and thermal nonequilibrium. The method requires a few hundred time steps and small computer times for axisymmetric flows about simple body shapes. The extension to more complex two-dimensional body geometries appears straightforward.
A numerical method for predicting hypersonic flowfields
NASA Technical Reports Server (NTRS)
Maccormack, Robert W.; Candler, Graham V.
1988-01-01
The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to react chemically and reach states in thermal nonequilibrium. In this paper, a new procedure based on Gauss-Seidel line relaxation is shown to solve the equations of hypersonic flow fields containing finite reaction rate chemistry and thermal nonequilibrium. The method requires a few hundred time steps and small computer times for axisymmetric flows about simple body shapes. The extension to more complex two-dimensional body geometries appears straightforward.
A survey of spectrum prediction methods in cognitive radio networks
NASA Astrophysics Data System (ADS)
Wu, Jianwei; Li, Yanling
2017-04-01
Spectrum prediction technology is an effective way to solve the problems of processing latency, spectrum access, spectrum collision and energy consumption in cognitive radio networks. Spectral prediction technology is divided into three categories according to its nature, namely, spectral prediction method based on regression analysis, spectrum prediction method based on Markov model and spectrum prediction method based on machine learning. By analyzing and comparing the three kinds of prediction models, the author hopes to provide some reference for the later researchers. In this paper, the development situation, practical application and existent problems of three kinds of forecasting models are analyzed and summarized. On this basis, this paper discusses the development trend of the next step.
Study on noise prediction model and control schemes for substation.
Chen, Chuanmin; Gao, Yang; Liu, Songtao
2014-01-01
With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods.
Study on Noise Prediction Model and Control Schemes for Substation
Gao, Yang; Liu, Songtao
2014-01-01
With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356
The predictive integration method for dynamics of infrequent events
NASA Astrophysics Data System (ADS)
Cubuk, Ekin; Waterland, Amos; Kaxiras, Efthimios
2012-02-01
With the increasing prominence and availability of multi-processor computers, recasting problems in a form amenable to parallel solution is becoming a critical step in effective scientific computation. We present a method for parallelizing molecular dynamics simulations in time scale, by using predictive integration. Our method is closely related to Voter's parallel replica method, but goes beyond that approach in that it involves speculatively initializing processors in more than one basin. Our predictive integration method requires predicting possible future configurations while it does not suffer from restrictions due to correlation time after transitions between basins.
Model predictive control power management strategies for HEVs: A review
NASA Astrophysics Data System (ADS)
Huang, Yanjun; Wang, Hong; Khajepour, Amir; He, Hongwen; Ji, Jie
2017-02-01
This paper presents a comprehensive review of power management strategy (PMS) utilized in hybrid electric vehicles (HEVs) with an emphasis on model predictive control (MPC) based strategies for the first time. Research on MPC-based power management systems for HEVs has intensified recently due to its many inherent merits. The categories of the existing PMSs are identified from the latest literature, and a brief study of each type is conducted. Then, the MPC approach is introduced and its advantages are discussed. Based on the acquisition method of driver behavior used for state prediction and the dynamic model used, the MPC is classified and elaborated. Factors that affect the performance of the MPC are put forward, including prediction accuracy, design parameters, and solvers. Finally, several important issues in the application of MPC-based power management strategies and latest developing trends are discussed. This paper not only provides a comprehensive analysis of MPC-based power management strategies for HEVs but also puts forward the future and emphasis of future study, which will promote the development of energy management controller with high performance and low cost for HEVs.
Consensus and Stability Analysis of Networked Multiagent Predictive Control Systems.
Liu, Guo-Ping
2017-04-01
This paper is concerned with the consensus and stability problem of multiagent control systems via networks with communication delays and data loss. A networked multiagent predictive control scheme is proposed to achieve output consensus and also compensate for the communication delays and data loss actively. The necessary and sufficient conditions of achieving both consensus and stability of the closed-loop networked multiagent control systems are derived. An important result that is obtained is that the consensus and stability of closed-loop networked multiagent predictive control systems are not related to the communication delays and data loss. An example illustrates the performance of the networked multiagent predictive control scheme.
Scaling--which methods best predict performance?
Comfort, Paul; Pearson, Stephen J
2014-06-01
Athletes with a higher body mass (BM) tend to be stronger, with ratio scaling possibly eliminating this effect. The aim of this study was to compare relationships between sprint performances with scaled measures of strength and power. Fifteen professional rugby league players (age, 26.27 6 3.87 years; height, 183.33 6 6.37 cm; BM, 96.86 6 11.49 kg) performed 1 repetition maximum back squats, power cleans, squat jumps, and sprints (5, 10, and 20 m). Heavier athletes (forward) generated significantly greater absolute levels of power during the squat jump (5,659.11 6 710.35 vs.4,740.16 6 558.61 W; p , 0.001); however, when power data were scaled no differences were observed. Squat performance indicated no differences in absolute ability between the subgroups (190.6 6 14.25 vs. 205.7 6 18.35 kg), although the lighter group was significantly (p # 0.05) stronger than the heavier group when using ratio and allometric methods (2.1 vs. 1.9 kg · kg(-1) and 10.42 vs. 9.87 kg · kg(0.28)), respectively. Significant relationships with 5-m sprints were only observed for ratio and allometrically scaled power cleans (r = 20.625, p , 0.02; r = 20.675, p , 0.02), with similar correlations between allometrically scaled 10-m sprint and both back squat and power clean performances. Scaled power clean performances were also inversely correlated with 20-m sprints (r = 20.620, r = 20.638, p , 0.02). Where differences in absolute strength are apparent between individuals of different BM, then the use of scaling is required. Because of the similarity between ratio and allometric methods, simple ratio scaling is recommended.
Deep learning methods for protein torsion angle prediction.
Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin
2017-09-18
Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.
Multiplexed Predictive Control of a Large Commercial Turbofan Engine
NASA Technical Reports Server (NTRS)
Richter, hanz; Singaraju, Anil; Litt, Jonathan S.
2008-01-01
Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.
NASA Astrophysics Data System (ADS)
Maiwald, Thomas; Winterhalder, Matthias; Aschenbrenner-Scheibe, Richard; Voss, Henning U.; Schulze-Bonhage, Andreas; Timmer, Jens
2004-07-01
Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. The predictability of these seizures would render novel therapeutic approaches possible. Several prediction methods have claimed to be able to predict seizures based on EEG recordings minutes in advance. However, the term seizure prediction is not unequivocally defined, different criteria to assess prediction methods exist, and only little attention has been paid to issues of sensitivity and false prediction rate. We introduce an assessment criterion called the seizure prediction characteristic that incorporates the assessment of sensitivity and false prediction rate. Within this framework, three nonlinear seizure prediction methods were evaluated on a large EEG data pool of 21 patients. Altogether, 582 h intracranial EEG data and 88 seizures were examined. With a rate of 1-3.6 false predictions per day, the “dynamical similarity index” achieves a sensitivity between 21 and 42%, which was the best result of the three methods. Sensitivity was between 18 and 31% for the extended, prospective version of the “accumulated energy” and between 13 and 30% for the “effective correlation dimension”. These results still are not sufficient for clinical applications.
Constrained model predictive control, state estimation and coordination
NASA Astrophysics Data System (ADS)
Yan, Jun
In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to
Experimental validation of boundary element methods for noise prediction
NASA Technical Reports Server (NTRS)
Seybert, A. F.; Oswald, Fred B.
1992-01-01
Experimental validation of methods to predict radiated noise is presented. A combined finite element and boundary element model was used to predict the vibration and noise of a rectangular box excited by a mechanical shaker. The predicted noise was compared to sound power measured by the acoustic intensity method. Inaccuracies in the finite element model shifted the resonance frequencies by about 5 percent. The predicted and measured sound power levels agree within about 2.5 dB. In a second experiment, measured vibration data was used with a boundary element model to predict noise radiation from the top of an operating gearbox. The predicted and measured sound power for the gearbox agree within about 3 dB.
Integrated control system and method
Wang, Paul Sai Keat; Baldwin, Darryl; Kim, Myoungjin
2013-10-29
An integrated control system for use with an engine connected to a generator providing electrical power to a switchgear is disclosed. The engine receives gas produced by a gasifier. The control system includes an electronic controller associated with the gasifier, engine, generator, and switchgear. A gas flow sensor monitors a gas flow from the gasifier to the engine through an engine gas control valve and provides a gas flow signal to the electronic controller. A gas oversupply sensor monitors a gas oversupply from the gasifier and provides an oversupply signal indicative of gas not provided to the engine. A power output sensor monitors a power output of the switchgear and provide a power output signal. The electronic controller changes gas production of the gasifier and the power output rating of the switchgear based on the gas flow signal, the oversupply signal, and the power output signal.
A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction.
Singh, Shailendra; Kaur, Sukhbir; Goel, Neelam
2015-01-01
In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA that helps in transcription of a gene. Promoter is one such region and to find its location is a challenging problem. Various computational methods for promoter prediction have been developed over the past few years. This paper reviews these promoter prediction methods. Several difficulties and pitfalls encountered by these methods are also detailed, along with future research directions.
A method to predict electromigration failure of metal lines
NASA Astrophysics Data System (ADS)
Sasagawa, Kazuhiko; Naito, Kazushi; Saka, Masumi; Abé, Hiroyuki
1999-12-01
A new calculation method of atomic flux divergence (AFDgen) due to electromigration has recently been proposed by considering all the factors on void formation, and AFDgen has been identified as a parameter governing void formation by observing agreement of the numerical prediction of the void with experiment. In this article, a method to predict the electromigration failure of metal lines was proposed by using AFDgen. Lifetime and failure site in a polycrystalline line were predicted by numerical simulation of the processes of void initiation, its growth to line failure, where the change in distributions of current density and temperature with void growth was taken into account. The usefulness of this prediction method was verified by the experiment where the angled aluminum line was treated. The failure location was determined by the line shape and the operating condition. The present simulation accurately predicted the lifetime as well as the failure location of the metal line.
A K-nearest neighbors survival probability prediction method.
Lowsky, D J; Ding, Y; Lee, D K K; McCulloch, C E; Ross, L F; Thistlethwaite, J R; Zenios, S A
2013-05-30
We introduce a nonparametric survival prediction method for right-censored data. The method generates a survival curve prediction by constructing a (weighted) Kaplan-Meier estimator using the outcomes of the K most similar training observations. Each observation has an associated set of covariates, and a metric on the covariate space is used to measure similarity between observations. We apply our method to a kidney transplantation data set to generate patient-specific distributions of graft survival and to a simulated data set in which the proportional hazards assumption is explicitly violated. We compare the performance of our method with the standard Cox model and the random survival forests method.
Model-Based Predictive Control of Turbulent Channel Flow
NASA Astrophysics Data System (ADS)
Kellogg, Steven M.; Collis, S. Scott
1999-11-01
In recent simulations of optimal turbulence control, the time horizon over which the control is determined matches the time horizon over which the flow is advanced. A popular workhorse of the controls community, Model-Based Predictive Control (MBPC), suggests using longer predictive horizons than advancement windows. Including additional time information in the optimization may generate improved controls. When the advancement horizon is smaller than the predictive horizon, part of the optimization and resulting control are discarded. Although this inherent inefficiency may be justified by improved control predictions, it has hampered prior investigations of MBPC for turbulent flow due to the expense associated with optimal control based on Direct Numerical Simulation. The current approach overcomes this by using our optimal control formulation based on Large Eddy Simulation. This presentation summarizes the results of optimal control simulations for turbulent channel flow using various ratios of advancement and predictive horizons. These results provide clues as to the roles of foresight, control history, cost functional, and turbulence structures for optimal control of wall-bounded turbulence.
A graph kernel method for DNA-binding site prediction.
Yan, Changhui; Wang, Yingfeng
2014-01-01
Protein-DNA interactions play important roles in many biological processes. Computational methods that can accurately predict DNA-binding sites on proteins will greatly expedite research on problems involving protein-DNA interactions. This paper presents a method for predicting DNA-binding sites on protein structures. The method represents protein surface patches using labeled graphs and uses a graph kernel method to calculate the similarities between graphs. A new surface patch is predicted to be interface or non-interface patch based on its similarities to known DNA-binding patches and non-DNA-binding patches. The proposed method achieved high accuracy when tested on a representative set of 146 protein-DNA complexes using leave-one-out cross-validation. Then, the method was applied to identify DNA-binding sites on 13 unbound structures of DNA-binding proteins. In each of the unbound structure, the top 1 patch predicted by the proposed method precisely indicated the location of the DNA-binding site. Comparisons with other methods showed that the proposed method was competitive in predicting DNA-binding sites on unbound proteins. The proposed method uses graphs to encode the feature's distribution in the 3-dimensional (3D) space. Thus, compared with other vector-based methods, it has the advantage of taking into account the spatial distribution of features on the proteins. Using an efficient kernel method to compare graphs the proposed method also avoids the demanding computations required for 3D objects comparison. It provides a competitive method for predicting DNA-binding sites without requiring structure alignment.
Prospects for earthquake prediction and control
Healy, J.H.; Lee, W.H.K.; Pakiser, L.C.; Raleigh, C.B.; Wood, M.D.
1972-01-01
The San Andreas fault is viewed, according to the concepts of seafloor spreading and plate tectonics, as a transform fault that separates the Pacific and North American plates and along which relative movements of 2 to 6 cm/year have been taking place. The resulting strain can be released by creep, by earthquakes of moderate size, or (as near San Francisco and Los Angeles) by great earthquakes. Microearthquakes, as mapped by a dense seismograph network in central California, generally coincide with zones of the San Andreas fault system that are creeping. Microearthquakes are few and scattered in zones where elastic energy is being stored. Changes in the rate of strain, as recorded by tiltmeter arrays, have been observed before several earthquakes of about magnitude 4. Changes in fluid pressure may control timing of seismic activity and make it possible to control natural earthquakes by controlling variations in fluid pressure in fault zones. An experiment in earthquake control is underway at the Rangely oil field in Colorado, where the rates of fluid injection and withdrawal in experimental wells are being controlled. ?? 1972.
Tuning the Model Predictive Control of a Crude Distillation Unit.
Yamashita, André Shigueo; Zanin, Antonio Carlos; Odloak, Darci
2016-01-01
Tuning the parameters of the Model Predictive Control (MPC) of an industrial Crude Distillation Unit (CDU) is considered here. A realistic scenario is depicted where the inputs of the CDU system have optimizing targets, which are provided by the Real Time Optimization layer of the control structure. It is considered the nominal case, in which both the CDU model and the MPC model are the same. The process outputs are controlled inside zones instead of at fixed set points. Then, the tuning procedure has to define the weights that penalize the output error with respect to the control zone, the weights that penalize the deviation of the inputs from their targets, as well as the weights that penalize the input moves. A tuning approach based on multi-objective optimization is proposed and applied to the MPC of the CDU system. The performance of the controller tuned with the proposed approach is compared through simulation with the results of an existing approach also based on multi-objective optimization. The simulation results are similar, but the proposed approach has a computational load significantly lower than the existing method. The tuning effort is also much lower than in the conventional practical approaches that are usually based on ad-hoc procedures.
Tetrahedron-tiling method for crystal structure prediction
NASA Astrophysics Data System (ADS)
Hong, Qi-Jun; Yasi, Joseph; van de Walle, Axel
2017-07-01
Reliable and robust methods of predicting the crystal structure of a compound, based only on its chemical composition, is crucial to the study of materials and their applications. Despite considerable ongoing research efforts, crystal structure prediction remains a challenging problem that demands large computational resources. Here we propose an efficient approach for first-principles crystal structure prediction. The new method explores and finds crystal structures by tiling together elementary tetrahedra that are energetically favorable and geometrically matching each other. This approach has three distinguishing features: a favorable building unit, an efficient calculation of local energy, and a stochastic Monte Carlo simulation of crystal growth. By applying the method to the crystal structure prediction of various materials, we demonstrate its validity and potential as a promising alternative to current methods.
What Predicts Use of Learning-Centered, Interactive Engagement Methods?
ERIC Educational Resources Information Center
Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael
2014-01-01
What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…
Bagos, Pantelis G; Liakopoulos, Theodore D; Hamodrakas, Stavros J
2005-01-12
Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at http://bioinformatics.biol.uoa.gr/ConBBPRED.
Integration simulation method concerning speed control of ultrasonic motor
NASA Astrophysics Data System (ADS)
Miyauchi, R.; Yue, B.; Matsunaga, N.; Ishizuka, S.
2016-09-01
In this paper, the configuration of control system of the ultrasonic motor (USM) from finite element method (FEM) model by applying the nonlinear model order reduction (MOR) is proposed. First, the USM and the FEM model is introduced. Second, FEM model order reduction method is described. Third, the result of comparing the computing time and accuracy of the FEM model and reduced order model is shown. Finaly, nominal model for control is derived by system identification from reduced order model. Nonlinear model predictive control (NMPC) is applied to the nominal model, and speed is controlled. the controller effect is comfirmed by applying the proposed reduced order model.
An Intelligent Predictive Controller for Autonomous Vehicles
1994-05-02
Controller for Autonomous Vehicles page 4. 2.2 Mail Marla =e The Map Manager is distinguished from a perception system in the following way. It is considered...872-884. [38] K. Olin, and D. Tseng , "Autonomous Cross Country Navigation", IEEE Expert, August 1991, pp. 16-30. [39] D. A. Pomerleau, "Efficient
Interim prediction method for low frequency core engine noise
NASA Technical Reports Server (NTRS)
Huff, R. G.; Clark, B. J.; Dorsch, R. G.
1974-01-01
A literature survey on low-frequency core engine noise is presented. Possible sources of low frequency internally generated noise in core engines are discussed with emphasis on combustion and component scrubbing noise. An interim method is recommended for predicting low frequency core engine noise that is dominant when jet velocities are low. Suggestions are made for future research on low frequency core engine noise that will aid in improving the prediction method and help define possible additional internal noise sources.
Predictive control of SOFC based on a GA-RBF neural network model
NASA Astrophysics Data System (ADS)
Wu, Xiao-Juan; Zhu, Xin-Jian; Cao, Guang-Yi; Tu, Heng-Yong
Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) system. One of the main reasons is that the fuel utilization changes drastically due to the load change. Therefore, in order to guarantee the fuel utilization to operate within a safe range, a nonlinear model predictive control (MPC) method is proposed to control the stack terminal voltage as a proper constant in this paper. The nonlinear predictive controller is based on an improved radial basis function (RBF) neural network identification model. During the process of modeling, the genetic algorithm (GA) is used to optimize the parameters of RBF neural networks. And then a nonlinear predictive control algorithm is applied to track the voltage of the SOFC. Compared with the constant fuel utilization control method, the simulation results show that the nonlinear predictive control algorithm based on the GA-RBF model performs much better.
Power maximization of a point absorber wave energy converter using improved model predictive control
NASA Astrophysics Data System (ADS)
Milani, Farideh; Moghaddam, Reihaneh Kardehi
2017-08-01
This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.
Assessment of a method for the prediction of mandibular rotation.
Lee, R S; Daniel, F J; Swartz, M; Baumrind, S; Korn, E L
1987-05-01
A new method to predict mandibular rotation developed by Skieller and co-workers on a sample of 21 implant subjects with extreme growth patterns has been tested against an alternative sample of 25 implant patients with generally similar mean values, but with less extreme facial patterns. The method, which had been highly successful in retrospectively predicting changes in the sample of extreme subjects, was much less successful in predicting individual patterns of mandibular rotation in the new, less extreme sample. The observation of a large difference in the strength of the predictions for these two samples, even though their mean values were quite similar, should serve to increase our awareness of the complexity of the problem of predicting growth patterns in individual cases.
Prediction methods for jet V/STOL propulsion aerodynamics
NASA Technical Reports Server (NTRS)
Platzer, M. F.; Margason, R. J.
1976-01-01
The current status of prediction methods for propulsive flows and propulsion-induced effects which occur on jet V/STOL aircraft is reviewed. Among the major topics studied are flows in propulsive ducts, propulsion-induced ground and thermal effects, aerodynamic loads induced during V/STOL and transition flight, flow vectoring devices, and thrust augmented ejector and lift-fan studies. The current predictive capability in jet V/STOL propulsion aerodynamics is assessed. Future research needs are identified, with particular reference to activities that can improve the usefulness of prediction methods for jet V/STOL aircraft.
Comparison of prediction performance using statistical postprocessing methods
NASA Astrophysics Data System (ADS)
Han, Keunhee; Choi, JunTae; Kim, Chansoo
2016-11-01
As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.
Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems
NASA Technical Reports Server (NTRS)
McMillan, Michelle L.; Mackie, Scott A.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James L.
2011-01-01
Fail-safe, hybrid, flow control (HFC) is a promising technology for meeting high-speed cruise efficiency, low-noise signature, and reduced fuel-burn goals for future, Hybrid-Wing-Body (HWB) aircraft with embedded engines. This report details the development of HFC technology that enables improved inlet performance in HWB vehicles with highly integrated inlets and embedded engines without adversely affecting vehicle performance. In addition, new test techniques for evaluating Boundary-Layer-Ingesting (BLI)-inlet flow-control technologies developed and demonstrated through this program are documented, including the ability to generate a BLI-like inlet-entrance flow in a direct-connect, wind-tunnel facility, as well as, the use of D-optimal, statistically designed experiments to optimize test efficiency and enable interpretation of results. Validated improvements in numerical analysis tools and methods accomplished through this program are also documented, including Reynolds-Averaged Navier-Stokes CFD simulations of steady-state flow physics for baseline, BLI-inlet diffuser flow, as well as, that created by flow-control devices. Finally, numerical methods were employed in a ground-breaking attempt to directly simulate dynamic distortion. The advances in inlet technologies and prediction tools will help to meet and exceed "N+2" project goals for future HWB aircraft.
NASA Astrophysics Data System (ADS)
Zhang, Hui; Shi, Yang; Wang, Junmin
2013-10-01
This paper is concerned with a tracking controller design problem for discrete-time networked predictive control systems. The control law used here is a combined state-feedback control and integral control. Since not all the states are available in practice, a local Luenberger observer is utilised to estimate the state vector. The measured output and estimated state vector are packed together and transmitted to the tracking controller via a communication channel with a limited capacity. Meanwhile, the control signal is also transmitted through a communication network.Network-induced delays on both links are considered for the signal transmission and modelled by Markov chains. Moreover, it is assumed that the elements in Markov transition matrices are subject to uncertainties. In order to fully compensate for network-induced delays, the controller generates a sequence of control signals which are dependent on each possible delay in the feedforward channel. By taking the augmentation twice, we obtain delay-free stochastic closed-loop systems and the controlled output is chosen as the tracking error. Sufficient conditions are provided for the energy-to-peak performance of the closed-loop systems. The feedback gains of the controller can be derived by solving a minimisation problem. Two examples are illustrated to demonstrate the effectiveness of the proposed design method.
Method and device for predicting wavelength dependent radiation influences in thermal systems
Kee, Robert J.; Ting, Aili
1996-01-01
A method and apparatus for predicting the spectral (wavelength-dependent) radiation transport in thermal systems including interaction by the radiation with partially transmitting medium. The predicted model of the thermal system is used to design and control the thermal system. The predictions are well suited to be implemented in design and control of rapid thermal processing (RTP) reactors. The method involves generating a spectral thermal radiation transport model of an RTP reactor. The method also involves specifying a desired wafer time dependent temperature profile. The method further involves calculating an inverse of the generated model using the desired wafer time dependent temperature to determine heating element parameters required to produce the desired profile. The method also involves controlling the heating elements of the RTP reactor in accordance with the heating element parameters to heat the wafer in accordance with the desired profile.
In vitro method for prediction of plaque reduction by dentifrice.
Tepper, Bruce; Howard, Brian; Schnell, Daniel; Mills, Lisa; Xu, Jian
2015-11-01
An in vitro Particle Based Biofilm (PBB) model was developed to enable high throughput screening tests to predict clinical plaque reduction. Multi-species oral biofilms were cultured from pooled stimulated human saliva on continuously-colliding hydroxyapatite particles. After three days PBBs were saline washed prior to use in screening tests. Testing involved dosing PBBs for 1min followed by neutralization of test materials and rinsing. PBBs were then assayed for intact biofilm activity measured as ATP. The ranking of commercial dentifrices from most to least reduction of intact biofilm activity was Crest ProHealth Clinical Gum Protection, Crest ProHealth, Colgate Total and Crest Cavity Protection. We demonstrated five advantages of the PBB model: 1) the ATP metric had a linear response over ≥1000-fold dynamic range, 2) potential interference with the ATP assay by treatments was easily eliminated by rinsing PBBs with saline, 3) discriminating power was statistically excellent between all treatment comparisons with the negative controls, 4) screening test results were reproducible across four tests, and 5) the screening test produced the same rank order for dentifrices as clinical studies that measured plaque reduction. In addition, 454 pyrosequencing of the PBBs indicated an oral microbial consortium was present. The most prevalent genera were Neisseria, Rothia, Streptococcus, Porphyromonas, Prevotella, Actinomyces, Fusobacterium, Veillonella and Haemophilus. We conclude these in vitro methods offer an efficient, effective and relevant screening tool for reduction of intact biofilm activity by dentifrices. Moreover, dentifrice rankings by the in vitro test method are expected to predict clinical results for plaque reduction. Copyright © 2015 Elsevier B.V. All rights reserved.
A method to objectively optimize coral bleaching prediction techniques
NASA Astrophysics Data System (ADS)
van Hooidonk, R. J.; Huber, M.
2007-12-01
Thermally induced coral bleaching is a global threat to coral reef health. Methodologies, e.g. the Degree Heating Week technique, have been developed to predict bleaching induced by thermal stress by utilizing remotely sensed sea surface temperature (SST) observations. These techniques can be used as a management tool for Marine Protected Areas (MPA). Predictions are valuable to decision makers and stakeholders on weekly to monthly time scales and can be employed to build public awareness and support for mitigation. The bleaching problem is only expected to worsen because global warming poses a major threat to coral reef health. Indeed, predictive bleaching methods combined with climate model output have been used to forecast the global demise of coral reef ecosystems within coming decades due to climate change. Accuracy of these predictive techniques has not been quantitatively characterized despite the critical role they play. Assessments have typically been limited, qualitative or anecdotal, or more frequently they are simply unpublished. Quantitative accuracy assessment, using well established methods and skill scores often used in meteorology and medical sciences, will enable objective optimization of existing predictive techniques. To accomplish this, we will use existing remotely sensed data sets of sea surface temperature (AVHRR and TMI), and predictive values from techniques such as the Degree Heating Week method. We will compare these predictive values with observations of coral reef health and calculate applicable skill scores (Peirce Skill Score, Hit Rate and False Alarm Rate). We will (a) quantitatively evaluate the accuracy of existing coral reef bleaching predictive methods against state-of- the-art reef health databases, and (b) present a technique that will objectively optimize the predictive method for any given location. We will illustrate this optimization technique for reefs located in Puerto Rico and the US Virgin Islands.
