DEFF Research Database (Denmark)
Krogsdal, Iben
2007-01-01
Artiklen søger at problematisere forståelsen af terapiformen NLP (Neuro Linguistisk Programmering) som fremelsker af selvstyrende individer. Den viser, hvordan NLP - gennem sin underliggende fortælling om det autentiske menneske som ideal - fordrer et fortsat selvudviklingarbejde via terapeutisk...... intervention. NLP-praksis kan derfor anskues som en løbende bemyndigelsesproces, hvor klientens selvforståelse først afvises som uautentisk hvorefter en ny selvforståelse installeres gennem terapeutiske tolkninger af "det ubevidste"....
Recent Advances in Explicit Multiparametric Nonlinear Model Predictive Control
Domínguez, Luis F.
2011-01-19
In this paper we present recent advances in multiparametric nonlinear programming (mp-NLP) algorithms for explicit nonlinear model predictive control (mp-NMPC). Three mp-NLP algorithms for NMPC are discussed, based on which novel mp-NMPC controllers are derived. The performance of the explicit controllers are then tested and compared in a simulation example involving the operation of a continuous stirred-tank reactor (CSTR). © 2010 American Chemical Society.
NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES
Directory of Open Access Journals (Sweden)
R. G. SILVA
1999-03-01
Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.
Progovac, Ana M.; Chen, Pei; Mullin, Brian; Hou, Sherry
2016-01-01
Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, “how do you feel today?” We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4) were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible. PMID:27752278
Directory of Open Access Journals (Sweden)
Benjamin L. Cook
2016-01-01
Full Text Available Natural language processing (NLP and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12. Predictor variables included structured items (e.g., relating to sleep and well-being and responses to one unstructured question, “how do you feel today?” We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4 were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.
Explicit Nonlinear Model Predictive Control Theory and Applications
Grancharova, Alexandra
2012-01-01
Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: Ø Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; �...
Improved nonlinear prediction method
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.
Karamanis, N.; Schneider, A.; van der Sluis, Ielka; Schlogl, S.; Doherty, G.; Luz, S.
2009-01-01
We examine the relationship between HCI and Natural Language Processing (NLP) by performing a bibliometric analysis and looking at the specific example of BioNLP. We identify opportunities for HCI to fertilise current NLP research and suggest that HCI will benefit from looking at advances in NLP
Karamanis, N.; Schneider, A.; van der Sluis, Ielka; Schlogl, S.; Doherty, G.; Luz, S.
2009-01-01
We examine the relationship between HCI and Natural Language Processing (NLP) by performing a bibliometric analysis and looking at the specific example of BioNLP. We identify opportunities for HCI to fertilise current NLP research and suggest that HCI will benefit from looking at advances in NLP mor
Predictive simulation of nonlinear ultrasonics
Shen, Yanfeng; Giurgiutiu, Victor
2012-04-01
Most of the nonlinear ultrasonic studies to date have been experimental, but few theoretical predictive studies exist, especially for Lamb wave ultrasonic. Compared with nonlinear bulk waves and Rayleigh waves, nonlinear Lamb waves for structural health monitoring become more challenging due to their multi-mode dispersive features. In this paper, predictive study of nonlinear Lamb waves is done with finite element simulation. A pitch-catch method is used to interrogate a plate with a "breathing crack" which opens and closes under tension and compression. Piezoelectric wafer active sensors (PWAS) used as transmitter and receiver are modeled with coupled field elements. The "breathing crack" is simulated via "element birth and death" technique. The ultrasonic waves generated by the transmitter PWAS propagate into the structure, interact with the "breathing crack", acquire nonlinear features, and are picked up by the receiver PWAS. The features of the wave packets at the receiver PWAS are studied and discussed. The received signal is processed with Fast Fourier Transform to show the higher harmonics nonlinear characteristics. A baseline free damage index is introduced to assess the presence and the severity of the crack. The paper finishes with summary, conclusions, and suggestions for future work.
Estimating relative risks for common outcome using PROC NLP.
Yu, Binbing; Wang, Zhuoqiao
2008-05-01
In cross-sectional or cohort studies with binary outcomes, it is biologically interpretable and of interest to estimate the relative risk or prevalence ratio, especially when the response rates are not rare. Several methods have been used to estimate the relative risk, among which the log-binomial models yield the maximum likelihood estimate (MLE) of the parameters. Because of restrictions on the parameter space, the log-binomial models often run into convergence problems. Some remedies, e.g., the Poisson and Cox regressions, have been proposed. However, these methods may give out-of-bound predicted response probabilities. In this paper, a new computation method using the SAS Nonlinear Programming (NLP) procedure is proposed to find the MLEs. The proposed NLP method was compared to the COPY method, a modified method to fit the log-binomial model. Issues in the implementation are discussed. For illustration, both methods were applied to data on the prevalence of microalbuminuria (micro-protein leakage into urine) for kidney disease patients from the Diabetes Control and Complications Trial. The sample SAS macro for calculating relative risk is provided in the appendix.
Adaptive nonlinear prediction of ocean reverberation
Institute of Scientific and Technical Information of China (English)
GAN Weiming; LI Fenghua
2009-01-01
An adaptive nonlinear prediction algorithm is proposed to predict ocean reverber-ation based on the phase space reconstruction of nonlinear dynamic system. The prediction algorithm is tested by experimental reverberation data measured in two areas, and the one-step forward prediction results are in good agreement with the experimental data. If the errors between the predicted and experimental data are chosen as the variable to detect the target in the reverberation series, the reverberation is suppressed and the signal-to-reverberation ratio is improved.
Nonlinear predictive control in the LHC accelerator
Blanco, E; Cristea, S; Casas, J
2009-01-01
This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation\\ maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.
Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market
Directory of Open Access Journals (Sweden)
Junhai Ma
2008-01-01
Full Text Available This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.
Nonlinear chaotic model for predicting storm surges
Siek, M.; Solomatine, D.P.
This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables.
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
NONLINEAR PREDICTIVE CONTROL FOR TERRAIN FOLLOWING
Institute of Scientific and Technical Information of China (English)
1998-01-01
A nonlinear continuous predictive control method was used for design of cruise missile terrain-following controller. A performance index which combined the tracking error and rate of tracking error is presented. Then an optimal nonlinear feedback control law is generated to minimize the performance index. The tracking performance and robustness of controller are discussed. The advantage of the control law is demonstrated by successfully designing cruise missile terrain following controllers. The results show that the controller exhibits robustness and excellent tracking performance.
Nonlinear chaotic model for predicting storm surges
Directory of Open Access Journals (Sweden)
M. Siek
2010-09-01
Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.
Launch flexibility using NLP guidance and remote wind sensing
Cramer, Evin J.; Bradt, Jerre E.; Hardtla, John W.
1990-01-01
This paper examines the use of lidar wind measurements in the implementation of a guidance strategy for a nonlinear programming (NLP) launch guidance algorithm. The NLP algorithm uses B-spline command function representation for flexibility in the design of the guidance steering commands. Using this algorithm, the guidance system solves a two-point boundary value problem at each guidance update. The specification of different boundary value problems at each guidance update provides flexibility that can be used in the design of the guidance strategy. The algorithm can use lidar wind measurements for on pad guidance retargeting and for load limiting guidance steering commands. Examples presented in the paper use simulated wind updates to correct wind induced final orbit errors and to adjust the guidance steering commands to limit the product of the dynamic pressure and angle-of-attack for launch vehicle load alleviation.
Hierarchical semantic structures for medical NLP.
Taira, Ricky K; Arnold, Corey W
2013-01-01
We present a framework for building a medical natural language processing (NLP) system capable of deep understanding of clinical text reports. The framework helps developers understand how various NLP-related efforts and knowledge sources can be integrated. The aspects considered include: 1) computational issues dealing with defining layers of intermediate semantic structures to reduce the dimensionality of the NLP problem; 2) algorithmic issues in which we survey the NLP literature and discuss state-of-the-art procedures used to map between various levels of the hierarchy; and 3) implementation issues to software developers with available resources. The objective of this poster is to educate readers to the various levels of semantic representation (e.g., word level concepts, ontological concepts, logical relations, logical frames, discourse structures, etc.). The poster presents an architecture for which diverse efforts and resources in medical NLP can be integrated in a principled way.
Predicting nonlinear properties of metamaterials from the linear response.
O'Brien, Kevin; Suchowski, Haim; Rho, Junsuk; Salandrino, Alessandro; Kante, Boubacar; Yin, Xiaobo; Zhang, Xiang
2015-04-01
The discovery of optical second harmonic generation in 1961 started modern nonlinear optics. Soon after, R. C. Miller found empirically that the nonlinear susceptibility could be predicted from the linear susceptibilities. This important relation, known as Miller's Rule, allows a rapid determination of nonlinear susceptibilities from linear properties. In recent years, metamaterials, artificial materials that exhibit intriguing linear optical properties not found in natural materials, have shown novel nonlinear properties such as phase-mismatch-free nonlinear generation, new quasi-phase matching capabilities and large nonlinear susceptibilities. However, the understanding of nonlinear metamaterials is still in its infancy, with no general conclusion on the relationship between linear and nonlinear properties. The key question is then whether one can determine the nonlinear behaviour of these artificial materials from their exotic linear behaviour. Here, we show that the nonlinear oscillator model does not apply in general to nonlinear metamaterials. We show, instead, that it is possible to predict the relative nonlinear susceptibility of large classes of metamaterials using a more comprehensive nonlinear scattering theory, which allows efficient design of metamaterials with strong nonlinearity for important applications such as coherent Raman sensing, entangled photon generation and frequency conversion.
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.
Stability analysis of embedded nonlinear predictor neural generalized predictive controller
Directory of Open Access Journals (Sweden)
Hesham F. Abdel Ghaffar
2014-03-01
Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.
Prediction of peptide bonding affinity: kernel methods for nonlinear modeling
Bergeron, Charles; Sundling, C Matthew; Krein, Michael; Katt, Bill; Sukumar, Nagamani; Breneman, Curt M; Bennett, Kristin P
2011-01-01
This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.
Nonlinear analysis and prediction of time series in multiphase reactors
Liu, Mingyan
2014-01-01
This book reports on important nonlinear aspects or deterministic chaos issues in the systems of multi-phase reactors. The reactors treated in the book include gas-liquid bubble columns, gas-liquid-solid fluidized beds and gas-liquid-solid magnetized fluidized beds. The authors take pressure fluctuations in the bubble columns as time series for nonlinear analysis, modeling and forecasting. They present qualitative and quantitative non-linear analysis tools which include attractor phase plane plot, correlation dimension, Kolmogorov entropy and largest Lyapunov exponent calculations and local non-linear short-term prediction.
Prediction of nonlinear optical properties of large organic molecules
Cardelino, Beatriz H.
1992-01-01
The preparation of materials with large nonlinear responses usually requires involved synthetic processes. Thus, it is very advantageous for materials scientists to have a means of predicting nonlinear optical properties. The prediction of nonlinear optical properties has to be addressed first at the molecular level and then as bulk material. For relatively large molecules, two types of calculations may be used, which are the sum-over-states and the finite-field approach. The finite-field method was selected for this research, because this approach is better suited for larger molecules.
Validation of Eye Movements Model of NLP through Stressed Recalls.
Sandhu, Daya S.
Neurolinguistic Progamming (NLP) has emerged as a new approach to counseling and psychotherapy. Though not to be confused with computer programming, NLP does claim to program, deprogram, and reprogram clients' behaviors with the precision and expedition akin to computer processes. It is as a tool for therapeutic communication that NLP has rapidly…
Quantification and prediction of rare events in nonlinear waves
Sapsis, Themistoklis; Cousins, Will; Mohamad, Mustafa
2014-11-01
The scope of this work is the quantification and prediction of rare events characterized by extreme intensity, in nonlinear dispersive models that simulate water waves. In particular we are interested for the understanding and the short-term prediction of rogue waves in the ocean and to this end, we consider 1-dimensional nonlinear models of the NLS type. To understand the energy transfers that occur during the development of an extreme event we perform a spatially localized analysis of the energy distribution along different wavenumbers by means of the Gabor transform. A stochastic analysis of the Gabor coefficients reveals i) the low-dimensionality of the intermittent structures, ii) the interplay between non-Gaussian statistical properties and nonlinear energy transfers between modes, as well as iii) the critical scales (or Gabor coefficients) where a critical energy can trigger the formation of an extreme event. The unstable character of these critical localized modes is analysed directly through the system equation and it is shown that it is defined as the result of the system nonlinearity and the wave dissipation (that mimics wave breaking). These unstable modes are randomly triggered through the dispersive ``heat bath'' of random waves that propagate in the nonlinear medium. Using these properties we formulate low-dimensional functionals of these Gabor coefficients that allow for the prediction of extreme event well before the strongly nonlinear interactions begin to occur. The prediction window is further enhanced by the combination of the developed scheme with traditional filtering schemes.
Nonlinear Economic Model Predictive Control Strategy for Active Smart Buildings
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm...... for solving the nonconvex optimization problem is proposed in this paper. A simulation using the nonlinear model-based controller to control the temperature levels of an intelligent office building (PowerFlexHouse) is addressed. Its performance is compared with a linear model-based controller. The nonlinear...
Federated nonlinear predictive filtering for the gyroless attitude determination system
Zhang, Lijun; Qian, Shan; Zhang, Shifeng; Cai, Hong
2016-11-01
This paper presents a federated nonlinear predictive filter (NPF) for the gyroless attitude determination system with star sensor and Global Positioning System (GPS) sensor. This approach combines the good qualities of both the NPF and federated filter. In order to combine them, the equivalence relationship between the NPF and classical Kalman filter (KF) is demonstrated from algorithm structure and estimation criterion. The main features of this approach include a nonlinear predictive filtering algorithm to estimate uncertain model errors and determine the spacecraft attitude by using attitude kinematics and dynamics, and a federated filtering algorithm to process measurement data from multiple attitude sensors. Moreover, a fault detection and isolation algorithm is applied to the proposed federated NPF to improve the estimation accuracy even when one sensor fails. Numerical examples are given to verify the navigation performance and fault-tolerant performance of the proposed federated nonlinear predictive attitude determination algorithm.
Nonlinear prediction of the aerodynamic loads on lifting surfaces
Kandil, O. A.; Mook, D. T.; Nayfeh, A. H.
1974-01-01
A numerical procedure is used to predict the nonlinear aerodynamic characteristics of lifting surfaces of low aspect ratio at high angles of attack for low subsonic Mach numbers. The procedure utilizes a vortex-lattice method and accounts for separation at sharp tips and leading edges. The shapes of the wakes emanating from the edges are predicted, and hence the nonlinear characteristics are calculated. Parallelogram and delta wings are presented as numerical examples. The numerical results are in good agreement with the experimental data.
GENERAL ASPECTS OF NLP IN TEACHING LANGUAGES
Directory of Open Access Journals (Sweden)
Clementina Niţă
2011-11-01
Full Text Available This article is focus on NLP as a tool helping to improve personal excellence in teaching languages. NLP states that learning begins in the students’ frame of reference and thus, it is important for teachers to increase their interpersonal skills and ability to recognize it. A map of how language operates is drawn by the Meta Model - a tool for a fuller understanding of what people say -, which deals with the concepts of surface structure and deep structure. The meta programs deal with deletions, distortions and generalisations which appear in language during communication. Establish rapport is a helpful method to create favourable teaching atmosphere and humanising teaching is a way to improve students’ performance.
Sensor fusion and nonlinear prediction for anomalous event detection
Energy Technology Data Exchange (ETDEWEB)
Hernandez, J.V.; Moore, K.R.; Elphic, R.C.
1995-03-07
The authors consider the problem of using the information from various time series, each one characterizing a different physical quantity, to predict the future state of the system and, based on that information, to detect and classify anomalous events. They stress the application of principal components analysis (PCA) to analyze and combine data from different sensors. They construct both linear and nonlinear predictors. In particular, for linear prediction the authors use the least-mean-square (LMS) algorithm and for nonlinear prediction they use both backpropagation (BP) networks and fuzzy predictors (FP). As an application, they consider the prediction of gamma counts from past values of electron and gamma counts recorded by the instruments of a high altitude satellite.
UAV Formation Flight Based on Nonlinear Model Predictive Control
Directory of Open Access Journals (Sweden)
Zhou Chao
2012-01-01
Full Text Available We designed a distributed collision-free formation flight control law in the framework of nonlinear model predictive control. Formation configuration is determined in the virtual reference point coordinate system. Obstacle avoidance is guaranteed by cost penalty, and intervehicle collision avoidance is guaranteed by cost penalty combined with a new priority strategy.
Neural Generalized Predictive Control of a non-linear Process
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...
Neural Generalized Predictive Control of a non-linear Process
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....
Nonlinear Predictive Control for PEMFC Stack Operation Temperature
Institute of Scientific and Technical Information of China (English)
LI Xi; CAO Guang-yi; ZHU Xin-jian
2005-01-01
Operating temperature of proton exchange membrane fuel cell stack should be controlled within a special range. The input-output data and operating experiences were used to establish a PEMFC stack model and operating temperature control system. A nonlinear predictive control algorithm based on fuzzy model was presented for a family of complex system with severe nonlinearity such as PEMFC. Based on the obtained fuzzy model, a discrete optimization of the control action was carried out according to the principle of Branch and Bound method. The test results demonstrate the effectiveness and advantage of this approach.
A nonlinear viscoelastic constitutive equation - Yield predictions in multiaxial deformations
Shay, R. M., Jr.; Caruthers, J. M.
1987-01-01
Yield stress predictions of a nonlinear viscoelastic constitutive equation for amorphous polymer solids have been obtained and are compared with the phenomenological von Mises yield criterion. Linear viscoelasticity theory has been extended to include finite strains and a material timescale that depends on the instantaneous temperature, volume, and pressure. Results are presented for yield and the correct temperature and strain-rate dependence in a variety of multiaxial deformations. The present nonlinear viscoelastic constitutive equation can be formulated in terms of either a Cauchy or second Piola-Kirchhoff stress tensor, and in terms of either atmospheric or hydrostatic pressure.
Neuro-fuzzy predictive control for nonlinear application
Institute of Scientific and Technical Information of China (English)
CHEN Dong-xiang; WANG Gang; LV Shi-xia
2008-01-01
Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.
Nonlinear local Lyapunov exponent and atmospheric predictability research
Institute of Scientific and Technical Information of China (English)
CHEN; Baohua; LI; Jianping; DING; Ruiqiang
2006-01-01
Because atmosphere itself is a nonlinear system and there exist some problems using the linearized equations to study the initial error growth, in this paper we try to use the error nonlinear growth theory to discuss its evolution, based on which we first put forward a new concept: nonlinear local Lyapunov exponent. It is quite different from the classic Lyapunov exponent because it may characterize the finite time error local average growth and its value depends on the initial condition,initial error, variables, evolution time, temporal and spatial scales. Based on its definition and the atmospheric features, we provide a reasonable algorithm to the exponent for the experimental data,obtain the atmospheric initial error growth in finite time and gain the maximal prediction time. Lastly,taking 500 hPa height field as example, we discuss the application of the nonlinear local Lyapunov exponent in the study of atmospheric predictability and get some reliable results: atmospheric predictability has a distinct spatial structure. Overall, predictability shows a zonal distribution. Prediction time achieves the maximum over tropics, the second near the regions of Antarctic, it is also longer next to the Arctic and in subtropics and the mid-latitude the predictability is lowest. Particularly speaking, the average prediction time near the equation is 12 days and the maximum is located in the tropical Indian, Indonesia and the neighborhood, tropical eastern Pacific Ocean, on these regions the prediction time is about two weeks. Antarctic has a higher predictability than the neighboring latitudes and the prediction time is about 9 days. This feature is more obvious on Southern Hemispheric summer. In Arctic, the predictability is also higher than the one over mid-high latitudes but it is not pronounced as in Antarctic. Mid-high latitude of both Hemispheres (30°S-60°S, 30°-60°N) have the lowest predictability and the mean prediction time is just 3-4 d. In addition
Nonlinear model predictive control of a packed distillation column
Energy Technology Data Exchange (ETDEWEB)
Patwardhan, A.A.; Edgar, T.F. (Univ. of Texas, Austin, TX (United States). Dept. of Chemical Engineering)
1993-10-01
A rigorous dynamic model based on fundamental chemical engineering principles was formulated for a packed distillation column separating a mixture of cyclohexane and n-heptane. This model was simplified to a form suitable for use in on-line model predictive control calculations. A packed distillation column was operated at several operating conditions to estimate two unknown model parameters in the rigorous and simplified models. The actual column response to step changes in the feed rate, distillate rate, and reboiler duty agreed well with dynamic model predictions. One unusual characteristic observed was that the packed column exhibited gain-sign changes, which are very difficult to treat using conventional linear feedback control. Nonlinear model predictive control was used to control the distillation column at an operating condition where the process gain changed sign. An on-line, nonlinear model-based scheme was used to estimate unknown/time-varying model parameters.
Online prediction and control in nonlinear stochastic systems
DEFF Research Database (Denmark)
Nielsen, Torben Skov
2002-01-01
of systems which are inherently non-stationary. The third part concerns the issue of predicting the power production from wind turbines in the presence of Numerical Weather Predictions (NWP) of selected climatical variables. Here the transformation through the wind turbines from (primarily) wind speed....... The papers G , H and J investigate models and methods for predicting wind power from a wind farm on basis of observations and numerical weather predictions. All three papers consider multistep prediction models, but uses di erent estimation methods as well as dierent models for the diurnal variation of wind......The present thesis consists of a summary report and ten research papers. The subject of the thesis is on-line prediction and control of non-linear and non-stationary systems based on stochastic modelling. The thesis consists of three parts where the rst part deals with on-line estimation in linear...
Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G
2012-01-01
Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.
Institute of Scientific and Technical Information of China (English)
CHEN Bomin; JI Liren; YANG Peicai; ZHANG Daomin
2006-01-01
Based on Chen et al. (2006), the scheme of the combination of the pentad-mean zonal height departure nonlinear prediction with the T42L9 model prediction was designed, in which the pentad zonal heights at all the 12-initial-value-input isobar levels from 50 hPa to 1000 hPa except 200, 300, 500, and 700 hPa were derived from nonlinear forecasts of the four levels by means of a good correlation between neighboring levels.Then the above pentad zonal heights at 12 isobar-levels were transformed to the spectrum coefficients of the temperature at each integration step of T42L9 model. At last, the nudging was made. On account of a variety of error accumulation, the pentad zonal components of the monthly height at isobar levels output by T42L9 model were replaced by the corresponding nonlinear results once more when integration was over.Multiple case experiments showed that such combination of two kinds of prediction made an improvement in the wave component as a result of wave-flow nonlinear interaction while reducing the systematical forecast errors. Namely the monthly-mean height anomaly correlation coefficients over the high- and mid-latitudes of the Northern Hemisphere, over the Southern Hemisphere and over the globe increased respectively from 0.249 to 0.347, from 0.286 to 0.387, and from 0.343 to 0.414 (relative changes of 31.5%, 41.0%, and 18.3%).The monthly-mean root-mean-square error (RMSE) of T42L9 model over the three areas was considerably decreased, the relative change over the globe reached 44.2%. The monthly-mean anomaly correlation coefficients of wave 4-9 over the areas were up to 0.392, 0.200, and 0.295, with the relative change of 53.8%, 94.1%,and 61.2%, and correspondingly their RMSEs were decreased respectively with the rate of 8.5%, 6.3%, and 8.1%. At the same time the monthly-mean pattern of parts of cases were presented better.
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.
Speaker Recognition Through NLP and CWT Modeling
Energy Technology Data Exchange (ETDEWEB)
Brown-VanHoozer, S.A.; Kercel, S.W.; Tucker, R.W.
1999-06-16
The objective of this research is to develop a system capable of identifying speakers on wiretaps from a large database (>500 speakers) with a short search time duration (<30 seconds), and with better than 90% accuracy. Much previous research in speaker recognition has led to algorithms that produced encouraging preliminary results, but were overwhelmed when applied to populations of more than a dozen or so different speakers. The authors are investigating a solution to the "large population" problem by seeking two completely different kinds of characterizing features. These features are he techniques of Neuro-Linguistic Programming (NLP) and the continuous wavelet transform (CWT). NLP extracts precise neurological, verbal and non-verbal information, and assimilates the information into useful patterns. These patterns are based on specific cues demonstrated by each individual, and provide ways of determining congruency between verbal and non-verbal cues. The primary NLP modalities are characterized through word spotting (or verbal predicates cues, e.g., see, sound, feel, etc.) while the secondary modalities would be characterized through the speech transcription used by the individual. This has the practical effect of reducing the size of the search space, and greatly speeding up the process of identifying an unknown speaker. The wavelet-based line of investigation concentrates on using vowel phonemes and non-verbal cues, such as tempo. The rationale for concentrating on vowels is there are a limited number of vowels phonemes, and at least one of them usually appears in even the shortest of speech segments. Using the fast, CWT algorithm, the details of both the formant frequency and the glottal excitation characteristics can be easily extracted from voice waveforms. The differences in the glottal excitation waveforms as well as the formant frequency are evident in the CWT output. More significantly, the CWT reveals significant detail of the glottal excitation
Speaker recognition through NLP and CWT modeling.
Energy Technology Data Exchange (ETDEWEB)
Brown-VanHoozer, A.; Kercel, S. W.; Tucker, R. W.
1999-06-23
The objective of this research is to develop a system capable of identifying speakers on wiretaps from a large database (>500 speakers) with a short search time duration (<30 seconds), and with better than 90% accuracy. Much previous research in speaker recognition has led to algorithms that produced encouraging preliminary results, but were overwhelmed when applied to populations of more than a dozen or so different speakers. The authors are investigating a solution to the ''huge population'' problem by seeking two completely different kinds of characterizing features. These features are extracted using the techniques of Neuro-Linguistic Programming (NLP) and the continuous wavelet transform (CWT). NLP extracts precise neurological, verbal and non-verbal information, and assimilates the information into useful patterns. These patterns are based on specific cues demonstrated by each individual, and provide ways of determining congruency between verbal and non-verbal cues. The primary NLP modalities are characterized through word spotting (or verbal predicates cues, e.g., see, sound, feel, etc.) while the secondary modalities would be characterized through the speech transcription used by the individual. This has the practical effect of reducing the size of the search space, and greatly speeding up the process of identifying an unknown speaker. The wavelet-based line of investigation concentrates on using vowel phonemes and non-verbal cues, such as tempo. The rationale for concentrating on vowels is there are a limited number of vowels phonemes, and at least one of them usually appears in even the shortest of speech segments. Using the fast, CWT algorithm, the details of both the formant frequency and the glottal excitation characteristics can be easily extracted from voice waveforms. The differences in the glottal excitation waveforms as well as the formant frequency are evident in the CWT output. More significantly, the CWT reveals significant
Prediction of ventricular fibrillation based on nonlinear multi-parameter
Institute of Scientific and Technical Information of China (English)
SI Junfeng; NING Xinbao; ZHOU Lingling; ZHANG Song
2003-01-01
Ventricular fibrillation (VF) caused by myocardial ischemia is one of the leading factors of death attributed to cardiovascular diseases. It is particularly significant to predict VF and gain valuable time for clinic therapy. Fivedogs are taken as the research objects and a VF model is introduced. The nonlinear characteristics of the ECGs before and after VF are investigated with nonlinear multi-parame- ter analysis methods, Gaussian kernel (GK) correlation estimation algorithm and Lyapunov exponent estimation algorithm. Correlation entropy h2is also presented. The results indicate that there are three parameters which will change at the same time with the conditions of myocardial ischemia, and any changes of a single parameter may be caused by other factors and mislead the judgment. Multi-parameter analysis is more reliable to reveal the heart conditions,and to predict VF without misjudgments.
Nonlinear predictive energy management of residential buildings with photovoltaics & batteries
Sun, Chao; Sun, Fengchun; Moura, Scott J.
2016-09-01
This paper studies a nonlinear predictive energy management strategy for a residential building with a rooftop photovoltaic (PV) system and second-life lithium-ion battery energy storage. A key novelty of this manuscript is closing the gap between building energy management formulations, advanced load forecasting techniques, and nonlinear battery/PV models. Additionally, we focus on the fundamental trade-off between lithium-ion battery aging and economic performance in energy management. The energy management problem is formulated as a model predictive controller (MPC). Simulation results demonstrate that the proposed control scheme achieves 96%-98% of the optimal performance given perfect forecasts over a long-term horizon. Moreover, the rate of battery capacity loss can be reduced by 25% with negligible losses in economic performance, through an appropriate cost function formulation.
Predicting catastrophes in nonlinear dynamical systems by compressive sensing.
Wang, Wen-Xu; Yang, Rui; Lai, Ying-Cheng; Kovanis, Vassilios; Grebogi, Celso
2011-04-15
An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in nonlinear dynamical systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic systems are provided to demonstrate our idea.
Predicting catastrophes in nonlinear dynamical systems by compressive sensing
Wang, Wen-Xu; Lai, Ying-Cheng; Kovanis, Vassilios; Grebogi, Celso
2011-01-01
An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in nonlinear dynamical systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic systems are provided to demonstrate our idea.
Nonlinear system PID-type multi-step predictive control
Institute of Scientific and Technical Information of China (English)
Yan ZHANG; Zengqiang CHEN; Zhuzhi YUAN
2004-01-01
A compound neural network was constructed during the process of identification and multi-step prediction. Under the PlD-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller' s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Simulation study shows the effectiveness and good performance.
Predicting speech intelligibility in conditions with nonlinearly processed noisy speech
DEFF Research Database (Denmark)
Jørgensen, Søren; Dau, Torsten
2013-01-01
The speech-based envelope power spectrum model (sEPSM; [1]) was proposed in order to overcome the limitations of the classical speech transmission index (STI) and speech intelligibility index (SII). The sEPSM applies the signal-tonoise ratio in the envelope domain (SNRenv), which was demonstrated...... to successfully predict speech intelligibility in conditions with nonlinearly processed noisy speech, such as processing with spectral subtraction. Moreover, a multiresolution version (mr-sEPSM) was demonstrated to account for speech intelligibility in various conditions with stationary and fluctuating...... from computational auditory scene analysis and further support the hypothesis that the SNRenv is a powerful metric for speech intelligibility prediction....
[Structuralization of digestive endoscopy report based on NLP].
Kong, Xiao-feng; Li, Ying; Li, Hao-min; Lu, Xu-dong
2008-09-01
This paper presents a method based on NLP to realize structuralization of digestive endoscopy reports. The method is taking advantage of existing NLP's processing technologies and introducing minimal standard terminology (MST) to transform a narrative gastroscopy report into the structuralization report based on MST, whose accuracy rate is 92.3%.
NLP Meets the Jabberwocky: Natural Language Processing in Information Retrieval.
Feldman, Susan
1999-01-01
Focuses on natural language processing (NLP) in information retrieval. Defines the seven levels at which people extract meaning from text/spoken language. Discusses the stages of information processing; how an information retrieval system works; advantages to adding full NLP to information retrieval systems; and common problems with information…
The Role of NLP in Teachers' Classroom Discourse
Millrood, Radislav
2004-01-01
Neuro-linguistic programming (NLP) is an approach to language teaching which is claimed to help achieve excellence in learner performance. Yet there is little evidence of the impact that NLP techniques in teachers' discourse can have on learners. The article draws on workshops with teachers where classroom simulations were used to raise teachers'…
Aurora B interaction of centrosomal Nlp regulates cytokinesis.
Yan, Jie; Jin, Shunqian; Li, Jia; Zhan, Qimin
2010-12-17
Cytokinesis is a fundamental cellular process, which ensures equal abscission and fosters diploid progenies. Aberrant cytokinesis may result in genomic instability and cell transformation. However, the underlying regulatory machinery of cytokinesis is largely undefined. Here, we demonstrate that Nlp (Ninein-like protein), a recently identified BRCA1-associated centrosomal protein that is required for centrosomes maturation at interphase and spindle formation in mitosis, also contributes to the accomplishment of cytokinesis. Through immunofluorescent analysis, Nlp is found to localize at midbody during cytokinesis. Depletion of endogenous Nlp triggers aborted division and subsequently leads to multinucleated phenotypes. Nlp can be recruited by Aurora B to the midbody apparatus via their physical association at the late stage of mitosis. Disruption of their interaction induces aborted cytokinesis. Importantly, Nlp is characterized as a novel substrate of Aurora B and can be phosphorylated by Aurora B. The specific phosphorylation sites are mapped at Ser-185, Ser-448, and Ser-585. The phosphorylation at Ser-448 and Ser-585 is likely required for Nlp association with Aurora B and localization at midbody. Meanwhile, the phosphorylation at Ser-185 is vital to Nlp protein stability. Disruptions of these phosphorylation sites abolish cytokinesis and lead to chromosomal instability. Collectively, these observations demonstrate that regulation of Nlp by Aurora B is critical for the completion of cytokinesis, providing novel insights into understanding the machinery of cell cycle progression.
The NLP Swish Pattern: An Innovative Visualizing Technique.
Masters, Betsy J.; And Others
1991-01-01
Describes swish pattern, one of many innovative therapeutic interventions that developers of neurolinguistic programing (NLP) have contributed to counseling profession. Presents brief overview of NLP followed by an explanation of the basic theory and expected outcomes of the swish. Presents description of the intervention process and case studies…
Contribution of NLP to the Content Indexing of Multimedia Documents
Declerck, T.; Saggion, H.; Kuper, J.; Samiotou, A.; Wittenburg, P.; Contreras, J.; Enser, P.; Kompatsiaris, Y.; O'Connor, N. E.
2004-01-01
This paper describes the role natural language processing (NLP) can play for multimedia applications. As an example of such an application, we present an approach dealing with the conceptual indexing of soccer videos which the help of structured information automatically extracted by NLP tools from
Non-linear aeroelastic prediction for aircraft applications
de C. Henshaw, M. J.; Badcock, K. J.; Vio, G. A.; Allen, C. B.; Chamberlain, J.; Kaynes, I.; Dimitriadis, G.; Cooper, J. E.; Woodgate, M. A.; Rampurawala, A. M.; Jones, D.; Fenwick, C.; Gaitonde, A. L.; Taylor, N. V.; Amor, D. S.; Eccles, T. A.; Denley, C. J.
2007-05-01
Current industrial practice for the prediction and analysis of flutter relies heavily on linear methods and this has led to overly conservative design and envelope restrictions for aircraft. Although the methods have served the industry well, it is clear that for a number of reasons the inclusion of non-linearity in the mathematical and computational aeroelastic prediction tools is highly desirable. The increase in available and affordable computational resources, together with major advances in algorithms, mean that non-linear aeroelastic tools are now viable within the aircraft design and qualification environment. The Partnership for Unsteady Methods in Aerodynamics (PUMA) Defence and Aerospace Research Partnership (DARP) was sponsored in 2002 to conduct research into non-linear aeroelastic prediction methods and an academic, industry, and government consortium collaborated to address the following objectives: To develop useable methodologies to model and predict non-linear aeroelastic behaviour of complete aircraft. To evaluate the methodologies on real aircraft problems. To investigate the effect of non-linearities on aeroelastic behaviour and to determine which have the greatest effect on the flutter qualification process. These aims have been very effectively met during the course of the programme and the research outputs include: New methods available to industry for use in the flutter prediction process, together with the appropriate coaching of industry engineers. Interesting results in both linear and non-linear aeroelastics, with comprehensive comparison of methods and approaches for challenging problems. Additional embryonic techniques that, with further research, will further improve aeroelastics capability. This paper describes the methods that have been developed and how they are deployable within the industrial environment. We present a thorough review of the PUMA aeroelastics programme together with a comprehensive review of the relevant research
Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant
Institute of Scientific and Technical Information of China (English)
CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian
2007-01-01
This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
Prediction and simulation errors in parameter estimation for nonlinear systems
Aguirre, Luis A.; Barbosa, Bruno H. G.; Braga, Antônio P.
2010-11-01
This article compares the pros and cons of using prediction error and simulation error to define cost functions for parameter estimation in the context of nonlinear system identification. To avoid being influenced by estimators of the least squares family (e.g. prediction error methods), and in order to be able to solve non-convex optimisation problems (e.g. minimisation of some norm of the free-run simulation error), evolutionary algorithms were used. Simulated examples which include polynomial, rational and neural network models are discussed. Our results—obtained using different model classes—show that, in general the use of simulation error is preferable to prediction error. An interesting exception to this rule seems to be the equation error case when the model structure includes the true model. In the case of error-in-variables, although parameter estimation is biased in both cases, the algorithm based on simulation error is more robust.
Linear and non-linear bias: predictions vs. measurements
Hoffmann, Kai; Gaztanaga, Enrique
2016-01-01
We study the linear and non-linear bias parameters which determine the mapping between the distributions of galaxies and the full matter density fields, comparing different measurements and predictions. Accociating galaxies with dark matter haloes in the MICE Grand Challenge N-body simulation we directly measure the bias parameters by comparing the smoothed density fluctuations of halos and matter in the same region at different positions as a function of smoothing scale. Alternatively we measure the bias parameters by matching the probablility distributions of halo and matter density fluctuations, which can be applied to observations. These direct bias measurements are compared to corresponding measurements from two-point and different third-order correlations, as well as predictions from the peak-background model, which we presented in previous articles using the same data. We find an overall variation of the linear bias measurements and predictions of $\\sim 5 \\%$ with respect to results from two-point corr...
Prediction of biodegradation kinetics using a nonlinear group contribution method
Energy Technology Data Exchange (ETDEWEB)
Tabak, H.H. (Environmental Protection Agency, Cincinnati, OH (United States)); Govind, R. (Univ. of Cincinnati, OH (United States))
1993-02-01
The fate of organic chemicals in the environment depends on their susceptibility to biodegradation. Hence, development of regulations concerning their manufacture and use requires information on the extent and rate of biodegradation. Recent studies have attempted to correlate the kinetics of biodegradation with the molecular structure of the compound. This has led to the development of structure-biodegradation relationships (SBRs) using the group contribution approach. Each defined group present in the chemical structure of the compound is assigned a unique numerical contribution toward the calculation of the biodegradation kinetic constants. In this paper, a nonlinear group contribution method has been developed using neural networks; it is trained using literature data on the first-order biodegradation kinetic rate constant for a number of priority pollutants. The trained neural network is then used to predict the biodegradation kinetic constant for a new list of compounds, and results have been compared with the experimental values and the predictions obtained from a linear group contribution method. It has been shown that the nonlinear group contribution method using neural networks is able to provide a superior fit to the training set data and test data set and produce a lower prediction error than the previous linear method.
Linear and nonlinear dynamic systems in financial time series prediction
Directory of Open Access Journals (Sweden)
Salim Lahmiri
2012-10-01
Full Text Available Autoregressive moving average (ARMA process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX are compared by evaluating their ability to predict financial time series; for instance the S&P500 returns. Two classes of ARMA are considered. The first one is the standard ARMA model which is a linear static system. The second one uses Kalman filter (KF to estimate and predict ARMA coefficients. This model is a linear dynamic system. The forecasting ability of each system is evaluated by means of mean absolute error (MAE and mean absolute deviation (MAD statistics. Simulation results indicate that the ARMA-KF system performs better than the standard ARMA alone. Thus, introducing dynamics into the ARMA process improves the forecasting accuracy. In addition, the ARMA-KF outperformed the NARX. This result may suggest that the linear component found in the S&P500 return series is more dominant than the nonlinear part. In sum, we conclude that introducing dynamics into the ARMA process provides an effective system for S&P500 time series prediction.
Predictability of extremes in non-linear hierarchically organized systems
Kossobokov, V. G.; Soloviev, A.
2011-12-01
Understanding the complexity of non-linear dynamics of hierarchically organized systems progresses to new approaches in assessing hazard and risk of the extreme catastrophic events. In particular, a series of interrelated step-by-step studies of seismic process along with its non-stationary though self-organized behaviors, has led already to reproducible intermediate-term middle-range earthquake forecast/prediction technique that has passed control in forward real-time applications during the last two decades. The observed seismic dynamics prior to and after many mega, great, major, and strong earthquakes demonstrate common features of predictability and diverse behavior in course durable phase transitions in complex hierarchical non-linear system of blocks-and-faults of the Earth lithosphere. The confirmed fractal nature of earthquakes and their distribution in space and time implies that many traditional estimations of seismic hazard (from term-less to short-term ones) are usually based on erroneous assumptions of easy tractable analytical models, which leads to widespread practice of their deceptive application. The consequences of underestimation of seismic hazard propagate non-linearly into inflicted underestimation of risk and, eventually, into unexpected societal losses due to earthquakes and associated phenomena (i.e., collapse of buildings, landslides, tsunamis, liquefaction, etc.). The studies aimed at forecast/prediction of extreme events (interpreted as critical transitions) in geophysical and socio-economical systems include: (i) large earthquakes in geophysical systems of the lithosphere blocks-and-faults, (ii) starts and ends of economic recessions, (iii) episodes of a sharp increase in the unemployment rate, (iv) surge of the homicides in socio-economic systems. These studies are based on a heuristic search of phenomena preceding critical transitions and application of methodologies of pattern recognition of infrequent events. Any study of rare
Syntactic dependency parsers for biomedical-NLP.
Cohen, Raphael; Elhadad, Michael
2012-01-01
Syntactic parsers have made a leap in accuracy and speed in recent years. The high order structural information provided by dependency parsers is useful for a variety of NLP applications. We present a biomedical model for the EasyFirst parser, a fast and accurate parser for creating Stanford Dependencies. We evaluate the models trained in the biomedical domains of EasyFirst and Clear-Parser in a number of task oriented metrics. Both parsers provide stat of the art speed and accuracy in the Genia of over 89%. We show that Clear-Parser excels at tasks relating to negation identification while EasyFirst excels at tasks relating to Named Entities and is more robust to changes in domain.
Open Source Clinical NLP - More than Any Single System.
Masanz, James; Pakhomov, Serguei V; Xu, Hua; Wu, Stephen T; Chute, Christopher G; Liu, Hongfang
2014-01-01
The number of Natural Language Processing (NLP) tools and systems for processing clinical free-text has grown as interest and processing capability have surged. Unfortunately any two systems typically cannot simply interoperate, even when both are built upon a framework designed to facilitate the creation of pluggable components. We present two ongoing activities promoting open source clinical NLP. The Open Health Natural Language Processing (OHNLP) Consortium was originally founded to foster a collaborative community around clinical NLP, releasing UIMA-based open source software. OHNLP's mission currently includes maintaining a catalog of clinical NLP software and providing interfaces to simplify the interaction of NLP systems. Meanwhile, Apache cTAKES aims to integrate best-of-breed annotators, providing a world-class NLP system for accessing clinical information within free-text. These two activities are complementary. OHNLP promotes open source clinical NLP activities in the research community and Apache cTAKES bridges research to the health information technology (HIT) practice.
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...
Gene expression and pharmacology of nematode NLP-12 neuropeptides.
McVeigh, Paul; Leech, Suzie; Marks, Nikki J; Geary, Timothy G; Maule, Aaron G
2006-05-31
This study examines the biology of NLP-12 neuropeptides in Caenorhabditis elegans, and in the parasitic nematodes Ascaris suum and Trichostrongylus colubriformis. DYRPLQFamide (1 nM-10 microM; n > or =6) produced contraction of innervated dorsal and ventral Ascaris body wall muscle preparations (10 microM, 6.8+/-1.9 g; 1 microM, 4.6+/-1.8 g; 0.1 microM, 4.1+/-2.0 g; 10 nM, 3.8+/-2.0 g; n > or =6), and also caused a qualitatively similar, but quantitatively lower contractile response (10 microM, 4.0+/-1.5 g, n=6) on denervated muscle strips. Ovijector muscle displayed no measurable response (10 microM, n=5). nlp-12 cDNAs were characterised from A. suum (As-nlp-12) and T. colubriformis (Tc-nlp-12), both of which show sequence similarity to C. elegans nlp-12, in that they encode multiple copies of -LQFamide peptides. In C. elegans, reverse transcriptase (RT)-PCR analysis showed that nlp-12 was transcribed throughout the life cycle, suggesting that DYRPLQFamide plays a constitutive role in the nervous system of this nematode. Transcription was also identified in both L3 and adult stages of T. colubriformis, in which Tc-nlp-12 is expressed in a single tail neurone. Conversely, As-nlp-12 is expressed in both head and tail tissue of adult female A. suum, suggesting species-specific differences in the transcription pattern of this gene.
T-shirts with Brains, or is NLP magic?
Sharapova, Ksenia
2009-01-01
This thesis deals with Neuro-Linguistic Programming techniques in advertising. NLP is a study that claims close interrelation between the brain, language and behavior. It claims that persuading people is possible through using the definite code of language - a number of techniques that appeal to many people. That is why advertising and social media is a good platform for NLP techniques and that is why this topic was accepted by the advertising agency Aptual. It was decided by the company t...
Nonlinear model predictive control based on collective neurodynamic optimization.
Yan, Zheng; Wang, Jun
2015-04-01
In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.
Nonlinear Model Predictive Control for Oil Reservoirs Management
DEFF Research Database (Denmark)
Capolei, Andrea
. With this objective function we link the optimization problem in production optimization to the Markowitz portfolio optimization problem in finance or to the the robust design problem in topology optimization. In this study we focus on open-loop configuration, i.e. without measurement feedback. We demonstrate......, the research community is working on improving current feedback model-based optimal control technologies. The topic of this thesis is production optimization for water flooding in the secondary phase of oil recovery. We developed numerical methods for nonlinear model predictive control (NMPC) of an oil field....... Further, we studied the use of robust control strategies in both open-loop, i.e. without measurement feedback, and closed-loop, i.e. with measurement feedback, configurations. This thesis has three main original contributions: The first contribution in this thesis is to improve the computationally...
Stabilizing model predictive control for constrained nonlinear distributed delay systems.
Mahboobi Esfanjani, R; Nikravesh, S K Y
2011-04-01
In this paper, a model predictive control scheme with guaranteed closed-loop asymptotic stability is proposed for a class of constrained nonlinear time-delay systems with discrete and distributed delays. A suitable terminal cost functional and also an appropriate terminal region are utilized to achieve asymptotic stability. To determine the terminal cost, a locally asymptotically stabilizing controller is designed and an appropriate Lyapunov-Krasoskii functional of the locally stabilized system is employed as the terminal cost. Furthermore, an invariant set for locally stabilized system which is established by using the Razumikhin Theorem is used as the terminal region. Simple conditions are derived to obtain terminal cost and terminal region in terms of Bilinear Matrix Inequalities. The method is illustrated by a numerical example.
