Hybrid modeling and prediction of dynamical systems
Lloyd, Alun L.; Flores, Kevin B.
2017-01-01
Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642
Active diagnosis of hybrid systems - A model predictive approach
2009-01-01
A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeate...
Active diagnosis of hybrid systems - A model predictive approach
DEFF Research Database (Denmark)
Tabatabaeipour, Seyed Mojtaba; Ravn, Anders P.; Izadi-Zamanabadi, Roozbeh;
2009-01-01
A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty...... outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeated until the fault is detected by a passive diagnoser. It is demonstrated how the generated excitation signal...
Hybrid multiscale modeling and prediction of cancer cell behavior.
Zangooei, Mohammad Hossein; Habibi, Jafar
2017-01-01
Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.
Hybrid CFD/CAA Modeling for Liftoff Acoustic Predictions
Strutzenberg, Louise L.; Liever, Peter A.
2011-01-01
This paper presents development efforts at the NASA Marshall Space flight Center to establish a hybrid Computational Fluid Dynamics and Computational Aero-Acoustics (CFD/CAA) simulation system for launch vehicle liftoff acoustics environment analysis. Acoustic prediction engineering tools based on empirical jet acoustic strength and directivity models or scaled historical measurements are of limited value in efforts to proactively design and optimize launch vehicles and launch facility configurations for liftoff acoustics. CFD based modeling approaches are now able to capture the important details of vehicle specific plume flow environment, identifY the noise generation sources, and allow assessment of the influence of launch pad geometric details and sound mitigation measures such as water injection. However, CFD methodologies are numerically too dissipative to accurately capture the propagation of the acoustic waves in the large CFD models. The hybrid CFD/CAA approach combines the high-fidelity CFD analysis capable of identifYing the acoustic sources with a fast and efficient Boundary Element Method (BEM) that accurately propagates the acoustic field from the source locations. The BEM approach was chosen for its ability to properly account for reflections and scattering of acoustic waves from launch pad structures. The paper will present an overview of the technology components of the CFD/CAA framework and discuss plans for demonstration and validation against test data.
Runoff prediction using an integrated hybrid modelling scheme
Remesan, Renji; Shamim, Muhammad Ali; Han, Dawei; Mathew, Jimson
2009-06-01
SummaryRainfall runoff is a very complicated process due to its nonlinear and multidimensional dynamics, and hence difficult to model. There are several options for a modeller to consider, for example: the type of input data to be used, the length of model calibration (training) data and whether or not the input data be treated as signals with different frequency bands so that they can be modelled separately. This paper describes a new hybrid modelling scheme to answer the above mentioned questions. The proposed methodology is based on a hybrid model integrating wavelet transformation, a modelling engine (Artificial Neural Network) and the Gamma Test. First, the Gamma Test is used to decide the required input data dimensions and its length. Second, the wavelet transformation decomposes the input signals into different frequency bands. Finally, a modelling engine (ANN in this study) is used to model the decomposed signals separately. The proposed scheme was tested using the Brue catchment, Southwest England, as a case study and has produced very positive results. The hybrid model outperforms all other models tested. This study has a wider implication in the hydrological modelling field since its general framework could be applied to other model combinations (e.g., model engine could be Support Vector Machines, neuro-fuzzy systems, or even a conceptual model. The signal decomposition could be carried out by Fourier transformation).
Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
Voyant, Cyril; Paoli, Christophe; Nivet, Marie Laure
2012-01-01
We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/ARMA is 14.9% compared to 26.2% for the na\\"ive persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed
Directory of Open Access Journals (Sweden)
Juliana Yim
2009-06-01
Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN’s, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.
Directory of Open Access Journals (Sweden)
Juliana Yim
2005-01-01
Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.
Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues
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Mingheng Zhang
2014-01-01
Full Text Available This paper presents a hybrid model for early onset prediction of driver fatigue, which is the major reason of severe traffic accidents. The proposed method divides the prediction problem into three stages, that is, SVM-based model for predicting the early onset driver fatigue state, GA-based model for optimizing the parameters in the SVM, and PCA-based model for reducing the dimensionality of the complex features datasets. The model and algorithm are illustrated with driving experiment data and comparison results also show that the hybrid method can generally provide a better performance for driver fatigue state prediction.
Hybrid Corporate Performance Prediction Model Considering Technical Capability
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Joonhyuck Lee
2016-07-01
Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.
A Hybrid Neural Network Prediction Model of Air Ticket Sales
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Han-Chen Huang
2013-11-01
Full Text Available Air ticket sales revenue is an important source of revenue for travel agencies, and if future air ticket sales revenue can be accurately forecast, travel agencies will be able to advance procurement to achieve a sufficient amount of cost-effective tickets. Therefore, this study applied the Artificial Neural Network (ANN and Genetic Algorithms (GA to establish a prediction model of travel agency air ticket sales revenue. By verifying the empirical data, this study proved that the established prediction model has accurate prediction power, and MAPE (mean absolute percentage error is only 9.11%. The established model can provide business operators with reliable and efficient prediction data as a reference for operational decisions.
Hybrid experimental/analytical models of structural dynamics - Creation and use for predictions
Balmes, Etienne
1993-01-01
An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.
Kuo, K. A.; Verbraken, H.; Degrande, G.; Lombaert, G.
2016-07-01
Along with the rapid expansion of urban rail networks comes the need for accurate predictions of railway induced vibration levels at grade and in buildings. Current computational methods for making predictions of railway induced ground vibration rely on simplifying modelling assumptions and require detailed parameter inputs, which lead to high levels of uncertainty. It is possible to mitigate against these issues using a combination of field measurements and state-of-the-art numerical methods, known as a hybrid model. In this paper, two hybrid models are developed, based on the use of separate source and propagation terms that are quantified using in situ measurements or modelling results. These models are implemented using term definitions proposed by the Federal Railroad Administration and assessed using the specific illustration of a surface railway. It is shown that the limitations of numerical and empirical methods can be addressed in a hybrid procedure without compromising prediction accuracy.
Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction
Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro
Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.
Ocean U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM)
2008-10-01
Ocean U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM) By Eric P. Chassignet1 and Harley E. Hurlburt2 1 COAPS ...UAcademia:U Florida State University/Center for Ocean-Atmospheric Prediction Studies ( COAPS ); University of Miami/Rosenstiel School of Marine and
A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments
Gokhale, Sharad; Khare, Mukesh
Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the 'extreme' concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the 'extreme' concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only 'extreme' ranges but also the 'middle' ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the 'entire range' of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models ( Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO) concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous in nature and meteorology is 'tropical'. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM-LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5-99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well ( d=0.91) in 10-95 percentiles range. The regulatory compliance is also developed to estimate the probability of exceedance of hourly CO concentration beyond the National Ambient Air Quality Standards (NAAQS) of India. It consists of light vehicles, heavy vehicles, three- wheelers (auto rickshaws) and two
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.
Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W
2014-12-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
A discrete-time hybrid model of a permanent magnet synchronous motor (PMSM) with saturation in voltage and current is formulated.The controller design with incorporated constraints is achieved in a systematic way from modeling to control synthesis and implementation.The Hybrid System Description Language is used to obtain a mixed-logical dynamical (MLD) model.Based on the MLD model,a model predictive controller is designed for an optimal speed regulation of the motor.For reducing computation complexity and ...
Ultra-Short-Term Wind Power Prediction Using a Hybrid Model
Mohammed, E.; Wang, S.; Yu, J.
2017-05-01
This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.