Performance Comparison of Capacity Control Methods for Reciprocating Compressors
NASA Astrophysics Data System (ADS)
Wang, L.; Liu, G. B.; Zhao, Y. Y.; Li, L. L.
2015-08-01
Different capacity control methods are used for adjusting suction flow of reciprocating compressors to meet process need. Compared with recycle or bypass and suction throttling, three capacity control methods of speed control, clearance pockets and suction valve unloading are preferred due to their energy-saving at operating condition of partial load. The paper reviewed state of the art of the current capacity control technologies and their principles. A comprehensive mathematical model was developed to predict thermodynamic and dynamic performance of reciprocating compressors equipped with the capacity control systems of four above-mentioned methods. Comparison of shaft work and mechanical efficiency were conducted for different capacity control methods at the same condition. In addition, their influence on p-v diagram and valve motion were also studied, which is important for reliability and life of the reciprocating compressors. These results were helpful for selection of the capacity control systems by end-users and optimum design by manufacturers.
Machine Learning Methods for Predicting HLA–Peptide Binding Activity
Luo, Heng; Ye, Hao; Ng, Hui Wen; Shi, Leming; Tong, Weida; Mendrick, Donna L.; Hong, Huixiao
2015-01-01
As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA–peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA–peptide binding prediction. We also summarize the descriptors based on which the HLA–peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA–peptide binding prediction method based on network analysis. PMID:26512199
Preview Scheduled Model Predictive Control For Horizontal Axis Wind Turbines
NASA Astrophysics Data System (ADS)
Laks, Jason H.
This research investigates the use of model predictive control (MPC) in application to wind turbine operation from start-up to cut-out. The studies conducted are focused on the design of an MPC controller for a 650˜KW, three-bladed horizontal axis turbine that is in operation at the National Renewable Energy Laboratory's National Wind Technology Center outside of Golden, Colorado. This turbine is at the small end of utility scale turbines, but it provides advanced instrumentation and control capabilities, and there is a good probability that the approach developed in simulation for this thesis, will be field tested on the actual turbine. A contribution of this thesis is a method to combine the use of preview measurements with MPC while also providing regulation of turbine speed and cyclic blade loading. A common MPC technique provides integral-like control to achieve offset-free operation. At the same time in wind turbine applications, multiple studies have developed "feed-forward" controls based on applying a gain to an estimate of the wind speed changes obtained from an observer incorporating a disturbance model. These approaches are based on a technique that can be referred to as disturbance accommodating control (DAC). In this thesis, it is shown that offset-free tracking MPC is equivalent to a DAC approach when the disturbance gain is computed to satisfy a regulator equation. Although the MPC literature has recognized that this approach provides "structurally stable" disturbance rejection and tracking, this step is not typically divorced from the MPC computations repeated each sample hit. The DAC formulation is conceptually simpler, and essentially uncouples regulation considerations from MPC related issues. This thesis provides a self contained proof that the DAC formulation (an observer-controller and appropriate disturbance gain) provides structurally stable regulation.
Design and Performance Analysis of Incremental Networked Predictive Control Systems.
Pang, Zhong-Hua; Liu, Guo-Ping; Zhou, Donghua
2016-06-01
This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.
COMSAC: Computational Methods for Stability and Control. Part 1
NASA Technical Reports Server (NTRS)
Fremaux, C. Michael (Compiler); Hall, Robert M. (Compiler)
2004-01-01
Work on stability and control included the following reports:Introductory Remarks; Introduction to Computational Methods for Stability and Control (COMSAC); Stability & Control Challenges for COMSAC: a NASA Langley Perspective; Emerging CFD Capabilities and Outlook A NASA Langley Perspective; The Role for Computational Fluid Dynamics for Stability and Control:Is it Time?; Northrop Grumman Perspective on COMSAC; Boeing Integrated Defense Systems Perspective on COMSAC; Computational Methods in Stability and Control:WPAFB Perspective; Perspective: Raytheon Aircraft Company; A Greybeard's View of the State of Aerodynamic Prediction; Computational Methods for Stability and Control: A Perspective; Boeing TacAir Stability and Control Issues for Computational Fluid Dynamics; NAVAIR S&C Issues for CFD; An S&C Perspective on CFD; Issues, Challenges & Payoffs: A Boeing User s Perspective on CFD for S&C; and Stability and Control in Computational Simulations for Conceptual and Preliminary Design: the Past, Today, and Future?
Modified-Fibonacci-Dual-Lucas method for earthquake prediction
NASA Astrophysics Data System (ADS)
Boucouvalas, A. C.; Gkasios, M.; Tselikas, N. T.; Drakatos, G.
2015-06-01
The FDL method makes use of Fibonacci, Dual and Lucas numbers and has shown considerable success in predicting earthquake events locally as well as globally. Predicting the location of the epicenter of an earthquake is one difficult challenge the other being the timing and magnitude. One technique for predicting the onset of earthquakes is the use of cycles, and the discovery of periodicity. Part of this category is the reported FDL method. The basis of the reported FDL method is the creation of FDL future dates based on the onset date of significant earthquakes. The assumption being that each occurred earthquake discontinuity can be thought of as a generating source of FDL time series The connection between past earthquakes and future earthquakes based on FDL numbers has also been reported with sample earthquakes since 1900. Using clustering methods it has been shown that significant earthquakes (<6.5R) can be predicted with very good accuracy window (+-1 day). In this contribution we present an improvement modification to the FDL method, the MFDL method, which performs better than the FDL. We use the FDL numbers to develop possible earthquakes dates but with the important difference that the starting seed date is a trigger planetary aspect prior to the earthquake. Typical planetary aspects are Moon conjunct Sun, Moon opposite Sun, Moon conjunct or opposite North or South Modes. In order to test improvement of the method we used all +8R earthquakes recorded since 1900, (86 earthquakes from USGS data). We have developed the FDL numbers for each of those seeds, and examined the earthquake hit rates (for a window of 3, i.e. +-1 day of target date) and for <6.5R. The successes are counted for each one of the 86 earthquake seeds and we compare the MFDL method with the FDL method. In every case we find improvement when the starting seed date is on the planetary trigger date prior to the earthquake. We observe no improvement only when a planetary trigger coincided with
Gillis, Rachel; Palerm, Cesar C.; Zisser, Howard; Jovanovič, Lois; Seborg, Dale E.; Doyle, Francis J.
2007-01-01
Background A primary challenge for closed-loop glucose control in type 1 diabetes mellitus (T1DM) is the development of a control strategy that will be applicable during all daily activities, including meals, stress, and exercise. A model-based control algorithm requires a mathematical model that has the simplicity for online glucose prediction, yet retains the complexity necessary to cope with variations in insulin sensitivities and carbohydrate ingestion. Methods A modified Bergman minimal model was linearized for Kalman filter (KF) state estimation on data from T1DM subjects, and multiple methods of parameter augmentation were developed for online adaptation. In addition, model deterioration for glucose prediction was assessed to determine an appropriate prediction horizon for model predictive control (MPC). Furthermore, MPC strategies were validated using advisory mode simulations. Results Twenty days of continuous glucose data, which included 97 meals, were evaluated for three subjects. A constant parameter minimal model was used to predict glucose levels for normal days with meal announcement and with a maximum prediction horizon of approximately 45 minutes. In order to attain this prediction horizon in the absence of meal announcement, parameter adaptation was necessary to capture the glucose disturbance. Evaluation of advisory mode MPC permitted effective tuning for a moderately aggressive controller that responded well to meal disturbances. Conclusions Estimation and prediction of glucose were accomplished using a KF based on a modified Bergman model. For a model with no meal announcement, parameter adaptation provided the means for closed-loop implementation. This state estimation and model validation scheme established the necessary framework for advisory mode MPC. PMID:19885154
Method for crystal growth control
Yates, Douglas A.; Hatch, Arthur E.; Goldsmith, Jeff M.
1981-01-01
The growth of a crystalline body of a selected material is controlled so that the body has a selected cross-sectional shape. The apparatus is of the type which includes the structure normally employed in known capillary die devices as well as means for observing at least the portion of the surfaces of the growing crystalline body and the meniscus (of melt material from which the body is being pulled) including the solid/liquid/vapor junction in a direction substantially perpendicular to the meniscus surface formed at the junction when the growth of the crystalline body is under steady state conditions. The cross-sectional size of the growing crystalline body can be controlled by determining which points exhibit a sharp change in the amount of reflected radiation of a preselected wavelength and controlling the speed at which the body is being pulled or the temperature of the growth pool of melt so as to maintain those points exhibiting a sharp change at a preselected spatial position relative to a predetermined reference position. The improvement comprises reference object means positioned near the solid/liquid/vapor junction and capable of being observed by the means for observing so as to define said reference position so that the problems associated with convection current jitter are overcome.
Method for predicting impulsive noise generated by wind turbine rotors
NASA Technical Reports Server (NTRS)
Viterna, L. A.
1982-01-01
Large wind turbines can generate both broad band and impulsive noises. These noises can be controlled by proper choice of rotor design parameters such as rotor location with respect to the supporting tower, tower geometry and tip speed. A method was developed to calculate the impulsive noise generated when the wind turbine blade experiences air forces that are periodic functions of the rotational frequency. This phenomenon can occur when the blades operate in the wake of the support tower and the nonuniform velocity field near the ground due to wind shear. Results from this method were compared with measured sound spectra taken at locations of one to two rotor diameters from the DOE/NASA Mod-1 wind turbine. The calculated spectra generally agreed with the measured data in both the amplitude of the predominant harmonics and the roll of rate with frequency. Measured sound pressure levels far from the Mod-1 (15 rotor diameters), however, were higher than predicted. Simultaneous measurements in the near and far field indicated that the propagation effects could enhance the sound levels by more than 10 dB above that expected by spherical dispersion. These propagation effects are believed to be due to terrain and atmospheric characteristics of the Mod-1 site.
Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft
NASA Technical Reports Server (NTRS)
Barlow, Jonathan Spencer; Acosta, Diana Michelle; Phan, Minh Q.
2010-01-01
Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs.
A review of statistical updating methods for clinical prediction models.
Su, Ting-Li; Jaki, Thomas; Hickey, Graeme L; Buchan, Iain; Sperrin, Matthew
2016-07-26
A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods. © The Author(s) 2016.
Model predictive control of P-time event graphs
NASA Astrophysics Data System (ADS)
Hamri, H.; Kara, R.; Amari, S.
2016-12-01
This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.
Pilots Rate Augmented Generalized Predictive Control for Reconfiguration
NASA Technical Reports Server (NTRS)
Soloway, Don; Haley, Pam
2004-01-01
The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.
Rate-Based Model Predictive Control of Turbofan Engine Clearance
NASA Technical Reports Server (NTRS)
DeCastro, Jonathan A.
2006-01-01
An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.
Connecting clinical and actuarial prediction with rule-based methods.
Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H
2015-06-01
Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).
New tuning method for PID controller.
Shen, Jing-Chung
2002-10-01
In this paper, a tuning method for proportional-integral-derivative (PID) controller and the performance assessment formulas for this method are proposed. This tuning method is based on a genetic algorithm based PID controller design method. For deriving the tuning formula, the genetic algorithm based design method is applied to design PID controllers for a variety of processes. The relationship between the controller parameters and the parameters that characterize the process dynamics are determined and the tuning formula is then derived. Using simulation studies, the rules for assessing the performance of a PID controller tuned by the proposed method are also given. This makes it possible to incorporate the capability to determine if the PID controller is well tuned or not into an autotuner. An autotuner based on this new tuning method and the corresponding performance assessment rules is also established. Simulations and real-time experimental results are given to demonstrate the effectiveness and usefulness of these formulas.
Prediction methods of spudcan penetration for jack-up units
NASA Astrophysics Data System (ADS)
Zhang, Ai-xia; Duan, Meng-lan; Li, Hai-ming; Zhao, Jun; Wang, Jian-jun
2012-12-01
Jack-up units are extensively playing a successful role in drilling engineering around the world, and their safety and efficiency take more and more attraction in both research and engineering practice. An accurate prediction of the spudcan penetration depth is quite instrumental in deciding on whether a jack-up unit is feasible to operate at the site. The prediction of a too large penetration depth may lead to the hesitation or even rejection of a site due to potential difficulties in the subsequent extraction process; the same is true of a too small depth prediction due to the problem of possible instability during operation. However, a deviation between predictive results and final field data usually exists, especially when a strong-over-soft soil is included in the strata. The ultimate decision sometimes to a great extent depends on the practical experience, not the predictive results given by the guideline. It is somewhat risky, but no choice. Therefore, a feasible predictive method for the spudcan penetration depth, especially in strata with strong-over-soft soil profile, is urgently needed by the jack-up industry. In view of this, a comprehensive investigation on methods of predicting spudcan penetration is executed. For types of different soil profiles, predictive methods for spudcan penetration depth are proposed, and the corresponding experiment is also conducted to validate these methods. In addition, to further verify the feasibility of the proposed methods, a practical engineering case encountered in the South China Sea is also presented, and the corresponding numerical and experimental results are also presented and discussed.
Adaptive extended state space predictive control for a kind of nonlinear systems.
Zhang, Ri-Dong; Wang, Shu-Qing; Xue, An-Ke; Ren, Zheng-Yun; Li, Ping
2009-10-01
This paper presents an adaptive nonlinear predictive control design strategy for a kind of nonlinear systems with output feedback coupling and results in the improvement of regulatory capacity for reference tracking, robustness and disturbance rejection. The nonlinear system is first transformed into an equal time-variant system by analyzing the nonlinear part. Then an extended state space predictive controller with a similar structure of a PI optimal regulator and with P-step setpoint feedforward control is designed. Because changes of the system state variables are considered in the objective function, the control performance is superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is tested and compared with latest methods in literature. Tracking performance, robustness and disturbance rejection are improved.
The trajectory prediction of spacecraft by grey method
NASA Astrophysics Data System (ADS)
Wang, Qiyue; Zhang, Zili; Wang, Zhongyu; Wang, Yanqing; Zhou, Weihu
2016-08-01
The real-time and high-precision trajectory prediction of a moving object is a core technology in the field of aerospace engineering. The real-time monitoring and tracking technology are also significant guarantees of aerospace equipment. A dynamic trajectory prediction method called grey dynamic filter (GDF) which combines the dynamic measurement theory and grey system theory is proposed. GDF can use coordinates of the current period to extrapolate coordinates of the following period. At meantime, GDF can also keep the instantaneity of measured coordinates by the metabolism model. In this paper the optimal model length of GDF is firstly selected to improve the prediction accuracy. Then the simulation for uniformly accelerated motion and variably accelerated motion is conducted. The simulation results indicate that the mean composite position error of GDF prediction is one-fifth to that of Kalman filter (KF). By using a spacecraft landing experiment, the prediction accuracy of GDF is compared with the KF method and the primitive grey method (GM). The results show that the motion trajectory of spacecraft predicted by GDF is much closer to actual trajectory than the other two methods. The mean composite position error calculated by GDF is one-eighth to KF and one-fifth to GM respectively.
HOPE: a homotopy optimization method for protein structure prediction.
Dunlavy, Daniel M; O'Leary, Dianne P; Klimov, Dmitri; Thirumalai, D
2005-12-01
We use a homotopy optimization method, HOPE, to minimize the potential energy associated with a protein model. The method uses the minimum energy conformation of one protein as a template to predict the lowest energy structure of a query sequence. This objective is achieved by following a path of conformations determined by a homotopy between the potential energy functions for the two proteins. Ensembles of solutions are produced by perturbing conformations along the path, increasing the likelihood of predicting correct structures. Successful results are presented for pairs of homologous proteins, where HOPE is compared to a variant of Newton's method and to simulated annealing.
Interim prediction method for fan and compressor source noise
NASA Technical Reports Server (NTRS)
Heidmann, M. F.
1975-01-01
A method is presented for interim use in assessing the noise generated by fans and compressors in turbojet and turbofan engines. One-third octave band sound pressure levels consisting of broadband, discrete tone, and combination-tone noise components are predicted. Spectral distributions and directivity variations are specified. The method is based on that developed by other investigators with modifications derived from an analysis of full-scale, single-stage fan data. Comparisons of predicted and measured noise performance are presented, and requirements for improving the method are discussed.
Predicting the past: a simple reverse stand table projection method
Quang V. Cao; Shanna M. McCarty
2006-01-01
A stand table gives number of trees in each diameter class. Future stand tables can be predicted from current stand tables using a stand table projection method. In the simplest form of this method, a future stand table can be expressed as the product of a matrix of transitional proportions (based on diameter growth rates) and a vector of the current stand table. There...
Toxicologic methods: controlled human exposures.
Utell, M J; Frampton, M W
2000-01-01
The assessment of risk from exposure to environmental air pollutants is complex, and involves the disciplines of epidemiology, animal toxicology, and human inhalation studies. Controlled, quantitative studies of exposed humans help determine health-related effects that result from breathing the atmosphere. The major unique feature of the clinical study is the ability to select, control, and quantify pollutant exposures of subjects of known clinical status, and determine their effects under ideal experimental conditions. The choice of outcomes to be assessed in human clinical studies can be guided by both scientific and practical considerations, but the diversity of human responses and responsiveness must be considered. Subjects considered to be among the most susceptible include those with asthma, chronic obstructive lung disease, and cardiovascular disease. New experimental approaches include exposures to concentrated ambient air particles, diesel engine exhaust, combustion products from smoking machines, and experimental model particles. Future investigations of the health effects of air pollution will benefit from collaborative efforts among the disciplines of epidemiology, animal toxicology, and human clinical studies. PMID:10931779
Toxicologic methods: controlled human exposures.
Utell, M J; Frampton, M W
2000-08-01
The assessment of risk from exposure to environmental air pollutants is complex, and involves the disciplines of epidemiology, animal toxicology, and human inhalation studies. Controlled, quantitative studies of exposed humans help determine health-related effects that result from breathing the atmosphere. The major unique feature of the clinical study is the ability to select, control, and quantify pollutant exposures of subjects of known clinical status, and determine their effects under ideal experimental conditions. The choice of outcomes to be assessed in human clinical studies can be guided by both scientific and practical considerations, but the diversity of human responses and responsiveness must be considered. Subjects considered to be among the most susceptible include those with asthma, chronic obstructive lung disease, and cardiovascular disease. New experimental approaches include exposures to concentrated ambient air particles, diesel engine exhaust, combustion products from smoking machines, and experimental model particles. Future investigations of the health effects of air pollution will benefit from collaborative efforts among the disciplines of epidemiology, animal toxicology, and human clinical studies.
Prediction-based association control scheme in dense femtocell networks
Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond
2017-01-01
The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992
Power control system and method
Steigerwald, Robert Louis; Anderson, Todd Alan
2006-11-07
A power system includes an energy harvesting device, a battery coupled to the energy harvesting device, and a circuit coupled to the energy harvesting device and the battery. The circuit is adapted to deliver power to a load by providing power generated by the energy harvesting device to the load without delivering excess power to the battery and to supplement the power generated by the energy harvesting device with power from the battery if the power generated by the energy harvesting device is insufficient to fully power the load. A method of operating the power system is also provided.
Power control system and method
Steigerwald, Robert Louis [Burnt Hills, NY; Anderson, Todd Alan [Niskayuna, NY
2008-02-19
A power system includes an energy harvesting device, a battery coupled to the energy harvesting device, and a circuit coupled to the energy harvesting device and the battery. The circuit is adapted to deliver power to a load by providing power generated by the energy harvesting device to the load without delivering excess power to the battery and to supplement the power generated by the energy harvesting device with power from the battery if the power generated by the energy harvesting device is insufficient to fully power the load. A method of operating the power system is also provided.
Rapid Evaluation of Prediction Methods with DIPPR's Automated Property Prediction Package
NASA Astrophysics Data System (ADS)
Rowley, J. R.; Wilding, W. V.; Oscarson, J. L.; Rowley, R. L.
2007-06-01
An automated property prediction package has been developed that permits rapid evaluation of group-contribution, corresponding states, empirical, and theoretical property estimation methods. The property prediction package, which is part of the DIPPR® Information And Data Evaluation Manager (DIADEM) software, is used in conjunction with the DIPPR® 801 database to develop and test new prediction methods. The software is freely available to all DIPPR sponsor companies, but is also commercially available. The estimation engine is based on an automated SMILES (Simplified Molecular Input Line Entry Specification) formula parser to provide required molecular structural information, retrieval of required secondary properties from the DIPPR® database, and defined rules for the method. Automatic comparisons of predicted values to experimental data in the DIPPR® database can be made for properties at specified accuracy levels, by chemical family or type, or over the entire database. This allows evaluation of the relative effectiveness of methods for specific chemical families and tailoring of the selected method to specific chemical classes. New methods can readily be added by input using a simple input form. Nearly 200 thermophysical property prediction methods are currently available in DIADEM.
Computational Methods for Failure Analysis and Life Prediction
NASA Technical Reports Server (NTRS)
Noor, Ahmed K. (Compiler); Harris, Charles E. (Compiler); Housner, Jerrold M. (Compiler); Hopkins, Dale A. (Compiler)
1993-01-01
This conference publication contains the presentations and discussions from the joint UVA/NASA Workshop on Computational Methods for Failure Analysis and Life Prediction held at NASA Langley Research Center 14-15 Oct. 1992. The presentations focused on damage failure and life predictions of polymer-matrix composite structures. They covered some of the research activities at NASA Langley, NASA Lewis, Southwest Research Institute, industry, and universities. Both airframes and propulsion systems were considered.
Methods, apparatus and system for notification of predictable memory failure
Cher, Chen-Yong; Andrade Costa, Carlos H.; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.
2017-01-03
A method for providing notification of a predictable memory failure includes the steps of: obtaining information regarding at least one condition associated with a memory; calculating a memory failure probability as a function of the obtained information; calculating a failure probability threshold; and generating a signal when the memory failure probability exceeds the failure probability threshold, the signal being indicative of a predicted future memory failure.
Predictive active disturbance rejection control for processes with time delay.
Zheng, Qinling; Gao, Zhiqiang
2014-07-01
Active disturbance rejection control (ADRC) has been shown to be an effective tool in dealing with real world problems of dynamic uncertainties, disturbances, nonlinearities, etc. This paper addresses its existing limitations with plants that have a large transport delay. In particular, to overcome the delay, the extended state observer (ESO) in ADRC is modified to form a predictive ADRC, leading to significant improvements in the transient response and stability characteristics, as shown in extensive simulation studies and hardware-in-the-loop tests, as well as in the frequency response analysis. In this research, it is assumed that the amount of delay is approximately known, as is the approximated model of the plant. Even with such uncharacteristic assumptions for ADRC, the proposed method still exhibits significant improvements in both performance and robustness over the existing methods such as the dead-time compensator based on disturbance observer and the Filtered Smith Predictor, in the context of some well-known problems of chemical reactor and boiler control problems.
Three-dimensional protein structure prediction: Methods and computational strategies.
Dorn, Márcio; E Silva, Mariel Barbachan; Buriol, Luciana S; Lamb, Luis C
2014-10-12
A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction.
A simple method of predicting S-wave velocity
Lee, M.W.
2006-01-01
Prediction of shear-wave velocity plays an important role in seismic modeling, amplitude analysis with offset, and other exploration applications. This paper presents a method for predicting S-wave velocity from the P-wave velocity on the basis of the moduli of dry rock. Elastic velocities of water-saturated sediments at low frequencies can be predicted from the moduli of dry rock by using Gassmann's equation; hence, if the moduli of dry rock can be estimated from P-wave velocities, then S-wave velocities easily can be predicted from the moduli. Dry rock bulk modulus can be related to the shear modulus through a compaction constant. The numerical results indicate that the predicted S-wave velocities for consolidated and unconsolidated sediments agree well with measured velocities if differential pressure is greater than approximately 5 MPa. An advantage of this method is that there are no adjustable parameters to be chosen, such as the pore-aspect ratios required in some other methods. The predicted S-wave velocity depends only on the measured P-wave velocity and porosity. ?? 2006 Society of Exploration Geophysicists.
Supervised learning method for predicting chromatin boundary associated insulator elements.
Bednarz, Paweł; Wilczyński, Bartek
2014-12-01
In eukaryotic cells, the DNA material is densely packed inside the nucleus in the form of a DNA-protein complex structure called chromatin. Since the actual conformation of the chromatin fiber defines the possible regulatory interactions between genes and their regulatory elements, it is very important to understand the mechanisms governing folding of chromatin. In this paper, we show that supervised methods for predicting chromatin boundary elements are much more effective than the currently popular unsupervised methods. Using boundary locations from published Hi-C experiments and modEncode tracks as features, we can tell the insulator elements from randomly selected background sequences with great accuracy. In addition to accurate predictions of the training boundary elements, our classifiers make new predictions. Many of them correspond to the locations of known insulator elements. The key features used for predicting boundary elements do not depend on the prediction method. Because of its miniscule size, chromatin state cannot be measured directly, we need to rely on indirect measurements, such as ChIP-Seq and fill in the gaps with computational models. Our results show that currently, at least in the model organisms, where we have many measurements including ChIP-Seq and Hi-C, we can make accurate predictions of insulator positions.