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.
HTP-NLP: A New NLP System for High Throughput Phenotyping.
Schlegel, Daniel R; Crowner, Chris; Lehoullier, Frank; Elkin, Peter L
2017-01-01
Secondary use of clinical data for research requires a method to quickly process the data so that researchers can quickly extract cohorts. We present two advances in the High Throughput Phenotyping NLP system which support the aim of truly high throughput processing of clinical data, inspired by a characterization of the linguistic properties of such data. Semantic indexing to store and generalize partially-processed results and the use of compositional expressions for ungrammatical text are discussed, along with a set of initial timing results for the system.
Parallel Nonlinear Optimization for Astrodynamic Navigation Project
National Aeronautics and Space Administration — CU Aerospace proposes the development of a new parallel nonlinear program (NLP) solver software package. NLPs allow the solution of complex optimization problems,...
Ouari, Kamel; Rekioua, Toufik; Ouhrouche, Mohand
2014-01-01
In order to make a wind power generation truly cost-effective and reliable, an advanced control techniques must be used. In this paper, we develop a new control strategy, using nonlinear generalized predictive control (NGPC) approach, for DFIG-based wind turbine. The proposed control law is based on two points: NGPC-based torque-current control loop generating the rotor reference voltage and NGPC-based speed control loop that provides the torque reference. In order to enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. Finally, a real-time simulation is carried out to illustrate the performance of the proposed controller.
Sridhar, Upasana Manimegalai; Govindarajan, Anand; Rhinehart, R Russell
2016-01-01
This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques.
On the prediction of stress relaxation from known creep of nonlinear materials
Energy Technology Data Exchange (ETDEWEB)
Touati, D.; Cederbaum, G. [Ben-Gurion Univ. of the Negev, Beer Sheva (Israel)
1997-04-01
A method to predict the nonlinear relaxation behavior from creep experiments of nonlinear viscoelastic materials is presented. It is shown that for given nonlinear creep properties, and creep compliance represented by the Prony series, the Schapery creep model can be transformed into a set of first order nonlinear equations. The solution of these equations enables the obtaining of the nonlinear stress relaxation curves. The strain-dependent constitutive equation can then be constructed for a given nonlinear viscoelastic model, as needed for engineering applications. A comparison example of the calculated stress relaxation curves, with test data for polyurethane demonstrates the very good accuracy of the proposed method.
Nonlinear turbulence models for predicting strong curvature effects
Institute of Scientific and Technical Information of China (English)
XU Jing-lei; MA Hui-yang; HUANG Yu-ning
2008-01-01
Prediction of the characteristics of turbulent flows with strong streamline curvature, such as flows in turbomachines, curved channel flows, flows around airfoils and buildings, is of great importance in engineering applicatious and poses a very practical challenge for turbulence modeling. In this paper, we analyze qualitatively the curvature effects on the structure of turbulence and conduct numerical simulations of a turbulent U- duct flow with a number of turbulence models in order to assess their overall performance. The models evaluated in this work are some typical linear eddy viscosity turbulence models, nonlinear eddy viscosity turbulence models (NLEVM) (quadratic and cubic), a quadratic explicit algebraic stress model (EASM) and a Reynolds stress model (RSM) developed based on the second-moment closure. Our numerical results show that a cubic NLEVM that performs considerably well in other benchmark turbulent flows, such as the Craft, Launder and Suga model and the Huang and Ma model, is able to capture the major features of the highly curved turbulent U-duct flow, including the damping of turbulence near the convex wall, the enhancement of turbulence near the concave wall, and the subsequent turbulent flow separation. The predictions of the cubic models are quite close to that of the RSM, in relatively good agreement with the experimental data, which suggests that these inodels may be employed to simulate the turbulent curved flows in engineering applications.
Prediction of municipal solid waste generation using nonlinear autoregressive network.
Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A
2015-12-01
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.
PCI-SS: MISO dynamic nonlinear protein secondary structure prediction
Directory of Open Access Journals (Sweden)
Aboul-Magd Mohammed O
2009-07-01
Full Text Available Abstract Background Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing α-helices, β-strands, and non-regular structures from primary sequence data which makes use of Parallel Cascade Identification (PCI, a powerful technique from the field of nonlinear system identification. Results Using PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at http://bioinf.sce.carleton.ca/PCISS. In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input
Linear and non-linear bias: predictions versus measurements
Hoffmann, K.; Bel, J.; Gaztañaga, E.
2017-02-01
We study the linear and non-linear bias parameters which determine the mapping between the distributions of galaxies and the full matter density fields, comparing different measurements and predictions. Associating galaxies with dark matter haloes in the Marenostrum Institut de Ciències de l'Espai (MICE) Grand Challenge N-body simulation, we directly measure the bias parameters by comparing the smoothed density fluctuations of haloes and matter in the same region at different positions as a function of smoothing scale. Alternatively, we measure the bias parameters by matching the probability distributions of halo and matter density fluctuations, which can be applied to observations. These direct bias measurements are compared to corresponding measurements from two-point and different third-order correlations, as well as predictions from the peak-background model, which we presented in previous papers using the same data. We find an overall variation of the linear bias measurements and predictions of ˜5 per cent with respect to results from two-point correlations for different halo samples with masses between ˜1012and1015 h-1 M⊙ at the redshifts z = 0.0 and 0.5. Variations between the second- and third-order bias parameters from the different methods show larger variations, but with consistent trends in mass and redshift. The various bias measurements reveal a tight relation between the linear and the quadratic bias parameters, which is consistent with results from the literature based on simulations with different cosmologies. Such a universal relation might improve constraints on cosmological models, derived from second-order clustering statistics at small scales or higher order clustering statistics.
NLP-1: a DNA intercalating hypoxic cell radiosensitizer and cytotoxin
Energy Technology Data Exchange (ETDEWEB)
Panicucci, R.; Heal, R.; Laderoute, K.; Cowan, D.; McClelland, R.A.; Rauth, A.M.
1989-04-01
The 2-nitroimidazole linked phenanthridine, NLP-1 (5-(3-(2-nitro-1-imidazoyl)-propyl)-phenanthridinium bromide), was synthesized with the rationale of targeting the nitroimidazole to DNA via the phenanthridine ring. The drug is soluble in aqueous solution (greater than 25 mM) and stable at room temperature. It binds to DNA with a binding constant 1/30 that of ethidium bromide. At a concentration of 0.5 mM, NLP-1 is 8 times more toxic to hypoxic than aerobic cells at 37 degrees C. This concentration is 40 times less than the concentration of misonidazole, a non-intercalating 2-nitroimidazole, required for the same degree of hypoxic cell toxicity. The toxicity of NLP-1 is reduced at least 10-fold at 0 degrees C. Its ability to radiosensitize hypoxic cells is similar to misonidazole at 0 degrees C. Thus the putative targeting of the 2-nitroimidazole, NLP-1, to DNA, via its phenanthridine group, enhances its hypoxic toxicity, but not its radiosensitizing ability under the present test conditions. NLP-1 represents a lead compound for intercalating 2-nitroimidazoles with selective toxicity for hypoxic cells.
Predictive Dynamic Stimulation of Structures with Non-Smooth Nonlinearities
2005-06-30
bang- bang, dead band, and Duffing type nonlinearity. Nonlinear damping has been considered in the form of Coulomb damping, velocity-squared damping...or 2,000 DOF reduced to 5 or 10 DOF) of simple oscillator systems capture the free oscillation decay and the steady state response to harmonic...smooth or non-smooth), the linear based reduced model tends to overestimate the change in oscillation frequency due to the nonlinearity. Specifically
Ensemble prediction experiments using conditional nonlinear optimal perturbation
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Two methods for initialization of ensemble forecasts are compared, namely, singular vector (SV) and conditional nonlinear optimal perturbation (CNOP). The comparison is done for forecast lengths of up to 10 days with a three-level quasi-geostrophic (QG) atmospheric model in a perfect model scenario. Ten cases are randomly selected from 1982/1983 winter to 1993/1994 winter (from December to the following February). Anomaly correlation coefficient (ACC) is adopted as a tool to measure the quality of the predicted ensembles on the Northern Hemisphere 500 hPa geopotential height. The results show that the forecast quality of ensemble samples in which the first SV is replaced by CNOP is higher than that of samples composed of only SVs in the medium range, based on the occurrence of weather re-gime transitions in Northern Hemisphere after about four days. Besides, the reliability of ensemble forecasts is evaluated by the Rank Histograms. The above conclusions confirm and extend those reached earlier by the authors, which stated that the introduction of CNOP improves the forecast skill under the condition that the analysis error belongs to a kind of fast-growing error by using a barotropic QG model.
Ensemble prediction experiments using conditional nonlinear optimal perturbation
Institute of Scientific and Technical Information of China (English)
JIANG ZhiNa; MU Mu; WANG DongHai
2009-01-01
Two methods for initialization of ensemble forecasts are compared, namely, singular vector (SV) and conditional nonlinear optimal perturbation (CNOP). The comparison is done for forecast lengths of up to 10 days with a three-level quasi-geostrophic (QG) atmospheric model in a perfect model scenario. Ten cases are randomly selected from 1982/1983 winter to 1993/1994 winter (from 12 to the following February). Anomaly correlation coefficient (ACC) is adopted as a tool to measure the quality of the predicted ensembles on the Northern Hemisphere 500 hPa geopotential height. The results show that the forecast quality of ensemble samples in which the first SV is replaced by CNOP is higher than that of samples composed of only SVs in the medium range, based on the occurrence of weather re-gime transitions in Northern Hemisphere after about four days. Besides, the reliability of ensemble forecasts is evaluated by the Rank Histograms. The above conclusions confirm .and extend those reached earlier by the authors, which stated that the introduction of CNOP improves the forecast skill under the condition that the analysis error belongs to a kind of fast-growing error by using a barotropic QG model.
Nonlinear fastest growing perturbation and the first kind of predictability
Institute of Scientific and Technical Information of China (English)
MU; Mu
2001-01-01
［1］Jiao Jiujiu, Grey hydrogeologic system analysis and time series model, Survey Science and Technology (in Chinese), 1987,(10): 39-43.［2］Li Shuwen, Wang Baolai, Xiao Guoqiang, A compound model of grey and periodic scrape and its application in groundwater prediction, Journal of Hebei Institute of Architectural Science & Technology (in Chinese), 1992, (3): 246-251.［3］Wang Qingyin, Li Shuwen, Grey distributed parameter model and groundwater analog, Journal of Hebei Institute of Architectural Science & Technology (in Chinese), 1992, (3): 66-70.［4］Guo Chunqing, Xia Riyuan, Liu Zhenglin, Gray Systematic Theory and Methodological Study of Krast Groundwater Resources Evaluation (in Chinese), Beijing: Geological Publishing House, 1993, 3-60.［5］Wang Qingyin, Liu Kaidi, The Mathematical Method of Grey Systematic Theory and Its Application (in Chinese), Chengdu: Publishing House of Southwestern China University of Communication, 1990, 23-27.［6］Wang Qingyin, Wu Heqing, The concept of grey number and its property, in Proceedings of NAFIPS98, USA, 1998,45-49.［7］Givoli, D., Doukhovni, I., Finite element programming approach for contact problems with geometrical nonlinearity, Computers and Structures, 1996, (8): 31-41.［8］Li Shuwen, Wang Zhiqiang, Wu Qiang, The superiority of storage-centered finite element method in solving seepage problem, Coal Geology and Exploration (in Chinese), 1999, (5): 46-49.
A cloud-based approach to medical NLP.
Chard, Kyle; Russell, Michael; Lussier, Yves A; Mendonça, Eneida A; Silverstein, Jonathan C
2011-01-01
Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN.
New Methods, Current Trends and Software Infrastructure for NLP
Cunningham, H; Gaizauskas, R J; Cunningham, Hamish; Wilks, Yorick; Gaizauskas, Robert J.
1996-01-01
The increasing use of `new methods' in NLP, which the NeMLaP conference series exemplifies, occurs in the context of a wider shift in the nature and concerns of the discipline. This paper begins with a short review of this context and significant trends in the field. The review motivates and leads to a set of requirements for support software of general utility for NLP research and development workers. A freely-available system designed to meet these requirements is described (called GATE - a General Architecture for Text Engineering). Information Extraction (IE), in the sense defined by the Message Understanding Conferences (ARPA \\cite{Arp95}), is an NLP application in which many of the new methods have found a home (Hobbs \\cite{Hob93}; Jacobs ed. purposes, and this is described. Lastly we review related work.
Nonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant
Directory of Open Access Journals (Sweden)
Xiangjie Liu
2014-01-01
Full Text Available Reliable power and temperature control in pressurized water reactor (PWR nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC, by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation.
Min-max model predictive control for constrained nonlinear systems via multiple LPV embeddings
Institute of Scientific and Technical Information of China (English)
ZHAO Min; LI Ning; LI ShaoYuan
2009-01-01
A min-max model predictive control strategy is proposed for a class of constrained nonlinear system whose trajectories can be embedded within those of a bank of linear parameter varying (LPV) models. The embedding LPV models can yield much better approximation of the nonlinear system dynamics than a single LTV model. For each LPV model, a parameter-dependent Lyapunov function is introduced to obtain poly-quadratically stable control law and to guarantee the feasibility and stability of the original nonlinear system. This approach can greatly reduce computational burden in traditional nonlinear predictive control strategy. Finally a simulation example illustrating the strategy is presented.
BRCA1 interaction of centrosomal protein Nlp is required for successful mitotic progression.
Jin, Shunqian; Gao, Hua; Mazzacurati, Lucia; Wang, Yang; Fan, Wenhong; Chen, Qiang; Yu, Wei; Wang, Mingrong; Zhu, Xueliang; Zhang, Chuanmao; Zhan, Qimin
2009-08-21
Breast cancer susceptibility gene BRCA1 is implicated in the control of mitotic progression, although the underlying mechanism(s) remains to be further defined. Deficiency of BRCA1 function leads to disrupted mitotic machinery and genomic instability. Here, we show that BRCA1 physically interacts and colocalizes with Nlp, an important molecule involved in centrosome maturation and spindle formation. Interestingly, Nlp centrosomal localization and its protein stability are regulated by normal cellular BRCA1 function because cells containing BRCA1 mutations or silenced for endogenous BRCA1 exhibit disrupted Nlp colocalization to centrosomes and enhanced Nlp degradation. Its is likely that the BRCA1 regulation of Nlp stability involves Plk1 suppression. Inhibition of endogenous Nlp via the small interfering RNA approach results in aberrant spindle formation, aborted chromosomal segregation, and aneuploidy, which mimic the phenotypes of disrupted BRCA1. Thus, BRCA1 interaction of Nlp might be required for the successful mitotic progression, and abnormalities of Nlp lead to genomic instability.
Comparison of Caenorhabditis elegans NLP peptides with arthropod neuropeptides.
Husson, Steven J; Lindemans, Marleen; Janssen, Tom; Schoofs, Liliane
2009-04-01
Neuropeptides are small messenger molecules that can be found in all metazoans, where they govern a diverse array of physiological processes. Because neuropeptides seem to be conserved among pest species, selected peptides can be considered as attractive targets for drug discovery. Much can be learned from the model system Caenorhabditis elegans because of the availability of a sequenced genome and state-of-the-art postgenomic technologies that enable characterization of endogenous peptides derived from neuropeptide-like protein (NLP) precursors. Here, we provide an overview of the NLP peptide family in C. elegans and discuss their resemblance with arthropod neuropeptides and their relevance for anthelmintic discovery.
Discussion of Some Problems About Nonlinear Time Series Prediction Using v-Support Vector Machine
Institute of Scientific and Technical Information of China (English)
GAO Cheng-Feng; CHEN Tian-Lun; NAN Tian-Shi
2007-01-01
Some problems in using v-support vector machine (v-SVM) for the prediction of nonlinear time series are discussed. The problems include selection of various net parameters, which affect the performance of prediction, mixture of kernels, and decomposition cooperation linear programming v-SVM regression, which result in improvements of the algorithm. Computer simulations in the prediction of nonlinear time series produced by Mackey-Glass equation and Lorenz equation provide some improved results.
Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.
2011-08-01
This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.
Model Predictive Control of a Nonlinear System with Known Scheduling Variable
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon.......Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...
Prediction of the nonlinear creep deformation of plastic products
Spoormaker, Jan; Skrypnyk, Ihor; Heidweiller, Anton
2015-01-01
Based on an example of the non-linear creep deformations of an air inlet, thispaper demonstrates modern capabilities in the FEA modeling of complex 3D visco-elastic deformations in relation to the design of plastic products. The importance of such capabilities for designing complex plastic components is discussed. Because commercial FEA packages do not yet render these capabilities "off the shelf", the non-linear visco-elasticity model is incorporated through a user subroutine. The specifics ...
Institute of Scientific and Technical Information of China (English)
钟伟民; 何国龙; 皮道映; 孙优贤
2005-01-01
A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.
Overexpression of centrosomal protein Nlp confers breast carcinoma resistance to paclitaxel.
Zhao, Weihong; Song, Yongmei; Xu, Binghe; Zhan, Qimin
2012-02-01
Nlp (ninein-like protein), an important molecule involved in centrosome maturation and spindle formation, plays an important role in tumorigenesis and its abnormal expression was recently observed in human breast and lung cancers. In this study, the correlation between overexpression of Nlp and paclitaxel chemosensitivity was investigated to explore the mechanisms of resistance to paclitaxel and to understand the effect of Nlp upon apoptosis induced by chemotherapeutic agents. Nlp expression vector was stably transfected into breast cancer MCF-7 cells. With Nlp overexpression, the survival rates, cell cycle distributions and apoptosis were analyzed in transfected MCF-7 cells by MTT test and FCM approach. The immunofluorescent assay was employed to detect the changes of microtubule after paclitaxel treatment. Immunoblotting analysis was used to examine expression of centrosomal proteins and apoptosis associated proteins. Subsequently, Nlp expression was retrospectively examined with 55 breast cancer samples derived from paclitaxel treated patients. Interestingly, the survival rates of MCF-7 cells with Nlp overexpressing were higher than that of control after paclitaxel treatment. Nlp overexpression promoted G2-M arrest and attenuated apoptosis induced by paclitaxel, which was coupled with elevated Bcl-2 protein. Nlp expression significantly lessened the microtubule polymerization and bundling elicited by paclitaxel attributing to alteration on the structure or dynamics of β-tubulin but not on its expression. The breast cancer patients with high expression of Nlp were likely resistant to the treatment of paclitaxel, as the response rate in Nlp negative patients was 62.5%, whereas was 58.3 and 15.8% in Nlp (+) and Nlp (++) patients respectively (p = 0.015). Nlp expression was positive correlated with those of Plk1 and PCNA. These findings provide insights into more rational chemotherapeutic regimens in clinical practice, and more effective approaches might be
Development and evaluation of task-specific NLP framework in China.
Ge, Caixia; Zhang, Yinsheng; Huang, Zhenzhen; Jia, Zheng; Ju, Meizhi; Duan, Huilong; Li, Haomin
2015-01-01
Natural language processing (NLP) has been designed to convert narrative text into structured data. Although some general NLP architectures have been developed, a task-specific NLP framework to facilitate the effective use of data is still a challenge in lexical resource limited regions, such as China. The purpose of this study is to design and develop a task-specific NLP framework to extract targeted information from particular documents by adopting dedicated algorithms on current limited lexical resources. In this framework, a shared and evolving ontology mechanism was designed. The result has shown that such a free text driven platform will accelerate the NLP technology acceptance in China.
Nonlinear softening as a predictive precursor to climate tipping
Sieber, Jan
2011-01-01
Approaching a dangerous bifurcation, from which a dynamical system such as the Earth's climate will jump (tip) to a different state, the current stable state lies within a shrinking basin of attraction. Persistence of the state becomes increasingly precarious in the presence of noisy disturbances. We consider an underlying potential, as defined theoretically for a saddle-node fold and (via averaging) for a Hopf bifurcation. Close to a stable state, this potential has a parabolic form; but approaching a jump it becomes increasingly dominated by softening nonlinearities. If we have already detected a decrease in the linear decay rate, nonlinear information allows us to estimate the propensity for early tipping due to noise. If there is no discernable trend in the linear analysis, nonlinear softening is even more important in showing the proximity to tipping. After extensive normal form calibration studies, we apply our technique to two geological time series from paleo-climate tipping events. For the ending of ...
Errouissi, Rachid; Yang, Jun; Chen, Wen-Hua; Al-Durra, Ahmed
2016-08-01
In this paper, a robust nonlinear generalised predictive control (GPC) method is proposed by combining an integral sliding mode approach. The composite controller can guarantee zero steady-state error for a class of uncertain nonlinear systems in the presence of both matched and unmatched disturbances. Indeed, it is well known that the traditional GPC based on Taylor series expansion cannot completely reject unknown disturbance and achieve offset-free tracking performance. To deal with this problem, the existing approaches are enhanced by avoiding the use of the disturbance observer and modifying the gain function of the nonlinear integral sliding surface. This modified strategy appears to be more capable of achieving both the disturbance rejection and the nominal prescribed specifications for matched disturbance. Simulation results demonstrate the effectiveness of the proposed approach.
Nonlinear Time Series Prediction Using Chaotic Neural Networks
Institute of Scientific and Technical Information of China (English)
LI KePing; CHEN TianLun
2001-01-01
A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm.``
Weissel, Florian; Huber, Marco F.; Hanebeck, Uwe D.
2007-01-01
Model identification and measurement acquisition is always to some degree uncertain. Therefore, a framework for Nonlinear Model Predictive Control (NMPC) is proposed that explicitly considers the noise influence on nonlinear dynamic systems with continuous state spaces and a finite set of control inputs in order to significantly increase the control quality. Integral parts of NMPC are the prediction of system states over a finite horizon as well as the problem specific modeling of reward func...
Coordinate regulation of the mother centriole component nlp by nek2 and plk1 protein kinases.
Rapley, Joseph; Baxter, Joanne E; Blot, Joelle; Wattam, Samantha L; Casenghi, Martina; Meraldi, Patrick; Nigg, Erich A; Fry, Andrew M
2005-02-01
Mitotic entry requires a major reorganization of the microtubule cytoskeleton. Nlp, a centrosomal protein that binds gamma-tubulin, is a G(2)/M target of the Plk1 protein kinase. Here, we show that human Nlp and its Xenopus homologue, X-Nlp, are also phosphorylated by the cell cycle-regulated Nek2 kinase. X-Nlp is a 213-kDa mother centriole-specific protein, implicating it in microtubule anchoring. Although constant in abundance throughout the cell cycle, it is displaced from centrosomes upon mitotic entry. Overexpression of active Nek2 or Plk1 causes premature displacement of Nlp from interphase centrosomes. Active Nek2 is also capable of phosphorylating and displacing a mutant form of Nlp that lacks Plk1 phosphorylation sites. Importantly, kinase-inactive Nek2 interferes with Plk1-induced displacement of Nlp from interphase centrosomes and displacement of endogenous Nlp from mitotic spindle poles, while active Nek2 stimulates Plk1 phosphorylation of Nlp in vitro. Unlike Plk1, Nek2 does not prevent association of Nlp with gamma-tubulin. Together, these results provide the first example of a protein involved in microtubule organization that is coordinately regulated at the G(2)/M transition by two centrosomal kinases. We also propose that phosphorylation by Nek2 may prime Nlp for phosphorylation by Plk1.
Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.
2014-09-01
The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).
Implementation of neural network based non-linear predictive control
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1999-01-01
of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...
A Novel Method for Prediction of Nonlinear Aeroelastic Responses
2010-01-01
Brian A. Freno Graduate Student, Texas A&M University Publications Journal articles: 1. Gargoloff, J. I. and Cizmas, P. G. A., “Mesh Generation and...papers: 1. Cizmas, P. G. A., Freno , B. A., Brenner, T. A., Worley, G. D., “A High-Fidelity Nonlinear Aeroelastic Model for Aircraft with Large Wing
Determining the input dimension of a neural network for nonlinear time series prediction
Institute of Scientific and Technical Information of China (English)
张胜; 刘红星; 高敦堂; 都思丹
2003-01-01
Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the mostimportant feature of it,the paper presents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Finally,some wlidation examples and results are given.
A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes
Directory of Open Access Journals (Sweden)
Qi-Zhi Zhang
2005-01-01
Full Text Available In this paper, an improved nonlinear Active Noise Control (ANC system is achieved by introducing an appropriate secondary source. For ANC system to be successfully implemented, the nonlinearity of the primary path and time delay of the secondary path must be overcome. A nonlinear Model Predictive Control (MPC strategy is introduced to deal with the time delay in the secondary path and the nonlinearity in the primary path of the ANC system. An overall online modeling technique is utilized for online secondary path and primary path estimation. The secondary path is estimated using an adaptive FIR filter, and the primary path is estimated using a Neural Network (NN. The two models are connected in parallel with the two paths. In this system, the mutual disturbances between the operation of the nonlinear ANC controller and modeling of the secondary can be greatly reduced. The coefficients of the adaptive FIR filter and weight vector of NN are adjusted online. Computer simulations are carried out to compare the proposed nonlinear MPC method with the nonlinear Filter-x Least Mean Square (FXLMS algorithm. The results showed that the convergence speed of the proposed nonlinear MPC algorithm is faster than that of nonlinear FXLMS algorithm. For testing the robust performance of the proposed nonlinear ANC system, the sudden changes in the secondary path and primary path of the ANC system are considered. Results indicated that the proposed nonlinear ANC system can rapidly track the sudden changes in the acoustic paths of the nonlinear ANC system, and ensure the adaptive algorithm stable when the nonlinear ANC system is time variable.
Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter
Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling
2017-07-01
The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.
Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter
Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling
2017-08-01
The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization.We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.
Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali
2014-05-01
Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.
The role of centrosomal Nlp in the control of mitotic progression and tumourigenesis.
Li, J; Zhan, Q
2011-05-10
The human centrosomal ninein-like protein (Nlp) is a new member of the γ-tubulin complexes binding proteins (GTBPs) that is essential for proper execution of various mitotic events. The primary function of Nlp is to promote microtubule nucleation that contributes to centrosome maturation, spindle formation and chromosome segregation. Its subcellular localisation and protein stability are regulated by several crucial mitotic kinases, such as Plk1, Nek2, Cdc2 and Aurora B. Several lines of evidence have linked Nlp to human cancer. Deregulation of Nlp in cell models results in aberrant spindle, chromosomal missegregation and multinulei, and induces chromosomal instability and renders cells tumourigenic. Overexpression of Nlp induces anchorage-independent growth and immortalised primary cell transformation. In addition, we first demonstrate that the expression of Nlp is elevated primarily due to NLP gene amplification in human breast cancer and lung carcinoma. Consistently, transgenic mice overexpressing Nlp display spontaneous tumours in breast, ovary and testicle, and show rapid onset of radiation-induced lymphoma, indicating that Nlp is involved in tumourigenesis. This review summarises our current knowledge of physiological roles of Nlp, with an emphasis on its potentials in tumourigenesis.
Nonlinear model predictive control with guaraneed stability based on pesudolinear neural networks
Institute of Scientific and Technical Information of China (English)
WANG Yongji; WANG Hong
2004-01-01
A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because...... of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we...
TF/TA2 trajectory tracking using nonlinear predictive control approach
Institute of Scientific and Technical Information of China (English)
Tang Qiang; Zhang Xinguo; Liu Xicheng
2006-01-01
The use of a methodology of nonlinear continuous predictive control to design the guidance control law for the aircraft TF/TA2 trajectory tracking problem is emplojed. For the derivation of the predictive control law, by using Taylor series expansion, and based on optimizing a performance index which is a quadratic function of both the predictive value of the state variables and the control inputs, a state variable feedback controller for nonlinear systems is obtained, and it provides a tradeoff between satisfactory tracking performance and the control magnitude requirements. Numerical simulation results for a supersonic fighter aircraft model show the viability of this approach.
Foundations of Complex Systems Nonlinear Dynamics, Statistical Physics, and Prediction
Nicolis, Gregoire
2007-01-01
Complexity is emerging as a post-Newtonian paradigm for approaching a large body of phenomena of concern at the crossroads of physical, engineering, environmental, life and human sciences from a unifying point of view. This book outlines the foundations of modern complexity research as it arose from the cross-fertilization of ideas and tools from nonlinear science, statistical physics and numerical simulation. It is shown how these developments lead to an understanding, both qualitative and quantitative, of the complex systems encountered in nature and in everyday experience and, conversely, h
NLP as a communication strategy tool in libraries
Koulouris, Alexandros; Sakas, Damianos P.; Giannakopoulos, Georgios
2015-02-01
The role of communication is a catalyst for the proper function of an organization. This paper focuses on libraries, where the communication is crucial for their success. In our opinion, libraries in Greece are suffering from the lack of communication and marketing strategy. Communication has many forms and manifestations. A key aspect of communication is body language, which has a dominant communication tool the neuro-linguistic programming (NLP). The body language is a system that expresses and transfers messages, thoughts and emotions. More and more organizations in the public sector and companies in the private sector base their success on the communication skills of their personnel. The NLP suggests several methods to obtain excellent relations in the workplace and to develop ideal communication. The NLP theory is mainly based on the development of standards (communication model) that guarantees the expected results. This research was conducted and analyzed in two parts, the qualitative and the quantitative. The findings mainly confirm the need for proper communication within libraries. In the qualitative research, the interviewees were aware of communication issues, although some gaps in that knowledge were observed. Even this slightly lack of knowledge, highlights the need for constant information through educational programs. This is particularly necessary for senior executives of libraries, who should attend relevant seminars and refresh their knowledge on communication related issues.
A new method of determining the optimal embedding dimension based on nonlinear prediction
Institute of Scientific and Technical Information of China (English)
Meng Qing-Fang; Peng Yu-Hua; Xue Pei-Jun
2007-01-01
A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.
Determining the minimum embedding dimension of nonlinear time series based on prediction method
Institute of Scientific and Technical Information of China (English)
Bian Chun-Hua; Ning Xin-Bao
2004-01-01
Determining the embedding dimension of nonlinear time series plays an important role in the reconstruction of nonlinear dynamics. The paper first summarizes the current methods for determining the embedding dimension.Then, inspired by the fact that the optimum modelling dimension of nonlinear autoregressive (NAR) prediction model can characterize the embedding feature of the dynamics, the paper presents a new idea that the optimum modelling dimension of the NAR model can be taken as the minimum embedding dimension. Some validation examples and results are given and the present method shows its advantage for short data series.
Nonlinear model predictive control for chemical looping process
Energy Technology Data Exchange (ETDEWEB)
Joshi, Abhinaya; Lei, Hao; Lou, Xinsheng
2017-08-22
A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to a CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.
Polo-like kinase 1 regulates Nlp, a centrosome protein involved in microtubule nucleation.
Casenghi, Martina; Meraldi, Patrick; Weinhart, Ulrike; Duncan, Peter I; Körner, Roman; Nigg, Erich A
2003-07-01
In animal cells, most microtubules are nucleated at centrosomes. At the onset of mitosis, centrosomes undergo a structural reorganization, termed maturation, which leads to increased microtubule nucleation activity. Centrosome maturation is regulated by several kinases, including Polo-like kinase 1 (Plk1). Here, we identify a centrosomal Plk1 substrate, termed Nlp (ninein-like protein), whose properties suggest an important role in microtubule organization. Nlp interacts with two components of the gamma-tubulin ring complex and stimulates microtubule nucleation. Plk1 phosphorylates Nlp and disrupts both its centrosome association and its gamma-tubulin interaction. Overexpression of an Nlp mutant lacking Plk1 phosphorylation sites severely disturbs mitotic spindle formation. We propose that Nlp plays an important role in microtubule organization during interphase, and that the activation of Plk1 at the onset of mitosis triggers the displacement of Nlp from the centrosome, allowing the establishment of a mitotic scaffold with enhanced microtubule nucleation activity.
Cdc2/cyclin B1 regulates centrosomal Nlp proteolysis and subcellular localization.
Zhao, Xuelian; Jin, Shunqian; Song, Yongmei; Zhan, Qimin
2010-11-01
The formation of proper mitotic spindles is required for appropriate chromosome segregation during cell division. Aberrant spindle formation often causes aneuploidy and results in tumorigenesis. However, the underlying mechanism of regulating spindle formation and chromosome separation remains to be further defined. Centrosomal Nlp (ninein-like protein) is a recently characterized BRCA1-regulated centrosomal protein and plays an important role in centrosome maturation and spindle formation. In this study, we show that Nlp can be phosphorylated by cell cycle protein kinase Cdc2/cyclin B1. The phosphorylation sites of Nlp are mapped at Ser185 and Ser589. Interestingly, the Cdc2/cyclin B1 phosphorylation site Ser185 of Nlp is required for its recognition by PLK1, which enable Nlp depart from centrosomes to allow the establishment of a mitotic scaffold at the onset of mitosis . PLK1 fails to dissociate the Nlp mutant lacking Ser185 from centrosome, suggesting that Cdc2/cyclin B1 might serve as a primary kinase of PLK1 in regulating Nlp subcellular localization. However, the phosphorylation at the site Ser589 by Cdc2/cyclin B1 plays an important role in Nlp protein stability probably due to its effect on protein degradation. Furthermore, we show that deregulated expression or subcellular localization of Nlp lead to multinuclei in cells, indicating that scheduled levels of Nlp and proper subcellular localization of Nlp are critical for successful completion of normal cell mitosis, These findings demonstrate that Cdc2/cyclin B1 is a key regulator in maintaining appropriate degradation and subcellular localization of Nlp, providing novel insights into understanding on the role of Cdc2/cyclin B1 in mitotic progression.
Nonlinear Dependence of Global Warming Prediction on Ocean State
Liang, M.; Lin, L.; Tung, K. K.; Yung, Y. L.; Sun, S.
2010-12-01
Global temperature has increased by 0.8 C since the pre-industrial era, and is likely to increase further if greenhouse gas emission continues unchecked. Various mitigation efforts are being negotiated among nations to keep the increase under 2 C, beyond which the outcome is believed to be catastrophic. Such policy efforts are currently based on predictions by the state-of-the-art coupled atmosphere ocean models (AOGCM). Caution is advised for their use for the purpose of short-term (less than a century) climate prediction as the predicted warming and spatial patterns vary depending on the initial state of the ocean, even in an ensemble mean. The range of uncertainty in such predictions by Intergovernmental Panel on Climate Change (IPCC) models may be underreported when models were run with their oceans at various stages of adjustment with their atmospheres. By comparing a very long run (> 1000 years) of the coupled Goddard Institute for Space Studies (GISS) model with what was reported to IPCC Fourth Assessment Report (AR4), we show that the fully adjusted model transient climate sensitivity should be 30% higher for the same model, and the 2 C warming should occur sooner than previously predicted. Using model archives we further argue that this may be a common problem for the IPCC AR4 models, since few, if any, of the models has a fully adjusted ocean. For all models, multi-decadal climate predictions to 2050 are highly dependent on the initial ocean state (and so are unreliable). Such dependence cannot be removed simply by subtracting the climate drift from control runs.
Nonlinear wind prediction using a fuzzy modular temporal neural network
Energy Technology Data Exchange (ETDEWEB)
Wu, G.G. [GeoControl Systems, Inc., Houston, TX (United States); Zhijie Dou [West Texas A& M Univ., Canyon, TX (United States)
1995-12-31
This paper introduces a new approach utilizing a fuzzy classifier and a modular temporal neural network to predict wind speed and direction for advanced wind turbine control systems. The fuzzy classifier estimates wind patterns and then assigns weights accordingly to each module of the temporal neural network. A temporal network with the finite-duration impulse response and multiple-layer structure is used to represent the underlying dynamics of physical phenomena. Using previous wind measurements and information given by the classifier, the modular network trained by a standard back-propagation algorithm predicts wind speed and direction effectively. Meanwhile, the feedback from the network helps auto-tuning the classifier.
Energy Technology Data Exchange (ETDEWEB)
Rajkumar, V. [ABB Transmission Technology Institute, Raleigh, NC (United States); Mohler, R.R. [Oregon State Univ., Corvallis, OR (United States)
1994-12-31
This paper presents a framework for the development of discrete-time, nonlinear predictive controllers using thyristor-controlled-series-capacitors and phasor measurements of bus voltage magnitude and angle, for the stabilization and rapid damping of multimachine power systems which are subjected to large disturbances. When the faults of concern are large, the nonlinear predictive controllers are used to return the power system state to a small region about the post-fault equilibrium. In this region, linear controllers provide local asymptotic stability and rapid damping. Simulation results are provided on a sample four-machine power system.
Experimenting with semantic web services to understand the role of NLP technologies in healthcare.
Jagannathan, V
2006-01-01
NLP technologies can play a significant role in healthcare where a predominant segment of the clinical documentation is in text form. In a graduate course focused on understanding semantic web services at West Virginia University, a class project was designed with the purpose of exploring potential use for NLP-based abstraction of clinical documentation. The role of NLP-technology was simulated using human abstractors and various workflows were investigated using public domain workflow and semantic web service technologies. This poster explores the potential use of NLP and the role of workflow and semantic web technologies in developing healthcare IT environments.
Directory of Open Access Journals (Sweden)
Luis Gonzaga Baca Ruiz
2016-08-01
Full Text Available This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR and the nonlinear autoregressive neural network with exogenous inputs (NARX, respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.
Time-varying Combinations of Predictive Densities using Nonlinear Filtering
M. Billio (Monica); R. Casarin (Roberto); F. Ravazzolo (Francesco); H.K. van Dijk (Herman)
2012-01-01
textabstractWe propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics
Institute of Scientific and Technical Information of China (English)
Yun Li; Hiroshi Kashiwagi
2005-01-01
Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but existing design and implementation methods are restricted to linear process models. A chemical process, however, involves severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to accommodate nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC). It also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design, which relieves practising engineers from the need for deriving a physical-principles based model first. An on-line realisation technique for implementing NMPC is then developed and applied to a Mitsubishi Chemicals polymerisation reaction process. Results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the developed approach lie not only in control performance superior to existing NMPC methods, but also in eliminating the need for converting an analytical model and then convert it to a Volterra model obtainable only up to the second order.
Energy Technology Data Exchange (ETDEWEB)
Prill, Dennis; Class, Andreas G. [Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen (Germany). AREVA Nuclear Professional School (ANPS)
2013-07-01
Unexpected non-linear boiling water reactor (BWR) instability events in various plants, e.g. LaSalle II in 1988 and Oskarshamn II in 1990 amongst others, emphasize the major safety relevance and the existence of parameter regions with unstable behavior. A detailed description of the complete dynamical non-linear behavior is of paramount importance for BWR operation. An extension of state-of-the-art methodology towards a more general stability description, also applicable in the non-linear region, could lead to a deeper understanding of non-linear BWR stability phenomena. With the intention of a full non-linear stability analysis of the two-phase BWR system, the present paper aims at a general non-linear methodology capable to achieve reliable and numerical stable reduced order models (ROMs), representing the dynamical behavior of an original system based on a small number of transients. Model-specific options and aspects of the proposed methodology are focused on and illustrated by means of a strongly non-linear dynamical system showing complex oscillating behavior. Prediction capability of the proposed methodology is also addressed. (orig.)
Directory of Open Access Journals (Sweden)
Geoff Boeing
2016-11-01
Full Text Available Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems’ behavior.
Constrained predictive control based on T-S fuzzy model for nonlinear systems
Institute of Scientific and Technical Information of China (English)
Su Baili; Chen Zengqiang; Yuan Zhuzhi
2007-01-01
A constrained generalized predictive control (GPC) algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno (T-S) fuzzy model based on the fuzzy cluster algorithm and the orthogonal least square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented.This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
Reduced order prediction of rare events in unidirectional nonlinear water waves
Cousins, Will
2015-01-01
We consider the problem of short-term prediction of rare, extreme water waves in unidirectional fields, a critical topic for ocean structures and naval operations. One possible mechanism for the occurrence of such rare, unusually-intense waves is nonlinear wave focusing. Recent results have demonstrated that random localizations of energy, induced by the dispersive mixing of different harmonics, can grow significantly due to localized nonlinear focusing. Here we show how the interplay between i) statistical properties captured through linear information such as the waves power spectrum and ii) nonlinear dynamical properties of focusing localized wave groups defines a critical length scale associated with the formation of extreme events. The energy that is locally concentrated over this length scale acts as the "trigger" of nonlinear focusing for wave groups and the formation of subsequent rare events. We use this property to develop inexpensive, short-term predictors of large water waves. Specifically, we sho...
Ko, William L.; Fleischer, Van Tran; Lung, Shun-Fat
2017-01-01
For shape predictions of structures under large geometrically nonlinear deformations, Curved Displacement Transfer Functions were formulated based on a curved displacement, traced by a material point from the undeformed position to deformed position. The embedded beam (depth-wise cross section of a structure along a surface strain-sensing line) was discretized into multiple small domains, with domain junctures matching the strain-sensing stations. Thus, the surface strain distribution could be described with a piecewise linear or a piecewise nonlinear function. The discretization approach enabled piecewise integrations of the embedded-beam curvature equations to yield the Curved Displacement Transfer Functions, expressed in terms of embedded beam geometrical parameters and surface strains. By entering the surface strain data into the Displacement Transfer Functions, deflections along each embedded beam can be calculated at multiple points for mapping the overall structural deformed shapes. Finite-element linear and nonlinear analyses of a tapered cantilever tubular beam were performed to generate linear and nonlinear surface strains and the associated deflections to be used for validation. The shape prediction accuracies were then determined by comparing the theoretical deflections with the finiteelement- generated deflections. The results show that the newly developed Curved Displacement Transfer Functions are very accurate for shape predictions of structures under large geometrically nonlinear deformations.
RBFNN Model for Predicting Nonlinear Response of Uniformly Loaded Paddle Cantilever
Directory of Open Access Journals (Sweden)
Abdullah H. Abdullah
2009-01-01
Full Text Available The Radial basis Function neural network (RBFNN model has been developed for the prediction of nonlinear response for paddle Cantilever with built-in edges and different sizes, thickness and uniform loads. Learning data was performed by using a nonlinear finite element program, incremental stages of the nonlinear finite element analysis were generated by using 25 schemes of built paddle Cantilevers with different thickness and uniform distributed loads. The neural network model has 5 input nodes representing the uniform distributed load and paddle size, length, width and thickness, eight nodes at hidden layer and one output node representing the max. deflection response (1500×1 represent the deflection response of load. Regression analysis between finite element results and values predicted by the neural network model shows the least error.
Yu, Shukai; Talbayev, Diyar
2016-01-01
We present an experimental and computational study of the nonlinear optical response of conduction electrons to intense terahertz (THz) electric field. Our observations (saturable absorption and an amplitude-dependent group refractive index) can be understood on the qualitative level as the breakdown of the effective mass approximation. However, a predictive theoretical description of the nonlinearity has been missing. We propose a model based on the semiclassical electron dynamics, a realistic band structure, and the free electron Drude parameters to accurately calculate the experimental observables in InSb. Our results open a path to predictive modeling of the conduction-electron optical nonlinearity in semiconductors, metamaterials, as well as high-field effects in THz plasmonics.
CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL
Directory of Open Access Journals (Sweden)
Dr.A.TRIVEDI
2011-04-01
Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.
Convergence Guaranteed Nonlinear Constraint Model Predictive Control via I/O Linearization
Directory of Open Access Journals (Sweden)
Xiaobing Kong
2013-01-01
Full Text Available Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR demonstrate the effectiveness of the proposed method.
Nonlinear continuous-time generalized predictive control of solar power plant
Directory of Open Access Journals (Sweden)
Khoukhi Billal
2015-01-01
Full Text Available This paper presents an application of nonlinear continuous-time generalized predictive control (GPC to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A brief description of the solar power plant and its simulator is given. After that, basic concepts of predictive control and continuous-time generalized predictive control are introduced. A new control strategy, named nonlinear continuous-time generalized predictive control (NCGPC, is then derived to control the process. The simulation results show that the NCGPC gives a greater flexibility to achieve performance goals and better perturbation rejection than classical control.
Long-term non-linear predictability of ENSO events over the 20th century
Astudillo, H F; Borotto, F A
2015-01-01
We show that the monthly recorded history (1878-2013) of the Southern Oscillation Index (SOI), a descriptor of the El Ni\\~no Southern Oscillation (ENSO) phenomenon, can be well described as a dynamic system that supports an average nonlinear predictability well beyond the spring barrier. The predictability is strongly linked to a detailed knowledge of the topology of the attractor obtained by embedding the SOI index in a wavelets base state space. Using the state orbits on the attractor we show that the information contained in the Southern Oscillation Index (SOI) is sufficient to provide average nonlinear predictions for time periods of 2, 3 and 4 years in advance throughout the 20th century with an acceptable error. The simplicity of implementation and ease of use makes it suitable for studying non linear predictability in any area where observations are similar to those that describe the ENSO phenomenon.
C code generation applied to nonlinear model predictive control for an artificial pancreas
DEFF Research Database (Denmark)
Boiroux, Dimitri; Jørgensen, John Bagterp
2017-01-01
This paper presents a method to generate C code from MATLAB code applied to a nonlinear model predictive control (NMPC) algorithm. The C code generation uses the MATLAB Coder Toolbox. It can drastically reduce the time required for development compared to a manual porting of code from MATLAB to C...
2015-04-24
Allgwer and A. Zheng, Nonlinear model predictive control vol. 26: Springer , 2000. [10] J. M. Park, D. W. Kim, Y. S. Yoon, H. J. Kim, and K. S. Yi...include modeling, simulation, and control of dynamic systems, with applications to energy systems, multibody dynamics, vehicle systems, and biomechanics
Institute of Scientific and Technical Information of China (English)
ZHANG JIA-SHU; XIAO XIAN-CI
2001-01-01
A multistage adaptive higher-order nonlinear finite impulse response (MAHONFIR) filter is proposed to predict chaotic time series. Using this approach, we may readily derive the decoupled parallel algorithm for the adaptation of the coefficients of the MAHONFIR filter, to guarantee a more rapid convergence of the adaptive weights to their optimal values. Numerical simulation results show that the MAHONFIR filters proposed here illustrate a very good performance for making an adaptive prediction of chaotic time series.
Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine
Institute of Scientific and Technical Information of China (English)
XU Rui-Rui; BIAN Guo-Xing; GAO Chen-Feng; CHEN Tian-Lun
2005-01-01
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction.First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.
