Forecasting Value-at-Risk under Different Distributional Assumptions

Directory of Open Access Journals (Sweden)

Manuela Braione

2016-01-01

Full Text Available Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR. We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels.

Non-linear Model Predictive Control for cooling strings of superconducting magnets using superfluid helium

CERN Document Server

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...

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

Energy Technology Data Exchange (ETDEWEB)

Abbas, Nikhar [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Tom, Nathan M [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

Non-linear aeroelastic prediction for aircraft applications

Science.gov (United States)

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

in this domain. This is set within the context of a generic industrial process and the requirements of UK and US aeroelastic qualification. A range of test cases, from simple small DOF cases to full aircraft, have been used to evaluate and validate the non-linear methods developed and to make comparison with the linear methods in everyday use. These have focused mainly on aerodynamic non-linearity, although some results for structural non-linearity are also presented. The challenges associated with time domain (coupled computational fluid dynamics-computational structural model (CFD-CSM)) methods have been addressed through the development of grid movement, fluid-structure coupling, and control surface movement technologies. Conclusions regarding the accuracy and computational cost of these are presented. The computational cost of time-domain methods, despite substantial improvements in efficiency, remains high. However, significant advances have been made in reduced order methods, that allow non-linear behaviour to be modelled, but at a cost comparable with that of the regular linear methods. Of particular note is a method based on Hopf bifurcation that has reached an appropriate maturity for deployment on real aircraft configurations, though only limited results are presented herein. Results are also presented for dynamically linearised CFD approaches that hold out the possibility of non-linear results at a fraction of the cost of time coupled CFD-CSM methods. Local linearisation approaches (higher order harmonic balance and continuation method) are also presented; these have the advantage that no prior assumption of the nature of the aeroelastic instability is required, but currently these methods are limited to low DOF problems and it is thought that these will not reach a level of maturity appropriate to real aircraft problems for some years to come. Nevertheless, guidance on the most likely approaches has been derived and this forms the basis for ongoing

Non-linear multivariable predictive control of an alcoholic fermentation process using functional link networks

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.

Optimising Reactive Control in non-ideal Efficiency Wave Energy Converters

DEFF Research Database (Denmark)

Strager, Thomas; Lopez, Pablo Fernandez; Giorgio, Giuseppe

2014-01-01

When analytically optimising the control strategy in wave energy converters which use a point absorber, the efficiency aspect is generally neglected. The results presented in this paper provide an analytical expression for the mean harvested electrical power in non-ideal efficiency situations....... These have been derived under the assumptions of monochromatic incoming waves and linear system behaviour. This allows to establish the power factor of a system with non-ideal efficiency. The locus of the optimal reactive control parameters is then studied and an alternative method of representation...... is developed to model the optimal control parameters. Ultimately we present a simple method of choosing optimal control parameters for any combination of efficiency and wave frequency....

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

Energy Technology Data Exchange (ETDEWEB)

Abbas, Nikhar; Tom, Nathan

2017-09-01

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

Making Predictions about Chemical Reactivity: Assumptions and Heuristics

Science.gov (United States)

Maeyer, Jenine; Talanquer, Vicente

2013-01-01

Diverse implicit cognitive elements seem to support but also constrain reasoning in different domains. Many of these cognitive constraints can be thought of as either implicit assumptions about the nature of things or reasoning heuristics for decision-making. In this study we applied this framework to investigate college students' understanding of…

Generalized Predictive Control and Neural Generalized Predictive Control

Directory of Open Access Journals (Sweden)

Sadhana CHIDRAWAR

2008-12-01

Full Text Available As Model Predictive Control (MPC relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking three separate systems, performances of the system has been tested. Simulation results show the effect of neural network on Generalized Predictive Control. The performance comparison of this three system configurations has been given in terms of ISE and IAE.

Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing

Science.gov (United States)

Ren, Jingye; Zhang, Fang; Wang, Yuying; Collins, Don; Fan, Xinxin; Jin, Xiaoai; Xu, Weiqi; Sun, Yele; Cribb, Maureen; Li, Zhanqing

2018-05-01

Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted areas is crucial for accurately predicting CCN number concentrations (NCCN). In this study, we predict NCCN under five assumed schemes of aerosol chemical composition and mixing state based on field measurements in Beijing during the winter of 2016. Our results show that the best closure is achieved with the assumption of size dependent chemical composition for which sulfate, nitrate, secondary organic aerosols, and aged black carbon are internally mixed with each other but externally mixed with primary organic aerosol and fresh black carbon (external-internal size-resolved, abbreviated as EI-SR scheme). The resulting ratios of predicted-to-measured NCCN (RCCN_p/m) were 0.90 - 0.98 under both clean and polluted conditions. Assumption of an internal mixture and bulk chemical composition (INT-BK scheme) shows good closure with RCCN_p/m of 1.0 -1.16 under clean conditions, implying that it is adequate for CCN prediction in continental clean regions. On polluted days, assuming the aerosol is internally mixed and has a chemical composition that is size dependent (INT-SR scheme) achieves better closure than the INT-BK scheme due to the heterogeneity and variation in particle composition at different sizes. The improved closure achieved using the EI-SR and INT-SR assumptions highlight the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (with RCCN_p/m of 0.66 - 0.75) when using the schemes of external mixtures with bulk (EXT-BK scheme) or size-resolved composition (EXT-SR scheme), implying that primary particles experience rapid aging and physical mixing processes in urban Beijing. However, our results show that the aerosol mixing state plays a minor role in CCN prediction when the κorg exceeds 0.1.

Underlying assumptions and core beliefs in anorexia nervosa and dieting.

Science.gov (United States)

Cooper, M; Turner, H

2000-06-01

To investigate assumptions and beliefs in anorexia nervosa and dieting. The Eating Disorder Belief Questionnaire (EDBQ), was administered to patients with anorexia nervosa, dieters and female controls. The patients scored more highly than the other two groups on assumptions about weight and shape, assumptions about eating and negative self-beliefs. The dieters scored more highly than the female controls on assumptions about weight and shape. The cognitive content of anorexia nervosa (both assumptions and negative self-beliefs) differs from that found in dieting. Assumptions about weight and shape may also distinguish dieters from female controls.

Prediction of Non-Equilibrium Kinetics of Fuel-Rich Kerosene/LOX Combustion in Gas Generator

International Nuclear Information System (INIS)

Yu, Jung Min; Lee, Chang Jin

2007-01-01

Gas generator is the device to produce high enthalpy gases needed to drive turbo-pump system in liquid rocket engine. And, the combustion temperature in gas generator should be controlled below around 1,000K to avoid any possible thermal damages to turbine blade by using either fuel rich combustion or oxidizer rich combustion. Thus, nonequilibrium chemical reaction dominates in fuel-rich combustion of gas generator. Meanwhile, kerosene is a compounded fuel with various types of hydrocarbon elements and difficult to model the chemical kinetics. This study focuses on the prediction of the non-equilibrium reaction of fuel rich kerosene/LOX combustion with detailed kinetics developed by Dagaut using PSR (Perfectly Stirred Reactor) assumption. In Dagaut's surrogate model for kerosene, chemical kinetics of kerosene consists of 1,592 reaction steps with 207 chemical species. Also, droplet evaporation time is taken into account in the PSR calculation by changing the residence time of droplet in the gas generator. Frenklach's soot model was implemented along with detailed kinetics to calculate the gas properties of fuel rich combustion efflux. The results could provide very reliable and accurate numbers in the prediction of combustion gas temperature,species fraction and material properties

Direct numerical simulations of temporally developing hydrocarbon shear flames at elevated pressure: effects of the equation of state and the unity Lewis number assumption

Science.gov (United States)

Korucu, Ayse; Miller, Richard

2016-11-01

Direct numerical simulations (DNS) of temporally developing shear flames are used to investigate both equation of state (EOS) and unity-Lewis (Le) number assumption effects in hydrocarbon flames at elevated pressure. A reduced Kerosene / Air mechanism including a semi-global soot formation/oxidation model is used to study soot formation/oxidation processes in a temporarlly developing hydrocarbon shear flame operating at both atmospheric and elevated pressures for the cubic Peng-Robinson real fluid EOS. Results are compared to simulations using the ideal gas law (IGL). The results show that while the unity-Le number assumption with the IGL EOS under-predicts the flame temperature for all pressures, with the real fluid EOS it under-predicts the flame temperature for 1 and 35 atm and over-predicts the rest. The soot mass fraction, Ys, is only under-predicted for the 1 atm flame for both IGL and real gas fluid EOS models. While Ys is over-predicted for elevated pressures with IGL EOS, for the real gas EOS Ys's predictions are similar to results using a non-unity Le model derived from non-equilibrium thermodynamics and real diffusivities. Adopting the unity Le assumption is shown to cause misprediction of Ys, the flame temperature, and the mass fractions of CO, H and OH.

Frequency weighted model predictive control of wind turbine

DEFF Research Database (Denmark)

Klauco, Martin; Poulsen, Niels Kjølstad; Mirzaei, Mahmood

2013-01-01

This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work are the rotatio...... predictive controller are presented. Statistical comparison between frequency weighted MPC, standard MPC and baseline PI controller is shown as well.......This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work...... are the rotational degree of freedom of the rotor and the tower for-aft movement. The MPC design is based on a receding horizon policy and a linearised model of the wind turbine. Due to the change of dynamics according to wind speed, several linearisation points must be considered and the control design adjusted...

Real-time reservoir operation considering non-stationary inflow prediction

Science.gov (United States)

Zhao, J.; Xu, W.; Cai, X.; Wang, Z.

2011-12-01

Stationarity of inflow has been a basic assumption for reservoir operation rule design, which is now facing challenges due to climate change and human interferences. This paper proposes a modeling framework to incorporate non-stationary inflow prediction for optimizing the hedging operation rule of large reservoirs with multiple-year flow regulation capacity. A multi-stage optimization model is formulated and a solution algorithm based on the optimality conditions is developed to incorporate non-stationary annual inflow prediction through a rolling, dynamic framework that updates the prediction from period to period and adopt the updated prediction in reservoir operation decision. The prediction model is ARIMA(4,1,0), in which parameter 4 stands for the order of autoregressive, 1 represents a linear trend, and 0 is the order of moving average. The modeling framework and solution algorithm is applied to the Miyun reservoir in China, determining a yearly operating schedule during the period from 1996 to 2009, during which there was a significant declining trend of reservoir inflow. Different operation policy scenarios are modeled, including standard operation policy (SOP, matching the current demand as much as possible), hedging rule (i.e., leaving a certain amount of water for future to avoid large risk of water deficit) with forecast from ARIMA (HR-1), hedging (HR) with perfect forecast (HR-2 ). Compared to the results of these scenarios to that of the actual reservoir operation (AO), the utility of the reservoir operation under HR-1 is 3.0% lower than HR-2, but 3.7% higher than the AO and 14.4% higher than SOP. Note that the utility under AO is 10.3% higher than that under SOP, which shows that a certain level of hedging under some inflow prediction or forecast was used in the real-world operation. Moreover, the impacts of discount rate and forecast uncertainty level on the operation will be discussed.

Topographic controls on shallow groundwater levels in a steep, prealpine catchment: When are the TWI assumptions valid?

NARCIS (Netherlands)

Rinderer, M.; van Meerveld, H.J.; Seibert, J.

2014-01-01

Topographic indices like the Topographic Wetness Index (TWI) have been used to predict spatial patterns of average groundwater levels and to model the dynamics of the saturated zone during events (e.g., TOPMODEL). However, the assumptions underlying the use of the TWI in hydrological models, of

Applied predictive control

CERN Document Server

Sunan, Huang; Heng, Lee Tong

2002-01-01

The presence of considerable time delays in the dynamics of many industrial processes, leading to difficult problems in the associated closed-loop control systems, is a well-recognized phenomenon. The performance achievable in conventional feedback control systems can be significantly degraded if an industrial process has a relatively large time delay compared with the dominant time constant. Under these circumstances, advanced predictive control is necessary to improve the performance of the control system significantly. The book is a focused treatment of the subject matter, including the fundamentals and some state-of-the-art developments in the field of predictive control. Three main schemes for advanced predictive control are addressed in this book: • Smith Predictive Control; • Generalised Predictive Control; • a form of predictive control based on Finite Spectrum Assignment. A substantial part of the book addresses application issues in predictive control, providing several interesting case studie...

Heterosexual assumptions in verbal and non-verbal communication in nursing.

Science.gov (United States)

Röndahl, Gerd; Innala, Sune; Carlsson, Marianne

2006-11-01

This paper reports a study of what lesbian women and gay men had to say, as patients and as partners, about their experiences of nursing in hospital care, and what they regarded as important to communicate about homosexuality and nursing. The social life of heterosexual cultures is based on the assumption that all people are heterosexual, thereby making homosexuality socially invisible. Nurses may assume that all patients and significant others are heterosexual, and these heteronormative assumptions may lead to poor communication that affects nursing quality by leading nurses to ask the wrong questions and make incorrect judgements. A qualitative interview study was carried out in the spring of 2004. Seventeen women and 10 men ranging in age from 23 to 65 years from different parts of Sweden participated. They described 46 experiences as patients and 31 as partners. Heteronormativity was communicated in waiting rooms, in patient documents and when registering for admission, and nursing staff sometimes showed perplexity when an informant deviated from this heteronormative assumption. Informants had often met nursing staff who showed fear of behaving incorrectly, which could lead to a sense of insecurity, thereby impeding further communication. As partners of gay patients, informants felt that they had to deal with heterosexual assumptions more than they did when they were patients, and the consequences were feelings of not being accepted as a 'true' relative, of exclusion and neglect. Almost all participants offered recommendations about how nursing staff could facilitate communication. Heterosexual norms communicated unconsciously by nursing staff contribute to ambivalent attitudes and feelings of insecurity that prevent communication and easily lead to misconceptions. Educational and management interventions, as well as increased communication, could make gay people more visible and thereby encourage openness and awareness by hospital staff of the norms that they

Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

OpenAIRE

Díaz, Zuleyka; Segovia, María Jesús; Fernández, José

2005-01-01

Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques ...

Analysis Method of Common Cause Failure on Non-safety Digital Control System

Energy Technology Data Exchange (ETDEWEB)

Kim, Yun Goo; Oh, Eun Gse [KHNP, Daejeon (Korea, Republic of)

2014-08-15

The effects of common cause failure on safety digital instrumentation and control system had been considered in defense in depth analysis with safety analysis method. However, the effects of common cause failure on non-safety digital instrumentation and control system also should be evaluated. The common cause failure can be included in credible failure on the non-safety system. In the I and C architecture of nuclear power plant, many design feature has been applied for the functional integrity of control system. One of that is segmentation. Segmentation defenses the propagation of faults in the I and C architecture. Some of effects from common cause failure also can be limited by segmentation. Therefore, in this paper there are two type of failure mode, one is failures in one control group which is segmented, and the other is failures in multiple control group because that the segmentation cannot defense all effects from common cause failure. For each type, the worst failure scenario is needed to be determined, so the analysis method has been proposed in this paper. The evaluation can be qualitative when there is sufficient justification that the effects are bounded in previous safety analysis. When it is not bounded in previous safety analysis, additional analysis should be done with conservative assumptions method of previous safety analysis or best estimation method with realistic assumptions.

A damage cumulation method for crack initiation prediction under non proportional loading and overloading

International Nuclear Information System (INIS)

Taheri, S.

1992-04-01

For a sequence of constant amplitude cyclic loading containing overloads, we propose a method for damage cumulation in non proportional loading. This method uses as data cyclic stabilized states at non proportional loading and initiation or fatigue curve in uniaxial case. For that, we take into account the dependence of Cyclic Strain Stress Curves (C.S.S.C.) and mean cell size on prehardening and we define a stabilized uniaxial state cyclically equivalent to a non proportional stabilized state through a family of C.S.S.C. Although simple assumptions like linear damage function and linear cumulation is used we obtain a sequence effect for difficult cross slip materials as 316 stainless steel, but the Miner rule for easy cross-slip materials. We show then differences between a load-controlled test and a strain controlled test: for a 316 stainless steel in a load controlled test, the non proportional loading at each cycle is less damaging than the uniaxial one for the same equivalent stress, while the result is opposite in a strain controlled test. We show also that an overloading retards initiation in a load controlled test while it accelerates initiation in a strain controlled test. (author). 26 refs., 8 figs

Prediction of chaos in non-salient permanent-magnet synchronous machines

Energy Technology Data Exchange (ETDEWEB)

Rasoolzadeh, Arsalan [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Tavazoei, Mohammad Saleh, E-mail: tavazoei@sharif.edu [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)

2012-12-03

This Letter tries to find the area in parameter space of a non-salient Permanent-Magnet Synchronous Machine (PMSM) in which chaos can occur. This area is briefly named as chaotic area. The predicted chaotic area is obtained by checking some conditions which are necessary for existence of chaos in a dynamical system. In this Letter, it is assumed that this machine is in the generator mode, and its model is based on direct and quadrature axis of stator voltages and currents. The information of the predicted area is used in non-chaotic maximum power control of torque in the machine.

Non-linear model predictive supervisory controller for building, air handling unit with recuperator and refrigeration system with heat waste recovery

DEFF Research Database (Denmark)

Minko, Tomasz; Wisniewski, Rafal; Bendtsen, Jan Dimon

2016-01-01

. The retrieved heat excess can be stored in the water tank. For this purpose the charging and the discharging water loops has been designed. We present the non-linear model of the above described system and a non-linear model predictive supervisory controller that according to the received price signal......, occupancy information and ambient temperature minimizes the operation cost of the whole system and distributes set points to local controllers of supermarkets subsystems. We find that when reliable information about the high price period is available, it is profitable to use the refrigeration system...... to generate heat during the low price period, store it and use it to substitute the conventional heater during the high price period....

Development of advanced stability theory suction prediction techniques for laminar flow control. [on swept wings

Science.gov (United States)

Srokowski, A. J.

1978-01-01

The problem of obtaining accurate estimates of suction requirements on swept laminar flow control wings was discussed. A fast accurate computer code developed to predict suction requirements by integrating disturbance amplification rates was described. Assumptions and approximations used in the present computer code are examined in light of flow conditions on the swept wing which may limit their validity.

Data-Based Predictive Control with Multirate Prediction Step

Science.gov (United States)

Barlow, Jonathan S.

2010-01-01

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

40 CFR 144.66 - State assumption of responsibility.

Science.gov (United States)

2010-07-01

... PROGRAMS (CONTINUED) UNDERGROUND INJECTION CONTROL PROGRAM Financial Responsibility: Class I Hazardous Waste Injection Wells § 144.66 State assumption of responsibility. (a) If a State either assumes legal... 40 Protection of Environment 22 2010-07-01 2010-07-01 false State assumption of responsibility...

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.

Predictions of type 2 diabetes and complications in Greenland in 2014

DEFF Research Database (Denmark)

Martinsen, Nina; Jørgensen, Marit E; Bjerregaard, Peter

2006-01-01

population in 2014. RESULTS: The prevalence of type 2 diabetes was not predicted to increase by 2014 under the 1st assumption. It was predicted to increase from 11% to 23% for women, but not for men under the 2nd assumption. Approximately half of the cases of cardiovascular disease and cardiovascular risk......OBJECTIVES: The objective of this study was to predict the prevalence of type 2 diabetes and the associated burden to the health care system in Greenland posed by diabetic complications by 2014. The predictions were based on changes in demographic variables and obesity. STUDY DESIGN: Projection...... 2 diabetes was predicted under these assumptions and based on the observed association between BMI and type 2 diabetes. The prevalence of complications was estimated using the 2nd assumption, as was the prevalence of hypertension, dyslipidemia, Ischemic Heart Disease (IHD) and stroke in the non-diabetic...

Output Feedback Stabilization with Nonlinear Predictive Control: Asymptotic properties

Directory of Open Access Journals (Sweden)

Lars Imsland

2003-07-01

Full Text Available State space based nonlinear model predictive control (NM PC needs the state for the prediction of the system behaviour. Unfortunately, for most applications, not all states are directly measurable. To recover the unmeasured states, typically a stable state observer is used. However, this implies that the stability of the closed-loop should be examined carefully, since no general nonlinear separation principle exists. Recently semi-global practical stability results for output feedback NMPC using a high-gain observer for state estimation have been established. One drawback of this result is that (in general the observer gain must be increased, if the desired set the state should converge to is made smaller. We show that under slightly stronger assumptions, not only practical stability, but also convergence of the system states and observer error to the origin for a sufficiently large but bounded observer gain can be achieved.

Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

KAUST Repository

Wang, Jim Jing-Yan

2014-05-23

Protein-protein interactions are critically dependent on just a few residues (“hot spots”) at the interfaces. Hot spots make a dominant contribution to the binding free energy and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there exists a need for accurate and reliable computational hot spot prediction methods. Compared to the supervised hot spot prediction algorithms, the semi-supervised prediction methods can take into consideration both the labeled and unlabeled residues in the dataset during the prediction procedure. The transductive support vector machine has been utilized for this task and demonstrated a better prediction performance. To the best of our knowledge, however, none of the transductive semi-supervised algorithms takes all the three semisupervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during learning. In this paper, we propose a novel semi-supervised method for hot spot residue prediction, by considering all the three semisupervised assumptions using nonlinear models. Our algorithm, IterPropMCS, works in an iterative manner. In each iteration, the algorithm first propagates the labels of the labeled residues to the unlabeled ones, along the shortest path between them on a graph, assuming that they lie on a nonlinear manifold. Then it selects the most confident residues as the labeled ones for the next iteration, according to the cluster and smoothness criteria, which is implemented by a nonlinear density estimator. Experiments on a benchmark dataset, using protein structure-based features, demonstrate that our approach is effective in predicting hot spots and compares favorably to other available methods. The results also show that our method outperforms the state-of-the-art transductive learning methods.

Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor

Directory of Open Access Journals (Sweden)

Muayad Al-Qaisy

2015-02-01

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.

A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the Productivity Susceptibility Analysis (PSA)

Science.gov (United States)

2018-01-01

Qualitative risk assessment frameworks, such as the Productivity Susceptibility Analysis (PSA), have been developed to rapidly evaluate the risks of fishing to marine populations and prioritize management and research among species. Despite being applied to over 1,000 fish populations, and an ongoing debate about the most appropriate method to convert biological and fishery characteristics into an overall measure of risk, the assumptions and predictive capacity of these approaches have not been evaluated. Several interpretations of the PSA were mapped to a conventional age-structured fisheries dynamics model to evaluate the performance of the approach under a range of assumptions regarding exploitation rates and measures of biological risk. The results demonstrate that the underlying assumptions of these qualitative risk-based approaches are inappropriate, and the expected performance is poor for a wide range of conditions. The information required to score a fishery using a PSA-type approach is comparable to that required to populate an operating model and evaluating the population dynamics within a simulation framework. In addition to providing a more credible characterization of complex system dynamics, the operating model approach is transparent, reproducible and can evaluate alternative management strategies over a range of plausible hypotheses for the system. PMID:29856869

Emerging Assumptions About Organization Design, Knowledge And Action

Directory of Open Access Journals (Sweden)

Alan Meyer

2013-12-01

Full Text Available Participants in the Organizational Design Community’s 2013 Annual Conference faced the challenge of “making organization design knowledge actionable.”  This essay summarizes the opinions and insights participants shared during the conference.  I reflect on these ideas, connect them to recent scholarly thinking about organization design, and conclude that seeking to make design knowledge actionable is nudging the community away from an assumption set based upon linearity and equilibrium, and toward a new set of assumptions based on emergence, self-organization, and non-linearity.

How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning.

Science.gov (United States)

Voorspoels, Wouter; Navarro, Daniel J; Perfors, Amy; Ransom, Keith; Storms, Gert

2015-09-01

A robust finding in category-based induction tasks is for positive observations to raise the willingness to generalize to other categories while negative observations lower the willingness to generalize. This pattern is referred to as monotonic generalization. Across three experiments we find systematic non-monotonicity effects, in which negative observations raise the willingness to generalize. Experiments 1 and 2 show that this effect emerges in hierarchically structured domains when a negative observation from a different category is added to a positive observation. They also demonstrate that this is related to a specific kind of shift in the reasoner's hypothesis space. Experiment 3 shows that the effect depends on the assumptions that the reasoner makes about how inductive arguments are constructed. Non-monotonic reasoning occurs when people believe the facts were put together by a helpful communicator, but monotonicity is restored when they believe the observations were sampled randomly from the environment. Copyright © 2015 Elsevier Inc. All rights reserved.

Can you please put it out? Predicting non-smokers' assertiveness intentions at work.

Science.gov (United States)

Aspropoulos, Eleftherios; Lazuras, Lambros; Rodafinos, Angelos; Eiser, J Richard

2010-04-01

The present study aimed to identify the psychosocial predictors of non-smoker employee intentions to ask smokers not to smoke at work. The predictive effects of past behaviour, anticipated regret, social norms, attitudinal, outcome expectancy and behavioural control beliefs were investigated in relation to the Attitudes-Social influence-self-Efficacy (ASE) model. Data were collected from Greek non-smoker employees (n=137, mean age=33.5, SD=10.5, 54.7% female) in 15 companies. The main outcome measure was assertiveness intention. Data on participants' past smoking, age, gender and on current smoking policy in the company were also collected. The majority of employees (77.4%) reported being annoyed by exposure to passive smoking at work, but only 37% reported having asked a smoker colleague not to smoke in the last 30 days. Regression analysis showed that the strongest predictor of non-smokers' assertiveness intentions was how often they believed that other non-smokers were assertive. Perceived control over being assertive, annoyance with secondhand smoke (SHS) exposure at work and past assertive behaviour also significantly predicted assertiveness intentions. Assertiveness by non-smoker employees seems to be guided mainly by normative and behavioural control beliefs, annoyance with SHS exposure at work, and past behaviour. Interventions to promote assertiveness in non-smokers might benefit from efficacy training combined with conveying the messages that the majority of other non-smokers are frequently annoyed by exposure to SHS, and that nearly half of all non-smokers are assertive towards smokers.

Adolescent risk-taking is predicted by individual differences in cognitive control over emotional, but not non-emotional, response conflict.

Science.gov (United States)

Botdorf, Morgan; Rosenbaum, Gail M; Patrianakos, Jamie; Steinberg, Laurence; Chein, Jason M

2017-08-01

While much research on adolescent risk behaviour has focused on the development of prefrontal self-regulatory mechanisms, prior studies have elicited mixed evidence of a relationship between individual differences in the capacity for self-regulation and individual differences in risk taking. To explain these inconsistent findings, it has been suggested that the capacity for self-regulation may be, for most adolescents, adequately mature to produce adaptive behaviour in non-affective, "cold" circumstances, but that adolescents have a more difficult time exerting control in affective, "hot" contexts. To further explore this claim, the present study examined individual differences in self-control in the face of affective and non-affective response conflict, and examined whether differences in the functioning of cognitive control processes under these different conditions was related to risk taking. Participants completed a cognitive Stroop task, an emotional Stroop task, and a risky driving task known as the Stoplight game. Regression analyses showed that performance on the emotional Stroop task predicted laboratory risk-taking in the driving task, whereas performance on the cognitive Stroop task did not exhibit the same trend. This pattern of results is consistent with theories of adolescent risk-taking that emphasise the impacts of affective contextual influences on the ability to enact effective cognitive control.

SU-E-T-630: Predictive Modeling of Mortality, Tumor Control, and Normal Tissue Complications After Stereotactic Body Radiotherapy for Stage I Non-Small Cell Lung Cancer

International Nuclear Information System (INIS)

Lindsay, WD; Berlind, CG; Gee, JC; Simone, CB

2015-01-01

Purpose: While rates of local control have been well characterized after stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC), less data are available characterizing survival and normal tissue toxicities, and no validated models exist assessing these parameters after SBRT. We evaluate the reliability of various machine learning techniques when applied to radiation oncology datasets to create predictive models of mortality, tumor control, and normal tissue complications. Methods: A dataset of 204 consecutive patients with stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT) at the University of Pennsylvania between 2009 and 2013 was used to create predictive models of tumor control, normal tissue complications, and mortality in this IRB-approved study. Nearly 200 data fields of detailed patient- and tumor-specific information, radiotherapy dosimetric measurements, and clinical outcomes data were collected. Predictive models were created for local tumor control, 1- and 3-year overall survival, and nodal failure using 60% of the data (leaving the remainder as a test set). After applying feature selection and dimensionality reduction, nonlinear support vector classification was applied to the resulting features. Models were evaluated for accuracy and area under ROC curve on the 81-patient test set. Results: Models for common events in the dataset (such as mortality at one year) had the highest predictive power (AUC = .67, p < 0.05). For rare occurrences such as radiation pneumonitis and local failure (each occurring in less than 10% of patients), too few events were present to create reliable models. Conclusion: Although this study demonstrates the validity of predictive analytics using information extracted from patient medical records and can most reliably predict for survival after SBRT, larger sample sizes are needed to develop predictive models for normal tissue toxicities and more advanced

SU-E-T-630: Predictive Modeling of Mortality, Tumor Control, and Normal Tissue Complications After Stereotactic Body Radiotherapy for Stage I Non-Small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Lindsay, WD [University of Pennsylvania, Philadelphia, PA (United States); Oncora Medical, LLC, Philadelphia, PA (United States); Berlind, CG [Georgia Institute of Technology, Atlanta, GA (Georgia); Oncora Medical, LLC, Philadelphia, PA (United States); Gee, JC; Simone, CB [University of Pennsylvania, Philadelphia, PA (United States)

2015-06-15

Purpose: While rates of local control have been well characterized after stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC), less data are available characterizing survival and normal tissue toxicities, and no validated models exist assessing these parameters after SBRT. We evaluate the reliability of various machine learning techniques when applied to radiation oncology datasets to create predictive models of mortality, tumor control, and normal tissue complications. Methods: A dataset of 204 consecutive patients with stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT) at the University of Pennsylvania between 2009 and 2013 was used to create predictive models of tumor control, normal tissue complications, and mortality in this IRB-approved study. Nearly 200 data fields of detailed patient- and tumor-specific information, radiotherapy dosimetric measurements, and clinical outcomes data were collected. Predictive models were created for local tumor control, 1- and 3-year overall survival, and nodal failure using 60% of the data (leaving the remainder as a test set). After applying feature selection and dimensionality reduction, nonlinear support vector classification was applied to the resulting features. Models were evaluated for accuracy and area under ROC curve on the 81-patient test set. Results: Models for common events in the dataset (such as mortality at one year) had the highest predictive power (AUC = .67, p < 0.05). For rare occurrences such as radiation pneumonitis and local failure (each occurring in less than 10% of patients), too few events were present to create reliable models. Conclusion: Although this study demonstrates the validity of predictive analytics using information extracted from patient medical records and can most reliably predict for survival after SBRT, larger sample sizes are needed to develop predictive models for normal tissue toxicities and more advanced

Unbiased and non-supervised learning methods for disruption prediction at JET

International Nuclear Information System (INIS)

Murari, A.; Vega, J.; Ratta, G.A.; Vagliasindi, G.; Johnson, M.F.; Hong, S.H.