Prediction of Protein–Protein Interactions by Evidence Combining Methods
Chang, Ji-Wei; Zhou, Yan-Qing; Ul Qamar, Muhammad Tahir; Chen, Ling-Ling; Ding, Yu-Duan
2016-01-01
Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy. PMID:27879651
Recent advances in B-cell epitope prediction methods
2010-01-01
Identification of epitopes that invoke strong responses from B-cells is one of the key steps in designing effective vaccines against pathogens. Because experimental determination of epitopes is expensive in terms of cost, time, and effort involved, there is an urgent need for computational methods for reliable identification of B-cell epitopes. Although several computational tools for predicting B-cell epitopes have become available in recent years, the predictive performance of existing tools remains far from ideal. We review recent advances in computational methods for B-cell epitope prediction, identify some gaps in the current state of the art, and outline some promising directions for improving the reliability of such methods. PMID:21067544
A Micromechanics-Based Method for Multiscale Fatigue Prediction
NASA Astrophysics Data System (ADS)
Moore, John Allan
An estimated 80% of all structural failures are due to mechanical fatigue, often resulting in catastrophic, dangerous and costly failure events. However, an accurate model to predict fatigue remains an elusive goal. One of the major challenges is that fatigue is intrinsically a multiscale process, which is dependent on a structure's geometric design as well as its material's microscale morphology. The following work begins with a microscale study of fatigue nucleation around non- metallic inclusions. Based on this analysis, a novel multiscale method for fatigue predictions is developed. This method simulates macroscale geometries explicitly while concurrently calculating the simplified response of microscale inclusions. Thus, providing adequate detail on multiple scales for accurate fatigue life predictions. The methods herein provide insight into the multiscale nature of fatigue, while also developing a tool to aid in geometric design and material optimization for fatigue critical devices such as biomedical stents and artificial heart valves.
Evaluation of Methods to Predict Reactivity of Gold Nanoparticles
Allison, Thomas C.; Tong, Yu ye J.
2011-06-20
Several methods have appeared in the literature for predicting reactivity on metallic surfaces and on the surface of metallic nanoparticles. All of these methods have some relationship to the concept of frontier molecular orbital theory. The d-band theory of Hammer and Nørskov is perhaps the most widely used predictor of reactivity on metallic surfaces, and it has been successfully applied in many cases. Use of the Fukui function and the condensed Fukui function is well established in organic chemistry, but has not been so widely applied in predicting the reactivity of metallic nanoclusters. In this article, we will evaluate the usefulness of the condensed Fukui function in predicting the reactivity of a family of cubo-octahedral gold nanoparticles and make comparison with the d-band method.
Assessing Computational Methods of Cis-Regulatory Module Prediction
Su, Jing; Teichmann, Sarah A.; Down, Thomas A.
2010-01-01
Computational methods attempting to identify instances of cis-regulatory modules (CRMs) in the genome face a challenging problem of searching for potentially interacting transcription factor binding sites while knowledge of the specific interactions involved remains limited. Without a comprehensive comparison of their performance, the reliability and accuracy of these tools remains unclear. Faced with a large number of different tools that address this problem, we summarized and categorized them based on search strategy and input data requirements. Twelve representative methods were chosen and applied to predict CRMs from the Drosophila CRM database REDfly, and across the human ENCODE regions. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. When discriminating CRMs from non-coding regions, those methods considering evolutionary conservation have a stronger predictive power than methods designed to be run on a single genome. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. For example, some favour homotypical clusters of binding sites, while others perform best on short CRMs. Furthermore, most methods appear to be sensitive to the composition and structure of the genome to which they are applied. We analyze the principal features that distinguish the methods that performed well, identify weaknesses leading to poor performance, and provide a guide for users. We also propose key considerations for the development and evaluation of future CRM-prediction methods. PMID:21152003
Predicting Spacecraft Trajectories by the WeavEncke Method
NASA Technical Reports Server (NTRS)
Weaver, Jonathan K.; Adamo, Daniel R.
2011-01-01
A combination of methods is proposed of predicting spacecraft trajectories that possibly include multiple maneuvers and/or perturbing accelerations, with greater speed, accuracy, and repeatability than were heretofore achievable. The combination is denoted the WeavEncke method because it is based on unpublished studies by Jonathan Weaver of the orbit-prediction formulation of the noted astronomer Johann Franz Encke. Weaver evaluated a number of alternatives that arise within that formulation, arriving at an orbit-predicting algorithm optimized for complex trajectory operations. In the WeavEncke method, Encke's method of prediction of perturbed orbits is enhanced by application of modern numerical methods. Among these methods are efficient Kepler s-equation time-of-flight solutions and self-starting numerical integration with time as the independent variable. Self-starting numerical integration satisfies the requirements for accuracy, reproducibility, and efficiency (and, hence, speed). Self-starting numerical integration also supports fully analytic regulation of integration step sizes, thereby further increasing speed while maintaining accuracy.
Predictive toxicology: benchmarking molecular descriptors and statistical methods.
Feng, Jun; Lurati, Laura; Ouyang, Haojun; Robinson, Tracy; Wang, Yuanyuan; Yuan, Shenglan; Young, S Stanley
2003-01-01
The development of drugs depends on finding compounds that have beneficial effects with a minimum of toxic effects. The measurement of toxic effects is typically time-consuming and expensive, so there is a need to be able to predict toxic effects from the compound structure. Predicting toxic effects is expected to be challenging because there are usually multiple toxic mechanisms involved. In this paper, combinations of different chemical descriptors and popular statistical methods were applied to the problem of predictive toxicology. Four data sets were collected and cleaned, and four different sets of chemical descriptors were calculated for the compounds in each of the four data sets. Three statistical methods (recursive partitioning, neural networks, and partial least squares) were used to attempt to link chemical descriptors to the response. Good predictions were achieved in the two smaller data sets; we found for large data sets that the results were less effective, indicating that new chemical descriptors or statistical methods are needed. All of the methods and descriptors worked to a degree, but our work hints that certain descriptors work better with specific statistical methods so there is a need for better understanding and for continued methods development.
A Simple Microsoft Excel Method to Predict Antibiotic Outbreaks and Underutilization.
Miglis, Cristina; Rhodes, Nathaniel J; Avedissian, Sean N; Zembower, Teresa R; Postelnick, Michael; Wunderink, Richard G; Sutton, Sarah H; Scheetz, Marc H
2017-07-01
Benchmarking strategies are needed to promote the appropriate use of antibiotics. We have adapted a simple regressive method in Microsoft Excel that is easily implementable and creates predictive indices. This method trends consumption over time and can identify periods of over- and underuse at the hospital level. Infect Control Hosp Epidemiol 2017;38:860-862.
Quality Control Guidelines for SAM Pathogen Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the biotoxin methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
Quality Control Guidelines for SAM Biotoxin Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the pathogen methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
Quality Control Guidelines for SAM Radiochemical Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the radiochemistry methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
Quality Control Guidelines for SAM Chemical Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the chemistry methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.
Heck, Gabriela S; Pintro, Val O; Pereira, Richard R; de Ávila, Mauricio B; Levin, Nayara M B; de Azevedo, Walter F
2017-01-01
Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Reporting and Methods in Clinical Prediction Research: A Systematic Review
Mallett, Susan; Geerlings, Mirjam I.; Vergouwe, Yvonne; Steyerberg, Ewout W.; Altman, Douglas G.; Moons, Karel G. M.
2012-01-01
Background We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures. Methods and Findings We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%). Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively. Conclusions The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability. Please see later in the article for the Editors' Summary PMID:22629234
An analytical method to predict efficiency of aircraft gearboxes
NASA Technical Reports Server (NTRS)
Anderson, N. E.; Loewenthal, S. H.; Black, J. D.
1984-01-01
A spur gear efficiency prediction method previously developed by the authors was extended to include power loss of planetary gearsets. A friction coefficient model was developed for MIL-L-7808 oil based on disc machine data. This combined with the recent capability of predicting losses in spur gears of nonstandard proportions allows the calculation of power loss for complete aircraft gearboxes that utilize spur gears. The method was applied to the T56/501 turboprop gearbox and compared with measured test data. Bearing losses were calculated with large scale computer programs. Breakdowns of the gearbox losses point out areas for possible improvement.
Preface to the Focus Issue: Chaos Detection Methods and Predictability
Gottwald, Georg A.; Skokos, Charalampos
2014-06-01
This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17–21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.
An analytical method to predict efficiency of aircraft gearboxes
NASA Technical Reports Server (NTRS)
Anderson, N. E.; Loewenthal, S. H.; Black, J. D.
1984-01-01
A spur gear efficiency prediction method previously developed by the authors was extended to include power loss of planetary gearsets. A friction coefficient model was developed for MIL-L-7808 oil based on disc machine data. This combined with the recent capability of predicting losses in spur gears of nonstandard proportions allows the calculation of power loss for complete aircraft gearboxes that utilize spur gears. The method was applied to the T56/501 turboprop gearbox and compared with measured test data. Bearing losses were calculated with large scale computer programs. Breakdowns of the gearbox losses point out areas for possible improvement.
Preface to the Focus Issue: chaos detection methods and predictability.
Gottwald, Georg A; Skokos, Charalampos
2014-06-01
This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17-21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.
Development of aerodynamic prediction methods for irregular planform wings
NASA Technical Reports Server (NTRS)
Benepe, D. B., Sr.
1983-01-01
A set of empirical methods was developed to predict low-speed lift, drag and pitching-moment variations with angle of attack for a class of low aspect ratio irregular planform wings suitable for application to advanced aerospace vehicles. The data base, an extensive series of wind-tunnel tests accomplished by the Langley Research Center of the National Aeronautics and Space Administration, is summarized. The approaches used to analyze the wind tunnel data, the evaluation of previously existing methods, data correlation efforts, and the development of the selected methods are presented and discussed. A summary of the methods is also presented to document the equations, computational charts and design guides which have been programmed for digital computer solution. Comparisons of predictions and test data are presented which show that the new methods provide a significant improvement in capability for evaluating the landing characteristics of advanced aerospace vehicles during the preliminary design phase of the configuration development cycle.
Model and Predictive Control for a Wind Turbine
NASA Astrophysics Data System (ADS)
Gilev, B.; Slavchev, J.; Penev, D.; Yonchev, A.
2011-12-01
A mathematical model of the system consisting of wind turbine, gear box and asynchronous generator is presented in this work. The model is linearized. Then a controller, which provides a desire mode of frequency stabilization, is developed using the predictive control theory.
Towards feasible and effective predictive wavefront control for adaptive optics
Poyneer, L A; Veran, J
2008-06-04
We have recently proposed Predictive Fourier Control, a computationally efficient and adaptive algorithm for predictive wavefront control that assumes frozen flow turbulence. We summarize refinements to the state-space model that allow operation with arbitrary computational delays and reduce the computational cost of solving for new control. We present initial atmospheric characterization using observations with Gemini North's Altair AO system. These observations, taken over 1 year, indicate that frozen flow is exists, contains substantial power, and is strongly detected 94% of the time.
Development of Improved Surface Integral Methods for Jet Aeroacoustic Predictions
NASA Technical Reports Server (NTRS)
Pilon, Anthony R.; Lyrintzis, Anastasios S.
1997-01-01
The accurate prediction of aerodynamically generated noise has become an important goal over the past decade. Aeroacoustics must now be an integral part of the aircraft design process. The direct calculation of aerodynamically generated noise with CFD-like algorithms is plausible. However, large computer time and memory requirements often make these predictions impractical. It is therefore necessary to separate the aeroacoustics problem into two parts, one in which aerodynamic sound sources are determined, and another in which the propagating sound is calculated. This idea is applied in acoustic analogy methods. However, in the acoustic analogy, the determination of far-field sound requires the solution of a volume integral. This volume integration again leads to impractical computer requirements. An alternative to the volume integrations can be found in the Kirchhoff method. In this method, Green's theorem for the linear wave equation is used to determine sound propagation based on quantities on a surface surrounding the source region. The change from volume to surface integrals represents a tremendous savings in the computer resources required for an accurate prediction. This work is concerned with the development of enhancements of the Kirchhoff method for use in a wide variety of aeroacoustics problems. This enhanced method, the modified Kirchhoff method, is shown to be a Green's function solution of Lighthill's equation. It is also shown rigorously to be identical to the methods of Ffowcs Williams and Hawkings. This allows for development of versatile computer codes which can easily alternate between the different Kirchhoff and Ffowcs Williams-Hawkings formulations, using the most appropriate method for the problem at hand. The modified Kirchhoff method is developed primarily for use in jet aeroacoustics predictions. Applications of the method are shown for two dimensional and three dimensional jet flows. Additionally, the enhancements are generalized so that
Application of two direct runoff prediction methods in Puerto Rico
Sepulveda, N.
1997-01-01
Two methods for predicting direct runoff from rainfall data were applied to several basins and the resulting hydrographs compared to measured values. The first method uses a geomorphology-based unit hydrograph to predict direct runoff through its convolution with the excess rainfall hyetograph. The second method shows how the resulting hydraulic routing flow equation from a kinematic wave approximation is solved using a spectral method based on the matrix representation of the spatial derivative with Chebyshev collocation and a fourth-order Runge-Kutta time discretization scheme. The calibrated Green-Ampt (GA) infiltration parameters are obtained by minimizing the sum, over several rainfall events, of absolute differences between the total excess rainfall volume computed from the GA equations and the total direct runoff volume computed from a hydrograph separation technique. The improvement made in predicting direct runoff using a geomorphology-based unit hydrograph with the ephemeral and perennial stream network instead of the strictly perennial stream network is negligible. The hydraulic routing scheme presented here is highly accurate in predicting the magnitude and time of the hydrograph peak although the much faster unit hydrograph method also yields reasonable results.
Increased Fidelity in Prediction Methods For Landing Gear Noise
NASA Technical Reports Server (NTRS)
Lopes, Leonard V.; Brentner, Kenneth S.; Morris, Philip J.; Lockard, David P.
2006-01-01
An aeroacoustic prediction scheme has been developed for landing gear noise. The method is designed to handle the complex landing gear geometry of current and future aircraft. The gear is represented by a collection of subassemblies and simple components that are modeled using acoustic elements. These acoustic elements are generic, but generate noise representative of the physical components on a landing gear. The method sums the noise radiation from each component of the undercarriage in isolation accounting for interference with adjacent components through an estimate of the local upstream and downstream flows and turbulence intensities. The acoustic calculations are made in the code LGMAP, which computes the sound pressure levels at various observer locations. The method can calculate the noise from the undercarriage in isolation or installed on an aircraft for both main and nose landing gear. Comparisons with wind tunnel and flight data are used to initially calibrate the method, then it may be used to predict the noise of any landing gear. In this paper, noise predictions are compared with wind tunnel data for model landing gears of various scales and levels of fidelity, as well as with flight data on fullscale undercarriages. The present agreement between the calculations and measurements suggests the method has promise for future application in the prediction of airframe noise.
A new method to predict unsteady aeroelastic behavior
NASA Technical Reports Server (NTRS)
Strganac, Thomas W.; Mook, Dean T.
1987-01-01
A new method for predicting subsonic flutter and static deflections, including divergence, has been developed. The present method accounts for aspect ratio and, in the case of flutter, static deflections. The angle of attack is limited only by the occurrence of stall or vortex bursting near the wing. The innovation in the present method is to integrate simultaneously and interactively the equations of motion of the structure and the flowfield. The present approach employs an iterative scheme based on the predictor-corrector method. The general unsteady vortex-lattice method (UVLM) is used to predict the aerodynamic loads. Because the UVLM predicts the wakes as part of the solution, the history of the motion is taken into account; hysteresis is predicted. The deflection (for both bending and torsion) is expressed as an expansion in terms of the free-vibration modes. The time-dependent coefficients in these expansions serve as the generalized coordinates. Numerical examples illustrating the calculation of static deflections and transient dynamic responses above and below the flutter boundary are included.
The Energetic Cost of Walking: A Comparison of Predictive Methods
Kramer, Patricia Ann; Sylvester, Adam D.
2011-01-01
Background The energy that animals devote to locomotion has been of intense interest to biologists for decades and two basic methodologies have emerged to predict locomotor energy expenditure: those based on metabolic and those based on mechanical energy. Metabolic energy approaches share the perspective that prediction of locomotor energy expenditure should be based on statistically significant proxies of metabolic function, while mechanical energy approaches, which derive from many different perspectives, focus on quantifying the energy of movement. Some controversy exists as to which mechanical perspective is “best”, but from first principles all mechanical methods should be equivalent if the inputs to the simulation are of similar quality. Our goals in this paper are 1) to establish the degree to which the various methods of calculating mechanical energy are correlated, and 2) to investigate to what degree the prediction methods explain the variation in energy expenditure. Methodology/Principal Findings We use modern humans as the model organism in this experiment because their data are readily attainable, but the methodology is appropriate for use in other species. Volumetric oxygen consumption and kinematic and kinetic data were collected on 8 adults while walking at their self-selected slow, normal and fast velocities. Using hierarchical statistical modeling via ordinary least squares and maximum likelihood techniques, the predictive ability of several metabolic and mechanical approaches were assessed. We found that all approaches are correlated and that the mechanical approaches explain similar amounts of the variation in metabolic energy expenditure. Most methods predict the variation within an individual well, but are poor at accounting for variation between individuals. Conclusion Our results indicate that the choice of predictive method is dependent on the question(s) of interest and the data available for use as inputs. Although we used modern
The energetic cost of walking: a comparison of predictive methods.
Kramer, Patricia Ann; Sylvester, Adam D
2011-01-01
The energy that animals devote to locomotion has been of intense interest to biologists for decades and two basic methodologies have emerged to predict locomotor energy expenditure: those based on metabolic and those based on mechanical energy. Metabolic energy approaches share the perspective that prediction of locomotor energy expenditure should be based on statistically significant proxies of metabolic function, while mechanical energy approaches, which derive from many different perspectives, focus on quantifying the energy of movement. Some controversy exists as to which mechanical perspective is "best", but from first principles all mechanical methods should be equivalent if the inputs to the simulation are of similar quality. Our goals in this paper are 1) to establish the degree to which the various methods of calculating mechanical energy are correlated, and 2) to investigate to what degree the prediction methods explain the variation in energy expenditure. We use modern humans as the model organism in this experiment because their data are readily attainable, but the methodology is appropriate for use in other species. Volumetric oxygen consumption and kinematic and kinetic data were collected on 8 adults while walking at their self-selected slow, normal and fast velocities. Using hierarchical statistical modeling via ordinary least squares and maximum likelihood techniques, the predictive ability of several metabolic and mechanical approaches were assessed. We found that all approaches are correlated and that the mechanical approaches explain similar amounts of the variation in metabolic energy expenditure. Most methods predict the variation within an individual well, but are poor at accounting for variation between individuals. Our results indicate that the choice of predictive method is dependent on the question(s) of interest and the data available for use as inputs. Although we used modern humans as our model organism, these results can be extended
Accuracy of Wind Prediction Methods in the California Sea Breeze
NASA Astrophysics Data System (ADS)
Sumers, B. D.; Dvorak, M. J.; Ten Hoeve, J. E.; Jacobson, M. Z.
2010-12-01
In this study, we investigate the accuracy of measure-correlate-predict (MCP) algorithms and log law/power law scaling using data from two tall towers in coastal environments. We find that MCP algorithms accurately predict sea breeze winds and that log law/power law scaling methods struggle to predict 50-meter wind speeds. MCP methods have received significant attention as the wind industry has grown and the ability to accurately characterize the wind resource has become valuable. These methods are used to produce longer-term wind speed records from short-term measurement campaigns. A correlation is developed between the “target site,” where the developer is interested in building wind turbines, and a “reference site,” where long-term wind data is available. Up to twenty years of prior wind speeds are then are predicted. In this study, two existing MCP methods - linear regression and Mortimer’s method - are applied to predict 50-meter wind speeds at sites in the Salinas Valley and Redwood City, CA. The predictions are then verified with tall tower data. It is found that linear regression is poorly suited to MCP applications as the process produces inaccurate estimates of the cube of the wind speed at 50 meters. Meanwhile, Mortimer’s method, which bins data by direction and speed, is found to accurately predict the cube of the wind speed in both sea breeze and non-sea breeze conditions. We also find that log and power law are unstable predictors of wind speeds. While these methods produced accurate estimates of the average 50-meter wind speed at both sites, they predicted an average cube of the wind speed that was between 1.3 and 1.18 times the observed value. Inspection of time-series error reveals increased error in the mid-afternoon of the summer. This suggests that the cold sea breeze may disrupt the vertical temperature profile, create a stable atmosphere and violate the assumptions that allow log law scaling to work.
Plasma disruption prediction using machine learning methods: DIII-D
NASA Astrophysics Data System (ADS)
Lupin-Jimenez, L.; Kolemen, E.; Eldon, D.; Eidietis, N.
2016-10-01
Plasma disruption prediction is becoming more important with the development of larger tokamaks, due to the larger amount of thermal and magnetic energy that can be stored. By accurately predicting an impending disruption, the disruption's impact can be mitigated or, better, prevented. Recent approaches to disruption prediction have been through implementation of machine learning methods, which characterize raw and processed diagnostic data to develop accurate prediction models. Using disruption trials from the DIII-D database, the effectiveness of different machine learning methods are characterized. Developed real time disruption prediction approaches are focused on tearing and locking modes. Machine learning methods used include random forests, multilayer perceptrons, and traditional regression analysis. The algorithms are trained with data within short time frames, and whether or not a disruption occurs within the time window after the end of the frame. Initial results from the machine learning algorithms will be presented. Work supported by US DOE under the Science Undergraduate Laboratory Internship (SULI) program, DE-FC02-04ER54698, and DE-AC02-09CH11466.
Neural network based automatic limit prediction and avoidance system and method
NASA Technical Reports Server (NTRS)
Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)
2001-01-01
A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.
Flight-Test Evaluation of Flutter-Prediction Methods
NASA Technical Reports Server (NTRS)
Lind, RIck; Brenner, Marty
2003-01-01
The flight-test community routinely spends considerable time and money to determine a range of flight conditions, called a flight envelope, within which an aircraft is safe to fly. The cost of determining a flight envelope could be greatly reduced if there were a method of safely and accurately predicting the speed associated with the onset of an instability called flutter. Several methods have been developed with the goal of predicting flutter speeds to improve the efficiency of flight testing. These methods include (1) data-based methods, in which one relies entirely on information obtained from the flight tests and (2) model-based approaches, in which one relies on a combination of flight data and theoretical models. The data-driven methods include one based on extrapolation of damping trends, one that involves an envelope function, one that involves the Zimmerman-Weissenburger flutter margin, and one that involves a discrete-time auto-regressive model. An example of a model-based approach is that of the flutterometer. These methods have all been shown to be theoretically valid and have been demonstrated on simple test cases; however, until now, they have not been thoroughly evaluated in flight tests. An experimental apparatus called the Aerostructures Test Wing (ATW) was developed to test these prediction methods.
High accuracy operon prediction method based on STRING database scores.
Taboada, Blanca; Verde, Cristina; Merino, Enrique
2010-07-01
We present a simple and highly accurate computational method for operon prediction, based on intergenic distances and functional relationships between the protein products of contiguous genes, as defined by STRING database (Jensen,L.J., Kuhn,M., Stark,M., Chaffron,S., Creevey,C., Muller,J., Doerks,T., Julien,P., Roth,A., Simonovic,M. et al. (2009) STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416). These two parameters were used to train a neural network on a subset of experimentally characterized Escherichia coli and Bacillus subtilis operons. Our predictive model was successfully tested on the set of experimentally defined operons in E. coli and B. subtilis, with accuracies of 94.6 and 93.3%, respectively. As far as we know, these are the highest accuracies ever obtained for predicting bacterial operons. Furthermore, in order to evaluate the predictable accuracy of our model when using an organism's data set for the training procedure, and a different organism's data set for testing, we repeated the E. coli operon prediction analysis using a neural network trained with B. subtilis data, and a B. subtilis analysis using a neural network trained with E. coli data. Even for these cases, the accuracies reached with our method were outstandingly high, 91.5 and 93%, respectively. These results show the potential use of our method for accurately predicting the operons of any other organism. Our operon predictions for fully-sequenced genomes are available at http://operons.ibt.unam.mx/OperonPredictor/.
High accuracy operon prediction method based on STRING database scores
Taboada, Blanca; Verde, Cristina; Merino, Enrique
2010-01-01
We present a simple and highly accurate computational method for operon prediction, based on intergenic distances and functional relationships between the protein products of contiguous genes, as defined by STRING database (Jensen,L.J., Kuhn,M., Stark,M., Chaffron,S., Creevey,C., Muller,J., Doerks,T., Julien,P., Roth,A., Simonovic,M. et al. (2009) STRING 8–a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412–D416). These two parameters were used to train a neural network on a subset of experimentally characterized Escherichia coli and Bacillus subtilis operons. Our predictive model was successfully tested on the set of experimentally defined operons in E. coli and B. subtilis, with accuracies of 94.6 and 93.3%, respectively. As far as we know, these are the highest accuracies ever obtained for predicting bacterial operons. Furthermore, in order to evaluate the predictable accuracy of our model when using an organism's data set for the training procedure, and a different organism's data set for testing, we repeated the E. coli operon prediction analysis using a neural network trained with B. subtilis data, and a B. subtilis analysis using a neural network trained with E. coli data. Even for these cases, the accuracies reached with our method were outstandingly high, 91.5 and 93%, respectively. These results show the potential use of our method for accurately predicting the operons of any other organism. Our operon predictions for fully-sequenced genomes are available at http://operons.ibt.unam.mx/OperonPredictor/. PMID:20385580
Predictive neuro-fuzzy controller for multilink robot manipulator
NASA Astrophysics Data System (ADS)
Kaymaz, Emre; Mitra, Sunanda
1995-10-01
A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems including multilink robot manipulators. The proposed controller is particularly useful when the dynamics of the nonlinear system to be controlled are difficult to yield exact solutions and the system specification can be obtained in terms of crisp input-output pairs. It inherits the advantages of both fuzzy logic and predictive control. The identification of the nonlinear mapping of the system to be controlled is realized by a three- layer feed-forward neural network model employing the input-output data obtained from the system. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. The neural network model is then used as a simulation tool to generate the input-output data for developing the predictive fuzzy logic controller for the chosen nonlinear system. The use of fuzzy clustering facilitates automatic generation of membership relations of the input-output data. Unlike the linguistic fuzzy logic controller which requires approximate knowledge of the shape and the numbers of the membership functions in the input and output universes of the discourse, this integrated neuro-fuzzy approach allows one to find the fuzzy relations and the membership functions more accurately. Furthermore, it is not necessary to tune the controller. For a two-link robot manipulator, the performance of this predictive fuzzy controller is shown to be superior to that of a conventional controller employing an ARMA model of the system in terms of accuracy and consumption of energy.
Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control.
Mehrabi, Naser; Sharif Razavian, Reza; Ghannadi, Borna; McPhee, John
2016-01-01
This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.
Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control
Mehrabi, Naser; Sharif Razavian, Reza; Ghannadi, Borna; McPhee, John
2017-01-01
This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization. PMID:28133449
Pang, Herbert; Jung, Sin-Ho
2013-04-01
A variety of prediction methods are used to relate high-dimensional genome data with a clinical outcome using a prediction model. Once a prediction model is developed from a data set, it should be validated using a resampling method or an independent data set. Although the existing prediction methods have been intensively evaluated by many investigators, there has not been a comprehensive study investigating the performance of the validation methods, especially with a survival clinical outcome. Understanding the properties of the various validation methods can allow researchers to perform more powerful validations while controlling for type I error. In addition, sample size calculation strategy based on these validation methods is lacking. We conduct extensive simulations to examine the statistical properties of these validation strategies. In both simulations and a real data example, we have found that 10-fold cross-validation with permutation gave the best power while controlling type I error close to the nominal level. Based on this, we have also developed a sample size calculation method that will be used to design a validation study with a user-chosen combination of prediction. Microarray and genome-wide association studies data are used as illustrations. The power calculation method in this presentation can be used for the design of any biomedical studies involving high-dimensional data and survival outcomes.
Pang, Herbert; Jung, Sin-Ho
2013-01-01
A variety of prediction methods are used to relate high-dimensional genome data with a clinical outcome using a prediction model. Once a prediction model is developed from a data set, it should be validated using a resampling method or an independent data set. Although the existing prediction methods have been intensively evaluated by many investigators, there has not been a comprehensive study investigating the performance of the validation methods, especially with a survival clinical outcome. Understanding the properties of the various validation methods can allow researchers to perform more powerful validations while controlling for type I error. In addition, sample size calculation strategy based on these validation methods is lacking. We conduct extensive simulations to examine the statistical properties of these validation strategies. In both simulations and a real data example, we have found that 10-fold cross-validation with permutation gave the best power while controlling type I error close to the nominal level. Based on this, we have also developed a sample size calculation method that will be used to design a validation study with a user-chosen combination of prediction. Microarray and genome-wide association studies data are used as illustrations. The power calculation method in this presentation can be used for the design of any biomedical studies involving high-dimensional data and survival outcomes. PMID:23471879
Predicting new indications for approved drugs using a proteochemometric method.
Dakshanamurthy, Sivanesan; Issa, Naiem T; Assefnia, Shahin; Seshasayee, Ashwini; Peters, Oakland J; Madhavan, Subha; Uren, Aykut; Brown, Milton L; Byers, Stephen W
2012-08-09
The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching, or simple in silico ligand docking. We now describe a novel rapid computational proteochemometric method called "train, match, fit, streamline" (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology, and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3671 FDA approved drugs across 2335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84%, and 91% for agents ranked in the top 10, 20, 30, and 40, respectively, out of all 3671 drugs. Drugs ranked in the top 1-40 that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an antiparasitic with recently discovered and unexpected anticancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity and angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27
Orthology prediction methods: a quality assessment using curated protein families.
Trachana, Kalliopi; Larsson, Tomas A; Powell, Sean; Chen, Wei-Hua; Doerks, Tobias; Muller, Jean; Bork, Peer
2011-10-01
The increasing number of sequenced genomes has prompted the development of several automated orthology prediction methods. Tests to evaluate the accuracy of predictions and to explore biases caused by biological and technical factors are therefore required. We used 70 manually curated families to analyze the performance of five public methods in Metazoa. We analyzed the strengths and weaknesses of the methods and quantified the impact of biological and technical challenges. From the latter part of the analysis, genome annotation emerged as the largest single influencer, affecting up to 30% of the performance. Generally, most methods did well in assigning orthologous group but they failed to assign the exact number of genes for half of the groups. The publicly available benchmark set (http://eggnog.embl.de/orthobench/) should facilitate the improvement of current orthology assignment protocols, which is of utmost importance for many fields of biology and should be tackled by a broad scientific community. Copyright © 2011 WILEY Periodicals, Inc.
Orthology prediction methods: A quality assessment using curated protein families
Trachana, Kalliopi; Larsson, Tomas A; Powell, Sean; Chen, Wei-Hua; Doerks, Tobias; Muller, Jean; Bork, Peer
2011-01-01
The increasing number of sequenced genomes has prompted the development of several automated orthology prediction methods. Tests to evaluate the accuracy of predictions and to explore biases caused by biological and technical factors are therefore required. We used 70 manually curated families to analyze the performance of five public methods in Metazoa. We analyzed the strengths and weaknesses of the methods and quantified the impact of biological and technical challenges. From the latter part of the analysis, genome annotation emerged as the largest single influencer, affecting up to 30% of the performance. Generally, most methods did well in assigning orthologous group but they failed to assign the exact number of genes for half of the groups. The publicly available benchmark set (http://eggnog.embl.de/orthobench/) should facilitate the improvement of current orthology assignment protocols, which is of utmost importance for many fields of biology and should be tackled by a broad scientific community. PMID:21853451
Improving active space telescope wavefront control using predictive thermal modeling
NASA Astrophysics Data System (ADS)
Gersh-Range, Jessica; Perrin, Marshall D.
2015-01-01
Active control algorithms for space telescopes are less mature than those for large ground telescopes due to differences in the wavefront control problems. Active wavefront control for space telescopes at L2, such as the James Webb Space Telescope (JWST), requires weighing control costs against the benefits of correcting wavefront perturbations that are a predictable byproduct of the observing schedule, which is known and determined in advance. To improve the control algorithms for these telescopes, we have developed a model that calculates the temperature and wavefront evolution during a hypothetical mission, assuming the dominant wavefront perturbations are due to changes in the spacecraft attitude with respect to the sun. Using this model, we show that the wavefront can be controlled passively by introducing scheduling constraints that limit the allowable attitudes for an observation based on the observation duration and the mean telescope temperature. We also describe the implementation of a predictive controller designed to prevent the wavefront error (WFE) from exceeding a desired threshold. This controller outperforms simpler algorithms even with substantial model error, achieving a lower WFE without requiring significantly more corrections. Consequently, predictive wavefront control based on known spacecraft attitude plans is a promising approach for JWST and other future active space observatories.
A Method of Recording and Predicting the Pollen Count.
ERIC Educational Resources Information Center
Buck, M.
1985-01-01
A hair dryer, plastic funnel, and microscope slide can be used for predicting pollen counts on a day-to-day basis. Materials, methods for assembly, collection technique, meteorological influences, and daily patterns are discussed. Data collected using the apparatus suggest that airborne grass products other than pollen also affect hay fever…
Prediction of Preclinical Operative Dentistry Performance in Two Instructional Methods.
ERIC Educational Resources Information Center
Boyd, Marcia; And Others
1980-01-01
The accuracy of manual and academic variables in predicting preclinical operative technique was evaluated. Correlations were low, but significant using the Perceptual Motor Ability Test (PMAT) of the Dental Admission Test. Students with low or average two-dimension scores on the PMAT benefited from an alternate teaching method. (JSR)
Risk prediction with machine learning and regression methods.
Steyerberg, Ewout W; van der Ploeg, Tjeerd; Van Calster, Ben
2014-07-01
This is a discussion of issues in risk prediction based on the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.
Statistical Methods for Predicting Malaria Incidences Using Data from Sudan
Awadalla, Khidir E.
2017-01-01
Malaria is the leading cause of illness and death in Sudan. The entire population is at risk of malaria epidemics with a very high burden on government and population. The usefulness of forecasting methods in predicting the number of future incidences is needed to motivate the development of a system that can predict future incidences. The objective of this paper is to develop applicable and understood time series models and to find out what method can provide better performance to predict future incidences level. We used monthly incidence data collected from five states in Sudan with unstable malaria transmission. We test four methods of the forecast: (1) autoregressive integrated moving average (ARIMA); (2) exponential smoothing; (3) transformation model; and (4) moving average. The result showed that transformation method performed significantly better than the other methods for Gadaref, Gazira, North Kordofan, and Northern, while the moving average model performed significantly better for Khartoum. Future research should combine a number of different and dissimilar methods of time series to improve forecast accuracy with the ultimate aim of developing a simple and useful model for producing reasonably reliable forecasts of the malaria incidence in the study area. PMID:28367352
Tang, Xiaoming; Qu, Hongchun; Wang, Ping; Zhao, Meng
2015-03-01
This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Sediment rating curve & Co. - a contest of prediction methods
NASA Astrophysics Data System (ADS)
Francke, T.; Zimmermann, A.
2012-04-01
In spite of the recent technological progress in sediment monitoring, often the calculation of sediment yield (SSY) still relies on intermittent measurements because of the use of historic records, instrument-failure in continuous recording or financial constraints. Therefore, available measurements are usually inter- and even extrapolated using the sediment rating curve approach, which uses continuously available discharge data to predict sediment concentrations. Extending this idea by further aspects like the inclusion of other predictors (e.g. rainfall, discharge-characteristics, etc.), or the consideration of prediction uncertainty led to a variety of new methods. Now, with approaches such as Fuzzy Logic, Artificial Neural Networks, Tree-based regression, GLMs, etc., the user is left to decide which method to apply. Trying multiple approaches is usually not an option, as considerable effort and expertise may be needed for their application. To establish a helpful guideline in selecting the most appropriate method for SSY-computation, we initiated a study to compare and rank available methods. Depending on problem attributes like hydrological and sediment regime, number of samples, sampling scheme, and availability of ancillary predictors, the performance of different methods is compared. Our expertise allowed us to "register" Random Forests, Quantile Regression Forests and GLMs for the contest. To include many different methods and ensure their sophisticated use we invite scientists that are willing to benchmark their favourite method(s) with us. The more diverse the participating methods are, the more exciting the contest will be.
Optimized continuous pharmaceutical manufacturing via model-predictive control.
Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin
2016-08-20
This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.
Prediction and control of limit cycling motions in boosting rockets
NASA Astrophysics Data System (ADS)
Newman, Brett
An investigation concerning the prediction and control of observed limit cycling behavior in a boosting rocket is considered. The suspected source of the nonlinear behavior is the presence of Coulomb friction in the nozzle pivot mechanism. A classical sinusoidal describing function analysis is used to accurately recreate and predict the observed oscillatory characteristic. In so doing, insight is offered into the limit cycling mechanism and confidence is gained in the closed-loop system design. Nonlinear simulation results are further used to support and verify the results obtained from describing function theory. Insight into the limit cycling behavior is, in turn, used to adjust control system parameters in order to passively control the oscillatory tendencies. Tradeoffs with the guidance and control system stability/performance are also noted. Finally, active control of the limit cycling behavior, using a novel feedback algorithm to adjust the inherent nozzle sticking-unsticking characteristics, is considered.
Shannon, Ronald; Nelson, Andrea
2016-11-20
To compare data on time to healing from two separate cohorts: one treated with a new acellular synthetic matrix plus standard care (SC) and one matched from four large UK pragmatic, randomised controlled trials [venous leg ulcer (VLU) evidence network]. We introduce a new proof-of-concept strategy to a VLU clinical evidence network, propensity score matching and sensitivity analysis to predict the feasibility of the new acellular synthetic matrix plus SC for success in future randomised, controlled clinical trials. Prospective data on chronic VLUs from a safety and effectiveness study on an acellular synthetic matrix conducted in one wound centre in the UK (17 patients) and three wound centres in Australia (36 patients) were compared retrospectively to propensity score-matched data from patients with comparable leg ulcer disease aetiology, age, baseline ulcer area, ulcer duration, multi-layer compression bandaging and majority of care completed in specialist wound centres (average of 1 visit per week), with the outcome measures at comparable follow-up periods from patients enrolled in four prospective, multicentre, pragmatic, randomised studies of venous ulcers in the UK (the comparison group; VLU evidence network). Analysis using Kaplan-Meier survival curves showed a mean healing time of 73·1 days for ASM plus SC (ASM) treated ulcers in comparison with 83·5 days for comparison group ulcers treated with SC alone (Log rank test, χ(2) 5·779, P = 0·016) within 12 weeks. Sensitivity analysis indicates that an unobserved covariate would have to change the odds of healing for SC by a factor of 1·1 to impact the baseline results. Results from this study predict a significant effect on healing time when using a new ASM as an adjunct to SC in the treatment of non-healing venous ulcers in the UK, but results are sensitive to unobserved covariates that may be important in healing time comparison.
Modification of an Existing In vitro Method to Predict Relative ...
The soil matrix can sequester arsenic (As) and reduces its exposure by soil ingestion. In vivo dosing studies and in vitro gastrointestinal (IVG) methods have been used to predict relative bioavailable (RBA) As. Originally, the Ohio State University (OSU-IVG) method predicted RBA As for soils exclusively from mining and smelting sites with a median of 5,636 mg As kg-1. The objectives of the current study were to (i) evaluate the ability of the OSU-IVG method to predict RBA As for As contaminated soils with a wider range of As content and As contaminant sources, and (ii) evaluate a modified extraction procedure's ability to improve prediction of RBA As. In vitro bioaccessible (IVBA) by OSU-IVG and California Bioaccessibility Method (CAB) methods, RBA As, speciation, and properties of 33 As contaminated soils were determined. Total As ranged from 162 to 12,483 mg kg-1 with a median of 731 mg kg-1. RBA As ranged from 1.30 to 60.0% and OSU-IVG IVBA As ranged from 0.80 to 52.3%. Arsenic speciation was predominantly As(V) adsorbed to hydrous ferric oxide (HFO) or iron (Fe), manganese (Mn), and aluminum (Al) oxides. The OSU-IVG often extracted significantly less As in vitro than in vivo RBA As, in particularly for soils from historical gold mining. The CAB method, which is a modified OSU-IVG method extracted more As than OSU-IVG for most soils, resulting in a more accurate predictor than OSU-IVG, especially for low to moderately contaminated soils (<1,500 mg As
Prediction Models are Basis for Rational Air Quality Control
ERIC Educational Resources Information Center
Daniels, Anders; Bach, Wilfrid
1973-01-01
An air quality control scheme employing meteorological diffusion, time averaging and frequency, and cost-benefit models is discussed. The methods outlined provide a constant feedback system for air quality control. Flow charts and maps are included. (BL)
Prediction Models are Basis for Rational Air Quality Control
ERIC Educational Resources Information Center
Daniels, Anders; Bach, Wilfrid
1973-01-01
An air quality control scheme employing meteorological diffusion, time averaging and frequency, and cost-benefit models is discussed. The methods outlined provide a constant feedback system for air quality control. Flow charts and maps are included. (BL)
Model reduction methods for control design
NASA Technical Reports Server (NTRS)
Dunipace, K. R.
1988-01-01
Several different model reduction methods are developed and detailed implementation information is provided for those methods. Command files to implement the model reduction methods in a proprietary control law analysis and design package are presented. A comparison and discussion of the various reduction techniques is included.
A Review of Computational Methods for Predicting Drug Targets.
Huang, Guohua; Yan, Fengxia; Tan, Duoduo
2016-11-14
Drug discovery and development is not only a time-consuming and labor-intensive process but also full of risk. Identifying targets of small molecules helps evaluate safety of drugs and find new therapeutic applications. The biotechnology measures a wide variety of properties related to drug and targets from different perspectives, thus generating a large body of data. This undoubtedly provides a solid foundation to explore relationships between drugs and targets. A large number of computational techniques have recently been developed for drug target prediction. In this paper, we summarize these computational methods and classify them into structure-based, molecular activity-based, side-effect-based and multi-omics-based predictions according to the used data for inference. The multi-omics-based methods are further grouped into two types: classifier-based and network-based predictions. Furthermore，the advantages and limitations of each type of methods are discussed. Finally, we point out the future directions of computational predictions for drug targets.
Predictions of Thrombus Formation Using Lattice Boltzmann Method
NASA Astrophysics Data System (ADS)
Tamagawa, Masaaki; Matsuo, Sumiaki
This paper describes the prediction of index of thrombus formation in shear blood flow by computational fluid dynamics (CFD) with Lattice Boltzmann Method (LBM), applying to orifice-pipe blood flow and flow around a cylinder, which is simple model of turbulent shear stress in the high speed rotary blood pumps and complicated geometry of medical fluid machines. The results of the flow field in the orifice-pipe flow using LBM are compared with experimental data and those using finite difference method, and it is found that the reattachment length of the backward facing step flow is predicted as precise as that the experiment and the finite difference method. As for thrombus formation, from the computational data of flow around the cylinder in the channel, the thrombus formation (thickness) is estimated using (1) shear rate and adhesion force (effective distance) to the wall independently, and (2) shear rate function with adhesion force (effective distance), and it is found that the prediction method using shear rate function with adhesion force is more accurate than the method using the former one.
Structure-based Methods for Computational Protein Functional Site Prediction
Dukka, B KC
2013-01-01
Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details. PMID:24688745
Better Methods for Predicting Lifetimes of Seal Materials
Celina, M.; Gillen, K.T.; Keenan, M.R.
1999-03-16
We have been working for many years to develop better methods for predicting the lifetimes of polymer materials. Because of the recent interest in extending the lifetimes of nuclear weapons and the importance of environmental seals (o-rings, gaskets) for protecting weapon interiors against oxygen and water vapor, we have recently turned our attention to seal materials. Perhaps the most important environmental o-ring material is butyl rubber, used in various military applications. Although it is the optimum choice from a water permeability perspective, butyl can be marginal from an aging point-of-view. The purpose of the present work was to derive better methods for predicting seal lifetimes and applying these methods to an important butyl material, Parker compound B6 12-70.
Stabilization precision control methods of photoelectric aim-stabilized system
NASA Astrophysics Data System (ADS)
Song, Xiaoru; Chen, Hua; Xue, Yonggang
2015-09-01
To solve the question that photoelectric aim-stabilized system can be controlled with high precision and stability, this paper researches a new photoelectric aim-stabilized control algorithm, analyzes the photoelectric aim-stabilized system architecture, sets up stability control system mathematical model, designs the stability of the photoelectric aim-stabilized LSSVM identification and control system, discusses uncertain factors and calculates the LSSVM parameters by the Chaos theory, gives the predictive controller model by the LSSVM and designs new photoelectric aim-stabilized system. Through the simulation calculation and experimental analysis, new photoelectric aim-stabilized control algorithm was verified; the results show the new photoelectric aim-stabilized control method can meet the demand of high precision control in photoelectric aim-stabilized system.
A method for predicting protein-protein interaction types.
Silberberg, Yael; Kupiec, Martin; Sharan, Roded
2014-01-01
Protein-protein interactions (PPIs) govern basic cellular processes through signal transduction and complex formation. The diversity of those processes gives rise to a remarkable diversity of interactions types, ranging from transient phosphorylation interactions to stable covalent bonding. Despite our increasing knowledge on PPIs in humans and other species, their types remain relatively unexplored and few annotations of types exist in public databases. Here, we propose the first method for systematic prediction of PPI type based solely on the techniques by which the interaction was detected. We show that different detection methods are better suited for detecting specific types. We apply our method to ten interaction types on a large scale human PPI dataset. We evaluate the performance of the method using both internal cross validation and external data sources. In cross validation, we obtain an area under receiver operating characteristic (ROC) curve ranging from 0.65 to 0.97 with an average of 0.84 across the predicted types. Comparing the predicted interaction types to external data sources, we obtained significant agreements for phosphorylation and ubiquitination interactions, with hypergeometric p-value = 2.3e(-54) and 5.6e(-28) respectively. We examine the biological relevance of our predictions using known signaling pathways and chart the abundance of interaction types in cell processes. Finally, we investigate the cross-relations between different interaction types within the network and characterize the discovered patterns, or motifs. We expect the resulting annotated network to facilitate the reconstruction of process-specific subnetworks and assist in predicting protein function or interaction.
Factors Predicting Atypical Development of Nighttime Bladder Control
Sullivan, Sarah; Heron, Jon
2015-01-01
ABSTRACT: Objective: To derive latent classes (longitudinal “phenotypes”) of frequency of bedwetting from 4 to 9 years and to examine their association with developmental delay, parental history of bedwetting, length of gestation and birth weight. Method: The authors used data from 8,769 children from the UK Avon Longitudinal Study of Parents and Children cohort. Mothers provided repeated reports on their child's frequency of bedwetting from 4 to 9 years. The authors used longitudinal latent class analysis to derive latent classes of bedwetting and examined their association with sex, developmental level at 18 months, parental history of wetting, birth weight, and gestational length. Results: The authors identified 5 latent classes: (1) “normative”—low probability of bedwetting; (2) “infrequent delayed”—delayed attainment of nighttime bladder control with bedwetting
Polyadenylation site prediction using PolyA-iEP method.
Kavakiotis, Ioannis; Tzanis, George; Vlahavas, Ioannis
2014-01-01
This chapter presents a method called PolyA-iEP that has been developed for the prediction of polyadenylation sites. More precisely, PolyA-iEP is a method that recognizes mRNA 3'ends which contain polyadenylation sites. It is a modular system which consists of two main components. The first exploits the advantages of emerging patterns and the second is a distance-based scoring method. The outputs of the two components are finally combined by a classifier. The final results reach very high scores of sensitivity and specificity.
Nonlinear model identification and adaptive model predictive control using neural networks.
Akpan, Vincent A; Hassapis, George D
2011-04-01
This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.
Self-Control Assessments and Implications for Predicting Adolescent Offending.
Fine, Adam; Steinberg, Laurence; Frick, Paul J; Cauffman, Elizabeth
2016-04-01
Although low self-control is consistently related to adolescent offending, it is unknown whether self-report measures or laboratory behavior tasks yield better predictive utility, or if a combination yields incremental predictive power. This is particularly important because developmental theory indicates that self-control is related to adolescent offending and, consequently, risk assessments rely on self-control measures. The present study (a) examines relationships between self-reported self-control on the Weinberger Adjustment Inventory with Go/No-Go response inhibition, and (b) compares the predictive utility of both assessment strategies for short- and long-term adolescent reoffending. It uses longitudinal data from the Crossroads Study of male, first-time adolescent offenders ages 13-17 (N = 930; 46 % Hispanic/Latino, 37 % Black/African-American, 15 % non-Hispanic White, 2 % other race). The results of the study indicate that the measures are largely unrelated, and that the self-report measure is a better indicator of both short- and long-term reoffending. The laboratory task measure does not add value to what is already predicted by the self-report measure. Implications for assessing self-control during adolescence and consequences of assessment strategy are discussed.
Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Eure, Kenneth W.
1998-01-01
Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.
Predicted torque equilibrium attitude utilization for Space Station attitude control
NASA Technical Reports Server (NTRS)
Kumar, Renjith R.; Heck, Michael L.; Robertson, Brent P.
1990-01-01
An approximate knowledge of the torque equilibrium attitude (TEA) is shown to improve the performance of a control moment gyroscope (CMG) momentum management/attitude control law for Space Station Freedom. The linearized equations of motion are used in conjunction with a state transformation to obtain a control law which uses full state feedback and the predicted TEA to minimize both attitude excursions and CMG peak and secular momentum. The TEA can be computationally determined either by observing the steady state attitude of a 'controlled' spacecraft using arbitrary initial attitude, or by simulating a fixed attitude spacecraft flying in desired orbit subject to realistic environmental disturbance models.
Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter
NASA Astrophysics Data System (ADS)
Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç
2017-01-01
This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.
Evaluation of ride quality prediction methods for operational military helicopters
NASA Technical Reports Server (NTRS)
Leatherwood, J. D.; Clevenson, S. A.; Hollenbaugh, D. D.
1984-01-01
The results of a simulator study conducted to compare and validate various ride quality prediction methods for use in assessing passenger/crew ride comfort within helicopters are presented. Included are results quantifying 35 helicopter pilots' discomfort responses to helicopter interior noise and vibration typical of routine flights, assessment of various ride quality metrics including the NASA ride comfort model, and examination of possible criteria approaches. Results of the study indicated that crew discomfort results from a complex interaction between vibration and interior noise. Overall measures such as weighted or unweighted root-mean-square acceleration level and A-weighted noise level were not good predictors of discomfort. Accurate prediction required a metric incorporating the interactive effects of both noise and vibration. The best metric for predicting crew comfort to the combined noise and vibration environment was the NASA discomfort index.
Evaluation of ride quality prediction methods for operational military helicopters
NASA Technical Reports Server (NTRS)
Leatherwood, J. D.; Clevenson, S. A.; Hollenbaugh, D. D.
1984-01-01
The results of a simulator study conducted to compare and validate various ride quality prediction methods for use in assessing passenger/crew ride comfort within helicopters are presented. Included are results quantifying 35 helicopter pilots' discomfort responses to helicopter interior noise and vibration typical of routine flights, assessment of various ride quality metrics including the NASA ride comfort model, and examination of possible criteria approaches. Results of the study indicated that crew discomfort results from a complex interaction between vibration and interior noise. Overall measures such as weighted or unweighted root-mean-square acceleration level and A-weighted noise level were not good predictors of discomfort. Accurate prediction required a metric incorporating the interactive effects of both noise and vibration. The best metric for predicting crew comfort to the combined noise and vibration environment was the NASA discomfort index.
Congestion Control for TCP/AQM Networks using State Predictive Control
NASA Astrophysics Data System (ADS)
Azuma, Takehito; Fujita, Tsunetoshi; Fujita, Masayuki
The purpose of this paper is to design congestion controllers for TCP/AQM networks using state predictive control and illustrate the effectiveness of designed congestion controllers via SIMULINK and the ns-2 simulator. Linearized models of TCP/AQM networks can be described as linear systems with an information delay simply. Using state predictive control, these linear systems with an information delay is equivalent to linear systems with no delays. Thus congestion controllers (AQM mechanisms) can be designed using the linear control theory. In this paper, LQ control with an observer is adopted for linear systems with no delays which describe linearized systems of TCP/AQM networks. Finally the designed state predictive controllers using LQ control with an observer is implemented and some simulation results are shown via SIMULINK and the ns-2 simulator.