Robust Predictive Functional Control for Flight Vehicles Based on Nonlinear Disturbance Observer
Directory of Open Access Journals (Sweden)
Yinhui Zhang
2015-01-01
Full Text Available A novel robust predictive functional control based on nonlinear disturbance observer is investigated in order to address the control system design for flight vehicles with significant uncertainties, external disturbances, and measurement noise. Firstly, the nonlinear longitudinal dynamics of the flight vehicle are transformed into linear-like state-space equations with state-dependent coefficient matrices. And then the lumped disturbances are considered in the linear structure predictive model of the predictive functional control to increase the precision of the predictive output and resolve the intractable mismatched disturbance problem. As the lumped disturbances cannot be derived or measured directly, the nonlinear disturbance observer is applied to estimate the lumped disturbances, which are then introduced to the predictive functional control to replace the unknown actual lumped disturbances. Consequently, the robust predictive functional control for the flight vehicle is proposed. Compared with the existing designs, the effectiveness and robustness of the proposed flight control are illustrated and validated in various simulation conditions.
Directory of Open Access Journals (Sweden)
E. L. Dmitrieva
2016-05-01
Full Text Available Basic peculiarities of nonlinear Kalman filtering algorithm applied to processing of interferometric signals are considered. Analytical estimates determining statistical characteristics of signal values prediction errors were obtained and analysis of errors histograms taking into account variations of different parameters of interferometric signal was carried out. Modeling of the signal prediction procedure with known fixed parameters and variable parameters of signal in the algorithm of nonlinear Kalman filtering was performed. Numerical estimates of prediction errors for interferometric signal values were obtained by formation and analysis of the errors histograms under the influence of additive noise and random variations of amplitude and frequency of interferometric signal. Nonlinear Kalman filter is shown to provide processing of signals with randomly variable parameters, however, it does not take into account directly the linearization error of harmonic function representing interferometric signal that is a filtering error source. The main drawback of the linear prediction consists in non-Gaussian statistics of prediction errors including cases of random deviations of signal amplitude and/or frequency. When implementing stochastic filtering of interferometric signals, it is reasonable to use prediction procedures based on local statistics of a signal and its parameters taken into account.
Narayanan, Mani Shankar; Kushwaha, Manish; Ersfeld, Klaus; Fullbrook, Alexander; Stanne, Tara M; Rudenko, Gloria
2011-03-01
Trypanosoma brucei mono-allelically expresses one of approximately 1500 variant surface glycoprotein (VSG) genes while multiplying in the mammalian bloodstream. The active VSG is transcribed by RNA polymerase I in one of approximately 15 telomeric VSG expression sites (ESs). T. brucei is unusual in controlling gene expression predominantly post-transcriptionally, and how ESs are mono-allelically controlled remains a mystery. Here we identify a novel transcription regulator, which resembles a nucleoplasmin-like protein (NLP) with an AT-hook motif. NLP is key for ES control in bloodstream form T. brucei, as NLP knockdown results in 45- to 65-fold derepression of the silent VSG221 ES. NLP is also involved in repression of transcription in the inactive VSG Basic Copy arrays, minichromosomes and procyclin loci. NLP is shown to be enriched on the 177- and 50-bp simple sequence repeats, the non-transcribed regions around rDNA and procyclin, and both active and silent ESs. Blocking NLP synthesis leads to downregulation of the active ES, indicating that NLP plays a role in regulating appropriate levels of transcription of ESs in both their active and silent state. Discovery of the unusual transcription regulator NLP provides new insight into the factors that are critical for ES control.
LeBlanc, Linda A.; Geiger, Kaneen B.; Sautter, Rachael A.; Sidener, Tina M.
2007-01-01
The Natural Language Paradigm (NLP) has proven effective in increasing spontaneous verbalizations for children with autism. This study investigated the use of NLP with older adults with cognitive impairments served at a leisure-based adult day program for seniors. Three individuals with limited spontaneous use of functional language participated…
Predicate Matching in NLP: A Review of Research on the Preferred Representational System.
Sharpley, Christopher F.
1984-01-01
Reviews 15 studies that have investigated the use of the Preferred Representational System (PRS) in Neurolinguistic Programming (NLP). Aspects of design, methodology, population and dependent measures are evaluated, with comments on the outcomes obtained. Results suggested that there is little supportive evidence for the use of PRS in the NLP.…
Robo-Sensei's NLP-Based Error Detection and Feedback Generation
Nagata, Noriko
2009-01-01
This paper presents a new version of Robo-Sensei's NLP (Natural Language Processing) system which updates the version currently available as the software package "ROBO-SENSEI: Personal Japanese Tutor" (Nagata, 2004). Robo-Sensei's NLP system includes a lexicon, a morphological generator, a word segmentor, a morphological parser, a syntactic…
Yao, Weigang; Liou, Meng-Sing
2016-08-01
To preserve nonlinearity of a full-order system over a range of parameters of interest, we propose an accurate and robust nonlinear modeling approach by assembling a set of piecewise linear local solutions expanded about some sampling states. The work by Rewienski and White [1] on micromachined devices inspired our use of piecewise linear local solutions to study nonlinear unsteady aerodynamics. These local approximations are assembled via nonlinear weights of radial basis functions. The efficacy of the proposed procedure is validated for a two-dimensional airfoil moving with different pitching motions, specifically AGARD's CT2 and CT5 problems [27], in which the flows exhibit different nonlinear behaviors. Furthermore, application of the developed aerodynamic model to a two-dimensional aero-elastic system proves the approach is capable of predicting limit cycle oscillations (LCOs) by using AGARD's CT6 [28] as a benchmark test. All results, based on inviscid solutions, confirm that our nonlinear model is stable and accurate, against the full model solutions and measurements, and for predicting not only aerodynamic forces but also detailed flowfields. Moreover, the model is robust for inputs that considerably depart from the base trajectory in form and magnitude. This modeling provides a very efficient way for predicting unsteady flowfields with varying parameters because it needs only a tiny fraction of the cost of a full-order modeling for each new condition-the more cases studied, the more savings rendered. Hence, the present approach is especially useful for parametric studies, such as in the case of design optimization and exploration of flow phenomena.
Recursive prediction error methods for online estimation in nonlinear state-space models
Directory of Open Access Journals (Sweden)
Dag Ljungquist
1994-04-01
Full Text Available Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included, as well as a new method based on a line-search strategy. A comparison of the algorithms illustrates that they are very similar although the differences can be important for the online tracking capabilities and robustness. Simulation experiments on a simple nonlinear process show that the performance under certain conditions can be improved by including a line-search strategy.
On the Prediction of the Nonlinear Absorption in Reverse Saturable Absorbing Materials
Pachter, Ruth; Nguyen, Kiet A.; Day, Paul N.; Kennel, Joshua C.
2001-03-01
In our continuing efforts to design materials that exhibit reverse saturable absorption (RSA), we systematically examine the ability of the time-dependent density functional theory (TDDFT) method using local, nonlocal, and hybrid functionals, to predict the experimental nonlinear absorption for a variety of organic and organometallic molecular systems, including a number of free-base porphyrins, phthalocyanine and their metal complexes. The ground and triplet-triplet excitation energies, as well as the oscillator strengths are calculated, indicating good agreement with experiment. We conclude that the TDDFT approach with a hybrid functional provides good estimates for the nonlinear absorption of RSA materials.
Predicting the dielectric nonlinearity of anisotropic composite materials via tensorial analysis
Energy Technology Data Exchange (ETDEWEB)
Giordano, S [Department of Physics, University of Cagliari, Cittadella Universitaria, I-09042 Monserrato (Italy); Rocchia, W [NEST CNR-INFM, Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa (Italy)
2006-11-29
The discovery of new materials with peculiar optical properties as well as the prediction of their behaviour given the microstructure is a matter of remarkable interest in the community of material scientists. A complete theory allowing such a prediction is not yet available. We have formulated a theory able to analytically predict the effective second- and third-order nonlinear electrical behaviour of a dilute dispersion of randomly oriented anisotropic nonlinear spheres in a linear host. The inclusion medium has non-vanishing second- and third-order nonlinear hypersusceptibilities. As a result, the overall composite material is nonlinear but isotropic because of the random orientation of the inclusions. We derive the expressions for the equivalent permittivity and for the Kerr equivalent hypersusceptibility in terms of the characteristic electric tensors describing the electrical behaviour of the spheres. The complete averaging over inclusion positions and orientations led to general results in the dilute limit. We show that these results are consistent with earlier theories and that they provide null second-order hypersusceptibility as expected in a macroscopically isotropic medium. This theory generalizes the well-known Maxwell-Garnett formula and it can be easily specialized to any of the 32 crystallographic symmetry classes. Despite this study assuming static conditions, it can be generalized to the sinusoidal regime, pointing at an interesting way to engineer optically active materials with desired behaviour.
An efficient artificial bee colony algorithm with application to nonlinear predictive control
Ait Sahed, Oussama; Kara, Kamel; Benyoucef, Abousoufyane; Laid Hadjili, Mohamed
2016-05-01
In this paper a constrained nonlinear predictive control algorithm, that uses the artificial bee colony (ABC) algorithm to solve the optimization problem, is proposed. The main objective is to derive a simple and efficient control algorithm that can solve the nonlinear constrained optimization problem with minimal computational time. Indeed, a modified version, enhancing the exploring and the exploitation capabilities, of the ABC algorithm is proposed and used to design a nonlinear constrained predictive controller. This version allows addressing the premature and the slow convergence drawbacks of the standard ABC algorithm, using a modified search equation, a well-known organized distribution mechanism for the initial population and a new equation for the limit parameter. A convergence statistical analysis of the proposed algorithm, using some well-known benchmark functions is presented and compared with several other variants of the ABC algorithm. To demonstrate the efficiency of the proposed algorithm in solving engineering problems, the constrained nonlinear predictive control of the model of a Multi-Input Multi-Output industrial boiler is considered. The control performances of the proposed ABC algorithm-based controller are also compared to those obtained using some variants of the ABC algorithms.
Institute of Scientific and Technical Information of China (English)
唐圣金; 郭晓松; 于传强; 周志杰; 周召发; 张邦成
2014-01-01
Real time remaining useful life (RUL) prediction based on condition monitoring is an essential part in condition based maintenance (CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item’s individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
Optimal Parameter Tuning in a Predictive Nonlinear Control Method for a Mobile Robot
Directory of Open Access Journals (Sweden)
D. Hazry
2006-01-01
Full Text Available This study contributes to a new optimal parameter tuning in a predictive nonlinear control method for stable trajectory straight line tracking with a non-holonomic mobile robot. In this method, the focus lies in finding the optimal parameter estimation and to predict the path that the mobile robot will follow for stable trajectory straight line tracking system. The stability control contains three parameters: 1 deflection parameter for the traveling direction of the mobile robot 2 deflection parameter for the distance across traveling direction of the mobile robot and 3 deflection parameter for the steering angle of the mobile robot . Two hundred and seventy three experimental were performed and the results have been analyzed and described herewith. It is found that by using a new optimal parameter tuning in a predictive nonlinear control method derived from the extension of kinematics model, the movement of the mobile robot is stabilized and adhered to the reference posture
STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES
Institute of Scientific and Technical Information of China (English)
马军海; 陈予恕; 辛宝贵
2004-01-01
The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions.By combining neural networks and wavelet theories,the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given.Based on wavelet networks,a new method for parameter identification was suggested,which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series.Through pre-treatment and comparison of results before and after the treatment,several useful conclusions are reached:High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.
Variable structure control with sliding mode prediction for discrete-time nonlinear systems
Institute of Scientific and Technical Information of China (English)
Lingfei XIAO; Hongye SU; Xiaoyu ZHANG; Jian CHU
2006-01-01
A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.
Institute of Scientific and Technical Information of China (English)
SU BaiLi; LI ShaoYuan; ZHU QuanMin
2009-01-01
Stabilization of the constrained switched nonlinear systems is an attractive research subject. Predictive control can handle variable constraints well and make the system stable. Its stability is typically based on an assumption of initial feasibility of the optimization problem; however the set of initial conditions, starting from where a given predictive formulation is guaranteed to be feasible, is not explicitly char-acterized. In this paper, a hybrid predictive control method is proposed for a class of switched nonlin-ear systems with input constraints and un-measurable states. The main idea is to design a mixed con-troller using Lyapunov functions and a state observer, which switches appropriately between a bounded feedback controller and a predictive controller, and to give an explicitly characterized set of initial conditions to stabilize each closed-loop subsystem. For the whole switched nonlinear system, a suitable switched law based on the state estimation is designed to orchestrate the transitions between the consistituent modes and their respective controllers, and to ensure the whole closed-loop system's stability. The simulation results for a chemical process show the validity of the controller proposed in this paper.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Stabilization of the constrained switched nonlinear systems is an attractive research subject. Predictive control can handle variable constraints well and make the system stable. Its stability is typically based on an assumption of initial feasibility of the optimization problem; however the set of initial conditions, starting from where a given predictive formulation is guaranteed to be feasible, is not explicitly characterized. In this paper, a hybrid predictive control method is proposed for a class of switched nonlinear systems with input constraints and un-measurable states. The main idea is to design a mixed controller using Lyapunov functions and a state observer, which switches appropriately between a bounded feedback controller and a predictive controller, and to give an explicitly characterized set of initial conditions to stabilize each closed-loop subsystem. For the whole switched nonlinear system, a suitable switched law based on the state estimation is designed to orchestrate the transitions between the consistituent modes and their respective controllers, and to ensure the whole closed-loop system’s stability. The simulation results for a chemical process show the validity of the controller proposed in this paper.
Directory of Open Access Journals (Sweden)
A. Tata
2009-01-01
Full Text Available This paper presents a nonlinear finite element modeling and analysis of rectangular normal-strength reinforced concrete columns confined with transverse steel under axial compressive loading. In this study, the columns were modeled as discrete elements using ANSYS nonlinear finite element software. Concrete was modeled with 8-noded SOLID65 elements that can translate either in the x-, y-, or z-axis directions from ANSYS element library. Longitudinal and transverse steels were modeled as discrete elements using 3D-LINK8 bar elements available in the ANSYS element library. The nonlinear constitutive law of each material was also implemented in the model. The results indicate that the stress-strain relationships obtained from the analytical model using ANSYS are in good agreement with the experimental data. This has been confirmed with the insignificant difference between the analytical and experimental, i.e. 5.65 and 2.80 percent for the peak stress and the strain at the peak stress, respectively. The comparison shows that the ANSYS nonlinear finite element program is capable of modeling and predicting the actual nonlinear behavior of confined concrete column under axial loading. The actual stress-strain relationship, the strength gain and ductility improvement have also been confirmed to be satisfactorily.
A PREDICT-CORRECT NUMERICAL INTEGRATION SCHEME FOR SOLVING NONLINEAR DYNAMIC EQUATIONS
Institute of Scientific and Technical Information of China (English)
Fan Jianping; Huang Tao; Tang Chak-yin; Wang Cheng
2006-01-01
A new numerical integration scheme incorporating a predict-correct algorithm for solving the nonlinear dynamic systems was proposed in this paper. A nonlinear dynamic system governed by the equaton (v) = F(v, t) was transformed into the form as (v) = Hv+ f(v, t). The nonlinear part f(v, t) was then expanded by Taylor series and only the first-order term retained in the polynomial. Utilizing the theory of linear differential equation and the precise time-integration method, an exact solution for linearizing equation was obtained. In order to find the solution of the original system, a third-order interpolation polynomial of v was used and an equivalent nonlinear ordinary differential equation was regenerated. With a predicted solution as an initial value and an iteration scheme, a corrected result was achieved. Since the error caused by linearization could be eliminated in the correction process, the accuracy of calculation was improved greatly. Three engineering scenarios were used to assess the accuracy and reliability of the proposed method and the results were satisfactory.
Directory of Open Access Journals (Sweden)
Ronghui Zhang
2017-05-01
Full Text Available Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.
Traffic chaos and its prediction based on a nonlinear car-following model
Institute of Scientific and Technical Information of China (English)
Hui FU; Jianmin XU; Lunhui XU
2005-01-01
This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car's movement in a single lane.Traffic chaos is a promising field,and chaos theory has been applied to identify and predict its chaotic movement.A simulated traffic flow is generated using a car-following model(GM model),and the distance between two cars is investigated for its dynamic properties.A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model.A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos.The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent.The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series.
Model predictive control of non-linear systems over networks with data quantization and packet loss.
Yu, Jimin; Nan, Liangsheng; Tang, Xiaoming; Wang, Ping
2015-11-01
This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Considering the limitation of the linear theory of singular vector (SV), the authors and their collaborators proposed conditional nonlinear optimal perturbation (CNOP) and then applied it in the predictability study and the sensitivity analysis of weather and climate system. To celebrate the 20th anniversary of Chinese National Committee for World Climate Research Programme (WCRP), this paper is devoted to reviewing the main results of these studies. First, CNOP represents the initial perturbation that has largest nonlinear evolution at prediction time, which is different from linear singular vector (LSV) for the large magnitude of initial perturbation or/and the long optimization time interval. Second, CNOP,rather than linear singular vector (LSV), represents the initial anomaly that evolves into ENSO events most probably. It is also the CNOP that induces the most prominent seasonal variation of error growth for ENSO predictability; furthermore, CNOP was applied to investigate the decadal variability of ENSO asymmetry. It is demonstrated that the changing nonlinearity causes the change of ENSO asymmetry.Third, in the studies of the sensitivity and stability of ocean's thermohaline circulation (THC), the non-linear asymmetric response of THC to finite amplitude of initial perturbations was revealed by CNOP.Through this approach the passive mechanism of decadal variation of THC was demonstrated; Also the authors studies the instability and sensitivity analysis of grassland ecosystem by using CNOP and show the mechanism of the transitions between the grassland and desert states. Finally, a detailed discussion on the results obtained by CNOP suggests the applicability of CNOP in predictability studies and sensitivity analysis.
Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks
Institute of Scientific and Technical Information of China (English)
张燕; 陈增强; 袁著祉
2003-01-01
After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulated errors produced in the recursive process. Characterized by predictive control, this method can achieve a good control accuracy and has good robustness. A simulation study shows that this control algorithm is very effective.
Neural-network predictive control for nonlinear dynamic systems with time-delay.
Huang, Jin-Quan; Lewis, F L
2003-01-01
A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.
Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models
Daunizeau, J.; Friston, K. J.; Kiebel, S. J.
2009-11-01
In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Jørgensen, John Bagterp; Rawlings, James B.
2015-01-01
In this paper, we develop an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) for a complete spray drying plant with multiple stages. In the E-NMPC the initial state is estimated by an extended Kalman Filter (EKF) with noise covariances estimated by an autocovariance least...... squares method (ALS). We present a model for the spray drying plant and use this model for simulation as well as for prediction in the E-NMPC. The open-loop optimal control problem in the E-NMPC is solved using the single-shooting method combined with a quasi-Newton Sequential Quadratic programming (SQP...
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik;
2015-01-01
In this paper, we compare the performance of an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) to a linear tracking Model Predictive Controller (MPC) for a spray drying plant. We find in this simulation study, that the economic performance of the two controllers are almost...... equal. We evaluate the economic performance with an industrially recorded disturbance scenario, where unmeasured disturbances and model mismatch are present. The state of the spray dryer, used in the E-NMPC and MPC, is estimated using Kalman Filters with noise covariances estimated by a maximum...
Dong, Suomeng; Kong, Guanghui; Qutob, Dinah; Yu, Xiaoli; Tang, Junli; Kang, Jixiong; Dai, Tingting; Wang, Hai; Gijzen, Mark; Wang, Yuanchao
2012-07-01
Necrosis- and ethylene-inducing-like proteins (NLP) are widely distributed in eukaryotic and prokaryotic plant pathogens and are considered to be important virulence factors. We identified, in total, 70 potential Phytophthora sojae NLP genes but 37 were designated as pseudogenes. Sequence alignment of the remaining 33 NLP delineated six groups. Three of these groups include proteins with an intact heptapeptide (Gly-His-Arg-His-Asp-Trp-Glu) motif, which is important for necrosis-inducing activity, whereas the motif is not conserved in the other groups. In total, 19 representative NLP genes were assessed for necrosis-inducing activity by heterologous expression in Nicotiana benthamiana. Surprisingly, only eight genes triggered cell death. The expression of the NLP genes in P. sojae was examined, distinguishing 20 expressed and 13 nonexpressed NLP genes. Real-time reverse-transcriptase polymerase chain reaction results indicate that most NLP are highly expressed during cyst germination and infection stages. Amino acid substitution ratios (Ka/Ks) of 33 NLP sequences from four different P. sojae strains resulted in identification of positive selection sites in a distinct NLP group. Overall, our study indicates that expansion and pseudogenization of the P. sojae NLP family results from an ongoing birth-and-death process, and that varying patterns of expression, necrosis-inducing activity, and positive selection suggest that NLP have diversified in function.
Davis, Craig Warren; Di Toro, Dominic M
2015-07-07
Procedures for accurately predicting linear partition coefficients onto various sorbents (e.g., organic carbon, soils, clay) are reliable and well established. However, similar procedures for the prediction of sorption parameters of nonlinear isotherm models are not. The purpose of this paper is to present a procedure for predicting nonlinear isotherm parameters, specifically the median Langmuir binding constants, K̃L, obtained utilizing the single-chemical parameter log-normal Langmuir isotherm developed in the accompanying work. A reduced poly parameter linear free energy relationship (pp-LFER) is able to predict median Langmuir binding constants for graphite, charcoal, and Darco granular activated carbon (GAC) adsorption data. For the larger F400 GAC data set, a single pp-LFER model was insufficient, as a plateau is observed for the median Langmuir binding constants of larger molecular volume sorbates. This volumetric cutoff occurs in proximity to the median pore diameter for F400 GAC. A log-linear relationship exists between the aqueous solubility of these large compounds and their median Langmuir binding constants. Using this relationship for the chemicals above the volumetric cutoff and the pp-LFER below the cutoff, the median Langmuir binding constants can be predicted with a root-mean square error for graphite (n = 13), charcoal (n = 11), Darco GAC (n = 14), and F400 GAC (n = 44) of 0.129, 0.307, 0.407, and 0.424, respectively.
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.
Directory of Open Access Journals (Sweden)
Ahad Zeinali
2007-12-01
Full Text Available Introduction: Because of the importance of vertebral compressive fracture (VCF role in increasing the patients’ death rate and reducing their quality of life, many studies have been conducted for a noninvasive prediction of vertebral compressive strength based on bone mineral density (BMD determination and recently finite element analysis. In this study, QCT-voxel based nonlinear finite element method is used for predicting vertebral compressive strength. Material and Methods: Four thoracolumbar vertebrae were excised from 3 cadavers with an average age of 42 years. They were then put in a water phantom and were scanned using the QCT. Using a computer program prepared in MATLAB, detailed voxel based geometry and mechanical characteristics of the vertebra were extracted from the CT images. The three dimensional finite element models of the samples were created using ANSYS computer program. The compressive strength of each vertebra body was calculated based on a linearly elastic-linearly plastic model and large deformation analysis in ANSYS and was compared to the value measured experimentally for that sample. Results: Based on the obtained results the QCT-voxel based nonlinear finite element method (FEM can predict vertebral compressive strength more effectively and accurately than the common QCT-voxel based linear FEM. The difference between the predicted strength values using this method and the measured ones was less than 1 kN for all the samples. Discussion and Conclusion: It seems that the QCT-voxel based nonlinear FEM used in this study can predict more effectively and accurately the vertebral strengths based on every vertebrae specification by considering their detailed geometric and densitometric characteristics.
Wang, Dong; Zhao, Yang; Yang, Fangfang; Tsui, Kwok-Leung
2017-09-01
Brownian motion with adaptive drift has attracted much attention in prognostics because its first hitting time is highly relevant to remaining useful life prediction and it follows the inverse Gaussian distribution. Besides linear degradation modeling, nonlinear-drifted Brownian motion has been developed to model nonlinear degradation. Moreover, the first hitting time distribution of the nonlinear-drifted Brownian motion has been approximated by time-space transformation. In the previous studies, the drift coefficient is the only hidden state used in state space modeling of the nonlinear-drifted Brownian motion. Besides the drift coefficient, parameters of a nonlinear function used in the nonlinear-drifted Brownian motion should be treated as additional hidden states of state space modeling to make the nonlinear-drifted Brownian motion more flexible. In this paper, a prognostic method based on nonlinear-drifted Brownian motion with multiple hidden states is proposed and then it is applied to predict remaining useful life of rechargeable batteries. 26 sets of rechargeable battery degradation samples are analyzed to validate the effectiveness of the proposed prognostic method. Moreover, some comparisons with a standard particle filter based prognostic method, a spherical cubature particle filter based prognostic method and two classic Bayesian prognostic methods are conducted to highlight the superiority of the proposed prognostic method. Results show that the proposed prognostic method has lower average prediction errors than the particle filter based prognostic methods and the classic Bayesian prognostic methods for battery remaining useful life prediction.
Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle
Directory of Open Access Journals (Sweden)
Zhihui Xing
2013-01-01
Full Text Available A lifting body unmanned aerial vehicle (UAV generates lift by its body and shows many significant advantages due to the particular shape, such as huge loading space, small wetted area, high-strength fuselage structure, and large lifting area. However, designing the control law for a lifting body UAV is quite challenging because it has strong nonlinearity and coupling, and usually lacks it rudders. In this paper, an explicit nonlinear model predictive control (ENMPC strategy is employed to design a control law for a saucer-shaped UAV which can be adequately modeled with a rigid 6-degrees-of-freedom (DOF representation. In the ENMPC, control signal is calculated by approximation of the tracking error in the receding horizon by its Taylor-series expansion to any specified order. It enhances the advantages of the nonlinear model predictive control and eliminates the time-consuming online optimization. The simulation results show that ENMPC is a propriety strategy for controlling lifting body UAVs and can compensate the insufficient control surface area.
Nonlinear model predictive control using parameter varying BP-ARX combination model
Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.
2012-03-01
A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.
Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.
Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei
2016-02-01
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
Boiler-turbine control system design using continuous-time nonlinear model predictive control
Institute of Scientific and Technical Information of China (English)
ZHUO Xu-sheng; ZHOU Huai-chun
2008-01-01
A continuous-time nonlinear model predictive controller (NMPC) was designed for a boiler-turbine unit. The controller was designed by optimizing a receding-horizon performance index, with the nonlinear system approximated by its Taylor series expansion with a certain order, the magnitude saturation constraints on the inputs satisfied by increasing the predictive time, and the rate saturation conditions on the actuators satisfied by tuning the time constant of the reference trajectories in a reference governor. Simulation results showed that the controller can drive the drum pressure and output power of the nonlinear boiler-turbine unit to follow their respective reference trajectories throughout a varying operation range and keep the water level deviation within tolerances. Comparison of the NMPC scheme with the generic model control (GMC) scheme indicated that the responses are slower and there are more oscillations in the responses of the water level, fuel flow input and feed water flow input in the GMC scheme when the boiler-turbine unit is operating over a wide range.
Zhou, Feifan; Ding, Ruiqiang; Feng, Guolin; Fu, Zuntao; Duan, Wansuo
2012-09-01
Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007-2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types: (1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following: (1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Niño-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.
Institute of Scientific and Technical Information of China (English)
ZHOU Feifan; DING Ruiqiang; FENG Guolin; FU Zuntao; DUAN Wansuo
2012-01-01
Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007-2011) are briefly introduced in this article.Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types:(1) progress based on the analysis of solutions of simplified control equations,such as the dynamics of NAO,the optimal precursors for blocking onset,and the behavior of nonlinear waves,and (2) progress based on data analyses,such as the nonlinear analyses of fluctuations and recording-breaking temperature events,the long-range correlation of extreme events,and new methods of detecting abrupt dynamical change.Major achievements in the study of predictability include the following:(1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Ni(n)o-Southern Oscillation (ENSO) predictions,ensemble forecasting,targeted observation,and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies.The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion,and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.
The use of machine learning and nonlinear statistical tools for ADME prediction.
Sakiyama, Yojiro
2009-02-01
Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.
Rapid prediction method for nonlinear expansion process of medical vascular stent
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
A neural network model with high nonlinear recognition capability was constructed to describe the relationship between the deformation impact factors and the deformation results of vascular stent.Then,using the weighted correction method with the attached momentum term,the network training algorithm was optimized by introducing learning factor η and momentum factor ψ,so the speed of the network training and the system robustness were enhanced.The network was trained by some practi-cal cases,and the statistical hypothesis validation was made for the predictive errors.It was shown that the average difference between the intelligent predictive result of vascular stent deformation neu-ral network and the nonlinear finite element analysis result was less than 0.03%,and the trained net-work could perfectly predict the vascular stent deformation.Further more,the rapid evaluation tool for the vascular stent mechanics performance was established using the Pro/Toolkit and the intelligent neural network predictive model of vascular stent expansion.The proposed tool system with strong practicality and high efficiency can significantly shorten the product development cycle of vascular stent.
Directory of Open Access Journals (Sweden)
Xuliang Yao
2017-01-01
Full Text Available The attitude control and depth tracking issue of autonomous underwater vehicle (AUV are addressed in this paper. By introducing a nonsingular coordinate transformation, a novel nonlinear reduced-order observer (NROO is presented to achieve an accurate estimation of AUV’s state variables. A discrete-time model predictive control with nonlinear model online linearization (MPC-NMOL is applied to enhance the attitude control and depth tracking performance of AUV considering the wave disturbance near surface. In AUV longitudinal control simulation, the comparisons have been presented between NROO and full-order observer (FOO and also between MPC-NMOL and traditional NMPC. Simulation results show the effectiveness of the proposed method.
Model Predictive Control of Nonlinear Systems: Stability Region and Feasible Initial Control
Institute of Scientific and Technical Information of China (English)
Xiao-Bing Hu; Wen-Hua Chen
2007-01-01
This paper proposes a new method for model predictive control (MPC) of nonlinear systems to calculate stability region and feasible initial control profile/sequence, which are important to the implementations of MPC. Different from many existing methods,this paper distinguishes stability region from conservative terminal region. With global linearization, linear differential inclusion (LDI)and linear matrix inequality (LMI) techniques, a nonlinear system is transformed into a convex set of linear systems, and then the vertices of the set are used off-line to design the controller, to estimate stability region, and also to determine a feasible initial control profile/sequence. The advantages of the proposed method are demonstrated by simulation study.
A computer program for predicting nonlinear uniaxial material responses using viscoplastic models
Chang, T. Y.; Thompson, R. L.
1984-01-01
A computer program was developed for predicting nonlinear uniaxial material responses using viscoplastic constitutive models. Four specific models, i.e., those due to Miller, Walker, Krieg-Swearengen-Rhode, and Robinson, are included. Any other unified model is easily implemented into the program in the form of subroutines. Analysis features include stress-strain cycling, creep response, stress relaxation, thermomechanical fatigue loop, or any combination of these responses. An outline is given on the theoretical background of uniaxial constitutive models, analysis procedure, and numerical integration methods for solving the nonlinear constitutive equations. In addition, a discussion on the computer program implementation is also given. Finally, seven numerical examples are included to demonstrate the versatility of the computer program developed.
Nonlinear filtering techniques for noisy geophysical data: Using big data to predict the future
Moore, J. M.
2014-12-01
Chaos is ubiquitous in physical systems. Within the Earth sciences it is readily evident in seismology, groundwater flows and drilling data. Models and workflows have been applied successfully to understand and even to predict chaotic systems in other scientific fields, including electrical engineering, neurology and oceanography. Unfortunately, the high levels of noise characteristic of our planet's chaotic processes often render these frameworks ineffective. This contribution presents techniques for the reduction of noise associated with measurements of nonlinear systems. Our ultimate aim is to develop data assimilation techniques for forward models that describe chaotic observations, such as episodic tremor and slip (ETS) events in fault zones. A series of nonlinear filters are presented and evaluated using classical chaotic systems. To investigate whether the filters can successfully mitigate the effect of noise typical of Earth science, they are applied to sunspot data. The filtered data can be used successfully to forecast sunspot evolution for up to eight years (see figure).
Acikmese, Ahmet Behcet; Carson, John M., III
2006-01-01
A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.
Khazaee, Mostafa; Markazi, Amir H D; Omidi, Ehsan
2015-11-01
In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound.
A LQP BASED INTERIOR PREDICTION-CORRECTION METHOD FOR NONLINEAR COMPLEMENTARITY PROBLEMS
Institute of Scientific and Technical Information of China (English)
Bing-sheng He; Li-zhi Liao; Xiao-ming Yuan
2006-01-01
To solve nonlinear complementarity problems (NCP), at each iteration, the classical proximal point algorithm solves a well-conditioned sub-NCP while the LogarithmicQuadratic Proximal (LQP) method solves a system of nonlinear equations (LQP system). This paper presents a practical LQP method-based prediction-correction method for NCP.The predictor is obtained via solving the LQP system approximately under significantly relaxed restriction, and the new iterate (the corrector) is computed directly by an explicit formula derived from the original LQP method. The implementations are very easy to be carried out. Global convergence of the method is proved under the same mild assumptions as the original LQP method. Finally, numerical results for traffic equilibrium problems are provided to verify that the method is effective for some practical problems.
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.
Semantic characteristics of NLP-extracted concepts in clinical notes vs. biomedical literature.
Wu, Stephen; Liu, Hongfang
2011-01-01
Natural language processing (NLP) has become crucial in unlocking information stored in free text, from both clinical notes and biomedical literature. Clinical notes convey clinical information related to individual patient health care, while biomedical literature communicates scientific findings. This work focuses on semantic characterization of texts at an enterprise scale, comparing and contrasting the two domains and their NLP approaches. We analyzed the empirical distributional characteristics of NLP-discovered named entities in Mayo Clinic clinical notes from 2001-2010, and in the 2011 MetaMapped Medline Baseline. We give qualitative and quantitative measures of domain similarity and point to the feasibility of transferring resources and techniques. An important by-product for this study is the development of a weighted ontology for each domain, which gives distributional semantic information that may be used to improve NLP applications.
NLP-SIR: A Natural Language Approach for Spreadsheet Information Retrieval
Flood, Derek; Caffery, Fergal Mc
2009-01-01
Spreadsheets are a ubiquitous software tool, used for a wide variety of tasks such as financial modelling, statistical analysis and inventory management. Extracting meaningful information from such data can be a difficult task, especially for novice users unfamiliar with the advanced data processing features of many spreadsheet applications. We believe that through the use of Natural Language Processing (NLP) techniques this task can be made considerably easier. This paper introduces NLP-SIR, a Natural language interface for spreadsheet information retrieval. The results of a recent evaluation which compared NLP-SIR with existing Information retrieval tools are also outlined. This evaluation has shown that NLP-SIR is a more effective method of spreadsheet information retrieval.
NLP-SIR: A Natural Language Approach for Spreadsheet Information Retrieval
Flood, Derek; McDaid, Kevin; McCaffery, Fergal
2001-01-01
Spreadsheets are a ubiquitous software tool, used for a wide variety of tasks such as financial modelling, statistical analysis and inventory management. Extracting meaningful information from such data can be a difficult task, especially for novice users unfamiliar with the advanced data processing features of many spreadsheet applications. We believe that through the use of Natural Language Processing (NLP) techniques this task can be made considerably easier. This paper introduces NLP-SIR,...
Schwechheimer, Carmen; Rodriguez, Daniel L; Kuehn, Meta J
2015-06-01
Outer membrane vesicles (OMVs) are ubiquitously secreted from the outer membrane (OM) of Gram-negative bacteria. These heterogeneous structures are composed of OM filled with periplasmic content from the site of budding. By analyzing mutants that have vesicle production phenotypes, we can gain insight into the mechanism of OMV budding in wild-type cells, which has thus far remained elusive. In this study, we present data demonstrating that the hypervesiculation phenotype of the nlpI deletion mutant of Escherichia coli correlates with changes in peptidoglycan (PG) dynamics. Our data indicate that in stationary phase cultures the nlpI mutant exhibits increased PG synthesis that is dependent on spr, consistent with a model in which NlpI controls the activity of the PG endopeptidase Spr. In log phase, the nlpI mutation was suppressed by a dacB mutation, suggesting that NlpI regulates penicillin-binding protein 4 (PBP4) during exponential growth. The data support a model in which NlpI negatively regulates PBP4 activity during log phase, and Spr activity during stationary phase, and that in the absence of NlpI, the cell survives by increasing PG synthesis. Further, the nlpI mutant exhibited a significant decrease in covalent outer membrane (OM-PG) envelope stabilizing cross-links, consistent with its high level of OMV production. Based on these results, we propose that one mechanism wild-type Gram-negative bacteria can use to modulate vesiculation is by altering PG-OM cross-linking via localized modulation of PG degradation and synthesis. © 2015 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.
Ecological prediction with nonlinear multivariate time-frequency functional data models
Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.
2013-01-01
Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.
Data based identification and prediction of nonlinear and complex dynamical systems
Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso
2016-07-01
The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional dynamical systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the systems of interest are complex and high dimensional with many interacting components. A complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required. Nonlinear and complex systems identification aims at inferring, from data, the mathematical equations that govern the dynamical evolution and the complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The "inverse" problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology. The past fifteen years have witnessed rapid development of contemporary complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear dynamical
Data based identification and prediction of nonlinear and complex dynamical systems
Energy Technology Data Exchange (ETDEWEB)
Wang, Wen-Xu [School of Systems Science, Beijing Normal University, Beijing, 100875 (China); Business School, University of Shanghai for Science and Technology, Shanghai 200093 (China); Lai, Ying-Cheng, E-mail: Ying-Cheng.Lai@asu.edu [School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287 (United States); Department of Physics, Arizona State University, Tempe, AZ 85287 (United States); Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen AB24 3UE (United Kingdom); Grebogi, Celso [Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen AB24 3UE (United Kingdom)
2016-07-12
The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional dynamical systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the systems of interest are complex and high dimensional with many interacting components. A complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required. Nonlinear and complex systems identification aims at inferring, from data, the mathematical equations that govern the dynamical evolution and the complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The “inverse” problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology. The past fifteen years have witnessed rapid development of contemporary complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear
A pharmacological study of NLP-12 neuropeptide signaling in free-living and parasitic nematodes.
Peeters, Lise; Janssen, Tom; De Haes, Wouter; Beets, Isabel; Meelkop, Ellen; Grant, Warwick; Schoofs, Liliane
2012-03-01
NLP-12a and b have been identified as cholecystokinin/sulfakinin-like neuropeptides in the free-living nematode Caenorhabditis elegans. They are suggested to play an important role in the regulation of digestive enzyme secretion and fat storage. This study reports on the identification and characterization of an NLP-12-like peptide precursor gene in the rat parasitic nematode Strongyloides ratti. The S. ratti NLP-12 peptides are able to activate both C. elegans CKR-2 receptor isoforms in a dose-dependent way with affinities in the same nanomolar range as the native C. elegans NLP-12 peptides. The C-terminal RPLQFamide sequence motif of the NLP-12 peptides is perfectly conserved between free-living and parasitic nematodes. Based on systemic amino acid replacements the Arg-, Leu- and Phe- residues appear to be critical for high-affinity receptor binding. Finally, a SAR analysis revealed the essential pharmacophore in C. elegans NLP-12b to be the pentapeptide RPLQFamide.
Drzewiecki, Wojciech
2016-12-01
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
Wang, John T.; Bomarito, Geoffrey F.
2016-01-01
This study implements a plasticity tool to predict the nonlinear shear behavior of unidirectional composite laminates under multiaxial loadings, with an intent to further develop the tool for use in composite progressive damage analysis. The steps for developing the plasticity tool include establishing a general quadratic yield function, deriving the incremental elasto-plastic stress-strain relations using the yield function with associated flow rule, and integrating the elasto-plastic stress-strain relations with a modified Euler method and a substepping scheme. Micromechanics analyses are performed to obtain normal and shear stress-strain curves that are used in determining the plasticity parameters of the yield function. By analyzing a micromechanics model, a virtual testing approach is used to replace costly experimental tests for obtaining stress-strain responses of composites under various loadings. The predicted elastic moduli and Poisson's ratios are in good agreement with experimental data. The substepping scheme for integrating the elasto-plastic stress-strain relations is suitable for working with displacement-based finite element codes. An illustration problem is solved to show that the plasticity tool can predict the nonlinear shear behavior for a unidirectional laminate subjected to multiaxial loadings.
Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.
Kawashima, Issaku; Kumano, Hiroaki
2017-01-01
Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.
Prediction of nonlinear optical properties of organic materials. General theoretical considerations
Cardelino, B.; Moore, C.; Zutaut, S.
1993-01-01
The prediction of nonlinear optical properties of organic materials is geared to assist materials scientists in the selection of good candidate molecules. A brief summary of the quantum mechanical methods used for estimating hyperpolarizabilities will be presented. The advantages and limitations of each technique will be discussed. Particular attention will be given to the finite-field method for calculating first and second order hyperpolarizabilities, since this method is better suited for large molecules. Corrections for dynamic fields and bulk effects will be discussed in detail, focusing on solvent effects, conformational isomerization, core effects, dispersion, and hydrogen bonding. Several results will be compared with data obtained from third-harmonic-generation (THG) and dc-induced second harmonic generation (EFISH) measurements. These comparisons will demonstrate the qualitative ability of the method to predict the relative strengths of hyperpolarizabilities of a class of compounds. The future application of molecular mechanics, as well as other techniques, in the study of bulk properties and solid state defects will be addressed. The relationship between large values for nonlinear optical properties and large conjugation lengths is well known, and is particularly important for third-order processes. For this reason, the materials with the largest observed nonresonant third-order properties are conjugated polymers. An example of this type of polymer is polydiacetylene. One of the problems in dealing with polydiacetylene is that substituents which may enhance its nonlinear properties may ultimately prevent it from polymerizing. A model which attempts to predict the likelihood of solid-state polymerization is considered, along with the implications of the assumptions that are used. Calculations of the third-order optical properties and their relationship to first-order properties and energy gaps will be discussed. The relationship between monomeric and
A computerized implementation of a non-linear equation to predict barrier shielding requirements.
Chamberlain, A C; Strydom, W J
1997-04-01
A non-linear equation to predict barrier shielding thickness from the work function of x- and gamma-ray generators is presented. This equation is incorporated into a model that takes into account primary, scatter, and leakage radiation components to determine the amount of shielding necessary. The case of multiple wall materials is also considered. The equation accurately models the radiation attenuation curves given in NCRP 49 for concrete and lead, thus eliminating the necessity to use graphical or tabular methods to calculate shielding thickness, which can be inaccurate.
Improved Nonlinear Equation Method for Numerical Prediction of Jominy End-Quench Curves
Institute of Scientific and Technical Information of China (English)
SONG Yue-peng; LIU Guo-quan; LIU Sheng-xin; LIU Jian-tao; FENG Cheng-ming
2007-01-01
Without considering the effects of alloying interaction on the Jominy end-quench curves, the prediction results obtained by YU Bai-hai's nonlinear equation method for multi-alloying steels were different from those experimental ones reported in literature. Some alloying elements have marked influence on Jominy end-quench curves of steels. An improved mathematical model for simulating the Jominy end-quench curves is proposed by introducing a parameter named alloying interactions equivalent (Le). With the improved model, the Jominy end-quench curves of steels so obtained agree very well with the experimental ones.
Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2014-01-01
In this paper we investigate an economically optimizing Nonlinear Model Predictive Control (E-NMPC) for a spray drying process. By simulation we evaluate the economic potential of this E-NMPC compared to a conventional PID based control strategy. Spray drying is the preferred process to reduce......-shooting method combined with a quasi-Newton Sequential Quadratic Programming (SQP) algorithm and the adjoint method for computation of gradients. The E-NMPC improves the cost of spray drying by 26.7% compared to conventional PI control in our simulations....
Functional analysis of NLP genes from Botrytis elliptica.
Staats, Martijn; VAN Baarlen, Peter; Schouten, Alexander; VAN Kan, Jan A L
2007-03-01
SUMMARY We functionally analysed two Nep1-like protein (NLP) genes from Botrytis elliptica (a specialized pathogen of lily), encoding proteins homologous to the necrosis and ethylene-inducing protein (NEP1) from Fusarium oxysporum. Single gene replacement mutants were made for BeNEP1 and BeNEP2, providing the first example of transformation and successful targeted mutagenesis in this fungus. The virulence of both mutants on lily leaves was not affected. BeNEP1 and BeNEP2 were individually expressed in the yeast Pichia pastoris, and the necrosis-inducing activity was tested by infiltration of both proteins into leaves of several monocots and eudicots. Necrotic symptoms developed on the eudicots tobacco, Nicotiana benthamiana and Arabidopsis thaliana, and cell death was induced in tomato cell suspensions. No necrotic symptoms developed on leaves of the monocots rice, maize and lily. These results support the hypothesis that the necrosis-inducing activity of NLPs is limited to eudicots. We conclude that NLPs are not essential virulence factors and they do not function as host-selective toxins for B. elliptica.
Recent advances on NLP research in Harbin Institute of Technology
Institute of Scientific and Technical Information of China (English)
ZHAO Tiejun; GUAN Yi; LIU Ting; WANG Qiang
2007-01-01
In the 1960s,the researchers of Harbin Institute of Technology(HIT)attempted to do relevant research on natural language processing.With more than 40-year's effort,HIT has already established three research laboratories for Chinese information processing,i.e.the Machine Intelligence and Translation Laboratory(MI&T Lab),the Intelligent Technology and Natural Language Processing Laboratory(ITNLP)and the Information Retrieval Laboratory (IR-Lab).At present,it has a well-balanced research team of over 200 persons,and the research interests have extended to language processing,machine translation,text retrieval and other fields.Harbin Institute of Technology has accumulated a batch of key techniques and data resources,won many prizes in the technical evaluations at home and abroad.Harbin Institute of Technology has become one of the most important natural language processing bases for teaching and scientific research in China now.This paper gives an introduction to the achievements onNLP in HIT.
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Nahid Ardalani
2011-07-01
Full Text Available This article describes linear and nonlinear Artificial Neural Network(ANN-based predictors as Autoregressive Moving Average models with Auxiliary input (ARMAX process for Signal to Interference plus Noise Ratio (SINR prediction in Direct Sequence Code Division Multiple Access (DS/CDMA systems. The Multi Layer Perceptron (MLP neural network with nonlinear function is used as nonlinear neural network and Adaptive Linear (Adaline predictor is used as linear predictor. The problem of complexity of the MLP and Adaline structures is solved by using the Minimum Mean Squared Error (MMSE principle to select the optimal numbers of input and hidden nodes by try and error role. Simulation results show that both of MLP and Adaline optimal neural networks can track the effect of deep fading due to using a 1.8 GHZ carrier frequency at the urban mobile speeds of 10 km/h, 50 km/h and 120 km/h with tolerable estimation errors. Therefore, the neural networkbased predictor is well suitable SINR-based predictor in closedloop power control to combat multi path fading in CDMA systems.