2009-01-01

The importance of predicting the occurrence of disruptions is going to increase significantly in the next generation of tokamak devices. The expected energy content of ITER plasmas, for example, is such that disruptions could have a significant detrimental impact on various parts of the device, ranging from erosion of plasma facing components to structural damage. Early detection of disruptions is therefore needed with evermore increasing urgency. In this paper, the results of a series of methods to predict disruptions at JET are reported. The main objective of the investigation consists of trying to determine how early before a disruption it is possible to perform acceptable predictions on the basis of the raw data, keeping to a minimum the number of 'ad hoc' hypotheses. Therefore, the chosen learning techniques have the common characteristic of requiring a minimum number of assumptions. Classification and Regression Trees (CART) is a supervised but, on the other hand, a completely unbiased and nonlinear method, since it simply constructs the best classification tree by working directly on the input data. A series of unsupervised techniques, mainly K-means and hierarchical, have also been tested, to investigate to what extent they can autonomously distinguish between disruptive and non-disruptive groups of discharges. All these independent methods indicate that, in general, prediction with a success rate above 80% can be achieved not earlier than 180 ms before the disruption. The agreement between various completely independent methods increases the confidence in the results, which are also confirmed by a visual inspection of the data performed with pseudo Grand Tour algorithms.

Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

Science.gov (United States)

Cheval, Boris; Sarrazin, Philippe; Pelletier, Luc

2014-01-01

Understanding the determinants of non-exercise activity thermogenesis (NEAT) is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91) completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA) and sedentary behaviors (IASB). Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a) positively predicted by IAPA, (b) negatively predicted by IASB, and (c) was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

Model evaluation for the prediction of solubility of active pharmaceutical ingredients (APIs to guide solid–liquid separator design

Directory of Open Access Journals (Sweden)

Kuveneshan Moodley

2018-05-01

Full Text Available The assumptions and models for solubility modelling or prediction in systems using non-polar solvents, or water and complex triterpene and other active pharmaceutical ingredients as solutes aren't well studied. Furthermore, the assumptions concerning heat capacity effects (negligibility, experimental values or approximations are explored, using non-polar solvents (benzene, or water as reference solvents, for systems with solute melting points in the range of 306–528 K and molecular weights in the range of 90–442 g/mol. New empirical estimation methods for the ΔfusCpi of APIs are presented which correlate the solute molecular masses and van der Waals surface areas with ΔfusCpi. Separate empirical parameters were required for oxygenated and non-oxygenated solutes. Subsequently, the predictive capabilities of the various approaches to solubility modelling for complex pharmaceuticals, for which data is limited, are analysed. The solute selection is based on a principal component analysis, considering molecular weights, fusion temperatures, and solubilities in a non-polar solvent, alcohol, and water, where data was available. New NRTL-SAC parameters were determined for selected steroids, by regression. The original UNIFAC, modified UNIFAC (Dortmund, COSMO-RS (OL, and COSMO-SAC activity coefficient predictions are then conducted, based on the availability of group constants and sigma profiles. These are undertaken to assess the predictive capabilities of these models when each assumption concerning heat capacity is employed. The predictive qualities of the models are assessed, based on the mean square deviation and provide guidelines for model selection, and assumptions concerning phase equilibrium, when designing solid–liquid separators for the pharmaceutical industry on process simulation software. The most suitable assumption regarding ΔfusCpi was found to be system specific, with modified UNIFAC (Dortmund performing well in benzene as

Adaptive tracking control for active suspension systems with non-ideal actuators

Science.gov (United States)

Pan, Huihui; Sun, Weichao; Jing, Xingjian; Gao, Huijun; Yao, Jianyong

2017-07-01

As a critical component of transportation vehicles, active suspension systems are instrumental in the improvement of ride comfort and maneuverability. However, practical active suspensions commonly suffer from parameter uncertainties (e.g., the variations of payload mass and suspension component parameters), external disturbances and especially the unknown non-ideal actuators (i.e., dead-zone and hysteresis nonlinearities), which always significantly deteriorate the control performance in practice. To overcome these issues, this paper synthesizes an adaptive tracking control strategy for vehicle suspension systems to achieve suspension performance improvements. The proposed control algorithm is formulated by developing a unified framework of non-ideal actuators rather than a separate way, which is a simple yet effective approach to remove the unexpected nonlinear effects. From the perspective of practical implementation, the advantages of the presented controller for active suspensions include that the assumptions on the measurable actuator outputs, the prior knowledge of nonlinear actuator parameters and the uncertain parameters within a known compact set are not required. Furthermore, the stability of the closed-loop suspension system is theoretically guaranteed by rigorous mathematical analysis. Finally, the effectiveness of the presented adaptive control scheme is confirmed using comparative numerical simulation validations.

Hardness prediction of HAZ in temper bead welding by non-consistent layer technique

International Nuclear Information System (INIS)

Yu, Lina; Saida, Kazuyoshi; Mochizuki, Masahito; Kameyama, Masashi; Chigusa, Naoki; Nishimoto, Kazutoshi

2014-01-01

Based on the experimentally obtained hardness database, the neural network-based hardness prediction system of heat affect zone (HAZ) in temper bead welding by Consistent Layer (CSL) technique has been constructed by the authors. However in practical operation, CSL technique is sometimes difficult to perform because of difficulty of the precise heat input controlling, and in such case non-CSL techniques are mainly used in the actual repair process. Therefore in the present study, the neural network-based hardness prediction system of HAZ in temper bead welding by non-CSL techniques has been constructed through thermal cycle simplification, from the view of engineering. The hardness distribution in HAZ with non-CSL techniques was calculated based on the thermal cycles numerically obtained by finite element method. The experimental result has shown that the predicted hardness is in good accordance with the measured ones. It follows that the currently proposed method is effective for estimating the tempering effect during temper bead welding by non-CSL techniques. (author)

One Layer Nonlinear Economic Closed-Loop Generalized Predictive Control for a Wastewater Treatment Plant

Directory of Open Access Journals (Sweden)

Hicham El bahja

2018-04-01

Full Text Available The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC. In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP and the results prove the efficiency of the method and that it can be used profitably in practical cases.

Testing the assumptions behind emissions trading in non-market goods: the RECLAIM program in Southern California

International Nuclear Information System (INIS)

Lejano, Raul P.; Hirose, Rei

2005-01-01

Emissions trading is, essentially, a policy instrument that is designed to simulate a market for an otherwise public good. Conceptually, its justification hinges on a number of key assumptions, namely the negligibility of local impacts, the ability to separate and commodify the good in question, and characteristics of a well-functioning market. The authors examine the performance of RECLAIM, a NO x emissions trading program in Southern California, USA, and illustrate how to test these assumptions. There is some evidence that the trading of NO x generates new externalities, such as the possibility that other air pollutants, e.g. volatile organics, are essentially traded along with it. Moreover, the RECLAIM program has recently begun to experience difficulties due to the fact that the market is relatively thin. This analysis provides ways to assess more deeply and reform these trading regimes, including opening up RECLAIM to public review. The case study speaks to a wider arena, as emissions trading is presently being considered in other parts of the world to address issues ranging from acid rain to non-point source pollution to greenhouse gases. The analytic approach, illustrated herein, is a general one that has a wider applicability than the particular case of NO x trading. It is hoped that this kind of critical inquiry can lead to a more careful deliberation of the merits and challenges of emissions trading

Tests of the linearity assumption in the dose-effect relationship for radiation-induced cancer

International Nuclear Information System (INIS)

Cohen, A.F.; Cohen, B.L.

1980-01-01

The validity of the BEIR linear extrapolation to low doses of the dose-effect relationship for radiation induced cancer is tested by use of natural radiation making use of selectivity on type of cancer, smoking habits, sex, age group, geographic area and/or time period. For lung cancer, a linear interpolation between zero dose-zero effect and the data from radon-induced cancers in miners implies that the majority of all lung cancers among non-smokers are due to radon; since lung cancers in miners are mostly small-cell undifferentiated (SCU), a rather rare type in general, linearity over predicts the frequency of SCU lung cancers among non smokers by a factor of 10, and among non-smoking females age 25-44 by a factor of 24. Similarly, linearity predicts that the majority of all lung cancers early in this century were due to radon even after due consideration is given to cases missed by poor diagnostic efficiency (this matter is considered in some detail). For the 30-40 age range, linearity over predicts the total lung cancer rate at that time by a factor of 3-6; for SCU lung cancer, the over-prediction is by at least a factor of 10. Other causes of lung cancer are considered which further enhance the degree to which the linearity assumption over-estimates the effects of low level radiation. A similar analysis is applied to leukemia induced by natural radiation. It is concluded that the upper limit for this is not higher than estimates from the linearity hypothesis. (author)

Energy efficient non-road hybrid electric vehicles advanced modeling and control

CERN Document Server

Unger, Johannes; Jakubek, Stefan

2016-01-01

Analyzing the main problems in the real-time control of parallel hybrid electric powertrains in non-road applications, which work in continuous high dynamic operation, this book gives practical insight in to how to maximize the energetic efficiency and drivability of such powertrains. The book addresses an energy management control structure, which considers all constraints of the physical powertrain and uses novel methodologies for the prediction of the future load requirements to optimize the controller output in terms of an entire work cycle of a non-road vehicle. The load prediction includes a methodology for short term loads as well as for an entire load cycle by means of a cycle detection. A maximization of the energetic efficiency can so be achieved, which is simultaneously a reduction in fuel consumption and exhaust emissions. Readers will gain a deep insight into the necessary topics to be considered in designing an energy and battery management system for non-road vehicles and that only a combinatio...

Serum AMH Level to Predict the Hyper Response in Women with PCOS and Non-PCOS Undergoing Controlled Ovarian Stimulation in ART.

Science.gov (United States)

Vembu, Radha; Reddy, Nellepalli Sanjeeva

2017-01-01

It is essential to determine the cut-off value of serum anti-Mullerian hormone (AMH) to predict the hyper response in assisted reproductive technology (ART). There are few studies mentioning the cut-off value for the hyper response in infertile women but not specifically for polycystic ovary syndrome (PCOS) and non-PCOS groups. With this in background, this study was conducted. To determine the cut-off value of serum AMH to predict the hyper response in women with PCOS and non-PCOS undergoing a controlled ovarian stimulation (COS) in ART. To compare the outcome of stimulation in PCOS and non-PCOS groups. All 246 women enrolled for Intra Cytoplasmic Sperm Injection (ICSI) fulfilling the selection criteria were recruited. On the day 3 of the cycle, the serum AMH, Follicle Stimulating Hormone (FSH), Luteinizing Hormone (LH), estradiol and antral follicle count (AFC) were measured. They underwent COS as per the unit protocol. They were divided into PCOS and non-PCOS groups as per the Rotterdam's criteria. The mean age, duration of infertility, Body Mass Index (BMI), Ovarian reserve markers and outcome of stimulation were compared. Using the Statistical Package for the Social Sciences version 16.0 software, the significant difference was measured by multivariate analysis, as well as a one-way analysis of variance with Tukey's post-hoc test was used. Among 246 women, 31.3% were in PCOS group, and 68.7% were in non-PCOS group. Comparison of PCOS and non-PCOS groups showed a significant difference in the age with the mean age being 29.2 and 31.5 years, respectively. The mean AMH and AFC were 2-fold higher in PCOS group. The mean number of follicles, oocytes retrieved, MII and oocytes fertilised were significantly higher in PCOS group. The pregnancy rate was 52.6% in PCOS and 30.9% in non-PCOS group. In the PCOS group, 22.1% had ovarian hyper stimulation syndrome (OHSS), and only 4.7% had OHSS in non-PCOS group ( P = 0.0005). Receiving Operator Curve (ROC) curve was plotted

Predictive control, with restrictions for the climate of a greenhouse

International Nuclear Information System (INIS)

Pinon, Sandra; Pena, Miguel; Kuchen, Benjamin

2002-01-01

A proposal for controlling nonlinear systems under constraints is presented. a combination of model predictive control and feedback linearization is used. An alternative that uses extended kalman filter as non-measured variable estimator is applied for performing the constrained optimization. Finally, an observability analysis is done in closed loop in order to demonstrate observer convergence

Delta-Domain Predictive Control and Identification for Control

DEFF Research Database (Denmark)

Lauritsen, Morten Bach

1997-01-01

The present thesis is concerned with different aspects of modelling, control and identification of linear systems. Traditionally, discrete-time sampled-data systems are represented using shift-operator parametrizations. Such parametrizations are not suitable at fast sampling rates. An alternative...... minimum-variance predictor as a special case and to have a well-defined continuous-time limit. By means of this new prediction method a unified framework for discrete-time and continuous-time predictive control algorithms is developed. This contains a continuous-time like discrete-time predictive...... controller which is insensitive to the choice of sampling period and has a well-defined limit in the continuous-time case. Also more conventional discrete-time predictive control methods may be described within the unified approach. The predictive control algorithms are extended to frequency weighted...

Linear Model-Based Predictive Control of the LHC 1.8 K Cryogenic Loop

CERN Document Server

Blanco-Viñuela, E; De Prada-Moraga, C

1999-01-01

The LHC accelerator will employ 1800 superconducting magnets (for guidance and focusing of the particle beams) in a pressurized superfluid helium bath at 1.9 K. This temperature is a severely constrained control parameter in order to avoid the transition from the superconducting to the normal state. Cryogenic processes are difficult to regulate due to their highly non-linear physical parameters (heat capacity, thermal conductance, etc.) and undesirable peculiarities like non self-regulating process, inverse response and variable dead time. To reduce the requirements on either temperature sensor or cryogenic system performance, various control strategies have been investigated on a reduced-scale LHC prototype built at CERN (String Test). Model Based Predictive Control (MBPC) is a regulation algorithm based on the explicit use of a process model to forecast the plant output over a certain prediction horizon. This predicted controlled variable is used in an on-line optimization procedure that minimizes an approp...

The Wally plot approach to assess the calibration of clinical prediction models.

Science.gov (United States)

Blanche, Paul; Gerds, Thomas A; Ekstrøm, Claus T

2017-12-06

A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. Typically, the calibration assumption is assessed graphically but in practice it is often challenging to judge whether a "disappointing" calibration plot is the consequence of a departure from the calibration assumption, or alternatively just "bad luck" due to sampling variability. We propose a graphical approach which enables the visualization of how much a calibration plot agrees with the calibration assumption to address this issue. The approach is mainly based on the idea of generating new plots which mimic the available data under the calibration assumption. The method handles the common non-trivial situations in which the data contain censored observations and occurrences of competing events. This is done by building on ideas from constrained non-parametric maximum likelihood estimation methods. Two examples from large cohort data illustrate our proposal. The 'wally' R package is provided to make the methodology easily usable.

Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

Directory of Open Access Journals (Sweden)

Boris Cheval

Full Text Available Understanding the determinants of non-exercise activity thermogenesis (NEAT is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91 completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA and sedentary behaviors (IASB. Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a positively predicted by IAPA, (b negatively predicted by IASB, and (c was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

The Immoral Assumption Effect: Moralization Drives Negative Trait Attributions.

Science.gov (United States)

Meindl, Peter; Johnson, Kate M; Graham, Jesse

2016-04-01

Jumping to negative conclusions about other people's traits is judged as morally bad by many people. Despite this, across six experiments (total N = 2,151), we find that multiple types of moral evaluations--even evaluations related to open-mindedness, tolerance, and compassion--play a causal role in these potentially pernicious trait assumptions. Our results also indicate that moralization affects negative-but not positive-trait assumptions, and that the effect of morality on negative assumptions cannot be explained merely by people's general (nonmoral) preferences or other factors that distinguish moral and nonmoral traits, such as controllability or desirability. Together, these results suggest that one of the more destructive human tendencies--making negative assumptions about others--can be caused by the better angels of our nature. © 2016 by the Society for Personality and Social Psychology, Inc.

Social support, world assumptions, and exposure as predictors of anxiety and quality of life following a mass trauma.

Science.gov (United States)

Grills-Taquechel, Amie E; Littleton, Heather L; Axsom, Danny

2011-05-01

This study examined the influence of a mass trauma (the Virginia Tech campus shootings) on anxiety symptoms and quality of life, as well as the potential vulnerability/protective roles of world assumptions and social support. Pre-trauma adjustment data, collected in the six months prior to the shooting, was examined along with two-month post-shooting data in a sample of 298 female students enrolled at the university at the time of the shootings. Linear regression analyses revealed consistent predictive roles for world assumptions pertaining to control and self-worth as well as family support. In addition, for those more severely exposed to the shooting, greater belief in a lack of control over outcomes appeared to increase vulnerability for post-trauma physiological and emotional anxiety symptoms. Implications of the results for research and intervention following mass trauma are discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

Non-Vacuum Initial States for Cosmological Perturbations of Quantum-Mechanical Origin

CERN Document Server

Martín, J; Sakellariadou, M; Martin, Jerome; Riazuelo, Alain; Sakellariadou, Mairi

2000-01-01

In the context of inflation, non-vacuum initial states for cosmological perturbations that possess a built in scale are studied. It is demonstrated that this assumption leads to a falsifiable class of models. The question of whether they lead to conflicts with the available observations is addressed. For this purpose, the power spectrum of the Bardeen potential operator is calculated and compared with the CMBR anisotropies measurements and the redshift surveys of galaxies and clusters of galaxies. Generic predictions of the model are: a high first acoustic peak, the presence of a bump in the matter power spectrum and non-Gaussian statistics. The details are controlled by the number of quanta in the non-vacuum initial state. Comparisons with observations show that there exists a window for the free parameters such that good agreement between the data and the theoretical predictions is possible. However, in the case where the initial state is a state with a fixed number of quanta, it is shown that this number c...

Shattering world assumptions: A prospective view of the impact of adverse events on world assumptions.

Science.gov (United States)

Schuler, Eric R; Boals, Adriel

2016-05-01

Shattered Assumptions theory (Janoff-Bulman, 1992) posits that experiencing a traumatic event has the potential to diminish the degree of optimism in the assumptions of the world (assumptive world), which could lead to the development of posttraumatic stress disorder. Prior research assessed the assumptive world with a measure that was recently reported to have poor psychometric properties (Kaler et al., 2008). The current study had 3 aims: (a) to assess the psychometric properties of a recently developed measure of the assumptive world, (b) to retrospectively examine how prior adverse events affected the optimism of the assumptive world, and (c) to measure the impact of an intervening adverse event. An 8-week prospective design with a college sample (N = 882 at Time 1 and N = 511 at Time 2) was used to assess the study objectives. We split adverse events into those that were objectively or subjectively traumatic in nature. The new measure exhibited adequate psychometric properties. The report of a prior objective or subjective trauma at Time 1 was related to a less optimistic assumptive world. Furthermore, participants who experienced an intervening objectively traumatic event evidenced a decrease in optimistic views of the world compared with those who did not experience an intervening adverse event. We found support for Shattered Assumptions theory retrospectively and prospectively using a reliable measure of the assumptive world. We discuss future assessments of the measure of the assumptive world and clinical implications to help rebuild the assumptive world with current therapies. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Adult Learning Assumptions

Science.gov (United States)

Baskas, Richard S.

2011-01-01

The purpose of this study is to examine Knowles' theory of andragogy and his six assumptions of how adults learn while providing evidence to support two of his assumptions based on the theory of andragogy. As no single theory explains how adults learn, it can best be assumed that adults learn through the accumulation of formal and informal…

Comparative Analysis of Local Control Prediction Using Different Biophysical Models for Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

Directory of Open Access Journals (Sweden)

Bao-Tian Huang

2017-01-01

Full Text Available Purpose. The consistency for predicting local control (LC data using biophysical models for stereotactic body radiotherapy (SBRT treatment of lung cancer is unclear. This study aims to compare the results calculated from different models using the treatment planning data. Materials and Methods. Treatment plans were designed for 17 patients diagnosed with primary non-small cell lung cancer (NSCLC using 5 different fraction schemes. The Martel model, Ohri model, and the Tai model were used to predict the 2-year LC value. The Gucken model, Santiago model, and the Tai model were employed to estimate the 3-year LC data. Results. We found that the employed models resulted in completely different LC prediction except for the Gucken and the Santiago models which exhibited quite similar 3-year LC data. The predicted 2-year and 3-year LC values in different models were not only associated with the dose normalization but also associated with the employed fraction schemes. The greatest difference predicted by different models was up to 15.0%. Conclusions. Our results show that different biophysical models influence the LC prediction and the difference is not only correlated to the dose normalization but also correlated to the employed fraction schemes.

Online prediction and control in nonlinear stochastic systems

DEFF Research Database (Denmark)

Nielsen, Torben Skov

2002-01-01

speed and the relationship between (primarily) wind speed and wind power (the power curve). In paper G the model parameters are estimated using a RLS algorithm and any systematic time-variation of the model parameters is disregarded. Two di erent parameterizations of the power curve is considered...... are estimated using the algorithm proposed in paper C. The power curve and the diurnal variation of wind speed is estimated separately using the local polynomial regression procedure described in paper A . In paper J the parameters of the prediction model is assumed to be smooth functions of wind direction (and......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...

Unrealistic Assumptions in Economics: an Analysis under the Logic of Socioeconomic Processes

Directory of Open Access Journals (Sweden)

Leonardo Ivarola

2014-11-01

Full Text Available The realism of assumptions is an ongoing debate within the philosophy of economics. One of the most referenced papers in this matter belongs to Milton Friedman. He defends the use of unrealistic assumptions, not only because of a pragmatic issue, but also the intrinsic difficulties of determining the extent of realism. On the other hand, realists have criticized (and still do today the use of unrealistic assumptions - such as the assumption of rational choice, perfect information, homogeneous goods, etc. However, they did not accompany their statements with a proper epistemological argument that supports their positions. In this work it is expected to show that the realism of (a particular sort of assumptions is clearly relevant when examining economic models, since the system under study (the real economies is not compatible with logic of invariance and of mechanisms, but with the logic of possibility trees. Because of this, models will not function as tools for predicting outcomes, but as representations of alternative scenarios, whose similarity to the real world will be examined in terms of the verisimilitude of a class of model assumptions

Adaptive filtering prediction and control

CERN Document Server

Goodwin, Graham C

2009-01-01

Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o

A Design of a Hybrid Non-Linear Control Algorithm

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Farinaz Behrooz

2017-11-01

Full Text Available One of the high energy consuming devices in the buildings is the air-conditioning system. Designing a proper controller to consider the thermal comfort and simultaneously control the energy usage of the device will impact on the system energy efficiency and its performance. The aim of this study was to design a Multiple-Input and Multiple-Output (MIMO, non-linear, and intelligent controller on direct expansion air-conditioning system The control algorithm uses the Fuzzy Cognitive Map method as a main controller and the Generalized Predictive Control method is used for assigning the initial weights of the main controller. The results of the proposed controller shows that the controller was successfully designed and works in set point tracking and under disturbance rejection tests. The obtained results of the Generalized Predictive Control-Fuzzy Cognitive Map controller are compared with the previous MIMO Linear Quadratic Gaussian control design on the same direct expansion air-conditioning system under the same conditions. The comparative results indicate energy savings would be achieved with the proposed controller with long-term usage. Energy efficiency and thermal comfort conditions are achieved by the proposed controller.

Recognising the Effects of Costing Assumptions in Educational Business Simulation Games

Science.gov (United States)

Eckardt, Gordon; Selen, Willem; Wynder, Monte

2015-01-01

Business simulations are a powerful way to provide experiential learning that is focussed, controlled, and concentrated. Inherent in any simulation, however, are numerous assumptions that determine feedback, and hence the lessons learnt. In this conceptual paper we describe some common cost assumptions that are implicit in simulation design and…

Evaluating growth assumptions using diameter or radial increments in natural even-aged longleaf pine

Science.gov (United States)

John C. Gilbert; Ralph S. Meldahl; Jyoti N. Rayamajhi; John S. Kush

2010-01-01

When using increment cores to predict future growth, one often assumes future growth is identical to past growth for individual trees. Once this assumption is accepted, a decision has to be made between which growth estimate should be used, constant diameter growth or constant basal area growth. Often, the assumption of constant diameter growth is used due to the ease...

Do unreal assumptions pervert behaviour?

DEFF Research Database (Denmark)

Petersen, Verner C.

of the basic assumptions underlying the theories found in economics. Assumptions relating to the primacy of self-interest, to resourceful, evaluative, maximising models of man, to incentive systems and to agency theory. The major part of the paper then discusses how these assumptions and theories may pervert......-interested way nothing will. The purpose of this paper is to take a critical look at some of the assumptions and theories found in economics and discuss their implications for the models and the practices found in the management of business. The expectation is that the unrealistic assumptions of economics have...... become taken for granted and tacitly included into theories and models of management. Guiding business and manage¬ment to behave in a fashion that apparently makes these assumptions become "true". Thus in fact making theories and models become self-fulfilling prophecies. The paper elucidates some...

A simulation study to compare three self-controlled case series approaches: correction for violation of assumption and evaluation of bias.

Science.gov (United States)

Hua, Wei; Sun, Guoying; Dodd, Caitlin N; Romio, Silvana A; Whitaker, Heather J; Izurieta, Hector S; Black, Steven; Sturkenboom, Miriam C J M; Davis, Robert L; Deceuninck, Genevieve; Andrews, N J

2013-08-01

The assumption that the occurrence of outcome event must not alter subsequent exposure probability is critical for preserving the validity of the self-controlled case series (SCCS) method. This assumption is violated in scenarios in which the event constitutes a contraindication for exposure. In this simulation study, we compared the performance of the standard SCCS approach and two alternative approaches when the event-independent exposure assumption was violated. Using the 2009 H1N1 and seasonal influenza vaccines and Guillain-Barré syndrome as a model, we simulated a scenario in which an individual may encounter multiple unordered exposures and each exposure may be contraindicated by the occurrence of outcome event. The degree of contraindication was varied at 0%, 50%, and 100%. The first alternative approach used only cases occurring after exposure with follow-up time starting from exposure. The second used a pseudo-likelihood method. When the event-independent exposure assumption was satisfied, the standard SCCS approach produced nearly unbiased relative incidence estimates. When this assumption was partially or completely violated, two alternative SCCS approaches could be used. While the post-exposure cases only approach could handle only one exposure, the pseudo-likelihood approach was able to correct bias for both exposures. Violation of the event-independent exposure assumption leads to an overestimation of relative incidence which could be corrected by alternative SCCS approaches. In multiple exposure situations, the pseudo-likelihood approach is optimal; the post-exposure cases only approach is limited in handling a second exposure and may introduce additional bias, thus should be used with caution. Copyright © 2013 John Wiley & Sons, Ltd.

Genomic prediction based on data from three layer lines using non-linear regression models

NARCIS (Netherlands)

Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.

2014-01-01

Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate

Experimental evaluation of the pure configurational stress assumption in the flow dynamics of entangled polymer melts

DEFF Research Database (Denmark)

Rasmussen, Henrik K.; Bejenariu, Anca Gabriela; Hassager, Ole

2010-01-01

to the flow in the non-linear flow regime. This has allowed highly elastic measurements within the limit of pure orientational stress, as the time of the flow was considerably smaller than the Rouse time. A Doi-Edwards [J. Chem. Soc., Faraday Trans. 2 74, 1818-1832 (1978)] type of constitutive model...... with the assumption of pure configurational stress was accurately able to predict the startup as well as the reversed flow behavior. This confirms that this commonly used theoretical picture for the flow of polymeric liquids is a correct physical principle to apply. c 2010 The Society of Rheology. [DOI: 10.1122/1.3496378]...

Model predictive control for a thermostatic controlled system

DEFF Research Database (Denmark)

Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob

2013-01-01

This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperat......This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...

REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN

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A. I. Hinojosa

Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.

Control and prediction components of movement planning in stuttering vs. nonstuttering adults

Science.gov (United States)

Daliri, Ayoub; Prokopenko, Roman A.; Flanagan, J. Randall; Max, Ludo

2014-01-01

Purpose Stuttering individuals show speech and nonspeech sensorimotor deficiencies. To perform accurate movements, the sensorimotor system needs to generate appropriate control signals and correctly predict their sensory consequences. Using a reaching task, we examined the integrity of these control and prediction components, separately, for movements unrelated to the speech motor system. Method Nine stuttering and nine nonstuttering adults made fast reaching movements to visual targets while sliding an object under the index finger. To quantify control, we determined initial direction error and end-point error. To quantify prediction, we calculated the correlation between vertical and horizontal forces applied to the object—an index of how well vertical force (preventing slip) anticipated direction-dependent variations in horizontal force (moving the object). Results Directional and end-point error were significantly larger for the stuttering group. Both groups performed similarly in scaling vertical force with horizontal force. Conclusions The stuttering group's reduced reaching accuracy suggests limitations in generating control signals for voluntary movements, even for non-orofacial effectors. Typical scaling of vertical force with horizontal force suggests an intact ability to predict the consequences of planned control signals. Stuttering may be associated with generalized deficiencies in planning control signals rather than predicting the consequences of those signals. PMID:25203459

The sufficiency assumption of the reasoned approach to action

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David Trafimow

2015-12-01

Full Text Available The reasoned action approach to understanding and predicting behavior includes the sufficiency assumption. Although variables not included in the theory may influence behavior, these variables work through the variables in the theory. Once the reasoned action variables are included in an analysis, the inclusion of other variables will not increase the variance accounted for in behavioral intentions or behavior. Reasoned action researchers are very concerned with testing if new variables account for variance (or how much traditional variables account for variance, to see whether they are important, in general or with respect to specific behaviors under investigation. But this approach tacitly assumes that accounting for variance is highly relevant to understanding the production of variance, which is what really is at issue. Based on the variance law, I question this assumption.

Neural Network-Based Resistance Spot Welding Control and Quality Prediction

Energy Technology Data Exchange (ETDEWEB)

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

1999-07-10

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

Chaos in balance: non-linear measures of postural control predict individual variations in visual illusions of motion.

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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.

The incompressibility assumption in computational simulations of nasal airflow.

Science.gov (United States)

Cal, Ismael R; Cercos-Pita, Jose Luis; Duque, Daniel

2017-06-01

Most of the computational works on nasal airflow up to date have assumed incompressibility, given the low Mach number of these flows. However, for high temperature gradients, the incompressibility assumption could lead to a loss of accuracy, due to the temperature dependence of air density and viscosity. In this article we aim to shed some light on the influence of this assumption in a model of calm breathing in an Asian nasal cavity, by solving the fluid flow equations in compressible and incompressible formulation for different ambient air temperatures using the OpenFOAM package. At low flow rates and warm climatological conditions, similar results were obtained from both approaches, showing that density variations need not be taken into account to obtain a good prediction of all flow features, at least for usual breathing conditions. This agrees with most of the simulations previously reported, at least as far as the incompressibility assumption is concerned. However, parameters like nasal resistance and wall shear stress distribution differ for air temperatures below [Formula: see text]C approximately. Therefore, density variations should be considered for simulations at such low temperatures.

Model predictive Controller for Mobile Robot

OpenAIRE

Alireza Rezaee

2017-01-01

This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

The predictive value of leucocyte progression for one-week mortality on acutely admitted medical patients to the emergency department

DEFF Research Database (Denmark)

Brabrand, Mikkel; Soltau, Matilde Røgilds

2018-01-01

female. Using logistic regression, we found significantly lower one-week mortality with falling leucocyte count progression, even when controlling for confounders. A decreasing leucocyte count had a sensitivity for one-week mortality of 65%, specificity of 62%, positive predictive value of 4......%, and negative predictive value of 99%. Difference in admission to the intensive care unit was non-significant between the three groups. Difference in length of stay was significant, but with one day difference, the clinical significance is questionable. CONCLUSION: Leucocyte count progression is not sensitive...... enough to predict one-week mortality, nor specific enough to discount it. It is important for physicians to be aware of this to avoid faulty assessments based on imprecise assumptions....

Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage

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Douglas Halamay

2014-09-01

Full Text Available This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps, can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.

Predicting glycated hemoglobin levels in the non-diabetic general population

DEFF Research Database (Denmark)

Rauh, Simone P; Heymans, Martijn W; Koopman, Anitra D M

2017-01-01

AIMS/HYPOTHESIS: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. METHODS: Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, I...