Flow Control Predictions using URANS Modeling: A Parametric Study
NASA Technical Reports Server (NTRS)
Rumsey, Christopher L.; Greenblatt, David
2007-01-01
A computational study was performed for steady and oscillatory flow control over a hump model with flow separation to assess how well the steady and unsteady Reynolds-averaged Navier-Stokes equations predict trends due to Reynolds number, control magnitude, and control frequency. As demonstrated previously, the hump model case is useful because it clearly demonstrates a failing in all known turbulence models: they under-predict the turbulent shear stress in the separated region and consequently reattachment occurs too far downstream. In spite of this known failing, three different turbulence models were employed to determine if trends can be captured even though absolute levels are not. The three turbulence models behaved similarly. Overall they showed very similar trends as experiment for steady suction, but only agreed qualitatively with some of the trends for oscillatory control.
Neural Generalized Predictive Control: A Newton-Raphson Implementation
NASA Technical Reports Server (NTRS)
Soloway, Donald; Haley, Pamela J.
1997-01-01
An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.
Interpolation techniques in robust constrained model predictive control
NASA Astrophysics Data System (ADS)
Kheawhom, Soorathep; Bumroongsri, Pornchai
2017-05-01
This work investigates interpolation techniques that can be employed on off-line robust constrained model predictive control for a discrete time-varying system. A sequence of feedback gains is determined by solving off-line a series of optimal control optimization problems. A sequence of nested corresponding robustly positive invariant set, which is either ellipsoidal or polyhedral set, is then constructed. At each sampling time, the smallest invariant set containing the current state is determined. If the current invariant set is the innermost set, the pre-computed gain associated with the innermost set is applied. If otherwise, a feedback gain is variable and determined by a linear interpolation of the pre-computed gains. The proposed algorithms are illustrated with case studies of a two-tank system. The simulation results showed that the proposed interpolation techniques significantly improve control performance of off-line robust model predictive control without much sacrificing on-line computational performance.
PROSO II--a new method for protein solubility prediction.
Smialowski, Pawel; Doose, Gero; Torkler, Phillipp; Kaufmann, Stefanie; Frishman, Dmitrij
2012-06-01
Many fields of science and industry depend on efficient production of active protein using heterologous expression in Escherichia coli. The solubility of proteins upon expression is dependent on their amino acid sequence. Prediction of solubility from sequence is therefore highly valuable. We present a novel machine-learning-based model called PROSO II which makes use of new classification methods and growth in experimental data to improve coverage and accuracy of solubility predictions. The classification algorithm is organized as a two-layered structure in which the output of a primary Parzen window model for sequence similarity and a logistic regression classifier of amino acid k-mer composition serve as input for a second-level logistic regression classifier. Compared with previously published research our model is trained on five times more data than used by any other method before (82 000 proteins). When tested on a separate holdout set not used at any point of method development our server attained the best results in comparison with other currently available methods: accuracy 75.4%, Matthew's correlation coefficient 0.39, sensitivity 0.731, specificity 0.759, gain (soluble) 2.263. In summary, due to utilization of cutting edge machine learning technologies combined with the largest currently available experimental data set the PROSO II server constitutes a substantial improvement in protein solubility predictions. PROSO II is available at http://mips.helmholtz-muenchen.de/prosoII. © 2012 The Authors Journal compilation © 2012 FEBS.
Epidemiology and statistical methods in prediction of patient outcome.
Bostwick, David G; Adolfsson, Jan; Burke, Harry B; Damber, Jan-Erik; Huland, Hartwig; Pavone-Macaluso, Michele; Waters, David J
2005-05-01
Substantial gaps exist in the data of the assessment of risk and prognosis that limit our understanding of the complex mechanisms that contribute to the greatest cancer epidemic, prostate cancer, of our time. This report was prepared by an international multidisciplinary committee of the World Health Organization to address contemporary issues of epidemiology and statistical methods in prostate cancer, including a summary of current risk assessment methods and prognostic factors. Emphasis was placed on the relative merits of each of the statistical methods available. We concluded that: 1. An international committee should be created to guide the assessment and validation of molecular biomarkers. The goal is to achieve more precise identification of those who would benefit from treatment. 2. Prostate cancer is a predictable disease despite its biologic heterogeneity. However, the accuracy of predicting it must be improved. We expect that more precise statistical methods will supplant the current staging system. The simplicity and intuitive ease of using the current staging system must be balanced against the serious compromise in accuracy for the individual patient. 3. The most useful new statistical approaches will integrate molecular biomarkers with existing prognostic factors to predict conditional life expectancy (i.e. the expected remaining years of a patient's life) and take into account all-cause mortality.
Pedophilia: an evaluation of diagnostic and risk prediction methods.
Wilson, Robin J; Abracen, Jeffrey; Looman, Jan; Picheca, Janice E; Ferguson, Meaghan
2011-06-01
One hundred thirty child sexual abusers were diagnosed using each of following four methods: (a) phallometric testing, (b) strict application of Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision [DSM-IV-TR]) criteria, (c) Rapid Risk Assessment of Sex Offender Recidivism (RRASOR) scores, and (d) "expert" diagnoses rendered by a seasoned clinician. Comparative utility and intermethod consistency of these methods are reported, along with recidivism data indicating predictive validity for risk management. Results suggest that inconsistency exists in diagnosing pedophilia, leading to diminished accuracy in risk assessment. Although the RRASOR and DSM-IV-TR methods were significantly correlated with expert ratings, RRASOR and DSM-IV-TR were unrelated to each other. Deviant arousal was not associated with any of the other methods. Only the expert ratings and RRASOR scores were predictive of sexual recidivism. Logistic regression analyses showed that expert diagnosis did not add to prediction of sexual offence recidivism over and above RRASOR alone. Findings are discussed within a context of encouragement of clinical consistency and evidence-based practice regarding treatment and risk management of those who sexually abuse children.
Predicting worsening asthma control following the common cold
Walter, Michael J.; Castro, Mario; Kunselman, Susan J.; Chinchilli, Vernon M; Reno, Melissa; Ramkumar, Thiruvamoor P.; Avila, Pedro C.; Boushey, Homer A.; Ameredes, Bill T.; Bleecker, Eugene R.; Calhoun, William J.; Cherniack, Reuben M.; Craig, Timothy J.; Denlinger, Loren C.; Israel, Elliot; Fahy, John V.; Jarjour, Nizar N.; Kraft, Monica; Lazarus, Stephen C.; Lemanske, Robert F.; Martin, Richard J.; Peters, Stephen P.; Ramsdell, Joe W.; Sorkness, Christine A.; Rand Sutherland, E.; Szefler, Stanley J.; Wasserman, Stephen I.; Wechsler, Michael E.
2008-01-01
The asthmatic response to the common cold is highly variable and early characteristics that predict worsening of asthma control following a cold have not been identified. In this prospective multi-center cohort study of 413 adult subjects with asthma, we used the mini-Asthma Control Questionnaire (mini-ACQ) to quantify changes in asthma control and the Wisconsin Upper Respiratory Symptom Survey-21 (WURSS-21) to measure cold severity. Univariate and multivariable models examined demographic, physiologic, serologic, and cold-related characteristics for their relationship to changes in asthma control following a cold. We observed a clinically significant worsening of asthma control following a cold (increase in mini-ACQ of 0.69 ± 0.93). Univariate analysis demonstrated season, center location, cold length, and cold severity measurements all associated with a change in asthma control. Multivariable analysis of the covariates available within the first 2 days of cold onset revealed the day 2 and the cumulative sum of the day 1 and 2 WURSS-21 scores were significant predictors for the subsequent changes in asthma control. In asthmatic subjects the cold severity measured within the first 2 days can be used to predict subsequent changes in asthma control. This information may help clinicians prevent deterioration in asthma control following a cold. PMID:18768579
Researches on High Accuracy Prediction Methods of Earth Orientation Parameters
NASA Astrophysics Data System (ADS)
Xu, X. Q.
2015-09-01
The Earth rotation reflects the coupling process among the solid Earth, atmosphere, oceans, mantle, and core of the Earth on multiple spatial and temporal scales. The Earth rotation can be described by the Earth's orientation parameters, which are abbreviated as EOP (mainly including two polar motion components PM_X and PM_Y, and variation in the length of day ΔLOD). The EOP is crucial in the transformation between the terrestrial and celestial reference systems, and has important applications in many areas such as the deep space exploration, satellite precise orbit determination, and astrogeodynamics. However, the EOP products obtained by the space geodetic technologies generally delay by several days to two weeks. The growing demands for modern space navigation make high-accuracy EOP prediction be a worthy topic. This thesis is composed of the following three aspects, for the purpose of improving the EOP forecast accuracy. (1) We analyze the relation between the length of the basic data series and the EOP forecast accuracy, and compare the EOP prediction accuracy for the linear autoregressive (AR) model and the nonlinear artificial neural network (ANN) method by performing the least squares (LS) extrapolations. The results show that the high precision forecast of EOP can be realized by appropriate selection of the basic data series length according to the required time span of EOP prediction: for short-term prediction, the basic data series should be shorter, while for the long-term prediction, the series should be longer. The analysis also showed that the LS+AR model is more suitable for the short-term forecasts, while the LS+ANN model shows the advantages in the medium- and long-term forecasts. (2) We develop for the first time a new method which combines the autoregressive model and Kalman filter (AR+Kalman) in short-term EOP prediction. The equations of observation and state are established using the EOP series and the autoregressive coefficients
Method for controlled hydrogen charging of metals
Cheng, Bo-Ching; Adamson, Ronald B.
1984-05-29
A method for controlling hydrogen charging of hydride forming metals through a window of a superimposed layer of a non-hydriding metal overlying the portion of the hydride forming metals to be charged.
Approach, Avoidance, and Inhibition: Personality Traits Predict Cognitive Control Abilities
Prabhakaran, Ranjani; Kraemer, David J.M.; Thompson-Schill, Sharon L.
2011-01-01
The extent to which approach and avoidance personality trait sensitivities are associated with specific cognitive control abilities as well as with verbal and nonverbal domains remains unclear. In the current study, we investigated whether approach and avoidance trait sensitivities predict performance on verbal and nonverbal versions of the Stroop task, which taps the specific cognitive control ability of inhibiting task-irrelevant information. The findings from the current study indicate that whereas approach (specifically, Extraversion) sensitivity was predictive of verbal Stroop performance, avoidance (specifically, Behavioral Inhibition System) sensitivity was predictive of nonverbal Stroop performance. These results provide novel evidence suggestive of the integration of motivational personality traits and the ability to inhibit task-irrelevant information in a domain-specific fashion. PMID:21765574
Prediction of active control of subsonic centrifugal compressor rotating stall
NASA Technical Reports Server (NTRS)
Lawless, Patrick B.; Fleeter, Sanford
1993-01-01
A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.
On Application of Model Predictive Control to Power Converter with Switching
NASA Astrophysics Data System (ADS)
Zanma, Tadanao; Fukuta, Junichi; Doki, Shinji; Ishida, Muneaki; Okuma, Shigeru; Matsumoto, Takashi; Nishimori, Eiji
This paper concerns a DC-DC converter control. In DC-DC converters, there exist both continuous components such as inductance, conductance and resistance and discrete ones, IGBT and MOSFET as semiconductor switching elements. Such a system can be regarded as a hybrid dynamical system. Thus, this paper presents a dc-dc control technique based on the model predictive control. Specifically, a case in which the load of the dc-dc converter changes from active to sleep is considered. In the case, a control method which makes the output voltage follow to the reference quickly in transition, and the switching frequency be constant in steady state. In addition, in applying the model predictive control to power electronics circuits, the switching characteristic of the device and the restriction condition for protection are also considered. The effectiveness of the proposed method is illustrated by comparing a conventional method through some simulation results.
Strike Point Control on EAST Using an Isoflux Control Method
NASA Astrophysics Data System (ADS)
Xing, Zhe; Xiao, Bingjia; Luo, Zhengping; L. Walker, M.; A. Humphreys, D.
2015-09-01
For the advanced tokamak, the particle deposition and thermal load on the divertor is a big challenge. By moving the strike points on divertor target plates, the position of particle deposition and thermal load can be shifted. We could adjust the Poloidal Field (PF) coil current to achieve the strike point position feedback control. Using isoflux control method, the strike point position can be controlled by controlling the X point position. On the basis of experimental data, we establish relational expressions between X point position and strike point position. Benchmark experiments are carried out to validate the correctness and robustness of the control methods. The strike point position is successfully controlled following our command in the EAST operation. supported by the National Magnetic Confinement Fusion Science Program of China (Nos. 2012GB105000 and 2014GB103000)
Model predictive control for a class of systems with isolated nonlinearity.
Tao, Jili; Zhu, Yong; Fan, Qinru
2014-07-01
The paper is concerned with an overall convergent nonlinear model predictive control design for a kind of nonlinear mechatronic drive systems. The proposed nonlinear model predictive control results in the improvement of regulatory capacity for reference tracking and load disturbance rejection. The design of the nonlinear model predictive controller consists of two steps: the first step is to design a linear model predictive controller based on the linear part of the system at each sample instant, then an overall convergent nonlinear part is added to the linear model predictive controller to combine a nonlinear controller using error driven. The structure of the proposed controller is similar to that of classical PI optimal regulator but it also bears a set-point feed forward control loop, thus tracking ability and disturbance rejection are improved. The proposed method is compared with the results from recent literature, where control performance under both model match and mismatch cases are enlightened. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Method to predict external store carriage characteristics at transonic speeds
NASA Technical Reports Server (NTRS)
Rosen, Bruce S.
1988-01-01
Development of a computational method for prediction of external store carriage characteristics at transonic speeds is described. The geometric flexibility required for treatment of pylon-mounted stores is achieved by computing finite difference solutions on a five-level embedded grid arrangement. A completely automated grid generation procedure facilitates applications. Store modeling capability consists of bodies of revolution with multiple fore and aft fins. A body-conforming grid improves the accuracy of the computed store body flow field. A nonlinear relaxation scheme developed specifically for modified transonic small disturbance flow equations enhances the method's numerical stability and accuracy. As a result, treatment of lower aspect ratio, more highly swept and tapered wings is possible. A limited supersonic freestream capability is also provided. Pressure, load distribution, and force/moment correlations show good agreement with experimental data for several test cases. A detailed computer program description for the Transonic Store Carriage Loads Prediction (TSCLP) Code is included.
Linear reduction method for predictive and informative tag SNP selection.
He, Jingwu; Westbrooks, Kelly; Zelikovsky, Alexander
2005-01-01
Constructing a complete human haplotype map is helpful when associating complex diseases with their related SNPs. Unfortunately, the number of SNPs is very large and it is costly to sequence many individuals. Therefore, it is desirable to reduce the number of SNPs that should be sequenced to a small number of informative representatives called tag SNPs. In this paper, we propose a new linear algebra-based method for selecting and using tag SNPs. We measure the quality of our tag SNP selection algorithm by comparing actual SNPs with SNPs predicted from selected linearly independent tag SNPs. Our experiments show that for sufficiently long haplotypes, knowing only 0.4% of all SNPs the proposed linear reduction method predicts an unknown haplotype with the error rate below 2% based on 10% of the population.
Auxiliary particle filter-model predictive control of the vacuum arc remelting process
NASA Astrophysics Data System (ADS)
Lopez, F.; Beaman, J.; Williamson, R.
2016-07-01
Solidification control is required for the suppression of segregation defects in vacuum arc remelting of superalloys. In recent years, process controllers for the VAR process have been proposed based on linear models, which are known to be inaccurate in highly-dynamic conditions, e.g. start-up, hot-top and melt rate perturbations. A novel controller is proposed using auxiliary particle filter-model predictive control based on a nonlinear stochastic model. The auxiliary particle filter approximates the probability of the state, which is fed to a model predictive controller that returns an optimal control signal. For simplicity, the estimation and control problems are solved using Sequential Monte Carlo (SMC) methods. The validity of this approach is verified for a 430 mm (17 in) diameter Alloy 718 electrode melted into a 510 mm (20 in) diameter ingot. Simulation shows a more accurate and smoother performance than the one obtained with an earlier version of the controller.
When Predictions Take Control: The Effect of Task Predictions on Task Switching Performance
Duthoo, Wout; De Baene, Wouter; Wühr, Peter; Notebaert, Wim
2012-01-01
In this paper, we aimed to investigate the role of self-generated predictions in the flexible control of behavior. Therefore, we ran a task switching experiment in which participants were asked to try to predict the upcoming task in three conditions varying in switch rate (30, 50, and 70%). Irrespective of their predictions, the color of the target indicated which task participants had to perform. In line with previous studies (Mayr, 2006; Monsell and Mizon, 2006), the switch cost was attenuated as the switch rate increased. Importantly, a clear task repetition bias was found in all conditions, yet the task repetition prediction rate dropped from 78 over 66 to 49% with increasing switch probability in the three conditions. Irrespective of condition, the switch cost was strongly reduced in expectation of a task alternation compared to the cost of an unexpected task alternation following repetition predictions. Hence, our data suggest that the reduction in the switch cost with increasing switch probability is caused by a diminished expectancy for the task to repeat. Taken together, this paper highlights the importance of predictions in the flexible control of behavior, and suggests a crucial role for task repetition expectancy in the context-sensitive adjusting of task switching performance. PMID:22891063
A review of statistical methods for prediction of proteolytic cleavage.
duVerle, David A; Mamitsuka, Hiroshi
2012-05-01
A fundamental component of systems biology, proteolytic cleavage is involved in nearly all aspects of cellular activities: from gene regulation to cell lifecycle regulation. Current sequencing technologies have made it possible to compile large amount of cleavage data and brought greater understanding of the underlying protein interactions. However, the practical impossibility to exhaustively retrieve substrate sequences through experimentation alone has long highlighted the need for efficient computational prediction methods. Such methods must be able to quickly mark substrate candidates and putative cleavage sites for further analysis. Available methods and expected reliability depend heavily on the type and complexity of proteolytic action, as well as the availability of well-labelled experimental data sets: factors varying greatly across enzyme families. For this review, we chose to give a quick overview of the general issues and challenges in cleavage prediction methods followed by a more in-depth presentation of major techniques and implementations, with a focus on two particular families of cysteine proteases: caspases and calpains. Through their respective differences in proteolytic specificity (high for caspases, broader for calpains) and data availability (much lower for calpains), we aimed to illustrate the strengths and limitations of techniques ranging from position-based matrices and decision trees to more flexible machine-learning methods such as hidden Markov models and Support Vector Machines. In addition to a technical overview for each family of algorithms, we tried to provide elements of evaluation and performance comparison across methods.
Method and apparatus for controlling electroslag remelting
Maguire, Michael C.; Zanner, Frank J.; Damkroger, Brian K.; Miszkiel, Mark E.; Aronson, Eugene A.
1994-01-01
Method and apparatus for controlling electrode immersion depth in an electroslag remelting furnace. The phase difference of the alternating current circuit established in the furnace is calculated in real time and employed to more accurately control immersion depth than possible with voltage-swing systems.
Method of controlling fusion reaction rates
Kulsrud, Russell M.; Furth, Harold P.; Valeo, Ernest J.; Goldhaber, Maurice
1988-01-01
A method of controlling the reaction rates of the fuel atoms in a fusion reactor comprises the step of polarizing the nuclei of the fuel atoms in a particular direction relative to the plasma confining magnetic field. Fusion reaction rates can be increased or decreased, and the direction of emission of the reaction products can be controlled, depending on the choice of polarization direction.
Method of controlling fusion reaction rates
Kulsrud, Russell M.; Furth, Harold P.; Valeo, Ernest J.; Goldhaber, Maurice
1988-03-01
A method of controlling the reaction rates of the fuel atoms in a fusion reactor comprises the step of polarizing the nuclei of the fuel atoms in a particular direction relative to the plasma confining magnetic field. Fusion reaction rates can be increased or decreased, and the direction of emission of the reaction products can be controlled, depending on the choice of polarization direction.
Barrier methods of birth control - slideshow
... gov/ency/presentations/100107.htm Barrier methods of birth control - series—Female normal anatomy To use the sharing ... A.M. Editorial team. Related MedlinePlus Health Topics Birth Control A.D.A.M., Inc. is accredited by ...
Practical Methods for Robust Multivariable Control
1991-12-31
objectives in the face of both structured and unstructured uncertainty. Advances in the past two years have included relative-error methods for system ... identification , model reduction and control, better algorithms for H- and H2 control computations and new results on the analysis of stability
Fiber composite structural durability and damage tolerance: Simplified predictive methods
NASA Technical Reports Server (NTRS)
Chamis, Christos C.; Ginty, Carol A.
1987-01-01
Simplified predictive methods and models (theory) to evaluate fiber/polymer-matrix composite material for determining structural durability and damage tolerance are presented and described. This theory includes equations for (1) fatigue and fracture of composites without and with defects, (2) impact resistance and residual strength after impact, (3) thermal fatigue, and (4) combined stress fatigue. Several examples are included to illustrate applications of the theory and to identify significant parameters and sensitivities. Comparisons with limited experimental data are made.
Sixth blind test of organic crystal-structure prediction methods.
Groom, Colin R; Reilly, Anthony M
2014-08-01
Over the past 15 years progress in predicting crystal structures of small organic molecules has been charted by a series of blind tests hosted by the Cambridge Crystallographic Data Centre. This letter announces a sixth blind test to take place between September 2014 and August 2015, giving details of the target systems and the revised procedure. We hope that as many methods as possible will be assessed and benchmarked in this new blind test.
Epileptic seizure prediction by non-linear methods
Hively, Lee M.; Clapp, Ned E.; Daw, C. Stuart; Lawkins, William F.
1999-01-01
Methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming.
Epileptic seizure prediction by non-linear methods
Hively, L.M.; Clapp, N.E.; Day, C.S.; Lawkins, W.F.
1999-01-12
This research discloses methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming. 76 figs.
Predicting asthma control: the role of psychological triggers.
Ritz, Thomas; Bobb, Carol; Griffiths, Chris
2014-01-01
Asthma triggers have been linked to adverse health outcomes in asthma, but little is known about their association with asthma control. Because trigger avoidance is an integral part of successful asthma management, psychological triggers in particular may be associated with suboptimal asthma control, given the difficulty of controlling them. We examined cross-sectional and longitudinal associations of perceived asthma triggers with self-report of asthma control impairment, symptoms, and spirometric lung function (forced expiratory volume in the 1st second, [FEV1]) in 179 adult primary care asthma patients. Perceived asthma triggers explained up to 42.5% of the variance in asthma control and symptoms, but not in FEV1 alone. Allergic triggers explained up to 12.1% of the asthma control and symptom variance, three nonallergic trigger types, air pollution/irritants, physical activity, and infection, explained up to 26.2% over and above allergic triggers, and psychological triggers up to 9.5% over and above all other triggers. Psychological triggers alone explained up to 33.9% of the variance and were the only trigger class that was consistently significant in all final multiple regression models predicting control and symptoms. Psychological triggers also predicted lower asthma control 3-6 months later, although controlling for initial asthma control eliminated this association. In free reports of individually relevant triggers, only psychological triggers were associated with suboptimal asthma control. Trigger factors are important predictors of self-reported asthma control and symptoms but not actual lung function. Particular attention should be directed to psychological triggers as indicators of patients' perceptions of suboptimal asthma control.
Kuniya, Toshikazu; Sano, Hideki
2016-05-10
In mathematical epidemiology, age-structured epidemic models have usually been formulated as the boundary-value problems of the partial differential equations. On the other hand, in engineering, the backstepping method has recently been developed and widely studied by many authors. Using the backstepping method, we obtained a boundary feedback control which plays the role of the threshold criteria for the prediction of increase or decrease of newly infected population. Under an assumption that the period of infectiousness is same for all infected individuals (that is, the recovery rate is given by the Dirac delta function multiplied by a sufficiently large positive constant), the prediction method is simplified to the comparison of the numbers of reported cases at the current and previous time steps. Our prediction method was applied to the reported cases per sentinel of influenza in Japan from 2006 to 2015 and its accuracy was 0.81 (404 correct predictions to the total 500 predictions). It was higher than that of the ARIMA models with different orders of the autoregressive part, differencing and moving-average process. In addition, a proposed method for the estimation of the number of reported cases, which is consistent with our prediction method, was better than that of the best-fitted ARIMA model ARIMA(1,1,0) in the sense of mean square error. Our prediction method based on the backstepping method can be simplified to the comparison of the numbers of reported cases of the current and previous time steps. In spite of its simplicity, it can provide a good prediction for the spread of influenza in Japan.
Model predictive control based on reduced order models applied to belt conveyor system.
Chen, Wei; Li, Xin
2016-11-01
In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system.
Fast prediction unit selection method for HEVC intra prediction based on salient regions
NASA Astrophysics Data System (ADS)
Feng, Lei; Dai, Ming; Zhao, Chun-lei; Xiong, Jing-ying
2016-07-01
In order to reduce the computational complexity of the high efficiency video coding (HEVC) standard, a new algorithm for HEVC intra prediction, namely, fast prediction unit (PU) size selection method for HEVC based on salient regions is proposed in this paper. We first build a saliency map for each largest coding unit (LCU) to reduce its texture complexity. Secondly, the optimal PU size is determined via a scheme that implements an information entropy comparison among sub-blocks of saliency maps. Finally, we apply the partitioning result of saliency map on the original LCUs, obtaining the optimal partitioning result. Our algorithm can determine the PU size in advance to the angular prediction in intra coding, reducing computational complexity of HEVC. The experimental results show that our algorithm achieves a 37.9% reduction in encoding time, while producing a negligible loss in Bjontegaard delta bit rate ( BDBR) of 0.62%.
Eggert, Thomas
2017-01-01
Abstract The smooth pursuit eye movement system incorporates various control features enabling adaptation to specific tracking situations. In this work, we analyzed the interplay between two of these mechanisms: gain control and predictive pursuit. We tested human responses to high-frequency perturbations during step-ramp pursuit, as well as the pursuit of a periodically moving target. For the latter task, we found a nonlinear interaction between perturbation response and carrier acceleration. Responses to perturbations where the initial perturbation acceleration was contradirectional to carrier acceleration increased with carrier velocity, in a manner similar to that observed during step-ramp pursuit. In contrast, responses to perturbations with ipsidirectional initial perturbation and carrier acceleration were large for all carrier velocities. Modeling the pursuit system suggests that gain control and short-term prediction are separable elements. The observed effect may be explained by combining the standard gain control mechanism with a derivative-based short-term predictive mechanism. The nonlinear interaction between perturbation and carrier acceleration can be reproduced by assuming a signal saturation, which is acting on the derivative of the target velocity signal. Our results therefore argue for the existence of an internal estimate of target acceleration as a basis for a simple yet efficient short-term predictive mechanism. PMID:28560317
CREME96 and Related Error Rate Prediction Methods
NASA Technical Reports Server (NTRS)
Adams, James H., Jr.