AUTHOR|(SzGeCERN)673023; Blanco Viñuela, Enrique
In each of eight arcs of the 27 km circumference Large Hadron Collider (LHC), 2.5 km long strings of super-conducting magnets are cooled with superfluid Helium II at 1.9 K. The temperature stabilisation is a challenging control problem due to complex non-linear dynamics of the magnets temperature and presence of multiple operational constraints. Strong nonlinearities and variable dead-times of the dynamics originate at strongly heat-flux dependent effective heat conductivity of superfluid that varies three orders of magnitude over the range of possible operational conditions. In order to improve the temperature stabilisation, a proof of concept on-line economic output-feedback Non-linear Model Predictive Controller (NMPC) is presented in this thesis. The controller is based on a novel complex first-principles distributed parameters numerical model of the temperature dynamics over a 214 m long sub-sector of the LHC that is characterized by very low computational cost of simulation needed in real-time optimizat...
Luna, Julio; Jemei, Samir; Yousfi-Steiner, Nadia; Husar, Attila; Serra, Maria; Hissel, Daniel
2016-10-01
In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.
Ability of non-linear mixed models to predict growth in laying hens
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Luis Fernando Galeano-Vasco
2014-11-01
Full Text Available In this study, the Von Bertalanffy, Richards, Gompertz, Brody, and Logistics non-linear mixed regression models were compared for their ability to estimate the growth curve in commercial laying hens. Data were obtained from 100 Lohmann LSL layers. The animals were identified and then weighed weekly from day 20 after hatch until they were 553 days of age. All the nonlinear models used were transformed into mixed models by the inclusion of random parameters. Accuracy of the models was determined by the Akaike and Bayesian information criteria (AIC and BIC, respectively, and the correlation values. According to AIC, BIC, and correlation values, the best fit for modeling the growth curve of the birds was obtained with Gompertz, followed by Richards, and then by Von Bertalanffy models. The Brody and Logistic models did not fit the data. The Gompertz nonlinear mixed model showed the best goodness of fit for the data set, and is considered the model of choice to describe and predict the growth curve of Lohmann LSL commercial layers at the production system of University of Antioquia.
Basant, Nikita; Gupta, Shikha; Singh, Kunwar P
2015-11-01
In this study, we established nonlinear quantitative-structure toxicity relationship (QSTR) models for predicting the toxicities of chemical pesticides in multiple aquatic test species following the OECD (Organization for Economic Cooperation and Development) guidelines. The decision tree forest (DTF) and decision tree boost (DTB) based QSTR models were constructed using a pesticides toxicity dataset in Selenastrum capricornutum and a set of six descriptors. Other six toxicity data sets were used for external validation of the constructed QSTRs. Global QSTR models were also constructed using the combined dataset of all the seven species. The diversity in chemical structures and nonlinearity in the data were evaluated. Model validation was performed deriving several statistical coefficients for the test data and the prediction and generalization abilities of the QSTRs were evaluated. Both the QSTR models identified WPSA1 (weighted charged partial positive surface area) as the most influential descriptor. The DTF and DTB QSTRs performed relatively better than the single decision tree (SDT) and support vector machines (SVM) models used as a benchmark here and yielded R(2) of 0.886 and 0.964 between the measured and predicted toxicity values in the complete dataset (S. capricornutum). The QSTR models applied to six other aquatic species toxicity data yielded R(2) of >0.92 (DTF) and >0.97 (DTB), respectively. The prediction accuracies of the global models were comparable with those of the S. capricornutum models. The results suggest for the appropriateness of the developed QSTR models to reliably predict the aquatic toxicity of chemicals and can be used for regulatory purpose.
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Qingping Xu
Full Text Available NlpC/P60 superfamily papain-like enzymes play important roles in all kingdoms of life. Two members of this superfamily, LRAT-like and YaeF/YiiX-like families, were predicted to contain a catalytic domain that is circularly permuted such that the catalytic cysteine is located near the C-terminus, instead of at the N-terminus. These permuted enzymes are widespread in virus, pathogenic bacteria, and eukaryotes. We determined the crystal structure of a member of the YaeF/YiiX-like family from Bacillus cereus in complex with lysine. The structure, which adopts a ligand-induced, "closed" conformation, confirms the circular permutation of catalytic residues. A comparative analysis of other related protein structures within the NlpC/P60 superfamily is presented. Permutated NlpC/P60 enzymes contain a similar conserved core and arrangement of catalytic residues, including a Cys/His-containing triad and an additional conserved tyrosine. More surprisingly, permuted enzymes have a hydrophobic S1 binding pocket that is distinct from previously characterized enzymes in the family, indicative of novel substrate specificity. Further analysis of a structural homolog, YiiX (PDB 2if6 identified a fatty acid in the conserved hydrophobic pocket, thus providing additional insights into possible function of these novel enzymes.
Energy Technology Data Exchange (ETDEWEB)
Xu, Qingping; Rawlings, Neil D.; Chiu, Hsiu-Ju; Jaroszewski, Lukasz; Klock, Heath E.; Knuth, Mark W.; Miller, Mitchell D.; Elsliger, Marc-Andre; Deacon, Ashley M.; Godzik, Adam; Lesley, Scott A.; Wilson, Ian A. (SG); (Wellcome)
2012-07-11
NlpC/P60 superfamily papain-like enzymes play important roles in all kingdoms of life. Two members of this superfamily, LRAT-like and YaeF/YiiX-like families, were predicted to contain a catalytic domain that is circularly permuted such that the catalytic cysteine is located near the C-terminus, instead of at the N-terminus. These permuted enzymes are widespread in virus, pathogenic bacteria, and eukaryotes. We determined the crystal structure of a member of the YaeF/YiiX-like family from Bacillus cereus in complex with lysine. The structure, which adopts a ligand-induced, 'closed' conformation, confirms the circular permutation of catalytic residues. A comparative analysis of other related protein structures within the NlpC/P60 superfamily is presented. Permutated NlpC/P60 enzymes contain a similar conserved core and arrangement of catalytic residues, including a Cys/His-containing triad and an additional conserved tyrosine. More surprisingly, permuted enzymes have a hydrophobic S1 binding pocket that is distinct from previously characterized enzymes in the family, indicative of novel substrate specificity. Further analysis of a structural homolog, YiiX (PDB 2if6) identified a fatty acid in the conserved hydrophobic pocket, thus providing additional insights into possible function of these novel enzymes.
Institute of Scientific and Technical Information of China (English)
SU Cheng-li; WANG Shu-qing
2006-01-01
An extended robust model predictive control approach for input constrained discrete uncertain nonlinear systems with time-delay based on a class of uncertain T-S fuzzy models that satisfy sector bound condition is presented. In this approach, the minimization problem of the "worst-case" objective function is converted into the linear objective minimization problem involving linear matrix inequalities (LMIs) constraints. The state feedback control law is obtained by solving convex optimization of a set of LMIs. Sufficient condition for stability and a new upper bound on robust performance index are given for these kinds of uncertain fuzzy systems with state time-delay. Simulation results of CSTR process show that the proposed robust predictive control approach is effective and feasible.
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.
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Qihong Chen
2014-01-01
Full Text Available This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX, and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.
Chen, Qihong; Long, Rong; Quan, Shuhai; Zhang, Liyan
2014-01-01
This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.
Generalized nonlinear models applied to the prediction of basal area and volume of Eucalyptus sp
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Samuel de Pádua Chaves e Carvalho
2011-12-01
Full Text Available This paper aims to propose the use of generalized nonlinear models for prediction of basal area growth and yield of total volume of the hybrid Eucalyptus urocamaldulensis, in a stand situation in a central region in state of Minas Gerais. The used methodology allows to work with data in its original form without the necessity of transformation of variables, and generate highly accurate models. To evaluate the fitting quality, it was proposed the Bayesian information criterion, of the Akaike, and test the maximum likelihood, beyond the standard error of estimate, and residual graphics. The models were used with a good performance, highly accurate and parsimonious estimates of the variables proposed, with errors reduced to 12% for basal area and 4% for prediction of the volume.
Miksovsky, J.; Raidl, A.
Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.
Nonlinear quantitative radiation sensitivity prediction model based on NCI-60 cancer cell lines.
Zhang, Chunying; Girard, Luc; Das, Amit; Chen, Sun; Zheng, Guangqiang; Song, Kai
2014-01-01
We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy γ-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines
Directory of Open Access Journals (Sweden)
Chunying Zhang
2014-01-01
Full Text Available We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT related genes were selected by significance analysis of microarrays (SAM. Orthogonal latent variables (LVs were then extracted by the partial least squares (PLS method as the new compressive input variables. Finally, support vector machine (SVM regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy γ-ray values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a reducing the root mean square error (RMSE of the radiation sensitivity prediction model from 0.20 to 0.011; and (b improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
Using Lyapunov exponents to predict the onset of chaos in nonlinear oscillators.
Ryabov, Vladimir B
2002-07-01
An analytic technique for predicting the emergence of chaotic instability in nonlinear nonautonomous dissipative oscillators is proposed. The method is based on the Lyapunov-type stability analysis of an arbitrary phase trajectory and the standard procedure of calculating the Lyapunov characteristic exponents. The concept of temporally local Lyapunov exponents is then utilized for specifying the area in the phase space where any trajectory is asymptotically stable, and, therefore, the existence of chaotic attractors is impossible. The procedure of linear coordinate transform optimizing the linear part of the vector field is developed for the purpose of maximizing the stability area in the vicinity of a stable fixed point. By considering the inverse conditions of asymptotic stability, this approach allows formulating a necessary condition for chaotic motion in a broad class of nonlinear oscillatory systems, including many cases of practical interest. The examples of externally excited one- and two-well Duffing oscillators and a planar pendulum demonstrate efficiency of the proposed method, as well as a good agreement of the theoretical predictions with the results of numerical experiments. The comparison of the proposed method with Melnikov's criterion shows a potential advantage of using the former one at high values of dissipation parameter and/or multifrequency type of excitation in dynamical systems.
Prediction of Ship Unsteady Maneuvering in Calm Water by a Fully Nonlinear Ship Motion Model
Directory of Open Access Journals (Sweden)
Ray-Qing Lin
2012-01-01
Full Text Available This is the continuation of our research on development of a fully nonlinear, dynamically consistent, numerical ship motion model (DiSSEL. In this study we will report our results in predicting ship motions in unsteady maneuvering in calm water. During the unsteady maneuvering, both the rudder angle, and ship forward speed vary with time. Therefore, not only surge, sway, and yaw motions occur, but roll, pitch and heave motions will also occur even in calm water as heel, trim, and sinkage, respectively. When the rudder angles and ship forward speed vary rapidly with time, the six degrees-of-freedom ship motions and their interactions become strong. To accurately predict the six degrees-of-freedom ship motions in unsteady maneuvering, a universal method for arbitrary ship hull requires physics-based fully-nonlinear models for ship motion and for rudder forces and moments. The numerical simulations will be benchmarked by experimental data of the Pre-Contract DDG51 design and an Experimental Hull Form. The benchmarking shows a good agreement between numerical simulations by the enhancement DiSSEL and experimental data. No empirical parameterization is used, except for the influence of the propeller slipstream on the rudder, which is included using a flow acceleration factor.
Li, Wangnan; Cai, Hongneng; Li, Chao
2014-11-01
This paper deals with the characterization of the strength of the constituents of carbon fiber reinforced plastic laminate (CFRP), and a prediction of the static compressive strength of open-hole structure of polymer composites. The approach combined with non-linear analysis in macro-level and a linear elastic micromechanical failure analysis in microlevel (non-linear MMF) is proposed to improve the prediction accuracy. A face-centered cubic micromechanics model is constructed to analyze the stresses in fiber and matrix in microlevel. Non-interactive failure criteria are proposed to characterize the strength of fiber and matrix. The non-linear shear behavior of the laminate is studied experimentally, and a novel approach of cubic spline interpolation is used to capture significant non-linear shear behavior of laminate. The user-defined material subroutine UMAT for the non-linear share behavior is developed and combined in the mechanics analysis in the macro-level using the Abaqus Python codes. The failure mechanism and static strength of open-hole compressive (OHC) structure of polymer composites is studied based on non-linear MMF. The UTS50/E51 CFRP is used to demonstrate the application of theory of non-linear MMF.
Santosa, H.; Hobara, Y.
2017-01-01
The electric field amplitude of very low frequency (VLF) transmitter from Hawaii (NPM) has been continuously recorded at Chofu (CHF), Tokyo, Japan. The VLF amplitude variability indicates lower ionospheric perturbation in the D region (60-90 km altitude range) around the NPM-CHF propagation path. We carried out the prediction of daily nighttime mean VLF amplitude by using Nonlinear Autoregressive with Exogenous Input Neural Network (NARX NN). The NARX NN model, which was built based on the daily input variables of various physical parameters such as stratospheric temperature, total column ozone, cosmic rays, Dst, and Kp indices possess good accuracy during the model building. The fitted model was constructed within the training period from 1 January 2011 to 4 February 2013 by using three algorithms, namely, Bayesian Neural Network (BRANN), Levenberg Marquardt Neural Network (LMANN), and Scaled Conjugate Gradient (SCG). The LMANN has the largest Pearson correlation coefficient (r) of 0.94 and smallest root-mean-square error (RMSE) of 1.19 dB. The constructed models by using LMANN were applied to predict the VLF amplitude from 5 February 2013 to 31 December 2013. As a result the one step (1 day) ahead predicted nighttime VLF amplitude has the r of 0.93 and RMSE of 2.25 dB. We conclude that the model built according to the proposed methodology provides good predictions of the electric field amplitude of VLF waves for NPM-CHF (midlatitude) propagation path.
The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression
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Chunxiao Zhang
2012-01-01
Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.
The neuropeptide NLP-22 regulates a sleep-like state in Caenorhabditis elegans.
Nelson, M D; Trojanowski, N F; George-Raizen, J B; Smith, C J; Yu, C-C; Fang-Yen, C; Raizen, D M
2013-01-01
Neuropeptides have central roles in the regulation of homoeostatic behaviours such as sleep and feeding. Caenorhabditis elegans displays sleep-like quiescence of locomotion and feeding during a larval transition stage called lethargus and feeds during active larval and adult stages. Here we show that the neuropeptide NLP-22 is a regulator of Caenorhabditis elegans sleep-like quiescence observed during lethargus. nlp-22 shows cyclical mRNA expression in synchrony with lethargus; it is regulated by LIN-42, an orthologue of the core circadian protein PERIOD; and it is expressed solely in the two RIA interneurons. nlp-22 and the RIA interneurons are required for normal lethargus quiescence, and forced expression of nlp-22 during active stages causes anachronistic locomotion and feeding quiescence. Optogenetic stimulation of the RIA interneurons has a movement-promoting effect, demonstrating functional complexity in a single-neuron type. Our work defines a quiescence-regulating role for NLP-22 and expands our knowledge of the neural circuitry controlling Caenorhabditis elegans behavioural quiescence.
Expression of Caenorhabditis elegans antimicrobial peptide NLP-31 in Escherichia coli
Lim, Mei-Perng; Nathan, Sheila
2014-09-01
Burkholderia pseudomallei is the causative agent of melioidosis, a fulminant disease endemic in Southeast Asia and Northern Australia. The standardized form of therapy is antibiotics treatment; however, the bacterium has become increasingly resistant to these antibiotics. This has spurred the need to search for alternative therapeutic agents. Antimicrobial peptides (AMPs) are small proteins that possess broad-spectrum antimicrobial activity. In a previous study, the nematode Caenorhabditis elegans was infected by B. pseudomallei and a whole animal transcriptome analysis identified a number of AMP-encoded genes which were induced significantly in the infected worms. One of the AMPs identified is NLP-31 and to date, there are no reports of anti-B. pseudomallei activity demonstrated by NLP-31. To produce NLP-31 protein for future studies, the gene encoding for NLP-31 was cloned into the pET32b expression vector and transformed into Escherichia coli BL21(DE3). Protein expression was induced with 1 mM IPTG for 20 hours at 20°C and recombinant NLP-31 was detected in the soluble fraction. Taken together, a simple optimized heterologous production of AMPs in an E. coli expression system has been successfully developed.
Razzaq, Zia
1989-01-01
Straight or curved hat-section members are often used as structural stiffeners in aircraft. For instance, they are employed as stiffeners for the dorsal skin as well as in the aerial refueling adjacent area structure in F-106 aircraft. The flanges of the hat-section are connected to the aircraft skin. Thus, the portion of the skin closing the hat-section interacts with the section itself when resisting the stresses due to service loads. The flexural fatigue life of such a closed section is estimated using materially nonlinear axial fatigue characteristics. It should be recognized that when a structural shape is subjected to bending, the fatigue life at the neutral axis is infinity since the normal stresses are zero at that location. Conversely, the fatigue life at the extreme fibers where the normal bending stresses are maximum can be expected to be finite. Thus, different fatigue life estimates can be visualized at various distances from the neural axis. The problem becomes compounded further when significant portions away from the neutral axis are stressed into plastic range. A theoretical analysis of the closed hat-section subjected to flexural cyclic loading is first conducted. The axial fatigue characteristics together with the related axial fatigue life formula and its inverted form given by Manson and Muralidharan are adopted for an aluminum alloy used in aircraft construction. A closed-form expression for predicting the flexural fatigue life is then derived for the closed hat-section including materially nonlinear action. A computer program is written to conduct a study of the variables such as the thicknesses of the hat-section and the skin, and the type of alloy used. The study has provided a fundamental understanding of the flexural fatigue life characteristics of a practical structural component used in aircraft when materially nonlinear action is present.
Tofighi, Elham; Mahdizadeh, Amin
2016-09-01
This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.
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Avital Tidhar
Full Text Available Yersinia pestis is the causative agent of plague. Previously we have isolated an attenuated Y. pestis transposon insertion mutant in which the pcm gene was disrupted. In the present study, we investigated the expression and the role of pcm locus genes in Y. pestis pathogenesis using a set of isogenic surE, pcm, nlpD and rpoS mutants of the fully virulent Kimberley53 strain. We show that in Y. pestis, nlpD expression is controlled from elements residing within the upstream genes surE and pcm. The NlpD lipoprotein is the only factor encoded from the pcm locus that is essential for Y. pestis virulence. A chromosomal deletion of the nlpD gene sequence resulted in a drastic reduction in virulence to an LD(50 of at least 10(7 cfu for subcutaneous and airway routes of infection. The mutant was unable to colonize mouse organs following infection. The filamented morphology of the nlpD mutant indicates that NlpD is involved in cell separation; however, deletion of nlpD did not affect in vitro growth rate. Trans-complementation experiments with the Y. pestis nlpD gene restored virulence and all other phenotypic defects. Finally, we demonstrated that subcutaneous administration of the nlpD mutant could protect animals against bubonic and primary pneumonic plague. Taken together, these results demonstrate that Y. pestis NlpD is a novel virulence factor essential for the development of bubonic and pneumonic plague. Further, the nlpD mutant is superior to the EV76 prototype live vaccine strain in immunogenicity and in conferring effective protective immunity. Thus it could serve as a basis for a very potent live vaccine against bubonic and pneumonic plague.
Nonlinear Model-Based Predictive Control applied to Large Scale Cryogenic Facilities
Blanco Vinuela, Enrique; de Prada Moraga, Cesar
2001-01-01
The thesis addresses the study, analysis, development, and finally the real implementation of an advanced control system for the 1.8 K Cooling Loop of the LHC (Large Hadron Collider) accelerator. The LHC is the next accelerator being built at CERN (European Center for Nuclear Research), it will use superconducting magnets operating below a temperature of 1.9 K along a circumference of 27 kilometers. The temperature of these magnets is a control parameter with strict operating constraints. The first control implementations applied a procedure that included linear identification, modelling and regulation using a linear predictive controller. It did improve largely the overall performance of the plant with respect to a classical PID regulator, but the nature of the cryogenic processes pointed out the need of a more adequate technique, such as a nonlinear methodology. This thesis is a first step to develop a global regulation strategy for the overall control of the LHC cells when they will operate simultaneously....
Nonlinear Dynamics and Chaos Applications for Prediction of Weather and Climate
Pethkar, J S
2001-01-01
Turbulence, namely, irregular fluctuations in space and time characterize fluid flows in general and atmospheric flows in particular.The irregular,i.e., nonlinear space-time fluctuations on all scales contribute to the unpredictable nature of both short-term weather and long-term climate.It is of importance to quantify the total pattern of fluctuations for predictability studies. The power spectra of temporal fluctuations are broadband and exhibit inverse power law form with different slopes for different scale ranges. Inverse power-law form for power spectra implies scaling (self similarity) for the scale range over which the slope is constant. Atmospheric flows therefore exhibit multiple scaling or multifractal structure.Standard meteorological theory cannot explain satisfactorily the observed multifractal structure of atmospheric flows.Selfsimilar spatial pattern implies long-range spatial correlations. Atmospheric flows therefore exhibit long-range spatiotemporal correlations, namely,self-organized critic...
Zhao, Meng; Ding, Baocang
2015-03-01
This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable.
Dual-orthogonal radial basis function networks for nonlinear time series prediction.
Hong, X; Billings, Steve A.
1998-04-01
A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.
Naumann, H D; Tedeschi, L O; Fonseca, M A
2015-11-01
Methane (CH) is a potent greenhouse gas that is normally produced by microbial fermentation in the rumen and released to the environment mainly during eructation. Prediction of ruminal CH production is important for ruminant nutrition, especially for the determination of ME intake to assess the amount of total GE available for metabolism by an animal. Equations have been developed to predict ruminal CH production based on dietary constituents, but none have considered condensed tannins (CT), which are known to impact CH production by ruminants. The objective was to develop an equation to predict ruminal CH, accounting for CT effects. Methane production data were acquired from 48-h in vitro fermentation of a diverse group of warm-season perennial forage legumes containing different concentrations of CT over the course of 3 yr ( = 113). The following nonlinear exponential decay regression equation was developed: CH₄ = 113.6 × exp (-0.1751 x CT) - 2.18), [corrected] in which CH is expressed in grams per kilogram of fermentable organic matter and CT is in percentage of the DM. This equation predicted that CH production could be reduced by approximately 50% when CT is 3.9% DM. This equation is likely more accurate when screening CT-containing forages for their potential ability to mitigate in vitro CH production by ruminants when the CT concentration is greater than 3% DM. Therefore, despite the degree of variability in ruminal CH production, this equation could be used as a tool for screening CT-containing forages for their potential to inhibit ruminal CH. Future research should focus on the development of predictive equations when other potential reducers of ruminal CH are used in conjunction with CT.
Finding 'Evidence of Absence' in Medical Notes: Using NLP for Clinical Inferencing.
Carter, Marjorie E; Divita, Guy; Redd, Andrew; Rubin, Michael A; Samore, Matthew H; Gupta, Kalpana; Trautner, Barbara W; Gundlapalli, Adi V
2016-01-01
Extracting evidence of the absence of a target of interest from medical text can be useful in clinical inferencing. The purpose of our study was to develop a natural language processing (NLP) pipelineto identify the presence of indwelling urinary catheters from electronic medical notes to aid in detection of catheter-associated urinary tract infections (CAUTI). Finding clear evidence that a patient does not have an indwelling urinary catheter is useful in making a determination regarding CAUTI. We developed a lexicon of seven core concepts to infer the absence of a urinary catheter. Of the 990,391 concepts extractedby NLP from a large corpus of 744,285 electronic medical notes from 5589 hospitalized patients, 63,516 were labeled as evidence of absence.Human review revealed three primary causes for false negatives. The lexicon and NLP pipeline were refined using this information, resulting in outputs with an acceptable false positive rate of 11%.
Institute of Scientific and Technical Information of China (English)
郭红梅; 高晓博
2013-01-01
In this paper the auther first gives a brief introduction to Neuro-linguistic Programming(NLP) and its four principles. Then from the point of the four principles ,the auther illustrates the application of NLP in foreign language teaching and learning.
Wood, Peter
2011-01-01
"QuickAssist," the program presented in this paper, uses natural language processing (NLP) technologies. It places a range of NLP tools at the disposal of learners, intended to enable them to independently read and comprehend a German text of their choice while they extend their vocabulary, learn about different uses of particular words,…
A generic tool to generate a lexicon for NLP from Lexicon-Grammar tables
Constant, Matthieu
2010-01-01
Lexicon-Grammar tables constitute a large-coverage syntactic lexicon but they cannot be directly used in Natural Language Processing (NLP) applications because they sometimes rely on implicit information. In this paper, we introduce LGExtract, a generic tool for generating a syntactic lexicon for NLP from the Lexicon-Grammar tables. It is based on a global table that contains undefined information and on a unique extraction script including all operations to be performed for all tables. We also present an experiment that has been conducted to generate a new lexicon of French verbs and predicative nouns.
NLP-12 engages different UNC-13 proteins to potentiate tonic and evoked release.
Hu, Zhitao; Vashlishan-Murray, Amy B; Kaplan, Joshua M
2015-01-21
A neuropeptide (NLP-12) and its receptor (CKR-2) potentiate tonic and evoked ACh release at Caenorhabditis elegans neuromuscular junctions. Increased evoked release is mediated by a presynaptic pathway (egl-30 Gαq and egl-8 PLCβ) that produces DAG, and by DAG binding to short and long UNC-13 proteins. Potentiation of tonic ACh release persists in mutants deficient for egl-30 Gαq and egl-8 PLCβ and requires DAG binding to UNC-13L (but not UNC-13S). Thus, NLP-12 adjusts tonic and evoked release by distinct mechanisms.
A Brief Analysis of Amharic NLP: From POS Tagging to Question Answering
Seid Muhie Yimam
2016-01-01
In the first part of my talk, I will discuss some NLP research and applications done for Amharic so far. I specifically discuss POS tagging [http://aflat.org/files/HLTD201109.pdf ], Morphological processing [HornMorpho, see http://homes.soic.indiana.edu/gasser/L3/horn2.5.pdf ], Spell checking, Named entity recognition, and Questions Answering [http://etd.aau.edu.et/bitstream/123456789/8587/1/Brook%20Eshetu%20Bete.pdf ]. A brief list of the challenges in Amharic NLP will be also discussed, rel...
Buchko, Garry W; Weinfeld, Michael
2002-09-01
The nitroimidazole-linked phenanthridines 2-NLP-3 (5-[3-(2-nitro-1-imidazoyl)-propyl]-phenanthridinium bromide) and 2-NLP-4 (5-[3-(2-nitro-1-imidazoyl)-butyl]-phenanthridinium bromide) are composed of the radiosensitizer, 2-nitroimidazole, attached to the DNA intercalator phenanthridine by a 3- and 4-carbon linker, respectively. Previous in vitro assays showed both compounds to be 10-100 times more efficient as hypoxic cell radiosensitizers (based on external drug concentrations) than the untargeted 2-nitroimidazole radiosensitizer, misonidazole (Cowan et al., Radiat. Res. 127, 81-89, 1991). Here we have used a (32)P postlabeling assay and 5'-end-labeled oligonucleotide assay to compare the radiation-induced DNA damage generated in the presence of 2-NLP-3, 2-NLP-4, phenanthridine and misonidazole. After irradiation of the DNA under anoxic conditions, we observed a significantly greater level of 3'-phosphoglycolate DNA damage in the presence of 2-NLP-3 or 2-NLP-4 compared to irradiation of the DNA in the presence of misonidazole. This may account at least in part for the greater cellular radiosensitization shown by the nitroimidazole-linked phenanthridines over misonidazole. Of the two nitroimidazole-linked phenanthridines, the better in vitro radiosensitizer, 2-NLP-4, generated more 3'-phosphoglycolate in DNA than did 2-NLP-3. At all concentrations, phenanthridine had little effect on the levels of DNA damage, suggesting that the enhanced radiosensitization displayed by 2-NLP-3 and 2-NLP-4 is due to the localization of the 2-nitroimidazole to the DNA by the phenanthridine substituent and not to radiosensitization by the phenanthridine moiety itself.
A non-linear model predictive controller with obstacle avoidance for a space robot
Wang, Mingming; Luo, Jianjun; Walter, Ulrich
2016-04-01
This study investigates the use of the non-linear model predictive control (NMPC) strategy for a kinematically redundant space robot to approach an un-cooperative target in complex space environment. Collision avoidance, traditionally treated as a high level planning problem, can be effectively translated into control constraints as part of the NMPC. The objective of this paper is to evaluate the performance of the predictive controller in a constrained workspace and to investigate the feasibility of imposing additional constraints into the NMPC. In this paper, we reformulated the issue of the space robot motion control by using NMPC with predefined objectives under input, output and obstacle constraints over a receding horizon. An on-line quadratic programming (QP) procedure is employed to obtain the constrained optimal control decisions in real-time. This study has been implemented for a 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a 6 DOF free-floating spacecraft via simulation studies. Real-time trajectory tracking and collision avoidance particularly demonstrate the effectiveness and potential of the proposed NMPC strategy for the space robot.
A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials
Matouš, Karel; Geers, Marc G. D.; Kouznetsova, Varvara G.; Gillman, Andrew
2017-02-01
Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.
A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials
Energy Technology Data Exchange (ETDEWEB)
Matouš, Karel, E-mail: kmatous@nd.edu [Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556 (United States); Geers, Marc G.D.; Kouznetsova, Varvara G. [Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven (Netherlands); Gillman, Andrew [Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556 (United States)
2017-02-01
Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.
Directory of Open Access Journals (Sweden)
Luiz Augusto da Cruz Meleiro
2005-06-01
Full Text Available In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs, identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.Neste trabalho um controlador preditivo não linear multivariável foi desenvolvido para um processo de fermentação alcoólica extrativa. O modelo interno do controlador foi representado por duas redes do tipo Functional Link (FLN, identificadas usando dados de simulação gerados a partir de um modelo validado experimentalmente. A estrutura FLN apresenta como vantagem o treinamento rápido e convergência garantida, já que a estimação dos seus pesos é um problema de otimização linear. Além disso, a eliminação de pesos não significativos gera modelos parsimoniosos, o que permite a rápida execução em algoritmos de controle preditivo baseado em modelo. Os resultados mostram que o algoritmo proposto tem grande potencial para identificação e controle de processos não lineares.
Nonlinear short-term heart rate variability prediction of spontaneous ventricular tachyarrhythmia
Institute of Scientific and Technical Information of China (English)
ZHUANG JianJun; NING XinBao; DU SiDan; WANG ZhenZhou; HUO ChengYu; YANG Xi; FAN AiHua
2008-01-01
As malign ventricular tachyarrhythmias triggering sudden cardiac death (SCD),both ventricular tachycardia (VT) and ventricular fibrillation (VF) are major causes of mortality.The most efficient ther-apy for SCD prevention is implantable cardioverter defibrillators (ICD).The ICD can accurately and ef-fectively identify the forthcoming of fatal ventricular tachyarrhythmias and deliver a shock in order to restore patients' normal sinus rhythm.In this study,two nonlinear complexity measures based on en-tropy:approximate entropy (ApEn) and sample entropy (SampEn) as well as two time linear indices:the mean RR interval (the average of time intervals between consecutive R-waves) and the standard devia-tion of RR intervals were used for short-term forecasting of VT-VF occurrence.The last small sections of interbeat intervals preceding 135 VT-VF episodes from 78 patients stored by the ICD were analyzed and compared with individually acquired control time series (CON series) from the same patients,which are normally intrinsic sinus rhythms.The results demonstrate that in addition to an obvious in-crease in heart rates of the patients,the values of two entropy measures are significantly smaller for VT-VF episodes than those for CON series.Conclusions can be drawn that when a ventricular tach-yarrhythmia approaches,the sympathetic tone of the patients is increased,and the complexity of their RR intervals immediately before the onset of VT-VF events is obviously lower than that of RR Intervals recorded during sinus rhythms.For a better separation,the optimal range of threshold r is determined for two algorithms.ApEn and SampEn measures might be the suitable nonlinear parameters for shod-term prediction of life-threatening ventricular tachyarrhythmias in the application of the cardioversion and defibrillation.
DEFF Research Database (Denmark)
Boiroux, Dimitri; Hagdrup, Morten; Mahmoudi, Zeinab
2016-01-01
This paper presents a novel ensemble nonlinear model predictive control (NMPC) algorithm for glucose regulation in type 1 diabetes. In this approach, we consider a number of scenarios describing different uncertainties, for instance meals or metabolic variations. We simulate a population of 9 pat...
Tao, Jing; Sang, Yu; Teng, Qihui; Ni, Jinjing; Yang, Yi; Tsui, Stephen Kwok-Wing; Yao, Yu-Feng
2015-01-01
Lipoprotein NlpI of Escherichia coli is involved in the cell division, virulence, and bacterial interaction with eukaryotic host cells. To elucidate the functional mechanism of NlpI, we examined how NlpI affects cell division and found that induction of NlpI inhibits nucleoid division and halts cell growth. Consistent with these results, the cell division protein FtsZ failed to localize at the septum but diffused in the cytosol. Elevation of NlpI expression enhanced the transcription and the outer membrane localization of the heat shock protein IbpA and IbpB. Deletion of either ibpA or ibpB abolished the effects of NlpI induction, which could be restored by complementation. The C-terminus of NlpI is critical for the enhancement in IbpA and IbpB production, and the N-terminus of NlpI is required for the outer membrane localization of NlpI, IbpA, and IbpB. Furthermore, NlpI physically interacts with IbpB. These results indicate that over-expression of NlpI can interrupt the nucleoids division and the assembly of FtsZ at the septum, mediated by IbpA/IbpB, suggesting a role of the NlpI/IbpA/IbpB complex in the cell division.
Directory of Open Access Journals (Sweden)
Jing eTao
2015-02-01
Full Text Available Lipoprotein NlpI of Escherichia coli is involved in the cell division, virulence and bacterial interaction with eukaryotic host cells. To elucidate the functional mechanism of NlpI, we examined how NlpI affects cell division and found that induction of NlpI inhibits nucleoid division and halts cell growth. Consistent with these results, the cell division protein FtsZ failed to localize at the septum but diffused in the cytosol. Elevation of NlpI expression enhanced the transcription and the outer membrane localization of the heat shock protein IbpA and IbpB. Deletion of either ibpA or ibpB abolished the effects of NlpI induction, which could be restored by complementation. The C-terminus of NlpI is critical for the enhancement in IbpA and IbpB production, and the N-terminus of NlpI is required for the outer membrane localization of NlpI, IbpA, and IbpB. Furthermore, NlpI physically interacts with IbpB. These results indicate that over-expression of NlpI can interrupt the nucleoids division and the assembly of FtsZ at the septum, mediated by IbpA/IbpB, suggesting a role of the NlpI/IbpA/IbpB complex in the cell division.
Directory of Open Access Journals (Sweden)
Kohei Arai
2013-01-01
Full Text Available Method for image prediction with nonlinear control lines which are derived from extracted feature points from the previously acquired imagery data based on Kriging method and morphing method is proposed. Through comparisons between the proposed method and the conventional linear interpolation and widely used Cubic Spline interpolation methods, it is found that the proposed method is superior to the conventional methods in terms of prediction accuracy.
Predicting path from undulations for C. elegans using linear and nonlinear resistive force theory
Keaveny, Eric E.; Brown, André E. X.
2017-04-01
A basic issue in the physics of behaviour is the mechanical relationship between an animal and its surroundings. The model nematode C. elegans provides an excellent platform to explore this relationship due to its anatomical simplicity. Nonetheless, the physics of nematode crawling, in which the worm undulates its body to move on a wet surface, is not completely understood and the mathematical models often used to describe this phenomenon are empirical. We confirm that linear resistive force theory, one such empirical model, is effective at predicting a worm’s path from its sequence of body postures for forward crawling, reversing, and turning and for a broad range of different behavioural phenotypes observed in mutant worms. Worms recently isolated from the wild have a higher effective drag anisotropy than the laboratory-adapted strain N2 and most mutant strains. This means the wild isolates crawl with less surface slip, perhaps reflecting more efficient gaits. The drag anisotropies required to fit the observed locomotion data (70 ± 28 for the wild isolates) are significantly larger than the values measured by directly dragging worms along agar surfaces (3–10 in Rabets et al (2014 Biophys. J. 107 1980–7)). A proposed nonlinear extension of the resistive force theory model also provides accurate predictions, but does not resolve the discrepancy between the parameters required to achieve good path prediction and the experimentally measured parameters. We confirm that linear resistive force theory provides a good effective model of worm crawling that can be used in applications such as whole-animal simulations and advanced tracking algorithms, but that the nature of the physical interaction between worms and their most commonly studied laboratory substrate remains unresolved.
Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.
Brylinski, Michal
2013-11-25
A common strategy for virtual screening considers a systematic docking of a large library of organic compounds into the target sites in protein receptors with promising leads selected based on favorable intermolecular interactions. Despite a continuous progress in the modeling of protein-ligand interactions for pharmaceutical design, important challenges still remain, thus the development of novel techniques is required. In this communication, we describe eSimDock, a new approach to ligand docking and binding affinity prediction. eSimDock employs nonlinear machine learning-based scoring functions to improve the accuracy of ligand ranking and similarity-based binding pose prediction, and to increase the tolerance to structural imperfections in the target structures. In large-scale benchmarking using the Astex/CCDC data set, we show that 53.9% (67.9%) of the predicted ligand poses have RMSD of <2 Å (<3 Å). Moreover, using binding sites predicted by recently developed eFindSite, eSimDock models ligand binding poses with an RMSD of 4 Å for 50.0-39.7% of the complexes at the protein homology level limited to 80-40%. Simulations against non-native receptor structures, whose mean backbone rearrangements vary from 0.5 to 5.0 Å Cα-RMSD, show that the ratio of docking accuracy and the estimated upper bound is at a constant level of ∼0.65. Pearson correlation coefficient between experimental and predicted by eSimDock Ki values for a large data set of the crystal structures of protein-ligand complexes from BindingDB is 0.58, which decreases only to 0.46 when target structures distorted to 3.0 Å Cα-RMSD are used. Finally, two case studies demonstrate that eSimDock can be customized to specific applications as well. These encouraging results show that the performance of eSimDock is largely unaffected by the deformations of ligand binding regions, thus it represents a practical strategy for across-proteome virtual screening using protein models. eSimDock is freely
Directory of Open Access Journals (Sweden)
Shandilya Sharad
2012-10-01
.6% and 60.9%, respectively. Conclusion We report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.
Kudliskis, Voldis; Burden, Robert
2009-01-01
The strengths and weaknesses of Neuro-Linguistic Programming (NLP) are described with reference to its origins, previous research and comments from critics and supporters. A case is made for this allegedly theoretical approach to provide the kind of outcomes focused intervention that psychology and psychologists can offer to schools. In…
Applications of NLP Techniques to Computer-Assisted Authoring of Test Items for Elementary Chinese
Liu, Chao-Lin; Lin, Jen-Hsiang; Wang, Yu-Chun
2010-01-01
The authors report an implemented environment for computer-assisted authoring of test items and provide a brief discussion about the applications of NLP techniques for computer assisted language learning. Test items can serve as a tool for language learners to examine their competence in the target language. The authors apply techniques for…
The Promise of NLP and Speech Processing Technologies in Language Assessment
Chapelle, Carol A.; Chung, Yoo-Ree
2010-01-01
Advances in natural language processing (NLP) and automatic speech recognition and processing technologies offer new opportunities for language testing. Despite their potential uses on a range of language test item types, relatively little work has been done in this area, and it is therefore not well understood by test developers, researchers or…
Kudliskis, Voldis; Burden, Robert
2009-01-01
The strengths and weaknesses of Neuro-Linguistic Programming (NLP) are described with reference to its origins, previous research and comments from critics and supporters. A case is made for this allegedly theoretical approach to provide the kind of outcomes focused intervention that psychology and psychologists can offer to schools. In…
Hong, Ming; Mao, Zhu; Todd, Michael D.; Su, Zhongqing
2017-01-01
Nonlinear features extracted from Lamb wave signals (e.g., second harmonic generation) are demonstrably sensitive to microscopic damage, such as fatigue and material thermal degradation. While a majority of the existing studies in this context is focused on detecting undersized damage in metallic materials, the present study is aimed at expanding such a detection philosophy to the domain of composites, by linking the relative acoustic nonlinearity parameter (RANP) - a prominent nonlinear signal feature of Lamb waves - to barely visible impact damage (BVID) in composites. Nevertheless, considering immense uncertainties inevitably embedded in acquired signals (due to instrumentation, environment, operation, computation/estimation, etc.) which can adversely obfuscate nonlinear features, it is necessary to quantify the uncertainty of the RANP (i.e., its statistics) in order to enhance decision-making associated with its use as a detection feature. A probabilistic model is established to numerically evaluate the statistical distribution of the RANP. Using piezoelectric wafers, Lamb waves are acquired and processed to produce histograms of RANP estimates in both the healthy and damaged conditions of a CF/EP laminate, to which the model is compared, with good agreement observed between the model-predicted and experimentally-obtained statistic distributions of the RANP. With the model, BVID in the laminate is predicted. The model is further made use of to quantify the level of confidence in damage prediction results based on the concept of a receiver operating characteristic, enabling the practitioners to better understand the obtained results in the presence of uncertainties.
Nonlinear programming technique for analyzing flocculent settling data.
Rashid, Md Mamunur; Hayes, Donald F
2014-04-01
The traditional graphical approach for drawing iso-concentration curves to analyze flocculent settling data and design sedimentation basins poses difficulties for computer-based design methods. Thus, researchers have developed empirical approaches to analyze settling data. In this study, the ability of five empirical approaches to fit flocculent settling test data is compared. Particular emphasis is given to compare rule-based SETTLE and rule-based nonlinear programming (NLP) techniques as a viable alternative to the modeling methods of Berthouex and Stevens (1982), San (1989), and Ozer (1994). Published flocculent settling data are used to test the suitability of these empirical approaches. The primary objective, however, is to determine if the results of a NLP optimization technique are more reliable than those of other approaches. For this, mathematical curve fitting is conducted and the modeled concentration data are graphically compared to the observed data. The design results in terms of average solid removal efficiency as a function of detention times are also compared. Finally, the sum of squared errors values from these approaches are compared. The results indicate a strong correlation between observed and NLP modeled concentration data. The SETTLE and NLP approaches tend to be more conservative at lower retention times and less conservative at longer retention times. The SETTLE approach appears to be the most conservative. In terms of sum of squared errors values, NLP appears to be rank number one (i.e., best model) for eight data sets and number two for six data sets among 15 data sets. Therefore, NLP is recommended for analyzing flocculent settling data as a logical extension of other approaches. The NLP approach is further recommended as it is an optimization technique and uses conventional mathematical algorithms that can be solved using widely available software such as EXCEL and LINGO.
Ahn, Joong-Bae; Lee, Joonlee
2016-08-01
A new multimodel ensemble (MME) method that uses a genetic algorithm (GA) is developed and applied to the prediction of winter surface air temperature (SAT) and precipitation. The GA based on the biological process of natural evolution is a nonlinear method which solves nonlinear optimization problems. Hindcast data of winter SAT and precipitation from the six coupled general circulation models participating in the seasonal MME prediction system of the Asia-Pacific Economic Conference Climate Center are used. Three MME methods using GA (MME/GAs) are examined in comparison with a simple composite MME strategy (MS0): MS1 which applies GA to single-model ensembles (SMEs), MS2 which applies GA to each ensemble member and then performs a simple composite method for MME, and MS3 which applies GA to both MME and SME. MS3 shows the highest predictability compared to MS0, MS1, and MS2 for both winter SAT and precipitation. These results indicate that biases of ensemble members of each model and model ensemble are more reduced with MS3 than with other MME/GAs and MS0. The predictability of the MME/GAs shows a greater improvement than that of MS0, particularly in higher-latitude land areas. The reason for the more improved increase of predictability over the land area, particularly in MS3, seems to be the fact that GA is more efficient in finding an optimum solution in a complex region where nonlinear physical properties are evident.
Nonlinear continuous predictive controller for robot manipulator%机器人非线性连续预测控制
Institute of Scientific and Technical Information of China (English)
周建锁; 叶青; 刘志远; 裴润
2000-01-01
研究了一种机器人非线性连续预测控制方案，其预测模型通过对系统状态进行泰勒级数展开并作适当的截尾处理来获得，并通过优化性能指标求得控制律.与其他非线性模型预测控制相比，所得到的控制器具有解析形式，方法简单、计算量小、利于在线应用.仿真结果表明了非线性预测控制器的有效性%A time-continuous nonlinear model predictive control scheme for robot manipulator is studied with a predictive model acquired by truncating appropriately Taylor series expansion for system states, and control law identified by optimizing the cost function. Compared with other nonlinear predictive control using such empirical predictive model as Hammerstein model, Volterra model, NARMAX model and so on, the resulting controller, which has analytical formula with less calculation, is simple and fit for on-line application. Simulation results show the nonlinear predictive controller is very effective
Institute of Scientific and Technical Information of China (English)
马军海; 陈予恕
2001-01-01
The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time series in the phase space adopting one kind of nonlinear chaotic model were reconstructed. At first, the model parameters were estimated by using the improved least square method. Then as the precision was satisfied,the optimization method was used to estimate these parameters. At the end by using the obtained chaotic model, the future data of the chaotic time series in the phase space was predicted. Some representative experimental examples were analyzed to testify the models and the algorithms developed in this paper. The results show that if the algorithms developed here are adopted, the parameters of the corresponding chaotic model will be easily calculated well and true. Predictions of chaotic series in phase space make the traditional methods change from outer iteration to interpolations. And if the optimal model rank is chosen, the prediction precision will increase notably. Long term superior predictability of nonlinear chaotic models is proved to be irrational and unreasonable.