Bergström, J S; Rimnac, C M; Kurtz, S M
2003-04-01
The development of theoretical failure, fatigue, and wear models for ultra-high molecular weight polyethylene (UHMWPE) used in joint replacements has been hindered by the lack of a validated constitutive model that can accurately predict large deformation mechanical behavior under clinically relevant, multiaxial loading conditions. Recently, a new Hybrid constitutive model for unirradiated UHMWPE was developed Bergström et al., (Biomaterials 23 (2002) 2329) based on a physics-motivated framework which incorporates the governing micro-mechanisms of polymers into an effective and accurate continuum representation. The goal of the present study was to compare the predictive capability of the new Hybrid model with the J(2)-plasticity model for four conventional and highly crosslinked UHMWPE materials during multiaxial loading. After calibration under uniaxial loading, the predictive capabilities of the J(2)-plasticity and Hybrid model were tested by comparing the load-displacement curves from experimental multiaxial (small punch) tests with simulated load-displacement curves calculated using a finite element model of the experimental apparatus. The quality of the model predictions was quantified using the coefficient of determination (r(2)). The results of the study demonstrate that the Hybrid model outperforms the J(2)-plasticity model both for combined uniaxial tension and compression predictions and for simulating multiaxial large deformation mechanical behavior produced by the small punch test. The results further suggest that the parameters of the HM may be generalizable for a wide range of conventional, highly crosslinked, and thermally treated UHMWPE materials, based on the characterization of four material properties related to the elastic modulus, yield stress, rate of strain hardening, and locking stretch of the polymer chains. Most importantly, from a practical perspective, these four key material properties for the Hybrid constitutive model can be measured
DEFF Research Database (Denmark)
Fontenete, Sílvia; Guimarães, Nuno; Wengel, Jesper
2016-01-01
Abstract The thermodynamics and kinetics of DNA hybridization, i.e. the process of self-assembly of one, two or more complementary nucleic acid strands, has been studied for many years. The appearance of the nearest-neighbor model led to several theoretical and experimental papers on DNA thermody......Abstract The thermodynamics and kinetics of DNA hybridization, i.e. the process of self-assembly of one, two or more complementary nucleic acid strands, has been studied for many years. The appearance of the nearest-neighbor model led to several theoretical and experimental papers on DNA...... thermodynamics that provide reasonably accurate thermodynamic information on nucleic acid duplexes and allow estimation of the melting temperature. Because there are no thermodynamic models specifically developed to predict the hybridization temperature of a probe used in a fluorescence in situ hybridization...
One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction
Shen, Yi; Guo, Jinyun; Liu, Xin; Wei, Xiaobei; Li, Wudong
2017-01-01
Accurate real-time polar motion parameters play an important role in satellite navigation and positioning and spacecraft tracking. To meet the needs for real-time and high-accuracy polar motion prediction, a hybrid model that integrated singular spectrum analysis (SSA), least-squares (LS) extrapolation and an autoregressive moving average (ARMA) model was proposed. SSA was applied to separate the trend, the annual and the Chandler components from a given polar motion time series. LS extrapolation models were constructed for the separated trend, annual and Chandler components. An ARMA model was established for a synthetic sequence that contained the remaining SSA component and the residual series of LS fitting. In applying this hybrid model, multiple sets of polar motion predictions with lead times of 360 days were made based on an IERS 08 C04 series. The results showed that the proposed method could effectively predict the polar motion parameters.
Model Predictive Control of the Hybrid Ventilation for Livestock
DEFF Research Database (Denmark)
Wu, Zhuang; Stoustrup, Jakob; Trangbæk, Klaus
2006-01-01
and performing off-set free tracking. The purpose of this paper is to apply MPC taking into account of the random disturbances from animals and weather condition to calculate the optimal ventilation rate and air flow distribution and the prediction of indoor horizontal variation of temperature through an optimum...
Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India
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Vipul K. Dabhi
2014-01-01
Full Text Available An accurate prediction of rainfall is crucial for national economy and management of water resources. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. The present work investigates the applicability of a hybrid wavelet-postfix-GP model for daily rainfall prediction of Anand region using meteorological variables. The wavelet analysis is used as a data preprocessing technique to remove the stochastic (noise component from the original time series of each meteorological variable. The Postfix-GP, a GP variant, and ANN are then employed to develop models for rainfall using newly generated subseries of meteorological variables. The developed models are then used for rainfall prediction. The out-of-sample prediction performance of Postfix-GP and ANN models is compared using statistical measures. The results are comparable and suggest that Postfix-GP could be explored as an alternative tool for rainfall prediction.
Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.
Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng
2013-09-01
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.
A hybrid deep neural network and physically based distributed model for river stage prediction
hitokoto, Masayuki; sakuraba, Masaaki
2016-04-01
We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network
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Abdelkrim Moussaoui
2006-01-01
Full Text Available The authors discuss the combination of an Artificial Neural Network (ANN with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.
A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars
Martin, A; Saranya, G; Gayathri, P; Venkatesan, Prasanna
2011-01-01
Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.
A HYBRID MODEL FOR BANKRUPTCY PREDICTION USING GENETIC ALGORITHM, FUZZY C-MEANS AND MARS
Directory of Open Access Journals (Sweden)
A.Martin
2011-05-01
Full Text Available Bankruptcy prediction is very important for all the organization since it affects the economy and rise manysocial problems with high costs. There are large number of techniques have been developed to predict thebankruptcy, which helps the decision makers such as investors and financial analysts. One of thebankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which usesstatic ratios taken from the bank financial statements for prediction, which has its own theoreticaladvantages. The performance of existing bankruptcy model can be improved by selecting the best featuresdynamically depend on the nature of the firm. This dynamic selection can be accomplished by GeneticAlgorithm and it improves the performance of prediction model. .
Ocean Prediction with the Hybrid Coordinate Ocean Model (HYCOM)
2006-01-01
Smedstad, George R. Hlalliwpll, Patrick J. Hogan, Alan J. Wallcraft, Rainer Bleck 5e. TASK NUMBER 5f. WORK UNIT NUMBER 73-7840-04-5 . •PERFORMING...OCEAN MODEL (HYCOM) Eric P. Chassignet’, Harley E. Hurlburt2, Ole Martin Smedstad 3, George R. Halliwell’, Patrick J. Hogan2, Alan J. Wallcraft2, and...Tellus, 19, 98-106. Large, W.G., G. Danabasoglu, S.C. Doney and J.C. McWilliams , 1997: Sensitivity to surface forcing and boundary layer mixing in a global
Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction
Directory of Open Access Journals (Sweden)
Jianzhou Wang
2014-01-01
Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.
Jackson, Chris J; Izadikah, Zahra; Oei, Tian P S
2012-06-01
Jackson's (2005, 2008a) hybrid model of learning identifies a number of learning mechanisms that lead to the emergence and maintenance of the balance between rationality and irrationality. We test a general hypothesis that Jackson's model will predict depressive symptoms, such that poor learning is related to depression. We draw comparisons between Jackson's model and Ellis' (2004) Rational Emotive Behavior Therapy and Theory (REBT) and thereby provide a set of testable learning mechanisms potentially underlying REBT. Results from 80 patients diagnosed with depression completed the learning styles profiler (LSP; Jackson, 2005) and two measures of depression. Results provide support for the proposed model of learning and further evidence that low rationality is a key predictor of depression. We conclude that the hybrid model of learning has the potential to explain some of the learning and cognitive processes related to the development and maintenance of irrational beliefs and depression. Copyright © 2011. Published by Elsevier B.V.
Model predictive control for power fluctuation supression in hybrid wind/PV/battery systems
DEFF Research Database (Denmark)
You, Shi; Liu, Zongyu; Zong, Yi
2015-01-01
predictive control (MPC)-based algorithm for battery management in a hybrid wind/PV/battery system to suppress the short-term power fluctuation on the ‘minute’ scale. A case study with data collected from a practical hybrid system setup is used to demonstrate the effectiveness of the proposed algorithm......A hybrid energy system, the combination of wind turbines, PV panels and battery storage with effective control mechanism, represents a promising solution to the power fluctuation problem when integrating renewable energy resources (RES) into conventional power systems. This paper proposes a model...... together with a Monte Carlo simulation-based sensitivity analysis. In addition to illustrating the complementarity between the fluctuations of wind power and PV power, the results prove the proposed MPC algorithm is effective in fluctuation suppression but sensitive to factors such as forecast accuracy...