2012-01-01
Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and
[Statistical prediction methods in violence risk assessment and its application].
Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song
2013-06-01
It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.
COMSAC: Computational Methods for Stability and Control. Part 2
NASA Technical Reports Server (NTRS)
Fremaux, C. Michael (Compiler); Hall, Robert M. (Compiler)
2004-01-01
The unprecedented advances being made in computational fluid dynamic (CFD) technology have demonstrated the powerful capabilities of codes in applications to civil and military aircraft. Used in conjunction with wind-tunnel and flight investigations, many codes are now routinely used by designers in diverse applications such as aerodynamic performance predictions and propulsion integration. Typically, these codes are most reliable for attached, steady, and predominantly turbulent flows. As a result of increasing reliability and confidence in CFD, wind-tunnel testing for some new configurations has been substantially reduced in key areas, such as wing trade studies for mission performance guarantees. Interest is now growing in the application of computational methods to other critical design challenges. One of the most important disciplinary elements for civil and military aircraft is prediction of stability and control characteristics. CFD offers the potential for significantly increasing the basic understanding, prediction, and control of flow phenomena associated with requirements for satisfactory aircraft handling characteristics.
Daylight control system device and method
Paton, John Douglas
2007-03-13
A system and device for and a method of programming and controlling light fixtures is disclosed. A system in accordance with the present invention includes a stationary controller unit that is electrically coupled to the light fixtures. The stationary controller unit is configured to be remotely programmed with a portable commissioning device to automatically control the lights fixtures. The stationary controller unit and the portable commissioning device include light sensors, micro-computers and transceivers for measuring light levels, running programs, storing data and transmitting data between the stationary controller unit and the portable commissioning device. In operation, target light levels selected with the portable commissioning device and the controller unit is remotely programmed to automatically maintain the target level.
Daylight control system, device and method
Paton, John Douglas
2012-08-28
A system and device for and a method of programming and controlling light fixtures is disclosed. A system in accordance with the present invention includes a stationary controller unit that is electrically coupled to the light fixtures. The stationary controller unit is configured to be remotely programmed with a portable commissioning device to automatically control the lights fixtures. The stationary controller unit and the portable commissioning device include light sensors, micro-computers and transceivers for measuring light levels, running programs, storing data and transmitting data between the stationary controller unit and the portable commissioning device. In operation, target light levels selected with the portable commissioning device and the controller unit is remotely programmed to automatically maintain the target level.
LED lamp color control system and method
Gaines, James; Clauberg, Bernd; Van Erp, Josephus A.M.
2013-02-05
An LED lamp color control system and method including an LED lamp having an LED controller 58; and a plurality of LED channels 60 operably connected to the LED controller 58, each of the plurality of LED channels 60 having a channel switch 62 in series with at least one shunted LED circuit 83, the shunted LED circuit 83 having a shunt switch 68 in parallel with an LED source 80. The LED controller 58 determines whether the LED source 80 is in a feedback controllable range, stores measured optical flux for the LED source 80 when the LED source 80 is in the feedback controllable range, and bypasses storing the measured optical flux when the LED source 80 is not in the feedback controllable range.
Daylight control system device and method
Paton, John Douglas
2009-12-01
A system and device for and a method of programming and controlling light fixtures is disclosed. A system in accordance with the present invention includes a stationary controller unit that is electrically coupled to the light fixtures. The stationary controller unit is configured to be remotely programmed with a portable commissioning device to automatically control the lights fixtures. The stationary controller unit and the portable commissioning device include light sensors, micro-computers and transceivers for measuring light levels, running programs, storing data and transmitting data between the stationary controller unit and the portable commissioning device. In operation, target light levels selected with the portable commissioning device and the controller unit is remotely programmed to automatically maintain the target level.
Dubay, Rickey; Hassan, Marwan; Li, Chunying; Charest, Meaghan
2014-09-01
This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
OVERVIEW ON ALTERNATIVE ASBESTOS CONTROL METHOD RESEARCH
The alternative asbestos control method (AACM) is an experimental approach to building demolition. Unlike the NESHAP method, the AACM allows some regulated asbestos-containing material to remain in the building and a surfactant-water solution is used to suppress asbestos fibers ...
Method to control artifacts of microstructural fabrication
Shul, Randy J.; Willison, Christi G.; Schubert, W. Kent; Manginell, Ronald P.; Mitchell, Mary-Anne; Galambos, Paul C.
2006-09-12
New methods for fabrication of silicon microstructures have been developed. In these methods, an etching delay layer is deposited and patterned so as to provide differential control on the depth of features being etched into a substrate material. Compensation for etching-related structural artifacts can be accomplished by proper use of such an etching delay layer.
OVERVIEW ON ALTERNATIVE ASBESTOS CONTROL METHOD RESEARCH
The alternative asbestos control method (AACM) is an experimental approach to building demolition. Unlike the NESHAP method, the AACM allows some regulated asbestos-containing material to remain in the building and a surfactant-water solution is used to suppress asbestos fibers ...
Turbulent heat transfer prediction method for application to scramjet engines
NASA Technical Reports Server (NTRS)
Pinckney, S. Z.
1974-01-01
An integral method for predicting boundary layer development in turbulent flow regions on two-dimensional or axisymmetric bodies was developed. The method has the capability of approximating nonequilibrium velocity profiles as well as the local surface friction in the presence of a pressure gradient. An approach was developed for the problem of predicting the heat transfer in a turbulent boundary layer in the presence of a high pressure gradient. The solution was derived with particular emphasis on its applicability to supersonic combustion; thus, the effects of real gas flows were included. The resulting integrodifferential boundary layer method permits the estimation of cooling reguirements for scramjet engines. Theoretical heat transfer results are compared with experimental combustor and noncombustor heat transfer data. The heat transfer method was used in the development of engine design concepts which will produce an engine with reduced cooling requirements. The Langley scramjet engine module was designed by utilizing these design concepts and this engine design is discussed along with its corresponding cooling requirements. The heat transfer method was also used to develop a combustor cooling correlation for a combustor whose local properties are computed one dimensionally by assuming a linear area variation and a given heat release schedule.
Implementation of model predictive control on a hydrothermal oxidation reactor
Muske, K.R.; Dell`Orco, P.C.; Le, L.A.; Flesner, R.L.
1998-12-31
This paper describes the model-based control algorithm developed for a hydrothermal oxidation reactor at the Pantex Department of Energy facility in Amarillo, Texas. The combination of base hydrolysis and hydrothermal oxidation is used for the disposal of PBX 9404 high explosive at Pantex. The reactor oxidizes the organic compounds in the hydrolysate solutions obtained from the base hydrolysis process. The objective of the model predictive controller is to minimize the total aqueous nitrogen compounds in the effluent of the reactor. The controller also maintains a desired excess oxygen concentration in the reactor effluent to ensure the complete destruction of the organic carbon compounds in the hydrolysate.
Motivation to control prejudice predicts categorization of multiracials.
Chen, Jacqueline M; Moons, Wesley G; Gaither, Sarah E; Hamilton, David L; Sherman, Jeffrey W
2014-05-01
Multiracial individuals often do not easily fit into existing racial categories. Perceivers may adopt a novel racial category to categorize multiracial targets, but their willingness to do so may depend on their motivations. We investigated whether perceivers' levels of internal motivation to control prejudice (IMS) and external motivation to control prejudice (EMS) predicted their likelihood of categorizing Black-White multiracial faces as Multiracial. Across four studies, IMS positively predicted perceivers' categorizations of multiracial faces as Multiracial. The association between IMS and Multiracial categorizations was strongest when faces were most racially ambiguous. Explicit prejudice, implicit prejudice, and interracial contact were ruled out as explanations for the relationship between IMS and Multiracial categorizations. EMS may be negatively associated with the use of the Multiracial category. Therefore, perceivers' motivations to control prejudice have important implications for racial categorization processes.
Discontinuous Galerkin method for predicting heat transfer in hypersonic environments
NASA Astrophysics Data System (ADS)
Ching, Eric; Lv, Yu; Ihme, Matthias
2016-11-01
This study is concerned with predicting surface heat transfer in hypersonic flows using high-order discontinuous Galerkin methods. A robust and accurate shock capturing method designed for steady calculations that uses smooth artificial viscosity for shock stabilization is developed. To eliminate parametric dependence, an optimization method is formulated that results in the least amount of artificial viscosity necessary to sufficiently suppress nonlinear instabilities and achieve steady-state convergence. Performance is evaluated in two canonical hypersonic tests, namely a flow over a circular half-cylinder and flow over a double cone. Results show this methodology to be significantly less sensitive than conventional finite-volume techniques to mesh topology and inviscid flux function. The method is benchmarked against state-of-the-art finite-volume solvers to quantify computational cost and accuracy. Financial support from a Stanford Graduate Fellowship and the NASA Early Career Faculty program are gratefully acknowledged.
Method of predicting mechanical properties of decayed wood
Kelley, Stephen S.
2003-07-15
A method for determining the mechanical properties of decayed wood that has been exposed to wood decay microorganisms, comprising: a) illuminating a surface of decayed wood that has been exposed to wood decay microorganisms with wavelengths from visible and near infrared (VIS-NIR) spectra; b) analyzing the surface of the decayed wood using a spectrometric method, the method generating a first spectral data of wavelengths in VIS-NIR spectra region; and c) using a multivariate analysis to predict mechanical properties of decayed wood by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of wavelengths in VIS-NIR spectra obtained from a reference decay wood, the second spectral data being correlated with a known mechanical property analytical result obtained from the reference decayed wood.
Control of Liquid Sloshing Container using Active Force Control Method
NASA Astrophysics Data System (ADS)
Setyo Purnomo, Didik; Rachmad Anom Besari, Adnan; Darojah, Zaqiatud
2017-04-01
This paper presents a robust control method to relieve the sloshing of liquid container transport using Active Force Control (AFC) method. A model of two degree-of-freedom (2-DOF) liquid container transfer was implemented in this research as the main dynamical system to be controlled. The surface of liquid is maintained in a flat position, so that changes the slope of liquid surface countered by changing the acceleration of container. The focus of this research is how to use AFC method being applied to the system, so that it can suppress liquid sloshing. The control scheme were simulated, compare between PID-AFC and pure PID. Simulations has been conducted, the results show that the PID-AFC have superior performance to suppress the sloshing compared with pure PID, especially if disturbance occurred.
Cognitive demand and predictive adaptational responses in dynamic stability control.
Bohm, Sebastian; Mersmann, Falk; Bierbaum, Stefanie; Dietrich, Ralf; Arampatzis, Adamantios
2012-09-21
We studied the effects of a concurrent cognitive task on predictive motor control, a feedforward mechanism of dynamic stability control, during disturbed gait in young and old adults. Thirty-two young and 27 elderly male healthy subjects participated and were randomly assigned to either control or dual task groups. By means of a covered exchangeable element the surface condition on a gangway could be altered to induce gait perturbations. The experimental protocol included a baseline on hard surface and an adaptation phase with twelve trials on soft surface. After the first, sixth and last soft surface trial, the surface condition was changed to hard (H1-3), to examine after-effects and, thus, to quantify predictive motor control. Dynamic stability was assessed using the 'margin of stability (MoS)' as a criterion for the stability state of the human body (extrapolated center of mass concept). In H1-3 the young participants significantly increased the MoS at touchdown of the disturbed leg compared to baseline. The magnitude and the rate of these after-effects were unaffected by the dual task condition. The old participants presented a trend to after-effects (i.e., increase of MoS) in H3 but only under the dual task condition.In conclusion, the additional cognitive demand did not compromise predictive motor control during disturbed walking in the young and old participants. In contrast to the control group, the old dual task group featured a trend to predictive motor adjustments, which may be a result of a higher state of attention or arousal due to the dual task paradigm.
Robustness analysis method for orbit control
NASA Astrophysics Data System (ADS)
Zhang, Jingrui; Yang, Keying; Qi, Rui; Zhao, Shuge; Li, Yanyan
2017-08-01
Satellite orbits require periodical maintenance due to the presence of perturbations. However, random errors caused by inaccurate orbit determination and thrust implementation may lead to failure of the orbit control strategy. Therefore, it is necessary to analyze the robustness of the orbit control methods. Feasible strategies which are tolerant to errors of a certain magnitude can be developed to perform reliable orbit control for the satellite. In this paper, first, the orbital dynamic model is formulated by Gauss' form of the planetary equation using the mean orbit elements; the atmospheric drag and the Earth's non-spherical perturbations are taken into consideration in this model. Second, an impulsive control strategy employing the differential correction algorithm is developed to maintain the satellite trajectory parameters in given ranges. Finally, the robustness of the impulsive control method is analyzed through Monte Carlo simulations while taking orbit determination error and thrust error into account.
Monadi, Mahmoud; Firouzjahi, Alireza; Hosseini, Amin; Javadian, Yahya; Sharbatdaran, Majid; Heidari, Behzad
2016-01-01
Background: Increased serum high sensitive C-reactive protein (hs-CRP) in asthma and its association with disease severity has been investigated in many studies. This study aimed to determine serum hs-CRP status in asthma versus healthy controls and to examine its ability in predicting asthma control. Methods: Serum CRP was measured by ELISA method using a high sensitive CRP kit. Severity of asthma was determined using Asthma Control Test. Spearman and chi square tests were used for association and correlation respectively. The predictive ability was determined by receiver operating characteristics (ROC) analysis. Accuracy was determined by determination of area under the ROC curve (AUC). Results: A total of 120 patients and 115 controls were studied. Median serum hs-CRP in asthma was higher than control (P=0.001. In well controlled asthma the hs-CRP decreased significantly compared with poorly controlled (P=0.024) but still was higher than control (P=0.017). Serum hs-CRP at cutoff level of 1.45 mg/L differentiated the patients and controls with accuracy of 63.5 % (AUC= 0.635±0.037, P=0.001). Serum hs-CRP ≤ 2.15 mg/L predicted well controlled asthma with accuracy of 62.5% (AUC= 0.625±0.056, p=0.025). After adjusting for age, sex, weight and smoking, there was an independent association between serum hs-CRP >1.45 mg/L and asthma by adjusted OR=2.49, p=0.018). Conclusion: These findings indicate that serum hs-CRP in asthma is higher than healthy control and increases with severity of asthma and decreases with. Thus, serum hs-CRP measurement can be helpful in predicting asthma control and treatment response. PMID:26958331
Exploring Function Prediction in Protein Interaction Networks via Clustering Methods
Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco
2014-01-01
Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach. PMID:24972109
Comparison of RF spectrum prediction methods for dynamic spectrum access
NASA Astrophysics Data System (ADS)
Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.
2017-05-01
Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.
Prediction of seizure control in non-ketotic hyperglycemic induced seizures
2009-01-01
Background To study the factors predictive for seizure control in non-ketotic hyperglycemic induced seizures (NKHS). Methods We studied 21 patients who were clinically diagnosed as NKHS at Khon Kaen University hospital, Thailand. Multiple linear regression analysis was used to identify the factors predictive for seizure control. Results Most patients had no previous history of diabetes and presented with repetitive partial seizures. The mean number of seizure attacks was 45 times prior to admission. The average duration to terminate seizure was 36 hours and significantly predicted by frequency of seizures (estimate 0.9, p value 0.013). Conclusion Frequency of seizures is the only predictive factor for the success of seizure control in NKHS. PMID:20003412
Efficient Unstructured Grid Adaptation Methods for Sonic Boom Prediction
NASA Technical Reports Server (NTRS)
Campbell, Richard L.; Carter, Melissa B.; Deere, Karen A.; Waithe, Kenrick A.
2008-01-01
This paper examines the use of two grid adaptation methods to improve the accuracy of the near-to-mid field pressure signature prediction of supersonic aircraft computed using the USM3D unstructured grid flow solver. The first method (ADV) is an interactive adaptation process that uses grid movement rather than enrichment to more accurately resolve the expansion and compression waves. The second method (SSGRID) uses an a priori adaptation approach to stretch and shear the original unstructured grid to align the grid with the pressure waves and reduce the cell count required to achieve an accurate signature prediction at a given distance from the vehicle. Both methods initially create negative volume cells that are repaired in a module in the ADV code. While both approaches provide significant improvements in the near field signature (< 3 body lengths) relative to a baseline grid without increasing the number of grid points, only the SSGRID approach allows the details of the signature to be accurately computed at mid-field distances (3-10 body lengths) for direct use with mid-field-to-ground boom propagation codes.
Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions
NASA Technical Reports Server (NTRS)
Miller, Christopher J.
2011-01-01
A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.
Model predictive control of room temperature with disturbance compensation
NASA Astrophysics Data System (ADS)
Kurilla, Jozef; Hubinský, Peter
2017-08-01
This paper deals with temperature control of multivariable system of office building. The system is simplified to several single input-single output systems by decoupling their mutual linkages, which are separately controlled by regulator based on generalized model predictive control. Main part of this paper focuses on the accuracy of the office temperature with respect to occupancy profile and effect of disturbance. Shifting of desired temperature and changing of weighting coefficients are used to achieve the desired accuracy of regulation. The final structure of regulation joins advantages of distributed computing power and possibility to use network communication between individual controllers to consider the constraints. The advantage of using decoupled MPC controllers compared to conventional PID regulators is demonstrated in a simulation study.
Low trait self-control predicts self-handicapping.
Uysal, Ahmet; Knee, C Raymond
2012-02-01
Past research has shown that self-handicapping stems from uncertainty about one's ability and self-presentational concerns. The present studies suggest that low dispositional self-control is also associated with self-handicapping. In 3 studies (N = 289), the association between self-control and self-handicapping was tested. Self-control was operationalized as trait self-control, whereas self-handicapping was operationalized as trait self-handicapping in Study 1 (N = 160), self-reported self-handicapping in Study 2 (N = 74), and behavioral self-handicapping in Study 3 (N = 55). In all 3 studies, hierarchical regression analyses revealed that low self-control predicts self-handicapping, independent of self-esteem, self-doubt, social desirability, and gender. © 2012 The Authors. Journal of Personality © 2012, Wiley Periodicals, Inc.
Health-aware Model Predictive Control of Pasteurization Plant
NASA Astrophysics Data System (ADS)
Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos
2017-01-01
In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.
Control method for physical systems and devices
Guckenheimer, John
1997-01-01
A control method for stabilizing systems or devices that are outside the control domain of a linear controller is provided. When applied to nonlinear systems, the effectiveness of this method depends upon the size of the domain of stability that is produced for the stabilized equilibrium. If this domain is small compared to the accuracy of measurements or the size of disturbances within the system, then the linear controller is likely to fail within a short period. Failure of the system or device can be catastrophic: the system or device can wander far from the desired equilibrium. The method of the invention presents a general procedure to recapture the stability of a linear controller, when the trajectory of a system or device leaves its region of stability. By using a hybrid strategy based upon discrete switching events within the state space of the system or device, the system or device will return from a much larger domain to the region of stability utilized by the linear controller. The control procedure is robust and remains effective under large classes of perturbations of a given underlying system or device.
Controlling flexible structures: A survey of methods
NASA Technical Reports Server (NTRS)
Benson, Russell A.; Coleman, Edward E.
1989-01-01
Most of the presently available control system design techniques applicable to flexible structure problems were developed to design controllers for rigid body systems. Although many of these design methods can be applied to flexible dynamics problems, recently developed techniques may be more suitable for flexible structure controller design. The purpose of this presentation is to examine briefly the peculiarities of the dynamics of flexible structures and to stimulate discussion about top level controller design approaches when designing controllers for flexible structures. Presented here is a suggestion of a set of categories of design methods for designing controllers for flexible structures as well as a discussion of the advantages and disadvantages of each category. No attempt has been made herein to select one category of design techniques as the best for flexible structure controller design. Instead, it is hoped that the structure suggested by these categories will facilitate further discussion on the merits of particular methods that will eventually point to those design techniques suitable for further development.
Current methods of gene prediction, their strengths and weaknesses.
Mathé, Catherine; Sagot, Marie-France; Schiex, Thomas; Rouzé, Pierre
2002-10-01
While the genomes of many organisms have been sequenced over the last few years, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed that try to address one part of this problem, which consists of locating the genes along a genome. This paper reviews the existing approaches to predicting genes in eukaryotic genomes and underlines their intrinsic advantages and limitations. The main mathematical models and computational algorithms adopted are also briefly described and the resulting software classified according to both the method and the type of evidence used. Finally, the several difficulties and pitfalls encountered by the programs are detailed, showing that improvements are needed and that new directions must be considered.
Methods for exploring uncertainty in groundwater management predictions
Guillaume, Joseph H. A.; Hunt, Randall J.; Comunian, Alessandro; Fu, Baihua; Blakers, Rachel S; Jakeman, Anthony J; Barreteau, Olivier; Hunt, Randall J.; Rinaudo, Jean-Daniel; Ross, Andrew
2016-01-01
Models of groundwater systems help to integrate knowledge about the natural and human system covering different spatial and temporal scales, often from multiple disciplines, in order to address a range of issues of concern to various stakeholders. A model is simply a tool to express what we think we know. Uncertainty, due to lack of knowledge or natural variability, means that there are always alternative models that may need to be considered. This chapter provides an overview of uncertainty in models and in the definition of a problem to model, highlights approaches to communicating and using predictions of uncertain outcomes and summarises commonly used methods to explore uncertainty in groundwater management predictions. It is intended to raise awareness of how alternative models and hence uncertainty can be explored in order to facilitate the integration of these techniques with groundwater management.
Tuning Proportional-Integral controllers to approximate simplified predictive control performance.
Mansour, S E
2009-10-01
An exact equivalence between PI (Proportional-Integral) and two-parameter SPC (Simplified Predictive Control) is developed to provide identical control of first order linear plants. A relationship between the PI control parameters and the SPC control parameters is described. This relationship that allows the same control in the case of first order linear plants is also found to provide tuning formulas that yield PI control which approximates SPC performance in the case of second order linear plants with widely separated Eigenvalues. Finally, an extension of the PI control algorithm to include future errors provides another exact PI-SPC equivalence for networked control of first order plants.
Decentralized robust nonlinear model predictive controller for unmanned aerial systems
NASA Astrophysics Data System (ADS)
Garcia Garreton, Gonzalo A.
The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1. A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2. A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3. An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4. A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible.
A Bayesian nonparametric method for prediction in EST analysis.
Lijoi, Antonio; Mena, Ramsés H; Prünster, Igor
2007-09-14
Expressed sequence tags (ESTs) analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library. In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a) the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b) the number of new unique genes to be observed in a future sample; c) the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries, previously studied with frequentist methods, are analyzed in detail. The Bayesian nonparametric approach we undertake yields valuable tools for gene capture and prediction in EST libraries. The estimators we obtain do not feature the kind of drawbacks associated with frequentist estimators and are reliable for any size of the additional sample.
T-cell epitope prediction methods: an overview.
Desai, Dattatraya V; Kulkarni-Kale, Urmila
2014-01-01
The scientific community is overwhelmed by the voluminous increase in the quantum of data on biological systems, including but not limited to the immune system. Consequently, immunoinformatics databases are continually being developed to accommodate this ever increasing data and analytical tools are continually being developed to analyze the same. Therefore, researchers are now equipped with numerous databases, analytical and prediction tools, in anticipation of better means of prevention of and therapeutic intervention in diseases of humans and other animals. Epitope is a part of an antigen, recognized either by B- or T-cells and/or molecules of the host immune system. Since only a few amino acid residues that comprise an epitope (instead of the whole protein) are sufficient to elicit an immune response, attempts are being made to identify or predict this critical stretch or patch of amino acid residues, i.e., T-cell epitopes and B-cell epitopes to be included in multiple-subunit vaccines. T-cell epitope prediction is a challenge owing to the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. Many algorithms/methods developed to predict T-cell epitopes and Web servers incorporating the same are available. These are based on approaches like considering amphipathicity profiles of proteins, sequence motifs, quantitative matrices (QM), artificial neural networks (ANN), support vector machines (SVM), quantitative structure activity relationship (QSAR) and molecular docking simulations, etc. This chapter aims to introduce the reader to the principle(s) underlying some of these methods/algorithms as well as procedural and practical aspects of using the same.
A Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture.
Shibayama, Shojiro; Kaneko, Hiromasa; Funatsu, Kimito
2017-04-01
This article proposes a novel concentration prediction model that requires little training data and is useful for rapid process understanding. Process analytical technology is currently popular, especially in the pharmaceutical industry, for enhancement of process understanding and process control. A calibration-free method, iterative optimization technology (IOT), was proposed to predict pure component concentrations, because calibration methods such as partial least squares, require a large number of training samples, leading to high costs. However, IOT cannot be applied to concentration prediction in non-ideal mixtures because its basic equation is derived from the Beer-Lambert law, which cannot be applied to non-ideal mixtures. We proposed a novel method that realizes prediction of pure component concentrations in mixtures from a small number of training samples, assuming that spectral changes arising from molecular interactions can be expressed as a function of concentration. The proposed method is named IOT with virtual molecular interaction spectra (IOT-VIS) because the method takes spectral change as a virtual spectrum x nonlin,i into account. It was confirmed through the two case studies that the predictive accuracy of IOT-VIS was the highest among existing IOT methods.
Model Predictive Control of Integrated Gasification Combined Cycle Power Plants
B. Wayne Bequette; Priyadarshi Mahapatra
2010-08-31
The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.
A nonlinear regression model-based predictive control algorithm.
Dubay, R; Abu-Ayyad, M; Hernandez, J M
2009-04-01
This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.