Predictability of solute transport in diffusion-controlled hydrogeologic regimes

International Nuclear Information System (INIS)

Gillham, R.W.; Cherry, J.A.

1983-01-01

Hydrogeologic regimes that are favourable for the subsurface management of low-level radioactive wastes must have transport properties that will limit the migration velocity of contaminants to some acceptably low value. Of equal importance, for the purpose of impact assessment and licensing, is the need to be able to predict, with a reasonable degree of certainty and over long time periods, what the migration velocity of the various contaminants of interest will be. This paper presents arguments to show that in addition to having favourable velocity characteristics, transport in saturated, diffusion-controlled hydrogeologic regimes is considerably more predictable than in the most common alternatives. The classical transport models for unsaturated, saturated-advection-controlled and saturated-diffusion-controlled environments are compared, with particular consideration being given to the difficulties associated with the characterization of the respective transport parameters. Results are presented which show that the diffusion of non-reactive solutes and solutes that react according to a constant partitioning ratio (K/sub d/) are highly predictable under laboratory conditions and that the diffusion coefficients for the reactive solutes can be determined with a reasonable degree of accuracy from independent measurements of bulk density, porosity, distribution coefficient and tortuosity. Field evidence is presented which shows that the distribution of environmental isotopes and chloride in thick clayey deposits is consistent with a diffusion-type transport process in these media. These results are particularly important in that they not only demonstrate the occurrence of diffusion-controlled hydrogeologic regimes, but they also demonstrate the predictability of the migration characteristics over very long time periods

Stability analysis of embedded nonlinear predictor neural generalized predictive controller

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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.

Optimal control predicts human performance on objects with internal degrees of freedom.

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Arne J Nagengast

2009-06-01

Full Text Available On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

Predicting gross wages of non-employed persons in Croatia

Directory of Open Access Journals (Sweden)

Slavko Bezeredi

2016-03-01

Full Text Available We present the findings of a study aimed at building a model for predicting wages of non-employed persons in Croatia. The predictions will be used in the calculation of marginal effective tax rate at the extensive margin and in labour supply modelling. The database used is 2012 “EU statistics on income and living conditions”. The paper comprehensively explains the data source, variables, subgroups of employed and non-employed, and the results of the linear regression model, the Heckman selection model and the quantile regression model. The quality of predictions obtained by different models is compared and discussed.

Model predictive control using fuzzy decision functions

NARCIS (Netherlands)

Kaymak, U.; Costa Sousa, da J.M.

2001-01-01

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

NARCIS (Netherlands)

Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

2014-01-01

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

Unreachable Setpoints in Model Predictive Control

DEFF Research Database (Denmark)

Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

2008-01-01

In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

Dynamic Algorithm for LQGPC Predictive Control

DEFF Research Database (Denmark)

Hangstrup, M.; Ordys, A.W.; Grimble, M.J.

1998-01-01

In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control......In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated...... into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how well-known GPC controller can be obtained as a special case of the LQGPC controller design. The important advantage of using the LQGPC framework for designing predictive, e.g. GPS is that LQGPC enables...

A narrow-band k-distribution model with single mixture gas assumption for radiative flows

Science.gov (United States)

Jo, Sung Min; Kim, Jae Won; Kwon, Oh Joon

2018-06-01

In the present study, the narrow-band k-distribution (NBK) model parameters for mixtures of H2O, CO2, and CO are proposed by utilizing the line-by-line (LBL) calculations with a single mixture gas assumption. For the application of the NBK model to radiative flows, a radiative transfer equation (RTE) solver based on a finite-volume method on unstructured meshes was developed. The NBK model and the RTE solver were verified by solving two benchmark problems including the spectral radiance distribution emitted from one-dimensional slabs and the radiative heat transfer in a truncated conical enclosure. It was shown that the results are accurate and physically reliable by comparing with available data. To examine the applicability of the methods to realistic multi-dimensional problems in non-isothermal and non-homogeneous conditions, radiation in an axisymmetric combustion chamber was analyzed, and then the infrared signature emitted from an aircraft exhaust plume was predicted. For modeling the plume flow involving radiative cooling, a flow-radiation coupled procedure was devised in a loosely coupled manner by adopting a Navier-Stokes flow solver based on unstructured meshes. It was shown that the predicted radiative cooling for the combustion chamber is physically more accurate than other predictions, and is as accurate as that by the LBL calculations. It was found that the infrared signature of aircraft exhaust plume can also be obtained accurately, equivalent to the LBL calculations, by using the present narrow-band approach with a much improved numerical efficiency.

Collective motion of predictive swarms.

Directory of Open Access Journals (Sweden)

Nathaniel Rupprecht

Full Text Available Theoretical models of populations and swarms typically start with the assumption that the motion of agents is governed by the local stimuli. However, an intelligent agent, with some understanding of the laws that govern its habitat, can anticipate the future, and make predictions to gather resources more efficiently. Here we study a specific model of this kind, where agents aim to maximize their consumption of a diffusing resource, by attempting to predict the future of a resource field and the actions of other agents. Once the agents make a prediction, they are attracted to move towards regions that have, and will have, denser resources. We find that the further the agents attempt to see into the future, the more their attempts at prediction fail, and the less resources they consume. We also study the case where predictive agents compete against non-predictive agents and find the predictors perform better than the non-predictors only when their relative numbers are very small. We conclude that predictivity pays off either when the predictors do not see too far into the future or the number of predictors is small.

Commentary: Considering Assumptions in Associations Between Music Preferences and Empathy-Related Responding

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Susan A O'Neill

2015-09-01

Full Text Available This commentary considers some of the assumptions underpinning the study by Clark and Giacomantonio (2015. Their exploratory study examined relationships between young people's music preferences and their cognitive and affective empathy-related responses. First, the prescriptive assumption that music preferences can be measured according to how often an individual listens to a particular music genre is considered within axiology or value theory as a multidimensional construct (general, specific, and functional values. This is followed by a consideration of the causal assumption that if we increase young people's empathy through exposure to prosocial song lyrics this will increase their prosocial behavior. It is suggested that the predictive power of musical preferences on empathy-related responding might benefit from a consideration of the larger pattern of psychological and subjective wellbeing within the context of developmental regulation across ontogeny that involves mutually influential individual—context relations.

Biological control agents elevate hantavirus by subsidizing deer mouse populations

Science.gov (United States)

Dean E. Pearson; Ragan M. Callaway

2006-01-01

Biological control of exotic invasive plants using exotic insects is practiced under the assumption that biological control agents are safe if they do not directly attack non-target species. We tested this assumption by evaluating the potential for two host-specific biological control agents (Urophora spp.), widely established in North America for spotted...

The Emperors sham - wrong assumption that sham needling is sham.

Science.gov (United States)

Lundeberg, Thomas; Lund, Iréne; Näslund, Jan; Thomas, Moolamanil

2008-12-01

During the last five years a large number of randomised controlled clinical trials (RCTs) have been published on the efficacy of acupuncture in different conditions. In most of these studies verum is compared with sham acupuncture. In general both verum and sham have been found to be effective, and often with little reported difference in outcome. This has repeatedly led to the conclusion that acupuncture is no more effective than placebo treatment. However, this conclusion is based on the assumption that sham acupuncture is inert. Since sham acupuncture evidently is merely another form of acupuncture from the physiological perspective, the assumption that sham is sham is incorrect and conclusions based on this assumption are therefore invalid. Clinical guidelines based on such conclusions may therefore exclude suffering patients from valuable treatments.

On testing the missing at random assumption

DEFF Research Database (Denmark)

Jaeger, Manfred

2006-01-01

Most approaches to learning from incomplete data are based on the assumption that unobserved values are missing at random (mar). While the mar assumption, as such, is not testable, it can become testable in the context of other distributional assumptions, e.g. the naive Bayes assumption...

Wind turbine control and model predictive control for uncertain systems

DEFF Research Database (Denmark)

Thomsen, Sven Creutz

as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

Improved fuzzy PID controller design using predictive functional control structure.

Science.gov (United States)

Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

2017-11-01

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Linear regression and the normality assumption.

Science.gov (United States)

Schmidt, Amand F; Finan, Chris

2017-12-16

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

Science.gov (United States)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method

Symbolic regression via genetic programming for data driven derivation of confinement scaling laws without any assumption on their mathematical form

International Nuclear Information System (INIS)

Murari, A; Peluso, E; Gelfusa, M; Lupelli, I; Lungaroni, M; Gaudio, P

2015-01-01

Many measurements are required to control thermonuclear plasmas and to fully exploit them scientifically. In the last years JET has shown the potential to generate about 50 GB of data per shot. These amounts of data require more sophisticated data analysis methodologies to perform correct inference and various techniques have been recently developed in this respect. The present paper covers a new methodology to extract mathematical models directly from the data without any a priori assumption about their expression. The approach, based on symbolic regression via genetic programming, is exemplified using the data of the International Tokamak Physics Activity database for the energy confinement time. The best obtained scaling laws are not in power law form and suggest a revisiting of the extrapolation to ITER. Indeed the best non-power law scalings predict confinement times in ITER approximately between 2 and 3 s. On the other hand, more comprehensive and better databases are required to fully profit from the power of these new methods and to discriminate between the hundreds of thousands of models that they can generate. (paper)

Non-Fourier based thermal-mechanical tissue damage prediction for thermal ablation.

Science.gov (United States)

Li, Xin; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-01-02

Prediction of tissue damage under thermal loads plays important role for thermal ablation planning. A new methodology is presented in this paper by combing non-Fourier bio-heat transfer, constitutive elastic mechanics as well as non-rigid motion of dynamics to predict and analyze thermal distribution, thermal-induced mechanical deformation and thermal-mechanical damage of soft tissues under thermal loads. Simulations and comparison analysis demonstrate that the proposed methodology based on the non-Fourier bio-heat transfer can account for the thermal-induced mechanical behaviors of soft tissues and predict tissue thermal damage more accurately than classical Fourier bio-heat transfer based model.

Non-destructive controls

International Nuclear Information System (INIS)

Nouvet, A.

1978-01-01

The non-destructive controls permit, while respecting their integrity, the direct and individual examination of parts or complete objects as they are manufactured, as well as to follow the evolution of their eventual defects while in operation. The choice of control methods depends on the manufacturing process and shapes of parts, on the physical properties of their components as well as the nature, position and size of the defects which are likely to be detected. Whether it is a question of controls by means of ionizing radiation, flux of neutrons, ultrasons, acoustic source, sweating, magnetoscopy. Foucault currents, thermography, detection of leaks or non-destructive metallography, each has a limited field of application such that they are less competitive than complementary [fr

Hybrid Predictive Control for Dynamic Transport Problems

CERN Document Server

Núñez, Alfredo A; Cortés, Cristián E

2013-01-01

Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) ...

Model predictive control classical, robust and stochastic

CERN Document Server

Kouvaritakis, Basil

2016-01-01

For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

Planning the unknown: the simultaneity of predictive and non-predictive entrepreneurial strategies

NARCIS (Netherlands)

Kraaijenbrink, Jeroen; Ratinho, Tiago; Groen, Arend J.

2012-01-01

Two distinct approaches have emerged to categorize entrepreneurial strategies. While some argue that planning is beneficial for entrepreneurs, a growing body of literature argues that non-predictive strategies can also lead to successful outcomes. The effectuation framework gained attention and it

Structural Dynamic Analyses And Test Predictions For Spacecraft Structures With Non-Linearities

Science.gov (United States)

Vergniaud, Jean-Baptiste; Soula, Laurent; Newerla, Alfred

2012-07-01

The overall objective of the mechanical development and verification process is to ensure that the spacecraft structure is able to sustain the mechanical environments encountered during launch. In general the spacecraft structures are a-priori assumed to behave linear, i.e. the responses to a static load or dynamic excitation, respectively, will increase or decrease proportionally to the amplitude of the load or excitation induced. However, past experiences have shown that various non-linearities might exist in spacecraft structures and the consequences of their dynamic effects can significantly affect the development and verification process. Current processes are mainly adapted to linear spacecraft structure behaviour. No clear rules exist for dealing with major structure non-linearities. They are handled outside the process by individual analysis and margin policy, and analyses after tests to justify the CLA coverage. Non-linearities can primarily affect the current spacecraft development and verification process on two aspects. Prediction of flights loads by launcher/satellite coupled loads analyses (CLA): only linear satellite models are delivered for performing CLA and no well-established rules exist how to properly linearize a model when non- linearities are present. The potential impact of the linearization on the results of the CLA has not yet been properly analyzed. There are thus difficulties to assess that CLA results will cover actual flight levels. Management of satellite verification tests: the CLA results generated with a linear satellite FEM are assumed flight representative. If the internal non- linearities are present in the tested satellite then there might be difficulties to determine which input level must be passed to cover satellite internal loads. The non-linear behaviour can also disturb the shaker control, putting the satellite at risk by potentially imposing too high levels. This paper presents the results of a test campaign performed in

Bayou Corne sinkhole : control measurements of State Highway 70 in Assumption Parish, Louisiana.

Science.gov (United States)

2014-01-01

This project measured and assessed the surface stability of the portion of LA Highway 70 that is : potentially vulnerable to the Assumption Parish sinkhole. Using Global Positioning Systems (GPS) : enhanced by a real-time network (RTN) of continuousl...

Quasi-experimental study designs series-paper 7: assessing the assumptions.

Science.gov (United States)

Bärnighausen, Till; Oldenburg, Catherine; Tugwell, Peter; Bommer, Christian; Ebert, Cara; Barreto, Mauricio; Djimeu, Eric; Haber, Noah; Waddington, Hugh; Rockers, Peter; Sianesi, Barbara; Bor, Jacob; Fink, Günther; Valentine, Jeffrey; Tanner, Jeffrey; Stanley, Tom; Sierra, Eduardo; Tchetgen, Eric Tchetgen; Atun, Rifat; Vollmer, Sebastian

2017-09-01

Quasi-experimental designs are gaining popularity in epidemiology and health systems research-in particular for the evaluation of health care practice, programs, and policy-because they allow strong causal inferences without randomized controlled experiments. We describe the concepts underlying five important quasi-experimental designs: Instrumental Variables, Regression Discontinuity, Interrupted Time Series, Fixed Effects, and Difference-in-Differences designs. We illustrate each of the designs with an example from health research. We then describe the assumptions required for each of the designs to ensure valid causal inference and discuss the tests available to examine the assumptions. Copyright © 2017 Elsevier Inc. All rights reserved.

Spatiotemporal property and predictability of large-scale human mobility

Science.gov (United States)

Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

2018-04-01

Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

Reflections on assumption of energetic politics. Viewpoint of a sceptial observer

International Nuclear Information System (INIS)

Taczanowski, S.; Pohorecki, W.

2000-01-01

The Polish assumptions of energetic politics up to 2020 have been critically assessed. Energy sources availability as well as predicted fuel prices have been discussed for interesting period. Fossil fuels and uranium have been taken into account. On the presented basis it has been concluded that rejection the nuclear option in Poland for energetics development plans up to 2020 seems to be a serious mistake

Predictive Modelling Risk Calculators and the Non Dialysis Pathway.

Science.gov (United States)

Robins, Jennifer; Katz, Ivor

2013-04-16

This guideline will review the current prediction models and survival/mortality scores available for decision making in patients with advanced kidney disease who are being considered for a non-dialysis treatment pathway. Risk prediction is gaining increasing attention with emerging literature suggesting improved patient outcomes through individualised risk prediction (1). Predictive models help inform the nephrologist and the renal palliative care specialists in their discussions with patients and families about suitability or otherwise of dialysis. Clinical decision making in the care of end stage kidney disease (ESKD) patients on a non-dialysis treatment pathway is currently governed by several observational trials (3). Despite the paucity of evidence based medicine in this field, it is becoming evident that the survival advantages associated with renal replacement therapy in these often elderly patients with multiple co-morbidities and limited functional status may be negated by loss of quality of life (7) (6), further functional decline (5, 8), increased complications and hospitalisations. This article is protected by copyright. All rights reserved.

Non-Compliance and Follow-Up in Swedish Official and Private Animal Welfare Control of Dairy Cows

Directory of Open Access Journals (Sweden)

Frida Lundmark Hedman

2018-05-01

Full Text Available Farmers often have to comply with several sets of animal welfare regulations, since private standards have been developed in addition to legislation. Using an epidemiological approach, we analysed protocols from animal welfare inspections carried out in Swedish dairy herds by the County Administrative Board (CAB; official control of legislation and by the dairy company Arla Foods (private control of Arlagården standard during 2010–2013 in the county of Västra Götaland. CAB and Arla inspections were not carried out simultaneously. We aimed to identify common non-compliances, quantify risk factors of non-compliance, and investigate if non-compliances were based on animal-, resource-, or management-based requirements, as well as determining the time period allowed for achieving compliance. Non-compliance was found in 58% of CAB cases, and 51% of Arla cases (each case comprising a sequence of one or several inspections. Dirty dairy cattle was one of the most frequent non-compliances in both control systems. However, the differences in control results were large, suggesting a difference in focus between the two systems. Tie-stall housing and winter season (Dec–Feb were common risk factors for non-compliance, and overall organic farms had a lower predicted number of non-compliances compared to conventional farms. The presence of both similarities and differences between the systems underlines the need for transparency, predictability, and clarity of inspections.

Model Predictive Control for Smart Energy Systems

DEFF Research Database (Denmark)

Halvgaard, Rasmus

pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

Validation of Individual Non-Linear Predictive Pharmacokinetic ...

African Journals Online (AJOL)

3Department of Veterinary Medicine, Faculty of Agriculture, University of Novi Sad, Novi Sad, Republic of Serbia ... Purpose: To evaluate the predictive performance of phenytoin multiple dosing non-linear pharmacokinetic ... status epilepticus affects an estimated 152,000 ..... causal factors, i.e., infection, inflammation, tissue.

Sensitivity Analysis Without Assumptions.

Science.gov (United States)

Ding, Peng; VanderWeele, Tyler J

2016-05-01

Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having an unmeasured confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. Without imposing any assumptions on the unmeasured confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.

Sensitivity of Rooftop PV Projections in the SunShot Vision Study to Market Assumptions

Energy Technology Data Exchange (ETDEWEB)

Drury, E.; Denholm, P.; Margolis, R.

2013-01-01

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The SolarDS model was used to simulate rooftop PV demand for this study, based on several PV market assumptions--future electricity rates, customer access to financing, and others--in addition to the SunShot PV price projections. This paper finds that modeled PV demand is highly sensitive to several non-price market assumptions, particularly PV financing parameters.

Residual Strength Prediction of Debond Damaged Sandwich Panels

DEFF Research Database (Denmark)

Berggreen, Carl Christian

followed by debond growth. The developed theoretical procedure is an extension of the Crack Surface Displacement method, here denoted the Crack Surface Displacement Extrapolation method. The method is first developed in 2D and then extended to 3D by use of a number of realistic assumptions...... for the considered configurations. Comparison of the theoretical predictions to two series of large-scale experiments with loadings (uniform and non-uniform in-plane compression) comparable with real life loading scenarios for sandwich ships shows that the model is indeed able to predict the failure modes...

Early Validation of Automation Plant Control Software using Simulation Based on Assumption Modeling and Validation Use Cases

Directory of Open Access Journals (Sweden)

Veronika Brandstetter

2015-10-01

Full Text Available In automation plants, technical processes must be conducted in a way that products, substances, or services are produced reliably, with sufficient quality and with minimal strain on resources. A key driver in conducting these processes is the automation plant’s control software, which controls the technical plant components and thereby affects the physical, chemical, and mechanical processes that take place in automation plants. To this end, the control software of an automation plant must adhere to strict process requirements arising from the technical processes, and from the physical plant design. Currently, the validation of the control software often starts late in the engineering process in many cases – once the automation plant is almost completely constructed. However, as widely acknowledged, the later the control software of the automation plant is validated, the higher the effort for correcting revealed defects is, which can lead to serious budget overruns and project delays. In this article we propose an approach that allows the early validation of automation control software against the technical plant processes and assumptions about the physical plant design by means of simulation. We demonstrate the application of our approach on the example of an actual plant project from the automation industry and present it’s technical implementation

Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting

Directory of Open Access Journals (Sweden)

Mayr Andreas

2012-01-01

Full Text Available Abstract Background The construction of prediction intervals (PIs for future body mass index (BMI values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. Methods We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. Results The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Conclusions Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.

Monitoring Assumptions in Assume-Guarantee Contracts

Directory of Open Access Journals (Sweden)

Oleg Sokolsky

2016-05-01

Full Text Available Pre-deployment verification of software components with respect to behavioral specifications in the assume-guarantee form does not, in general, guarantee absence of errors at run time. This is because assumptions about the environment cannot be discharged until the environment is fixed. An intuitive approach is to complement pre-deployment verification of guarantees, up to the assumptions, with post-deployment monitoring of environment behavior to check that the assumptions are satisfied at run time. Such a monitor is typically implemented by instrumenting the application code of the component. An additional challenge for the monitoring step is that environment behaviors are typically obtained through an I/O library, which may alter the component's view of the input format. This transformation requires us to introduce a second pre-deployment verification step to ensure that alarms raised by the monitor would indeed correspond to violations of the environment assumptions. In this paper, we describe an approach for constructing monitors and verifying them against the component assumption. We also discuss limitations of instrumentation-based monitoring and potential ways to overcome it.

Composite control for raymond mill based on model predictive control and disturbance observer

Directory of Open Access Journals (Sweden)

Dan Niu

2016-03-01

Full Text Available In the raymond mill grinding process, precise control of operating load is vital for the high product quality. However, strong external disturbances, such as variations of ore size and ore hardness, usually cause great performance degradation. It is not easy to control the current of raymond mill constant. Several control strategies have been proposed. However, most of them (such as proportional–integral–derivative and model predictive control reject disturbances just through feedback regulation, which may lead to poor control performance in the presence of strong disturbances. For improving disturbance rejection, a control method based on model predictive control and disturbance observer is put forward in this article. The scheme employs disturbance observer as feedforward compensation and model predictive control controller as feedback regulation. The test results illustrate that compared with model predictive control method, the proposed disturbance observer–model predictive control method can obtain significant superiority in disturbance rejection, such as shorter settling time and smaller peak overshoot under strong disturbances.

Model Predictive Vibration Control Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

CERN Document Server

Takács, Gergely

2012-01-01

Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: ·         the implementation of ...

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

Science.gov (United States)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

Elevated Ictal Brain Network Ictogenicity Enables Prediction of Optimal Seizure Control

Directory of Open Access Journals (Sweden)

Marinho A. Lopes

2018-03-01

Full Text Available Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI, which was demonstrated to have the capability of differentiating between functional connectivity (FC of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG. This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

Science.gov (United States)

Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

How Symmetrical Assumptions Advance Strategic Management Research

DEFF Research Database (Denmark)

Foss, Nicolai Juul; Hallberg, Hallberg

2014-01-01

We develop the case for symmetrical assumptions in strategic management theory. Assumptional symmetry obtains when assumptions made about certain actors and their interactions in one of the application domains of a theory are also made about this set of actors and their interactions in other...... application domains of the theory. We argue that assumptional symmetry leads to theoretical advancement by promoting the development of theory with greater falsifiability and stronger ontological grounding. Thus, strategic management theory may be advanced by systematically searching for asymmetrical...

Assumption-versus data-based approaches to summarizing species' ranges.

Science.gov (United States)

Peterson, A Townsend; Navarro-Sigüenza, Adolfo G; Gordillo, Alejandro

2018-06-01

For conservation decision making, species' geographic distributions are mapped using various approaches. Some such efforts have downscaled versions of coarse-resolution extent-of-occurrence maps to fine resolutions for conservation planning. We examined the quality of the extent-of-occurrence maps as range summaries and the utility of refining those maps into fine-resolution distributional hypotheses. Extent-of-occurrence maps tend to be overly simple, omit many known and well-documented populations, and likely frequently include many areas not holding populations. Refinement steps involve typological assumptions about habitat preferences and elevational ranges of species, which can introduce substantial error in estimates of species' true areas of distribution. However, no model-evaluation steps are taken to assess the predictive ability of these models, so model inaccuracies are not noticed. Whereas range summaries derived by these methods may be useful in coarse-grained, global-extent studies, their continued use in on-the-ground conservation applications at fine spatial resolutions is not advisable in light of reliance on assumptions, lack of real spatial resolution, and lack of testing. In contrast, data-driven techniques that integrate primary data on biodiversity occurrence with remotely sensed data that summarize environmental dimensions (i.e., ecological niche modeling or species distribution modeling) offer data-driven solutions based on a minimum of assumptions that can be evaluated and validated quantitatively to offer a well-founded, widely accepted method for summarizing species' distributional patterns for conservation applications. © 2016 Society for Conservation Biology.

More electric aircraft starter-generator system with utilization of hybrid modulated model predictive control

OpenAIRE

Yoeh, Seang Shen; Yang, Tao; Tarisciotti, Luca; Hill, Christopher Ian; Bozhko, Serhiy

2016-01-01

The current trend for future aircraft is the adoption of the More Electric Aircraft (MEA) concept. The electrical based starter-generator (S/G) system is one of the core ideas from the MEA concept. The PI based control scheme has been investigated in various papers for the permanent magnet based S/G system. Different control schemes are to be considered to improve the control performance of the S/G system. A type of non-linear control called Model Predictive Control (MPC) is considered for it...

Intelligent Predictive Control of Nonlienar Processes Using

DEFF Research Database (Denmark)

Nørgård, Peter Magnus; Sørensen, Paul Haase; Poulsen, Niels Kjølstad

1996-01-01

This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has...... frequently been discussed in the neural network community. This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting...... linear models from a nonlinear neural network and using them in designing the control system. The performance of the controller is demonstrated in a simulation study of a pneumatic servo system...

System Identification, Prediction, Simulation and Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1997-01-01

a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

Obstacle avoidance handling and mixed integer predictive control for space robots

Science.gov (United States)

Zong, Lijun; Luo, Jianjun; Wang, Mingming; Yuan, Jianping

2018-04-01

This paper presents a novel obstacle avoidance constraint and a mixed integer predictive control (MIPC) method for space robots avoiding obstacles and satisfying physical limits during performing tasks. Firstly, a novel kind of obstacle avoidance constraint of space robots, which needs the assumption that the manipulator links and the obstacles can be represented by convex bodies, is proposed by limiting the relative velocity between two closest points which are on the manipulator and the obstacle, respectively. Furthermore, the logical variables are introduced into the obstacle avoidance constraint, which have realized the constraint form is automatically changed to satisfy different obstacle avoidance requirements in different distance intervals between the space robot and the obstacle. Afterwards, the obstacle avoidance constraint and other system physical limits, such as joint angle ranges, the amplitude boundaries of joint velocities and joint torques, are described as inequality constraints of a quadratic programming (QP) problem by using the model predictive control (MPC) method. To guarantee the feasibility of the obtained multi-constraint QP problem, the constraints are treated as soft constraints and assigned levels of priority based on the propositional logic theory, which can realize that the constraints with lower priorities are always firstly violated to recover the feasibility of the QP problem. Since the logical variables have been introduced, the optimization problem including obstacle avoidance and system physical limits as prioritized inequality constraints is termed as MIPC method of space robots, and its computational complexity as well as possible strategies for reducing calculation amount are analyzed. Simulations of the space robot unfolding its manipulator and tracking the end-effector's desired trajectories with the existence of obstacles and physical limits are presented to demonstrate the effectiveness of the proposed obstacle avoidance

Model predictive control technologies for efficient and flexible power consumption in refrigeration systems

DEFF Research Database (Denmark)

Hovgaard, Tobias Gybel; Larsen, Lars F. S.; Edlund, Kristian

2012-01-01

. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we...... explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial...... of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller....

Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Directory of Open Access Journals (Sweden)

Jun Zhang

2017-06-01

Full Text Available Designing robust control for hypersonic vehicles in reentry is difficult, due to the features of the vehicles including strong coupling, non-linearity, and multiple constraints. This paper proposed a characteristic model-based robust model predictive control (MPC for hypersonic vehicles with reentry constraints. First, the hypersonic vehicle is modeled by a characteristic model composed of a linear time-varying system and a lumped disturbance. Then, the identification data are regenerated by the accumulative sum idea in the gray theory, which weakens effects of the random noises and strengthens regularity of the identification data. Based on the regenerated data, the time-varying parameters and the disturbance are online estimated according to the gray identification. At last, the mixed H2/H∞ robust predictive control law is proposed based on linear matrix inequalities (LMIs and receding horizon optimization techniques. Using active tackling system constraints of MPC, the input and state constraints are satisfied in the closed-loop control system. The validity of the proposed control is verified theoretically according to Lyapunov theory and illustrated by simulation results.

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

Science.gov (United States)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is

Model Predictive Control of Grid Connected Modular Multilevel Converter for Integration of Photovoltaic Power Systems

DEFF Research Database (Denmark)

Hajizadeh, Amin; Shahirinia, Amir

2017-01-01

Investigation of an advanced control structure for integration of Photovoltaic Power Systems through Grid Connected-Modular Multilevel Converter (GC-MMC) is proposed in this paper. To achieve this goal, a non-linear model of MMC regarding considering of negative and positive sequence components has...... been presented. Then, due to existence of unbalance voltage faults in distribution grid, non-linarites and uncertainties in model, model predictive controller which is developed for GC-MMC. They are implemented based upon positive and negative components of voltage and current to mitigate the power...

Bootstrap prediction and Bayesian prediction under misspecified models

OpenAIRE

Fushiki, Tadayoshi

2005-01-01

We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

MOTORCYCLE CRASH PREDICTION MODEL FOR NON-SIGNALIZED INTERSECTIONS

Directory of Open Access Journals (Sweden)

S. HARNEN

2003-01-01

Full Text Available This paper attempts to develop a prediction model for motorcycle crashes at non-signalized intersections on urban roads in Malaysia. The Generalized Linear Modeling approach was used to develop the model. The final model revealed that an increase in motorcycle and non-motorcycle flows entering an intersection is associated with an increase in motorcycle crashes. Non-motorcycle flow on major road had the greatest effect on the probability of motorcycle crashes. Approach speed, lane width, number of lanes, shoulder width and land use were also found to be significant in explaining motorcycle crashes. The model should assist traffic engineers to decide the need for appropriate intersection treatment that specifically designed for non-exclusive motorcycle lane facilities.

System reliability prediction using data from non-identical environments

International Nuclear Information System (INIS)

Bergman, B.; Ringi, M.

1997-01-01

Since information changes one's mind and probability assessments reflect one's degree of beliefs, a reliability prediction model should enclose all relevant information. Almost always ignored in existing reliability models is the dependence on component life lengths, induced by a common but unknown environment. Furthermore, existing models seldom permit learning from components' performance in similar systems, under the knowledge of non-identical operating environments. In an earlier paper by the present authors the first type of aspects were taken into account and in this paper that model is generalised so that failure data generated from several similar systems in non-identical environments may be used for the prediction of any similar system in its specific environment

Wrong assumptions in the financial crisis

NARCIS (Netherlands)

Aalbers, M.B.

2009-01-01

Purpose - The purpose of this paper is to show how some of the assumptions about the current financial crisis are wrong because they misunderstand what takes place in the mortgage market. Design/methodology/approach - The paper discusses four wrong assumptions: one related to regulation, one to

Modeling and Control of CSTR using Model based Neural Network Predictive Control

OpenAIRE

Shrivastava, Piyush

2012-01-01

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

[Predictive ocular motor control in Parkinson's disease].