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.
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
Beetz, M; 10.1613/jair.1565
2011-01-01
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results.
Energy Technology Data Exchange (ETDEWEB)
Sinha, S.; Matsumoto, Tsuyoshi; Kojima, Toshinori [Seikei University, Tokyo (Japan). Dept. of Industrial Chemistry; Sanjay Kumar [Kyoto University (Japan). Dept. of Global Environment Engineering
2001-03-01
Uncertainties in local solar radiation, ambient temperature and thermal load data have been one of the major factors limiting the reliability and efficiency of solar thermal hybrid systems. In the present paper, moving average auto regressive erogenous (ARX) model based reasoning has been mooted and modified to include moving average method, as an effective tool for predictions of these data. The results show that the method is quite robust and is capable of predicting fairly accurate results, which would make these systems more viable in areas where meteorological data are not available or vague. (author)
Fontenete, Sílvia; Guimarães, Nuno; Wengel, Jesper; Azevedo, Nuno Filipe
2016-01-01
The thermodynamics and kinetics of DNA hybridization, i.e. the process of self-assembly of one, two or more complementary nucleic acid strands, has been studied for many years. The appearance of the nearest-neighbor model led to several theoretical and experimental papers on DNA thermodynamics that provide reasonably accurate thermodynamic information on nucleic acid duplexes and allow estimation of the melting temperature. Because there are no thermodynamic models specifically developed to predict the hybridization temperature of a probe used in a fluorescence in situ hybridization (FISH) procedure, the melting temperature is used as a reference, together with corrections for certain compounds that are used during FISH. However, the quantitative relation between melting and experimental FISH temperatures is poorly described. In this review, various models used to predict the melting temperature for rRNA targets, for DNA oligonucleotides and for nucleic acid mimics (chemically modified oligonucleotides), will be addressed in detail, together with a critical assessment of how this information should be used in FISH.
Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.
Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam
2015-11-01
Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.
Directory of Open Access Journals (Sweden)
You Zhu
2016-05-01
Full Text Available Based on logistic regression (LR and artificial neural network (ANN methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China’s small and medium-sized enterprises (SMEs for financial institutions (FIs in the supply chain financing (SCF by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs in the period of 2012–2013 are chosen as the samples. The empirical results show that: (i the “negative signal” prediction accuracy ratio of the ANN model is better than that of LR model; (ii the two-stage hybrid model type I has a better performance of predicting “positive signals” than that of the ANN model; (iii the two-stage hybrid model type II has a stronger ability both in aspects of predicting “positive signals” and “negative signals” than that of the two-stage hybrid model type I; and (iv “negative signal” predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China’s FIs.
A Hybrid Model Predictive Control for Handling Infeasibility and Constraint Prioritization
Institute of Scientific and Technical Information of China (English)
王宇红; 黄德先; 金以慧
2005-01-01
A hybrid approach using MLD (mixed logical dynamical) framework to handle infeasibility and constraint prioritization issues in MPC (model predictive control) based on input-output model is introduced. By expressing constraint priorities as propositional logics and by transforming the propositional logics into inequalities,the infeasibility and constraint prioritization issues are solved in the MPC. Constraints with higher priorities are met first, and then these with lower priorities are satisfied as much as possible. This new approach is illustrated in the control of a heavy oil fractionator-Shell column. The overall control performance has been significantly improved through the infeasibility and control priorities handling.