Flow resistance and its prediction methods in compound channels
NASA Astrophysics Data System (ADS)
Yang, Kejun; Cao, Shuyou; Liu, Xingnian
2007-02-01
A series of experiments was carried out in a large symmetric compound channel composed of a rough main channel and rough floodplains to investigate the resistance characteristics of inbank and overbank flows. The effective Manning, Darcy-Weisbach, Chezy coefficients and the relative Nikuradse roughness height were analyzed. Many different representative methods for predicting the composite roughness were systematically summarized. Besides the measured data, a vast number of laboratory data and field data for compound channels were collected and used to check the validity of these methods for different subsection divisions including the vertical, horizontal, diagonal and bisectional divisions. The computation showed that these methods resulted in big errors in assessing the composite roughness in compound channels, and the reasons were analyzed in detail. The error magnitude is related to the subsection divisions.
Predicting recreational water quality advisories: A comparison of statistical methods
Brooks, Wesley R.; Corsi, Steven R.; Fienen, Michael N.; Carvin, Rebecca B.
2016-01-01
Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.
Satellite attitude prediction by multiple time scales method
NASA Technical Reports Server (NTRS)
Tao, Y. C.; Ramnath, R.
1975-01-01
An investigation is made of the problem of predicting the attitude of satellites under the influence of external disturbing torques. The attitude dynamics are first expressed in a perturbation formulation which is then solved by the multiple scales approach. The independent variable, time, is extended into new scales, fast, slow, etc., and the integration is carried out separately in the new variables. The theory is applied to two different satellite configurations, rigid body and dual spin, each of which may have an asymmetric mass distribution. The disturbing torques considered are gravity gradient and geomagnetic. Finally, as multiple time scales approach separates slow and fast behaviors of satellite attitude motion, this property is used for the design of an attitude control device. A nutation damping control loop, using the geomagnetic torque for an earth pointing dual spin satellite, is designed in terms of the slow equation.
NASA Astrophysics Data System (ADS)
Itahashi, S.; Yumimoto, K.; Uno, I.; Kim, S.
2012-12-01
Air quality studies based on the chemical transport model have been provided many important results for promoting our knowledge of air pollution phenomena, however, discrepancies between modeling results and observation data are still important issue to overcome. One of the concerning issue would be an over-prediction of summertime tropospheric ozone in remote area of Japan. This problem has been pointed out in the model comparison study of both regional scale (e.g., MICS-Asia) and global scale model (e.g., TH-FTAP). Several reasons for this issue can be listed as, (i) the modeled reproducibility on the penetration of clean oceanic air mass, (ii) correct estimation of the anthropogenic NOx / VOC emissions over East Asia, (iii) the chemical reaction scheme used in model simulation. In this study, we attempt to inverse estimation of some important chemical reactions based on the combining system of DDM (decoupled direct method) sensitivity analysis and modeled Green's function approach. The decoupled direct method (DDM) is an efficient and accurate way of performing sensitivity analysis to model inputs, calculates sensitivity coefficients representing the responsiveness of atmospheric chemical concentrations to perturbations in a model input or parameter. The inverse solutions with the Green's functions are given by a linear, least-squares method but are still robust against nonlinearities, To construct the response matrix (i.e., Green's functions), we can directly use the results of DDM sensitivity analysis. The solution of chemical reaction constants which have relatively large uncertainties are determined with constraints of observed ozone concentration data over the remote area in Japan. Our inversed estimation demonstrated that the underestimation of reaction constant to produce HNO3 (NO2 + OH + M → HNO3 + M) in SAPRC99 chemical scheme, and the inversed results indicated the +29.0 % increment to this reaction. This estimation has good agreement when compared
Diabetes management and glycemic control in youth with type 1 diabetes: test of a predictive model.
Drotar, Dennis; Ittenbach, Richard; Rohan, Jennifer M; Gupta, Resmi; Pendley, Jennifer Shroff; Delamater, Alan
2013-06-01
The objective of this study was to test a comprehensive model of biologic (pubertal status), family (communication and conflict), and psychological influences (behavioral autonomy) on diabetes management and glycemic control in a sample of youth (N = 226) with type 1 diabetes recruited during late childhood/early adolescence (ages 9-11 years). The study design was a prospective, multisite, multi-method study involving prediction of diabetes management and glycemic control 1 year post-baseline. The primary outcome measures included diabetes management behaviors based on the Diabetes Self-Management Profile (DSMP) administered separately to mothers and youth and glycemic control measured by glycated hemoglobin (HbA1c) obtained by blood samples and analyzed by a central laboratory to ensure standardization. Our hypothesized predictive model received partial support based on structural equation modeling analyses. Family conflict predicted less adequate glycemic control 1 year later (p < 0.05). Higher conflict predicted less adequate diabetes management and less adequate glycemic control. More advanced pubertal status also predicted less adequate glycemic control, but behavioral autonomy did not. Family conflict is an important, potentially clinically significant influence on glycemic control that should be considered in primary and secondary prevention in the management of type 1 diabetes in youth.
Diabetes management and glycemic control in youth with type 1 diabetes: test of a predictive model
Ittenbach, Richard; Rohan, Jennifer M.; Gupta, Resmi; Pendley, Jennifer Shroff; Delamater, Alan
2014-01-01
The objective of this study was to test a comprehensive model of biologic (pubertal status), family (communication and conflict), and psychological influences (behavioral autonomy) on diabetes management and glycemic control in a sample of youth (N = 226) with type 1 diabetes recruited during late childhood/early adolescence (ages 9–11 years). The study design was a prospective, multisite, multi-method study involving prediction of diabetes management and glycemic control 1 year post-baseline. The primary outcome measures included diabetes management behaviors based on the Diabetes Self-Management Profile (DSMP) administered separately to mothers and youth and glycemic control measured by glycated hemoglobin (HbA1c) obtained by blood samples and analyzed by a central laboratory to ensure standardization. Our hypothesized predictive model received partial support based on structural equation modeling analyses. Family conflict predicted less adequate glycemic control 1 year later (p < 0.05). Higher conflict predicted less adequate diabetes management and less adequate glycemic control. More advanced pubertal status also predicted less adequate glycemic control, but behavioral autonomy did not. Family conflict is an important, potentially clinically significant influence on glycemic control that should be considered in primary and secondary prevention in the management of type 1 diabetes in youth. PMID:22569775
Model predictive control of a wind turbine modelled in Simpack
NASA Astrophysics Data System (ADS)
Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.
2014-06-01
Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to
A survey of quantum Lyapunov control methods.
Cong, Shuang; Meng, Fangfang
2013-01-01
The condition of a quantum Lyapunov-based control which can be well used in a closed quantum system is that the method can make the system convergent but not just stable. In the convergence study of the quantum Lyapunov control, two situations are classified: nondegenerate cases and degenerate cases. For these two situations, respectively, in this paper the target state is divided into four categories: the eigenstate, the mixed state which commutes with the internal Hamiltonian, the superposition state, and the mixed state which does not commute with the internal Hamiltonian. For these four categories, the quantum Lyapunov control methods for the closed quantum systems are summarized and analyzed. Particularly, the convergence of the control system to the different target states is reviewed, and how to make the convergence conditions be satisfied is summarized and analyzed.
Temperature Effects and Compensation-Control Methods
Xia, Dunzhu; Chen, Shuling; Wang, Shourong; Li, Hongsheng
2009-01-01
In the analysis of the effects of temperature on the performance of microgyroscopes, it is found that the resonant frequency of the microgyroscope decreases linearly as the temperature increases, and the quality factor changes drastically at low temperatures. Moreover, the zero bias changes greatly with temperature variations. To reduce the temperature effects on the microgyroscope, temperature compensation-control methods are proposed. In the first place, a BP (Back Propagation) neural network and polynomial fitting are utilized for building the temperature model of the microgyroscope. Considering the simplicity and real-time requirements, piecewise polynomial fitting is applied in the temperature compensation system. Then, an integral-separated PID (Proportion Integration Differentiation) control algorithm is adopted in the temperature control system, which can stabilize the temperature inside the microgyrocope in pursuing its optimal performance. Experimental results reveal that the combination of microgyroscope temperature compensation and control methods is both realizable and effective in a miniaturized microgyroscope prototype. PMID:22408509
A Survey of Quantum Lyapunov Control Methods
2013-01-01
The condition of a quantum Lyapunov-based control which can be well used in a closed quantum system is that the method can make the system convergent but not just stable. In the convergence study of the quantum Lyapunov control, two situations are classified: nondegenerate cases and degenerate cases. For these two situations, respectively, in this paper the target state is divided into four categories: the eigenstate, the mixed state which commutes with the internal Hamiltonian, the superposition state, and the mixed state which does not commute with the internal Hamiltonian. For these four categories, the quantum Lyapunov control methods for the closed quantum systems are summarized and analyzed. Particularly, the convergence of the control system to the different target states is reviewed, and how to make the convergence conditions be satisfied is summarized and analyzed. PMID:23766732
USDA-ARS?s Scientific Manuscript database
Water resources are limited in many agricultural areas. One method to improve the effective use of water is to improve delivery service from irrigation canals. This can be done by applying automatic control methods that control the gates in an irrigation canal. The model predictive control MPC is ...
Control system health test system and method
Hoff, Brian D.; Johnson, Kris W.; Akasam, Sivaprasad; Baker, Thomas M.
2006-08-15
A method is provided for testing multiple elements of a work machine, including a control system, a component, a sub-component that is influenced by operations of the component, and a sensor that monitors a characteristic of the sub-component. In one embodiment, the method is performed by the control system and includes sending a command to the component to adjust a first parameter associated with an operation of the component. Also, the method includes detecting a sensor signal from the sensor reflecting a second parameter associated with a characteristic of the sub-component and determining whether the second parameter is acceptable based on the command. The control system may diagnose at least one of the elements of the work machine when the second parameter of the sub-component is not acceptable.
Application of infinite model predictive control methodology to other advanced controllers.
Abu-Ayyad, M; Dubay, R; Hernandez, J M
2009-01-01
This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.
Method of Controlling Lasing Wavelength(s)
NASA Technical Reports Server (NTRS)
Barnes, Norman P. (Inventor); Murray, Keith E. (Inventor); Hutcheson, Ralph L. (Inventor)
2000-01-01
A method is provided to control the lasing wavelength of a laser material without changing or adjusting the mechanical components of a laser device, The rate at which the laser material is pumped with the pumping energy is controlled so that lasing occurs at one or more lasing wavelengths based on the rate. The lasing wavelengths are determined by transition lifetimes and/or energy transfer rates.
Distributed predictive control of spiral wave in cardiac excitable media
NASA Astrophysics Data System (ADS)
Gan, Zheng-Ning; Cheng, Xin-Ming
2010-05-01
In this paper, we propose the distributed predictive control strategies of spiral wave in cardiac excitable media. The modified FitzHugh-Nagumo model was used to express the cardiac excitable media approximately. Based on the control-Lyapunov theory, we obtained the distributed control equation, which consists of a positive control-Lyapunov function and a positive cost function. Using the equation, we investigate two kinds of robust control strategies: the time-dependent distributed control strategy and the space-time dependent distributed control strategy. The feasibility of the strategies was demonstrated via an illustrative example, in which the spiral wave was prevented to occur, and the possibility for inducing ventricular fibrillation was eliminated. The strategies are helpful in designing various cardiac devices. Since the second strategy is more efficient and robust than the first one, and the response time in the second strategy is far less than that in the first one, the former is suitable for the quick-response control systems. In addition, our spatiotemporal control strategies, especially the second strategy, can be applied to other cardiac models, even to other reaction-diffusion systems.
Unstructured CFD and Noise Prediction Methods for Propulsion Airframe Aeroacoustics
NASA Technical Reports Server (NTRS)
Pao, S. Paul; Abdol-Hamid, Khaled S.; Campbell, Richard L.; Hunter, Craig A.; Massey, Steven J.; Elmiligui, Alaa A.
2006-01-01
Using unstructured mesh CFD methods for Propulsion Airframe Aeroacoustics (PAA) analysis has the distinct advantage of precise and fast computational mesh generation for complex propulsion and airframe integration arrangements that include engine inlet, exhaust nozzles, pylon, wing, flaps, and flap deployment mechanical parts. However, accurate solution values of shear layer velocity, temperature and turbulence are extremely important for evaluating the usually small noise differentials of potential applications to commercial transport aircraft propulsion integration. This paper describes a set of calibration computations for an isolated separate flow bypass ratio five engine nozzle model and the same nozzle system with a pylon. These configurations have measured data along with prior CFD solutions and noise predictions using a proven structured mesh method, which can be used for comparison to the unstructured mesh solutions obtained in this investigation. This numerical investigation utilized the TetrUSS system that includes a Navier-Stokes solver, the associated unstructured mesh generation tools, post-processing utilities, plus some recently added enhancements to the system. New features necessary for this study include the addition of two equation turbulence models to the USM3D code, an h-refinement utility to enhance mesh density in the shear mixing region, and a flow adaptive mesh redistribution method. In addition, a computational procedure was developed to optimize both solution accuracy and mesh economy. Noise predictions were completed using an unstructured mesh version of the JeT3D code.
Biomarkers: Non-destructive Method for Predicting Meat Tenderization.
Singh, Arashdeep; Ahluwalia, Preeti; Rafiq, Aasima; Sharma, Savita
2015-07-06
Meat tenderness is the primary and most important quality attribute for the consumers worldwide. Tenderness is the process of breakdown of collagen tissue in meat to make it palatable. The earlier methods of tenderness evaluation like taste panels and shear force methods are destructive, time consuming and ill suited as they requires removing a piece of steak from the carcass for performing the test. Therefore, a non-destructive method for predicting the tenderness would be more desirable. The development of a meat quality grading and guarantee system through muscle profiling research can help to meet this demand. Biomarkers have the ability to identify if an exposure has occurred. Biomarkers of the meat quality are of prime importance for meat industry, which has ability to satisfy consumers' expectations. The biomarkers so far identified have been then sorted and grouped according to their common biological functions. All of them refer to a series of biological pathways including glycolytic and oxidative energy production, cell detoxification, protease inhibition and production of Heat Shock Proteins. On this basis, a detailed analysis of these metabolic pathways helps in identifying tenderization of meat having some domains of interest. It was, therefore, stressed forward that biomarkers can be used to determine meat tenderness. This review article summarizes the uses of several biomarkers for predicting the meat tenderness.
Non-animal test methods for predicting skin sensitization potentials.
Mehling, Annette; Eriksson, Tove; Eltze, Tobias; Kolle, Susanne; Ramirez, Tzutzuy; Teubner, Wera; van Ravenzwaay, Bennard; Landsiedel, Robert
2012-08-01
Contact allergies are complex diseases, and it is estimated that 15-20 % of the general population suffers from contact allergy, with increasing prevalence. Evaluation of the sensitization potential of a substance is usually carried out in animal models. Nowadays, there is much interest in reducing and ultimately replacing current animal tests. Furthermore, as of 2013, the EU has posed a ban on animal testing of cosmetic ingredients that includes skin sensitization. Therefore, predictive and robust in vitro tests are urgently needed. In order to establish alternatives to animal testing, the in vitro tests must mimic the very complex interactions between the sensitizing chemical and the different parts of the immune system. This review article summarizes recent efforts to develop in vitro tests for predicting skin sensitizers. Cell-based assays, in chemico methods and, to a lesser extent, in silico methods are presented together with a discussion of their current status. With considerable progress having been achieved during the last years, the rationale today is that data from different non-animal test methods will have to be combined in order to obtain reliable hazard and potency information on potential skin sensitizers.
NASA Astrophysics Data System (ADS)
TayyebTaher, M.; Esmaeilzadeh, S. Majid
2017-07-01
This article presents an application of Model Predictive Controller (MPC) to the attitude control of a geostationary flexible satellite. SIMO model has been used for the geostationary satellite, using the Lagrange equations. Flexibility is also included in the modelling equations. The state space equations are expressed in order to simplify the controller. Naturally there is no specific tuning rule to find the best parameters of an MPC controller which fits the desired controller. Being an intelligence method for optimizing problem, Genetic Algorithm has been used for optimizing the performance of MPC controller by tuning the controller parameter due to minimum rise time, settling time, overshoot of the target point of the flexible structure and its mode shape amplitudes to make large attitude maneuvers possible. The model included geosynchronous orbit environment and geostationary satellite parameters. The simulation results of the flexible satellite with attitude maneuver shows the efficiency of proposed optimization method in comparison with LQR optimal controller.
Real-time model predictive control using a self-organizing neural network.
Han, Hong-Gui; Wu, Xiao-Long; Qiao, Jun-Fei
2013-09-01
In this paper, a real-time model predictive control (RT-MPC) based on self-organizing radial basis function neural network (SORBFNN) is proposed for nonlinear systems. This RT-MPC has its simplicity in parallelism to model predictive control design and efficiency to deal with computational complexity. First, a SORBFNN with concurrent structure and parameter learning is developed as the predictive model of the nonlinear systems. The model performance can be significantly improved through SORBFNN, and the modeling error is uniformly ultimately bounded. Second, a fast gradient method (GM) is enhanced for the solution of optimal control problem. This proposed GM can reduce computational cost and suboptimize the RT-MPC online. Then, the conditions of the stability analysis and steady-state performance of the closed-loop systems are presented. Finally, numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance rejection performances. Experimental results demonstrate its effectiveness.
Sham Electroacupuncture Methods in Randomized Controlled Trials
Chen, Zi-xian; Li, Yan; Zhang, Xiao-guang; Chen, Shuang; Yang, Wen-ting; Zheng, Xia-wei; Zheng, Guo-qing
2017-01-01
Sham electroacupuncture (EA) control is commonly used to evaluate the specific effects of EA in randomized-controlled trials (RCTs). However, establishing an inert and concealable sham EA control remains methodologically challenging. Here, we aimed to systematically investigate the sham EA methods. Eight electronic databases were searched from their inception to April 2015. Ten out of the 17 sham EA methods were identified from 94 RCTs involving 6134 participants according to three aspects: needle location, depth of needle insertion and electrical stimulation. The top three most frequently used types were sham EA type A, type L and type O ordinally. Only 24 out of the 94 trials reported credibility tests in six types of sham EA methods and the results were mainly as follows: sham EA type A (10/24), type B (5/24) and type Q (5/24). Compared with sham EA controls, EA therapy in 56.2% trials reported the specific effects, of which the highest positive rate was observed in type N (3/4), type F (5/7), type D (4/6) and type M (2/3). In conclusion, several sham EA types were identified as a promising candidate for further application in RCTs. Nonetheless, more evidence for inert and concealable sham EA control methods is needed. PMID:28106094
Model predictive control for tracking randomly varying references
NASA Astrophysics Data System (ADS)
Falugi, Paola
2015-04-01
This paper proposes a model predictive control scheme for tracking a-priori unknown references varying in a wide range and analyses its performance. It is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we remove this simplifying assumption and characterise the set to which the tracking error converges and the associated region of convergence.
Predicting mooring system fatigue life by probabilistic methods
Saders, D.R.; Dominguez, R.F.; Ho, K.C.; Lai, N.W.
1983-05-01
Failure of moored structures from accumulated fatigue damage in shackles, connecting links, chain and wire rope components is common. When systems will be deployed for long periods, it is especially important to determine at the design, inspection and maintenance stages the fatigue damage. Since slack moored structures behave in a highly nonlinear manner, commonly used fatigue analysis procedures are normally inadequate. This paper reviews present probablistic fatigue analysis methods, and provides a means for incorporating nonlinear mooring behavior into analysis and design to predict accumulated damage and remaining service life. The procedures presented are general, and they are also applicable to ship and buoy moorings, offshore terminals, and guyed and tension leg platforms.
Method for predicting peptide detection in mass spectrometry
Kangas, Lars [West Richland, WA; Smith, Richard D [Richland, WA; Petritis, Konstantinos [Richland, WA
2010-07-13
A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.
Prediction and control of chaotic processes using nonlinear adaptive networks
Jones, R.D.; Barnes, C.W.; Flake, G.W.; Lee, K.; Lewis, P.S.; O'Rouke, M.K.; Qian, S.
1990-01-01
We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We then present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series, tidal prediction in Venice lagoon, finite differencing, sonar transient detection, control of nonlinear processes, control of a negative ion source, balancing a double inverted pendulum and design advice for free electron lasers and laser fusion targets.
Experimental Investigations of Generalized Predictive Control for Tiltrotor Stability Augmentation
NASA Technical Reports Server (NTRS)
Nixon, Mark W.; Langston, Chester W.; Singleton, Jeffrey D.; Piatak, David J.; Kvaternik, Raymond G.; Bennett, Richard L.; Brown, Ross K.
2001-01-01
A team of researchers from the Army Research Laboratory, NASA Langley Research Center (LaRC), and Bell Helicopter-Textron, Inc. have completed hover-cell and wind-tunnel testing of a 1/5-size aeroelastically-scaled tiltrotor model using a new active control system for stability augmentation. The active system is based on a generalized predictive control (GPC) algorithm originally developed at NASA LaRC in 1997 for un-known disturbance rejection. Results of these investigations show that GPC combined with an active swashplate can significantly augment the damping and stability of tiltrotors in both hover and high-speed flight.
Punishment sensitivity predicts the impact of punishment on cognitive control.
Braem, Senne; Duthoo, Wout; Notebaert, Wim
2013-01-01
Cognitive control theories predict enhanced conflict adaptation after punishment. However, no such effect was found in previous work. In the present study, we demonstrate in a flanker task how behavioural adjustments following punishment signals are highly dependent on punishment sensitivity (as measured by the Behavioural Inhibition System (BIS) scale): Whereas low punishment-sensitive participants do show increased conflict adaptation after punishment, high punishment-sensitive participants show no such modulation. Interestingly, participants with a high punishment-sensitivity showed an overall reaction time increase after punishments. Our results stress the role of individual differences in explaining motivational modulations of cognitive control.
Scattering waves using exact controllability methods
NASA Astrophysics Data System (ADS)
Bristeau, M. O.; Glowinski, R.; Periaux, J.
1993-01-01
The main goal of this paper is to introduce a novel method for solving the Helmholtz equations from acoustics and two-dimensional electromagnetics. The key idea of the method is to go back to the original wave equation and look for time periodic solutions. In order to find these solutions, we essentially use a least squares/shooting method which is closely related to exact controllability and to the Hilbert uniqueness method of Lions (1988). From this formulation and by analogy with other controllability problems, we derive a conjugate gradient algorithm (in an appropriate Hilbert space) which has quite good convergence properties. Numerical experiments concerning the scattering of planar waves by convex or nonconvex obstacles show the efficiency of the new algorithm, particularly for air intakelike reflectors and two-dimensional aircraft related bodies.
An experiment in hurricane track prediction using parallel computing methods
NASA Technical Reports Server (NTRS)
Song, Chang G.; Jwo, Jung-Sing; Lakshmivarahan, S.; Dhall, S. K.; Lewis, John M.; Velden, Christopher S.
1994-01-01
The barotropic model is used to explore the advantages of parallel processing in deterministic forecasting. We apply this model to the track forecasting of hurricane Elena (1985). In this particular application, solutions to systems of elliptic equations are the essence of the computational mechanics. One set of equations is associated with the decomposition of the wind into irrotational and nondivergent components - this determines the initial nondivergent state. Another set is associated with recovery of the streamfunction from the forecasted vorticity. We demonstrate that direct parallel methods based on accelerated block cyclic reduction (BCR) significantly reduce the computational time required to solve the elliptic equations germane to this decomposition and forecast problem. A 72-h track prediction was made using incremental time steps of 16 min on a network of 3000 grid points nominally separated by 100 km. The prediction took 30 sec on the 8-processor Alliant FX/8 computer. This was a speed-up of 3.7 when compared to the one-processor version. The 72-h prediction of Elena's track was made as the storm moved toward Florida's west coast. Approximately 200 km west of Tampa Bay, Elena executed a dramatic recurvature that ultimately changed its course toward the northwest. Although the barotropic track forecast was unable to capture the hurricane's tight cycloidal looping maneuver, the subsequent northwesterly movement was accurately forecasted as was the location and timing of landfall near Mobile Bay.
A Primer In Advanced Fatigue Life Prediction Methods
NASA Technical Reports Server (NTRS)
Halford, Gary R.
2000-01-01
Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable more cost effective, and better performing products. In other words, as the envelop is expanded, components are then designed to operate just as close to the newly expanded envelop as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.
Control of nonlinear processes by using linear model predictive control algorithms.
Gu, Bingfeng; Gupta, Yash P
2008-04-01
Most chemical processes are inherently nonlinear. However, because of their simplicity, linear control algorithms have been used for the control of nonlinear processes. In this study, the use of the dynamic matrix control algorithm and a simplified model predictive control algorithm for control of a bench-scale pH neutralization process is investigated. The nonlinearity is handled by dividing the operating region into sub-regions and by switching the controller model as the process moves from one sub-region to another. A simple modification for model predictive control algorithms is presented to handle the switching. The simulation and experimental results show that the modification can provide a significant improvement in the control of nonlinear processes.
Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias
2015-01-01
The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305
Augmented Lagrangian method for optimal laser control
NASA Astrophysics Data System (ADS)
Shen, Hai; Dussault, Jean-Pierre; Bandrauk, Andre D.
1994-06-01
We use penalty methods derived from Augmented Lagrangians coupled with unitary exponential operator methods to solve the optimal control problem for molecular time-dependent Schodinger equations involving laser pulse excitations. A stable numerical algorithm is presented which propagates directly from initial states to given final states. Results are reported for an analytically solvable model for the complete inversion of a three-state system.
NASA Astrophysics Data System (ADS)
Kamel, Ouari; Mohand, Ouhrouche; Toufik, Rekioua; Taib, Nabil
2015-01-01
In order to improvement of the performances for wind energy conversions systems (WECS), an advanced control techniques must be used. In this paper, as an alternative to conventional PI-type control methods, a nonlinear predictive control (NPC) approach is developed for DFIG-based wind turbine. To enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. An explicitly analytical form of the optimal predictive controller is given consequently on-line optimization is not necessary The DFIG is fed through the rotor windings by a back-to-back converter controlled by Pulse Width Modulation (PWM), where the stator winding is directly connected to the grid. The presented simulation results show a good performance in trajectory tracking of the proposed strategy and rejection of disturbances is successfully achieved.