Science.gov (United States)

Ying, Li; Liu, Zhen-Guo; Chen, Wei; Gan, Jing; Wang, Wen-An

2008-02-19

To investigate the changes of predictive ocular motor function in the patients with Parkinson's disease (PD), and to discuss its clinical value. Videonystagmography (VNG) was used to examine 24 patients with idiopathic Parkinson's disease, 15 males and 9 females, aged 61 +/- 6 (50-69), and 24 sex and age-matched healthy control subjects on random ocular saccade (with the target moving at random intervals to random positions) and predictive ocular saccade (with the 1.25-second light target moving 10 degrees right or left from the center). In the random ocular saccade program, the latency of saccade of the PD patients was 284 ms +/- 58 ms, significantly longer than that of the healthy controls (236 ms +/- 37 ms, P = 0.003). In the predictive ocular saccade pattern, the latency of saccades the PD patients was 150 ms +/- 138 ms, significantly longer than that of the healthy controls (59 ms +/- 102 ms, P = 0.002). The appearance rate of predictive saccades (with the latency of saccade <80 ms) in the PD group was 21%, significantly lower than that in the control group (31%, P = 0.003). There is dysfunction of predictive ocular motor control in the PD patients, and the cognitive function may be impaired at the early stage of PD.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

Directory of Open Access Journals (Sweden)

Michael F Sloma

2017-11-01

Full Text Available Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

Science.gov (United States)

Sloma, Michael F; Mathews, David H

2017-11-01

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

An online re-linearization scheme suited for Model Predictive and Linear Quadratic Control

DEFF Research Database (Denmark)

Henriksen, Lars Christian; Poulsen, Niels Kjølstad

This technical note documents the equations for primal-dual interior-point quadratic programming problem solver used for MPC. The algorithm exploits the special structure of the MPC problem and is able to reduce the computational burden such that the computational burden scales with prediction...... horizon length in a linear way rather than cubic, which would be the case if the structure was not exploited. It is also shown how models used for design of model-based controllers, e.g. linear quadratic and model predictive, can be linearized both at equilibrium and non-equilibrium points, making...

Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

Directory of Open Access Journals (Sweden)

Wenbiao Li

2016-04-01

Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

Non-Compliance and Follow-Up in Swedish Official and Private Animal Welfare Control of Dairy Cows

Science.gov (United States)

Hultgren, Jan; Röcklinsberg, Helena; Wahlberg, Birgitta

2018-01-01

Simple Summary In many cases, different animal welfare inspections are taking place at an animal farm over time, as the farmer has to comply with both the legislation and with various private standards. In this study, we compared official inspections carried out by CAB (the County Administrative Board, a governmental agency) with private inspections carried out by Arla Foods (a private company) on dairy farms in one Swedish county. For example, we looked at seasonal effects and compared the incidence of different non-compliances. This study shows that long time periods were sometimes allowed for correction, that follow-up systems are diverse, and that there were differences in the inspection result between CAB and Arla due to different focuses during the inspections. Dirty dairy cattle were, however, a common non-compliance found by both CAB and Arla. Tie-stall housing and winter season (Dec–Feb) were risk factors for non-compliance, while the risk was lower for both CAB and Arla to find non-compliances at organic farms compared to conventional farms. We conclude that the presence of both similarities and differences between different control systems underlines the need for transparency, predictability, and clarity of inspections. Abstract Farmers often have to comply with several sets of animal welfare regulations, since private standards have been developed in addition to legislation. Using an epidemiological approach, we analysed protocols from animal welfare inspections carried out in Swedish dairy herds by the County Administrative Board (CAB; official control of legislation) and by the dairy company Arla Foods (private control of Arlagården standard) during 2010–2013 in the county of Västra Götaland. CAB and Arla inspections were not carried out simultaneously. We aimed to identify common non-compliances, quantify risk factors of non-compliance, and investigate if non-compliances were based on animal-, resource-, or management-based requirements, as

Damage to the anterior arcuate fasciculus predicts non-fluent speech production in aphasia.

Science.gov (United States)

Fridriksson, Julius; Guo, Dazhou; Fillmore, Paul; Holland, Audrey; Rorden, Chris

2013-11-01

Non-fluent aphasia implies a relatively straightforward neurological condition characterized by limited speech output. However, it is an umbrella term for different underlying impairments affecting speech production. Several studies have sought the critical lesion location that gives rise to non-fluent aphasia. The results have been mixed but typically implicate anterior cortical regions such as Broca's area, the left anterior insula, and deep white matter regions. To provide a clearer picture of cortical damage in non-fluent aphasia, the current study examined brain damage that negatively influences speech fluency in patients with aphasia. It controlled for some basic speech and language comprehension factors in order to better isolate the contribution of different mechanisms to fluency, or its lack. Cortical damage was related to overall speech fluency, as estimated by clinical judgements using the Western Aphasia Battery speech fluency scale, diadochokinetic rate, rudimentary auditory language comprehension, and executive functioning (scores on a matrix reasoning test) in 64 patients with chronic left hemisphere stroke. A region of interest analysis that included brain regions typically implicated in speech and language processing revealed that non-fluency in aphasia is primarily predicted by damage to the anterior segment of the left arcuate fasciculus. An improved prediction model also included the left uncinate fasciculus, a white matter tract connecting the middle and anterior temporal lobe with frontal lobe regions, including the pars triangularis. Models that controlled for diadochokinetic rate, picture-word recognition, or executive functioning also revealed a strong relationship between anterior segment involvement and speech fluency. Whole brain analyses corroborated the findings from the region of interest analyses. An additional exploratory analysis revealed that involvement of the uncinate fasciculus adjudicated between Broca's and global aphasia

Robot trajectory tracking with self-tuning predicted control

Science.gov (United States)

Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

Breakdown of Hydrostatic Assumption in Tidal Channel with Scour Holes

Directory of Open Access Journals (Sweden)

Chunyan Li

2016-10-01

Full Text Available Hydrostatic condition is a common assumption in tidal and subtidal motions in oceans and estuaries.. Theories with this assumption have been largely successful. However, there is no definite criteria separating the hydrostatic from the non-hydrostatic regimes in real applications because real problems often times have multiple scales. With increased refinement of high resolution numerical models encompassing smaller and smaller spatial scales, the need for non-hydrostatic models is increasing. To evaluate the vertical motion over bathymetric changes in tidal channels and assess the validity of the hydrostatic approximation, we conducted observations using a vessel-based acoustic Doppler current profiler (ADCP. Observations were made along a straight channel 18 times over two scour holes of 25 m deep, separated by 330 m, in and out of an otherwise flat 8 m deep tidal pass leading to the Lake Pontchartrain over a time period of 8 hours covering part of the diurnal tidal cycle. Out of the 18 passages over the scour holes, 11 of them showed strong upwelling and downwelling which resulted in the breakdown of hydrostatic condition. The maximum observed vertical velocity was ~ 0.35 m/s, a high value in a tidal channel, and the estimated vertical acceleration reached a high value of 1.76×10-2 m/s2. Analysis demonstrated that the barotropic non-hydrostatic acceleration was dominant. The cause of the non-hydrostatic flow was the that over steep slopes. This demonstrates that in such a system, the bathymetric variation can lead to the breakdown of hydrostatic conditions. Models with hydrostatic restrictions will not be able to correctly capture the dynamics in such a system with significant bathymetric variations particularly during strong tidal currents.

Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption.

Science.gov (United States)

Nasejje, Justine B; Mwambi, Henry

2017-09-07

age of five in a household, number of births in the past 5 years, wealth index, total number of children ever born and the child's birth order. The results further indicated that the predictive performance for random survival forests built using covariates including those that violate the PH assumption was higher than that for random survival forests built using only covariates that satisfy the PH assumption. Random survival forests are appealing methods in analysing public health data to understand factors strongly associated with under-five child mortality rates especially in the presence of covariates that violate the proportional hazards assumption.

Prediction Error During Functional and Non-Functional Action Sequences

DEFF Research Database (Denmark)

Nielbo, Kristoffer Laigaard; Sørensen, Jesper

2013-01-01

recurrent networks were made and the results are presented in this article. The simulations show that non-functional action sequences do indeed increase prediction error, but that context representations, such as abstract goal information, can modulate the error signal considerably. It is also shown...... that the networks are sensitive to boundaries between sequences in both functional and non-functional actions....

PFP issues/assumptions development and management planning guide

International Nuclear Information System (INIS)

SINCLAIR, J.C.

1999-01-01

The PFP Issues/Assumptions Development and Management Planning Guide presents the strategy and process used for the identification, allocation, and maintenance of an Issues/Assumptions Management List for the Plutonium Finishing Plant (PFP) integrated project baseline. Revisions to this document will include, as attachments, the most recent version of the Issues/Assumptions Management List, both open and current issues/assumptions (Appendix A), and closed or historical issues/assumptions (Appendix B). This document is intended be a Project-owned management tool. As such, this document will periodically require revisions resulting from improvements of the information, processes, and techniques as now described. Revisions that suggest improved processes will only require PFP management approval

Predictive Variable Gain Iterative Learning Control for PMSM

Directory of Open Access Journals (Sweden)

Huimin Xu

2015-01-01

Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

Sensitivity of probabilistic MCO water content estimates to key assumptions

International Nuclear Information System (INIS)

DUNCAN, D.R.

1999-01-01

Sensitivity of probabilistic multi-canister overpack (MCO) water content estimates to key assumptions is evaluated with emphasis on the largest non-cladding film-contributors, water borne by particulates adhering to damage sites, and water borne by canister particulate. Calculations considered different choices of damage state degree of independence, different choices of percentile for reference high inputs, three types of input probability density function (pdfs): triangular, log-normal, and Weibull, and the number of scrap baskets in an MCO

Predictive Control of Networked Multiagent Systems via Cloud Computing.

Science.gov (United States)

Liu, Guo-Ping

2017-01-18

This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

The zero-sum assumption in neutral biodiversity theory

NARCIS (Netherlands)

Etienne, R.S.; Alonso, D.; McKane, A.J.

2007-01-01

The neutral theory of biodiversity as put forward by Hubbell in his 2001 monograph has received much criticism for its unrealistic simplifying assumptions. These are the assumptions of functional equivalence among different species (neutrality), the assumption of point mutation speciation, and the

A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors

NARCIS (Netherlands)

Schoups, G.; Vrugt, J.A.

2010-01-01

Estimation of parameter and predictive uncertainty of hydrologic models has traditionally relied on several simplifying assumptions. Residual errors are often assumed to be independent and to be adequately described by a Gaussian probability distribution with a mean of zero and a constant variance.

Towards New Probabilistic Assumptions in Business Intelligence

Directory of Open Access Journals (Sweden)

Schumann Andrew

2015-01-01

Full Text Available One of the main assumptions of mathematical tools in science is represented by the idea of measurability and additivity of reality. For discovering the physical universe additive measures such as mass, force, energy, temperature, etc. are used. Economics and conventional business intelligence try to continue this empiricist tradition and in statistical and econometric tools they appeal only to the measurable aspects of reality. However, a lot of important variables of economic systems cannot be observable and additive in principle. These variables can be called symbolic values or symbolic meanings and studied within symbolic interactionism, the theory developed since George Herbert Mead and Herbert Blumer. In statistical and econometric tools of business intelligence we accept only phenomena with causal connections measured by additive measures. In the paper we show that in the social world we deal with symbolic interactions which can be studied by non-additive labels (symbolic meanings or symbolic values. For accepting the variety of such phenomena we should avoid additivity of basic labels and construct a new probabilistic method in business intelligence based on non-Archimedean probabilities.

Challenging the assumptions for thermal sensation scales

DEFF Research Database (Denmark)

Schweiker, Marcel; Fuchs, Xaver; Becker, Susanne

2016-01-01

Scales are widely used to assess the personal experience of thermal conditions in built environments. Most commonly, thermal sensation is assessed, mainly to determine whether a particular thermal condition is comfortable for individuals. A seven-point thermal sensation scale has been used...... extensively, which is suitable for describing a one-dimensional relationship between physical parameters of indoor environments and subjective thermal sensation. However, human thermal comfort is not merely a physiological but also a psychological phenomenon. Thus, it should be investigated how scales for its...... assessment could benefit from a multidimensional conceptualization. The common assumptions related to the usage of thermal sensation scales are challenged, empirically supported by two analyses. These analyses show that the relationship between temperature and subjective thermal sensation is non...

Philosophy of Technology Assumptions in Educational Technology Leadership

Science.gov (United States)

Webster, Mark David

2017-01-01

A qualitative study using grounded theory methods was conducted to (a) examine what philosophy of technology assumptions are present in the thinking of K-12 technology leaders, (b) investigate how the assumptions may influence technology decision making, and (c) explore whether technological determinist assumptions are present. Subjects involved…

Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded Hybrid Microgrids

OpenAIRE

Yi, Zhehan; Babqi, Abdulrahman J.; Wang, Yishen; Shi, Di; Etemadi, Amir H.; Wang, Zhiwei; Huang, Bibin

2018-01-01

Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining t...

Wind turbine control with constraint handling: a model predictive control approach

DEFF Research Database (Denmark)

Henriksen, Lars Christian; Hansen, Morten Hartvig; Poulsen, Niels Kjølstad

2012-01-01

on model predictive control, a control method well suited for constraint handling. The performance of the presented controller during an extreme operating gust is compared to that of a proportional-integral controller with integrator anti-windup. Furthermore, the presented controller-s capability...

I Assumed You Knew: Teaching Assumptions as Co-Equal to Observations in Scientific Work

Science.gov (United States)

Horodyskyj, L.; Mead, C.; Anbar, A. D.

2016-12-01

Introductory science curricula typically begin with a lesson on the "nature of science". Usually this lesson is short, built with the assumption that students have picked up this information elsewhere and only a short review is necessary. However, when asked about the nature of science in our classes, student definitions were often confused, contradictory, or incomplete. A cursory review of how the nature of science is defined in a number of textbooks is similarly inconsistent and excessively loquacious. With such confusion both from the student and teacher perspective, it is no surprise that students walk away with significant misconceptions about the scientific endeavor, which they carry with them into public life. These misconceptions subsequently result in poor public policy and personal decisions on issues with scientific underpinnings. We will present a new way of teaching the nature of science at the introductory level that better represents what we actually do as scientists. Nature of science lessons often emphasize the importance of observations in scientific work. However, they rarely mention and often hide the importance of assumptions in interpreting those observations. Assumptions are co-equal to observations in building models, which are observation-assumption networks that can be used to make predictions about future observations. The confidence we place in these models depends on whether they are assumption-dominated (hypothesis) or observation-dominated (theory). By presenting and teaching science in this manner, we feel that students will better comprehend the scientific endeavor, since making observations and assumptions and building mental models is a natural human behavior. We will present a model for a science lab activity that can be taught using this approach.

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.

Directory of Open Access Journals (Sweden)

Anne Hsu

Full Text Available A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning

Science.gov (United States)

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning. PMID:27310576

On Practical tuning of Model Uncertainty in Wind Turbine Model Predictive Control

DEFF Research Database (Denmark)

Odgaard, Peter Fogh; Hovgaard, Tobias

2015-01-01

Model predictive control (MPC) has in previous works been applied on wind turbines with promising results. These results apply linear MPC, i.e., linear models linearized at different operational points depending on the wind speed. The linearized models are derived from a nonlinear first principles...... model of a wind turbine. In this paper, we investigate the impact of this approach on the performance of a wind turbine. In particular, we focus on the most non-linear operational ranges of a wind turbine. The MPC controller is designed for, tested, and evaluated at an industrial high fidelity wind...

Passive impedance-based second-order sliding mode control for non-linear teleoperators

Directory of Open Access Journals (Sweden)

Luis G García-Valdovinos

2017-02-01

Full Text Available Bilateral teleoperation systems have attracted significant attention in the last decade mainly because of technological advancements in both the communication channel and computers performance. In addition, non-linear multi-degree-of-freedom bilateral teleoperators along with state observers have become an open research area. In this article, a model-free exact differentiator is used to estimate the full state along with a chattering-free second-order sliding mode controller to guarantee a robust impedance tracking under both constant and an unknown time delay of non-linear multi-degree-of-freedom robots. The robustness of the proposed controller is improved by introducing a change of coordinates in terms of a new nominal reference similar to that used in adaptive control theory. Experimental results that validate the predicted behaviour are presented and discussed using a Phantom Premium 1.0 as the master robot and a Catalyst-5 virtual model as the slave robot. The dynamics of the Catalyst-5 system is solved online.

Non-"g" Residuals of the SAT and ACT Predict Specific Abilities

Science.gov (United States)

Coyle, Thomas R.; Purcell, Jason M.; Snyder, Anissa C.; Kochunov, Peter

2013-01-01

This research examined whether non-"g" residuals of the SAT and ACT subtests, obtained after removing g, predicted specific abilities. Non-"g" residuals of the verbal and math subtests of the SAT and ACT were correlated with academic (verbal and math) and non-academic abilities (speed and shop), both based on the Armed Services…

Predicting establishment of non-native fishes in Greece: identifying key features

Directory of Open Access Journals (Sweden)

Christos Gkenas

2015-11-01

Full Text Available Non-native fishes are known to cause economic damage to human society and are considered a major threat to biodiversity loss in freshwater ecosystems. The growing concern about these impacts has driven to an investigation of the biological traits that facilitate the establishment of non-native fish. However, invalid assessment in choosing the appropriate statistical model can lead researchers to ambiguous conclusions. Here, we present a comprehensive comparison of traditional and alternative statistical methods for predicting fish invasions using logistic regression, classification trees, multicorrespondence analysis and random forest analysis to determine characteristics of successful and failed non-native fishes in Hellenic Peninsula through establishment. We defined fifteen categorical predictor variables with biological relevance and measures of human interest. Our study showed that accuracy differed according to the model and the number of factors considered. Among all the models tested, random forest and logistic regression performed best, although all approaches predicted non-native fish establishment with moderate to excellent results. Detailed evaluation among the models corresponded with differences in variables importance, with three biological variables (parental care, distance from nearest native source and maximum size and two variables of human interest (prior invasion success and propagule pressure being important in predicting establishment. The analyzed statistical methods presented have a high predictive power and can be used as a risk assessment tool to prevent future freshwater fish invasions in this region with an imperiled fish fauna.

Closed-loop analysis and control of a non-inverting buck-boost converter

Science.gov (United States)

Chen, Zengshi; Hu, Jiangang; Gao, Wenzhong

2010-11-01

In this article, a cascade controller is designed and analysed for a non-inverting buck-boost converter. The fast inner current loop uses sliding mode control. The slow outer voltage loop uses the proportional-integral (PI) control. Stability analysis and selection of PI gains are based on the nonlinear closed-loop error dynamics incorporating both the inner and outer loop controllers. The closed-loop system is proven to have a nonminimum phase structure. The voltage transient due to step changes of input voltage or resistance is predictable. The operating range of the reference voltage is discussed. The controller is validated by a simulation circuit. The simulation results show that the reference output voltage is well-tracked under system uncertainties or disturbances, confirming the validity of the proposed controller.

Large-scale analyses of synonymous substitution rates can be sensitive to assumptions about the process of mutation.

Science.gov (United States)

Aris-Brosou, Stéphane; Bielawski, Joseph P

2006-08-15

A popular approach to examine the roles of mutation and selection in the evolution of genomes has been to consider the relationship between codon bias and synonymous rates of molecular evolution. A significant relationship between these two quantities is taken to indicate the action of weak selection on substitutions among synonymous codons. The neutral theory predicts that the rate of evolution is inversely related to the level of functional constraint. Therefore, selection against the use of non-preferred codons among those coding for the same amino acid should result in lower rates of synonymous substitution as compared with sites not subject to such selection pressures. However, reliably measuring the extent of such a relationship is problematic, as estimates of synonymous rates are sensitive to our assumptions about the process of molecular evolution. Previous studies showed the importance of accounting for unequal codon frequencies, in particular when synonymous codon usage is highly biased. Yet, unequal codon frequencies can be modeled in different ways, making different assumptions about the mutation process. Here we conduct a simulation study to evaluate two different ways of modeling uneven codon frequencies and show that both model parameterizations can have a dramatic impact on rate estimates and affect biological conclusions about genome evolution. We reanalyze three large data sets to demonstrate the relevance of our results to empirical data analysis.

Predictive torque and flux control of an induction machine drive ...

Indian Academy of Sciences (India)

Finite-state model predictive control; fuzzy decision making; multi-objective optimization; predictive torque control. Abstract. Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to ...

Rate-Based Model Predictive Control of Turbofan Engine Clearance

Science.gov (United States)

DeCastro, Jonathan A.

2006-01-01

An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

Domestic appliances energy optimization with model predictive control

International Nuclear Information System (INIS)

Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.

2017-01-01

Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new

Model predictive control for spacecraft rendezvous in elliptical orbit

Science.gov (United States)

Li, Peng; Zhu, Zheng H.

2018-05-01

This paper studies the control of spacecraft rendezvous with attitude stable or spinning targets in an elliptical orbit. The linearized Tschauner-Hempel equation is used to describe the motion of spacecraft and the problem is formulated by model predictive control. The control objective is to maximize control accuracy and smoothness simultaneously to avoid unexpected change or overshoot of trajectory for safe rendezvous. It is achieved by minimizing the weighted summations of control errors and increments. The effects of two sets of horizons (control and predictive horizons) in the model predictive control are examined in terms of fuel consumption, rendezvous time and computational effort. The numerical results show the proposed control strategy is effective.

The relevance of ''theory rich'' bridge assumptions

NARCIS (Netherlands)

Lindenberg, S

1996-01-01

Actor models are increasingly being used as a form of theory building in sociology because they can better represent the caul mechanisms that connect macro variables. However, actor models need additional assumptions, especially so-called bridge assumptions, for filling in the relatively empty

Bayou Corne sinkhole : control measurements of State Highway 70 in Assumption Parish, Louisiana, tech summary.

Science.gov (United States)

2014-01-01

The sinkhole located in Assumption Parish, Louisiana, threatens the stability of Highway 70, a state maintained route. In order to : mitigate the potential damaging e ects of the sinkhole on this infrastructure, the Louisiana Department of Transpo...

“Marginal land” for energy crops: Exploring definitions and embedded assumptions

International Nuclear Information System (INIS)

Shortall, O.K.

2013-01-01

The idea of using less productive or “marginal land” for energy crops is promoted as a way to overcome the previous land use controversies faced by biofuels. It is argued that marginal land use would not compete with food production, is widely available and would incur fewer environmental impacts. This term is notoriously vague however, as are the details of how marginal land use for energy crops would work in practice. This paper explores definitions of the term “marginal land” in academic, consultancy, NGO, government and industry documents in the UK. It identifies three separate definitions of the term: land unsuitable for food production; ambiguous lower quality land; and economically marginal land. It probes these definitions further by exploring the technical, normative and political assumptions embedded within them. It finds that the first two definitions are normatively motivated: this land should be used to overcome controversies and the latter definition is predictive: this land is likely to be used. It is important that the different advantages, disadvantages and implications of the definitions are spelled out so definitions are not conflated to create unrealistic expectations about the role of marginal land in overcoming biofuels land use controversies. -- Highlights: •Qualitative methods were used to explore definitions of the term “marginal land”. •Three definitions were identified. •Two definitions focus on overcoming biomass land use controversies. •One definition predicts what land will be used for growing biomass. •Definitions contain problematic assumptions

Applying model predictive control to power system frequency control

OpenAIRE

Ersdal, AM; Imsland, L; Cecilio, IM; Fabozzi, D; Thornhill, NF

2013-01-01

16.07.14 KB Ok to add accepted version to Spiral Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) cont...

[Risk factors and clinical prediction of shoulder dystocia in non-macrosomia].

Science.gov (United States)

Li, Na; Li, Qiuling; Chang, Liang; Liu, Caixia

2015-01-01

To investigate the risk factors, clinical prediction and intrapartum management of shoulder dystocia in non-macrosomia. Totally 7 811 cases of vaginal delivery were retrospectively reviewed from Juanary 2009 to December 2013 in Shengjing Hospital. Shoulder dystocia was found in 11 cases (0.14% , 11/7 811), including 1 case of macrosomia and 10 cases of non-macrosomia (shoulder dystocia group). Each non-macrosomia shoulder dystocia case was matched with 10 cases of normal delivery in the same week, which were selected randomly as the control group. The tendency and risk factors of shoulder dystocia in macrosomia and non-macrosomia were analyzed, and the following data between the two groups were compared, including the height of uterus fundus, abdominal circumference of the pregnant woman, the increasing of body mass index (BMI), fetal biparietal diameter (BPD), fetal femur length (FL), duration of every stage of labor, birth weight of the newborn, head circumference and chest circumference of the newborn, Apgar score. (1) There were 213 macrosomias among the 7 811 vaginal deliveries, with the incidence of 2.73% (213/7 811). Only 1 shoulder dystocia was macrosomia (0.46%, 1/213); while the other 10 cases were non-macrosomia ( 0.13%, 10/7 598). (2) From 2009 to 2013, the macrosomia happened by 24 cases (2.32%, 24/1 034), 42 cases (3.61%, 42/1 164), 46 cases (2.60%, 46/1 772), 62 cases (3.01%, 62/2 060), 39 cases (2.19%, 39/1781), respectively. The incidence of macrosomia had no significant difference among these 5 years (P > 0.05). The shoulder dystosia occurrence without macrosia in these 5 years were 1 case ( 0.10% , 1/1 034), 3 cases (0.26%, 3/1 164), 2 cases ( 0.11%, 2/1 172), 2 cases (0.10%, 2/2 060), 2 cases ( 0.11%, 1/1 781), respectively. The incidence of shoulder dystocia without macrosomia had no significant difference among these 5 years (P > 0.05). (3) In the should dystocia group, 5 cases were complicated with premature rupture of membrane (5/10), 4

Texture analysis on MR images helps predicting non-response to NAC in breast cancer

International Nuclear Information System (INIS)

Michoux, N.; Van den Broeck, S.; Lacoste, L.; Fellah, L.; Galant, C.; Berlière, M.; Leconte, I.

2015-01-01

To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI. Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC. A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction. Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model

Assumptions and Policy Decisions for Vital Area Identification Analysis

Energy Technology Data Exchange (ETDEWEB)

Kim, Myungsu; Bae, Yeon-Kyoung; Lee, Youngseung [KHNP CRI, Daejeon (Korea, Republic of)

2016-10-15

U.S. Nuclear Regulatory Commission and IAEA guidance indicate that certain assumptions and policy questions should be addressed to a Vital Area Identification (VAI) process. Korea Hydro and Nuclear Power conducted a VAI based on current Design Basis Threat and engineering judgement to identify APR1400 vital areas. Some of the assumptions were inherited from Probabilistic Safety Assessment (PSA) as a sabotage logic model was based on PSA logic tree and equipment location data. This paper illustrates some important assumptions and policy decisions for APR1400 VAI analysis. Assumptions and policy decisions could be overlooked at the beginning stage of VAI, however they should be carefully reviewed and discussed among engineers, plant operators, and regulators. Through APR1400 VAI process, some of the policy concerns and assumptions for analysis were applied based on document research and expert panel discussions. It was also found that there are more assumptions to define for further studies for other types of nuclear power plants. One of the assumptions is mission time, which was inherited from PSA.

[A case-control study: association between oral hygiene and oral cancer in non-smoking and non-drinking women].

Science.gov (United States)

Wu, J F; Lin, L S; Chen, F; Liu, F Q; Huang, J F; Yan, L J; Liu, F P; Qiu, Y; Zheng, X Y; Cai, L; He, B C

2017-08-06

Objective: To evaluate the influence of oral hygiene on risk of oral cancer in non-smoking and non-drinking women. Methods: From September 2010 to February 2016, 242 non-smoking and non-drinking female patients with pathologically confirmed oral cancer were recruited in a hospital of Fuzhou, and another 856 non-smoking and non-drinking healthy women from health examination center in the same hospital were selected as control group. Five oral hygiene related variables including the frequency of teeth brushing, number of teeth lost, poor prosthesis, regular dental visits and recurrent dental ulceration were used to develop oral hygiene index model. Unconditional logistic regression was used to calculate odds ratios ( OR ) and 95% confidence intervals (95 %CI ). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the predictability of the oral hygiene index model. Multivariate logistic regression model was used to analyze the association between oral hygiene index and the incidence of oral cancer. Results: Teeth brushing oral cancer in non-smoking and non-drinking women, the corresponding OR (95 %CI ) were 1.50 (1.08-2.09), 1.81 (1.15-2.85), 1.51 (1.03-2.23), 1.73 (1.15-2.59), 7.30 (4.00-13.30), respectively. The AUROC of the oral hygiene index model was 0.705 9, indicating a high predictability. Multivariate logistic regression showed that the oral hygiene index was associated with risk of oral cancer. The higher the score, the higher risk was observed. The corresponding OR (95 %CI ) of oral hygiene index scores (score 1, score 2, score 3, score 4-5) were 2.51 (0.84-7.53), 4.68 (1.59-13.71), 6.47 (2.18-19.25), 15.29 (5.08-45.99), respectively. Conclusion: Oral hygiene could influence the incidence of oral cancer in non-smoking and non-drinking women, and oral hygiene index has a certain significance in assessing the combined effects of oral hygiene.

Prediction future asset price which is non-concordant with the historical distribution

Science.gov (United States)

Seong, Ng Yew; Hin, Pooi Ah

2015-12-01

This paper attempts to predict the major characteristics of the future asset price which is non-concordant with the distribution estimated from the price today and the prices on a large number of previous days. The three major characteristics of the i-th non-concordant asset price are the length of the interval between the occurrence time of the previous non-concordant asset price and that of the present non-concordant asset price, the indicator which denotes that the non-concordant price is extremely small or large by its values -1 and 1 respectively, and the degree of non-concordance given by the negative logarithm of the probability of the left tail or right tail of which one of the end points is given by the observed future price. The vector of three major characteristics of the next non-concordant price is modelled to be dependent on the vectors corresponding to the present and l - 1 previous non-concordant prices via a 3-dimensional conditional distribution which is derived from a 3(l + 1)-dimensional power-normal mixture distribution. The marginal distribution for each of the three major characteristics can then be derived from the conditional distribution. The mean of the j-th marginal distribution is an estimate of the value of the j-th characteristics of the next non-concordant price. Meanwhile, the 100(α/2) % and 100(1 - α/2) % points of the j-th marginal distribution can be used to form a prediction interval for the j-th characteristic of the next non-concordant price. The performance measures of the above estimates and prediction intervals indicate that the fitted conditional distribution is satisfactory. Thus the incorporation of the distribution of the characteristics of the next non-concordant price in the model for asset price has a good potential of yielding a more realistic model.

Support Vector Machine-Based Prediction of Local Tumor Control After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

International Nuclear Information System (INIS)

Klement, Rainer J.; Allgäuer, Michael; Appold, Steffen; Dieckmann, Karin; Ernst, Iris; Ganswindt, Ute; Holy, Richard; Nestle, Ursula; Nevinny-Stickel, Meinhard; Semrau, Sabine; Sterzing, Florian; Wittig, Andrea; Andratschke, Nicolaus; Guckenberger, Matthias

2014-01-01

Background: Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control. Methods and Materials: We investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performance by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection. Results: Local failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED ISO ) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED ISO and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED ISO , age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P<.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively. Conclusions: These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP modeling are

Non-destructive control of castings

International Nuclear Information System (INIS)

Boutault, J.; Mascre, C.