Diboson excess and Z‧-predictions via left-right nonlinear Higgs
Shu, Jing; Yepes, Juan
2016-12-01
The excess events reported by the ATLAS collaboration in the WZ-final state, and by the CMS collaboration in the e+e-jj, Wh and jj-final states, may be induced by the decays of a heavy boson W‧ in the 1.8-2 TeV mass range, here modeled via the larger local group SU(2)L × SU(2)R × U(1)B-L in a nonlinear dynamical Higgs scenario. The W‧-production cross-section at the 13 TeV LHC is around 700-1200 fb. This framework also predicts a heavy Z‧ boson with a mass of 2.5-4 TeV, and some decay channels testable in the LHC Run II. We determine the cross-section times branching fractions for the dijet, dilepton and top-pair Z‧-decay channels at the 13 TeV LHC around 2.3, 7.1, 70.2 fb, respectively, for MZ‧ = 2.5 TeV, while one/two orders of magnitude smaller for the dijet/dilepton and top-pair modes at MZ‧ = 4 TeV. Nonzero contributions from the effective operators, and the underlying Higgs sector of the model, will induce sizeable enhancement in the W+W- and Zh-final states that could be probed in the future LHC Run II.
Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control.
Wen, Chengjian; Mu, Yifen
2015-01-01
The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work) no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller.
Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
1997-01-01
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Diboson excess and $Z'$-predictions via left-right non-linear Higgs
Shu, Jing
2016-01-01
The excess events reported by the ATLAS Collaboration in the $WZ$-final state, and by the CMS Collaboration in the $e^+\\!e^- jj$, $Wh$ and $jj$-final states, may be induced by the decays of a heavy boson $W'$ in the 1.8-2 TeV mass range, here modelled via the larger local group $SU(2)_L\\times SU(2)_R\\times U(1)_{B-L}$ in a non-linear dynamical Higgs scenario. The $W'$-production cross section at the 13 TeV LHC is around 700-1200 fb. This framework also predicts a heavy $Z'$ boson with a mass of 2.5-4 TeV, and some decay channels testable in the LHC Run II. We determine the cross section times branching fractions for the dijet, dilepton and top-pair $Z'$-decay channels at the 13 TeV LHC around 2.3, 7.1, 70.2 fb respectively, for $M_{Z'}= 2.5$ TeV, while one/two orders of magnitude smaller for the dijet/dilepton and top-pair modes at $M_{Z'}= 4$ TeV. Non-zero contributions from the effective operators, and the underlying Higgs sector of the model, will induce sizeable enhancement in the $W^+W^-$ and $Z h$-final...
Power and Performance Management in Nonlinear Virtualized Computing Systems via Predictive Control.
Directory of Open Access Journals (Sweden)
Chengjian Wen
Full Text Available The problem of power and performance management captures growing research interest in both academic and industrial field. Virtulization, as an advanced technology to conserve energy, has become basic architecture for most data centers. Accordingly, more sophisticated and finer control are desired in virtualized computing systems, where multiple types of control actions exist as well as time delay effect, which make it complicated to formulate and solve the problem. Furthermore, because of improvement on chips and reduction of idle power, power consumption in modern machines shows significant nonlinearity, making linear power models(which is commonly adopted in previous work no longer suitable. To deal with this, we build a discrete system state model, in which all control actions and time delay effect are included by state transition and performance and power can be defined on each state. Then, we design the predictive controller, via which the quadratic cost function integrating performance and power can be dynamically optimized. Experiment results show the effectiveness of the controller. By choosing a moderate weight, a good balance can be achieved between performance and power: 99.76% requirements can be dealt with and power consumption can be saved by 33% comparing to the case with open loop controller.
Energy Technology Data Exchange (ETDEWEB)
Zhou, Ping; Song, Heda; Wang, Hong; Chai, Tianyou
2017-09-01
Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multi-phase and multi-field coupling and large time delay occur during its operation. In BF operation, the molten iron temperature (MIT) as well as Si, P and S contents of molten iron are the most essential molten iron quality (MIQ) indices, whose measurement, modeling and control have always been important issues in metallurgic engineering and automation field. This paper develops a novel data-driven nonlinear state space modeling for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques. First, to improve modeling efficiency, a data-driven hybrid method combining canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modeling inputs from multitudinous factors would affect the MIQ indices. Then, a Hammerstein model for the prediction of MIQ indices is established using the LS-SVM based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method to fit the complex nonlinear kernel function. Compared to the original Hammerstein model, this simplified model can not only significantly reduce the computational complexity, but also has almost the same reliability and accuracy for a stable prediction of MIQ indices. Last, in order to verify the practicability of the developed model, it is applied in designing a genetic algorithm based nonlinear predictive controller for multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approach.
Perdigão, Rui A. P.; Hall, Julia; Pires, Carlos A. L.; Blöschl, Günter
2017-04-01
Classical and stochastic dynamical system theories assume structural coherence and dynamic recurrence with invariants of motion that are not necessarily so. These are grounded on the unproven assumption of universality in the dynamic laws derived from statistical kinematic evaluation of non-representative empirical records. As a consequence, the associated formulations revolve around a restrictive set of configurations and intermittencies e.g. in an ergodic setting, beyond which any predictability is essentially elusive. Moreover, dynamical systems are fundamentally framed around dynamic codependence among intervening processes, i.e. entail essentially redundant interactions such as couplings and feedbacks. That precludes synergistic cooperation among processes that, whilst independent from each other, jointly produce emerging dynamic behaviour not present in any of the intervening parties. In order to overcome these fundamental limitations, we introduce a broad class of non-recursive dynamical systems that formulate dynamic emergence of unprecedented states in a fundamental synergistic manner, with fundamental principles in mind. The overall theory enables innovations to be predicted from the internal system dynamics before any a priori information is provided about the associated dynamical properties. The theory is then illustrated to anticipate, from non-emergent records, the spatiotemporal emergence of multiscale hyper chaotic regimes, critical transitions and structural coevolutionary changes in synthetic and real-world complex systems. Example applications are provided within the hydro-climatic context, formulating and dynamically forecasting evolving hydro-climatic distributions, including the emergence of extreme precipitation and flooding in a structurally changing hydro-climate system. Validation is then conducted with a posteriori verification of the simulated dynamics against observational records. Agreement between simulations and observations is
Park, Sang-Kyu; Link, Christopher D; Johnson, Thomas E
2010-02-01
Recent studies have shown that the rate of aging can be modulated by diverse interventions. Dietary restriction is the most widely used intervention to promote longevity; however, the mechanisms underlying the effect of dietary restriction remain elusive. In a previous study, we identified two novel genes, nlp-7 and cup-4, required for normal longevity in Caenorhabditis elegans. nlp-7 is one of a set of neuropeptide-like protein genes; cup-4 encodes an ion-channel involved in endocytosis by coelomocytes. Here, we assess whether nlp-7 and cup-4 mediate longevity increases by dietary restriction. RNAi of nlp-7 or cup-4 significantly reduces the life span of the eat-2 mutant, a genetic model of dietary restriction, but has no effect on the life span of long-lived mutants resulting from reduced insulin/IGF-1 signaling or dysfunction of the mitochondrial electron transport chain. The life-span extension observed in wild-type N2 worms by dietary restriction using bacterial dilution is prevented significantly in nlp-7 and cup-4 mutants. RNAi knockdown of genes encoding candidate receptors of NLP-7 and genes involved in endocytosis by coelomocytes also specifically shorten the life span of the eat-2 mutant. We conclude that two novel pathways, NLP-7 signaling and endocytosis by coelomocytes, are required for life extension under dietary restriction in C. elegans.
Directory of Open Access Journals (Sweden)
Gang Chen
2012-01-01
Full Text Available It is not easy for the system identification-based reduced-order model (ROM and even eigenmode based reduced-order model to predict the limit cycle oscillation generated by the nonlinear unsteady aerodynamics. Most of these traditional ROMs are sensitive to the flow parameter variation. In order to deal with this problem, a support vector machine- (SVM- based ROM was investigated and the general construction framework was proposed. The two-DOF aeroelastic system for the NACA 64A010 airfoil in transonic flow was then demonstrated for the new SVM-based ROM. The simulation results show that the new ROM can capture the LCO behavior of the nonlinear aeroelastic system with good accuracy and high efficiency. The robustness and computational efficiency of the SVM-based ROM would provide a promising tool for real-time flight simulation including nonlinear aeroelastic effects.
Fleming, David P.; Poplawski, J. V.
2002-01-01
Rolling-element bearing forces vary nonlinearly with bearing deflection. Thus an accurate rotordynamic transient analysis requires bearing forces to be determined at each step of the transient solution. Analyses have been carried out to show the effect of accurate bearing transient forces (accounting for non-linear speed and load dependent bearing stiffness) as compared to conventional use of average rolling-element bearing stiffness. Bearing forces were calculated by COBRA-AHS (Computer Optimized Ball and Roller Bearing Analysis - Advanced High Speed) and supplied to the rotordynamics code ARDS (Analysis of Rotor Dynamic Systems) for accurate simulation of rotor transient behavior. COBRA-AHS is a fast-running 5 degree-of-freedom computer code able to calculate high speed rolling-element bearing load-displacement data for radial and angular contact ball bearings and also for cylindrical and tapered roller beatings. Results show that use of nonlinear bearing characteristics is essential for accurate prediction of rotordynamic behavior.
A hybrid nonlinear programming method for design optimization
Rajan, S. D.
1986-01-01
Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.
Directory of Open Access Journals (Sweden)
J. Miksovsky
2005-01-01
Full Text Available We investigated the usability of the method of local linear models (LLM, multilayer perceptron neural network (MLP NN and radial basis function neural network (RBF NN for the construction of temporal and spatial transfer functions between different meteorological quantities, and compared the obtained results both mutually and to the results of multiple linear regression (MLR. The tested methods were applied for the short-term prediction of daily mean temperatures and for the downscaling of NCEP/NCAR reanalysis data, using series of daily mean, minimum and maximum temperatures from 25 European stations as predictands. None of the tested nonlinear methods was recognized to be distinctly superior to the others, but all nonlinear techniques proved to be better than linear regression in the majority of the cases. It is also discussed that the most frequently used nonlinear method, the MLP neural network, may not be the best choice for processing the climatic time series - LLM method or RBF NNs can offer a comparable or slightly better performance and they do not suffer from some of the practical disadvantages of MLPs. Aside from comparing the performance of different methods, we paid attention to geographical and seasonal variations of the results. The forecasting results showed that the nonlinear character of relations between climate variables is well apparent over most of Europe, in contrast to rather weak nonlinearity in the Mediterranean and North Africa. No clear large-scale geographical structure of nonlinearity was identified in the case of downscaling. Nonlinearity also seems to be noticeably stronger in winter than in summer in most locations, for both forecasting and downscaling.
Zitelli, Gregory; Djouadi, Seddik M; Day, Judy D
2015-10-01
The inflammatory response aims to restore homeostasis by means of removing a biological stress, such as an invading bacterial pathogen. In cases of acute systemic inflammation, the possibility of collateral tissue damage arises, which leads to a necessary down-regulation of the response. A reduced ordinary differential equations (ODE) model of acute inflammation was presented and investigated in [10]. That system contains multiple positive and negative feedback loops and is a highly coupled and nonlinear ODE. The implementation of nonlinear model predictive control (NMPC) as a methodology for determining proper therapeutic intervention for in silico patients displaying complex inflammatory states was initially explored in [5]. Since direct measurements of the bacterial population and the magnitude of tissue damage/dysfunction are not readily available or biologically feasible, the need for robust state estimation was evident. In this present work, we present results on the nonlinear reachability of the underlying model, and then focus our attention on improving the predictability of the underlying model by coupling the NMPC with a particle filter. The results, though comparable to the initial exploratory study, show that robust state estimation of this highly nonlinear model can provide an alternative to prior updating strategies used when only partial access to the unmeasurable states of the system are available.
Energy Technology Data Exchange (ETDEWEB)
Buchko, Garry W.(BATTELLE (PACIFIC NW LAB)); Weinfeld, Michael (Cross Cancer Institute)
2002-01-01
The nitroimidazole-linked phenanthridines 2-NLP-3 (5-[3-(2-nitro-1-imidazoyl)-propyl]-phenanthridinium bromide) and 2-NLP-4 (5-[3-(2-nitro-1-imidazoyl)-butyl1]-phenanthridinium bromide) are composed of the radiosensitizer, 2-nitroimidazole, attached to the DNA intercalator phenanthridine via a 3- and 4-carbon linker, respectively. Previous in vitro assays show both compounds to be 10 - 100 times more efficient as hypoxic cell radiosensitizer, misonidazole[Cowan et al., Radiat. Res. 127, 81-89, 1991]. Here we have used a 32P postlabeling assay and 5'-end labeled oligonucleotide assay to compare the radiogenic DNA damage generated in the presence of 2-NLP-3, 2-NLP-4 compared to irradiation in the presence of misonidazole. This may account, at least in part, for the greater cellular radiosensitization shown by the nitroimidazole-linked phenanthridines over misonidazole.
Carrassi, Alberto; Ghil, Michael; Trevisan, Anna; Uboldi, Francesco
2008-06-01
We study prediction-assimilation systems, which have become routine in meteorology and oceanography and are rapidly spreading to other areas of the geosciences and of continuum physics. The long-term, nonlinear stability of such a system leads to the uniqueness of its sequentially estimated solutions and is required for the convergence of these solutions to the system's true, chaotic evolution. The key ideas of our approach are illustrated for a linearized Lorenz system. Stability of two nonlinear prediction-assimilation systems from dynamic meteorology is studied next via the complete spectrum of their Lyapunov exponents; these two systems are governed by a large set of ordinary and of partial differential equations, respectively. The degree of data-induced stabilization is crucial for the performance of such a system. This degree, in turn, depends on two key ingredients: (i) the observational network, either fixed or data-adaptive, and (ii) the assimilation method.
Ensemble prediction of floods – catchment non-linearity and forecast probabilities
Directory of Open Access Journals (Sweden)
C. Reszler
2007-07-01
Full Text Available Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less, the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.
Performance prediction of gas turbines by solving a system of non-linear equations
Energy Technology Data Exchange (ETDEWEB)
Kaikko, J.
1998-09-01
This study presents a novel method for implementing the performance prediction of gas turbines from the component models. It is based on solving the non-linear set of equations that corresponds to the process equations, and the mass and energy balances for the engine. General models have been presented for determining the steady state operation of single components. Single and multiple shad arrangements have been examined with consideration also being given to heat regeneration and intercooling. Emphasis has been placed upon axial gas turbines of an industrial scale. Applying the models requires no information of the structural dimensions of the gas turbines. On comparison with the commonly applied component matching procedures, this method incorporates several advantages. The application of the models for providing results is facilitated as less attention needs to be paid to calculation sequences and routines. Solving the set of equations is based on zeroing co-ordinate functions that are directly derived from the modelling equations. Therefore, controlling the accuracy of the results is easy. This method gives more freedom for the selection of the modelling parameters since, unlike for the matching procedures, exchanging these criteria does not itself affect the algorithms. Implicit relationships between the variables are of no significance, thus increasing the freedom for the modelling equations as well. The mathematical models developed in this thesis will provide facilities to optimise the operation of any major gas turbine configuration with respect to the desired process parameters. The computational methods used in this study may also be adapted to any other modelling problems arising in industry. (orig.) 36 refs.
Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines
Janakiraman, Vijay Manikandan; Nguyen, XuanLong; Assanis, Dennis
2015-01-01
Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with a high fuel efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates a model based control approach for automotive application. HCCI engine control is a nonlinear, multi-input multi-output problem with state and actuator constraints which makes controller design a challenging task. Typical HCCI controllers make use of a first princi...
The nucleoplasmin homolog NLP mediates centromere clustering and anchoring to the nucleolus.
Padeken, Jan; Mendiburo, María José; Chlamydas, Sarantis; Schwarz, Hans-Jürgen; Kremmer, Elisabeth; Heun, Patrick
2013-04-25
Centromere clustering during interphase is a phenomenon known to occur in many different organisms and cell types, yet neither the factors involved nor their physiological relevance is well understood. Using Drosophila tissue culture cells and flies, we identified a network of proteins, including the nucleoplasmin-like protein (NLP), the insulator protein CTCF, and the nucleolus protein Modulo, to be essential for the positioning of centromeres. Artificial targeting further demonstrated that NLP and CTCF are sufficient for clustering, while Modulo serves as the anchor to the nucleolus. Centromere clustering was found to depend on centric chromatin rather than specific DNA sequences. Moreover, unclustering of centromeres results in the spatial destabilization of pericentric heterochromatin organization, leading to partial defects in the silencing of repetitive elements, defects during chromosome segregation, and genome instability.
UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text.
Demner-Fushman, Dina; Mork, James G; Shooshan, Sonya E; Aronson, Alan R
2010-08-01
Identification of medical terms in free text is a first step in such Natural Language Processing (NLP) tasks as automatic indexing of biomedical literature and extraction of patients' problem lists from the text of clinical notes. Many tools developed to perform these tasks use biomedical knowledge encoded in the Unified Medical Language System (UMLS) Metathesaurus. We continue our exploration of automatic approaches to creation of subsets (UMLS content views) which can support NLP processing of either the biomedical literature or clinical text. We found that suppression of highly ambiguous terms in the conservative AutoFilter content view can partially replace manual filtering for literature applications, and suppression of two character mappings in the same content view achieves 89.5% precision at 78.6% recall for clinical applications.
Mehrabi, Saeed; Krishnan, Anand; Roch, Alexandra M; Schmidt, Heidi; Li, DingCheng; Kesterson, Joe; Beesley, Chris; Dexter, Paul; Schmidt, Max; Palakal, Mathew; Liu, Hongfang
2015-01-01
In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework and consisted of section segmentation, relation discovery, and negation detection. The system was evaluated on data from two institutions. The family history identification precision was consistent across the institutions shifting from 88.9% on Indiana University (IU) dataset to 87.8% on Mayo Clinic dataset. Customizing the algorithm on the the Mayo Clinic data, increased its precision to 88.1%. The family member relation discovery achieved precision, recall, and F-measure of 75.3%, 91.6% and 82.6% respectively. Negation detection resulted in precision of 99.1%. The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance.
Liang, Jennifer J; Tsou, Ching-Huei; Devarakonda, Murthy V
2017-01-01
Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management. A critical need in developing such methods is the "ground truth" dataset needed for training and testing the algorithms. Beyond localizable, relatively simple tasks, ground truth creation is a significant challenge because medical experts, just as physicians in patient care, have to assimilate vast amounts of data in EHR systems. To mitigate potential inaccuracies of the cognitive challenges, we present an iterative vetting approach for creating the ground truth for complex NLP tasks. In this paper, we present the methodology, and report on its use for an automated problem list generation task, its effect on the ground truth quality and system accuracy, and lessons learned from the effort.
Liang, Jennifer J.; Tsou, Ching-Huei; Devarakonda, Murthy V.
2017-01-01
Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management. A critical need in developing such methods is the “ground truth” dataset needed for training and testing the algorithms. Beyond localizable, relatively simple tasks, ground truth creation is a significant challenge because medical experts, just as physicians in patient care, have to assimilate vast amounts of data in EHR systems. To mitigate potential inaccuracies of the cognitive challenges, we present an iterative vetting approach for creating the ground truth for complex NLP tasks. In this paper, we present the methodology, and report on its use for an automated problem list generation task, its effect on the ground truth quality and system accuracy, and lessons learned from the effort. PMID:28815130
Shao, Shujuan; Liu, Rong; Wang, Yang; Song, Yongmei; Zuo, Lihui; Xue, Liyan; Lu, Ning; Hou, Ning; Wang, Mingrong; Yang, Xiao; Zhan, Qimin
2010-02-01
Disruption of mitotic events contributes greatly to genomic instability and results in mutator phenotypes. Indeed, abnormalities of mitotic components are closely associated with malignant transformation and tumorigenesis. Here we show that ninein-like protein (Nlp), a recently identified BRCA1-associated centrosomal protein involved in microtubule nucleation and spindle formation, is an oncogenic protein. Nlp was found to be overexpressed in approximately 80% of human breast and lung carcinomas analyzed. In human lung cancers, this deregulated expression was associated with NLP gene amplification. Further analysis revealed that Nlp exhibited strong oncogenic properties; for example, it conferred to NIH3T3 rodent fibroblasts the capacity for anchorage-independent growth in vitro and tumor formation in nude mice. Consistent with these data, transgenic mice overexpressing Nlp displayed spontaneous tumorigenesis in the breast, ovary, and testicle within 60 weeks. In addition, Nlp overexpression induced more rapid onset of radiation-induced lymphoma. Furthermore, mouse embryonic fibroblasts (MEFs) derived from Nlp transgenic mice showed centrosome amplification, suggesting that Nlp overexpression mimics BRCA1 loss. These findings demonstrate that Nlp abnormalities may contribute to genomic instability and tumorigenesis and suggest that Nlp might serve as a potential biomarker for clinical diagnosis and therapeutic target.
Complete Complimentary Results Report of the MARF's NLP Approach to the DEFT 2010 Competition
Mokhov, Serguei A
2010-01-01
This paper complements the main DEFT'10 article describing the MARF approach to the DEFT'10 NLP competition. This paper is aimed to present the complete result sets of all the conducted experiments and their settings in the resulting tables highlighting the approach and the best results, but also showing the worse and the worst and their analysis. This is the first iteration of the initial release of the results.
Directory of Open Access Journals (Sweden)
Zhi-yuan Li
2014-01-01
Full Text Available As the core of the effective financial crisis prevention, enterprise finance crisis prediction has been the focal attention of both theorists and businessmen. Financial crisis predictions need to apply a variety of financial and operating indicators for its analysis. Therefore, a new evaluation model based on nonlinear programming is established, the nature of the model is proved, the detailed solution steps of the model are given, and the significance and algorithm of the model are thoroughly discussed in this study. The proposed model can deal with the case of missing data, and has the good isotonic property and profound theoretical background. In the empirical analysis to predict the financial crisis and through the comparison of the analysis of historical data and the real enterprises with financial crisis, we find that the results are in accordance with the real enterprise financial conditions and the proposed model has a good predictive ability.
Pérez-Marín, D; Garrido-Varo, A; Guerrero, J E; Fearn, T; Davies, A M C
2008-05-01
For quantitative applications, the most common usage of near-infrared reflection spectroscopy (NIRS) technology, calibration involves establishing a mathematical relationship between spectral data and data provided by the reference. This model may be fairly complex, since the near-infrared spectrum is highly variable and contains physical/chemical information for the sample that may be redundant, and multivariate calibration is usually required. When the relationship to be modeled is nonlinear, classical regression methods are inadequate, and more complex strategies and algorithms must be sought in order to model this nonlinearity. The development of NIRS calibrations to predict the ingredient composition, i.e., the inclusion percentage of each ingredient, in compound feeds is a complex task, due to the nature of the parameters to be predicted and to the heterogeneous nature of the matrices/formulas in which each ingredient participates. The present paper evaluates the use of least squares support vector machines (LSSVM) and two local calibration methods, CARNAC and locally biased regression, for developing NIRS models to predict two of the most representative ingredients in compound feed formulations, wheat and sunflower meal, using a large spectral library of 7523 commercial compound feed samples. For both ingredients, the best results were obtained using CARNAC, with standard errors of prediction (SEP) of 1.7% and 0.60% for wheat and sunflower meal, respectively, and even better results when the algorithm was allowed to refuse to predict 10% of the unknowns. Meanwhile, LSSVM performed less well on wheat (SEP 2.6%) but comparably on sunflower meal (SEP 0.60%), giving results very similar to those reported previously for artificial neural networks. Locally biased regression was the least successful of the three methods, with SEPs of 3.3% for wheat and 0.72% for sunflower meal. All the nonlinear methods improved on the standard approach using partial least squares
Zheng, Qin; Yang, Zubin; Sha, Jianxin; Yan, Jun
2017-02-01
In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the
Directory of Open Access Journals (Sweden)
Fan Liang
2013-01-01
Full Text Available Off‐pump coronary artery bypass graft surgery outperforms the traditional on‐pump surgery because the assisted robotic tools can cancel the relative motion between the beating heart and the robotic tools, which reduces post‐surgery complications for patients. The challenge for the robot assisted tool when tracking the beating heart is the abrupt change caused by the nonlinear nature of heart motion and high precision surgery requirements. A characteristic analysis of 3D heart motion data through bi‐spectral analysis demonstrates the quadratic nonlinearity in heart motion. Therefore, it is necessary to introduce nonlinear heart motion prediction into the motion tracking control procedures. In this paper, the heart motion tracking problem is transformed into a heart motion model following problem by including the adaptive heart motion model into the controller. Moreover, the model following algorithm with the nonlinear heart motion model embedded inside provides more accurate future reference by the quadratic term of sinusoid series, which could enhance the tracking accuracy of sharp change point and approximate the motion with sufficient detail. The experiment results indicate that the proposed algorithm outperforms the linear prediction‐based model following controller in terms of tracking accuracy (root mean square.
An early illness recognition framework using a temporal Smith Waterman algorithm and NLP.
Hajihashemi, Zahra; Popescu, Mihail
2013-01-01
In this paper we propose a framework for detecting health patterns based on non-wearable sensor sequence similarity and natural language processing (NLP). In TigerPlace, an aging in place facility from Columbia, MO, we deployed 47 sensor networks together with a nursing electronic health record (EHR) system to provide early illness recognition. The proposed framework utilizes sensor sequence similarity and NLP on EHR nursing comments to automatically notify the physician when health problems are detected. The reported methodology is inspired by genomic sequence annotation using similarity algorithms such as Smith Waterman (SW). Similarly, for each sensor sequence, we associate health concepts extracted from the nursing notes using Metamap, a NLP tool provided by Unified Medical Language System (UMLS). Since sensor sequences, unlike genomics ones, have an associated time dimension we propose a temporal variant of SW (TSW) to account for time. The main challenges presented by our framework are finding the most suitable time sequence similarity and aggregation of the retrieved UMLS concepts. On a pilot dataset from three Tiger Place residents, with a total of 1685 sensor days and 626 nursing records, we obtained an average precision of 0.64 and a recall of 0.37.
Directory of Open Access Journals (Sweden)
Bo Wang
2014-01-01
Full Text Available When predicting the nonlinear stability of high-speed spindle system, it is necessary to create an accurate model that reflects the dynamic characteristics of the whole system, including the spindle-bearing joint and spindle-holder-tool joints. In this paper, the distribution spring model of spindle-holder-tool joints was built with the consideration of its dynamic characteristics; the five-DOF dynamic model of the angle contact ball bearing was also established to study the influence of speed and preload on the spindle-bearing joint, both of which were used in the general whole complete spindle system FEM model. The rationality of the model was verified by comparison with the FRF of traditional rigid model and experiments. At last, the influences of speed and cutting force on the nonlinear stability were analyzed by amplitude spectrum, bifurcation, and Poincaré mapping. The results provided a theoretical basis and an evaluating criterion for nonlinear stability prediction and product surface quality improvement.
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Hui Cao
2014-01-01
Full Text Available Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.
Cao, Hui; Yan, Xingyu; Li, Yaojiang; Wang, Yanxia; Zhou, Yan; Yang, Sanchun
2014-01-01
Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.
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Ming Dong
2010-01-01
Full Text Available The primary objective of engineering asset management is to optimize assets service delivery potential and to minimize the related risks and costs over their entire life through the development and application of asset health and usage management in which the health and reliability prediction plays an important role. In real-life situations where an engineering asset operates under dynamic operational and environmental conditions, the lifetime of an engineering asset is generally described as monitored nonlinear time-series data and subject to high levels of uncertainty and unpredictability. It has been proved that application of data mining techniques is very useful for extracting relevant features which can be used as parameters for assets diagnosis and prognosis. In this paper, a tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction is given. Besides that an overview on health and reliability prediction techniques for engineering assets is covered, this tutorial will focus on concepts, models, algorithms, and applications of hidden Markov models (HMMs and hidden semi-Markov models (HSMMs in engineering asset health prognosis, which are representatives of recent engineering asset health prediction techniques.
Energy Technology Data Exchange (ETDEWEB)
Geniet, F; Leon, J [Physique Mathematique et Theorique, CNRS-UMR 5825, 34095 Montpellier (France)
2003-05-07
A nonlinear system possessing a natural forbidden band gap can transmit energy of a signal with a frequency in the gap, as recently shown for a nonlinear chain of coupled pendulums (Geniet and Leon 2002 Phys. Rev. Lett. 89 134102). This process of nonlinear supratransmission, occurring at a threshold that is exactly predictable in many cases, is shown to have a simple experimental realization with a mechanical chain of pendulums coupled by a coil spring. It is then analysed in more detail. First we go to different (nonintegrable) systems which do sustain nonlinear supratransmission. Then a Josephson transmission line (a one-dimensional array of short Josephson junctions coupled through superconducting wires) is shown to also sustain nonlinear supratransmission, though being related to a different class of boundary conditions, and despite the presence of damping, finiteness, and discreteness. Finally, the mechanism at the origin of nonlinear supratransmission is found to be a nonlinear instability, and this is briefly discussed here.
Ogi, Hirotsugu; Hirao, Masahiko; Aoki, Shinji
2001-07-01
A nonlinear acoustic measurement is studied for fatigue damage monitoring. An electromagnetic acoustic transducer (EMAT) magnetostrictively couples to a surface-shear-wave resonance along the circumference of a rod specimen during rotating bending fatigue of carbon steels. Excitation of the EMAT at half of the resonance frequency caused the standing wave to contain only the second-harmonic component, which was received by the same EMAT to determine the second-harmonic amplitude. Thus measured surface-wave nonlinearity always showed two distinct peaks at 60% and 85% of the total life. We attribute the earlier peak to crack nucleation and growth, and the later peak to an increase of free dislocations associated with crack extension in the final stage. This noncontact resonance-EMAT measurement can monitor the evolution of the surface-shear-wave nonlinearity throughout the metal's fatigue life and detect the pertinent precursors of the eventual failure.
Baring, M G; Reynolds, S P; Grenier, I; Goret, P; Baring, Matthew G.; Ellison, Donald C.; Reynolds, Stephen P; Grenier, Isabelle; Goret, Philippe
1999-01-01
Supernova remnants (SNRs) are widely believed to be the principal source of galactic cosmic rays. Such energetic particles can produce gamma-rays and lower energy photons via interactions with the ambient plasma. In this paper, we present results from a Monte Carlo simulation of non-linear shock structure and acceleration coupled with photon emission in shell-like SNRs. These non-linearities are a by-product of the dynamical influence of the accelerated cosmic rays on the shocked plasma and result in distributions of cosmic rays which deviate from pure power-laws. Such deviations are crucial to acceleration efficiency and spectral considerations, producing GeV/TeV intensity ratios that are quite different from test particle predictions. The Sedov scaling solution for SNR expansions is used to estimate important shock parameters for input into the Monte Carlo simulation. We calculate ion and electron distributions that spawn neutral pion decay, bremsstrahlung, inverse Compton, and synchrotron emission, yieldin...
Energy Technology Data Exchange (ETDEWEB)
Burr, T.L.
1994-04-01
This report is a primer on the analysis of both linear and nonlinear time series with applications in nuclear safeguards and nonproliferation. We analyze eight simulated and two real time series using both linear and nonlinear modeling techniques. The theoretical treatment is brief but references to pertinent theory are provided. Forecasting is our main goal. However, because our most common approach is to fit models to the data, we also emphasize checking model adequacy by analyzing forecast errors for serial correlation or nonconstant variance.
Energy Technology Data Exchange (ETDEWEB)
Burr, T.L.
1994-04-01
This report is a primer on the analysis of both linear and nonlinear time series with applications in nuclear safeguards and nonproliferation. We analyze eight simulated and two real time series using both linear and nonlinear modeling techniques. The theoretical treatment is brief but references to pertinent theory are provided. Forecasting is our main goal. However, because our most common approach is to fit models to the data, we also emphasize checking model adequacy by analyzing forecast errors for serial correlation or nonconstant variance.
Directory of Open Access Journals (Sweden)
Mohammad M. Kashani
2016-01-01
Full Text Available A numerical model is presented that enables simulation of the nonlinear flexural response of corroded reinforced concrete (RC components. The model employs a force-based nonlinear fibre beam-column element. A new phenomenological uniaxial material model for corroded reinforcing steel is used. This model accounts for the impact of corrosion on buckling strength, postbuckling behaviour, and low-cycle fatigue degradation of vertical reinforcement under cyclic loading. The basic material model is validated through comparison of simulated and observed responses for uncorroded RC columns. The model is used to explore the impact of corrosion on the inelastic response of corroded RC columns.
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Sharad Shandilya
Full Text Available The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR, rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals.Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA technique.358 defibrillations were evaluated (218 unsuccessful and 140 successful. Non-linear properties (Lyapunov exponent > 0 of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity outperformed AMSA (53.6% sensitivity. At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity.At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations
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Muayad Al-Qaisy
2013-04-01
Full Text Available In this article, multi-input multi-output (MIMO linear model predictive controller (LMPC based on state space model and nonlinear model predictive controller based on neural network (NNMPC are applied on a continuous stirred tank reactor (CSTR. The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA and reactor temperature (T. NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.
Fast numerical methods for mixed-integer nonlinear model-predictive control
Kirches, Christian
2011-01-01
Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates can be given even after discretization and rounding.
Samareh, Hossein; Khoshrou, Seyed Hassan; Shahriar, Kourosh; Ebadzadeh, Mohammad Mehdi; Eslami, Mohammad
2017-09-01
When particle's wave velocity resulting from mining blasts exceeds a certain level, then the intensity of produced vibrations incur damages to the structures around the blasting regions. Development of mathematical models for predicting the peak particle velocity (PPV) based on the properties of the wave emission environment is an appropriate method for better designing of blasting parameters, since the probability of incurred damages can considerably be mitigated by controlling the intensity of vibrations at the building sites. In this research, first out of 11 blasting and geo-mechanical parameters of rock masses, four parameters which had the greatest influence on the vibrational wave velocities were specified using regression analysis. Thereafter, some models were developed for predicting the PPV by nonlinear regression analysis (NLRA) and artificial neural network (ANN) with correlation coefficients of 0.854 and 0.662, respectively. Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm. The values of PPV were estimated for 18 testing dataset in order to evaluate the accuracy of the prediction and performance of the developed models. By calculating statistical indices for the test recorded maps, it was found that the optimized model can predict the PPV with a lower error than the other two models. Furthermore, considering the correlation coefficient (0.75) between the values of the PPV measured and predicted by the optimized nonlinear model, it was found that this model possesses a more desirable performance for predicting the PPV than the other two models.
Xu, Qingping; Mengin-Lecreulx, Dominique; Patin, Delphine; Grant, Joanna C; Chiu, Hsiu-Ju; Jaroszewski, Lukasz; Knuth, Mark W; Godzik, Adam; Lesley, Scott A; Elsliger, Marc-André; Deacon, Ashley M; Wilson, Ian A
2014-12-02
GlcNAc-1,6-anhydro-MurNAc-tetrapeptide is a major peptidoglycan degradation intermediate and a cytotoxin. It is generated by lytic transglycosylases and further degraded and recycled by various enzymes. We have identified and characterized a highly specific N-acetylmuramoyl-L-alanine amidase (AmiA) from Bacteroides uniformis, a member of the DUF1460 protein family, that hydrolyzes GlcNAc-1,6-anhydro-MurNAc-peptide into disaccharide and stem peptide. The high-resolution apo structure at 1.15 Å resolution shows that AmiA is related to NlpC/P60 γ-D-Glu-meso-diaminopimelic acid amidases and shares a common catalytic core and cysteine peptidase-like active site. AmiA has evolved structural adaptations that reconfigure the substrate recognition site. The preferred substrates for AmiA were predicted in silico based on structural and bioinformatics data, and subsequently were characterized experimentally. Further crystal structures of AmiA in complexes with GlcNAc-1,6-anhydro-MurNAc and GlcNAc have enabled us to elucidate substrate recognition and specificity. DUF1460 is highly conserved in structure and defines another amidase family.
Solutions manual to accompany Nonlinear programming
Bazaraa, Mokhtar S; Shetty, C M
2014-01-01
As the Solutions Manual, this book is meant to accompany the main title, Nonlinear Programming: Theory and Algorithms, Third Edition. This book presents recent developments of key topics in nonlinear programming (NLP) using a logical and self-contained format. The volume is divided into three sections: convex analysis, optimality conditions, and dual computational techniques. Precise statements of algortihms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations, and numerous exercises to aid readers in understanding the concepts a
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Hongbo Liu
2015-11-01
Full Text Available The electrocaloric (EC effect has been paid great attentions recently for applications on cooling or electricity generation. However, the directly commercial measurement equipment for the effect is still unavailable. Here we report a novel method to predict EC effect by non-linear behaviors of dielectric permittivity under temperature and electric fields. According to the method, the analytical equations of EC temperature change ΔT are directly given for normal ferroelectrics and relaxor. The calculations have been performed on several materials and it is shown that the method is suitable for both inorganic and organic ferroelectrics, and relaxor.
Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan
2017-06-01
Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.
Fracture prediction using modified mohr coulomb theory for non-linear strain paths using AA3104-H19
Dick, Robert; Yoon, Jeong Whan
2016-08-01
Experiment results from uniaxial tensile tests, bi-axial bulge tests, and disk compression tests for a beverage can AA3104-H19 material are presented. The results from the experimental tests are used to determine material coefficients for both Yld2000 and Yld2004 models. Finite element simulations are developed to study the influence of materials model on the predicted earing profile. It is shown that only the YLD2004 model is capable of accurately predicting the earing profile as the YLD2000 model only predicts 4 ears. Excellent agreement with the experimental data for earing is achieved using the AA3104-H19 material data and the Yld2004 constitutive model. Mechanical tests are also conducted on the AA3104-H19 to generate fracture data under different stress triaxiality conditions. Tensile tests are performed on specimens with a central hole and notched specimens. Torsion of a double bridge specimen is conducted to generate points near pure shear conditions. The Nakajima test is utilized to produce points in bi-axial tension. The data from the experiments is used to develop the fracture locus in the principal strain space. Mapping from principal strain space to stress triaxiality space, principal stress space, and polar effective plastic strain space is accomplished using a generalized mapping technique. Finite element modeling is used to validate the Modified Mohr-Coulomb (MMC) fracture model in the polar space. Models of a hole expansion during cup drawing and a cup draw/reverse redraw/expand forming sequence demonstrate the robustness of the modified PEPS fracture theory for the condition with nonlinear forming paths and accurately predicts the onset of failure. The proposed methods can be widely used for predicting failure for the examples which undergo nonlinear strain path including rigid-packaging and automotive forming.
Analytical Predictions of Field and Plasma Dynamics during Nonlinear Weibel-Mediated Flow Collisions
Ruyer, C.; Gremillet, L.; Bonnaud, G.; Riconda, C.
2016-08-01
The formation of collisionless shocks mediated by the ion Weibel instability is addressed theoretically and numerically in the nonrelativistic limit. First, the model developed in C. Ruyer et al., Phys. Plasmas 22, 032102 (2015) for the weakly nonlinear ion Weibel instability in a symmetric two-stream system is shown to be consistent with recent experimental and simulation results. Large-scale kinetic simulations are then performed to clarify the spatiotemporal evolution of the magnetic-field and plasma properties in the subsequent strongly nonlinear phase leading to shock formation. A simple analytical model is proposed which captures the simulation results up to a point close to ion isotropization. Electron screening effects are found important in the instability dynamics, so that numerical simulations using a nonphysical electron mass should be considered with caution.
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Ming Zhang
Full Text Available BACKGROUND: The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients' survival with benign end-stage liver diseases (BESLD by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD and a general-illness severity model (the sequential organ failure assessment score, or SOFA score. METHODOLOGY/PRINCIPAL FINDINGS: With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na(+; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, with c-statistic of 0.91 (P<0.001 in one-year and 0.88 (P<0.001 in two-year prediction. The performances of SOFA and MELD were fairly poor in prognostic assessment, with c-statistics of 0.70 and 0.66, respectively, in one-year prediction, and 0.67 and 0.65 in two-year prediction. CONCLUSIONS/SIGNIFICANCE: The posttransplant prognosis is a multidimensional nonlinear
solveME: fast and reliable solution of nonlinear ME models
DEFF Research Database (Denmark)
Yang, Laurence; Ma, Ding; Ebrahim, Ali
2016-01-01
reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. Results: Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models...
Nagode, Marko; Šeruga, Domen
An approach is presented that enables the calculation of elastic strain energy in linear and nonlinear elastic solids during arbitrary thermomechanical load cycles. The approach uses the simple fact that the variation of both strain and complementary energies always forms a rectangular shape in stress-strain space, hence integration is no longer required to calculate the energy. Furthermore, the approach considers the mean stress effect so that predictions of fatigue damage are more realistically representative of real-life experimental observations. By doing so, a parameter has been proposed to adjust the mean stress effect. This parameter α is based on the well-known Smith-Watson-Topper energy criterion, but allows consideration of other arbitrary mean stress effects, e.g. the Bergmann type criterion. The approach has then been incorporated into a numerical method which can be applied to uniaxial and multiaxial, proportional and non-proportional loadings to predict fatigue damage. The end result of the method is the cyclic evolution of accumulated damage. Numerical examples show how the method presented in this paper could be applied to a nonlinear elastic material.
Institute of Scientific and Technical Information of China (English)
JIANG Tie-zheng; CHEN Chen; CAO Guo-yun
2006-01-01
The main objectives of this paper are to simultaneously improve power system damping and to maintain voltage at the static var compensator (SVC) location bus simultaneously.A new controller for SVC with closed-form analytic solution nonlinear optimal predictive control (NOPC) law was presented.The controller does not require online optimization and the huge calculation burden can be avoided,so that the demand of real-time control can be satisfied.In addition,there are only two design parameters,which are the predictive period and control order;so it is easy to implement and test in practical use.Simulation results have shown that the controller can not only attenuate power system oscillation effectively but can also maintain voltage at the SVC bus location.
Hou, Zhaolu; Li, Jianping; Ding, Ruiqiang; Feng, Jie
2017-04-01
Nonlinear local Lyapunov vectors (NLLVs) have been developed to indicate orthogonal directions in phase space with different error growth rates. Comparing to the breeding vectors (BVs), NLLVs can span the fast-growing perturbation subspace efficiently and may gasp more components in analysis errors than the BVs in the nonlinear dynamical system. Here, NLLVs are employed in the Zebiak-Cane (ZC) atmosphere-ocean coupled model and represent a nonlinear, finite-time extension of the local Lyapunov vectors of the ZC model. The statistical properties of NLLVs is not very sensitive to the choice of the breeding parameter. However, the non-leading NLLVs have some randomness, which increase the diversity of NLLVs. Not only the leading NLLV but also the non-leading NLLVs are flow-dependent and related to the background ENSO evolution of the ZC model in the aspect of spatial structure and error growth rate. the non-leading NLLVs also are the instability direction related to the ENSO process in the ZC model. Due to the non-leading NLLVs, the subspace of the first few NLLVs can describe better the analysis error than that of the same number BVs in the ZC model. NLLVs as initial ensemble perturbations are applied to the ensemble prediction of ENSO and the performance are systematically compared to those of the random perturbation (RP) technique, and the BV method in the prefect environment. The results demonstrate that the RP technique has the worst performance and the NLLVs method is the best in the ensemble forecasts. In particular, the NLLV technique can reduce the "spring barrier" for ENSO prediction further than the other ensemble method.
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Ali Akbar Akbari
2014-08-01
Full Text Available Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG, as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are and , respectively. Conclusion EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.
Wang, Qiang; Mu, Mu; Dijkstra, Henk A.
2012-01-01
A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.
Institute of Scientific and Technical Information of China (English)
WANG Qiang; MU Mu; Henk A. DIJKSTRA
2012-01-01
A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations.The results show that the model was able to capture the essential features of these path variations.We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method.Because of their relatively large uncertainties,three model parameters were considcred:the interfacial friction coefficient,the wind-stress amplitude,and the lateral friction coefficient.We determined the CNOP-Ps optimized for each of these three parameters independently,and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm.Similarly,the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method.Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days.But the prediction error caused by CNOP-I is greater than that caused by CNOP-P.The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored.Hence,to enhance the forecast skill of the KLM in this model,the initial conditions should first be improved,the model parameters should use the best possible estimates.
Manejo de las complicaiones de la Nefrolitotricia Percutánea (NLP)
2013-01-01
La Cirugía Renal Percutánea nace a principios de la década de 1980 con la Nefrolitotomía Percutánea (NLP). A los grupos de Alken y Wickham se les atribuye la paternidad del método por haber sido los primeros en reportar series de casos tratados con sistematización de la técnica y la descripción de los aparatos necesarios para llevarla a cabo. La punción percutánea siguiendo la técnica de Seldinger, en un principio y guiada únicamente por R...
DEFF Research Database (Denmark)
Troen, Ib; Bechmann, Andreas; Kelly, Mark C.
2014-01-01
Using the Wind Atlas methodology to predict the average wind speed at one location from measured climatological wind frequency distributions at another nearby location we analyse the relative prediction errors using a linearized flow model (IBZ) and a more physically correct fully non-linear 3D...
Institute of Scientific and Technical Information of China (English)
Qin Ni
2001-01-01
An NGTN method was proposed for solving large-scale sparse nonlinear programming (NLP) problems. This is a hybrid method of a truncated Newton direction and a modified negative gradient direction, which is suitable for handling sparse data structure and possesses Q-quadratic convergence rate. The global convergence of this new method is proved,the convergence rate is further analysed, and the detailed implementation is discussed in this paper. Some numerical tests for solving truss optimization and large sparse problems are reported. The theoretical and numerical results show that the new method is efficient for solving large-scale sparse NLP problems.
Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data
M. Billio (Monica); R. Casarin (Roberto); F. Ravazzolo (Francesco); H.K. van Dijk (Herman)
2011-01-01
textabstractWe propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying weigh
Ma, Xiao-Juan; Shang, Li; Zhang, Wei-Min; Wang, Ming-Rong; Zhan, Qi-Min
2016-04-10
Cellular response to DNA damage, including ionizing radiation (IR) and UV radiation, is critical for the maintenance of genomic fidelity. Defects of DNA repair often result in genomic instability and malignant cell transformation. Centrosomal protein Nlp (ninein-like protein) has been characterized as an important cell cycle regulator that is required for proper mitotic progression. In this study, we demonstrate that Nlp is able to improve nucleotide excision repair (NER) activity and protects cells against UV radiation. Upon exposure of cells to UVC, Nlp is translocated into the nucleus. The C-terminus (1030-1382) of Nlp is necessary and sufficient for its nuclear import. Upon UVC radiation, Nlp interacts with XPA and ERCC1, and enhances their association. Interestingly, down-regulated expression of Nlp is found to be associated with human skin cancers, indicating that dysregulated Nlp might be related to the development of human skin cancers. Taken together, this study identifies mitotic protein Nlp as a new and important member of NER pathway and thus provides novel insights into understanding of regulatory machinery involved in NER.