Neural network modeling and model predictive control of ionic electroactive polymer actuators
NASA Astrophysics Data System (ADS)
Nakshatharan, Sunjai; Punning, Andres; Aabloo, Alvo
2016-04-01
This work reports on the modelling and control of ionic electroactive polymer actuators with electrodes based on nanoporous carbon, which are working in ambient environment. The model incorporates the humidity level value as one of the input parameters, and so captures the environment-dependent dynamics of the actuator. The effect of ambient humidity on the actuators is studied through the frequency response analysis and is followed by neural network method of modelling. A closed loop set point tracking control system based on gain scheduled model predictive control is designed and developed for position control of actuator and is verified experimentally. The developed model and controller is capable to predict and control the actuators at under the humidity conditions varying in the range of 3% - 97%.
Lai, Ying-Cheng; Harrison, Mary Ann F; Frei, Mark G; Osorio, Ivan
2004-09-01
Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy. Copyright 2004 American Institute of Physics
NASA Astrophysics Data System (ADS)
Lai, Ying-Cheng; Harrison, Mary Ann F.; Frei, Mark G.; Osorio, Ivan
2004-09-01
Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy.
Jet Noise Diagnostics Supporting Statistical Noise Prediction Methods
NASA Technical Reports Server (NTRS)
Bridges, James E.
2006-01-01
compared against measurements of mean and rms velocity statistics over a range of jet speeds and temperatures. Models for flow parameters used in the acoustic analogy, most notably the space-time correlations of velocity, have been compared against direct measurements, and modified to better fit the observed data. These measurements have been extremely challenging for hot, high speed jets, and represent a sizeable investment in instrumentation development. As an intermediate check that the analysis is predicting the physics intended, phased arrays have been employed to measure source distributions for a wide range of jet cases. And finally, careful far-field spectral directivity measurements have been taken for final validation of the prediction code. Examples of each of these experimental efforts will be presented. The main result of these efforts is a noise prediction code, named JeNo, which is in middevelopment. JeNo is able to consistently predict spectral directivity, including aft angle directivity, for subsonic cold jets of most geometries. Current development on JeNo is focused on extending its capability to hot jets, requiring inclusion of a previously neglected second source associated with thermal fluctuations. A secondary result of the intensive experimentation is the archiving of various flow statistics applicable to other acoustic analogies and to development of time-resolved prediction methods. These will be of lasting value as we look ahead at future challenges to the aeroacoustic experimentalist.
Nouretdinov, Ilia; Gammerman, Alex; Qi, Yanjun; Klein-Seetharaman, Judith
2012-01-01
Identifying protein-protein interactions (PPI's) is critical for understanding virtually all cellular molecular mechanisms. Previously, predicting PPI's was treated as a binary classification task and has commonly been solved in a supervised setting which requires a positive labeled set of known PPI's and a negative labeled set of non-interacting protein pairs. In those methods, the learner provides the likelihood of the predicted interaction, but without a confidence level associated with each prediction. Here, we apply a conformal prediction framework to make predictions and estimate confidence of the predictions. The conformal predictor uses a function measuring relative 'strangeness' interacting pairs to check whether prediction of a new example added to the sequence of already known PPI's would conform to the 'exchangeability' assumption: distribution of interacting pairs is invariant with any permutations of the pairs. In fact, this is the only assumption we make about the data. Another advantage is that the user can control a number of errors by providing a desirable confidence level. This feature of CP is very useful for a ranking list of possible interactive pairs. In this paper, the conformal method has been developed to deal with just one class - class interactive proteins - while there is not clearly defined of 'non-interactive'pairs. The confidence level helps the biologist in the interpretation of the results, and better assists the choices of pairs for experimental validation. We apply the proposed conformal framework to improve the identification of interacting pairs between HIV-1 and human proteins.
Method for Controlled Mitochondrial Perturbation during Phosphorus MRS in Children
Cree-Green, Melanie; Newcomer, Bradley R.; Brown, Mark; Hull, Amber; West, Amy D.; Singel, Debra; Reusch, Jane E.B.; McFann, Kim; Regensteiner, Judith G.; Nadeau, Kristen J.
2014-01-01
Introduction Insulin resistance (IR) is increasingly prevalent in children, and may be related to muscle mitochondrial dysfunction, necessitating development of mitochondrial assessment techniques. Recent studies used 31Phosphorus magnetic resonance spectroscopy (31P-MRS), a non-invasive technique appealing for clinical research. 31P-MRS requires exercise at a precise percentage of maximum volitional contraction (MVC). MVC measurement in children, particularly with disease, is problematic due to variability in perception of effort and motivation. We therefore developed a method to predict MVC, using maximal calf muscle cross-sectional area (MCSA) to assure controlled and reproducible muscle metabolic perturbations. Methods Data were collected from 66 sedentary 12–20 year-olds. Plantar flexion-volitional MVC was assessed using a MRI-compatible exercise treadle device. MCSA of the calf muscles were measured from MRI images. Data from the first 26 participants were utilized to model the relationship between MVC and MCSA (predicted MVC = 24.763+0.0047*MCSA). This model was then applied to the subsequent 40 participants. Results Volitional vs. model-predicted mean MVC was 43.9±0.8 kg vs. 44.2±1.81 (P=0.90). 31P-MRS results when predicted and volitional MVC were similar showed expected changes during volitional MVC-based exercise. In contrast, volitional MVC was markedly lower than predicted in 4 participants, and produced minimal metabolic perturbation. Upon repeat testing, these individuals could perform their predicted MVC with coaching, which produced expected metabolic perturbations. Conclusions Compared to using MVC testing alone, utilizing MRI to predict muscle strength allows for a more accurate and standardized 31P-MRS protocol during exercise in children. This method overcomes a major obstacle in assessing mitochondrial function in youth. These studies have importance as we seek to determine the role of mitochondrial function in youth with IR and diabetes
Unity power factor converter based on a fuzzy controller and predictive input current.
Bouafassa, Amar; Rahmani, Lazhar; Kessal, Abdelhalim; Babes, Badreddine
2014-11-01
This paper proposes analysis and control of a single-phase power factor corrector (PFC). The proposed control is capable of achieving a unity power factor for each DC link voltage or load fluctuation. The method under study is composed of two intelligent approaches, a fuzzy logic controller to ensure an output voltage at a suitable value and predictive current control. The fuzzy controller is used with minimum rules to attain a low cost. The method is verified and discussed through simulation on the MATLAB/Simulink platform. It presents high dynamic performance under various parameter changes. Moreover, in order to examine and evaluate the method in real-time, a test bench is built using dSPACE 1104. The implantation of the proposed method is very easy and flexible and allows for operation under parameter variations. Additionally, the obtained results are very significant. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Control and prediction components of movement planning in stuttering vs. nonstuttering adults
Daliri, Ayoub; Prokopenko, Roman A.; Flanagan, J. Randall; Max, Ludo
2014-01-01
Purpose Stuttering individuals show speech and nonspeech sensorimotor deficiencies. To perform accurate movements, the sensorimotor system needs to generate appropriate control signals and correctly predict their sensory consequences. Using a reaching task, we examined the integrity of these control and prediction components, separately, for movements unrelated to the speech motor system. Method Nine stuttering and nine nonstuttering adults made fast reaching movements to visual targets while sliding an object under the index finger. To quantify control, we determined initial direction error and end-point error. To quantify prediction, we calculated the correlation between vertical and horizontal forces applied to the object—an index of how well vertical force (preventing slip) anticipated direction-dependent variations in horizontal force (moving the object). Results Directional and end-point error were significantly larger for the stuttering group. Both groups performed similarly in scaling vertical force with horizontal force. Conclusions The stuttering group's reduced reaching accuracy suggests limitations in generating control signals for voluntary movements, even for non-orofacial effectors. Typical scaling of vertical force with horizontal force suggests an intact ability to predict the consequences of planned control signals. Stuttering may be associated with generalized deficiencies in planning control signals rather than predicting the consequences of those signals. PMID:25203459
Problems With Risk Reclassification Methods for Evaluating Prediction Models
Pepe, Margaret S.
2011-01-01
For comparing the performance of a baseline risk prediction model with one that includes an additional predictor, a risk reclassification analysis strategy has been proposed. The first step is to cross-classify risks calculated according to the 2 models for all study subjects. Summary measures including the percentage of reclassification and the percentage of correct reclassification are calculated, along with 2 reclassification calibration statistics. The author shows that interpretations of the proposed summary measures and P values are problematic. The author's recommendation is to display the reclassification table, because it shows interesting information, but to use alternative methods for summarizing and comparing model performance. The Net Reclassification Index has been suggested as one alternative method. The author argues for reporting components of the Net Reclassification Index because they are more clinically relevant than is the single numerical summary measure. PMID:21555714
Mathematical analysis method for predicting temperature rise in BIPV
NASA Astrophysics Data System (ADS)
Ibarahim, Zahari; Ruslan, Mohd Hafidz; Ibrahim, Zamry
2013-05-01
In order to estimate the airflow rate and temperature in the channel of building an integrated photovoltaic (BIPV) system, a simplified mathematical method has been derived in which the buoyancy force balances the friction along the channel. The channel is represented as a closed duct and the pressure losses are calculated in a similar fashion to those in a pipe circuit. Pressure losses at ventilation inlets and outlets are calculated using pressure loss factors, Kf. The effect of wind pressure on mass flow rate and temperature in the channel can be estimated with the inclusion of the wind speed pressure into the formulation. The benefit of wind pressure applied normal to the inlet and tangential to the outlet of the channel can be seen. The procedure yields the mass flow rate and temperature directly by solution of a simple cubic equation. This method allows the engineers and scientist to predict optimum configuration of their PV cladding design.
Decision tree methods: applications for classification and prediction
SONG, Yan-yan; LU, Ying
2015-01-01
Summary Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure. PMID:26120265
Novel Methods for Mosquito Control using RNAi.
USDA-ARS?s Scientific Manuscript database
The discovery and development of novel insecticides for vector control is a primary focus of toxicology research conducted at the Mosquito and Fly Research Unit, Gainesville, FL. Targeting critical genes/proteins in mosquitoes using RNA interference (RNAi) is being investigated as a method to devel...
Forward Stochastic Nonlinear Adaptive Control Method
NASA Technical Reports Server (NTRS)
Bayard, David S.
1990-01-01
New method of computation for optimal stochastic nonlinear and adaptive control undergoing development. Solves systematically stochastic dynamic programming equations forward in time, using nested-stochastic-approximation technique. Main advantage, simplicity of programming and reduced complexity with clear performance/computation trade-offs.
Alternative Asbestos Control Method (AACM), Washington
This presentation describes the status to date of the Alternative Asbestos Control Method research, which is intended as a possible alternative technology for use in the demolition of buildings that contain asbestos and are covered under the regulatory requirements of the Asbesto...
Alternative Asbestos Control Method (AACM) Research
This presentation describes the status to date of the Alternative Asbestos Control Method research, which is intended as a possible alternative technology for use in the demolition of buildings that contain asbestos and are covered under the regulatory requirements of the Asbesto...
Osteoporosis risk prediction using machine learning and conventional methods.
Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won
2013-01-01
A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
Soil salinity prediction using electromagnetic induction method in gypsiferous soil
NASA Astrophysics Data System (ADS)
Bouksila, Fethi; Persson, Magnus; Bahri, Akiça; Berndtsson, Ronny; Ben Slimane, Abir
2017-04-01
In arid and semiarid regions, secondary soil salinization is considered a main danger to the sustainability of irrigated land and agricultural production. Thus, accurate and rapid estimation of soil salinity should be readily available to farmers during crop development to increase productivity and to contribute to sustainable land planning aimed at mitigating soil degradation. Measurement of electrical conductivity in saturated paste extracts (ECe) is a standard method for which other salinity estimation methods are referenced. In the present study, we investigated the possibilities to use the EM38 to predict field ECe in a saline gypsiferous soil of the Saharian-climate Fatnassa oasis (Tunisia) under shallow and saline groundwater. On the 114 ha oasis, an experimental network system of 27 agricultural plots was chosen for monitoring soil properties (ECa-EM38, soil particle size, gypsum content, soil moisture, and ECe) and groundwater (depth, Dgw, electrical conductivity, and ECgw). Samples were taken during 4 years (2001 to 2004) at experimental plots and soil profiles were sampled at 0.2 m depth intervals to 1.2 m for physical and chemical analysis. The results showed that significant lnECe-EM relationships could be developed. However, results also indicated that for better accuracy of soil salinity prediction using the EM38, it is advisable to perform calibrations for each measurement period.
Popular theoretical methods predict benzene and arenes to be nonplanar.
Moran, Damian; Simmonett, Andrew C; Leach, Franklin E; Allen, Wesley D; Schleyer, Paul V R; Schaefer, Henry F
2006-07-26
Planar (D6h) benzene has one (1181i cm-1, b2g) and three (1844i cm-1, b2g; 462i cm-1, e2u) imaginary vibrational frequencies at the MP2/6-311++G(d,p) and MP2/6-311++G levels of theory, respectively! The spurious frequencies correspond to D3d chair (b2g) and C2v boat (e2u) out-of-plane distortions. Numerous, similar examples where planar benzene is not a minimum are documented at the MP2, MP3, and CISD levels with popular Pople-type basis sets, while the RHF, B3LYP, and BLYP methods exhibit no such problems. We show that, in the sp and spd atomic-orbital limits of MP2 theory, benzene is nonplanar. The observed failure of electron correlation methods with unbalanced basis sets to predict planar minima is not unique to benzene but is also found for other pi-delocalized molecules, including pyridine, naphthalene, anthracene, the cyclopentadienyl and indenyl anions, and the tropylium cation. Detailed mathematical analysis reveals that an insidious, geometry-dependent, two-electron basis set incompleteness error (BSIE) is responsible for the problem and that balanced, correlation-consistent constructions of basis sets are generally required to ensure reliable predictions for arenes with correlated wave functions.
Conditional Weather Resampling Method for Seasonal Ensemble Streamflow Prediction
NASA Astrophysics Data System (ADS)
Beckers, Joost; Weerts, Albrecht; Welles, Edwin
2014-05-01
Ensemble Streamflow Prediction (ESP) is a commonly used method for water resources planning on the seasonal time scale. The starting point for the ESP is the current state of the hydrological system, which is generated form a short historical simulation up to the time of forecast. Starting from this initial state, a hydrologic model is run to produce an ensemble of possible realizations of future streamflows, taking meteorological time series from historical years as input. It is assumed that these historical weather time series represent climatology. One disadvantage of the original ESP method is that an expected deviation from average climatology is not accounted for. Here, we propose a variation to the ESP, in which shorter periods from historical time years are resampled and assembled to generate additional possible realizations of future weather. The resampling is done in such a way as to incorporate statistical deviations from the average climate that are linked to climate modes, such as El Niño Southern Oscillation (ENSO) or Pacific Decadal Oscillation (PDO). These climate modes are known to affect the local weather in many regions around the world. The resampling of historical weather periods is conditioned on the climate mode indices, starting with the current climate index value and searching for historical years with similar climate indices. The resampled weather time series are used as input for the hydrological model, similar to the original ESP procedure. The method was implemented in the operational forecasting environment of Bonneville Power Administration (BPA), which based on Delft-FEWS. The method was run for 55 non-operational years of hindcasts (forecasts in retrospect) for the Columbia River in the North-West of the U.S. An increase in forecast skill up to 5% was found relative to the standard ESP for streamflow predictions at three test-locations.
Survey on large scale system control methods
NASA Technical Reports Server (NTRS)
Mercadal, Mathieu
1987-01-01
The problem inherent to large scale systems such as power network, communication network and economic or ecological systems were studied. The increase in size and flexibility of future spacecraft has put those dynamical systems into the category of large scale systems, and tools specific to the class of large systems are being sought to design control systems that can guarantee more stability and better performance. Among several survey papers, reference was found to a thorough investigation on decentralized control methods. Especially helpful was the classification made of the different existing approaches to deal with large scale systems. A very similar classification is used, even though the papers surveyed are somehow different from the ones reviewed in other papers. Special attention is brought to the applicability of the existing methods to controlling large mechanical systems like large space structures. Some recent developments are added to this survey.
Adaptive Accommodation Control Method for Complex Assembly
NASA Astrophysics Data System (ADS)
Kang, Sungchul; Kim, Munsang; Park, Shinsuk
Robotic systems have been used to automate assembly tasks in manufacturing and in teleoperation. Conventional robotic systems, however, have been ineffective in controlling contact force in multiple contact states of complex assemblythat involves interactions between complex-shaped parts. Unlike robots, humans excel at complex assembly tasks by utilizing their intrinsic impedance, forces and torque sensation, and tactile contact clues. By examining the human behavior in assembling complex parts, this study proposes a novel geometry-independent control method for robotic assembly using adaptive accommodation (or damping) algorithm. Two important conditions for complex assembly, target approachability and bounded contact force, can be met by the proposed control scheme. It generates target approachable motion that leads the object to move closer to a desired target position, while contact force is kept under a predetermined value. Experimental results from complex assembly tests have confirmed the feasibility and applicability of the proposed method.
NASA Astrophysics Data System (ADS)
Itoh, Masato; Hagimori, Yuki; Nonaka, Kenichiro; Sekiguchi, Kazuma
2016-09-01
In this study, we apply a hierarchical model predictive control to omni-directional mobile vehicle, and improve the tracking performance. We deal with an independent four-wheel driving/steering vehicle (IFWDS) equipped with four coaxial steering mechanisms (CSM). The coaxial steering mechanism is a special one composed of two steering joints on the same axis. In our previous study with respect to IFWDS with ideal steering, we proposed a model predictive tracking control. However, this method did not consider constraints of the coaxial steering mechanism which causes delay of steering. We also proposed a model predictive steering control considering constraints of this mechanism. In this study, we propose a hierarchical system combining above two control methods for IFWDS. An upper controller, which deals with vehicle kinematics, runs a model predictive tracking control, and a lower controller, which considers constraints of coaxial steering mechanism, runs a model predictive steering control which tracks the predicted steering angle optimized an upper controller. We verify the superiority of this method by comparing this method with the previous method.
Birth control method should suit your lifestyle.
1986-01-01
In choosing a method of birth control, effectiveness and safety are the key considerations. Yet, one's decisions about contraception also must be balanced against important considerations regarding children and family. Many other factors play a role in the choice. Women who live in rural areas where medical services are not easily available may not want to use oral contraceptives (OCs) or IUDs because both carry the risk of serious complications. Costs of the contraceptive method and of related medical examinations are a consideration for women in some financial circumstances. A partner's attitude also is crucial to selecting a birth control method. Methods such as condoms, withdrawal, spermicides, and diaphragm require his cooperation if they are to be effective. Health problems which might be worsened by a particular type of contraception require the advice of a physician. The physician should take a woman's health history and sexual pattern into account when prescribing a birth control technique. The contraceptive decision should be based on whether a woman plans to have children eventually. A woman who has several partners may find condoms, foams, and diaphragms inconvenient, even though they help prevention infection, but she should not use in IUD, which is convenient but carries a high risk of infection for women who have more than 1 sexual partner. OCs might be the best choice under these circumstances. A chart identifies the relative advantages and disadvantages of contraceptive methods.
Robust model predictive control for optimal continuous drug administration.
Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos
2014-10-01
In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
A model predictive control approach for the Italian LBE XADS
NASA Astrophysics Data System (ADS)
Cammi, Antonio; Casella, Francesco; Luzzi, Lelio; Milano, Alessandro; Ricotti, Marco E.
2008-06-01
In this paper, model predictive control (MPC) is applied to the Italian 80 MW th experimental accelerator driven system (XADS), referring to a simple, non-linear model for the dynamic simulation of the plant, which has been developed and described in a previous work [A. Cammi, L. Luzzi, A.A. Porta, M.E. Ricotti, Prog. Nucl. Energ. 48 (2006) 578], in order to describe the interactions among the different subsystems: i.e., the accelerator-core coupling, the lead bismuth eutectic (LBE) primary system, the secondary system with diathermic oil and air coolers batteries, which reject the thermal power to the environment. Hereinafter, a model predictive controller is proposed, with the objective to minimize the difference between the average temperature of the diathermic oil and its reference value, while also minimizing the variations of the control input, which is the air coolers mass flow rate. The dynamic response of the LBE-XADS has been evaluated with reference to a reduction of 20% in the reactor power from nominal load conditions: this transient is very demanding for the overall plant, nevertheless the obtained results indicate the effectiveness of the proposed controller.
Hartley, Stephen W.; Monti, Stefano; Liu, Ching-Ti; Steinberg, Martin H.; Sebastiani, Paola
2012-01-01
Genome-wide association studies (GWAS) have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction. The proposed method includes two phases: (1) Bayesian model comparison, to identify Single-Nucleotide Polymorphisms (SNPs) associated with one or more traits; and (2) cross-validation feature selection, in which a final set of SNPs is selected to optimize prediction. To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with two dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs. Across the 16 scenarios, prediction accuracy varied from 90 to 50%. In the 14 scenarios that included pleiotropically associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the two scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal. To further evaluate the method on real data, a discovery set of 1071 sickle cell disease (SCD) patients was used to search for pleiotropic associations between cerebral vascular accidents and fetal hemoglobin level. Classification was performed on a smaller validation set of 352 SCD patients, and showed that the inclusion of pleiotropic SNPs may slightly improve prediction, although the difference was not statistically significant. The
Actively controlled vibration welding system and method
Cai, Wayne W.; Kang, Bongsu; Tan, Chin-An
2013-04-02
A vibration welding system includes a controller, welding horn, an active material element, and anvil assembly. The assembly may include an anvil body connected to a back plate and support member. The element, e.g., a piezoelectric stack or shape memory alloy, is positioned with respect to the assembly. The horn vibrates in a desirable first direction to form a weld on a work piece. The element controls any vibrations in a second direction by applying calibrated response to the anvil body in the second direction. A method for controlling undesirable vibrations in the system includes positioning the element with respect to the anvil assembly, connecting the anvil body to the support member through the back plate, vibrating the horn in a desirable first direction, and transmitting an input signal to the element to control vibration in an undesirable second direction.
NASA Astrophysics Data System (ADS)
Rajapakse, G.; Jayasinghe, S. G.; Fleming, A.; Shahnia, F.
2017-07-01
Australia’s extended coastline asserts abundance of wave and tidal power. The predictability of these energy sources and their proximity to cities and towns make them more desirable. Several tidal current turbine and ocean wave energy conversion projects have already been planned in the coastline of southern Australia. Some of these projects use air turbine technology with air driven turbines to harvest the energy from an oscillating water column. This study focuses on the power take-off control of a single stage unidirectional oscillating water column air turbine generator system, and proposes a model predictive control-based speed controller for the generator-turbine assembly. The proposed method is verified with simulation results that show the efficacy of the controller in extracting power from the turbine while maintaining the speed at the desired level.
Prediction and control of neural responses to pulsatile electrical stimulation
NASA Astrophysics Data System (ADS)
Campbell, Luke J.; Sly, David James; O'Leary, Stephen John
2012-04-01
This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.
Prediction and control of neural responses to pulsatile electrical stimulation.
Campbell, Luke J; Sly, David James; O'Leary, Stephen John
2012-04-01
This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s(-1). A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s(-1). Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.
Prakash, J; Srinivasan, K
2009-07-01
In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.
Limits of Executive Control: Sequential Effects in Predictable Environments.
Verbruggen, Frederick; McAndrew, Amy; Weidemann, Gabrielle; Stevens, Tobias; McLaren, Ian P L
2016-05-01
Cognitive-control theories attribute action control to executive processes that modulate behavior on the basis of expectancy or task rules. In the current study, we examined corticospinal excitability and behavioral performance in a go/no-go task. Go and no-go trials were presented in runs of five, and go and no-go runs alternated predictably. At the beginning of each trial, subjects indicated whether they expected a go trial or a no-go trial. Analyses revealed that subjects immediately adjusted their expectancy ratings when a new run started. However, motor excitability was primarily associated with the properties of the previous trial, rather than the predicted properties of the current trial. We also observed a large latency cost at the beginning of a go run (i.e., reaction times were longer for the first trial in a go run than for the second trial). These findings indicate that actions in predictable environments are substantially influenced by previous events, even if this influence conflicts with conscious expectancies about upcoming events. © The Author(s) 2016.
Error correction, sensory prediction, and adaptation in motor control.
Shadmehr, Reza; Smith, Maurice A; Krakauer, John W
2010-01-01
Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.
A GIS modeling method applied to predicting forest songbird habitat
Dettmers, Randy; Bart, Jonathan
1999-01-01
We have developed an approach for using a??presencea?? data to construct habitat models. Presence data are those that indicate locations where the target organism is observed to occur, but that cannot be used to define locations where the organism does not occur. Surveys of highly mobile vertebrates often yield these kinds of data. Models developed through our approach yield predictions of the amount and the spatial distribution of good-quality habitat for the target species. This approach was developed primarily for use in a GIS context; thus, the models are spatially explicit and have the potential to be applied over large areas. Our method consists of two primary steps. In the first step, we identify an optimal range of values for each habitat variable to be used as a predictor in the model. To find these ranges, we employ the concept of maximizing the difference between cumulative distribution functions of (1) the values of a habitat variable at the observed presence locations of the target organism, and (2) the values of that habitat variable for all locations across a study area. In the second step, multivariate models of good habitat are constructed by combining these ranges of values, using the Boolean operators a??anda?? and a??or.a?? We use an approach similar to forward stepwise regression to select the best overall model. We demonstrate the use of this method by developing species-specific habitat models for nine forest-breeding songbirds (e.g., Cerulean Warbler, Scarlet Tanager, Wood Thrush) studied in southern Ohio. These models are based on speciesa?? microhabitat preferences for moisture and vegetation characteristics that can be predicted primarily through the use of abiotic variables. We use slope, land surface morphology, land surface curvature, water flow accumulation downhill, and an integrated moisture index, in conjunction with a land-cover classification that identifies forest/nonforest, to develop these models. The performance of these
Reducing usage of the computational resources by event driven approach to model predictive control
NASA Astrophysics Data System (ADS)
Misik, Stefan; Bradac, Zdenek; Cela, Arben
2017-08-01
This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.
Structural Acoustic Prediction and Interior Noise Control Technology
NASA Technical Reports Server (NTRS)
Mathur, G. P.; Chin, C. L.; Simpson, M. A.; Lee, J. T.; Palumbo, Daniel L. (Technical Monitor)
2001-01-01
This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.