1978-01-01

The object of non-destructive control in foundries is to verify the metal structure, the absence of unacceptable discontinuity, total tightness, etc. This leads to a range of very varied controls according to the importance of the series, the quality level required by the specifications, the nature of the alloy. The originality of the solutions which are imperative for castings is shown through examples: casting of high quality complex forms in short series; very thick unit parts; very large series of parts requiring on efficient automation of non-destructive control. Lastly the publishing of testing methods and interpretating rules, which are the base of a friendly understanding between constructors and founders are recalled [fr

World assumptions, posttraumatic stress and quality of life after a natural disaster: A longitudinal study

Science.gov (United States)

2012-01-01

Background Changes in world assumptions are a fundamental concept within theories that explain posttraumatic stress disorder. The objective of the present study was to gain a greater understanding of how changes in world assumptions are related to quality of life and posttraumatic stress symptoms after a natural disaster. Methods A longitudinal study of 574 Norwegian adults who survived the Southeast Asian tsunami in 2004 was undertaken. Multilevel analyses were used to identify which factors at six months post-tsunami predicted quality of life and posttraumatic stress symptoms two years post-tsunami. Results Good quality of life and posttraumatic stress symptoms were negatively related. However, major differences in the predictors of these outcomes were found. Females reported significantly higher quality of life and more posttraumatic stress than men. The association between level of exposure to the tsunami and quality of life seemed to be mediated by posttraumatic stress. Negative perceived changes in the assumption “the world is just” were related to adverse outcome in both quality of life and posttraumatic stress. Positive perceived changes in the assumptions “life is meaningful” and “feeling that I am a valuable human” were associated with higher levels of quality of life but not with posttraumatic stress. Conclusions Quality of life and posttraumatic stress symptoms demonstrate differences in their etiology. World assumptions may be less specifically related to posttraumatic stress than has been postulated in some cognitive theories. PMID:22742447

Dynamics and control of quadcopter using linear model predictive control approach

Science.gov (United States)

Islam, M.; Okasha, M.; Idres, M. M.

2017-12-01

This paper investigates the dynamics and control of a quadcopter using the Model Predictive Control (MPC) approach. The dynamic model is of high fidelity and nonlinear, with six degrees of freedom that include disturbances and model uncertainties. The control approach is developed based on MPC to track different reference trajectories ranging from simple ones such as circular to complex helical trajectories. In this control technique, a linearized model is derived and the receding horizon method is applied to generate the optimal control sequence. Although MPC is computer expensive, it is highly effective to deal with the different types of nonlinearities and constraints such as actuators’ saturation and model uncertainties. The MPC parameters (control and prediction horizons) are selected by trial-and-error approach. Several simulation scenarios are performed to examine and evaluate the performance of the proposed control approach using MATLAB and Simulink environment. Simulation results show that this control approach is highly effective to track a given reference trajectory.

Improving LMA predictions with non standard interactions

CERN Document Server

Das, C R

2010-01-01

It has been known for some time that the well established LMA solution to the observed solar neutrino deficit fails to predict a flat energy spectrum for SuperKamiokande as opposed to what the data indicates. It also leads to a Chlorine rate which appears to be too high as compared to the data. We investigate the possible solution to these inconsistencies with non standard neutrino interactions, assuming that they come as extra contributions to the $\

Quitting smoking: The importance of non-smoker identity in predicting smoking behaviour and responses to a smoking ban.

Science.gov (United States)

Meijer, Eline; Gebhardt, Winifred A; Dijkstra, Arie; Willemsen, Marc C; Van Laar, Colette

2015-01-01

We examined how 'smoker' and 'non-smoker' self- and group-identities and socio-economic status (SES) may predict smoking behaviour and responses to antismoking measures (i.e., the Dutch smoking ban in hospitality venues). We validated a measure of responses to the smoking ban. Longitudinal online survey study with one-year follow-up (N = 623 at T1 in 2011; N = 188 at T2 in 2012) among daily smokers. Intention to quit, quit attempts and 'rejecting', 'victimizing', 'socially conscious smoking' and 'active quitting' responses to the smoking ban. Non-smoker identities are more important than smoker identities in predicting intention to quit, quit attempts and responses to the smoking ban, even when controlling for other important predictors such as nicotine dependence. Smokers with stronger non-smoker identities had stronger intentions to quit, were more likely to attempt to quit between measurements, and showed less negative and more positive responses to the smoking ban. The association between non-smoker self-identity and intention to quit was stronger among smokers with lower than higher SES. Antismoking measures might be more effective if they would focus also on the identity of smokers, and help smokers to increase identification with non-smoking and non-smokers.

The Preoperative Controlling Nutritional Status Score Predicts Survival After Curative Surgery in Patients with Pathological Stage I Non-small Cell Lung Cancer.

Science.gov (United States)

Shoji, Fumihiro; Haratake, Naoki; Akamine, Takaki; Takamori, Shinkichi; Katsura, Masakazu; Takada, Kazuki; Toyokawa, Gouji; Okamoto, Tatsuro; Maehara, Yoshihiko

2017-02-01

The prognostic Controlling Nutritional Status (CONUT) score is used to evaluate immuno-nutritional conditions and is a predictive factor of postoperative survival in patients with digestive tract cancer. We retrospectively analyzed clinicopathological features of patients with pathological stage I non-small cell lung cancer (NSCLC) to identify predictors or prognostic factors of postoperative survival and to investigate the role of preoperative CONUT score in predicting survival. We selected 138 consecutive patients with pathological stage I NSCLC treated from August 2005 to August 2010. We measured their preoperative CONUT score in uni- and multivariate Cox regression analyses of postoperative survival. A high CONUT score was positively associated with preoperative serum carcinoembryonic antigen level (p=0.0100) and postoperative recurrence (p=0.0767). In multivariate analysis, the preoperative CONUT score [relative risk (RR)=6.058; 95% confidence interval (CI)=1.068-113.941; p=0.0407), increasing age (RR=7.858; 95% CI=2.034-36.185; p=0.0029), and pleural invasion (RR=36.615; 95% CI=5.900-362.620; pcancer-specific survival (CS), and overall survival (OS), the group with high CONUT score had a significantly shorter RFS, CS, and OS than did the low-CONUT score group by log-rank test (p=0.0458, p=0.0104 and p=0.0096, respectively). The preoperative CONUT score is both a predictive and prognostic factor in patients with pathological stage I NSCLC. This immuno-nutritional score can indicate patients at high risk of postoperative recurrence and death. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Multi-model predictive control method for nuclear steam generator water level

International Nuclear Information System (INIS)

Hu Ke; Yuan Jingqi

2008-01-01

The dynamics of a nuclear steam generator (SG) is very different according to the power levels and changes as time goes on. Therefore, it is an intractable as well as challenging task to improve the water level control system of the SG. In this paper, a robust model predictive control (RMPC) method is developed for the level control problem. Based on a multi-model framework, a combination of a local nominal model with a polytopic uncertain linear parameter varying (LPV) model is built to approximate the system's non-linear behavior. The optimization problem solved here is based on a receding horizon scheme involving the linear matrix inequality (LMI) technique. Closed loop stability and constraints satisfaction in the entire operating range are guaranteed by the feasibility of the optimization problem. Finally, simulation results show the effectiveness and the good performance of the proposed method

Distributed automata in an assumption-commitment framework

Indian Academy of Sciences (India)

We propose a class of finite state systems of synchronizing distributed processes, where processes make assumptions at local states about the state of other processes in the system. This constrains the global states of the system to those where assumptions made by a process about another are compatible with the ...

Contemporary assumptions on human nature and work and approach to human potential managing

Directory of Open Access Journals (Sweden)

Vujić Dobrila

2006-01-01

Full Text Available A general problem of this research is to identify if there is a relationship between the assumption on human nature and work (Mcgregor, Argyris, Schein, Steers and Porter and a general organizational model preference, as well as a mechanism of human resource management? This research was carried out in 2005/2006. The sample consisted of 317 subjects (197 managers, 105 highly educated subordinates and 15 entrepreneurs in 7 big enterprises in a group of small business enterprises differentiating in terms of the entrepreneur’s structure and a type of activity. A general hypothesis "that assumptions on human nature and work are statistically significant in connection to the preference approach (models, of work motivation commitment", has been confirmed. A specific hypothesis have been also confirmed: ·The assumptions on a human as a rational economic being are statistically significant in correlation with only two mechanisms of traditional models, the mechanism of method work control and the working discipline mechanism. ·Statistically significant assumptions on a human as a social being are correlated with all mechanisms of engaging employees, which belong to the model of the human relations, except the mechanism introducing the adequate type of prizes for all employees independently of working results. ·The assumptions on a human as a creative being are statistically significant, positively correlating with preference of two mechanisms belonging to the human resource model by investing into education and training and making conditions for the application of knowledge and skills. The young with assumptions on a human as a creative being prefer much broader repertoire of mechanisms belonging to the human resources model from the remaining category of subjects in the pattern. The connection between the assumption on human nature and preference models of engaging appears especially in the sub-pattern of managers, in the category of young subjects

HYPROLOG: A New Logic Programming Language with Assumptions and Abduction

DEFF Research Database (Denmark)

Christiansen, Henning; Dahl, Veronica

2005-01-01

We present HYPROLOG, a novel integration of Prolog with assumptions and abduction which is implemented in and partly borrows syntax from Constraint Handling Rules (CHR) for integrity constraints. Assumptions are a mechanism inspired by linear logic and taken over from Assumption Grammars. The lan......We present HYPROLOG, a novel integration of Prolog with assumptions and abduction which is implemented in and partly borrows syntax from Constraint Handling Rules (CHR) for integrity constraints. Assumptions are a mechanism inspired by linear logic and taken over from Assumption Grammars....... The language shows a novel flexibility in the interaction between the different paradigms, including all additional built-in predicates and constraints solvers that may be available. Assumptions and abduction are especially useful for language processing, and we can show how HYPROLOG works seamlessly together...

The Impact of Modeling Assumptions in Galactic Chemical Evolution Models

Science.gov (United States)

Côté, Benoit; O'Shea, Brian W.; Ritter, Christian; Herwig, Falk; Venn, Kim A.

2017-02-01

We use the OMEGA galactic chemical evolution code to investigate how the assumptions used for the treatment of galactic inflows and outflows impact numerical predictions. The goal is to determine how our capacity to reproduce the chemical evolution trends of a galaxy is affected by the choice of implementation used to include those physical processes. In pursuit of this goal, we experiment with three different prescriptions for galactic inflows and outflows and use OMEGA within a Markov Chain Monte Carlo code to recover the set of input parameters that best reproduces the chemical evolution of nine elements in the dwarf spheroidal galaxy Sculptor. This provides a consistent framework for comparing the best-fit solutions generated by our different models. Despite their different degrees of intended physical realism, we found that all three prescriptions can reproduce in an almost identical way the stellar abundance trends observed in Sculptor. This result supports the similar conclusions originally claimed by Romano & Starkenburg for Sculptor. While the three models have the same capacity to fit the data, the best values recovered for the parameters controlling the number of SNe Ia and the strength of galactic outflows, are substantially different and in fact mutually exclusive from one model to another. For the purpose of understanding how a galaxy evolves, we conclude that only reproducing the evolution of a limited number of elements is insufficient and can lead to misleading conclusions. More elements or additional constraints such as the Galaxy’s star-formation efficiency and the gas fraction are needed in order to break the degeneracy between the different modeling assumptions. Our results show that the successes and failures of chemical evolution models are predominantly driven by the input stellar yields, rather than by the complexity of the Galaxy model itself. Simple models such as OMEGA are therefore sufficient to test and validate stellar yields. OMEGA

Non-invasive prediction of hematocrit levels by portable visible and near-infrared spectrophotometer.

Science.gov (United States)

Sakudo, Akikazu; Kato, Yukiko Hakariya; Kuratsune, Hirohiko; Ikuta, Kazuyoshi

2009-10-01

After blood donation, in some individuals having polycythemia, dehydration causes anemia. Although the hematocrit (Ht) level is closely related to anemia, the current method of measuring Ht is performed after blood drawing. Furthermore, the monitoring of Ht levels contributes to a healthy life. Therefore, a non-invasive test for Ht is warranted for the safe donation of blood and good quality of life. A non-invasive procedure for the prediction of hematocrit levels was developed on the basis of a chemometric analysis of visible and near-infrared (Vis-NIR) spectra of the thumbs using portable spectrophotometer. Transmittance spectra in the 600- to 1100-nm region from thumbs of Japanese volunteers were subjected to a partial least squares regression (PLSR) analysis and leave-out cross-validation to develop chemometric models for predicting Ht levels. Ht levels of masked samples predicted by this model from Vis-NIR spectra provided a coefficient of determination in prediction of 0.6349 with a standard error of prediction of 3.704% and a detection limit in prediction of 17.14%, indicating that the model is applicable for normal and abnormal value in Ht level. These results suggest portable Vis-NIR spectrophotometer to have potential for the non-invasive measurement of Ht levels with a combination of PLSR analysis.

Why are predictions of general relativity theory for gravitational effects non-unique?

International Nuclear Information System (INIS)

Loskutov, Yu.M.

1990-01-01

Reasons of non-uniqueness of predictions of the general relativity theory (GRT) for gravitational effects are analyzed in detail. To authors' opinion, the absence of comparison mechanism of curved and plane metrics is the reason of non-uniqueness

A generic Approach for Reliability Predictions considering non-uniformly Deterioration Behaviour

International Nuclear Information System (INIS)

Krause, Jakob; Kabitzsch, Klaus

2012-01-01

Predictive maintenance offers the possibility to prognosticate the remaining time until a maintenance action of a machine has to be scheduled. Unfortunately, current predictive maintenance solutions are only suitable for very specific use cases like reliability predictions based on vibration monitoring. Furthermore, they do not consider the fact that machines may deteriorate non-uniformly, depending on external influences (e.g., the work piece material in a milling machine or the changing fruit acid concentration in a bottling plant). In this paper two concepts for a generic predictive maintenance solution which also considers non-uniformly aging behaviour are introduced. The first concept is based on system models representing the health state of a technical system. As these models are usually statically (viz. without a timely dimension) their coefficients are determined periodically and the resulting time series is used as aging indicator. The second concept focuses on external influences (contexts) which change the behaviour of the previous mentioned aging indicators in order to increase the accuracy of reliability predictions. Therefore, context-depended time series models are determined and used to predict machine reliability. Both concepts were evaluated on data of an air ventilation system. Thereby, it could be shown that they are suitable to determine aging indicators in a generic way and to incorporate external influences in the reliability prediction. Through this, the quality of reliability predictions can be significantly increased. In reality this leads to a more accurate scheduling of maintenance actions. Furthermore, the generic character of the solutions makes the concepts suitable for a wide range of aging processes.

Basic concepts and assumptions behind the new ICRP recommendations

International Nuclear Information System (INIS)

Lindell, B.

1979-01-01

A review is given of some of the basic concepts and assumptions behind the current recommendations by the International Commission on Radiological Protection in ICRP Publications 26 and 28, which form the basis for the revision of the Basic Safety Standards jointly undertaken by IAEA, ILO, NEA and WHO. Special attention is given to the assumption of a linear, non-threshold dose-response relationship for stochastic radiation effects such as cancer and hereditary harm. The three basic principles of protection are discussed: justification of practice, optimization of protection and individual risk limitation. In the new ICRP recommendations particular emphasis is given to the principle of keeping all radiation doses as low as is reasonably achievable. A consequence of this is that the ICRP dose limits are now given as boundary conditions for the justification and optimization procedures rather than as values that should be used for purposes of planning and design. The fractional increase in total risk at various ages after continuous exposure near the dose limits is given as an illustration. The need for taking other sources, present and future, into account when applying the dose limits leads to the use of the commitment concept. This is briefly discussed as well as the new quantity, the effective dose equivalent, introduced by ICRP. (author)

Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

Science.gov (United States)

Nguyen, Nhan; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

2018-01-01

This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

Science.gov (United States)

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Validation of the mortality prediction equation for damage control ...

African Journals Online (AJOL)

, preoperative lowest pH and lowest core body temperature to derive an equation for the purpose of predicting mortality in damage control surgery. It was shown to reliably predict death despite damage control surgery. The equation derivation ...

FPGA implementation of optimal and approximate model predictive control for a buck-boost DC-DC converter

NARCIS (Netherlands)

Spinu, V.; Oliveri, A.; Lazar, M.; Storace, M.

2012-01-01

This paper proposes a method for FPGA implementation of explicit, piecewise af¿ne (PWA) model predictive control (MPC) laws for non-inverting buck-boost DC-DC converters. A novel approach to obtain a PWA model of the power converter is proposed and two explicit MPC laws are derived, i.e., one based

Letters: Milk and Mortality : Study used wrong assumption about galactose content of fermented dairy products

NARCIS (Netherlands)

Hettinga, K.A.

2014-01-01

Michaëlsson and colleagues’ proposed mechanism for the effect of milk intake on the risk of mortality and fractures is based on the assumption that fermented dairy products (which had the opposite effects to those of non-fermented milk) are free of galactose.1 For most fermented dairy products,

Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

KAUST Repository

Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed

2016-01-01

In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

KAUST Repository

Siddiqui, Bilal A.

2016-07-26

In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

Prediction of forces and moments for flight vehicle control effectors. Part 1: Validation of methods for predicting hypersonic vehicle controls forces and moments

Science.gov (United States)

Maughmer, Mark D.; Ozoroski, L.; Ozoroski, T.; Straussfogel, D.

1990-01-01

Many types of hypersonic aircraft configurations are currently being studied for feasibility of future development. Since the control of the hypersonic configurations throughout the speed range has a major impact on acceptable designs, it must be considered in the conceptual design stage. The ability of the aerodynamic analysis methods contained in an industry standard conceptual design system, APAS II, to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds is considered. Predicted control forces and moments generated by various control effectors are compared with previously published wind tunnel and flight test data for three configurations: the North American X-15, the Space Shuttle Orbiter, and a hypersonic research airplane concept. Qualitative summaries of the results are given for each longitudinal force and moment and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage. Results for most lateral/directional control derivatives are acceptable for conceptual design purposes; however, predictions at supersonic Mach numbers for the change in yawing moment due to aileron deflection and the change in rolling moment due to rudder deflection are found to be unacceptable. Including shielding effects in the analysis is shown to have little effect on lift and pitching moment predictions while improving drag predictions.

Bayou Corne Sinkhole: Control Measurements of State Highway 70 in Assumption Parish, Louisiana : Research Project Capsule

Science.gov (United States)

2012-09-01

The sinkhole located in northern Assumption Parish, Louisiana, threatens : the stability of Highway 70, a state-maintained route. In order to monitor : and mitigate potential damage eff ects on this infrastructure, the Louisiana : Department of Trans...

Occupancy estimation and the closure assumption

Science.gov (United States)

Rota, Christopher T.; Fletcher, Robert J.; Dorazio, Robert M.; Betts, Matthew G.

2009-01-01

1. Recent advances in occupancy estimation that adjust for imperfect detection have provided substantial improvements over traditional approaches and are receiving considerable use in applied ecology. To estimate and adjust for detectability, occupancy modelling requires multiple surveys at a site and requires the assumption of 'closure' between surveys, i.e. no changes in occupancy between surveys. Violations of this assumption could bias parameter estimates; however, little work has assessed model sensitivity to violations of this assumption or how commonly such violations occur in nature. 2. We apply a modelling procedure that can test for closure to two avian point-count data sets in Montana and New Hampshire, USA, that exemplify time-scales at which closure is often assumed. These data sets illustrate different sampling designs that allow testing for closure but are currently rarely employed in field investigations. Using a simulation study, we then evaluate the sensitivity of parameter estimates to changes in site occupancy and evaluate a power analysis developed for sampling designs that is aimed at limiting the likelihood of closure. 3. Application of our approach to point-count data indicates that habitats may frequently be open to changes in site occupancy at time-scales typical of many occupancy investigations, with 71% and 100% of species investigated in Montana and New Hampshire respectively, showing violation of closure across time periods of 3 weeks and 8 days respectively. 4. Simulations suggest that models assuming closure are sensitive to changes in occupancy. Power analyses further suggest that the modelling procedure we apply can effectively test for closure. 5. Synthesis and applications. Our demonstration that sites may be open to changes in site occupancy over time-scales typical of many occupancy investigations, combined with the sensitivity of models to violations of the closure assumption, highlights the importance of properly addressing

Fuzzy model predictive control algorithm applied in nuclear power plant

International Nuclear Information System (INIS)

Zuheir, Ahmad

2006-01-01

The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

Fault Tolerant Control Using Gaussian Processes and Model Predictive Control

Directory of Open Access Journals (Sweden)

Yang Xiaoke

2015-03-01

Full Text Available Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.

Model Predictive Control with Constraints of a Wind Turbine

DEFF Research Database (Denmark)

Henriksen, Lars Christian; Poulsen, Niels Kjølstad

2007-01-01

Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure a...... an efficient control of the wind turbine over the entire range of wind speeds. Both onshore and floating offshore wind turbines are tested with the controllers.......Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure...

Estimating disease prevalence from two-phase surveys with non-response at the second phase

Science.gov (United States)

Gao, Sujuan; Hui, Siu L.; Hall, Kathleen S.; Hendrie, Hugh C.

2010-01-01

SUMMARY In this paper we compare several methods for estimating population disease prevalence from data collected by two-phase sampling when there is non-response at the second phase. The traditional weighting type estimator requires the missing completely at random assumption and may yield biased estimates if the assumption does not hold. We review two approaches and propose one new approach to adjust for non-response assuming that the non-response depends on a set of covariates collected at the first phase: an adjusted weighting type estimator using estimated response probability from a response model; a modelling type estimator using predicted disease probability from a disease model; and a regression type estimator combining the adjusted weighting type estimator and the modelling type estimator. These estimators are illustrated using data from an Alzheimer’s disease study in two populations. Simulation results are presented to investigate the performances of the proposed estimators under various situations. PMID:10931514

Nonlinear predictive control in the LHC accelerator

CERN Document Server

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.

Generalized predictive control in the delta-domain

DEFF Research Database (Denmark)

Lauritsen, Morten Bach; Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad

1995-01-01

This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater...... than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers...

Contextuality under weak assumptions

International Nuclear Information System (INIS)

Simmons, Andrew W; Rudolph, Terry; Wallman, Joel J; Pashayan, Hakop; Bartlett, Stephen D

2017-01-01

The presence of contextuality in quantum theory was first highlighted by Bell, Kochen and Specker, who discovered that for quantum systems of three or more dimensions, measurements could not be viewed as deterministically revealing pre-existing properties of the system. More precisely, no model can assign deterministic outcomes to the projectors of a quantum measurement in a way that depends only on the projector and not the context (the full set of projectors) in which it appeared, despite the fact that the Born rule probabilities associated with projectors are independent of the context. A more general, operational definition of contextuality introduced by Spekkens, which we will term ‘probabilistic contextuality’, drops the assumption of determinism and allows for operations other than measurements to be considered contextual. Even two-dimensional quantum mechanics can be shown to be contextual under this generalised notion. Probabilistic noncontextuality represents the postulate that elements of an operational theory that cannot be distinguished from each other based on the statistics of arbitrarily many repeated experiments (they give rise to the same operational probabilities) are ontologically identical. In this paper, we introduce a framework that enables us to distinguish between different noncontextuality assumptions in terms of the relationships between the ontological representations of objects in the theory given a certain relation between their operational representations. This framework can be used to motivate and define a ‘possibilistic’ analogue, encapsulating the idea that elements of an operational theory that cannot be unambiguously distinguished operationally can also not be unambiguously distinguished ontologically. We then prove that possibilistic noncontextuality is equivalent to an alternative notion of noncontextuality proposed by Hardy. Finally, we demonstrate that these weaker noncontextuality assumptions are sufficient to prove

A History of Non-Parental Care in Childhood Predicts More Positive Adult Attitudes towards Non-Parental Care and Maternal Employment

Science.gov (United States)

Shpancer, Noam; Schweitzer, Stefanie N.

2018-01-01

Data were collected over a 15-year span from three comparable cohorts of students at a Midwestern university about their childcare histories and current attitudes towards non-parental childcare and maternal employment. Across cohorts, a history of non-parental childcare predicted adult attitudes towards non-parental childcare and maternal…

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer.

Science.gov (United States)

Mino-Kenudson, Mari

2017-10-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC.

Investigation of assumptions underlying current safety guidelines on EM-induced nerve stimulation

Science.gov (United States)

Neufeld, Esra; Vogiatzis Oikonomidis, Ioannis; Iacono, Maria Ida; Angelone, Leonardo M.; Kainz, Wolfgang; Kuster, Niels

2016-06-01

An intricate network of a variety of nerves is embedded within the complex anatomy of the human body. Although nerves are shielded from unwanted excitation, they can still be stimulated by external electromagnetic sources that induce strongly non-uniform field distributions. Current exposure safety standards designed to limit unwanted nerve stimulation are based on a series of explicit and implicit assumptions and simplifications. This paper demonstrates the applicability of functionalized anatomical phantoms with integrated coupled electromagnetic and neuronal dynamics solvers for investigating the impact of magnetic resonance exposure on nerve excitation within the full complexity of the human anatomy. The impact of neuronal dynamics models, temperature and local hot-spots, nerve trajectory and potential smoothing, anatomical inhomogeneity, and pulse duration on nerve stimulation was evaluated. As a result, multiple assumptions underlying current safety standards are questioned. It is demonstrated that coupled EM-neuronal dynamics modeling involving realistic anatomies is valuable to establish conservative safety criteria.

Predictive models of glucose control: roles for glucose-sensing neurones

Science.gov (United States)

Kosse, C.; Gonzalez, A.; Burdakov, D.

2018-01-01

The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the ‘fast’ senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they

Predictive models of glucose control: roles for glucose-sensing neurones.

Science.gov (United States)

Kosse, C; Gonzalez, A; Burdakov, D

2015-01-01

The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate

Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia

NARCIS (Netherlands)

Ionescu, Clara A.; De Keyser, Robin; Torrico, Bismark Claure; De Smet, Tom; Struys, Michel M. R. F.; Normey-Rico, Julio E.

This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The

Visuo-manual tracking: does intermittent control with aperiodic sampling explain linear power and non-linear remnant without sensorimotor noise?

Science.gov (United States)

Gollee, Henrik; Gawthrop, Peter J; Lakie, Martin; Loram, Ian D

2017-11-01

A human controlling an external system is described most easily and conventionally as linearly and continuously translating sensory input to motor output, with the inevitable output remnant, non-linearly related to the input, attributed to sensorimotor noise. Recent experiments show sustained manual tracking involves repeated refractoriness (insensitivity to sensory information for a certain duration), with the temporary 200-500 ms periods of irresponsiveness to sensory input making the control process intrinsically non-linear. This evidence calls for re-examination of the extent to which random sensorimotor noise is required to explain the non-linear remnant. This investigation of manual tracking shows how the full motor output (linear component and remnant) can be explained mechanistically by aperiodic sampling triggered by prediction error thresholds. Whereas broadband physiological noise is general to all processes, aperiodic sampling is associated with sensorimotor decision making within specific frontal, striatal and parietal networks; we conclude that manual tracking utilises such slow serial decision making pathways up to several times per second. The human operator is described adequately by linear translation of sensory input to motor output. Motor output also always includes a non-linear remnant resulting from random sensorimotor noise from multiple sources, and non-linear input transformations, for example thresholds or refractory periods. Recent evidence showed that manual tracking incurs substantial, serial, refractoriness (insensitivity to sensory information of 350 and 550 ms for 1st and 2nd order systems respectively). Our two questions are: (i) What are the comparative merits of explaining the non-linear remnant using noise or non-linear transformations? (ii) Can non-linear transformations represent serial motor decision making within the sensorimotor feedback loop intrinsic to tracking? Twelve participants (instructed to act in three prescribed

40 CFR 265.150 - State assumption of responsibility.

Science.gov (United States)

2010-07-01

..., STORAGE, AND DISPOSAL FACILITIES Financial Requirements § 265.150 State assumption of responsibility. (a) If a State either assumes legal responsibility for an owner's or operator's compliance with the... 40 Protection of Environment 25 2010-07-01 2010-07-01 false State assumption of responsibility...

Power Admission Control with Predictive Thermal Management in Smart Buildings

DEFF Research Database (Denmark)

Yao, Jianguo; Costanzo, Giuseppe Tommaso; Zhu, Guchuan

2015-01-01

This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First...

Optimization control of LNG regasification plant using Model Predictive Control

Science.gov (United States)

Wahid, A.; Adicandra, F. F.

2018-03-01

Optimization of liquified natural gas (LNG) regasification plant is important to minimize costs, especially operational costs. Therefore, it is important to choose optimum LNG regasification plant design and maintaining the optimum operating conditions through the implementation of model predictive control (MPC). Optimal tuning parameter for MPC such as P (prediction horizon), M (control of the horizon) and T (sampling time) are achieved by using fine-tuning method. The optimal criterion for design is the minimum amount of energy used and for control is integral of square error (ISE). As a result, the optimum design is scheme 2 which is developed by Devold with an energy savings of 40%. To maintain the optimum conditions, required MPC with P, M and T as follows: tank storage pressure: 90, 2, 1; product pressure: 95, 2, 1; temperature vaporizer: 65, 2, 2; and temperature heater: 35, 6, 5, with ISE value at set point tracking respectively 0.99, 1792.78, 34.89 and 7.54, or improvement of control performance respectively 4.6%, 63.5%, 3.1% and 58.2% compared to PI controller performance. The energy savings that MPC controllers can make when there is a disturbance in temperature rise 1°C of sea water is 0.02 MW.

Advanced Emergency Braking Control Based on a Nonlinear Model Predictive Algorithm for Intelligent Vehicles

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.

High level model predictive control for plug-and-play process control with stability guaranty

DEFF Research Database (Denmark)

Michelsen, Axel Gottlieb; Stoustrup, Jakob

2010-01-01

In this paper a method for designing a stabilizing high level model predictive controller for a hierarchical plug- and-play process is presented. This is achieved by abstracting the lower layers of the controller structure as low order models with uncertainty and by using a robust model predictive...... controller for generating the references for these. A simulation example, in which the actuators in a process control system are changed, is reported to show the potential of this approach for plug and play process control....

Towards a test of non-locality without 'supplementary assumptions'

International Nuclear Information System (INIS)

Barbieri, M.; De Martini, F.; Di Nepi, G.; Mataloni, P.

2005-01-01

We have experimentally tested the non-local properties of the two-photon states generated by a high brilliance source of entanglement which virtually allows the direct measurement of the full set of photon pairs created by the basic QED process implied by the parametric quantum scattering. Standard Bell measurements and Bell's inequality violation test have been realized over the entire cone of emission of the degenerate pairs. By the same source we have verified Hardy's ladder theory up to the 20th step and the contradiction between the standard quantum theory and the local realism has been tested for 41% of entangled pairs

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

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...

Quad-copter UAV BLDC Motor Control: Linear v/s non-linear control maps

Directory of Open Access Journals (Sweden)

Deep Parikh

2015-08-01

Full Text Available This paper presents some investigations and comparison of using linear versus non-linear static motor-control maps for the speed control of a BLDC (Brush Less Direct Current motors used in quad-copter UAV (Unmanned Aerial Vehicles. The motor-control map considered here is the inverse of the static map relating motor-speed output to motor-voltage input for a typical out-runner type Brushless DC Motors (BLDCM.  Traditionally, quad-copter BLDC motor speed control uses simple linear motor-control map defined by the motor-constant specification. However, practical BLDC motors show non-linear characteristic, particularly when operated across wide operating speed-range as is commonly required in quad-copter UAV flight operations. In this paper, our investigations to compare performance of linear versus non-linear motor-control maps are presented. The investigations cover simulation-based and experimental study of BLDC motor speed control systems for  quad-copter vehicle available. First the non-linear map relating rotor RPM to motor voltage for quad-copter BLDC motor is obtained experimentally using an optical speed encoder. The performance of the linear versus non-linear motor-control-maps for the speed control are studied. The investigations also cover study of time-responses for various standard test input-signals e.g. step, ramp and pulse inputs, applied as the reference speed-commands. Also, simple 2-degree of freedom test-bed is developed in our laboratory to help test the open-loop and closed-loop experimental investigations. The non-linear motor-control map is found to perform better in BLDC motor speed tracking control performance and thereby helping achieve better quad-copter roll-angle attitude control.