An RLP23-SOBIR1-BAK1 complex mediates NLP-triggered immunity.
Albert, Isabell; Böhm, Hannah; Albert, Markus; Feiler, Christina E; Imkampe, Julia; Wallmeroth, Niklas; Brancato, Caterina; Raaymakers, Tom M; Oome, Stan; Zhang, Heqiao; Krol, Elzbieta; Grefen, Christopher; Gust, Andrea A; Chai, Jijie; Hedrich, Rainer; Van den Ackerveken, Guido; Nürnberger, Thorsten
2015-10-05
Plants and animals employ innate immune systems to cope with microbial infection. Pattern-triggered immunity relies on the recognition of microbe-derived patterns by pattern recognition receptors (PRRs). Necrosis and ethylene-inducing peptide 1-like proteins (NLPs) constitute plant immunogenic patterns that are unique, as these proteins are produced by multiple prokaryotic (bacterial) and eukaryotic (fungal, oomycete) species. Here we show that the leucine-rich repeat receptor protein (LRR-RP) RLP23 binds in vivo to a conserved 20-amino-acid fragment found in most NLPs (nlp20), thereby mediating immune activation in Arabidopsis thaliana. RLP23 forms a constitutive, ligand-independent complex with the LRR receptor kinase (LRR-RK) SOBIR1 (Suppressor of Brassinosteroid insensitive 1 (BRI1)-associated kinase (BAK1)-interacting receptor kinase 1), and recruits a second LRR-RK, BAK1, into a tripartite complex upon ligand binding. Stable, ectopic expression of RLP23 in potato (Solanum tuberosum) confers nlp20 pattern recognition and enhanced immunity to destructive oomycete and fungal plant pathogens, such as Phytophthora infestans and Sclerotinia sclerotiorum. PRRs that recognize widespread microbial patterns might be particularly suited for engineering immunity in crop plants.
THE PROBLEM OF FORMING PLANTS EMPATHETIC JUNIOR SCHOOLCHILDREN BY MEANS OF NLP TECHNIQUE – METAPHOR
Directory of Open Access Journals (Sweden)
Nagornova Anna Yurevna
2013-04-01
Full Text Available In the article the problem of the formation of empathic attitudes in primary school means NLP - metaphor for metaphor can serve as a swarm - a direct analogy, history, personal experience, the parable, anecdote, proverbs, legends, fairy tales. Emphasizes the efficiency of the use of simple and complex metaphors as a means invigorated resource mechanisms of age when working with children. A distinction between literary and therapeutic metaphor, the main function of which is to dive into the world of a child, educators created by God. Emphasis on the need to form a tripartite em-pathic relationship between the child, the teacher and the narrative that allows the child is to identify with its characters and events. Underlines that one of the purposes of the metaphor in the therapeutic process of NLP is to gather information about the child to understand his world map, to find out the problems. States that the metaphor can serve as primary school children and speech, when he invests his experience in verbal expression.
A Description Of Space Relations In An NLP Model: The ABBYY Compreno Approach
Directory of Open Access Journals (Sweden)
Aleksey Leontyev
2015-12-01
Full Text Available The current paper is devoted to a formal analysis of the space category and, especially, to questions bound with the presentation of space relations in a formal NLP model. The aim is to demonstrate how linguistic and cognitive problems relating to spatial categorization, definition of spatial entities, and the expression of different locative senses in natural languages can be solved in an artificial intelligence system. We offer a description of the locative groups in the ABBYY Compreno formalism – an integral NLP framework applied for machine translation, semantic search, fact extraction, and other tasks based on the semantic analysis of texts. The model is based on a universal semantic hierarchy of the thesaurus type and includes a description of all possible semantic and syntactic links every word can attach. In this work we define the set of semantic locative relations between words, suggest different tools for their syntactic presentation, give formal restrictions for the word classes that can denote spaces, and show different strategies of dealing with locative prepositions, especially as far as the problem of their machine translation is concerned.
Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling
National Research Council Canada - National Science Library
Issaku Kawashima; Hiroaki Kumano
2017-01-01
Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables...
New Robust Nonlinear Controller Design Based on Predictive Control for Industrial Processes
Directory of Open Access Journals (Sweden)
Hossein Esfroghy
2007-12-01
Full Text Available In this paper a new sliding mode controller based on predictive control is used for the first order system, which is a good model for the industrial process. In this method a developed predictive control is used to optimize the sliding mode control including sliding surface and switching function coefficient at every moment. A new smooth function is used to reduce the chattering problems. Simulation results show the high effectiveness of the proposed controller.
1995-12-01
questions about the stock market data and calculations. I truly had no idea of the breadth of information available. I’m truly impressed and proud of...your technical expertise and insight. This research began with your suggestion and would be nowhere without your market insight. Despite some set backs...under study in the Air Force include aircraft position prediction, missile target prediction, and pilot head motion studies (Longinow, 1994). It has
A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks
Directory of Open Access Journals (Sweden)
Richard Cédric
2010-01-01
Full Text Available Wireless sensor networks rely on sensor devices deployed in an environment to support sensing and monitoring, including temperature, humidity, motion, and acoustic. Here, we propose a new approach to model physical phenomena and track their evolution by taking advantage of the recent developments of pattern recognition for nonlinear functional learning. These methods are, however, not suitable for distributed learning in sensor networks as the order of models scales linearly with the number of deployed sensors and measurements. In order to circumvent this drawback, we propose to design reduced order models by using an easy to compute sparsification criterion. We also propose a kernel-based least-mean-square algorithm for updating the model parameters using data collected by each sensor. The relevance of our approach is illustrated by two applications that consist of estimating a temperature distribution and tracking its evolution over time.
HIGH-PRECISION PREDICTIONS FOR THE ACOUSTIC SCALE IN THE NONLINEAR REGIME
Energy Technology Data Exchange (ETDEWEB)
Seo, Hee-Jong; Eckel, Jonathan; Eisenstein, Daniel J.; Mehta, Kushal; Metchnik, Marc; Padmanabhan, Nikhil; Pinto, Phillip; Takahashi, Ryuichi; White, Martin; Xu, Xiaoying
2010-09-10
We measure shifts of the acoustic scale due to nonlinear growth and redshift distortions to a high precision using a very large volume of high-force-resolution simulations. We compare results from various sets of simulations that differ in their force, volume, and mass resolution. We find a consistency within 1.5-sigma for shift values from different simulations and derive shift alpha(z) -1 = (0.300\\pm 0.015)% [D(z)/D(0)]^{2} using our fiducial set. We find a strong correlation with a non-unity slope between shifts in real space and in redshift space and a weak correlation between the initial redshift and low redshift. Density-field reconstruction not only removes the mean shifts and reduces errors on the mean, but also tightens the correlations: after reconstruction, we recover a slope of near unity for the correlation between the real and redshift space and restore a strong correlation between the low and the initial redshifts. We derive propagators and mode-coupling terms from our N-body simulations and compared with Zeldovich approximation and the shifts measured from the chi^2 fitting, respectively. We interpret the propagator and the mode-coupling term of a nonlinear density field in the context of an average and a dispersion of its complex Fourier coefficients relative to those of the linear density field; from these two terms, we derive a signal-to-noise ratio of the acoustic peak measurement. We attempt to improve our reconstruction method by implementing 2LPT and iterative operations: we obtain little improvement. The Fisher matrix estimates of uncertainty in the acoustic scale is tested using 5000 (Gpc/h)^3 of cosmological PM simulations from Takahashi et al. (2009). (abridged)
An application of Matlab c on Dimensional Nonlinear Programming
Fernando Giménez Palomares; María José Marín Fernández
2014-01-01
[EN] Nonlinear Programming (NLP) is a widely applicable tool in modeling real life problems applied to business, economics and engineering. Is to maximize or minimize a scalar field whose domain is given as a set of constraints given by equalities and/or inequalities not necessarily linear. In this paper we present a virtual laboratory to study the PNL graphically and numerically in the case of two variables [EN] La Programación No Lineal (PNL) constituye una herramienta de amp...
Luna, Julio; Ocampo-Martinez, Carlos; Serra, Maria
2015-05-01
In this work, a nonlinear model predictive control (NMPC) strategy is proposed to regulate the concentrations of the different gas species inside a Proton Exchange Membrane Fuel Cell (PEMFC) anode gas channel. The purpose of the regulation relies on the rejection of the unmeasurable perturbations that affect the system: the hydrogen reaction and water transport terms. The model of the anode channel is derived from the discretisation of the partial differential equations that define the nonlinear dynamics of the system, taking into account spatial variations along the channel. Forward and backward discretisations of the distributed model are employed to take advantage of the boundary conditions of the problem. A linear observer is designed and implemented to perform output-feedback control of the plant. This information is fed to the controller to regulate the states towards their desired values. Simulation results are presented to show the performance of the proposed control method over a given case study. Different cost functions are compared and the one with minimum state-regulation error is identified. Suitable dynamic responses are obtained facing the different considered disturbances.
Jamali, A.; Khaleghi, E.; Gholaminezhad, I.; Nariman-zadeh, N.
2016-05-01
In this paper, a new multi-objective genetic programming (GP) with a diversity preserving mechanism and a real number alteration operator is presented and successfully used for Pareto optimal modelling of some complex non-linear systems using some input-output data. In this study, two different input-output data-sets of a non-linear mathematical model and of an explosive cutting process are considered separately in three-objective optimisation processes. The pertinent conflicting objective functions that have been considered for such Pareto optimisations are namely, training error (TE), prediction error (PE), and the length of tree (complexity of the network) (TL) of the GP models. Such three-objective optimisation implementations leads to some non-dominated choices of GP-type models for both cases representing the trade-offs among those objective functions. Therefore, optimal Pareto fronts of such GP models exhibit the trade-off among the corresponding conflicting objectives and, thus, provide different non-dominated optimal choices of GP-type models. Moreover, the results show that no significant optimality in TE and PE may occur when the TL of the corresponding GP model exceeds some values.
Miranian, A; Abdollahzade, M
2013-02-01
Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.
Multi input single output model predictive control of non-linear bio-polymerization process
Energy Technology Data Exchange (ETDEWEB)
Arumugasamy, Senthil Kumar; Ahmad, Z. [School of Chemical Engineering, Univerisiti Sains Malaysia, Engineering Campus, Seri Ampangan,14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang (Malaysia)
2015-05-15
This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state space model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.
Liu, Changxin; Gao, Jian; Li, Huiping; Xu, Demin
2017-08-14
The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.
Directory of Open Access Journals (Sweden)
Zhi-Ren Tsai
2013-01-01
Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.
Directory of Open Access Journals (Sweden)
Umberto Melia
Full Text Available The level of sedation in patients undergoing medical procedures evolves continuously, affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG, have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to improve the prediction of nociceptive responses with linear and non-linear measures calculated from EEG signal filtered in frequency bands higher than the traditional bands. Power spectral density and auto-mutual information function was applied in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. The proposed measures exhibit better performances than the bispectral index (BIS. Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% were achieved combining EEG measures from the traditional frequency bands and higher frequency bands.
Sabeerali, C. T.; Ajayamohan, R. S.; Giannakis, Dimitrios; Majda, Andrew J.
2017-01-01
An improved index for real-time monitoring and forecast verification of monsoon intraseasonal oscillations (MISOs) is introduced using the recently developed nonlinear Laplacian spectral analysis (NLSA) technique. Using NLSA, a hierarchy of Laplace-Beltrami (LB) eigenfunctions are extracted from unfiltered daily rainfall data from the Global Precipitation Climatology Project over the south Asian monsoon region. Two modes representing the full life cycle of the northeastward-propagating boreal summer MISO are identified from the hierarchy of LB eigenfunctions. These modes have a number of advantages over MISO modes extracted via extended empirical orthogonal function analysis including higher memory and predictability, stronger amplitude and higher fractional explained variance over the western Pacific, Western Ghats, and adjoining Arabian Sea regions, and more realistic representation of the regional heat sources over the Indian and Pacific Oceans. Real-time prediction of NLSA-derived MISO indices is demonstrated via extended-range hindcasts based on NCEP Coupled Forecast System version 2 operational output. It is shown that in these hindcasts the NLSA MISO indices remain predictable out to ˜ 3 weeks.
Albajes-Eizagirre, Anton; Romero, Laia; Soria-Frisch, Aureli; Vanhellemont, Quinten
2011-11-01
Impact of jellyfish in human activities has been increasingly reported worldwide in recent years. Segments such as tourism, water sports and leisure, fisheries and aquaculture are commonly damaged when facing blooms of gelatinous zooplankton. Hence the prediction of the appearance and disappearance of jellyfish in our coasts, which is not fully understood from its biological point of view, has been approached as a pattern recognition problem in the paper presented herein, where a set of potential ecological cues was selected to test their usefulness for prediction. Remote sensing data was used to describe environmental conditions that could support the occurrence of jellyfish blooms with the aim of capturing physical-biological interactions: forcing, coastal morphology, food availability, and water mass characteristics are some of the variables that seem to exert an effect on jellyfish accumulation on the shoreline, under specific spatial and temporal windows. A data-driven model based on computational intelligence techniques has been designed and implemented to predict jellyfish events on the beach area as a function of environmental conditions. Data from 2009 over the NW Mediterranean continental shelf have been used to train and test this prediction protocol. Standard level 2 products are used from MODIS (NASA OceanColor) and MERIS (ESA - FRS data). The procedure for designing the analysis system can be described as following. The aforementioned satellite data has been used as feature set for the performance evaluation. Ground truth has been extracted from visual observations by human agents on different beach sites along the Catalan area. After collecting the evaluation data set, the performance between different computational intelligence approaches have been compared. The outperforming one in terms of its generalization capability has been selected for prediction recall. Different tests have been conducted in order to assess the prediction capability of the
An Efficient Implementation of Partial Condensing for Nonlinear Model Predictive Control
DEFF Research Database (Denmark)
Frison, Gianluca; Kouzoupis, Dimitris; Jørgensen, John Bagterp
2016-01-01
Partial (or block) condensing is a recently proposed technique to reformulate a Model Predictive Control (MPC) problem into a form more suitable for structure-exploiting Quadratic Programming (QP) solvers. It trades off horizon length for input vector size, and this degree of freedom can be emplo......Partial (or block) condensing is a recently proposed technique to reformulate a Model Predictive Control (MPC) problem into a form more suitable for structure-exploiting Quadratic Programming (QP) solvers. It trades off horizon length for input vector size, and this degree of freedom can...
Directory of Open Access Journals (Sweden)
Deborah Apthorp
Full Text Available Visually-induced illusions of self-motion (vection can be compelling for some people, but they are subject to large individual variations in strength. Do these variations depend, at least in part, on the extent to which people rely on vision to maintain their postural stability? We investigated by comparing physical posture measures to subjective vection ratings. Using a Bertec balance plate in a brightly-lit room, we measured 13 participants' excursions of the centre of foot pressure (CoP over a 60-second period with eyes open and with eyes closed during quiet stance. Subsequently, we collected vection strength ratings for large optic flow displays while seated, using both verbal ratings and online throttle measures. We also collected measures of postural sway (changes in anterior-posterior CoP in response to the same visual motion stimuli while standing on the plate. The magnitude of standing sway in response to expanding optic flow (in comparison to blank fixation periods was predictive of both verbal and throttle measures for seated vection. In addition, the ratio between eyes-open and eyes-closed CoP excursions during quiet stance (using the area of postural sway significantly predicted seated vection for both measures. Interestingly, these relationships were weaker for contracting optic flow displays, though these produced both stronger vection and more sway. Next we used a non-linear analysis (recurrence quantification analysis, RQA of the fluctuations in anterior-posterior position during quiet stance (both with eyes closed and eyes open; this was a much stronger predictor of seated vection for both expanding and contracting stimuli. Given the complex multisensory integration involved in postural control, our study adds to the growing evidence that non-linear measures drawn from complexity theory may provide a more informative measure of postural sway than the conventional linear measures.
Apthorp, Deborah; Nagle, Fintan; Palmisano, Stephen
2014-01-01
Visually-induced illusions of self-motion (vection) can be compelling for some people, but they are subject to large individual variations in strength. Do these variations depend, at least in part, on the extent to which people rely on vision to maintain their postural stability? We investigated by comparing physical posture measures to subjective vection ratings. Using a Bertec balance plate in a brightly-lit room, we measured 13 participants' excursions of the centre of foot pressure (CoP) over a 60-second period with eyes open and with eyes closed during quiet stance. Subsequently, we collected vection strength ratings for large optic flow displays while seated, using both verbal ratings and online throttle measures. We also collected measures of postural sway (changes in anterior-posterior CoP) in response to the same visual motion stimuli while standing on the plate. The magnitude of standing sway in response to expanding optic flow (in comparison to blank fixation periods) was predictive of both verbal and throttle measures for seated vection. In addition, the ratio between eyes-open and eyes-closed CoP excursions during quiet stance (using the area of postural sway) significantly predicted seated vection for both measures. Interestingly, these relationships were weaker for contracting optic flow displays, though these produced both stronger vection and more sway. Next we used a non-linear analysis (recurrence quantification analysis, RQA) of the fluctuations in anterior-posterior position during quiet stance (both with eyes closed and eyes open); this was a much stronger predictor of seated vection for both expanding and contracting stimuli. Given the complex multisensory integration involved in postural control, our study adds to the growing evidence that non-linear measures drawn from complexity theory may provide a more informative measure of postural sway than the conventional linear measures.
Non-linear dynamics in financial asset returns: the predictive power of the CBOE volatility index
Bekiros, S.D.; Georgoutsos, D.A.
2008-01-01
In this paper we attempt to predict the direction of change of the S&P500 index over the period 8 April 1998 to 5 February 2002 by means of a recurrent neural network (RNN). We demonstrate that the incorporation in the trading rule of the Chicago Board Options Exchange (CBOE) volatility index change
Ellison, D C; Baring, M G; Grenier, I A; Lagage, P O; Ellison, Donald C.; Goret, Philippe; Baring, Matthew G.; Grenier, Isabelle A.; Lagage, Pierre-Olivier
1999-01-01
We calculate particle spectra and continuum photon emission from the Cassiopeia A supernova remnant (SNR). The particle spectra, ion and electron, result from diffusive shock acceleration at the forward SNR shock and are determined with a nonlinear Monte Carlo calculation. The calculation self-consistently determines the shock structure under the influence of ion pressure, and includes a simple parameterized treatment of electron injection and acceleration. Our results are compared to photon observations, concentrating on the connection between the Radio and GeV-TeV gamma-ray range, and to cosmic ray ion observations. We include new upper limits from the Cherenkov Array at Themis (CAT) imaging Cherenkov telescope and the Whipple 10m gamma-ray telescope at > 400 GeV. These new limits support the suggestion (e.g. Cowsik & Sarkar 1980; Allen et. al. 1997) that energetic electrons are emitting synchrotron radiation in an extremely high magnetic field (~ 1000 microGauss), far greater than values routinely assi...
Barber, A J; Valageas, P; Barber, Andrew J.; Munshi, Dipak; Valageas, Patrick
2004-01-01
Weak lensing convergence can be used directly to map and probe the dark mass distribution in the universe. Building on earlier studies, we recall how the statistics of the convergence field are related to the statistics of the underlying mass distribution, in particular to the many-body density correlations. We describe two model-independent approximations which provide two simple methods to compute the probability distribution function, pdf, of the convergence. We apply one of these to the case where the density field can be described by a log-normal pdf. Next, we discuss two hierarchical models for the high-order correlations which allow one to perform exact calculations and evaluate the previous approximations in such specific cases. Finally, we apply these methods to a very simple model for the evolution of the density field from linear to highly non-linear scales. Comparisons with the results obtained from numerical simulations, obtained from a number of different realizations, show excellent agreement w...
Collier, W.; Milian Sanz, J.
2016-09-01
The length and flexibility of wind turbine blades are increasing over time. Typically, the dynamic response of the blades is analysed using linear models of blade deflection, enhanced by various ad-hoc non-linear correction models. For blades undergoing large deflections, the small deflection assumption inherent to linear models becomes less valid. It has previously been demonstrated that linear and nonlinear blade models can show significantly different blade response, particularly for blade torsional deflection, leading to load prediction differences. There is a need to evaluate how load predictions from these two approaches compare to measurement data from the field. In this paper, time domain simulations in turbulent wind are carried out using the aero-elastic code Bladed with linear and non-linear blade deflection models. The turbine blade load and deflection simulation results are compared to measurement data from an onshore prototype of the GE 6MW Haliade turbine, which features 73.5m long LM blades. Both linear and non-linear blade models show a good match to measurement turbine load and blade deflections. Only the blade loads differ significantly between the two models, with other turbine loads not strongly affected. The non-linear blade model gives a better match to the measured blade root flapwise damage equivalent load, suggesting that the flapwise dynamic behaviour is better captured by the non-linear blade model. Conversely, the linear blade model shows a better match to measurements in some areas such as blade edgewise damage equivalent load.
Casenghi, Martina; Barr, Francis A; Nigg, Erich A
2005-11-01
When cells enter mitosis the microtubule (MT) network undergoes a profound rearrangement, in part due to alterations in the MT nucleating and anchoring properties of the centrosome. Ninein and the ninein-like protein (Nlp) are centrosomal proteins involved in MT organisation in interphase cells. We show that the overexpression of these two proteins induces the fragmentation of the Golgi, and causes lysosomes to disperse toward the cell periphery. The ability of Nlp and ninein to perturb the cytoplasmic distribution of these organelles depends on their ability to interact with the dynein-dynactin motor complex. Our data also indicate that dynactin is required for the targeting of Nlp and ninein to the centrosome. Furthermore, phosphorylation of Nlp by the polo-like kinase 1 (Plk1) negatively regulates its association with dynactin. These findings uncover a mechanism through which Plk1 helps to coordinate changes in MT organisation with cell cycle progression, by controlling the dynein-dynactin-dependent transport of centrosomal proteins.
Kudliskis, Voldis
2014-01-01
This study examines how an understanding of two NLP concepts, the meta-model of language and the implementation of reframing, could be used to help teaching assistants enhance class-based interactions with students with mild SEN. Participants (students) completed a pre-intervention and a post-intervention "Beliefs About my Learning…
Applications of Kalman filters based on non-linear functions to numerical weather predictions
Directory of Open Access Journals (Sweden)
G. Galanis
2006-10-01
Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.
Arqub, Omar Abu; El-Ajou, Ahmad; Momani, Shaher
2015-07-01
Building fractional mathematical models for specific phenomena and developing numerical or analytical solutions for these fractional mathematical models are crucial issues in mathematics, physics, and engineering. In this work, a new analytical technique for constructing and predicting solitary pattern solutions of time-fractional dispersive partial differential equations is proposed based on the generalized Taylor series formula and residual error function. The new approach provides solutions in the form of a rapidly convergent series with easily computable components using symbolic computation software. For method evaluation and validation, the proposed technique was applied to three different models and compared with some of the well-known methods. The resultant simulations clearly demonstrate the superiority and potentiality of the proposed technique in terms of the quality performance and accuracy of substructure preservation in the construct, as well as the prediction of solitary pattern solutions for time-fractional dispersive partial differential equations.
Energy Technology Data Exchange (ETDEWEB)
Kanamori, Masashi, E-mail: kanamori.masashi@jaxa.jp; Takahashi, Takashi, E-mail: takahashi.takashi@jaxa.jp; Aoyama, Takashi, E-mail: aoyama.takashi@jaxa.jp [Japan Aerospace Exploration Agency, 7-44-1, Jindaijihigashi-machi, Chofu, Tokyo (Japan)
2015-10-28
Shown in this paper is an introduction of a prediction tool for the propagation of loud noise with the application to the aeronautics in mind. The tool, named SPnoise, is based on HOWARD approach, which can express almost exact multidimensionality of the diffraction effect at the cost of back scattering. This paper argues, in particular, the prediction of the effect of atmospheric turbulence on sonic boom as one of the important issues in aeronautics. Thanks to the simple and efficient modeling of the atmospheric turbulence, SPnoise successfully re-creates the feature of the effect, which often emerges in the region just behind the front and rear shock waves in the sonic boom signature.
Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking
2015-07-01
corresponding cost function to be J(u) = ( xd − x)TQx ( xd − x) + uTRu, (20) where Qx ∈ RKnx×Knx is positive semi-definite, R and u are as in (3), xd is a...sequence of desired states, xd = ( xd ,k+1, . . . , xd ,k+K), x is a sequence of predicted states, x = (xk+1, . . . ,xk+K), and K is the given prediction...vact,k−1+b, ωact,k−1+b), based ωk θk vk xd ,i−1 xd ,i xd ,i+1 xk yk Figure 5: Definition of the robot velocities, vk and ωk, and three pose variables
Identification of Nonlinear Rational Systems Using A Prediction-Error Estimation Algorithm
1987-01-01
Identification of discrete-time noninear stochastic systems which can be represented by a rational input-output model is considered. A prediction-error parameter estimation algorithm is developed and a criterion is derived using results from the theory of hypothesis testing to determine the correct model structure. The identification of a simulated system and a heat exchanger are included to illustrate the algorithms.
Karimi Moridani, Mohammad; Setarehdan, Seyed Kamaledin; Motie Nasrabadi, Ali; Hajinasrollah, Esmaeil
2016-01-01
Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.
Neural Network Nonlinear Predictive Control Based on Tent-map Chaos Optimization%基于Tent混沌优化的神经网络预测控制
Institute of Scientific and Technical Information of China (English)
宋莹; 陈增强; 袁著祉
2007-01-01
With the unique ergodicity, irregularity, and special ability to avoid being trapped in local optima, chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields, such as nonlinear programming problems. In this article, a novel neural network nonlinear predictive control (NNPC) strategy based on the new Tent-map chaos optimization algorithm (TCOA) is presented. The feedforward neural network is used as the multi-step predictive model. In addition, the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC. Simulation on a laboratory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.
Prediction of Nonlinear Evolution Character of Energetic-Particle-Driven Instabilities
Duarte, Vinicius; Gorelenkov, Nikolai; Heidbrink, William; Kramer, Gerrit; Nazikian, Raffi; Pace, David; Podesta, Mario; Tobias, Benjamin; Van Zeeland, Michael
2016-01-01
A general criterion is proposed and found to successfully predict the emergence of chirping oscillations of unstable Alfv\\'enic eigenmodes in tokamak plasma experiments. The model includes realistic eigenfunction structure, detailed phase-space dependences of the instability drive, stochastic scattering and the Coulomb drag. The stochastic scattering combines the effects of collisional pitch angle scattering and micro-turbulence spatial diffusion. The latter mechanism is essential to accurately identify the transition between the fixed-frequency mode behavior and rapid chirping in tokamaks and to resolve the disparity with respect to chirping observation in spherical and conventional tokamaks.
A Domain Based Approach for the Animated Cum Automated Modelfor Deaf and Dumb Using NLP
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T.F. Michael Raj
2013-05-01
Full Text Available Deaf and dumb people are using the hand gestures for their communication throughout the world. To understand and animate the hand gestures, we can help in facilitate communication between computers and the under privileged. In this study we propose a domain based approach which will be helpful to design a model using NLP This model will be useful for the learning of the recent trends in the technology. The proposed model processes the user input and converts into parameters which will produce the actions or gestures by the animated character which can be understandable by the deaf and dumb. The desired animations can be done by the software. The accuracy of the actions or animations based on the parameters of the actions that we extracted from the given information or the text by the users.
Using NLP to identify cancer cases in imaging reports drawn from radiology information systems.
Patrick, Jon; Asgari, Pooyan; Li, Min; Nguyen, Dung
2013-01-01
A Natural Language processing (NLP) classifier has been developed for the Victorian and NSW Cancer Registries with the purpose of automatically identifying cancer reports from imaging services, transmitting them to the Registries and then extracting pertinent cancer information. Large scale trials conducted on over 40,000 reports show the sensitivity for identifying reportable cancer reports is above 98% with a specificity above 96%. Detection of tumour stream, report purpose, and a variety of extracted content is generally above 90% specificity. The differences between report layout and authoring strategies across imaging services appear to require different classifiers to retain this high level of accuracy. Linkage of the imaging data with existing registry records (hospital and pathology reports) to derive stage and recurrence of cancer has commenced and shown very promising results.
Tailoring vocabularies for NLP in sub-domains: a method to detect unused word sense.
Figueroa, Rosa L; Zeng-Treitler, Qing; Goryachev, Sergey; Wiechmann, Eduardo P
2009-11-14
We developed a method to help tailor a comprehensive vocabulary system (e.g. the UMLS) for a sub-domain (e.g. clinical reports) in support of natural language processing (NLP). The method detects unused sense in a sub-domain by comparing the relational neighborhood of a word/term in the vocabulary with the semantic neighborhood of the word/term in the sub-domain. The semantic neighborhood of the word/term in the sub-domain is determined using latent semantic analysis (LSA). We trained and tested the unused sense detection on two clinical text corpora: one contains discharge summaries and the other outpatient visit notes. We were able to detect unused senses with precision from 79% to 87%, recall from 48% to 74%, and an area under receiver operation curve (AUC) of 72% to 87%.
McEwan, Reed; Melton, Genevieve B; Knoll, Benjamin C; Wang, Yan; Hultman, Gretchen; Dale, Justin L; Meyer, Tim; Pakhomov, Serguei V
2016-01-01
Many design considerations must be addressed in order to provide researchers with full text and semantic search of unstructured healthcare data such as clinical notes and reports. Institutions looking at providing this functionality must also address the big data aspects of their unstructured corpora. Because these systems are complex and demand a non-trivial investment, there is an incentive to make the system capable of servicing future needs as well, further complicating the design. We present architectural best practices as lessons learned in the design and implementation NLP-PIER (Patient Information Extraction for Research), a scalable, extensible, and secure system for processing, indexing, and searching clinical notes at the University of Minnesota.
Extending the adverbial coverage of a NLP oriented resource for French
Tolone, Elsa
2011-01-01
This paper presents a work on extending the adverbial entries of LGLex: a NLP oriented syntactic resource for French. Adverbs were extracted from the Lexicon-Grammar tables of both simple adverbs ending in -ment '-ly' (Molinier and Levrier, 2000) and compound adverbs (Gross, 1986; 1990). This work relies on the exploitation of fine-grained linguistic information provided in existing resources. Various features are encoded in both LG tables and they haven't been exploited yet. They describe the relations of deleting, permuting, intensifying and paraphrasing that associate, on the one hand, the simple and compound adverbs and, on the other hand, different types of compound adverbs. The resulting syntactic resource is manually evaluated and freely available under the LGPL-LR license.
A three-dimensional nonlinear reduced-order predictive joint model
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Mechanical joints can have significant effects on the dynamics of assembled structures. However, the lack of efficacious predictive dynamic models for joints hinders accurate prediction of their dynamic behavior. The goal of our work is to develop physics-based, reduced-order, finite element models that are capable of replicating the effects of joints on vibrating structures. The authors recently developed the so-called two-dimensional adjusted Iwan beam element (2-D AIBE) to simulate the hysteretic behavior of bolted joints in 2-D beam structures. In this paper, 2-D AIBE is extended to three-dimensional cases by formulating a three-dimensional adjusted Iwan beam element (3-D AIBE). Impulsive loading experiments are applied to a jointed frame structure and a beam structure containing the same joint. The frame is subjected to excitation out of plane so that the joint is under rotation and single axis bending. By assuming that the rotation in the joint is linear elastic, the parameters of the joint associated with bending in the frame are identified from acceleration responses of the jointed beam structure, using a multi-layer feed-forward neural network (MLFF). Numerical simulation is then performed on the frame structure using the identified parameters. The good agreement between the simulated and experimental impulsive acceleration responses of the frame structure validates the efficacy of the presented 3-D AIBE, and indicates that the model can potentially be applied to more complex structural systems with joint parameters identified from a relatively simple structure.
Yu, Lin-Hui; Wu, Jie; Tang, Hui; Yuan, Yang; Wang, Shi-Mei; Wang, Yu-Ping; Zhu, Qi-Sheng; Li, Shi-Gui; Xiang, Cheng-Bin
2016-06-13
Nitrogen is essential for plant survival and growth. Excessive application of nitrogenous fertilizer has generated serious environment pollution and increased production cost in agriculture. To deal with this problem, tremendous efforts have been invested worldwide to increase the nitrogen use ability of crops. However, only limited success has been achieved to date. Here we report that NLP7 (NIN-LIKE PROTEIN 7) is a potential candidate to improve plant nitrogen use ability. When overexpressed in Arabidopsis, NLP7 increases plant biomass under both nitrogen-poor and -rich conditions with better-developed root system and reduced shoot/root ratio. NLP7-overexpressing plants show a significant increase in key nitrogen metabolites, nitrogen uptake, total nitrogen content, and expression levels of genes involved in nitrogen assimilation and signalling. More importantly, overexpression of NLP7 also enhances photosynthesis rate and carbon assimilation, whereas knockout of NLP7 impaired both nitrogen and carbon assimilation. In addition, NLP7 improves plant growth and nitrogen use in transgenic tobacco (Nicotiana tabacum). Our results demonstrate that NLP7 significantly improves plant growth under both nitrogen-poor and -rich conditions by coordinately enhancing nitrogen and carbon assimilation and sheds light on crop improvement.
Ncibi, Mohamed Chaker
2008-05-01
In any single component isotherm study, determining the best-fitting model is a key analysis to mathematically describe the involved sorption system and, therefore, to explore the related theoretical assumptions. Hence, several error calculation functions have been widely used to estimate the error deviations between experimental and theoretically predicted equilibrium adsorption values (Q(e,exp)vs.Q(e,theo) as X- and Y-axis, respectively), including the average relative error deviation, the Marquardt's percent standard error deviation, the hybrid fractional error function, the sum of the squares of the errors, the correlation coefficient and the residuals. In this study, five other statistical functions are analysed to investigate their applicability as suitable tools to evaluate isotherm model fitness, namely the Pearson correlation coefficient, the coefficient of determination, the Chi-square test, the F-test and the Student's T-test, using the commonly-used functions as references. The adsorption of textile dye onto Posidonia oceanica seagrass fibres was carried out, as study case, in batch mode at 20 degrees C. Besides, and in order to get an overall approach of the possible utilization of these statistical functions within the studied item, the examination was realized for both linear and non-linear regression analysis. The related results showed that, among the five studied statistical tools, the chi(2) and Student's T-tests were suitable to determine the best-fitting isotherm model for the case of linear modelling approach. On the other hand, dealing with the non-linear analysis, despite the Student's T-test, all the other functions gave satisfactorily results, by agreeing the commonly-used error functions calculation.
Tagantsev, Alexander K.; Stolichnov, Igor; Setter, Nava; Cross, Jeffrey S.
2004-12-01
The phenomenon of polarization imprint consisting of the development of a preferential polarization state in ferroelectric films is known as one of the major issues impacting the development of high density ferroelectric memories. According to the commonly accepted scenario, the imprint is related to the charge injection and charge accumulation in the nearby-electrode passive layer of the ferroelectric film. Recent studies demonstrated that the coercive voltage shift induced by the imprint exhibits a nonlinear time dependence in a logarithmic scale. This result was interpreted as the presence of two different imprint mechanisms characterized by different activation energies. In the present work, an analytical theory of the injection scenario of imprint is developed. The charge accumulation at the interface is shown to provoke a voltage offset and polarization loss which are nonlinearly dependent on the time in logarithmic scale. This result is obtained for different charge injection mechanisms including Schottky, Pool-Frenkel, and tunneling scenarios. Thus, it is shown that a single imprint mechanism can be responsible for a nolinear (in logarithmic scale) time dependence of the voltage offset and polarization loss. Additionally, the temperature dependence of the logarithmic rate of imprint is shown to be nonexponential. The developed model ties together the time and temperature dependences of imprint. For the experimental verification of the model a study of imprint has been performed on (111) Pb(Zr ,Ti)O3 film capacitors with temperatures ranging from 25 to 150°C and exposure times up to 1000h. It has been found that the theory developed adequately describes the obtained experimental data. Based upon the theoretical and experimental results a test for ferroelectric memories is proposed, which enables the long-term prediction of polarization loss caused by imprint for a wide temperature range and for different operating voltages.
Gradient radial basis function networks for nonlinear and nonstationary time series prediction.
Chng, E S; Chen, S; Mulgrew, B
1996-01-01
We present a method of modifying the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node's function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node's center. This type of response, however, is highly sensitive to changes in the level and trend of the time series. To counter these effects, the hidden node's function is modified to one which detects and reacts to the gradient of the series. We call this new network the gradient RBF (GRBF) model. Single and multistep predictive performance for the Mackey-Glass chaotic time series were evaluated using the classical RBF and GRBF models. The simulation results for the series without and with a tine-varying mean confirm the superior performance of the GRBF predictor over the RBF predictor.
Hallbauer-Zadorozhnaya, Valeriya; Santarato, Giovanni; Abu Zeid, Nasser
2015-08-01
In this paper, two separate but related goals are tackled. The first one is to demonstrate that in some saturated rock textures the non-linear behaviour of induced polarization (IP) and the violation of Ohm's law not only are real phenomena, but they can also be satisfactorily predicted by a suitable physical-mathematical model, which is our second goal. This model is based on Fick's second law. As the model links the specific dependence of resistivity and chargeability of a laboratory sample to the injected current and this in turn to its pore size distribution, it is able to predict pore size distribution from laboratory measurements, in good agreement with mercury injection capillary pressure test results. This fact opens up the possibility for hydrogeophysical applications on a macro scale. Mathematical modelling shows that the chargeability acquired in the field under normal conditions, that is at low current, will always be very small and approximately proportional to the applied current. A suitable field test site for demonstrating the possible reliance of both resistivity and chargeability on current was selected and a specific measuring strategy was established. Two data sets were acquired using different injected current strengths, while keeping the charging time constant. Observed variations of resistivity and chargeability are in agreement with those predicted by the mathematical model. These field test data should however be considered preliminary. If confirmed by further evidence, these facts may lead to changing the procedure of acquiring field measurements in future, and perhaps may encourage the design and building of a new specific geo-resistivity meter. This paper also shows that the well-known Marshall and Madden's equations based on Fick's law cannot be solved without specific boundary conditions.
Predictive functional control of temperature in a pharmaceutical hybrid nonlinear batch reactor
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Štampar Simon
2013-01-01
Full Text Available These days, in times of recession, we are forced by competitiveness and the optimization of production to lower the costs of the temperature control in pharmaceutical batch reactors and increase the quantity and quality of the produced pharmaceutical product (active pharmaceutical substances. Therefore, a control algorithm is needed which provides us rapid and precise temperature control. This paper deals with the development of a control algorithm, where two predictive functional controllers are connected in a cascade for heating and cooling the content of the hybrid batch reactor. The algorithm has to be designed to cope with the constraints and the mixed discrete and continuous nature of the process of heating and cooling. The main goal of the control law is to achieve rapid and exact tracking of the reference temperature, good disturbance rejection and, in particular, a small number of heating and cooling medium switchings. The simulation results of the proposed algorithm give us much better performance compared to a conventional cascade PI algorithm.
Brands, Dave W A; Bovendeerd, Peter H M; Wismans, Jac S H M
2002-11-01
In current Finite Element (FE) head models, brain tissue is commonly assumed to display linear viscoelastic material behaviour. However, brain tissue behaves like a non-linear viscoelastic solid for shear strains above 1%. The main objective of this study was to study the effect of non-linear material behaviour on the predicted brain response. We used a non-linear viscoelastic constitutive model, developed on the basis of experimental shear data presented elsewere. First we tested the numerical implementation of the constitutive model by simulating the response of a silicone gel (Sylgard 572 A&B) filled cylindrical cup, subjected to a transient rotational acceleration. The experimental results could be reproduced within 9%. Subsequently, the effect of non-linear material modelling on computed brain response was investigated in an existing three-dimensional head model subjected to an eccentric rotation. At the applied external load strains in the brain were approximately ten times larger than was expected on the basis of published data. This is probably caused by the values of the shear moduli applied in the model. These are at least a factor of ten lower than the ones used in head models in literature but comparable to material data in recent literature. Non-linear material behaviour was found to influence the levels of predicted strains (+20%) and stresses (-11%) but not their temporal and spatial distribution. The pressure response was independent of non-linear material behaviour. In fact it could be predicted by the equilibrium of momentum, and thus it is independent of the choice of the brain constitutive model.
Energy Technology Data Exchange (ETDEWEB)
Xu, Qingping; Mengin-Lecreulx, Dominique; Liu, Xueqian W.; Patin, Delphine; Farr, Carol L.; Grant, Joanna C.; Chiu, Hsiu-Ju; Jaroszewski, Lukasz; Knuth, Mark W.; Godzik, Adam; Lesley, Scott A.; Elsliger, Marc-André; Deacon, Ashley M.; Wilson, Ian A.
2015-09-15
Bacterial SH3 (SH3b) domains are commonly fused with papain-like Nlp/P60 cell wall hydrolase domains. To understand how the modular architecture of SH3b and NlpC/P60 affects the activity of the catalytic domain, three putative NlpC/P60 cell wall hydrolases were biochemically and structurally characterized. These enzymes all have γ-
Institute of Scientific and Technical Information of China (English)
GU Ji-jun; AN Chen; LEVI Carlos; SU Jian
2012-01-01
The Generalized Integral Transform Technique (GITT) was applied to predict dynamic response of Vortex-Induced Vibration (VIV) of a long flexible cylinder.A nonlinear wake oscillator model was used to represent the cross-flow force acting on the cylinder,leading to a coupled system of second-order Partial Differential Equations (PDEs) in temporal variable.The GITT approach was used to transform the system of PDEs to a system of Ordinary Differential Equations (ODEs),which was numerically solved by using the Adams-Moulton and Gear method (DIVPAG) developed by the International Mathematics and Statistics Library (IMSL).Numerical results were presented for comparison to those given by the finite difference method and experimental results,allowing a critical evaluation of the technique performance.The influence of variation of mean axial tension induced by elongation of flexible cylinder was evaluated,which was shown to be not negligible in numerical simulation of VIV of a long flexible cylinder.
Directory of Open Access Journals (Sweden)
Yin Hua
2015-04-01
Full Text Available Estimation of state of charge (SOC is of great importance for lithium-ion (Li-ion batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.
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Sakaros Bogning Dongue
2013-01-01
Full Text Available This paper presents the modelling of electrical I-V response of illuminated photovoltaic crystalline modules. As an alternative method to the linear five-parameter model, our strategy uses advantages of a nonlinear analytical five-point model to take into account the effects of nonlinear variations of current with respect to solar irradiance and of voltage with respect to cells temperature. We succeeded in this work to predict with great accuracy the I-V characteristics of monocrystalline shell SP75 and polycrystalline GESOLAR GE-P70 photovoltaic modules. The good comparison of our calculated results to experimental data provided by the modules manufacturers makes it possible to appreciate the contribution of taking into account the nonlinear effect of operating conditions data on I-V characteristics of photovoltaic modules.
Li, Li; Chase, Herbert S; Patel, Chintan O; Friedman, Carol; Weng, Chunhua
2008-11-06
The prevalence of electronic medical record (EMR) systems has made mass-screening for clinical trials viable through secondary uses of clinical data, which often exist in both structured and free text formats. The tradeoffs of using information in either data format for clinical trials screening are understudied. This paper compares the results of clinical trial eligibility queries over ICD9-encoded diagnoses and NLP-processed textual discharge summaries. The strengths and weaknesses of both data sources are summarized along the following dimensions: information completeness, expressiveness, code granularity, and accuracy of temporal information. We conclude that NLP-processed patient reports supplement important information for eligibility screening and should be used in combination with structured data.
Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011
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Pyysalo Sampo
2012-06-01
Full Text Available Abstract We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID and Epigenetics and Post-translational Modifications (EPI, and the supporting task on Entity Relations (REL. The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09 to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level
Nonlinear singular vectors and nonlinear singular values
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A novel concept of nonlinear singular vector and nonlinear singular value is introduced, which is a natural generalization of the classical linear singular vector and linear singular value to the nonlinear category. The optimization problem related to the determination of nonlinear singular vectors and singular values is formulated. The general idea of this approach is demonstrated by a simple two-dimensional quasigeostrophic model in the atmospheric and oceanic sciences. The advantage and its applications of the new method to the predictability, ensemble forecast and finite-time nonlinear instability are discussed. This paper makes a necessary preparation for further theoretical and numerical investigations.
Reinhard, Joscha; Peiffer, Swati; Sänger, Nicole; Herrmann, Eva; Yuan, Juping; Louwen, Frank
2012-01-01
Objective. To examine the effects of clinical hypnosis versus NLP intervention on the success rate of ECV procedures in comparison to a control group. Methods. A prospective off-centre randomised trial of a clinical hypnosis intervention against NLP of women with a singleton breech fetus at or after 37(0/7) (259 days) weeks of gestation and normal amniotic fluid index. All 80 participants heard a 20-minute recorded intervention via head phones. Main outcome assessed was success rate of ECV. The intervention groups were compared with a control group with standard medical care alone (n = 122). Results. A total of 42 women, who received a hypnosis intervention prior to ECV, had a 40.5% (n = 17), successful ECV, whereas 38 women, who received NLP, had a 44.7% (n = 17) successful ECV (P > 0.05). The control group had similar patient characteristics compared to the intervention groups (P > 0.05). In the control group (n = 122) 27.3% (n = 33) had a statistically significant lower successful ECV procedure than NLP (P = 0.05) and hypnosis and NLP (P = 0.03). Conclusions. These findings suggest that prior clinical hypnosis and NLP have similar success rates of ECV procedures and are both superior to standard medical care alone.
Directory of Open Access Journals (Sweden)
Joscha Reinhard
2012-01-01
Full Text Available Objective. To examine the effects of clinical hypnosis versus NLP intervention on the success rate of ECV procedures in comparison to a control group. Methods. A prospective off-centre randomised trial of a clinical hypnosis intervention against NLP of women with a singleton breech fetus at or after 370/7 (259 days weeks of gestation and normal amniotic fluid index. All 80 participants heard a 20-minute recorded intervention via head phones. Main outcome assessed was success rate of ECV. The intervention groups were compared with a control group with standard medical care alone (=122. Results. A total of 42 women, who received a hypnosis intervention prior to ECV, had a 40.5% (=17, successful ECV, whereas 38 women, who received NLP, had a 44.7% (=17 successful ECV (>0.05. The control group had similar patient characteristics compared to the intervention groups (>0.05. In the control group (=122 27.3% (=33 had a statistically significant lower successful ECV procedure than NLP (=0.05 and hypnosis and NLP (=0.03. Conclusions. These findings suggest that prior clinical hypnosis and NLP have similar success rates of ECV procedures and are both superior to standard medical care alone.
Sornette, Didier
2010-01-01
This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides the substance of a general phase diagram for understanding the many facets of the spatio-temporal organization of these systems. A key insight is to organize the evidence and mechanisms in terms of two summarizing measures: (i) amplitude of disorder or heterogeneity in the system and (ii) level of coupling or interaction strength among the system's components. On the basis of the recently identified remarkable correspondence between earthquakes and seizures, we present detailed information on a class of stochastic point processes that has been found to be particu...
Zaghian, Maryam; Cao, Wenhua; Liu, Wei; Kardar, Laleh; Randeniya, Sharmalee; Mohan, Radhe; Lim, Gino
2017-03-01
Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so-called "worst case dose" and "minmax" robust optimization approaches and conventional planning target volume (PTV)-based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull-based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP-PTV-based, NLP-PTV-based, LP-worst case dose, NLP-worst case dose, LP-minmax, and NLP-minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP-based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP-based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP-based methods was superior for the skull-based and head and neck cancer patients. Overall, LP-based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet
Kaggal, Vinod C; Elayavilli, Ravikumar Komandur; Mehrabi, Saeed; Pankratz, Joshua J; Sohn, Sunghwan; Wang, Yanshan; Li, Dingcheng; Rastegar, Majid Mojarad; Murphy, Sean P; Ross, Jason L; Chaudhry, Rajeev; Buntrock, James D; Liu, Hongfang
2016-01-01
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.
MAP1272c encodes an NlpC/P60 protein, an antigen detected in cattle with Johne's disease.
Bannantine, John P; Lingle, Cari K; Stabel, Judith R; Ramyar, Kasra X; Garcia, Brandon L; Raeber, Alex J; Schacher, Pascal; Kapur, Vivek; Geisbrecht, Brian V
2012-07-01
The protein encoded by MAP1272c has been shown to be an antigen of Mycobacterium avium subsp. paratuberculosis that contains an NlpC/P60 superfamily domain found in lipoproteins or integral membrane proteins. Proteins containing this domain have diverse enzymatic functions that include peptidases, amidases, and acetyltransferases. The NlpC protein was examined in comparison to over 100 recombinant proteins and showed the strongest antigenicity when analyzed with sera from cattle with Johne's disease. To further localize the immunogenicity of NlpC, recombinant proteins representing defined regions were expressed and evaluated with sera from cattle with Johne's disease. The region from amino acids 74 to 279 was shown to be the most immunogenic. This fragment was also evaluated against a commercially available enzyme-linked immunosorbent assay (ELISA). Two monoclonal antibodies were produced in mice immunized with the full-length protein, and each recognized a distinct epitope. These antibodies cross-reacted with proteins from other mycobacterial species and demonstrated variable sizes of the proteins expressed from these subspecies. Both antibodies were further analyzed, and their interaction with MAP1272c and MAP1204 was characterized by a solution-based, luminescent binding assay. These tools provide additional means to study a strong antigen of M. avium subsp. paratuberculosis.
Predicting Nonlinear Time Series
1993-12-01
response becomes R,(k) = f (Y FV,(k)) (2.4) where Wy specifies the weight associated with the output of node i to the input of nodej in the next layer and...interconnections for each of these previous nodes. 18 prr~~~o• wfe :t iam i -- ---- --- --- --- Figure 5: Delay block for ATNN [9] Thus, nodej receives the...computed values, aj(tn), and dj(tn) denotes the desired output of nodej at time in. In this thesis, the weights and time delays update after each input
Lu, Bao-Liang; Ito, Koji
2003-09-01
In this paper we present a method for converting general nonlinear programming (NLP) problems into separable programming (SP) problems by using feedforward neural networks (FNNs). The basic idea behind the method is to use two useful features of FNNs: their ability to approximate arbitrary continuous nonlinear functions with a desired degree of accuracy and their ability to express nonlinear functions in terms of parameterized compositions of functions of single variables. According to these two features, any nonseparable objective functions and/or constraints in NLP problems can be approximately expressed as separable functions with FNNs. Therefore, any NLP problems can be converted into SP problems. The proposed method has three prominent features. (a) It is more general than existing transformation techniques; (b) it can be used to formulate optimization problems as SP problems even when their precise analytic objective function and/or constraints are unknown; (c) the SP problems obtained by the proposed method may highly facilitate the selection of grid points for piecewise linear approximation of nonlinear functions. We analyze the computational complexity of the proposed method and compare it with an existing transformation approach. We also present several examples to demonstrate the method and the performance of the simplex method with the restricted basis entry rule for solving SP problems.
Nonlinear prediction of gold prices based on BP neural network%基于 BP神经网络的黄金价格非线性预测
Institute of Scientific and Technical Information of China (English)
张延利
2013-01-01
针对黄金价格的非线性特征和神经网络的自身特点，利用BP神经网络建立了黄金价格的非线性预测模型。实证研究结果表明，BP神经网络模型具有较好的预测精度，可以为黄金投资和宏观经济决策提供一定的参考依据。%According to the neural network nonlinear characteristics of gold price and its own characteristics ,using BP neural network nonlinear prediction model was set up for the price of gold .The results show that the BP prediction has good accuracy and is available to provide references for the gold investment and macroeconomic decisions .
Mao, Junwen; Vosoughi, Azadeh; Carney, Laurel H
2013-07-01
Tone-in-noise detection has been studied for decades; however, it is not completely understood what cue or cues are used by listeners for this task. Model predictions based on energy in the critical band are generally more successful than those based on temporal cues, except when the energy cue is not available. Nevertheless, neither energy nor temporal cues can explain the predictable variance for all listeners. In this study, it was hypothesized that better predictions of listeners' detection performance could be obtained using a nonlinear combination of energy and temporal cues, even when the energy cue was not available. The combination of different cues was achieved using the logarithmic likelihood-ratio test (LRT), an optimal detector in signal detection theory. A nonlinear LRT-based combination of cues was proposed, given that the cues have Gaussian distributions and the covariance matrices of cue values from noise-alone and tone-plus-noise conditions are different. Predictions of listeners' detection performance for three different sets of reproducible noises were computed with the proposed model. Results showed that predictions for hit rates approached the predictable variance for all three datasets, even when an energy cue was not available.
Directory of Open Access Journals (Sweden)
E. Çelebi
2012-11-01
Full Text Available The objective of this paper focuses primarily on the numerical approach based on two-dimensional (2-D finite element method for analysis of the seismic response of infinite soil-structure interaction (SSI system. This study is performed by a series of different scenarios that involved comprehensive parametric analyses including the effects of realistic material properties of the underlying soil on the structural response quantities. Viscous artificial boundaries, simulating the process of wave transmission along the truncated interface of the semi-infinite space, are adopted in the non-linear finite element formulation in the time domain along with Newmark's integration. The slenderness ratio of the superstructure and the local soil conditions as well as the characteristics of input excitations are important parameters for the numerical simulation in this research. The mechanical behavior of the underlying soil medium considered in this prediction model is simulated by an undrained elasto-plastic Mohr-Coulomb model under plane-strain conditions. To emphasize the important findings of this type of problems to civil engineers, systematic calculations with different controlling parameters are accomplished to evaluate directly the structural response of the vibrating soil-structure system. When the underlying soil becomes stiffer, the frequency content of the seismic motion has a major role in altering the seismic response. The sudden increase of the dynamic response is more pronounced for resonance case, when the frequency content of the seismic ground motion is close to that of the SSI system. The SSI effects under different seismic inputs are different for all considered soil conditions and structural types.
ONTOLOGY EXTRACTION FOR AGRICULTURE DOMAIN IN MARATHI LANGUAGE USING NLP TECHNIQUES
Directory of Open Access Journals (Sweden)
Prachi Dalvi
2016-10-01
Full Text Available Ontology is defined as shared specification of conceptual vocabulary used for formulating knowledge-level theories about a domain of discourse. Dataset is created by manually collecting information about different diseases related to crops. Ontology modeling is used for knowledge representation of various domains. India is an agricultural based economic country. Majority of Indian population relies on farming but the technologies are sparsely used for the aid of farmers. Ontology based modeling for agricultural knowledge can change this scenario. The farmers can understand it easily in their native language. We proposed a system which will model and extract knowledge in Marathi language. In this paper, we review various existing agriculture ontology’s along with some of Natural Language Processing (NLP models. Model ontology for agriculture domain system aims to retrieve relevant answers to the farmer’s query. We explored Rule-Based and Conditional Random Fields based models for Ontology extraction. The extraction methods and preprocessing phases of proposed system is discussed.
A New Method to Build NLP Knowledge for Improving Term Disambiguation
Directory of Open Access Journals (Sweden)
E. MD. Abdelrahim
2016-07-01
Full Text Available Term sense disambiguation is very essential for different approaches of NLP, including Internet search engines, information retrieval, Data mining, classification etc. However, the old methods using case frames and semantic primitives are not qualify for solving term ambiguities which needs a lot of information with sentences. This new approach introduces a building structure system of natural language knowledge. In this paper all surface case patterns is classified in advance with the consideration of the meaning of noun. Moreover, this paper introduces an efficient data structure using a trie which define the linkage among leaves and multi-attribute relations. By using this linkage multi-attribute relations, we can get a high frequent access among verbs and noun with an automatic generation of hierarchical relationships. In our experiment a large tagged corpus (Pan Treebank is used to extract data. In our approach around 11,000 verbs and nouns is used for verifying the new method and made a hierarchy group of its noun. Moreover, the achievement of term disambiguating using our trie structure method and linking trie among leaves is 6% higher than old method.
Institute of Scientific and Technical Information of China (English)
ZHOU En-Bo; ZHANG Xin-Liang; YU Yu; HUANG De-Xiu
2009-01-01
Nonlinear patterning (NLP) effect in wavelength conversion based on transient cross-phase modulation (XPM) in semiconductor optical amplifier (SOA) assisted with a detuning filter is theoretically investigated.A nonadiabatic model is used to estimate the ultrafast dynamics o[ gain,phase and electron temperature in the SOA.Simulation results show that the NLP can be greatly suppressed by introducing an assist light,especially for the probe wavelength distant from gain peak.Furthermore,the results also indicate that the improvement is more evident for long wavelength probe light and assist light in counter-propagating configuration.
Cartmell, Matthew P.
2016-09-01
The Editor wishes to make the reader aware that the paper "A new method for predicting nonlinear structural vibrations induced by ground impact loading" by Jun Liu, Yu Zhang, Bin Yun, Journal of Sound and Vibration, 331 (2012) 2129-2140, did not contain a direct citation of the fundamental and original work in this field by Dr. Mark Svinkin. The Editor regrets that this omission was not noted at the time that the above paper was accepted and published.
Kim, Yu Shin; Galis, Zorina S; Rachev, Alexander; Han, Hai-Chao; Vito, Raymond P
2009-01-01
Arteries adapt to their mechanical environment by undergoing remodeling of the structural scaffold via the action of matrix metalloproteinases (MMPs). Cell culture studies have shown that stretching vascular smooth muscle cells (VSMCs) positively correlates to the production of MMP-2 and -9. In tissue level studies, the expressions and activations of MMP-2 and -9 are generally higher in the outer media. However, homogeneous mechanical models of arteries predict lower stress and strain in the outer media, which appear inconsistent with experimental findings. The effects of heterogeneity may be important to our understanding of VSMC function since arteries exhibit structural heterogeneity across the wall. We hypothesized that local stresses, computed using a heterogeneous mechanical model of arteries, positively correlate to the levels of MMP-2 and -9 in situ. We developed a model of the arterial wall accounting for nonlinearity, residual strain, anisotropy, and structural heterogeneity. The distributions of elastin and collagen fibers in situ, measured in the media of porcine carotid arteries, showed significant nonuniformities. Anisotropy was represented by the direction of collagen fibers measured by the helical angle of VSMC nuclei. The points at which the collagen fibers became load bearing were computed, assuming a uniform fiber strain and orientation under physiological loading conditions, an assumption motivated by morphological measurements. The distributions of circumferential stresses, computed using both heterogeneous and homogeneous models, were correlated to the distributions of expressions and activations of MMP-2 and -9 in porcine common carotid arteries incubated in an ex vivo perfusion organ culture system under physiological conditions for 48 h. While strains computed using incompressibility were identical in both models, the heterogeneous model, unlike the homogeneous model, predicted higher circumferential stresses in the outer layer correlated
Cai, Huawei; Singh, Ajay N; Sun, Xiankai; Peng, Fangyu
2015-01-01
To synthesize a fluorescent Her2-NLP peptide conjugate consisting of Her2/neu targeting peptide and nuclear localization sequence peptide (NLP) and assess its cellular uptake and intracellular localization for radionuclide cancer therapy targeting Her2/neu-positive circulating breast cancer cells (CBCC). Fluorescent Cy5.5 Her2-NLP peptide conjugate was synthesized by coupling a bivalent peptide sequence, which consisted of a Her2-binding peptide (NH2-GSGKCCYSL) and an NLP peptide (CGYGPKKKRKVGG) linked by a polyethylene glycol (PEG) chain with 6 repeating units, with an activated Cy5.5 ester. The conjugate was separated and purified by HPLC and then characterized by Maldi-MS. The intracellular localization of fluorescent Cy5.5 Her2-NLP peptide conjugate was assessed by fluorescent microscopic imaging using a confocal microscope after incubation of Cy5.5-Her2-NLP with Her2/neu positive breast cancer cells and Her2/neu negative control breast cancer cells, respectively. Fluorescent signals were detected in cytoplasm of Her2/neu positive breast cancer cells (SKBR-3 and BT474 cell lines), but not or little in cytoplasm of Her2/neu negative breast cancer cells (MDA-MB-231), after incubation of the breast cancer cells with Cy5.5-Her2-NLP conjugates in vitro. No fluorescent signals were detected within the nuclei of Her2/neu positive SKBR-3 and BT474 breast cancer cells, neither Her2/neu negative MDA-MB-231 cells, incubated with the Cy5.5-Her2-NLP peptide conjugates, suggesting poor nuclear localization of the Cy5.5-Her2-NLP conjugates localized within the cytoplasm after their cellular uptake and internalization by the Her2/neu positive breast cancer cells. Her2-binding peptide (KCCYSL) is a promising agent for radionuclide therapy of Her2/neu positive breast cancer using a β(-) or α emitting radionuclide, but poor nuclear localization of the Her2-NLP peptide conjugates may limit its use for eradication of Her2/neu-positive CBCC using I-125 or other Auger electron
Zhu, Qing; Zhou, Zhiwen; Duncan, Emily W.; Lv, Ligang; Liao, Kaihua; Feng, Huihui
2017-02-01
Spatio-temporal variability of soil moisture (θ) is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time θ monitoring methods. This restricted the comprehensive and intensive examination of θ dynamics. In this study, we integrated the manual and real-time monitored data to depict the hillslope θ dynamics with good spatial coverage and temporal resolution. Linear (stepwise multiple linear regression-SMLR) and non-linear (support vector machines-SVM) models were used to predict θ at 39 manual sites (collected 1-2 times per month) with θ collected at three real-time monitoring sites (collected every 5 mins). By comparing the accuracies of SMLR and SVM for each depth and manual site, an optimal prediction model was then determined at this depth of this site. Results showed that θ at the 39 manual sites can be reliably predicted (root mean square errors model. The subsurface flow dynamics was an important factor that determined whether the relationship was linear or non-linear. Depth to bedrock, elevation, topographic wetness index, profile curvature, and θ temporal stability influenced the selection of prediction model since they were related to the subsurface soil water distribution and movement. Using this approach, hillslope θ spatial distributions at un-sampled times and dates can be predicted. Missing information of hillslope θ dynamics can be acquired successfully.
Institute of Scientific and Technical Information of China (English)
钟伟民; 何国龙; 皮道映; 孙优贤
2005-01-01
A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.
Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach.
Ratkovic, Zorana; Golik, Wiktoria; Warnier, Pierre
2012-06-26
Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field. We present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors. We participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points. We have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data.
Ławryńczuk, Maciej
2017-03-01
This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
基于ESN和PSO的非线性模型预测控制%Nonlinear Model Predictive Control Based on ESN and PSO
Institute of Scientific and Technical Information of China (English)
柴毅; 周海林; 付东莉; 罗德超
2011-01-01
To the problem that the control objects in practical industry processes are nonlinear systems, and the traditional control theory can not deal with them perfectly, the nonlinear model predictive algorithm is introduced. The algorilhmn of nonlinear model predic-tive control system based on the echo stale network (ESN) model and the particle swarm optimization (PSO) is proposed. The ESN can identify nonlinear system perfectly, and has larger progress in computing time, data training and stability compared with the traditional recursive neural network. The PSO algorithm has the global optimization and faster speed for optimum. The simulation results of continue stirred tank reactor shows that it is significantly supcrinr cornered to the neural networks based prediction control and traditional PID control, end the effectiveness of it in nonlinear model predictive control is proved.%针对传统的控制理论对实际的工业生产过程中的被控系统,特别是具有强非线性的系统控制效果不是很理想,而应用非线性模型预测控制算法能够较好解决非线性系统的控制问题,提出了一种基于回声状态网络(Echo State Network,ESN)模型进行非线性系统辨识和粒子群优化(Particle Swarm Optimization,PSO)进行滚动优化的非线性模型预测控制系统的算法.ESN能够很好地辨证非线性系统,其计算时间、数据训练和稳定性相对于传统递归神经网络有了较太进步,PSO具有全局优化和较快的寻优速度.针对典型化工非线性对象连续搅拌槽反应器(Continue Stirred Tank Reactor,CSTR)的仿真实例表明,此模型在预测控制化于BP和PSO结合的非线性预测控制,以及传统的PID控制,证明了该算法运用于非线性模型预测控制中的有效性.
Zhong, Jian; Dong, Gang; Sun, Yimei; Zhang, Zhaoyang; Wu, Yuqin
2016-11-01
The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. Project supported by the National Natural Science Foundation of China (Grant Nos. 41230421 and 41605075) and the National Basic Research Program of China (Grant No. 2013CB430101).
The Use of Neuro-linguistic Programming (NLP) Technique in the Classroom%神经语言规划(NLP)技术在课堂教学中的运用
Institute of Scientific and Technical Information of China (English)
冉昕
2009-01-01
神经语言规划(NLP)作为一个新兴的心理训练技术,在激发大脑潜能、促发心理能量方面的巨大作用,正越来越多地受到学术界的重视.在课堂教学中教师运用这一技术能调动学生学习积极性,提高学习效率,促使学生走向成功.
da Silva, Leonardo F; Dias, Cristiano V; Cidade, Luciana C; Mendes, Juliano S; Pirovani, Carlos P; Alvim, Fátima C; Pereira, Gonçalo A G; Aragão, Francisco J L; Cascardo, Júlio C M; Costa, Marcio G C
2011-07-01
Oxalic acid (OA) and Nep1-like proteins (NLP) are recognized as elicitors of programmed cell death (PCD) in plants, which is crucial for the pathogenic success of necrotrophic plant pathogens and involves reactive oxygen species (ROS). To determine the importance of oxalate as a source of ROS for OA- and NLP-induced cell death, a full-length cDNA coding for an oxalate decarboxylase (FvOXDC) from the basidiomycete Flammulina velutipes, which converts OA into CO(2) and formate, was overexpressed in tobacco plants. The transgenic plants contained less OA and more formic acid compared with the control plants and showed enhanced resistance to cell death induced by exogenous OA and MpNEP2, an NLP of the hemibiotrophic fungus Moniliophthora perniciosa. This resistance was correlated with the inhibition of ROS formation in the transgenic plants inoculated with OA, MpNEP2, or a combination of both PCD elicitors. Taken together, these results have established a pivotal function for oxalate as a source of ROS required for the PCD-inducing activity of OA and NLP. The results also indicate that FvOXDC represents a potentially novel source of resistance against OA- and NLP-producing pathogens such as M. perniciosa, the causal agent of witches' broom disease of cacao (Theobroma cacao L.).
ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction.
Subramani, A; Wei, Y; Floudas, C A
2012-05-01
The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.
Duerr, R.; Myers, S.; Palmer, M.; Jenkins, C. J.; Thessen, A.; Martin, J.
2015-12-01
Semantics underlie many of the tools and services available from and on the web. From improving search results to enabling data mashups and other forms of interoperability, semantic technologies have proven themselves. But creating semantic resources, especially re-usable semantic resources, is extremely time consuming and labor intensive. Why? Because it is not just a matter of technology but also of obtaining rough consensus if not full agreement amongst community members on the meaning and order of things. One way to develop these resources in a more automated way would be to use NLP/ML techniques to extract the required resources from large corpora of subject-specific text such as peer-reviewed papers where presumably a rough consensus has been achieved at least about the basics of the particular discipline involved. While not generally applied to Earth Sciences, considerable resources have been spent in other fields such as medicine on these types of techniques with some success. The NSF-funded ClearEarth project is applying the techniques developed for biomedicine to the cryosphere, geology, and biology in order to spur faster development of the semantic resources needed in these fields. The first area being addressed by the project is the cryosphere, specifically sea ice nomenclature where an existing set of sea ice ontologies are being used as the "Gold Standard" against which to test and validate the NLP/ML techniques. The processes being used, lessons learned and early results will be described.
Rassinoux, A-M
2011-01-01
To summarize excellent current research in the field of knowledge representation and management (KRM). A synopsis of the articles selected for the IMIA Yearbook 2011 is provided and an attempt to highlight the current trends in the field is sketched. This last decade, with the extension of the text-based web towards a semantic-structured web, NLP techniques have experienced a renewed interest in knowledge extraction. This trend is corroborated through the five papers selected for the KRM section of the Yearbook 2011. They all depict outstanding studies that exploit NLP technologies whenever possible in order to accurately extract meaningful information from various biomedical textual sources. Bringing semantic structure to the meaningful content of textual web pages affords the user with cooperative sharing and intelligent finding of electronic data. As exemplified by the best paper selection, more and more advanced biomedical applications aim at exploiting the meaningful richness of free-text documents in order to generate semantic metadata and recently to learn and populate domain ontologies. These later are becoming a key piece as they allow portraying the semantics of the Semantic Web content. Maintaining their consistency with documents and semantic annotations that refer to them is a crucial challenge of the Semantic Web for the coming years.
Derras, Boumédiène; Bard, Pierre-Yves; Cotton, Fabrice
2017-09-01
The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m ( V S30), the topographical slope (slope), the fundamental resonance frequency ( f 0) and the depth beyond which V s exceeds 800 m/s ( H 800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [ V S30- f 0], [ V S30- H 800], [ f 0-slope], [ H 800-slope], [ V S30-slope] and [ f 0- H 800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA ( T), and M w, R JB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median ground-motion prediction, it does impact the level of aleatory uncertainty. V S30 is found to perform the best of single proxies at short periods ( T < 0.6 s), while f 0 and H 800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [ V S30- H 800] and [ f 0-slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the "stiff" spectral ordinate at the considered period.[Figure not available: see fulltext.
Varga, Peter; Schwiedrzik, Jakob; Zysset, Philippe K; Fliri-Hofmann, Ladina; Widmer, Daniel; Gueorguiev, Boyko; Blauth, Michael; Windolf, Markus
2016-04-01
Osteoporotic proximal femur fractures are caused by low energy trauma, typically when falling on the hip from standing height. Finite element simulations, widely used to predict the fracture load of femora in fall, usually include neither mass-related inertial effects, nor the viscous part of bone׳s material behavior. The aim of this study was to elucidate if quasi-static non-linear homogenized finite element analyses can predict in vitro mechanical properties of proximal femora assessed in dynamic drop tower experiments. The case-specific numerical models of 13 femora predicted the strength (R(2)=0.84, SEE=540N, 16.2%), stiffness (R(2)=0.82, SEE=233N/mm, 18.0%) and fracture energy (R(2)=0.72, SEE=3.85J, 39.6%); and provided fair qualitative matches with the fracture patterns. The influence of material anisotropy was negligible for all predictions. These results suggest that quasi-static homogenized finite element analysis may be used to predict mechanical properties of proximal femora in the dynamic sideways fall situation.
Ho, Kwok M
2017-08-31
Area under a receiver-operating-characteristic (AUROC) curve is widely used in medicine to summarize the ability of a continuous predictive marker to predict a binary outcome. This study illustrated how a U-shaped or inverted U-shaped continuous predictor would affect the shape and magnitude of its AUROC curve in predicting a binary outcome by comparing the ROC curves of the worst first 24-hour arterial pH values of 9549 consecutive critically ill patients in predicting hospital mortality before and after centering the predictor by its mean or median. A simulation dataset with an inverted U-shaped predictor was used to assess how this would affect the shape and magnitude of the AUROC curve. An asymmetrical U-shaped relationship between pH and hospital mortality, resulting in an inverse-sigmoidal ROC curve, was observed. The AUROC substantially increased after centering the predictor by its mean (0.611 vs 0.722, difference = 0.111, 95% confidence interval [CI] 0.087-0.135), and was further improved after centering by its median (0.611 vs 0.745, difference = 0.133, 95%CI 0.110-0.157). A sigmoidal-shaped ROC curve was observed for an inverted U-shaped predictor. In summary, a non-linear predictor can result in a biphasic-shaped ROC curve; and centering the predictor can reduce its bias towards null predictive ability.
Biyanto, Totok R.
2016-06-01
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO2 emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Energy Technology Data Exchange (ETDEWEB)
Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)
2016-06-03
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Nonlinear Predictive Control Algorithm for Variable Pitch Controller of Wind Turbine%风机变桨距非线性预测控制算法
Institute of Scientific and Technical Information of China (English)
雷涛; 贾利民
2011-01-01
Pitch-Controlled technique is one of the significant control techniques to MW level large variable speed pitch-controlled wind turbines, and the output power can be maintained close to the rated output power by pitch controlling. Due to the strong nonlinear characteristics of the blades, using the conventional PID controller is always hard to choose the parameters and lack of self-tuning capability. This paper proposes a design method of pitch-controlled nonlinear prediction controller based on genetic algorithms. A restricted nonlinear prediction control algorithm based on intelligent searching is designed for the nonlinear control objects based on the prediction control strategy of prediction model, rolling optimization and feedback verification. This algorithm has well restricted prediction control characteristics to the nonlinear objects referring to the results of the simulation. Compared to the conventional PID controller, this method has the advantages of fast responding, small overshoot and good anti-disturbance capability, which can also reduce the design work of controller parameters significantly by rolling local optimization. The results of the simulation demonstrate the effective of the approach proposed in this paper.%变桨控制技术是MW级大型变速变桨风力发电机组的核心控制技术之一,通过变桨控制可以保证风电机组输出功率恒定在额定功率附近.由于风轮的强非线性特性,采用常规的PID控制器往往面临参数难以设计以及参数缺乏自整定能力的问题.提出了基于遗传算法的变桨距非线性预测控制器设计方法,根据优化和反馈校正的预测控制思想,针对风轮非线性控制对象设计了一种基于智能搜索方法的带约束的非线性预测控制算法进行仿真.仿真结果表明算法对非线性对象控制中具有很好的约束预测控制性能.与传统PI算法相比,具有响应快速、超调小、抗干扰能力好的
Billings, S. A.
1988-03-01
Time and frequency domain identification methods for nonlinear systems are reviewed. Parametric methods, prediction error methods, structure detection, model validation, and experiment design are discussed. Identification of a liquid level system, a heat exchanger, and a turbocharge automotive diesel engine are illustrated. Rational models are introduced. Spectral analysis for nonlinear systems is treated. Recursive estimation is mentioned.
Kostoglou, K.; Hadjipapas, A.; Lowet, E.; Roberts, M.; de Weerd, P.; Mitsis, G.D.
2014-01-01
Aims: The relationship between collective population activity (LFP) and spikes underpins network computation, yet it remains poorly understood. Previous studies utilized pre-defined LFP features to predict spiking from simultaneously recorded LFP, and have reported good prediction of spike bursts bu
DEFF Research Database (Denmark)
Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian
2014-01-01
differences and differences between the solution found by each optimisation method. One of the investigated approaches utilises LP (linear programming) for optimisation, one uses LP with binary operation constraints, while the third approach uses NLP (non-linear programming). The LP model is used...... of selected units by 23%, while for a non-linear approach the increase can be higher than 39%. The results indicate a higher coherence between the two latter approaches, and that the MLP (mixed integer programming) optimisation is most appropriate from a viewpoint of accuracy and runtime. © 2014 Elsevier Ltd...
De, Saumyendu; Sahai, Atul Kumar; Nath Goswami, Bhupendra
2013-04-01
energy and the scale interactions in terms of the wave-wave exchanges among nonlinear triads are formulated and the above hypothesis is tested through a diagnostic analysis of the error energetics in two different model predictions at the lower troposphere (850hPa). It has been revealed that nonlinear triad interactions lead to advection of errors from short and synoptic waves (wave number >10) to long waves (wave numbers 5 - 10) and from long waves to ultra-long waves (wave numbers 1 - 4) and is responsible for the rapid growth of errors in the planetary waves. The continuous generation and then, non-linear propagation of error upto the planetary scales in the course of prediction increase the uncertainty in ultra-long scales which actually inhibit to predict accurately the planetary scale waves in tropics during medium range forecasts. Unraveling this exact mechanism through which upscale transfer of errors take place may help us devising a method to limit the mixing of small scale error with the error in forecast of tropical Intra-seasonal Oscillations and improve the prediction of lower tropospheric ISOs. Keywords: Predictability, Systematic error energetics, Scale interactions, Triads, Intra-seasonal Oscillations. Reference: The YOTC Science Plan (2008) prepared by Duane Waliser and Mitch Moncrieff. A joint WCRP-WWRP/THORPEX International Initiative, WMO/TD-No. 1452, pp. 20. Baumhefner D P and Downey P 1978 Forecast intercomparisons from three numerical weather prediction models; Mon. Weather Rev. 106 1245 - 1279. Krishnamurti T N, Subramanium M, Oosteroff D K, Daughenbaugh G. 1990 Predictability of low frequency modes. Meteorol. Atmos. phys. 44 63 - 83.
Hou, Zhongsheng; Liu, Shida; Tian, Taotao
2016-05-18
In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.
Institute of Scientific and Technical Information of China (English)
左衍秋; 马娜娜; 梁晓庆; 岳萌萌; 孟庆伟
2014-01-01
NAC (NAM-ATAF1,2-CUC2) family members play important roles in various environmental re-sponses. In order to investigate the function of LeNLP4 under chilling stress, LeNLP4 was isolated from tomato (Lycopersicon esculentum) and transgenic plants were obtained. qRT-PCR confirmed that the expression of LeNLP4 was induced by chilling stress. Compared with the wild type (WT), transgenic tomato plants have higher growth increment and the maximal photochemistry efifciency of photosystem II (Fv/Fm), higher capabili-ty for scavenging hydrogen peroxide (H2O2) and superoxide radical (O2.-), higher ascorbate peroxidase (APX) and superoxide dismutase (SOD), and lower malondialdehyde (MDA) content and the relative electric conduc-tivity (REC) under chilling treatment. Expression level of SlCBF1 in transgenic lines was higher than that in WT. These results indicated that overexpression of LeNLP4 enhanced chilling tolerance of transgenic tomato plants.%NAC (NAM-ATAF1,2-CUC2)转录因子在植物胁迫响应中起重要作用。为了探讨LeNLP4基因在番茄抗低温胁迫中的功能,分离了番茄LeNLP4转录因子基因,并获得转正义LeNLP4基因番茄植株。荧光定量PCR分析表明, LeNLP4的表达受低温诱导。与野生型植株相比,在4℃胁迫下转基因植株具有较高的生长量和光系统II (PSII)最大光化学效率(Fv/Fm)、过氧化氢(H2O2)和超氧阴离子(O2.-)清除速率、抗坏血酸过氧化物酶(APX)和超氧化物歧化酶(SOD)活性,以及较低的丙二醛(MDA)含量和相对电导率(REC)。过表达株系中SlCBF1的表达高于野生型。上述结果表明, LeNLP4的过表达提高了转基因番茄抗低温胁迫能力。
Santosa, H.; Hobara, Y.; Balikhin, M. A.
2015-12-01
Very Low Frequency (VLF) waves have been proposed as an approach to study and monitor the lower ionospheric conditions. The ionospheric perturbations are identified in relation with thunderstorm activity, geomagnetic storm and other factors. The temporal dependence of VLF amplitude has a complicated and large daily variabilities in general due to combinations of both effects from above (space weather effect) and below (atmospheric and crustal processes) of the ionosphere. Quantitative contributions from different external sources are not known well yet. Thus the modelling and prediction of VLF wave amplitude are important issues to study the lower ionospheric responses from various external parameters and to also detect the anomalies of the ionosphere. The purpose of the study is to model and predict nighttime average amplitude of VLF wave propagation from the VLF transmitter in Hawaii (NPM) to receiver in Chofu (CHO) Tokyo, Japan path using NARX neural network. The constructed model was trained for the target parameter of nighttime average amplitude of NPM-CHO path. The NARX model, which was built based on daily input variables of various physical parameters such as stratosphere temperature, cosmic rays and total column ozone, possessed good accuracies. As a result, the constructed models are capable of performing accurate multistep ahead predictions, while maintaining acceptable one step ahead prediction accuracy. The results of the predicted daily VLF amplitude are in good agreement with observed (true) value for one step ahead prediction (r = 0.92, RMSE = 1.99), multi-step ahead 5 days prediction (r = 0.91, RMSE = 1.14) and multi-step ahead 10 days prediction (r = 0.75, RMSE = 1.74). The developed model indicates the feasibility and reliability of predicting lower ionospheric properties by the NARX neural network approach, and provides physical insights on the responses of lower ionosphere due to various external forcing.
Emelyanov, Alexander V; Rabbani, Joshua; Mehta, Monika; Vershilova, Elena; Keogh, Michael C; Fyodorov, Dmitry V
2014-09-15
Nuclear DNA in the male gamete of sexually reproducing animals is organized as sperm chromatin compacted primarily by sperm-specific protamines. Fertilization leads to sperm chromatin remodeling, during which protamines are expelled and replaced by histones. Despite our increased understanding of the factors that mediate nucleosome assembly in the nascent male pronucleus, the machinery for protamine removal remains largely unknown. Here we identify four Drosophila protamine chaperones that mediate the dissociation of protamine-DNA complexes: NAP-1, NLP, and nucleophosmin are previously characterized histone chaperones, and TAP/p32 has no known function in chromatin metabolism. We show that TAP/p32 is required for the removal of Drosophila protamine B in vitro, whereas NAP-1, NLP, and Nph share roles in the removal of protamine A. Embryos from P32-null females show defective formation of the male pronucleus in vivo. TAP/p32, similar to NAP-1, NLP, and Nph, facilitates nucleosome assembly in vitro and is therefore a histone chaperone. Furthermore, mutants of P32, Nlp, and Nph exhibit synthetic-lethal genetic interactions. In summary, we identified factors mediating protamine removal from DNA and reconstituted in a defined system the process of sperm chromatin remodeling that exchanges protamines for histones to form the nucleosome-based chromatin characteristic of somatic cells.
Institute of Scientific and Technical Information of China (English)
JIANG Zhina; MU Mu
2009-01-01
The authors apply the technique of conditional nonlinear optimal perturbations (CNOPs) as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-gcostrophic (QG) model in a perfect-model scenario. Ensemble forecasts for the medium range (14 days) are made from the initial states perturbed by CNOPs and singular vectors (SVs). 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear supcrior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.
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 (Psurvival 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.
Doulamis, A D; Doulamis, N D; Kollias, S D
2003-01-01
Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.
Directory of Open Access Journals (Sweden)
Lucyna Szaniawska
2010-09-01
Full Text Available Access to information about cartographic materials in the National Library of Poland has been improved by new functionalities intended to keep up with users’ new needs and technological trends. From mid-January 2010 on readers of maps and atlases kept in the library can find catalogue descriptions of these materials through the special website. Three different forms of access to descriptions of cartographic materials are available on the website: (1 Catalogues/Catalogues online — database search in the central computer catalogue of the National Library of Poland (NLP; (2 Bibliographies online. Cartographic documents [download .pdf files — in Polish]; (3 Bibliographies online. Cartographic documents [search database] — access to the database in the MAK system containing items from the published bibliography of cartographic materials.
2016-01-01
Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d–1 (RTD-model) and 0.03 ± 0.03 L d–1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d–1 and 0.0437 ± 0.02 L d–1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations. PMID:27363449
Energy Technology Data Exchange (ETDEWEB)
Gentili, Pier Luigi, E-mail: pierluigi.gentili@unipg.it [Department of Chemistry, Biology and Biotechnology, University of Perugia, 06123 Perugia (Italy); Gotoda, Hiroshi [Department of Mechanical Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-shi, Shiga 525-8577 (Japan); Dolnik, Milos; Epstein, Irving R. [Department of Chemistry, Brandeis University, Waltham, Massachusetts 02454-9110 (United States)
2015-01-15
Forecasting of aperiodic time series is a compelling challenge for science. In this work, we analyze aperiodic spectrophotometric data, proportional to the concentrations of two forms of a thermoreversible photochromic spiro-oxazine, that are generated when a cuvette containing a solution of the spiro-oxazine undergoes photoreaction and convection due to localized ultraviolet illumination. We construct the phase space for the system using Takens' theorem and we calculate the Lyapunov exponents and the correlation dimensions to ascertain the chaotic character of the time series. Finally, we predict the time series using three distinct methods: a feed-forward neural network, fuzzy logic, and a local nonlinear predictor. We compare the performances of these three methods.
Fan, Jiajie; Mohamed, Moumouni Guero; Qian, Cheng; Fan, Xuejun; Zhang, Guoqi; Pecht, Michael
2017-07-18
With the expanding application of light-emitting diodes (LEDs), the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD), defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED's optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1) the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2) the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs), and color rendering indexes (CRIs) of phosphor-converted (pc)-white LEDs, and also can estimate the residual color life.
Oosterhuis, Ekke J.; Eidhof, Wouter B.; Hoogt, van der Peter J.M.; Boer, de André
2006-01-01
Force prediction can basically be done by two methods: direct methods and optimization methods. Direct methods use the inverse of the forward system model to calculate the excitation directly from the measured responses. Optimization methods use a forward model in an optimization loop wherein the in
Predictive factors for final outcome of severely traumatized eyes with no light perception
Directory of Open Access Journals (Sweden)
Agrawal Rupesh
2012-06-01
Full Text Available Abstract Background An eye injury that causes no light perception (NLP typically carries an unfavorable prognosis, and NLP because of trauma is a common indication for enucleation. With advances in vitreoretinal surgical techniques, however, the indication for enucleation is no longer determined by posttrauma NLP vision alone. There are limited studies in the literature to analyse the outcome of NLP eyes following open globe injury. The current study was aimed to evaluate the outcome of surgical repair of severely traumatized eyes with no light perception vision as preoperative visual acuity. Secondary objective was to possibly predict the factors affecting the final vision outcome in this eyes. Methods Retrospective case analysis of patients with surgical repair of open globe injury over last ten years at a tertiary referral eye care centre in Singapore. Results Out of one hundred and seventy two eyes with open globe injury 27 (15.7% eyes had no light perception (NLP. After surgical repair, final visual acuity remained NLP in 18 (66.7% eyes. Final vision improved to Light perception/ Hand movement (LP/HM in 2(7.4% eyes, 1/200 to 19/200(11.1% in 3 eyes and 20/50-20/200(14.8% in 4 eyes. The median follow up was 18.9 months (range: 4–60 months. The factors contributing to poor postoperative outcome were presence of RAPD (p = 0.014, wound extending into zone III (p = 0.023 and associated vitreoretinal trauma (p = 0.008. Conclusions One third of eyes had ambulatory vision or better though two third of eyes still remained NLP. Pre-operative visual acuity of NLP should not be an indication for primary enucleation or evisceration for severely traumatized eyes. Presence of afferent papillary defect, wound extending posterior to rectus insertion and associated vitreoretinal trauma can adversely affect the outcome in severely traumatized eyes with NLP. Timely intervention and state of art surgery may restore useful vision in severely
Dishion, Thomas J; Forgatch, Marion; Van Ryzin, Mark; Winter, Charlotte
2012-07-01
In this study we examined the videotaped family interactions of a community sample of adolescents and their parents. Youths were assessed in early to late adolescence on their levels of antisocial behavior. At age 16-17, youths and their parents were videotaped interacting while completing a variety of tasks, including family problem solving. The interactions were coded and compared for three developmental patterns of antisocial behavior: early onset, persistent; adolescence onset; and typically developing. The mean duration of conflict bouts was the only interaction pattern that discriminated the 3 groups. In the prediction of future antisocial behavior, parent and youth reports of transition entropy and conflict resolution interacted to account for antisocial behavior at age 18-19. Families with low entropy and peaceful resolutions predicted low levels of youth antisocial behavior at age 18-19. These findings suggest the need to study both attractors and repellers to understand family dynamics associated with health and social and emotional development.
Fukuda, Hiroki; Suwa, Hideaki; Nakano, Atsushi; Sakamoto, Mari; Imazu, Miki; Hasegawa, Takuya; Takahama, Hiroyuki; Amaki, Makoto; Kanzaki, Hideaki; Anzai, Toshihisa; Mochizuki, Naoki; Ishii, Akira; Asanuma, Hiroshi; Asakura, Masanori; Washio, Takashi; Kitakaze, Masafumi
2016-11-01
Brain natriuretic peptide (BNP) is the most effective predictor of outcomes in chronic heart failure (CHF). This study sought to determine the qualitative relationship between the BNP levels at discharge and on the day of cardiovascular events in CHF patients. We devised a mathematical probabilistic model between the BNP levels at discharge (y) and on the day (t) of cardiovascular events after discharge for 113 CHF patients (Protocol I). We then prospectively evaluated this model on another set of 60 CHF patients who were readmitted (Protocol II). P(t|y) was the probability of cardiovascular events occurring after >t, the probability on t was given as p(t|y) = -dP(t|y)/dt, and p(t|y) = pP(t|y) = αyβP(t|y), along with p = αyβ (α and β were constant); the solution was p(t|y) = αyβ exp(-αyβt). We fitted this equation to the data set of Protocol I using the maximum likelihood principle, and we obtained the model p(t|y) = 0.000485y0.24788 exp(-0.000485y0.24788t). The cardiovascular event-free rate was computed as P(t) = 1/60Σi=1,…,60 exp(-0.000485yi0.24788t), based on this model and the BNP levels yi in a data set of Protocol II. We confirmed no difference between this model-based result and the actual event-free rate. In conclusion, the BNP levels showed a non-linear relationship with the day of occurrence of cardiovascular events in CHF patients.
Kratochwil, Nicole A; Meille, Christophe; Fowler, Stephen; Klammers, Florian; Ekiciler, Aynur; Molitor, Birgit; Simon, Sandrine; Walter, Isabelle; McGinnis, Claudia; Walther, Johanna; Leonard, Brian; Triyatni, Miriam; Javanbakht, Hassan; Funk, Christoph; Schuler, Franz; Lavé, Thierry; Parrott, Neil J
2017-03-01
Early prediction of human clearance is often challenging, in particular for the growing number of low-clearance compounds. Long-term in vitro models have been developed which enable sophisticated hepatic drug disposition studies and improved clearance predictions. Here, the cell line HepG2, iPSC-derived hepatocytes (iCell®), the hepatic stem cell line HepaRG™, and human hepatocyte co-cultures (HμREL™ and HepatoPac®) were compared to primary hepatocyte suspension cultures with respect to their key metabolic activities. Similar metabolic activities were found for the long-term models HepaRG™, HμREL™, and HepatoPac® and the short-term suspension cultures when averaged across all 11 enzyme markers, although differences were seen in the activities of CYP2D6 and non-CYP enzymes. For iCell® and HepG2, the metabolic activity was more than tenfold lower. The micropatterned HepatoPac® model was further evaluated with respect to clearance prediction. To assess the in vitro parameters, pharmacokinetic modeling was applied. The determination of intrinsic clearance by nonlinear mixed-effects modeling in a long-term model significantly increased the confidence in the parameter estimation and extended the sensitive range towards 3% of liver blood flow, i.e., >10-fold lower as compared to suspension cultures. For in vitro to in vivo extrapolation, the well-stirred model was used. The micropatterned model gave rise to clearance prediction in man within a twofold error for the majority of low-clearance compounds. Further research is needed to understand whether transporter activity and drug metabolism by non-CYP enzymes, such as UGTs, SULTs, AO, and FMO, is comparable to the in vivo situation in these long-term culture models.
Sliding Mode Predictive Control Scheme for Constrained Nonlinear Systems%约束非线性系统的滑模预测控制方法
Institute of Scientific and Technical Information of China (English)
周建锁; 刘志远; 裴润
2001-01-01
将预测控制和滑模控制结合起来，提出一种新的非线性模型预测控制方法。通过对系统状态预测得到切换函数预测值，求解约束开环优化求得预测控制律，并将当前时刻的控制作用于对象，下一时刻重复此过程。该方法具有预测控制在线处理约束及滑模控制滑动模态对干扰的不变性的优点。分析了零终端滑模约束模型预测控制的稳定性。%A new nonlinear model predictive control(NLMPC) scheme isproposed, which combines the predictive control and the sliding mode control(SMC). By predicting the system state, the pre-designed switching function is predicted, then the control law can be found by solving constrained open-loop optimal control problem. The current control is implemented, and then the optimizing procedure is repeated at the next sampling time. The proposed scheme, which has advantages of NLMPC and SMC, can deal with system constraints on-line and has strong robustness on the sliding surface. By constraining terminal sliding mode to be zero, the stability of MPC system is analyzed.