Support Vector Machine-Based Prediction of Local Tumor Control After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Klement, Rainer J., E-mail: rainer_klement@gmx.de [Department of Radiation Oncology, University of Würzburg (Germany); Department of Radiotherapy and Radiation Oncology, Leopoldina Hospital, Schweinfurt (Germany); Allgäuer, Michael [Department of Radiotherapy, Barmherzige Brüder Regensburg, Regensburg (Germany); Appold, Steffen [Department of Radiation Oncology, Technische Universität Dresden (Germany); Dieckmann, Karin [Department of Radiotherapy, Medical University of Vienna (Austria); Ernst, Iris [Department of Radiotherapy, University Hospital Münster (Germany); Ganswindt, Ute [Department of Radiation Oncology, Ludwigs-Maximilians-University Munich, München (Germany); Holy, Richard [Department of Radiation Oncology, RWTH Aachen University, Aachen (Germany); Nestle, Ursula [Department of Radiation Oncology, University Hospital Freiburg, Freiburg i Br (Germany); Nevinny-Stickel, Meinhard [Department of Therapeutic Radiology and Oncology, Innsbruck Medical University (Austria); Semrau, Sabine [Department of Radiation Oncology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen (Germany); Sterzing, Florian [Department of Radiation Oncology, University Hospital Heidelberg (Germany); Wittig, Andrea [Department of Radiotherapy and Radiation Oncology, Philipps-University Marburg (Germany); Andratschke, Nicolaus [Department of Radiation Oncology, Technische Universität München (Germany); Guckenberger, Matthias [Department of Radiation Oncology, University of Würzburg (Germany)

2014-03-01

Background: Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control. Methods and Materials: We investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performance by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection. Results: Local failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED{sub ISO}) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED{sub ISO} and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED{sub ISO}, age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P<.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively. Conclusions: These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP

Prediction of human core body temperature using non-invasive measurement methods.

Science.gov (United States)

Niedermann, Reto; Wyss, Eva; Annaheim, Simon; Psikuta, Agnes; Davey, Sarah; Rossi, René Michel

2014-01-01

The measurement of core body temperature is an efficient method for monitoring heat stress amongst workers in hot conditions. However, invasive measurement of core body temperature (e.g. rectal, intestinal, oesophageal temperature) is impractical for such applications. Therefore, the aim of this study was to define relevant non-invasive measures to predict core body temperature under various conditions. We conducted two human subject studies with different experimental protocols, different environmental temperatures (10 °C, 30 °C) and different subjects. In both studies the same non-invasive measurement methods (skin temperature, skin heat flux, heart rate) were applied. A principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. We identified six parameters (three skin temperatures, two skin heat fluxes and heart rate), which were included for the calculation of two factors. The predictive value of these factors for core body temperature was evaluated by a multiple regression analysis. The calculated root mean square deviation (rmsd) was in the range from 0.28 °C to 0.34 °C for all environmental conditions. These errors are similar to previous models using non-invasive measures to predict core body temperature. The results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. In addition, the physiological measurements chosen in this study and the algorithm defined in this work are potentially applicable as real-time core body temperature monitoring to assess health risk in broad range of working conditions.

40 CFR 264.150 - State assumption of responsibility.

Science.gov (United States)

2010-07-01

... FACILITIES Financial Requirements § 264.150 State assumption of responsibility. (a) If a State either assumes legal responsibility for an owner's or operator's compliance with the closure, post-closure care, or... 40 Protection of Environment 25 2010-07-01 2010-07-01 false State assumption of responsibility...

40 CFR 261.150 - State assumption of responsibility.

Science.gov (United States)

2010-07-01

... Excluded Hazardous Secondary Materials § 261.150 State assumption of responsibility. (a) If a State either assumes legal responsibility for an owner's or operator's compliance with the closure or liability... 40 Protection of Environment 25 2010-07-01 2010-07-01 false State assumption of responsibility...

40 CFR 267.150 - State assumption of responsibility.

Science.gov (United States)

2010-07-01

... STANDARDIZED PERMIT Financial Requirements § 267.150 State assumption of responsibility. (a) If a State either assumes legal responsibility for an owner's or operator's compliance with the closure care or liability... 40 Protection of Environment 26 2010-07-01 2010-07-01 false State assumption of responsibility...

A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

Science.gov (United States)

Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

2016-11-23

A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

Science.gov (United States)

Han, Min; Fan, Jianchao; Wang, Jun

2011-09-01

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

Nonparametric predictive inference in statistical process control

NARCIS (Netherlands)

Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

2000-01-01

New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on

Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

Directory of Open Access Journals (Sweden)

Fitri Yakub

2016-01-01

Full Text Available We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

Predictive Accuracy of Violence Risk Scale-Sexual Offender Version Risk and Change Scores in Treated Canadian Aboriginal and Non-Aboriginal Sexual Offenders.

Science.gov (United States)

Olver, Mark E; Sowden, Justina N; Kingston, Drew A; Nicholaichuk, Terry P; Gordon, Audrey; Beggs Christofferson, Sarah M; Wong, Stephen C P

2018-04-01

The present study examined the predictive properties of Violence Risk Scale-Sexual Offender version (VRS-SO) risk and change scores among Aboriginal and non-Aboriginal sexual offenders in a combined sample of 1,063 Canadian federally incarcerated men. All men participated in sexual offender treatment programming through the Correctional Service of Canada (CSC) at sites across its five regions. The Static-99R was also examined for comparison purposes. In total, 393 of the men were identified as Aboriginal (i.e., First Nations, Métis, Circumpolar) while 670 were non-Aboriginal and primarily White. Aboriginal men scored significantly higher on the Static-99R and VRS-SO and had higher rates of sexual and violent recidivism; however, there were no significant differences between Aboriginal and non-Aboriginal groups on treatment change with both groups demonstrating close to a half-standard deviation of change pre and post treatment. VRS-SO risk and change scores significantly predicted sexual and violent recidivism over fixed 5- and 10-year follow-ups for both racial/ancestral groups. Cox regression survival analyses also demonstrated positive treatment changes to be significantly associated with reductions in sexual and violent recidivism among Aboriginal and non-Aboriginal men after controlling baseline risk. A series of follow-up Cox regression analyses demonstrated that risk and change score information accounted for much of the observed differences between Aboriginal and non-Aboriginal men in rates of sexual recidivism; however, marked group differences persisted in rates of general violent recidivism even after controlling for these covariates. The results support the predictive properties of VRS-SO risk and change scores with treated Canadian Aboriginal sexual offenders.

Utilizing assumption for project of stand for solid state targets activation on inner beams of AIC-144 cyclotron

International Nuclear Information System (INIS)

Petelenz, B.

1997-09-01

General assumptions for project of target activation stand at AIC-144 cyclotron are presented. The project predicts production of 67 Ga, 111 In, 201 Tl, 139 Ce, 88 Y, 123 I and 211 At isotopes using various target backings. Directions concerning target cooling and beam parameters are also described

When predictions take control: The effect of task predictions on task switching performance

Directory of Open Access Journals (Sweden)

Wout eDuthoo

2012-08-01

Full Text Available In this paper, we aimed to investigate the role of self-generated predictions in the flexible control of behaviour. Therefore, we ran a task switching experiment in which participants were asked to try to predict the upcoming task in three conditions varying in switch rate (30%, 50% and 70%. Irrespective of their predictions, the colour of the target indicated which task participants had to perform. In line with previous studies (Mayr, 2006; Monsell & Mizon, 2006, the switch cost was attenuated as the switch rate increased. Importantly, a clear task repetition bias was found in all conditions, yet the task repetition prediction rate dropped from 78% over 66% to 49% with increasing switch probability in the three conditions. Irrespective of condition, the switch cost was strongly reduced in expectation of a task alternation compared to the cost of an unexpected task alternation following repetition predictions. Hence, our data suggest that the reduction in the switch cost with increasing switch probability is caused by a diminished expectancy for the task to repeat. Taken together, this paper highlights the importance of predictions in the flexible control of behaviour, and suggests a crucial role for task repetition expectancy in the context-sensitive adjusting of task switching performance.

Utilizing the non-bridge oxygen model to predict the glass viscosity

International Nuclear Information System (INIS)

Choi, Kwansik; Sheng, Jiawei; Maeng, Sung Jun; Song, Myung Jae

1998-01-01

Viscosity is the most important process property of waste glass. Viscosity measurement is difficult and costs much. Non-bridging Oxygen (NBO) model which relates glass composition to viscosity had been developed for high level waste at the Savannah River Site (SRS). This research utilized this NBO model to predict the viscosity of KEPRI's 55 glasses. It was found that there was a linear relationship between the measured viscosity and the predicted viscosity. The NBO model could be used to predict glass viscosity in glass formulation development. However the precision of predicted viscosity is out of satisfaction because the composition ranges are very different between the SRS and KEPRI glasses. The modification of NBO calculation, which included modification of alkaline earth elements and TiO 2 , could not strikingly improve the precision of predicted values

Just-In-Time predictive control for a two-wheeled robot

OpenAIRE

Nakpong, Nuttapun; Yamamoto, Shigeru

2012-01-01

In this paper, we introduce the use of Just-In-Time predictive control to enhance the stability of a two-wheeled robot. Just-In-Time predictive control uses a database which includes a huge amounts of input-output data of the two-wheeled robot and predicts its future movements based on a Just-In-Time algorithm. © 2012 IEEE.

A Robust Practical Generalized Predictive Control for BoilerSuper Heater Temperature Control

OpenAIRE

Zaki Maki Mohialdeen

2015-01-01

A practical method of robust generalized predictive controller (GPC) application is developed using a combination of Ziegler-Nichols type functions relating the GPC controller parameters to a first order with time delay process parameters and a model matching controller. The GPC controller and the model matching controller are used in a master/slave configuration, with the GPC as the master controller and the model matching controller as the slave controlle...

Model Predictive Controller Combined with LQG Controller and Velocity Feedback to Control the Stewart Platform

DEFF Research Database (Denmark)

Nadimi, Esmaeil Sharak; Bak, Thomas; Izadi-Zamanabadi, Roozbeh

2006-01-01

The main objective of this paper is to investigate the erformance and applicability of two GPC (generalized predictive control) based control methods on a complete benchmark model of the Stewart platform made in MATLAB V6.5. The first method involves an LQG controller (Linear Quadratic Gaussian...

Formalization and Analysis of Reasoning by Assumption

OpenAIRE

Bosse, T.; Jonker, C.M.; Treur, J.

2006-01-01

This article introduces a novel approach for the analysis of the dynamics of reasoning processes and explores its applicability for the reasoning pattern called reasoning by assumption. More specifically, for a case study in the domain of a Master Mind game, it is shown how empirical human reasoning traces can be formalized and automatically analyzed against dynamic properties they fulfill. To this end, for the pattern of reasoning by assumption a variety of dynamic properties have been speci...

Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?

Science.gov (United States)

Heilbron, Micha; Chait, Maria

2017-08-04

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. While proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modeling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organized - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfill its expectations. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Estimating Risks and Relative Risks in Case-Base Studies under the Assumptions of Gene-Environment Independence and Hardy-Weinberg Equilibrium

Science.gov (United States)

Chui, Tina Tsz-Ting; Lee, Wen-Chung

2014-01-01

Many diseases result from the interactions between genes and the environment. An efficient method has been proposed for a case-control study to estimate the genetic and environmental main effects and their interactions, which exploits the assumptions of gene-environment independence and Hardy-Weinberg equilibrium. To estimate the absolute and relative risks, one needs to resort to an alternative design: the case-base study. In this paper, the authors show how to analyze a case-base study under the above dual assumptions. This approach is based on a conditional logistic regression of case-counterfactual controls matched data. It can be easily fitted with readily available statistical packages. When the dual assumptions are met, the method is approximately unbiased and has adequate coverage probabilities for confidence intervals. It also results in smaller variances and shorter confidence intervals as compared with a previous method for a case-base study which imposes neither assumption. PMID:25137392

Estimating risks and relative risks in case-base studies under the assumptions of gene-environment independence and Hardy-Weinberg equilibrium.

Directory of Open Access Journals (Sweden)

Tina Tsz-Ting Chui

Full Text Available Many diseases result from the interactions between genes and the environment. An efficient method has been proposed for a case-control study to estimate the genetic and environmental main effects and their interactions, which exploits the assumptions of gene-environment independence and Hardy-Weinberg equilibrium. To estimate the absolute and relative risks, one needs to resort to an alternative design: the case-base study. In this paper, the authors show how to analyze a case-base study under the above dual assumptions. This approach is based on a conditional logistic regression of case-counterfactual controls matched data. It can be easily fitted with readily available statistical packages. When the dual assumptions are met, the method is approximately unbiased and has adequate coverage probabilities for confidence intervals. It also results in smaller variances and shorter confidence intervals as compared with a previous method for a case-base study which imposes neither assumption.

Statistical prediction of Late Miocene climate

Digital Repository Service at National Institute of Oceanography (India)

Fernandes, A.A; Gupta, S.M.

by making certain simplifying assumptions; for example in modelling ocean 4 currents, the geostrophic approximation is made. In case of statistical prediction no such a priori assumption need be made. statistical prediction comprises of using observed data... the number of equations. In this case the equations are overdetermined, and therefore one has to look for a solution that best fits the sample data in a least squares sense. To this end we express the sample .... (2.1)+ ry = y + data as follows: n L c. (x...

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

Science.gov (United States)

Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

2017-12-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach

OpenAIRE

Mohd Faudzi, Ahmad ’Athif; Mustafa, Nu’man Din; Osman, Khairuddin

2014-01-01

Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC) is proposed as the control st...

Predicting Phosphorus Dynamics Across Physiographic Regions Using a Mixed Hortonian Non-Hortonian Hydrology Model

Science.gov (United States)

Collick, A.; Easton, Z. M.; Auerbach, D.; Buchanan, B.; Kleinman, P. J. A.; Fuka, D.

2017-12-01

Predicting phosphorus (P) loss from agricultural watersheds depends on accurate representation of the hydrological and chemical processes governing P mobility and transport. In complex landscapes, P predictions are complicated by a broad range of soils with and without restrictive layers, a wide variety of agricultural management, and variable hydrological drivers. The Soil and Water Assessment Tool (SWAT) is a watershed model commonly used to predict runoff and non-point source pollution transport, but is commonly only used with Hortonian (traditional SWAT) or non-Hortonian (SWAT-VSA) initializations. Many shallow soils underlain by a restricting layer commonly generate saturation excess runoff from variable source areas (VSA), which is well represented in a re-conceptualized version, SWAT-VSA. However, many watersheds exhibit traits of both infiltration excess and saturation excess hydrology internally, based on the hydrologic distance from the stream, distribution of soils across the landscape, and characteristics of restricting layers. The objective of this research is to provide an initial look at integrating distributed predictive capabilities that consider both Hortonian and Non-Hortonian solutions simultaneously within a single SWAT-VSA initialization. We compare results from all three conceptual watershed initializations against measured surface runoff and stream P loads and to highlight the model's ability to drive sub-field management of P. All three initializations predict discharge similarly well (daily Nash-Sutcliffe Efficiencies above 0.5), but the new conceptual SWAT-VSA initialization performed best in predicting P export from the watershed, while also identifying critical source areas - those areas generating large runoff and P losses at the sub field level. These results support the use of mixed Hortonian non-Hortonian SWAT-VSA initializations in predicting watershed-scale P losses and identifying critical source areas of P loss in landscapes

Modeling soil CO2 production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0

Science.gov (United States)

Ryan, Edmund M.; Ogle, Kiona; Kropp, Heather; Samuels-Crow, Kimberly E.; Carrillo, Yolima; Pendall, Elise

2018-05-01

The flux of CO2 from the soil to the atmosphere (soil respiration, Rsoil) is a major component of the global carbon (C) cycle. Methods to measure and model Rsoil, or partition it into different components, often rely on the assumption that soil CO2 concentrations and fluxes are in steady state, implying that Rsoil is equal to the rate at which CO2 is produced by soil microbial and root respiration. Recent research, however, questions the validity of this assumption. Thus, the aim of this work was two-fold: (1) to describe a non-steady state (NSS) soil CO2 transport and production model, DETECT, and (2) to use this model to evaluate the environmental conditions under which Rsoil and CO2 production are likely in NSS. The backbone of DETECT is a non-homogeneous, partial differential equation (PDE) that describes production and transport of soil CO2, which we solve numerically at fine spatial and temporal resolution (e.g., 0.01 m increments down to 1 m, every 6 h). Production of soil CO2 is simulated for every depth and time increment as the sum of root respiration and microbial decomposition of soil organic matter. Both of these factors can be driven by current and antecedent soil water content and temperature, which can also vary by time and depth. We also analytically solved the ordinary differential equation (ODE) corresponding to the steady-state (SS) solution to the PDE model. We applied the DETECT NSS and SS models to the six-month growing season period representative of a native grassland in Wyoming. Simulation experiments were conducted with both model versions to evaluate factors that could affect departure from SS, such as (1) varying soil texture; (2) shifting the timing or frequency of precipitation; and (3) with and without the environmental antecedent drivers. For a coarse-textured soil, Rsoil from the SS model closely matched that of the NSS model. However, in a fine-textured (clay) soil, growing season Rsoil was ˜ 3 % higher under the assumption of

General predictive control using the delta operator

DEFF Research Database (Denmark)

Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad; Ravn, Ole

1993-01-01

This paper deals with two-discrete-time operators, the conventional forward shift-operator and the δ-operator. Both operators are treated in view of construction of suitable solutions to the Diophantine equation for the purpose of prediction. A general step-recursive scheme is presented. Finally...... a general predictive control (GPC) is formulated and applied adaptively to a continuous-time plant...

Model predictive control for Z-source power converter

DEFF Research Database (Denmark)

Mo, W.; Loh, P.C.; Blaabjerg, Frede

2011-01-01

This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...

DDH-Like Assumptions Based on Extension Rings

DEFF Research Database (Denmark)

Cramer, Ronald; Damgård, Ivan Bjerre; Kiltz, Eike

2012-01-01

We introduce and study a new type of DDH-like assumptions based on groups of prime order q. Whereas standard DDH is based on encoding elements of $\\mathbb{F}_{q}$ “in the exponent” of elements in the group, we ask what happens if instead we put in the exponent elements of the extension ring $R_f=......-Reingold style pseudorandom functions, and auxiliary input secure encryption. This can be seen as an alternative to the known family of k-LIN assumptions....

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

Science.gov (United States)

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

2016-04-01

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

Predictive control and identification: Applications to steering dynamics

DEFF Research Database (Denmark)

Hansen, Anca Daniela

1996-01-01

and of the loss function, which defines the optimality of the control. Some guidelines on how to choose the design parameters, depending on the type of process to be controlled and on the required control performance, are presented. A predictive track keeping system for a Mariner Class Vessel is formulated based...... the under- standing of the connection between identification and control, analysed in Chapter 7. Chapter 7 focuses on how to make the on-line identification for predictive control more robust towards unmodelled dynamics. The theory is verified via simulation studies on a Mariner Class Vessel. The effects...... and the need of a prefilter in the estimation are analysed and illustrated. Based on the idea that the control criterion must be dual to the estimation criterion, an iterative optimal prefilter is designed. This seems to be an appealing way to tune the model towards the objective for which the model...

Catalytic cracking models developed for predictive control purposes

Directory of Open Access Journals (Sweden)

Dag Ljungqvist

1993-04-01

Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.

Economic Model Predictive Control for Smart Energy Systems

DEFF Research Database (Denmark)

Halvgaard, Rasmus

Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat and ...... and hereby shift the heat pump power consumption to periods with both low electricity prices and a high fraction of green energy in the grid.......Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat...

Multimodel Predictive Control Approach for UAV Formation Flight

Directory of Open Access Journals (Sweden)

Chang-jian Ru

2014-01-01

Full Text Available Formation flight problem is the most important and interesting problem of multiple UAVs (unmanned aerial vehicles cooperative control. In this paper, a novel approach for UAV formation flight based on multimodel predictive control is designed. Firstly, the state equation of relative motion is obtained and then discretized. By the geometrical method, the characteristic points of state are determined. Afterwards, based on the linearization technique, the standard linear discrete model is obtained at each characteristic state point. Then, weighted model set is proposed using the idea of T-S (Takagi-Sugeno fuzzy control and the predictive control is carried out based on the multimodel method. Finally, to verify the performance of the proposed method, two different simulation scenarios are performed.

A framework for the organizational assumptions underlying safety culture

International Nuclear Information System (INIS)

Packer, Charles

2002-01-01

The safety culture of the nuclear organization can be addressed at the three levels of culture proposed by Edgar Schein. The industry literature provides a great deal of insight at the artefact and espoused value levels, although as yet it remains somewhat disorganized. There is, however, an overall lack of understanding of the assumption level of safety culture. This paper describes a possible framework for conceptualizing the assumption level, suggesting that safety culture is grounded in unconscious beliefs about the nature of the safety problem, its solution and how to organize to achieve the solution. Using this framework, the organization can begin to uncover the assumptions at play in its normal operation, decisions and events and, if necessary, engage in a process to shift them towards assumptions more supportive of a strong safety culture. (author)

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

Science.gov (United States)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

An Application on Merton Model in the Non-efficient Market

Science.gov (United States)

Feng, Yanan; Xiao, Qingxian

Merton Model is one of the famous credit risk models. This model presumes that the only source of uncertainty in equity prices is the firm’s net asset value .But the above market condition holds only when the market is efficient which is often been ignored in modern research. Another, the original Merton Model is based on assumptions that in the event of default absolute priority holds, renegotiation is not permitted , liquidation of the firm is costless and in the Merton Model and most of its modified version the default boundary is assumed to be constant which don’t correspond with the reality. So these can influence the level of predictive power of the model. In this paper, we have made some extensions on some of these assumptions underlying the original model. The model is virtually a modification of Merton’s model. In a non-efficient market, we use the stock data to analysis this model. The result shows that the modified model can evaluate the credit risk well in the non-efficient market.

Formalization and analysis of reasoning by assumption.

Science.gov (United States)

Bosse, Tibor; Jonker, Catholijn M; Treur, Jan

2006-01-02

This article introduces a novel approach for the analysis of the dynamics of reasoning processes and explores its applicability for the reasoning pattern called reasoning by assumption. More specifically, for a case study in the domain of a Master Mind game, it is shown how empirical human reasoning traces can be formalized and automatically analyzed against dynamic properties they fulfill. To this end, for the pattern of reasoning by assumption a variety of dynamic properties have been specified, some of which are considered characteristic for the reasoning pattern, whereas some other properties can be used to discriminate among different approaches to the reasoning. These properties have been automatically checked for the traces acquired in experiments undertaken. The approach turned out to be beneficial from two perspectives. First, checking characteristic properties contributes to the empirical validation of a theory on reasoning by assumption. Second, checking discriminating properties allows the analyst to identify different classes of human reasoners. 2006 Lawrence Erlbaum Associates, Inc.

Nonlinear model predictive control theory and algorithms

CERN Document Server

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...

Privacy-preserving smart meter control strategy including energy storage losses

OpenAIRE

Avula, Chinni Venkata Ramana R.; Oechtering, Tobias J.; Månsson, Daniel

2018-01-01

Privacy-preserving smart meter control strategies proposed in the literature so far make some ideal assumptions such as instantaneous control without delay, lossless energy storage systems etc. In this paper, we present a one-step-ahead predictive control strategy using Bayesian risk to measure and control privacy leakage with an energy storage system. The controller estimates energy state using a three-circuit energy storage model to account for steady-state energy losses. With numerical exp...

MONITORED GEOLOGIC REPOSITORY LIFE CYCLE COST ESTIMATE ASSUMPTIONS DOCUMENT

International Nuclear Information System (INIS)

R.E. Sweeney

2001-01-01

The purpose of this assumptions document is to provide general scope, strategy, technical basis, schedule and cost assumptions for the Monitored Geologic Repository (MGR) life cycle cost (LCC) estimate and schedule update incorporating information from the Viability Assessment (VA) , License Application Design Selection (LADS), 1999 Update to the Total System Life Cycle Cost (TSLCC) estimate and from other related and updated information. This document is intended to generally follow the assumptions outlined in the previous MGR cost estimates and as further prescribed by DOE guidance

The stable model semantics under the any-world assumption

OpenAIRE

Straccia, Umberto; Loyer, Yann

2004-01-01

The stable model semantics has become a dominating approach to complete the knowledge provided by a logic program by means of the Closed World Assumption (CWA). The CWA asserts that any atom whose truth-value cannot be inferred from the facts and rules is supposed to be false. This assumption is orthogonal to the so-called the Open World Assumption (OWA), which asserts that every such atom's truth is supposed to be unknown. The topic of this paper is to be more fine-grained. Indeed, the objec...

Monitored Geologic Repository Life Cycle Cost Estimate Assumptions Document

International Nuclear Information System (INIS)

Sweeney, R.

2000-01-01

The purpose of this assumptions document is to provide general scope, strategy, technical basis, schedule and cost assumptions for the Monitored Geologic Repository (MGR) life cycle cost estimate and schedule update incorporating information from the Viability Assessment (VA), License Application Design Selection (LADS), 1999 Update to the Total System Life Cycle Cost (TSLCC) estimate and from other related and updated information. This document is intended to generally follow the assumptions outlined in the previous MGR cost estimates and as further prescribed by DOE guidance

Perturbed cooperative-state feedback strategy for model predictive networked control of interconnected systems.

Science.gov (United States)

Tran, Tri; Ha, Q P

2018-01-01

A perturbed cooperative-state feedback (PSF) strategy is presented for the control of interconnected systems in this paper. The subsystems of an interconnected system can exchange data via the communication network that has multiple connection topologies. The PSF strategy can resolve both issues, the sensor data losses and the communication network breaks, thanks to the two components of the control including a cooperative-state feedback and a perturbation variable, e.g., u i =K ij x j +w i . The PSF is implemented in a decentralized model predictive control scheme with a stability constraint and a non-monotonic storage function (ΔV(x(k))≥0), derived from the dissipative systems theory. Numerical simulation for the automatic generation control problem in power systems is studied to illustrate the effectiveness of the presented PSF strategy. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Predictive performance models and multiple task performance

Science.gov (United States)

Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

1989-01-01

Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

Model complexity control for hydrologic prediction

NARCIS (Netherlands)

Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

2008-01-01

A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

Nonparametric predictive inference in statistical process control

NARCIS (Netherlands)

Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

2004-01-01

Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric

ASTRAL-R score predicts non-recanalisation after intravenous thrombolysis in acute ischaemic stroke.

Science.gov (United States)

Vanacker, Peter; Heldner, Mirjam R; Seiffge, David; Mueller, Hubertus; Eskandari, Ashraf; Traenka, Christopher; Ntaios, George; Mosimann, Pascal J; Sztajzel, Roman; Mendes Pereira, Vitor; Cras, Patrick; Engelter, Stefan; Lyrer, Philippe; Fischer, Urs; Lambrou, Dimitris; Arnold, Marcel; Michel, Patrik

2015-05-01

Intravenous thrombolysis (IVT) as treatment in acute ischaemic strokes may be insufficient to achieve recanalisation in certain patients. Predicting probability of non-recanalisation after IVT may have the potential to influence patient selection to more aggressive management strategies. We aimed at deriving and internally validating a predictive score for post-thrombolytic non-recanalisation, using clinical and radiological variables. In thrombolysis registries from four Swiss academic stroke centres (Lausanne, Bern, Basel and Geneva), patients were selected with large arterial occlusion on acute imaging and with repeated arterial assessment at 24 hours. Based on a logistic regression analysis, an integer-based score for each covariate of the fitted multivariate model was generated. Performance of integer-based predictive model was assessed by bootstrapping available data and cross validation (delete-d method). In 599 thrombolysed strokes, five variables were identified as independent predictors of absence of recanalisation: Acute glucose > 7 mmol/l (A), significant extracranial vessel STenosis (ST), decreased Range of visual fields (R), large Arterial occlusion (A) and decreased Level of consciousness (L). All variables were weighted 1, except for (L) which obtained 2 points based on β-coefficients on the logistic scale. ASTRAL-R scores 0, 3 and 6 corresponded to non-recanalisation probabilities of 18, 44 and 74 % respectively. Predictive ability showed AUC of 0.66 (95 %CI, 0.61-0.70) when using bootstrap and 0.66 (0.63-0.68) when using delete-d cross validation. In conclusion, the 5-item ASTRAL-R score moderately predicts non-recanalisation at 24 hours in thrombolysed ischaemic strokes. If its performance can be confirmed by external validation and its clinical usefulness can be proven, the score may influence patient selection for more aggressive revascularisation strategies in routine clinical practice.

A parallel adaptive mesh refinement algorithm for predicting turbulent non-premixed combusting flows

International Nuclear Information System (INIS)

Gao, X.; Groth, C.P.T.

2005-01-01

A parallel adaptive mesh refinement (AMR) algorithm is proposed for predicting turbulent non-premixed combusting flows characteristic of gas turbine engine combustors. The Favre-averaged Navier-Stokes equations governing mixture and species transport for a reactive mixture of thermally perfect gases in two dimensions, the two transport equations of the κ-ψ turbulence model, and the time-averaged species transport equations, are all solved using a fully coupled finite-volume formulation. A flexible block-based hierarchical data structure is used to maintain the connectivity of the solution blocks in the multi-block mesh and facilitate automatic solution-directed mesh adaptation according to physics-based refinement criteria. This AMR approach allows for anisotropic mesh refinement and the block-based data structure readily permits efficient and scalable implementations of the algorithm on multi-processor architectures. Numerical results for turbulent non-premixed diffusion flames, including cold- and hot-flow predictions for a bluff body burner, are described and compared to available experimental data. The numerical results demonstrate the validity and potential of the parallel AMR approach for predicting complex non-premixed turbulent combusting flows. (author)

Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids

Science.gov (United States)

Babqi, Abdulrahman Jamal

This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control specifies the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and verified using PSCAD/EMTDC software

Distributed Model Predictive Control for Smart Energy Systems

DEFF Research Database (Denmark)

Halvgaard, Rasmus Fogtmann; Vandenberghe, Lieven; Poulsen, Niels Kjølstad

2016-01-01

Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem...

Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control

DEFF Research Database (Denmark)

Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.

2017-01-01

This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded...... into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers...

Quantized Predictive Control over Erasure Channels

DEFF Research Database (Denmark)

E. Quevedo, Daniel; Østergaard, Jan

2009-01-01

.i.d. dropouts, the controller transmits data packets containing quantized plant input predictions. These minimize a finite horizon cost function and are provided by an appropriate optimal entropy coded dithered lattice vector quantizer. Within this context, we derive an equivalent noise-shaping model...

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.

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

Directory of Open Access Journals (Sweden)

Roberto Mei

2018-01-01

Full Text Available The application of a multivariable predictive controller to the mixing process for the production of a non-Newtonian fluid is discussed in this work. A data-driven model has been developed to describe the dynamic behaviour of the rheological properties of the fluid as a function of the operating conditions using experimental data collected in a pilot plant. The developed model provides a realistic process representation and it is used to test and verify the multivariable controller, which has been designed to maintain viscosity curves of the non-Newtonian fluid within a given region of the viscosity-vs-shear rate plane in presence of process disturbances occurring in the mixing process.