A Nonlinear Prediction Model for Philip Infiltration Parameters%Philip入渗模型参数的非线性预报模型
Institute of Scientific and Technical Information of China (English)
郭华; 樊贵盛
2016-01-01
利用黄土高原区大田耕作土壤的水分入渗试验过程资料 ,拟合了Philip入渗模型参数 ,建立了以土壤体积含水率、干密度、粉、黏粒含量和有机质含量等土壤理化参数为输入变量 ,Philip入渗模型参数为输出变量的土壤传递函数 ,通过对函数的分析、检验 ,建立了土壤入渗参数 S和A 的多元非线性预测模型 ;在此基础上 ,运用灰色关联分析理论 ,将各输入变量进行了灰色排序.研究表明 :用土壤体积含水率、干密度、粉粒含量、黏粒含量和有机质含量作为预报模型的输入参数可实现对入渗参数的预测 ,预测参数实测值与预测值之间的相对误差可控制在8% 以下 ,所建立的非线性预测模型高度相关.%Using the soil water infiltration test data in the Loess Plateau as the background ,the Philip infiltration model parameters were fitted ,and the Pedo-Transfer Functions was established .Soil water content ,soil bulk density ,soil silt content and soil organic matter content were used as input parameters ,and soil infiltration parameters were used as the output factors in Pedo-Transfer Func-tions .Multivariate nonlinear prediction models of infiltration parameter S and A were established by analyzing and testing Pedo-Transfer Functions .On this basis ,each input parameter was gray sorted by the gray relational analysis theory .The results showed that :it was reasonable to use soil water content ,soil bulk density ,soil silt content and soil organic matter content as input parame-ters to predict infiltration parameters ,and the relative errors between the measured value and the predicted value could be controlled below 8% .The nonlinear relationship models were highly correlated .
Paschoal, Diego; Santos, Hélio F Dos
2013-05-01
In this paper, we assessed the quantum mechanical level of theory for prediction of linear and nonlinear optical (NLO) properties of push-pull organic molecules. The electric dipole moment (μ), mean polarizability ([Symbol: see text]α[Symbol: see text]) and total static first hyperpolarizability (βt) were calculated for a set of benzene, styrene, biphenyl and stilbene derivatives using HF, MP2 and DFT (31 different functionals) levels and over 71 distinct basis sets. In addition, we propose two new basis sets, NLO-V and aNLO-V, for NLO properties calculations. As the main outcomes it is shown that long-range corrected DFT functionals such as M062X, ωB97, cam-B3LYP, LC-BLYP and LC-ωPBE work satisfactorily for NLO properties when appropriate basis sets such as those proposed here (NLO-V or aNLO-V) are used. For most molecules with β ranging from 0 to 190 esu, the average absolute deviation was 13.2 esu for NLO-V basis sets, compared to 27.2 esu for the standard 6-31 G(2d) basis set. Therefore, we conclude that the new basis sets proposed here (NLO-V and aNLO-V), together with the cam-B3LYP functional, make an affordable calculation scheme to predict NLO properties of large organic molecules.
Ansys nonlinear buckling analysis for prediction of femoral neck fracture%股骨颈骨折预测的Ansys非线性屈曲分析
Institute of Scientific and Technical Information of China (English)
张国栋; 林海滨; 陈宣煌; 郑锋; 陈国立; 陶圣祥
2012-01-01
目的 实施10例股骨颈骨折的Ansys非线性屈曲分析,模拟与生物力学实验一致的股骨颈骨折实验全程.方法 采用10个股骨上段标本进行有限元建模,以十字坐标轴及绘图辅助软件TweakWindow实施精确的载荷施加及条件约束,进行特征值屈曲分析及非线性屈曲分析.结果 实现可控制的股骨颈骨折预测的Ansys非线性屈曲分析的载荷施加及条件约束；全部分析在达到结构崩溃时自动终止,发生股骨颈骨折的极限载荷为( 12 324.07±4439.733)N,最大位移为(7.6067 ±2.2716) mm,截取载荷-位移曲线；通过应力等值线图可判断股骨颈骨折位置.结论 非线性屈曲分析是一种适用于骨折预测的有限元算法；通过十字坐标轴及绘图辅助软件可以实现精确可控制的载荷施加及条件约束,实现与生物力学实验一致的条件设置.%Objective To simulate the whole femoral neck fractures experimental processes which were consistent with the biomechanical experiments by executing 10 cases of ansys nonlinear buckling analysis of femoral neck fracture.Methods 10 femoral superior segment specimens were used for finite element modeling.A cross axis as well as tweakwindow,which was a drawing auxiliary software,were used to execute the define loads and displacement procedures accurately,then the linear and nonlinear buckling analysis were executed successively.Results A controllable define loads and displacement in ansys nonlinear buckling analysis of prediction for femoral neck fracture; All analysises stop automatically when structural collapse happened.The ultimate load results of femoral neck fracture were (12 324.07 ±4439.73) N,the maximum displacement results were (7.6067 ± 2.2716) mm.The load-displacement curves were harvested; The positions of femoral neck fracture could be judged easily by stress contour plots.Conclusion The nonlinear buckling analysis was a suitable finite element calculation method for
Boehm, Holger F.; Link, Thomas M.; Monetti, Roberto A.; Mueller, Dirk; Rummeny, Ernst J.; Newitt, David; Majumdar, Sharmila; Raeth, Christoph W.
2004-05-01
Multi-dimensional convex objects can be characterized with respect to shape, structure, and the connectivity of their components using a set of morphological descriptors known as the Minkowski functionals. In a 3D Euclidian space, these correspond to volume, surface area, mean integral curvature, and the Euler-Poincaré characteristic. We introduce the Minkowski functionals to medical image processing for the morphological analysis of trabecular bone tissue. In the context of osteoporosis-a metabolic disorder leading to a weakening of bone due to deterioration of micro-architecture-the structure of bone increasingly gains attention in the quantification of bone quality. The trabecular architecture of healthy cancellous bone consists of a complex 3D system of inter-connected mineralised elements whereas in osteoporosis the micro-structure is dominated by gaps and disconnections. At present, the standard parameter for diagnosis and assessment of fracture risk in osteoporosis is the bone mineral density (BMD) - a bulk measure of mineralisation irrespective of structural texture characteristics. With the development of modern imaging modalities (high resolution MRI, micro-CT) with spatial resolutions allowing to depict individual trabeculae bone micro-architecture has successfully been analysed using linear, 2- dimensional structural measures adopted from standard histo-morphometry. The preliminary results of our study demonstrate that due to the complex - i.e. the non-linear - network of trabecular bone structures non-linear measures in 3D are superior to linear ones in predicting mechanical properties of trabecular bone from structural information extracted from high resolution MR image data.
Energy Technology Data Exchange (ETDEWEB)
Lissenden, Cliff [Pennsylvania State Univ., State College, PA (United States); Hassan, Tasnin [North Carolina State Univ., Raleigh, NC (United States); Rangari, Vijaya [Tuskegee Univ., Tuskegee, AL (United States)
2014-10-30
application of the harmonic generation method to tubular mechanical test specimens and pipes for nondestructive evaluation. Tubular specimens and pipes act as waveguides, thus we applied the acoustic harmonic generation method to guided waves in both plates and shells. Magnetostrictive transducers were used to generate and receive guided wave modes in the shell sample and the received signals were processed to show the sensitivity of higher harmonic generation to microstructure evolution. Modeling was initiated to correlate higher harmonic generation with the microstructure that will lead to development of a life prediction model that is informed by the nonlinear acoustics measurements.
A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion
Institute of Scientific and Technical Information of China (English)
Wen-yang DUAN; Li-min HUANG; Yang HAN; Ya-hui ZHANG; Shuo HUANG
2015-01-01
题目：用于非线性非平稳船舶运动极短期预报的一种复合自回归经验模态分解支持向量机回归模型 目的：基于支持向量机回归（SVR）模型在非线时间序列的预测能力及经验模态分解（EMD）方法在处理非线性非平稳性的优势，提出一种复合自回归经验模态分解支持向量机回归（AR-EMD-SVR）模型，提高非线性非平稳船舶运动极短期预报精度。 创新点：1.研究非线性非平稳船舶运动的极短期预报问题，提出一种复合的预报方法；2.基于不同层次的预报模型和模型试验数据，分析非线性非平稳性对极短期预报精度的影响。 方法：1.在SVR模型中引入基于自回归（AR）预报端点延拓的 EMD 方法，形成复合的 AR-EMD-SVR 预报模型；2.基于集装箱船模水池试验运动数据将 AR-EMD-SVR 模型与 AR、SVR 和EMD-AR 三种模型进行比较，分析非线性非平稳性对极短期预报的影响以及不同模型的预报性能。 结论：1. AR-EMD 方法能够有效的克服非平稳对极短期预报模型（AR和 SVR）在精度上所带来的不良影响；2.基于船模试验数据的预报结果表明：相较于 AR、SVR 和 EMD-AR 三种预报模型，基于 AR-EMD-SVR模型的非线性非平稳船舶运动极短期预报结果具有更高的精度。%Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector re-gression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends
Will Nonlinear Backcalculation Help?
DEFF Research Database (Denmark)
Ullidtz, Per
2000-01-01
demonstrates, that treating the subgrade as a nonlinear elastic material, can result in more realistic moduli and a much better agreement between measured and calculated stresses and strains.The response of nonlinear elastic materials can be calculated using the Finite Element Method (FEM). A much simpler...... approach is to use the Method of Equivalent Thicknesses (MET), modified for a nonlinear subgrade. The paper includes an example where moduli backcalculated using FEM, linear elastic theory and MET are compared. Stresses and strains predicted by the three methods are also compared to measured values...
Konop, Christopher J.; Knickelbine, Jennifer J.; Sygulla, Molly S.; Wruck, Colin D.; Vestling, Martha M.; Stretton, Antony O. W.
2015-12-01
Neuromodulators have become an increasingly important component of functional circuits, dramatically changing the properties of both neurons and synapses to affect behavior. To explore the role of neuropeptides in Ascaris suum behavior, we devised an improved method for cleanly dissecting single motorneuronal cell bodies from the many other cell processes and hypodermal tissue in the ventral nerve cord. We determined their peptide content using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). The reduced complexity of the peptide mixture greatly aided the detection of peptides; peptide levels were sufficient to permit sequencing by tandem MS from single cells. Inhibitory motorneurons, known to be GABAergic, contain a novel neuropeptide, As-NLP-22 (SLASGRWGLRPamide). From this sequence and information from the A. suum expressed sequence tag (EST) database, we cloned the transcript ( As-nlp-22) and synthesized a riboprobe for in situ hybridization, which labeled the inhibitory motorneurons; this validates the integrity of the dissection method, showing that the peptides detected originate from the cells themselves and not from adhering processes from other cells (e.g., synaptic terminals). Synthetic As-NLP-22 has potent inhibitory activity on acetylcholine-induced muscle contraction as well as on basal muscle tone. Both of these effects are dose-dependent: the inhibitory effect on ACh contraction has an IC50 of 8.3 × 10-9 M. When injected into whole worms, As-NLP-22 produces a dose-dependent inhibition of locomotory movements and, at higher levels, complete paralysis. These experiments demonstrate the utility of MALDI TOF/TOF MS in identifying novel neuromodulators at the single-cell level.
Pérez-Moreno, Javier; Clays, Koen; Kuzyk, Mark G.
2010-08-01
We introduce a self-consistent theory for the description of the optical linear and nonlinear response of molecules that is based strictly on the results of the experimental characterization. We show how the Thomas-Kuhn sum-rules can be used to eliminate the dependence of the nonlinear response on parameters that are not directly measurable. Our approach leads to the successful modeling of the dispersion of the nonlinear response of complex molecular structures with different geometries (dipolar and octupolar), and can be used as a guide towards the modeling in terms of fundamental physical parameters.
Kaggal, Vinod C.; Elayavilli, Ravikumar Komandur; Mehrabi, Saeed; Pankratz, Joshua J.; Sohn, Sunghwan; Wang, Yanshan; Li, Dingcheng; Rastegar, Majid Mojarad; Murphy, Sean P.; Ross, Jason L.; Chaudhry, Rajeev; Buntrock, James D.; Liu, Hongfang
2016-01-01
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future. PMID:27385912
Institute of Scientific and Technical Information of China (English)
包哲静; 皮道映; 孙优贤
2007-01-01
Multi-kernel-based support vector machine (SVM) model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
Husson, Steven J; Janssen, Tom; Baggerman, Geert; Bogert, Brigitte; Kahn-Kirby, Amanda H; Ashrafi, Kaveh; Schoofs, Liliane
2007-07-01
Biologically active peptides are synthesized from inactive pre-proproteins or peptide precursors by the sequential actions of processing enzymes. Proprotein convertases cleave the precursor at pairs of basic amino acids, which are then removed from the carboxyl terminus of the generated fragments by a specific carboxypeptidase. Caenorhabditis elegans strains lacking proprotein convertase EGL-3 display a severely impaired neuropeptide profile (Husson et al. 2006, J. Neurochem.98, 1999-2012). In the present study, we examined the role of the C. elegans carboxypeptidase E orthologue EGL-21 in the processing of peptide precursors. More than 100 carboxy-terminally extended neuropeptides were detected in egl-21 mutant strains. These findings suggest that EGL-21 is a major carboxypeptidase involved in the processing of FMRFamide-like peptide (FLP) precursors and neuropeptide-like protein (NLP) precursors. The impaired peptide profile of egl-3 and egl-21 mutants is reflected in some similar phenotypes. They both share a severe widening of the intestinal lumen, locomotion defects, and retention of embryos. In addition, egl-3 animals have decreased intestinal fat content. Taken together, these results suggest that EGL-3 and EGL-21 are key enzymes for the proper processing of neuropeptides that control egg-laying, locomotion, fat storage and the nutritional status.
Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro
2015-01-01
Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources.
Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro
2015-01-01
Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. PMID:25505034
Bloembergen, Nicolaas
1996-01-01
Nicolaas Bloembergen, recipient of the Nobel Prize for Physics (1981), wrote Nonlinear Optics in 1964, when the field of nonlinear optics was only three years old. The available literature has since grown by at least three orders of magnitude.The vitality of Nonlinear Optics is evident from the still-growing number of scientists and engineers engaged in the study of new nonlinear phenomena and in the development of new nonlinear devices in the field of opto-electronics. This monograph should be helpful in providing a historical introduction and a general background of basic ideas both for expe
Solitons in nonlinear lattices
Kartashov, Yaroslav V; Torner, Lluis
2010-01-01
This article offers a comprehensive survey of results obtained for solitons and complex nonlinear wave patterns supported by purely nonlinear lattices (NLs), which represent a spatially periodic modulation of the local strength and sign of the nonlinearity, and their combinations with linear lattices. A majority of the results obtained, thus far, in this field and reviewed in this article are theoretical. Nevertheless, relevant experimental settings are surveyed too, with emphasis on perspectives for implementation of the theoretical predictions in the experiment. Physical systems discussed in the review belong to the realms of nonlinear optics (including artificial optical media, such as photonic crystals, and plasmonics) and Bose-Einstein condensation (BEC). The solitons are considered in one, two, and three dimensions (1D, 2D, and 3D). Basic properties of the solitons presented in the review are their existence, stability, and mobility. Although the field is still far from completion, general conclusions c...
Nonlinearity in nanomechanical cantilevers
DEFF Research Database (Denmark)
Villanueva Torrijo, Luis Guillermo; Karabalin, R. B.; Matheny, M. H.
2013-01-01
Euler-Bernoulli beam theory is widely used to successfully predict the linear dynamics of micro-and nanocantilever beams. However, its capacity to characterize the nonlinear dynamics of these devices has not yet been rigorously assessed, despite its use in nanoelectromechanical systems development....... These findings underscore the delicate balance between inertial and geometric nonlinear effects in the fundamental mode, and strongly motivate further work to develop theories beyond the Euler-Bernoulli approximation. DOI: 10.1103/PhysRevB.87.024304....... In this article, we report the first highly controlled measurements of the nonlinear response of nanomechanical cantilevers using an ultralinear detection system. This is performed for an extensive range of devices to probe the validity of Euler-Bernoulli theory in the nonlinear regime. We find that its...
2016-07-01
Advanced Research Projects Agency (DARPA) Dynamics-Enabled Frequency Sources (DEFYS) program is focused on the convergence of nonlinear dynamics and...Early work in this program has shown that nonlinear dynamics can provide performance advantages. However, the pathway from initial results to...dependent nonlinear stiffness observed in these devices. This work is ongoing, and will continue through the final period of this program . Reference 9
Nayfeh, Ali Hasan
1995-01-01
Nonlinear Oscillations is a self-contained and thorough treatment of the vigorous research that has occurred in nonlinear mechanics since 1970. The book begins with fundamental concepts and techniques of analysis and progresses through recent developments and provides an overview that abstracts and introduces main nonlinear phenomena. It treats systems having a single degree of freedom, introducing basic concepts and analytical methods, and extends concepts and methods to systems having degrees of freedom. Most of this material cannot be found in any other text. Nonlinear Oscillations uses sim
Yoshida, Zensho
2010-01-01
This book gives a general, basic understanding of the mathematical structure "nonlinearity" that lies in the depths of complex systems. Analyzing the heterogeneity that the prefix "non" represents with respect to notions such as the linear space, integrability and scale hierarchy, "nonlinear science" is explained as a challenge of deconstruction of the modern sciences. This book is not a technical guide to teach mathematical tools of nonlinear analysis, nor a zoology of so-called nonlinear phenomena. By critically analyzing the structure of linear theories, and cl
Nanda, Sudarsan
2013-01-01
"Nonlinear analysis" presents recent developments in calculus in Banach space, convex sets, convex functions, best approximation, fixed point theorems, nonlinear operators, variational inequality, complementary problem and semi-inner-product spaces. Nonlinear Analysis has become important and useful in the present days because many real world problems are nonlinear, nonconvex and nonsmooth in nature. Although basic concepts have been presented here but many results presented have not appeared in any book till now. The book could be used as a text for graduate students and also it will be useful for researchers working in this field.
Tom, Nathan
2015-01-01
To further maximize power absorption in both regular and irregular ocean wave environments, nonlinear-model-predictive control (NMPC) was applied to a model-scale point absorber developed at the University of California Berkeley, Berkeley, CA, USA. The NMPC strategy requires a power-takeoff (PTO) unit that could be turned on and off, as the generator would be inactive for up to 60% of the wave period. To confirm the effectiveness of this NMPC strategy, an in-house-designed permanent magnet linear generator (PMLG) was chosen as the PTO. The time-varying performance of the PMLG was first characterized by dry-bench tests, using mechanical relays to control the electromagnetic conversion process. The on/off sequencing of the PMLG was tested under regular and irregular wave excitation to validate NMPC simulations using control inputs obtained from running the choice optimizer offline. Experimental results indicate that successful implementation was achieved and absorbed power using NMPC was up to 50% greater than the passive system, which utilized no controller. Previous investigations into MPC applied to wave energy converters have lacked the experimental results to confirm the reported gains in power absorption. However, after considering the PMLG mechanical-to-electrical conversion efficiency, the electrical power output was not consistently maximized. To improve output power, a mathematical relation between the efficiency and damping magnitude of the PMLG was inserted in the system model to maximize the electrical power output through continued use of NMPC which helps separate this work from previous investigators. Of significance, results from latter simulations provided a damping time series that was active over a larger portion of the wave period requiring the actuation of the applied electrical load, rather than on/off control.
Nonlinear Evolution of Ferroelectric Domains
Institute of Scientific and Technical Information of China (English)
WeiLU; Dai－NingFANG; 等
1997-01-01
The nonlinear evolution of ferroelectric domains is investigated in the paper and amodel is proposed which can be applied to numerical computation.Numerical results show that the model can accurately predict some nonlinear behavior and consist with those experimental results.
NLP-based identification of pneumonia cases from free-text radiological reports.
Elkin, Peter L; Froehling, David; Wahner-Roedler, Dietlind; Trusko, Brett; Welsh, Gail; Ma, Haobo; Asatryan, Armen X; Tokars, Jerome I; Rosenbloom, S Trent; Brown, Steven H
2008-11-06
Radiological reports are a rich source of clinical data which can be mined to assist with biosurveillance of emerging infectious diseases. In addition to biosurveillance, radiological reports are an important source of clinical data for health service research.Pneumonias and other radiological findings on chest x ray or chest computed tomography (CT) are one type of relevant finding to both biosurveillance and health services research. In this study we examined the ability of a Natural Language Processing system to accurately identify pneumonias and other lesions from within free text radiological reports. The system encoded the reports in the SNOMED CT Ontology and then a set of SNOMED CT based rules were created in our Health Archetype Language aimed at the identification of these radiological findings and diagnoses. The encoded rule was executed against the SNOMED CT encodings of the radiological reports. The accuracy of the reports was compared with a Clinician review of the Radiological Reports. The accuracy of the system in the identification of pneumonias was high with a Sensitivity (recall) of 100%, a specificity of 98%, and a positive predictive value (precision) of 97%. We conclude that SNOMED CT based computable rules are accurate enough for the automated biosurveillance of pneumonias from radiological reports.
Svinkin, Mark R.
2016-12-01
The authors suggested a hybrid method for modeling the time history of structural vibrations triggered by impact dynamic loads from construction equipment and blasting, and they stated, "In this work, a hybrid method has been proposed to calculate the theoretical seismograms of structural vibrations. The word "hybrid" denotes a combination of field measurements and computer simulations. Then, based on nonlinear system theory, a novel method is proposed to predict the signal induced by impact loading".
Nonlinear systems in medicine.
Higgins, John P
2002-01-01
Many achievements in medicine have come from applying linear theory to problems. Most current methods of data analysis use linear models, which are based on proportionality between two variables and/or relationships described by linear differential equations. However, nonlinear behavior commonly occurs within human systems due to their complex dynamic nature; this cannot be described adequately by linear models. Nonlinear thinking has grown among physiologists and physicians over the past century, and non-linear system theories are beginning to be applied to assist in interpreting, explaining, and predicting biological phenomena. Chaos theory describes elements manifesting behavior that is extremely sensitive to initial conditions, does not repeat itself and yet is deterministic. Complexity theory goes one step beyond chaos and is attempting to explain complex behavior that emerges within dynamic nonlinear systems. Nonlinear modeling still has not been able to explain all of the complexity present in human systems, and further models still need to be refined and developed. However, nonlinear modeling is helping to explain some system behaviors that linear systems cannot and thus will augment our understanding of the nature of complex dynamic systems within the human body in health and in disease states.
Zhu, Hong-Ming; Pen, Ue-Li; Chen, Xuelei; Yu, Hao-Ran
2016-01-01
We present a direct approach to non-parametrically reconstruct the linear density field from an observed non-linear map. We solve for the unique displacement potential consistent with the non-linear density and positive definite coordinate transformation using a multigrid algorithm. We show that we recover the linear initial conditions up to $k\\sim 1\\ h/\\mathrm{Mpc}$ with minimal computational cost. This reconstruction approach generalizes the linear displacement theory to fully non-linear fields, potentially substantially expanding the BAO and RSD information content of dense large scale structure surveys, including for example SDSS main sample and 21cm intensity mapping.
Boyd, Robert W
2013-01-01
Nonlinear Optics is an advanced textbook for courses dealing with nonlinear optics, quantum electronics, laser physics, contemporary and quantum optics, and electrooptics. Its pedagogical emphasis is on fundamentals rather than particular, transitory applications. As a result, this textbook will have lasting appeal to a wide audience of electrical engineering, physics, and optics students, as well as those in related fields such as materials science and chemistry.Key Features* The origin of optical nonlinearities, including dependence on the polarization of light* A detailed treatment of the q
Model-based optimal design of experiments - semidefinite and nonlinear programming formulations.
Duarte, Belmiro P M; Wong, Weng Kee; Oliveira, Nuno M C
2016-02-15
We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D-, A- and E-optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D-optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice.
Ruszczynski, Andrzej
2011-01-01
Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates t
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
In, Visarath; Longhini, Patrick; Kho, Andy; Neff, Joseph D.; Leung, Daniel; Liu, Norman; Meadows, Brian K.; Gordon, Frank; Bulsara, Adi R.; Palacios, Antonio
2012-12-01
The nonlinear channelizer is an integrated circuit made up of large parallel arrays of analog nonlinear oscillators, which, collectively, serve as a broad-spectrum analyzer with the ability to receive complex signals containing multiple frequencies and instantaneously lock-on or respond to a received signal in a few oscillation cycles. The concept is based on the generation of internal oscillations in coupled nonlinear systems that do not normally oscillate in the absence of coupling. In particular, the system consists of unidirectionally coupled bistable nonlinear elements, where the frequency and other dynamical characteristics of the emergent oscillations depend on the system's internal parameters and the received signal. These properties and characteristics are being employed to develop a system capable of locking onto any arbitrary input radio frequency signal. The system is efficient by eliminating the need for high-speed, high-accuracy analog-to-digital converters, and compact by making use of nonlinear coupled systems to act as a channelizer (frequency binning and channeling), a low noise amplifier, and a frequency down-converter in a single step which, in turn, will reduce the size, weight, power, and cost of the entire communication system. This paper covers the theory, numerical simulations, and some engineering details that validate the concept at the frequency band of 1-4 GHz.
Energy Technology Data Exchange (ETDEWEB)
Turchetti, G. (Bologna Univ. (Italy). Dipt. di Fisica)
1989-01-01
Research in nonlinear dynamics is rapidly expanding and its range of applications is extending beyond the traditional areas of science where it was first developed. Indeed while linear analysis and modelling, which has been very successful in mathematical physics and engineering, has become a mature science, many elementary phenomena of intrinsic nonlinear nature were recently experimentally detected and investigated, suggesting new theoretical work. Complex systems, as turbulent fluids, were known to be governed by intrinsically nonlinear laws since a long time ago, but received purely phenomenological descriptions. The pioneering works of Boltzmann and Poincare, probably because of their intrinsic difficulty, did not have a revolutionary impact at their time; it is only very recently that their message is reaching a significant number of mathematicians and physicists. Certainly the development of computers and computer graphics played an important role in developing geometric intuition of complex phenomena through simple numerical experiments, while a new mathematical framework to understand them was being developed.
Terahertz Nonlinearity in Graphene Plasmons
Jadidi, Mohammad M; Winnerl, Stephan; Sushkov, Andrei B; Drew, H Dennis; Murphy, Thomas E; Mittendorff, Martin
2015-01-01
Sub-wavelength graphene structures support localized plasmonic resonances in the terahertz and mid-infrared spectral regimes. The strong field confinement at the resonant frequency is predicted to significantly enhance the light-graphene interaction, which could enable nonlinear optics at low intensity in atomically thin, sub-wavelength devices. To date, the nonlinear response of graphene plasmons and their energy loss dynamics have not been experimentally studied. We measure and theoretically model the terahertz nonlinear response and energy relaxation dynamics of plasmons in graphene nanoribbons. We employ a THz pump-THz probe technique at the plasmon frequency and observe a strong saturation of plasmon absorption followed by a 10 ps relaxation time. The observed nonlinearity is enhanced by two orders of magnitude compared to unpatterned graphene with no plasmon resonance. We further present a thermal model for the nonlinear plasmonic absorption that supports the experimental results.
Fitzgerald, M.; Sharapov, S. E.; Rodrigues, P.; Borba, D.
2016-11-01
We use the HAGIS code to compute the nonlinear stability of the Q = 10 ITER baseline scenario to toroidal Alfvén eigenmodes (TAE) and the subsequent effects of these modes on fusion alpha-particle redistribution. Our calculations build upon an earlier linear stability survey (Rodrigues et al 2015 Nucl. Fusion 55 083003) which provides accurate values of bulk ion, impurity ion and electron thermal Landau damping for our HAGIS calculations. Nonlinear calculations of up to 129 coupled TAEs with toroidal mode numbers in the range n = 1-35 have been performed. The effects of frequency sweeping were also included to examine possible phase space hole and clump convective transport. We find that even parity core localised modes are dominant (expected from linear theory), and that linearly stable global modes are destabilised nonlinearly. Landau damping is found to be important in reducing saturation amplitudes of coupled modes to below δ {{B}r}/{{B}0}˜ 3× {{10}-4} . For these amplitudes, stochastic transport of alpha-particles occurs in a narrow region where predominantly core localised modes are found, implying the formation of a transport barrier at r/a≈ 0.5 , beyond which, the weakly driven global modes are found. We find that for flat q profiles in this baseline scenario, alpha particle transport losses and redistribution by TAEs is minimal.
Lalung, M.; Phukan, P.; Sarma, J. K.
2017-09-01
In this work we have solved the nonlinear GLR-MQ evolution equation upto next-to-leading order (NLO) by considering NLO terms of the gluon-gluon splitting functions and running coupling constant α s (Q 2). Here, we have incorporated a Regge-like behaviour of gluon distribution in order to obtain a solution of the GLR-MQ equation in the range of 5G e V 2 ≤ Q 2 ≤ 25G e V 2. We have studied the Q 2 evolution of the gluon distribution function G(x, Q 2) and its nonlinear effects at small-x. It can be observed from our analysis that the nonlinearities increase with decrease in the correlation radius R of two interacting gluons, as expected. We have compared our result of G(x, Q 2) as Q 2 increases and x decreases, for two different values of R, viz. R = 2G e V -1 and 5 G e V -1. We have also checked the sensitivity of the Regge intercept λ G on our results. We compare our computed results with those obtained by the global analysis to parton distribution functions (PDFs) by various collaborations where LHC data have been included viz. ABM12, CT14, MMHT14, PDF4LHC15, NNPDF3.0 and CJ15. Besides we have also shown comparison of our results with HERA PDF data viz. HERAPDF15.
Nonlinear effects in Thomson backscattering
Maroli, C.; Petrillo, V.; Tomassini, P.; Serafini, L.
2013-03-01
We analyze the nonlinear classical effects of the X/γ radiation produced by Thomson/Compton sources. We confirm the development of spectral fringes of the radiation on axis, which comports broadening, shift, and deformation of the spectrum. For the nominal parameters of the SPARC-LAB Thomson scattering and of the European Proposal for the gamma source ELI-NP, however, the radiation, when collected in the suitable acceptance angle, does not reveal many differences from that predicted by the linear model and the nonlinear redshift is subdominant with respect to the quantum recoil. An experiment aimed to the study of the nonlinearities is proposed on the SPARC-LAB source.
Seider, Warren D.; Ungar, Lyle H.
1987-01-01
Describes a course in nonlinear mathematics courses offered at the University of Pennsylvania which provides an opportunity for students to examine the complex solution spaces that chemical engineers encounter. Topics include modeling many chemical processes, especially those involving reaction and diffusion, auto catalytic reactions, phase…
Institute of Scientific and Technical Information of China (English)
赵洪山; 兰晓明; 周雪青
2013-01-01
This paper presented a multi-machine power system nonlinear excitation predictive control method which combined model predictive control and model reduction technology in order to tackle the problem that the optimal excitation control and the traditional proportional-integral-derivative (PID) excitation control could not consider the constraint of states and input of the system, and to reduce the complexity of the numerical calculation of high order dynamic model in nonlinear excitation predictive control. First, The theory of empirical Gramians balanced reduction was used to reduce the orders of power system nonlinear dynamic model to save the computing time of open-loop optimization of model predictive control. Then, it used the least-square residual of system input and output as the objection function, using reduced dynamic model as equivalent constraint and the change limits of system output and control input as unequivalent constrain to establish the excitation predictive control model based on reduced model. Next, the interior-point method was used to solve the optimal problem meanwhile to realize multi-step prediction. Finally, we took advantage of a four-machine power system to verify the effectiveness of the predictive control method. The simulation results show that nonlinear excitation predictive control method based on balanced reduced model for the multi-machine power systems can greatly shorten the optimization time, meanwhile maintain the voltage of generator terminals within the set points and improve the stability of power system.% 将预测控制与模型降阶技术相结合提出一种基于平衡降阶模型的多机电力系统非线性励磁预测控制方法，以解决最优励磁控制和传统比例积分微分励磁控制无法考虑系统复杂状态和控制输入约束的问题，并且降低非线性励磁预测控制高阶动态模型数值计算的复杂性。首先，利用经验Gramians 平衡降阶原理，对电力系统
2015-01-01
From the Back Cover: The emphasis throughout the present volume is on the practical application of theoretical mathematical models helping to unravel the underlying mechanisms involved in processes from mathematical physics and biosciences. It has been conceived as a unique collection of abstract methods dealing especially with nonlinear partial differential equations (either stationary or evolutionary) that are applied to understand concrete processes involving some important applications re...
Institute of Scientific and Technical Information of China (English)
戚海永
2013-01-01
利用正交试验法对输送带速度、喷气压力和分选次数等因素对煤矸分选效果进行了试验研究,并利用非线性PSO-BP神经网络对分选效果进行了预测.试验结果表明,分矸率与混矸率呈现相反的趋势,第3组试验,即带速为0.11 m/s,喷气压力为0.7 MPa,分选次数为3的时候,分选效果最好,混矸率最低；非线性PSO-BP神经网络预测效果与传统PSO-BP神经网络相比,具有更高的准确性,与试验值更加吻合,对煤矸分选预测具有更高的参考价值.%The influences of velocity of conveyance belt,air jet pressure and separation number on coal and gangue separation effects were studied by orthogonal experiments,and nonlinear PSO-BP neutral network was applied to predict the separation effects.The experimental result show:the gangue separation ratio and the gangue mixture ratio had the opposite variation trend; in the third group of experiment,while the belt velocity being 0.11 m/s,air jet pressure being 0.7 MPa and separation number being three,the gangue separation effects were the best and the gangue mixture ratio was the lowest; contrasted with traditional PSO-BP neural network,the nonlinear PSO-BP neural network had higher prediction accuracy that was much closer to the experimental value,which offered references for prediction of coal and gangue separation effects.
Optimal bipedal interactions with dynamic terrain: synthesis and analysis via nonlinear programming
Hubicki, Christian; Goldman, Daniel; Ames, Aaron
In terrestrial locomotion, gait dynamics and motor control behaviors are tuned to interact efficiently and stably with the dynamics of the terrain (i.e. terradynamics). This controlled interaction must be particularly thoughtful in bipeds, as their reduced contact points render them highly susceptible to falls. While bipedalism under rigid terrain assumptions is well-studied, insights for two-legged locomotion on soft terrain, such as sand and dirt, are comparatively sparse. We seek an understanding of how biological bipeds stably and economically negotiate granular media, with an eye toward imbuing those abilities in bipedal robots. We present a trajectory optimization method for controlled systems subject to granular intrusion. By formulating a large-scale nonlinear program (NLP) with reduced-order resistive force theory (RFT) models and jamming cone dynamics, the optimized motions are informed and shaped by the dynamics of the terrain. Using a variant of direct collocation methods, we can express all optimization objectives and constraints in closed-form, resulting in rapid solving by standard NLP solvers, such as IPOPT. We employ this tool to analyze emergent features of bipedal locomotion in granular media, with an eye toward robotic implementation.
Nonlinearity without Superluminality
Kent, A
2002-01-01
Quantum theory is compatible with special relativity. In particular, though measurements on entangled systems are correlated in a way that cannot be reproduced by local hidden variables, they cannot be used for superluminal signalling. As Gisin and Polchinski first pointed out, this is not true for general nonlinear modifications of the Schroedinger equation. Excluding superluminal signalling has thus been taken to rule out most nonlinear versions of quantum theory. The no superluminal signalling constraint has also been used for alternative derivations of the optimal fidelities attainable for imperfect quantum cloning and other operations. These results apply to theories satisfying the rule that their predictions for widely separated and slowly moving entangled systems can be approximated by non-relativistic equations of motion with respect to a preferred time coordinate. This paper describes a natural way in which this rule might fail to hold. In particular, it is shown that quantum readout devices which di...
Monte Carlo and nonlinearities
Dauchet, Jérémi; Blanco, Stéphane; Caliot, Cyril; Charon, Julien; Coustet, Christophe; Hafi, Mouna El; Eymet, Vincent; Farges, Olivier; Forest, Vincent; Fournier, Richard; Galtier, Mathieu; Gautrais, Jacques; Khuong, Anaïs; Pelissier, Lionel; Piaud, Benjamin; Roger, Maxime; Terrée, Guillaume; Weitz, Sebastian
2016-01-01
The Monte Carlo method is widely used to numerically predict systems behaviour. However, its powerful incremental design assumes a strong premise which has severely limited application so far: the estimation process must combine linearly over dimensions. Here we show that this premise can be alleviated by projecting nonlinearities on a polynomial basis and increasing the configuration-space dimension. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles and concentrated-solar-power-plant productions, we prove the real world usability of this advance on four test-cases that were so far regarded as impracticable by Monte Carlo approaches. We also illustrate an outstanding feature of our method when applied to sharp problems with interacting particles: handling rare events is now straightforward. Overall, our extension preserves the features that made the method popular: addressing nonlinearities does not compromise o...
Mohammadimehr, M.; Mohammadi-Dehabadi, A. A.; Maraghi, Z. Khoddami
2017-04-01
In this research, the effect of non-local higher order stress on the nonlinear vibration behavior of carbon nanotube conveying viscous nanoflow resting on elastic foundation is investigated. Physical intuition reveals that increasing nanoscale stress leads to decrease the stiffness of nanostructure which firstly established by Eringen's non-local elasticity theory (previous nonlocal method) while many of papers have concluded otherwise at microscale based on modified couple stress, modified strain gradient theories and surface stress effect. The non-local higher order stress model (new nonlocal method) is used in this article that has been studied by few researchers in other fields and the results from the present study show that the trend of the new nonlocal method and size dependent effect including modified couple stress theory is the same. In this regard, the nonlinear motion equations are derived using a variational principal approach considering essential higher-order non-local terms. The surrounded elastic medium is modeled by Pasternak foundation to increase the stability of system where the fluid flow may cause system instability. Effects of various parameters such as non-local parameter, elastic foundation coefficient, and fluid flow velocity on the stability and dimensionless natural frequency of nanotube are investigated. The results of this research show that the small scale parameter based on higher order stress help to increase the natural frequency which has been approved by other small scale theories such as strain gradient theory, modified couple stress theory and experiments, and vice versa for previous nonlocal method. This study may be useful to measure accurately the vibration characteristics of nanotubes conveying viscous nanoflow and to design nanofluidic devices for detecting blood Glucose.
Energy Technology Data Exchange (ETDEWEB)
Mohammadimehr, M., E-mail: mmohammadimehr@kashanu.ac.ir [Department of Solid Mechanics, Faculty of Mechanical Engineering, University of Kashan, P.O. Box: 87317-53153, Kashan (Iran, Islamic Republic of); Mohammadi-Dehabadi, A.A. [Department of Solid Mechanics, Faculty of Mechanical Engineering, University of Kashan, P.O. Box: 87317-53153, Kashan (Iran, Islamic Republic of); Department of Mechanical Engineering, Iran University of Science and Technology, Narmak, Tehran (Iran, Islamic Republic of); Maraghi, Z. Khoddami [Department of Solid Mechanics, Faculty of Mechanical Engineering, University of Kashan, P.O. Box: 87317-53153, Kashan (Iran, Islamic Republic of)
2017-04-01
In this research, the effect of non-local higher order stress on the nonlinear vibration behavior of carbon nanotube conveying viscous nanoflow resting on elastic foundation is investigated. Physical intuition reveals that increasing nanoscale stress leads to decrease the stiffness of nanostructure which firstly established by Eringen's non-local elasticity theory (previous nonlocal method) while many of papers have concluded otherwise at microscale based on modified couple stress, modified strain gradient theories and surface stress effect. The non-local higher order stress model (new nonlocal method) is used in this article that has been studied by few researchers in other fields and the results from the present study show that the trend of the new nonlocal method and size dependent effect including modified couple stress theory is the same. In this regard, the nonlinear motion equations are derived using a variational principal approach considering essential higher-order non-local terms. The surrounded elastic medium is modeled by Pasternak foundation to increase the stability of system where the fluid flow may cause system instability. Effects of various parameters such as non-local parameter, elastic foundation coefficient, and fluid flow velocity on the stability and dimensionless natural frequency of nanotube are investigated. The results of this research show that the small scale parameter based on higher order stress help to increase the natural frequency which has been approved by other small scale theories such as strain gradient theory, modified couple stress theory and experiments, and vice versa for previous nonlocal method. This study may be useful to measure accurately the vibration characteristics of nanotubes conveying viscous nanoflow and to design nanofluidic devices for detecting blood Glucose.
Nonlinear plasmonics at high temperatures
Directory of Open Access Journals (Sweden)
Sivan Yonatan
2017-01-01
Full Text Available We solve the Maxwell and heat equations self-consistently for metal nanoparticles under intense continuous wave (CW illumination. Unlike previous studies, we rely on experimentally-measured data for metal permittivity for increasing temperature and for the visible spectral range. We show that the thermal nonlinearity of the metal can lead to substantial deviations from the predictions of the linear model for the temperature and field distribution and, thus, can explain qualitatively the strong nonlinear scattering from such configurations observed experimentally. We also show that the incompleteness of existing data of the temperature dependence of the thermal properties of the system prevents reaching a quantitative agreement between the measured and calculated scattering data. This modeling approach is essential for the identification of the underlying physical mechanism responsible for the thermo-optical nonlinearity of the metal and should be adopted in all applications of high-temperature nonlinear plasmonics, especially for refractory metals, for both CW and pulsed illumination.
Nonlinear plasmonics at high temperatures
Sivan, Yonatan; Chu, Shi-Wei
2017-01-01
We solve the Maxwell and heat equations self-consistently for metal nanoparticles under intense continuous wave (CW) illumination. Unlike previous studies, we rely on experimentally-measured data for metal permittivity for increasing temperature and for the visible spectral range. We show that the thermal nonlinearity of the metal can lead to substantial deviations from the predictions of the linear model for the temperature and field distribution and, thus, can explain qualitatively the strong nonlinear scattering from such configurations observed experimentally. We also show that the incompleteness of existing data of the temperature dependence of the thermal properties of the system prevents reaching a quantitative agreement between the measured and calculated scattering data. This modeling approach is essential for the identification of the underlying physical mechanism responsible for the thermo-optical nonlinearity of the metal and should be adopted in all applications of high-temperature nonlinear plasmonics, especially for refractory metals, for both CW and pulsed illumination.
Directory of Open Access Journals (Sweden)
Dirk Dolle
Full Text Available Enhancers have been described to evolve by permutation without changing function. This has posed the problem of how to predict enhancer elements that are hidden from alignment-based approaches due to the loss of co-linearity. Alignment-free algorithms have been proposed as one possible solution. However, this approach is hampered by several problems inherent to its underlying working principle. Here we present a new approach, which combines the power of alignment and alignment-free techniques into one algorithm. It allows the prediction of enhancers based on the query and target sequence only, no matter whether the regulatory logic is co-linear or reshuffled. To test our novel approach, we employ it for the prediction of enhancers across the evolutionary distance of ~450Myr between human and medaka. We demonstrate its efficacy by subsequent in vivo validation resulting in 82% (9/11 of the predicted medaka regions showing reporter activity. These include five candidates with partially co-linear and four with reshuffled motif patterns. Orthology in flanking genes and conservation of the detected co-linear motifs indicates that those candidates are likely functionally equivalent enhancers. In sum, our results demonstrate that the proposed principle successfully predicts mutated as well as permuted enhancer regions at an encouragingly high rate.
Institute of Scientific and Technical Information of China (English)
王红罡; 吕炳全; 赵永辉; 孙小刚
2001-01-01
系统论和非线性方法为石油地球物理勘探注入了新的动力。本文试通过分形理论结合人工神经网络方法对测井资料进行解释，对油气分布进行平面成像预测。%The integration of Fractal process and artificial neural network analysis by adopting nonlinear techniques adds new power to the explanation of logging curves in geophysical exploration for hydrocarbon. In terms of the method, this paper attempts to make new explanation for logging data and to conduct a plane imaging prediction of hydrocarbon distribution.
Institute of Scientific and Technical Information of China (English)
闫孝姮; 陈伟华; 彭继慎; 赵忠建
2013-01-01
A nonlinear predictive control algorithm was proposed for multivariable die-casting processes of die-casting machine based on chaos adaptive differential evolution model.In the adaptive differential evolution model,the tent map was embedded to improve the process of production of crossed factors and compile factor in the DE model.The simulation results show that the injection control PID controller with the intelligence algorithm can optimize the die-casting machine injection velocity and make the nonlinear predictive control possess good dynamic characteristics and high control precision to achieve the system optimal control objective in the end.%提出了一种基于混沌自适应差分进化算法(CADE)的压铸机多变量压射过程非线性预测控制.其将帐篷映射嵌入到自适应差分进化算法(DE)中,改进了DE算法中交叉因子及编译因子的产生过程.通过仿真试验,结果表明,利用该智能算法对压射控制PID控制器进行参数优化,能实现压铸机压射速度的快速寻优,使其非线性预测控制动态特性更好,控制精度更高,从而达到系统的最优控制目的.
Institute of Scientific and Technical Information of China (English)
陈伟华; 彭继慎; 赵忠建
2013-01-01
In the electric hydraulic servo control system during injection process of die casting machine,this paper presents a nonlinear-predictive-control method of die casting machine for multivariable injection process based on adaptive differential chaotic evolutional algorithm.The tent map is embedded into the adaptive differential evolution algorithm (DE),which has improved the generating process of DE algorithm for the crossover factor and compiling factor.The results of simulation experiment show that by using the intelligent algorithm to optimize injection parameters of die-casting PID controller,we can achieve fast optimization of die-casting machine injection speed and make the dynamic characteristics of nonlinear predictive control better,and the control precision more higher,so as to achieve the optimal-control purpose for the system.%在压铸机压射过程的电液压伺服控制系统中,提出了一种基于混沌自适应差分进化算法(CADE)的压铸机多变量压射过程非线性预测控制方法.该方法将帐篷映射嵌入到自适应差分进化算法(DE)中,改进了DE算法中交叉因子及编译因子的产生过程.通过仿真实验结果表明,利用该智能算法对压射控制PID控制器进行参数优化,能实现压铸机压射速度的快速寻优,使其非线性预测控制动态特性更好,控制精度更高,从而达到系统的最优控制目的.
Directory of Open Access Journals (Sweden)
V. K. Karastathis
2010-11-01
Full Text Available We examine the possible non-linear behaviour of potentially liquefiable layers at selected sites located within the expansion area of the town of Nafplion, East Peloponnese, Greece. Input motion is computed for three scenario earthquakes, selected on the basis of historical seismicity data, using a stochastic strong ground motion simulation technique, which takes into account the finite dimensions of the earthquake sources. Site-specific ground acceleration synthetics and soil profiles are then used to evaluate the liquefaction potential at the sites of interest. The activation scenario of the Iria fault, which is the closest one to Nafplion (M=6.4, is found to be the most hazardous in terms of liquefaction initiation. In this scenario almost all the examined sites exhibit liquefaction features at depths of 6–12 m. For scenario earthquakes at two more distant seismic sources (Epidaurus fault – M6.3; Xylokastro fault – M6.7 strong ground motion amplification phenomena by the shallow soft soil layer are expected to be observed.