Sampling Assumptions in Inductive Generalization

Science.gov (United States)

Navarro, Daniel J.; Dry, Matthew J.; Lee, Michael D.

2012-01-01

Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key "sampling" assumption about how the available data were generated.…

Life Support Baseline Values and Assumptions Document

Science.gov (United States)

Anderson, Molly S.; Ewert, Michael K.; Keener, John F.

2018-01-01

The Baseline Values and Assumptions Document (BVAD) provides analysts, modelers, and other life support researchers with a common set of values and assumptions which can be used as a baseline in their studies. This baseline, in turn, provides a common point of origin from which many studies in the community may depart, making research results easier to compare and providing researchers with reasonable values to assume for areas outside their experience. This document identifies many specific physical quantities that define life support systems, serving as a general reference for spacecraft life support system technology developers.

Modeling, robust and distributed model predictive control for freeway networks

NARCIS (Netherlands)

Liu, S.

2016-01-01

In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

Legal assumptions for private company claim for additional (supplementary payment

Directory of Open Access Journals (Sweden)

Šogorov Stevan

2011-01-01

Full Text Available Subject matter of analyze in this article are legal assumptions which must be met in order to enable private company to call for additional payment. After introductory remarks discussion is focused on existence of provisions regarding additional payment in formation contract, or in shareholders meeting general resolution, as starting point for company's claim. Second assumption is concrete resolution of shareholders meeting which creates individual obligations for additional payments. Third assumption is defined as distinctness regarding sum of payment and due date. Sending of claim by relevant company body is set as fourth legal assumption for realization of company's right to claim additional payments from member of private company.

Gain-Scheduled Model Predictive Control of Wind Turbines using Laguerre Functions

DEFF Research Database (Denmark)

Adegas, Fabiano Daher; Wisniewski, Rafal; Larsen, Lars Finn Sloth

2014-01-01

This paper presents a systematic approach to design gain-scheduled predictive controllers for wind turbines. The predictive control law is based on Laguerre functions to parameterize control signals and a parameter-dependent cost function that is analytically determined from turbine data....... These properties facilitate the design of speed controllers by placement of the closed-loop poles (when constraints are not active) and systematic adaptation towards changes in the operating point. Vibration control of undamped modes is achieved by imposing a certain degree of stability to the closed-loop system....... The approach can be utilized to the design of new controllers and to represent existing gain-scheduled controllers as predictive controllers. The numerical example and simulations illustrate the design of a speed controller augmented with active damping of the tower fore-aft displacement....

Prediction based chaos control via a new neural network

International Nuclear Information System (INIS)

Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

2008-01-01

In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

What Money Can't Buy: Different Patterns in Decision Making About Sex and Money Predict Past Sexual Coercion Perpetration.

Science.gov (United States)

Carrier Emond, Fannie; Gagnon, Jean; Nolet, Kevin; Cyr, Gaëlle; Rouleau, Joanne-Lucine

2018-02-01

Self-reported impulsivity has been found to predict the perpetration of sexual coercion in both sexual offenders and male college students. Impulsivity can be conceptualized as a generalized lack of self-control (i.e., general perspective) or as a multifaceted construct that can vary from one context to the other (i.e., domain-specific perspective). Delay discounting, the tendency to prefer sooner smaller rewards over larger delayed rewards, is a measure of impulsive decision making. Recent sexual adaptations of delay discounting tasks can be used to test domain-specific assumptions. The present study used the UPPS-P impulsivity questionnaire, a standard money discounting task, and a sexual discounting task to predict past use of sexual coercion in a sample of 98 male college students. Results indicated that higher negative urgency scores, less impulsive money discounting, and more impulsive sexual discounting all predicted sexual coercion. Consistent with previous studies, sexuality was discounted more steeply than money by both perpetrators and non-perpetrators of sexual coercion, but this difference was twice as large in perpetrators compared to non-perpetrators. Our study identified three different predictors of sexual coercion in male college students: a broad tendency to act rashly under negative emotions, a specific difficulty to postpone sexual gratification, and a pattern of optimal non-sexual decision making. Results highlight the importance of using multiple measures, including sexuality-specific measures, to get a clear portrait of the links between impulsivity and sexual coercion.

Posttraumatic Growth and Shattered World Assumptions Among Ex-POWs

DEFF Research Database (Denmark)

Lahav, Y.; Bellin, Elisheva S.; Solomon, Z.

2016-01-01

Objective: The controversy regarding the nature of posttraumatic growth (PTG) includes two main competing claims: one which argues that PTG reflects authentic positive changes and the other which argues that PTG reflects illusionary defenses. The former also suggests that PTG evolves from shattered...... world assumptions (WAs) and that the co-occurrence of high PTG and negative WAs among trauma survivors reflects reconstruction of an integrative belief system. The present study aimed to test these claims by investigating, for the first time, the mediating role of dissociation in the relation between...... PTG and WAs. Method: Former prisoners of war (ex-POWs; n = 158) and comparable controls (n = 106) were assessed 38 years after the Yom Kippur War. Results: Ex-POWs endorsed more negative WAs and higher PTG and dissociation compared to controls. Ex-POWs with posttraumatic stress disorder (PTSD...

Fractional-Order Generalized Predictive Control: Application for Low-Speed Control of Gasoline-Propelled Cars

Directory of Open Access Journals (Sweden)

M. Romero

2013-01-01

Full Text Available There is an increasing interest in using fractional calculus applied to control theory generalizing classical control strategies as the PID controller and developing new ones with the intention of taking advantage of characteristics supplied by this mathematical tool for the controller definition. In this work, the fractional generalization of the successful and spread control strategy known as model predictive control is applied to drive autonomously a gasoline-propelled vehicle at low speeds. The vehicle is a Citroën C3 Pluriel that was modified to act over the throttle and brake pedals. Its highly nonlinear dynamics are an excellent test bed for applying beneficial characteristics of fractional predictive formulation to compensate unmodeled dynamics and external disturbances.

Including model uncertainty in the model predictive control with output feedback

Directory of Open Access Journals (Sweden)

Rodrigues M.A.

2002-01-01

Full Text Available This paper addresses the development of an efficient numerical output feedback robust model predictive controller for open-loop stable systems. Stability of the closed loop is guaranteed by using an infinite horizon predictive controller and a stable state observer. The performance and the computational burden of this approach are compared to a robust predictive controller from the literature. The case used for this study is based on an industrial gasoline debutanizer column.

Model-predictive control based on Takagi-Sugeno fuzzy model for electrical vehicles delayed model

DEFF Research Database (Denmark)

Khooban, Mohammad-Hassan; Vafamand, Navid; Niknam, Taher

2017-01-01

Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non......-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison...... is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation...

Selection of References in Wind Turbine Model Predictive Control Design

DEFF Research Database (Denmark)

Odgaard, Peter Fogh; Hovgaard, Tobias

2015-01-01

a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power......Lowering the cost of energy is one of the major focus areas in the wind turbine industry. Recent research has indicated that wind turbine controllers based on model predictive control methods can be useful in obtaining this objective. A number of design considerations have to be made when designing....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...

Non-emergency small bowel obstruction: assessment of CT findings that predict need for surgery

Energy Technology Data Exchange (ETDEWEB)

Deshmukh, Swati D.; Shin, David S.; Willmann, Juergen K.; Rosenberg, Jarrett; Shin, Lewis; Jeffrey, R.B. [Stanford University, School of Medicine, Department of Radiology, Stanford, CA (United States)

2011-05-15

To identify CT findings predictive of surgical management in non-emergency small bowel obstruction (SBO). Contrast-enhanced abdominal CT of 129 patients with non-emergency SBO were evaluated for small bowel luminal diameter, wall thickness, presence of the small bowel faeces sign (intraluminal particulate matter in a dilated small bowel) and length, transition point, submucosal oedema, mesenteric stranding, ascites and degree of obstruction (low grade partial, high grade partial and complete obstruction). Medical records were reviewed for age, gender, management and history of abdominal surgery, abdominal malignancy, or SBO. Statistical analyses were performed with Stata Release 9.2. Degree of obstruction was the only predictor of need for surgery. Whereas 18.0% of patients with low-grade partial obstruction (n = 50) underwent surgery, 32.5% of patients with high-grade partial obstruction (n = 77) and 100% of patients with complete obstruction (n = 2) required surgery (P = 0.004). The small bowel faeces sign was inversely predictive of surgery (P = 0.018). In non-emergency SBO patients with contrast-enhanced CT imaging, grade of obstruction predicts surgery, while the small bowel faeces sign inversely predicts need for surgery. (orig.)

Neural Network Predictive Control for Vanadium Redox Flow Battery

Directory of Open Access Journals (Sweden)

Hai-Feng Shen

2013-01-01

Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

Optimization of piezoelectric cantilever energy harvesters including non-linear effects

International Nuclear Information System (INIS)

Patel, R; McWilliam, S; Popov, A A

2014-01-01

This paper proposes a versatile non-linear model for predicting piezoelectric energy harvester performance. The presented model includes (i) material non-linearity, for both substrate and piezoelectric layers, and (ii) geometric non-linearity incorporated by assuming inextensibility and accurately representing beam curvature. The addition of a sub-model, which utilizes the transfer matrix method to predict eigenfrequencies and eigenvectors for segmented beams, allows for accurate optimization of piezoelectric layer coverage. A validation of the overall theoretical model is performed through experimental testing on both uniform and non-uniform samples manufactured in-house. For the harvester composition used in this work, the magnitude of material non-linearity exhibited by the piezoelectric layer is 35 times greater than that of the substrate layer. It is also observed that material non-linearity, responsible for reductions in resonant frequency with increases in base acceleration, is dominant over geometric non-linearity for standard piezoelectric harvesting devices. Finally, over the tested range, energy loss due to damping is found to increase in a quasi-linear fashion with base acceleration. During an optimization study on piezoelectric layer coverage, results from the developed model were compared with those from a linear model. Unbiased comparisons between harvesters were realized by using devices with identical natural frequencies—created by adjusting the device substrate thickness. Results from three studies, each with a different assumption on mechanical damping variations, are presented. Findings showed that, depending on damping variation, a non-linear model is essential for such optimization studies with each model predicting vastly differing optimum configurations. (paper)

Simulation research on multivariable fuzzy model predictive control of nuclear power plant

International Nuclear Information System (INIS)

Su Jie

2012-01-01

To improve the dynamic control capabilities of the nuclear power plant, the algorithm of the multivariable nonlinear predictive control based on the fuzzy model was applied in the main parameters control of the nuclear power plant, including control structure and the design of controller in the base of expounding the math model of the turbine and the once-through steam generator. The simulation results show that the respond of the change of the gas turbine speed and the steam pressure under the algorithm of multivariable fuzzy model predictive control is faster than that under the PID control algorithm, and the output value of the gas turbine speed and the steam pressure under the PID control algorithm is 3%-5% more than that under the algorithm of multi-variable fuzzy model predictive control. So it shows that the algorithm of multi-variable fuzzy model predictive control can control the output of the main parameters of the nuclear power plant well and get better control effect. (author)

Model Predictive Control of Mineral Column Flotation Process

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Yahui Tian

2018-06-01

Full Text Available Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs and ordinary differential equations (ODEs, which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.

Model Predictive Control Algorithms for Pen and Pump Insulin Administration

DEFF Research Database (Denmark)

Boiroux, Dimitri

at mealtime, and the case where the insulin sensitivity increases during the night. This thesis consists of a summary report, glucose and insulin proles of the clinical studies and research papers submitted, peer-reviewed and/or published in the period September 2009 - September 2012....... of current closed-loop controllers. In this thesis, we present different control strategies based on Model Predictive Control (MPC) for an artificial pancreas. We use Nonlinear Model Predictive Control (NMPC) in order to determine the optimal insulin and blood glucose profiles. The optimal control problem...

Apparent diffusion coefficient mapping in medulloblastoma predicts non-infiltrative surgical planes.

Science.gov (United States)

Marupudi, Neena I; Altinok, Deniz; Goncalves, Luis; Ham, Steven D; Sood, Sandeep

2016-11-01

An appropriate surgical approach for posterior fossa lesions is to start tumor removal from areas with a defined plane to where tumor is infiltrating the brainstem or peduncles. This surgical approach minimizes risk of damage to eloquent areas. Although magnetic resonance imaging (MRI) is the current standard preoperative imaging obtained for diagnosis and surgical planning of pediatric posterior fossa tumors, it offers limited information on the infiltrative planes between tumor and normal structures in patients with medulloblastomas. Because medulloblastomas demonstrate diffusion restriction on apparent diffusion coefficient map (ADC map) sequences, we investigated the role of ADC map in predicting infiltrative and non-infiltrative planes along the brain stem and/or cerebellar peduncles by medulloblastomas prior to surgery. Thirty-four pediatric patients with pathologically confirmed medulloblastomas underwent surgical resection at our facility from 2004 to 2012. An experienced pediatric neuroradiologist reviewed the brain MRIs/ADC map, assessing the planes between the tumor and cerebellar peduncles/brain stem. An independent evaluator documented surgical findings from operative reports for comparison to the radiographic findings. The radiographic findings were statistically compared to the documented intraoperative findings to determine predictive value of the test in identifying tumor infiltration of the brain stem cerebellar peduncles. Twenty-six patients had preoperative ADC mapping completed and thereby, met inclusion criteria. Mean age at time of surgery was 8.3 ± 4.6 years. Positive predictive value of ADC maps to predict tumor invasion of the brain stem and cerebellar peduncles ranged from 69 to 88 %; negative predictive values ranged from 70 to 89 %. Sensitivity approached 93 % while specificity approached 78 %. ADC maps are valuable in predicting the infiltrative and non-infiltrative planes along the tumor and brain stem interface in

Non interacting control by measurement feedback

NARCIS (Netherlands)

Woude, van der J.W.

1987-01-01

In this paper we shall solve the problem of non interacting control by measurement feedback for systems that in addition to a control input and a measurement output have two exogenous inputs and two exogenous outputs. That is, we shall derive necessary and sufficient conditions that can actually be

Testing Our Fundamental Assumptions

Science.gov (United States)

Kohler, Susanna

2016-06-01

fundamental assumptions.A recent focus set in the Astrophysical Journal Letters, titled Focus on Exploring Fundamental Physics with Extragalactic Transients, consists of multiple published studies doing just that.Testing General RelativitySeveral of the articles focus on the 4th point above. By assuming that the delay in photon arrival times is only due to the gravitational potential of the Milky Way, these studies set constraints on the deviation of our galaxys gravitational potential from what GR would predict. The study by He Gao et al. uses the different photon arrival times from gamma-ray bursts to set constraints at eVGeV energies, and the study by Jun-Jie Wei et al. complements this by setting constraints at keV-TeV energies using photons from high-energy blazar emission.Photons or neutrinos from different extragalactic transients each set different upper limits on delta gamma, the post-Newtonian parameter, vs. particle energy or frequency. This is a test of Einsteins equivalence principle: if the principle is correct, delta gamma would be exactly zero, meaning that photons of different energies move at the same velocity through a vacuum. [Tingay Kaplan 2016]S.J. Tingay D.L. Kaplan make the case that measuring the time delay of photons from fast radio bursts (FRBs; transient radio pulses that last only a few milliseconds) will provide even tighter constraints if we are able to accurately determine distances to these FRBs.And Adi Musser argues that the large-scale structure of the universe plays an even greater role than the Milky Way gravitational potential, allowing for even stricter testing of Einsteins equivalence principle.The ever-narrower constraints from these studies all support GR as a correct set of rules through which to interpret our universe.Other Tests of Fundamental PhysicsIn addition to the above tests, Xue-Feng Wu et al. show that FRBs can be used to provide severe constraints on the rest mass of the photon, and S. Croft et al. even touches on what we

Efficient predictive model-based and fuzzy control for green urban mobility

NARCIS (Netherlands)

Jamshidnejad, A.

2017-01-01

In this thesis, we develop efficient predictive model-based control approaches, including model-predictive control (MPC) andmodel-based fuzzy control, for application in urban traffic networks with the aim of reducing a combination of the total time spent by the vehicles within the network and the

Idaho National Engineering Laboratory installation roadmap assumptions document

International Nuclear Information System (INIS)

1993-05-01

This document is a composite of roadmap assumptions developed for the Idaho National Engineering Laboratory (INEL) by the US Department of Energy Idaho Field Office and subcontractor personnel as a key element in the implementation of the Roadmap Methodology for the INEL Site. The development and identification of these assumptions in an important factor in planning basis development and establishes the planning baseline for all subsequent roadmap analysis at the INEL

Temporal Distinctiveness in Task Switching: Assessing the Mixture-Distribution Assumption

Directory of Open Access Journals (Sweden)

James A Grange

2016-02-01

Full Text Available In task switching, increasing the response--cue interval has been shown to reduce the switch cost. This has been attributed to a time-based decay process influencing the activation of memory representations of tasks (task-sets. Recently, an alternative account based on interference rather than decay has been successfully applied to this data (Horoufchin et al., 2011. In this account, variation of the RCI is thought to influence the temporal distinctiveness (TD of episodic traces in memory, thus affecting their retrieval probability. This can affect performance as retrieval probability influences response time: If retrieval succeeds, responding is fast due to positive priming; if retrieval fails, responding is slow, due to having to perform the task via a slow algorithmic process. This account---and a recent formal model (Grange & Cross, 2015---makes the strong prediction that all RTs are a mixture of one of two processes: a fast process when retrieval succeeds, and a slow process when retrieval fails. The present paper assesses the evidence for this mixture-distribution assumption in TD data. In a first section, statistical evidence for mixture-distributions is found using the fixed-point property test. In a second section, a mathematical process model with mixture-distributions at its core is fitted to the response time distribution data. Both approaches provide good evidence in support of the mixture-distribution assumption, and thus support temporal distinctiveness accounts of the data.

Using the Speech Transmission Index for predicting non-native speech intelligibility

NARCIS (Netherlands)

Wijngaarden, S.J. van; Bronkhorst, A.W.; Houtgast, T.; Steeneken, H.J.M.

2004-01-01

While the Speech Transmission Index ~STI! is widely applied for prediction of speech intelligibility in room acoustics and telecommunication engineering, it is unclear how to interpret STI values when non-native talkers or listeners are involved. Based on subjectively measured psychometric functions

Distributed Model Predictive Control for Active Power Control of Wind Farm

DEFF Research Database (Denmark)

Zhao, Haoran; Wu, Qiuwei; Rasmussen, Claus Nygaard

2014-01-01

This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can...... be considered to achieve a trade-off between them. Additionally, D- MPC is based on communication among the subsystems. Through the interaction among the neighboring subsystems, the global optimization could be achieved, which significantly reduces the computation burden. It is suitable for the modern large......-scale wind farm control....

Wind farms production: Control and prediction

Science.gov (United States)

El-Fouly, Tarek Hussein Mostafa

Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect

Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

Science.gov (United States)

Aissa, Oualid; Moulahoum, Samir; Colak, Ilhami; Babes, Badreddine; Kabache, Nadir

2017-10-12

This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.

Revealing patterns of cultural transmission from frequency data: equilibrium and non-equilibrium assumptions

Science.gov (United States)

Crema, Enrico R.; Kandler, Anne; Shennan, Stephen

2016-12-01

A long tradition of cultural evolutionary studies has developed a rich repertoire of mathematical models of social learning. Early studies have laid the foundation of more recent endeavours to infer patterns of cultural transmission from observed frequencies of a variety of cultural data, from decorative motifs on potsherds to baby names and musical preferences. While this wide range of applications provides an opportunity for the development of generalisable analytical workflows, archaeological data present new questions and challenges that require further methodological and theoretical discussion. Here we examine the decorative motifs of Neolithic pottery from an archaeological assemblage in Western Germany, and argue that the widely used (and relatively undiscussed) assumption that observed frequencies are the result of a system in equilibrium conditions is unwarranted, and can lead to incorrect conclusions. We analyse our data with a simulation-based inferential framework that can overcome some of the intrinsic limitations in archaeological data, as well as handle both equilibrium conditions and instances where the mode of cultural transmission is time-variant. Results suggest that none of the models examined can produce the observed pattern under equilibrium conditions, and suggest. instead temporal shifts in the patterns of cultural transmission.

Genetic design of interpolated non-linear controllers for linear plants

International Nuclear Information System (INIS)

Ajlouni, N.

2000-01-01

The techniques of genetic algorithms are proposed as a means of designing non-linear PID control systems. It is shown that the use of genetic algorithms for this purpose results in highly effective non-linear PID control systems. These results are illustrated by using genetic algorithms to design a non-linear PID control system and contrasting the results with an optimally tuned linear PID controller. (author)

Prediction of active control of subsonic centrifugal compressor rotating stall

Science.gov (United States)

Lawless, Patrick B.; Fleeter, Sanford

1993-01-01

A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

Science.gov (United States)

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 Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

Directory of Open Access Journals (Sweden)

Issaku Kawashima

2017-07-01

Full Text Available 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.

Stability of the spreading in small-world network with predictive controller

International Nuclear Information System (INIS)

Bao, Z.J.; Jiang, Q.Y.; Yan, W.J.; Cao, Y.J.

2010-01-01

In this Letter, we apply the predictive control strategy to suppress the propagation of diseases or viruses in small-world network. The stability of small-world spreading model with predictive controller is investigated. The sufficient and necessary stability condition is given, which is closely related to the controller parameters and small-world rewiring probability p. Our simulations discover a phenomenon that, with the fixed predictive controller parameters, the spreading dynamics become more and more stable when p decreases from a larger value to a smaller one, and the suitable controller parameters can effectively suppress the spreading behaviors even when p varies within the whole spectrum, and the unsuitable controller parameters can lead to oscillation when p lies within a certain range.

School Principals' Assumptions about Human Nature: Implications for Leadership in Turkey

Science.gov (United States)

Sabanci, Ali

2008-01-01

This article considers principals' assumptions about human nature in Turkey and the relationship between the assumptions held and the leadership style adopted in schools. The findings show that school principals hold Y-type assumptions and prefer a relationship-oriented style in their relations with assistant principals. However, both principals…

Single-energy non-contrast hepatic steatosis criteria applied to virtual non-contrast images: is it still highly specific and positively predictive?

Science.gov (United States)

Haji-Momenian, S; Parkinson, W; Khati, N; Brindle, K; Earls, J; Zeman, R K

2018-06-01

To determine the sensitivity, specificity, and predictive values of single-energy non-contrast hepatic steatosis criteria on dual-energy virtual non-contrast (VNC) images. Forty-eight computed tomography (CT) examinations, which included single-energy non-contrast (TNC) and contrast-enhanced dual-energy CT angiography (CTA) of the abdomen, were enrolled. VNC images were reconstructed from the CTA. Region of interest (ROI) attenuations were measured in the right and left hepatic lobes, spleen, and aorta on TNC and VNC images. The right and left hepatic lobes were treated as separate samples. Steatosis was diagnosed based on TNC liver attenuation of ≤40 HU or liver attenuation index (LAI) of ≤-10 HU, which are extremely specific and predictive for moderate to severe steatosis. The sensitivity, specificity, and predictive values of VNC images for steatosis were calculated. VNC-TNC deviations were correlated with aortic enhancement and patient water equivalent diameter (PWED). Thirty-two liver ROIs met steatosis criteria based on TNC attenuation; VNC attenuation had sensitivity, specificity, and a positive predictive value of 66.7%, 100%, and 100%, respectively. Twenty-one liver ROIs met steatosis criteria based on TNC LAI. VNC LAI had sensitivity, specificity, and positive predictive values of 61.9%, 90.7%, and 65%, respectively. Hepatic and splenic VNC-TNC deviations did not correlate with one another (R 2 =0.08), aortic enhancement (R 2 predictive for moderate to severe steatosis on VNC reconstructions from the arterial phase. Hepatic attenuation performs better than LAI criteria. VNC deviations are independent of aortic enhancement and PWED. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Major Assumptions of Mastery Learning.

Science.gov (United States)

Anderson, Lorin W.

Mastery learning can be described as a set of group-based, individualized, teaching and learning strategies based on the premise that virtually all students can and will, in time, learn what the school has to teach. Inherent in this description are assumptions concerning the nature of schools, classroom instruction, and learners. According to the…

Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.

Science.gov (United States)

Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald

2017-07-01

The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A LIDAR-assisted model predictive controller added on a traditional wind turbine controller

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Hansen, Morten Hartvig

2016-01-01

control and opens the market of retrofitting existing wind turbines with the new technology. In this paper, we suggest a model predictive controller (MPC) that is added to the basic gain scheduled PI controller of a WT to enhance the performance of the closed loop system using LIDAR measurements...

Muscle Control and Non-specific Chronic Low Back Pain.

Science.gov (United States)

Russo, Marc; Deckers, Kristiaan; Eldabe, Sam; Kiesel, Kyle; Gilligan, Chris; Vieceli, John; Crosby, Peter

2018-01-01

Chronic low back pain (CLBP) is the most prevalent of the painful musculoskeletal conditions. CLBP is a heterogeneous condition with many causes and diagnoses, but there are few established therapies with strong evidence of effectiveness (or cost effectiveness). CLBP for which it is not possible to identify any specific cause is often referred to as non-specific chronic LBP (NSCLBP). One type of NSCLBP is continuing and recurrent primarily nociceptive CLBP due to vertebral joint overload subsequent to functional instability of the lumbar spine. This condition may occur due to disruption of the motor control system to the key stabilizing muscles in the lumbar spine, particularly the lumbar multifidus muscle (MF). This review presents the evidence for MF involvement in CLBP, mechanisms of action of disruption of control of the MF, and options for restoring control of the MF as a treatment for NSCLBP. Imaging assessment of motor control dysfunction of the MF in individual patients is fraught with difficulty. MRI or ultrasound imaging techniques, while reliable, have limited diagnostic or predictive utility. For some patients, restoration of motor control to the MF with specific exercises can be effective, but population results are not persuasive since most patients are unable to voluntarily contract the MF and may be inhibited from doing so due to arthrogenic muscle inhibition. Targeting MF control with restorative neurostimulation promises a new treatment option. © 2017 The Authors. Neuromodulation: Technology at the Neural Interface published by Wiley Periodicals, Inc. on behalf of International Neuromodulation Society.

Predictive Function Control for Communication-Based Train Control (CBTC Systems

Directory of Open Access Journals (Sweden)

Bing Bu

2013-01-01

Full Text Available In Communication-Based Train Control (CBTC systems, random transmission delays and packet drops are inevitable in the wireless networks, which could result in unnecessary traction, brakes or even emergency brakes of trains, losses of line capacity and passenger dissatisfaction. This paper applies predictive function control technology with a mixed H2/∞ control approach to improve the control performances. The controller is in the state feedback form and satisfies the requirement of quadratic input and state constraints. A linear matrix inequality (LMI approach is developed to solve the control problem. The proposed method attenuates disturbances by incorporating H2/∞ into the control scheme. The control command from the automatic train operation (ATO is included in the reward function to optimize the train's running profile. The influence of transmission delays and packet drops is alleviated through improving the performances of the controller. Simulation results show that the method is effective to improve the performances and robustness of CBTC systems.

Stability of a neural predictive controller scheme on a neural model

DEFF Research Database (Denmark)

Luther, Jim Benjamin; Sørensen, Paul Haase

2009-01-01

In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

Unchanged Levels of Soluble CD14 and IL-6 Over Time Predict Serious Non-AIDS Events in HIV-1-Infected People

Science.gov (United States)

Sunil, Meena; Nigalye, Maitreyee; Somasunderam, Anoma; Martinez, Maria Laura; Yu, Xiaoying; Arduino, Roberto C.; Bell, Tanvir K.

2016-01-01

Abstract HIV-1-infected persons have increased risk of serious non-AIDS events (SNAEs) despite suppressive antiretroviral therapy. Increased circulating levels of soluble CD14 (sCD14), soluble CD163 (sCD163), and interleukin-6 (IL-6) at a single time point have been associated with SNAEs. However, whether changes in these biomarker levels predict SNAEs in HIV-1-infected persons is unknown. We hypothesized that greater decreases in inflammatory biomarkers would be associated with fewer SNAEs. We identified 39 patients with SNAEs, including major cardiovascular events, end stage renal disease, decompensated cirrhosis, non-AIDS-defining malignancies, and death of unknown cause, and age- and sex-matched HIV-1-infected controls. sCD14, sCD163, and IL-6 were measured at study enrollment (T1) and proximal to the event (T2) or equivalent duration in matched controls. Over ∼34 months, unchanged rather than decreasing levels of sCD14 and IL-6 predicted SNAEs. Older age and current illicit substance abuse, but not HCV coinfection, were associated with SNAEs. In a multivariate analysis, older age, illicit substance use, and unchanged IL-6 levels remained significantly associated with SNAEs. Thus, the trajectories of sCD14 and IL-6 levels predict SNAEs. Interventions to decrease illicit substance use may decrease the risk of SNAEs in HIV-1-infected persons. PMID:27344921

Modeling, Prediction, and Control of Heating Temperature for Tube Billet

Directory of Open Access Journals (Sweden)

Yachun Mao

2015-01-01

Full Text Available Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.

Two stage neural network modelling for robust model predictive control.

Science.gov (United States)

Patan, Krzysztof

2018-01-01

The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Hybrid model predictive control applied to switching control of burner load for a compact marine boiler design

DEFF Research Database (Denmark)

Solberg, Brian; Andersen, Palle; Maciejowski, Jan

2008-01-01

This paper discusses the application of hybrid model predictive control to control switching between different burner modes in a novel compact marine boiler design. A further purpose of the present work is to point out problems with finite horizon model predictive control applied to systems for w...

Predictive Psychiatric Genetic Testing in Minors: An Exploration of the Non-Medical Benefits.

Science.gov (United States)

Manzini, Arianna; Vears, Danya F

2018-03-01

Predictive genetic testing for susceptibility to psychiatric conditions is likely to become part of standard practice. Because the onset of most psychiatric diseases is in late adolescence or early adulthood, testing minors could lead to early identification that may prevent or delay the development of these disorders. However, due to their complex aetiology, psychiatric genetic testing does not provide the immediate medical benefits that current guidelines require for testing minors. While several authors have argued non-medical benefits may play a crucial role in favour of predictive testing for other conditions, little research has explored such a role in psychiatric disorders. This paper outlines the potential non-medical benefits and harms of psychiatric genetic testing in minors in order to consider whether the non-medical benefits could ever make such testing appropriate. Five non-medical themes arise in the literature: psychological impacts, autonomy/self-determination, implications of the biomedical approach, use of financial and intellectual resources, and discrimination. Non-medical benefits were prominent in all of them, suggesting that psychiatric genetic testing in minors may be appropriate in some circumstances. Further research needs to empirically assess these potential non-medical benefits, incorporate minors in the debate, and include normative reflection to evaluate the very purposes and motivations of psychiatric genetic testing in minors.

Range-Space Predictive Control for Optimal Robot Motion

Czech Academy of Sciences Publication Activity Database

Belda, Květoslav; Böhm, Josef

2008-01-01

Roč. 1, č. 1 (2008), s. 1-7 ISSN 1998-0140 R&D Projects: GA ČR GP102/06/P275 Institutional research plan: CEZ:AV0Z10750506 Keywords : Accurate manipulation * Industrial robotics * Predictive control * Range-space control Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/historie/belda-0305644.pdf

Predicting worsening asthma control following the common cold

NARCIS (Netherlands)

Walter, M. J.; Castro, M.; Kunselman, S. J.; Chinchilli, V. M.; Reno, M.; Ramkumar, T. P.; Avila, P. C.; Boushey, H. A.; Ameredes, B. T.; Bleecker, E. R.; Calhoun, W. J.; Cherniack, R. M.; Craig, T. J.; Denlinger, L. C.; Israel, E.; Fahy, J. V.; Jarjour, N. N.; Kraft, M.; Lazarus, S. C.; Lemanske, R. F.; Martin, R. J.; Peters, S. P.; Ramsdell, J. W.; Sorkness, C. A.; Sutherland, E. R.; Szefler, S. J.; Wasserman, S. I.; Wechsler, M. E.

2008-01-01

The asthmatic response to the common cold is highly variable, and early characteristics that predict worsening of asthma control following a cold have not been identified. In this prospective multicentric cohort study of 413 adult subjects with asthma, the mini-Asthma Control Questionnaire

7 CFR 772.10 - Transfer and assumption-AMP loans.

Science.gov (United States)

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Transfer and assumption-AMP loans. 772.10 Section 772..., DEPARTMENT OF AGRICULTURE SPECIAL PROGRAMS SERVICING MINOR PROGRAM LOANS § 772.10 Transfer and assumption—AMP loans. (a) Eligibility. The Agency may approve transfers and assumptions of AMP loans when: (1) The...

Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control

Directory of Open Access Journals (Sweden)

Yuki Minami

2018-04-01

Full Text Available In recent years, automatic driving control has attracted attention. To achieve a satisfactory driving control performance, the prediction accuracy of the traveling route is important. If a highly accurate prediction method can be used, an accurate traveling route can be obtained. Despite the considerable efforts that have been invested in improving prediction methods, prediction errors do occur in general. Thus, a method to minimize the influence of prediction errors on automatic driving control systems is required. This need motivated us to focus on the design of a mechanism for shaping prediction signals, which is called a prediction governor. In this study, we first extended our previous study to the input-affine nonlinear system case. Then, we analytically derived a solution to an optimal design problem of prediction governors. Finally, we applied the solution to an automatic driving control system, and demonstrated its usefulness through a numerical example and an experiment using a radio controlled car.

Quantifying predictability through information theory: small sample estimation in a non-Gaussian framework

International Nuclear Information System (INIS)

Haven, Kyle; Majda, Andrew; Abramov, Rafail

2005-01-01

Many situations in complex systems require quantitative estimates of the lack of information in one probability distribution relative to another. In short term climate and weather prediction, examples of these issues might involve the lack of information in the historical climate record compared with an ensemble prediction, or the lack of information in a particular Gaussian ensemble prediction strategy involving the first and second moments compared with the non-Gaussian ensemble itself. The relative entropy is a natural way to quantify the predictive utility in this information, and recently a systematic computationally feasible hierarchical framework has been developed. In practical systems with many degrees of freedom, computational overhead limits ensemble predictions to relatively small sample sizes. Here the notion of predictive utility, in a relative entropy framework, is extended to small random samples by the definition of a sample utility, a measure of the unlikeliness that a random sample was produced by a given prediction strategy. The sample utility is the minimum predictability, with a statistical level of confidence, which is implied by the data. Two practical algorithms for measuring such a sample utility are developed here. The first technique is based on the statistical method of null-hypothesis testing, while the second is based upon a central limit theorem for the relative entropy of moment-based probability densities. These techniques are tested on known probability densities with parameterized bimodality and skewness, and then applied to the Lorenz '96 model, a recently developed 'toy' climate model with chaotic dynamics mimicking the atmosphere. The results show a detection of non-Gaussian tendencies of prediction densities at small ensemble sizes with between 50 and 100 members, with a 95% confidence level

One-Step-Ahead Predictive Control for Hydroturbine Governor

Directory of Open Access Journals (Sweden)

Zhihuai Xiao

2015-01-01

Full Text Available The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.

Distributed model predictive control made easy

CERN Document Server

Negenborn, Rudy

2014-01-01

The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

Predicting body temperature of endotherms during shuttling

NARCIS (Netherlands)

Rodriguez-Girones, M.A.

2002-01-01

This paper presents two models that can be used to predict the temporal dynamics of body temperature in endotherms. A first-order model is based on the assumption that body temperature is uniform at all times, while a second-order model is based on the assumption that animals can be divided in a

Model for predicting non-linear crack growth considering load sequence effects (LOSEQ)

International Nuclear Information System (INIS)

Fuehring, H.

1982-01-01

A new analytical model for predicting non-linear crack growth is presented which takes into account the retardation as well as the acceleration effects due to irregular loading. It considers not only the maximum peak of a load sequence to effect crack growth but also all other loads of the history according to a generalised memory criterion. Comparisons between crack growth predicted by using the LOSEQ-programme and experimentally observed data are presented. (orig.) [de

Direct model-based predictive control scheme without cost function for voltage source inverters with reduced common-mode voltage

Science.gov (United States)

Kim, Jae-Chang; Moon, Sung-Ki; Kwak, Sangshin

2018-04-01

This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of -Vdc/6 to +Vdc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±Vdc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.

Robust Output Model Predictive Control of an Unstable Rijke Tube

Directory of Open Access Journals (Sweden)

Fabian Jarmolowitz

2012-01-01

Full Text Available This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC. As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.

Non-destructive control in nuclear construction

International Nuclear Information System (INIS)

Banus; Barbier; Launay

1978-01-01

Having recalled the characteristics of the fundamental components of the main primary circuit of nuclear boilers (900 MW) and the means appropriated for their control, it is recalled that the 'French Electricity Board's specifications and control rules' often prescribe more severe criteria than those existing in the U.S.A. Then practical examples of non-destructive controls concerning the steam generator end plates, vessel stainless steel linings, pump attachements, steam generator pipes are given [fr

Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit

DEFF Research Database (Denmark)

Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan

2012-01-01

We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures...

Repetitive model predictive approach to individual pitch control of wind turbines

DEFF Research Database (Denmark)

Adegas, Fabiano Daher; Stoustrup, Jakob; Odgaard, Peter Fogh

2011-01-01

prediction. As a consequence, individual pitch feed-forward control action is generated by the controller, taking ”future” wind disturbance into account. Information about the estimated wind spatial distribution one blade experience can be used in the prediction model to better control the next passing blade......Wind turbines are inherently exposed to nonuniform wind fields with of wind shear, tower shadow, and possible wake contributions. Asymmetrical aerodynamic rotor loads are a consequence of such periodic, repetitive wind disturbances experienced by the blades. A controller may estimate and use...... this peculiar disturbance pattern to better attenuate loads and regulate power by controlling the blade pitch angles individually. A novel model predictive (MPC) approach for individual pitch control of wind turbines is proposed in this paper. A repetitive wind disturbance model is incorporated into the MPC...

Artificial neural network implementation of a near-ideal error prediction controller

Science.gov (United States)

Mcvey, Eugene S.; Taylor, Lynore Denise

1992-01-01

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error

Modelling carbonaceous aerosol from residential solid fuel burning with different assumptions for emissions

Directory of Open Access Journals (Sweden)

R. Ots

2018-04-01

Full Text Available Evidence is accumulating that emissions of primary particulate matter (PM from residential wood and coal combustion in the UK may be underestimated and/or spatially misclassified. In this study, different assumptions for the spatial distribution and total emission of PM from solid fuel (wood and coal burning in the UK were tested using an atmospheric chemical transport model. Modelled concentrations of the PM components were compared with measurements from aerosol mass spectrometers at four sites in central and Greater London (ClearfLo campaign, 2012, as well as with measurements from the UK black carbon network.The two main alternative emission scenarios modelled were Base4x and combRedist. For Base4x, officially reported PM2.5 from the residential and other non-industrial combustion source sector were increased by a factor of four. For the combRedist experiment, half of the baseline emissions from this same source were redistributed by residential population density to simulate the effect of allocating some emissions to the smoke control areas (that are assumed in the national inventory to have no emissions from this source. The Base4x scenario yielded better daily and hourly correlations with measurements than the combRedist scenario for year-long comparisons of the solid fuel organic aerosol (SFOA component at the two London sites. However, the latter scenario better captured mean measured concentrations across all four sites. A third experiment, Redist – all emissions redistributed linearly to population density, is also presented as an indicator of the maximum concentrations an assumption like this could yield.The modelled elemental carbon (EC concentrations derived from the combRedist experiments also compared well with seasonal average concentrations of black carbon observed across the network of UK sites. Together, the two model scenario simulations of SFOA and EC suggest both that residential solid fuel emissions may be higher than

Modelling carbonaceous aerosol from residential solid fuel burning with different assumptions for emissions

Science.gov (United States)

Ots, Riinu; Heal, Mathew R.; Young, Dominique E.; Williams, Leah R.; Allan, James D.; Nemitz, Eiko; Di Marco, Chiara; Detournay, Anais; Xu, Lu; Ng, Nga L.; Coe, Hugh; Herndon, Scott C.; Mackenzie, Ian A.; Green, David C.; Kuenen, Jeroen J. P.; Reis, Stefan; Vieno, Massimo

2018-04-01

Evidence is accumulating that emissions of primary particulate matter (PM) from residential wood and coal combustion in the UK may be underestimated and/or spatially misclassified. In this study, different assumptions for the spatial distribution and total emission of PM from solid fuel (wood and coal) burning in the UK were tested using an atmospheric chemical transport model. Modelled concentrations of the PM components were compared with measurements from aerosol mass spectrometers at four sites in central and Greater London (ClearfLo campaign, 2012), as well as with measurements from the UK black carbon network.The two main alternative emission scenarios modelled were Base4x and combRedist. For Base4x, officially reported PM2.5 from the residential and other non-industrial combustion source sector were increased by a factor of four. For the combRedist experiment, half of the baseline emissions from this same source were redistributed by residential population density to simulate the effect of allocating some emissions to the smoke control areas (that are assumed in the national inventory to have no emissions from this source). The Base4x scenario yielded better daily and hourly correlations with measurements than the combRedist scenario for year-long comparisons of the solid fuel organic aerosol (SFOA) component at the two London sites. However, the latter scenario better captured mean measured concentrations across all four sites. A third experiment, Redist - all emissions redistributed linearly to population density, is also presented as an indicator of the maximum concentrations an assumption like this could yield.The modelled elemental carbon (EC) concentrations derived from the combRedist experiments also compared well with seasonal average concentrations of black carbon observed across the network of UK sites. Together, the two model scenario simulations of SFOA and EC suggest both that residential solid fuel emissions may be higher than inventory

The ability of 'non-cognitive' traits to predict undergraduate performance in medical schools: a national linkage study.

Science.gov (United States)

Finn, Gabrielle M; Mwandigha, Lazaro; Paton, Lewis W; Tiffin, Paul A

2018-05-03

In addition to the evaluation of educational attainment and intellectual ability there has been interest in the potential to select medical school applicants on non-academic qualities. Consequently, a battery of self-report measures concerned with assessing 'non-cognitive' traits was piloted as part of the UK Clinical Aptitude Test (UKCAT) administration to evaluate their potential to be used in selection. The four non-cognitive instruments piloted were: 1) the Libertarian-communitarian scale, (2) The NACE (narcissism, aloofness, confidence and empathy, (3) the MEARS (Managing emotions and resilience scale; self-esteem, optimism, control, self-discipline, emotional-nondefensiveness and faking, and (4) an abridged version of instruments (1) and (2) combined. Non-cognitive scores and sociodemographic characteristics were available for 14,387 applicants. A series of univariable and multivariable analyses were conducted in order to assess the ability of the non-cognitive scores to predict knowledge and skills-based performance, as well as the odds of passing each academic year at first attempt. Non-cognitive scores and medical performance were standardised within cohorts. The scores on the non-cognitive scales showed only very small (magnitude of standardised betas< 0.2), though sometimes statistically significant (p < 0.01) univariable associations with subsequent performance on knowledge or skills-based assessments. The only statistically significant association between the non-cognitive scores and the probability of passing an academic year at first attempt was the narcissism score from one the abridged tests (OR 0.84,95% confidence intervals 0.71 to 0.97, p = 0.02). Our findings are consistent with previously published research. The tests had a very limited ability to predict undergraduate academic performance, though further research on identifying narcissism in medical students may be warranted. However, the validity of such self-report tools in high

Biocomputational prediction of small non-coding RNAs in Streptomyces

Czech Academy of Sciences Publication Activity Database

Pánek, Josef; Bobek, Jan; Mikulík, Karel; Basler, Marek; Vohradský, Jiří

2008-01-01

Roč. 9, č. 217 (2008), s. 1-14 ISSN 1471-2164 R&D Projects: GA ČR GP204/07/P361; GA ČR GA203/05/0106; GA ČR GA310/07/1009 Grant - others:XE(XE) EC Integrated Project ActinoGEN, LSHM-CT-2004-005224. Institutional research plan: CEZ:AV0Z50200510 Keywords : non-coding RNA * streptomyces * biocomputational prediction Subject RIV: IN - Informatics, Computer Science Impact factor: 3.926, year: 2008

Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

Science.gov (United States)

Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

2016-01-01

A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

Science.gov (United States)

Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

2010-09-01

The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the

Predictions for an invaded world: A strategy to predict the distribution of native and non-indigenous species at multiple scales

Science.gov (United States)

Reusser, D.A.; Lee, H.

2008-01-01

Habitat models can be used to predict the distributions of marine and estuarine non-indigenous species (NIS) over several spatial scales. At an estuary scale, our goal is to predict the estuaries most likely to be invaded, but at a habitat scale, the goal is to predict the specific locations within an estuary that are most vulnerable to invasion. As an initial step in evaluating several habitat models, model performance for a suite of benthic species with reasonably well-known distributions on the Pacific coast of the US needs to be compared. We discuss the utility of non-parametric multiplicative regression (NPMR) for predicting habitat- and estuary-scale distributions of native and NIS. NPMR incorporates interactions among variables, allows qualitative and categorical variables, and utilizes data on absence as well as presence. Preliminary results indicate that NPMR generally performs well at both spatial scales and that distributions of NIS are predicted as well as those of native species. For most species, latitude was the single best predictor, although similar model performance could be obtained at both spatial scales with combinations of other habitat variables. Errors of commission were more frequent at a habitat scale, with omission and commission errors approximately equal at an estuary scale. ?? 2008 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved.

Predicting scholars' scientific impact.

Directory of Open Access Journals (Sweden)

Amin Mazloumian

Full Text Available We tested the underlying assumption that citation counts are reliable predictors of future success, analyzing complete citation data on the careers of ~150,000 scientists. Our results show that i among all citation indicators, the annual citations at the time of prediction is the best predictor of future citations, ii future citations of a scientist's published papers can be predicted accurately (r(2 = 0.80 for a 1-year prediction, P<0.001 but iii future citations of future work are hardly predictable.

Understanding and predicting soot generation in turbulent non-premixed jet flames.

Energy Technology Data Exchange (ETDEWEB)

Wang, Hai (University of Southern California, Los Angeles, CA); Kook, Sanghoon; Doom, Jeffrey; Oefelein, Joseph Charles; Zhang, Jiayao; Shaddix, Christopher R.; Schefer, Robert W.; Pickett, Lyle M.

2010-10-01

This report documents the results of a project funded by DoD's Strategic Environmental Research and Development Program (SERDP) on the science behind development of predictive models for soot emission from gas turbine engines. Measurements of soot formation were performed in laminar flat premixed flames and turbulent non-premixed jet flames at 1 atm pressure and in turbulent liquid spray flames under representative conditions for takeoff in a gas turbine engine. The laminar flames and open jet flames used both ethylene and a prevaporized JP-8 surrogate fuel composed of n-dodecane and m-xylene. The pressurized turbulent jet flame measurements used the JP-8 surrogate fuel and compared its combustion and sooting characteristics to a world-average JP-8 fuel sample. The pressurized jet flame measurements demonstrated that the surrogate was representative of JP-8, with a somewhat higher tendency to soot formation. The premixed flame measurements revealed that flame temperature has a strong impact on the rate of soot nucleation and particle coagulation, but little sensitivity in the overall trends was found with different fuels. An extensive array of non-intrusive optical and laser-based measurements was performed in turbulent non-premixed jet flames established on specially designed piloted burners. Soot concentration data was collected throughout the flames, together with instantaneous images showing the relationship between soot and the OH radical and soot and PAH. A detailed chemical kinetic mechanism for ethylene combustion, including fuel-rich chemistry and benzene formation steps, was compiled, validated, and reduced. The reduced ethylene mechanism was incorporated into a high-fidelity LES code, together with a moment-based soot model and models for thermal radiation, to evaluate the ability of the chemistry and soot models to predict soot formation in the jet diffusion flame. The LES results highlight the importance of including an optically-thick radiation

Model Predictive Control for Connected Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Kaijiang Yu

2015-01-01

Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

Model Predictive Control based on Finite Impulse Response Models

DEFF Research Database (Denmark)

Prasath, Guru; Jørgensen, John Bagterp

2008-01-01

We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

Predictive IP controller for robust position control of linear servo system.

Science.gov (United States)

Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi

2016-07-01

Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Directory of Open Access Journals (Sweden)

Mohamed R. Al-Mulla

2011-03-01

Full Text Available Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results.

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Science.gov (United States)

Al-Mulla, Mohamed R.; Sepulveda, Francisco; Colley, Martin

2011-01-01

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results. PMID:22163810

Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

KAUST Repository

Wang, Jim Jing-Yan; Almasri, Islam; Shi, Yuexiang; Gao, Xin

2014-01-01

of the transductive semi-supervised algorithms takes all the three semisupervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during learning. In this paper, we propose a novel semi-supervised method for hot spot residue

Adaptive Non-linear Control of Hydraulic Actuator Systems

DEFF Research Database (Denmark)

Hansen, Poul Erik; Conrad, Finn

1998-01-01

Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF).......Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF)....

Model predictive controller design of hydrocracker reactors

OpenAIRE

GÖKÇE, Dila

2011-01-01

This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

Clinical review: Moral assumptions and the process of organ donation in the intensive care unit

OpenAIRE

Streat, Stephen

2004-01-01

The objective of the present article is to review moral assumptions underlying organ donation in the intensive care unit. Data sources used include personal experience, and a Medline search and a non-Medline search of relevant English-language literature. The study selection included articles concerning organ donation. All data were extracted and analysed by the author. In terms of data synthesis, a rational, utilitarian moral perspective dominates, and has captured and circumscribed, the lan...

Non linear predictive control of a LEGO mobile robot

Science.gov (United States)

Merabti, H.; Bouchemal, B.; Belarbi, K.; Boucherma, D.; Amouri, A.

2014-10-01

Metaheuristics are general purpose heuristics which have shown a great potential for the solution of difficult optimization problems. In this work, we apply the meta heuristic, namely particle swarm optimization, PSO, for the solution of the optimization problem arising in NLMPC. This algorithm is easy to code and may be considered as alternatives for the more classical solution procedures. The PSO- NLMPC is applied to control a mobile robot for the tracking trajectory and obstacles avoidance. Experimental results show the strength of this approach.

A distributed predictive control approach for periodic flow-based networks: application to drinking water systems

Science.gov (United States)

Grosso, Juan M.; Ocampo-Martinez, Carlos; Puig, Vicenç

2017-10-01

This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.

Robust Model Predictive Control of a Wind Turbine

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2012-01-01

In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...

Predictive capacity of a non-radioisotopic local lymph node assay using flow cytometry, LLNA:BrdU-FCM: Comparison of a cutoff approach and inferential statistics.

Science.gov (United States)

Kim, Da-Eun; Yang, Hyeri; Jang, Won-Hee; Jung, Kyoung-Mi; Park, Miyoung; Choi, Jin Kyu; Jung, Mi-Sook; Jeon, Eun-Young; Heo, Yong; Yeo, Kyung-Wook; Jo, Ji-Hoon; Park, Jung Eun; Sohn, Soo Jung; Kim, Tae Sung; Ahn, Il Young; Jeong, Tae-Cheon; Lim, Kyung-Min; Bae, SeungJin

2016-01-01

In order for a novel test method to be applied for regulatory purposes, its reliability and relevance, i.e., reproducibility and predictive capacity, must be demonstrated. Here, we examine the predictive capacity of a novel non-radioisotopic local lymph node assay, LLNA:BrdU-FCM (5-bromo-2'-deoxyuridine-flow cytometry), with a cutoff approach and inferential statistics as a prediction model. 22 reference substances in OECD TG429 were tested with a concurrent positive control, hexylcinnamaldehyde 25%(PC), and the stimulation index (SI) representing the fold increase in lymph node cells over the vehicle control was obtained. The optimal cutoff SI (2.7≤cutoff <3.5), with respect to predictive capacity, was obtained by a receiver operating characteristic curve, which produced 90.9% accuracy for the 22 substances. To address the inter-test variability in responsiveness, SI values standardized with PC were employed to obtain the optimal percentage cutoff (42.6≤cutoff <57.3% of PC), which produced 86.4% accuracy. A test substance may be diagnosed as a sensitizer if a statistically significant increase in SI is elicited. The parametric one-sided t-test and non-parametric Wilcoxon rank-sum test produced 77.3% accuracy. Similarly, a test substance could be defined as a sensitizer if the SI means of the vehicle control, and of the low, middle, and high concentrations were statistically significantly different, which was tested using ANOVA or Kruskal-Wallis, with post hoc analysis, Dunnett, or DSCF (Dwass-Steel-Critchlow-Fligner), respectively, depending on the equal variance test, producing 81.8% accuracy. The absolute SI-based cutoff approach produced the best predictive capacity, however the discordant decisions between prediction models need to be examined further. Copyright © 2015 Elsevier Inc. All rights reserved.

Treatment of two-phase turbulent mixing, void drift and diversion cross-flow in a hydraulically non-equilibrium subchannel flow

International Nuclear Information System (INIS)

Sadatomi, Michio; Kawahara, Akimaro; Sato, Yoshifusa

1997-01-01

A practical way of treating two-phase turbulent mixing, void drift and diversion cross-flow on a subchannel analysis has been studied. Experimental data on the axial variations of subchannel flow parameters, such as flow rates of both phases, pressure, void fraction and concentrations of tracers for both phases, were obtained for hydraulically non-equilibrium two-phase subchannel flows in a vertical multiple channel made up of two-identical circular subchannels. These data were analyzed on the basis of the following four assumptions: (1) the turbulent mixing is independent of both the void drift and the diversion cross-flow; (2) the turbulent mixing rates of both phases in a non-equilibrium flow are equal to those in the equilibrium flow that the flow under consideration will attain; (3) the void drift is independent of the diversion cross-flow; and (4) the lateral gas velocity due to the void drift is predictable from Lahey et al.'s void settling model even in a non-equilibrium flow with the diversion cross-flow. The validity of the assumptions (1) and (2) was assured by comparing the concentration distribution data with the calculations, and that of the assumptions (3) and (4) by analyzing the data on flow rates of both phases, pressure and void fraction (author)

Enhanced Voltage Control of VSC-HVDC Connected Offshore Wind Farms Based on Model Predictive Control

DEFF Research Database (Denmark)

Guo, Yifei; Gao, Houlei; Wu, Qiuwei

2018-01-01

This paper proposes an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage source converter based high voltage direct current (VSCHVDC) connected offshore wind farms (OWFs). In the proposed MPC based EVCS, all wind turbine generators (WTGs) as well...... as the wind farm side VSC are optimally coordinated to keep voltages within the feasible range and reduce system power losses. Considering the high ratio of the OWF collector system, the effects of active power outputs of WTGs on voltage control are also taken into consideration. The predictive model of VSC...

Nonlinear Predictive Sliding Mode Control for Active Suspension System

Directory of Open Access Journals (Sweden)

Dazhuang Wang

2018-01-01

Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.

Generic Model Predictive Control Framework for Advanced Driver Assistance Systems

NARCIS (Netherlands)

Wang, M.

2014-01-01

This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the

Symptoms of non-gonococcal urethritis in heterosexual men: a case control study.

Science.gov (United States)

Iser, P; Read, Tr H; Tabrizi, S; Bradshaw, C; Lee, D; Horvarth, L; Garland, S; Denham, I; Fairley, C K

2005-04-01

To determine microbial and behavioural factors contributing to non-gonococcal urethral symptoms in men. Case-control study of heterosexual men with non-gonococcal urethral symptoms (cases) and without urethral symptoms (controls) attending Melbourne Sexual Health Centre, Australia. Sexual behaviour and condom use were measured by questionnaire. First stream urine was tested for potential pathogens: Chlamydia trachomatis (ligase chain reaction), Mycoplasma genitalium (polymerase chain reaction, PCR), Ureaplasma urealyticum (culture and PCR), and Streptococcus spp, Gardnerella vaginalis, and Haemophilus species (culture). Urethral smears from cases were examined for polymorphonuclear leucocytes. 80 cases and 79 controls were recruited over 4 months in 2002-3. 49 cases (61%) had urethritis by microscopic criteria, 17 (21%) had Chlamydia trachomatis (adjusted odds ratio (OR) 27 (95% confidence interval (CI): 3.4 to 222)), five (6%) had Mycoplasma genitalium (OR 6.1 (95% CI: 0.6 to 61)), and 11 (14%) had Gardnerella vaginalis (OR 9.0 (95% CI: 1.6 to 52)). Other organisms were not significantly associated with urethral symptoms. The presence of urethritis on urethral smear did not predict the presence of Chlamydia trachomatis (OR 1.7 (95% CI: 0.5 to 5.4)). Urethral symptoms were significantly associated with unprotected vaginal sex with more than one casual partner (OR 9.3 (95% CI: 1.3 to 65)) and unprotected anal sex with a regular partner in the past month (OR 3.5 (95% CI: 1.0 to 13)). Gardnerella vaginalis and unprotected anal sex may cause symptoms of non-gonococcal urethritis. Microscopy of the urethral smear to diagnose urethritis in this population does not help to identify which men with urethral symptoms require treatment for chlamydia.

A Quasi-Dynamic Optimal Control Strategy for Non-Linear Multivariable Processes Based upon Non-Quadratic Objective Functions

Directory of Open Access Journals (Sweden)

Jens G. Balchen

1984-10-01

Full Text Available The problem of systematic derivation of a quasi-dynamic optimal control strategy for a non-linear dynamic process based upon a non-quadratic objective function is investigated. The wellknown LQG-control algorithm does not lead to an optimal solution when the process disturbances have non-zero mean. The relationships between the proposed control algorithm and LQG-control are presented. The problem of how to constrain process variables by means of 'penalty' - terms in the objective function is dealt with separately.

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.

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.

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

Directory of Open Access Journals (Sweden)

Jin Xin

2017-10-01

Full Text Available This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

An estimator-based distributed voltage-predictive control strategy for ac islanded microgrids

DEFF Research Database (Denmark)

Wang, Yanbo; Chen, Zhe; Wang, Xiongfei

2015-01-01

This paper presents an estimator-based voltage predictive control strategy for AC islanded microgrids, which is able to perform voltage control without any communication facilities. The proposed control strategy is composed of a network voltage estimator and a voltage predictive controller for each...... and has a good capability to reject uncertain perturbations of islanded microgrids....

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Directory of Open Access Journals (Sweden)

César Hernández-Hernández

2017-06-01

Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

Footbridge Response Predictions and Their Sensitivity to Stochastic Load Assumptions

DEFF Research Database (Denmark)

Pedersen, Lars; Frier, Christian

2011-01-01

Knowledge about footbridges response to actions of walking is important in assessments of vibration serviceability. In a number of design codes for footbridges, the vibration serviceability limit state is assessed using a walking load model in which the walking parameters (step frequency, pedestr......Knowledge about footbridges response to actions of walking is important in assessments of vibration serviceability. In a number of design codes for footbridges, the vibration serviceability limit state is assessed using a walking load model in which the walking parameters (step frequency...... of pedestrians for predicting footbridge response, which is meaningful, and a step forward. Modelling walking parameters stochastically, however, requires decisions to be made in terms of their statistical distribution and the parameters describing the statistical distribution. The paper investigates...... the sensitivity of results of computations of bridge response to some of the decisions to be made in this respect. This is a useful approach placing focus on which decisions (and which information) are important for sound estimation of bridge response. The studies involve estimating footbridge responses using...

Predictive control of irrigation canals – robust design and real-time implementation

NARCIS (Netherlands)

Aguilar, José V.; Langarita, Pedro; Rodellar, José; Linares, Lorenzo; Horváth, K.

2016-01-01

Predictive control is one of the most commonly used control methods in a variety of application areas, including hydraulic processes such as water distribution canals for irrigation. This article presents the design and application of predictive control for the water discharge entering into an

Enhanced pid vs model predictive control applied to bldc motor

Science.gov (United States)

Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

2018-01-01

BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

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 ...... controller is shown very reliable keeping the comfort levels in the two considered seasons and shifting the load away from peak hours in order to achieve the desired flexible electricity consumption.......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...

Predictive Factors of Suicide Attempt and Non-Suicidal Self-Harm in Emergency Department

Directory of Open Access Journals (Sweden)

Saad Salman

2014-09-01

Full Text Available Introduction: Suicide is the third cause of mortality in America, second leading cause of death in developed countries, and one of the major health problems. Self-harm is self-inflicted damage to one’s self with or without suicidal intent. In the present study, the predictive factors of suicide attempt and non-suicidal self-harm were evaluated in patients referred to emergency department (ED with these problem. Methods: The total number of 45 patients with suicide attempt or self-harm admitted to ED were included. Clinical symptoms, thoughts and behaviors of suicidal, and non-suicidal self-harm in these patients were evaluated at baseline. Suicidality, suicidal intent and ideation, non-suicidal self-injury, social withdrawal, disruptive behavior, and poor family functions were evaluated at admission time. Brief clinical visits were scheduled for the twelfth weeks. In the twelfth week, patients returned for their final visit to determine their maintenance treatment. Finally data were analyzed using chi-squared and multiple logistic regression. Results: Forty five patients were included in the study (56.1% female. The mean age of patients was 23.3±10.2 years (range: 15-75; 33.3% married. Significant association of suicide and self-injury was presented at the baseline and in the month before attempting (p=0.001. The most important predictive factors of suicide and self-harm based on univariate analysis were depression (suicidal and non-suicidal items of Hamilton depression rating scale, anxiety, hopelessness, younger age, history of non-suicidal self-harm and female gender (p<0.05. The participants’ quality of life analysis showed a significant higher quality in physical component summary (p=0.002, mental component summary (p=0.001, and general health (p=0.001 at follow up period. Conclusion: At the time of admission in ED, suicide attempt and non-suicidal self-harm are subsequent clinical markers for the patient attempting suicide again. The

Robust block bootstrap panel predictability tests

NARCIS (Netherlands)

Westerlund, J.; Smeekes, S.

2013-01-01

Most panel data studies of the predictability of returns presume that the cross-sectional units are independent, an assumption that is not realistic. As a response to this, the current paper develops block bootstrap-based panel predictability tests that are valid under very general conditions. Some

Robust multi-model predictive control of multi-zone thermal plate system

Directory of Open Access Journals (Sweden)

Poom Jatunitanon

2018-02-01

Full Text Available A modern controller was designed by using the mathematical model of a multi–zone thermal plate system. An important requirement for this type of controller is that it must be able to keep the temperature set-point of each thermal zone. The mathematical model used in the design was determined through a system identification process. The results showed that when the operating condition is changed, the performance of the controller may be reduced as a result of the system parameter uncertainties. This paper proposes a weighting technique of combining the robust model predictive controller for each operating condition into a single robust multi-model predictive control. Simulation and experimental results showed that the proposed method performed better than the conventional multi-model predictive control in rise time of transient response, when used in a system designed to work over a wide range of operating conditions.

Network control principles predict neuron function in the Caenorhabditis elegans connectome

Science.gov (United States)

Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László