Predicting Freeway Work Zone Delays and Costs with a Hybrid Machine-Learning Model
Directory of Open Access Journals (Sweden)
Bo Du
2017-01-01
Full Text Available A hybrid machine-learning model, integrating an artificial neural network (ANN and a support vector machine (SVM model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. The model is very user friendly, allowing the least inputs from the users. With that the delays caused by a work zone on any location of a New Jersey freeway can be predicted. To this end, tremendous amounts of data from different sources were collected to establish the relationship between the model inputs and outputs. A comparative analysis was conducted, and results indicate that the proposed model outperforms others in terms of the least root mean square error (RMSE. The proposed hybrid model can be used to calculate contractor penalty in terms of cost overruns as well as incentive reward schedule in case of early work competition. Additionally, it can assist work zone planners in determining the best start and end times of a work zone for developing and evaluating traffic mitigation and management plans.
Prediction of Coal Face Gas Concentration by Multi-Scale Selective Ensemble Hybrid Modeling
Directory of Open Access Journals (Sweden)
WU Xiang
2014-06-01
Full Text Available A selective ensemble hybrid modeling prediction method based on wavelet transformation is proposed to improve the fitting and generalization capability of the existing prediction models of the coal face gas concentration, which has a strong stochastic volatility. Mallat algorithm was employed for the multi-scale decomposition and single-scale reconstruction of the gas concentration time series. Then, it predicted every subsequence by sparsely weighted multi unstable ELM(extreme learning machine predictor within method SERELM(sparse ensemble regressors of ELM. At last, it superimposed the predicted values of these models to obtain the predicted values of the original sequence. The proposed method takes advantage of characteristics of multi scale analysis of wavelet transformation, accuracy and fast characteristics of ELM prediction and the generalization ability of L1 regularized selective ensemble learning method. The results show that the forecast accuracy has large increase by using the proposed method. The average relative error is 0.65%, the maximum relative error is 4.16% and the probability of relative error less than 1% reaches 0.785.
Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM
Directory of Open Access Journals (Sweden)
Łapczyński Mariusz
2014-08-01
Full Text Available Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models, customers who purchase additional products (cross- and up-sell models or customers intending to resign from the cooperation (churn models. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm and cluster analysis (k-means. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.
Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus
Zeng, Xiaohua; Yang, Nannan; Wang, Junnian; Song, Dafeng; Zhang, Nong; Shang, Mingli; Liu, Jianxin
2015-08-01
Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.
Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long
2017-09-01
This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.
Directory of Open Access Journals (Sweden)
Babankumar S. Bansod
2011-02-01
Full Text Available Starting from descriptive data on crop yield and various other properties, the aim of this study is to reveal the trends on soil behaviour, such as crop yield. This study has been carried out by developing web application that uses a well known technique- Cluster Analysis. The cluster analysis revealed linkages between soil classes for the same field as well as between different fields, which can be partly assigned to crops rotation and determination of variable soil input rates. A hybrid clustering algorithm has been developed taking into account the traits of two clustering technologies: i Hierarchical clustering, ii K-means clustering. This hybrid clustering algorithm is applied to sensor- gathered data about soil and analysed, resulting in the formation of well delineatedmanagement zones based on various properties of soil, such as, ECa , crop yield, etc. One of the purposes of the study was to identify the main factors affecting the crop yield and the results obtained were validated with existing techniques. To accomplish this purpose, geo-referenced soil information has been examined. Also, based on this data, statistical method has been used to classify and characterize the soil behaviour. This is done using a prediction model, developed to predict the unknown behaviour of clusters based on the known behaviour of other clusters. In predictive modeling, data has been collected for the relevant predictors, a statistical model has been formulated, predictions were made and the model can be validated (or revised as additional data becomes available. The model used in the web application has been formed taking into account neural network based minimum hamming distance criterion.
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...
Prediction of hot spots in protein interfaces using a random forest model with hybrid features.
Wang, Lin; Liu, Zhi-Ping; Zhang, Xiang-Sun; Chen, Luonan
2012-03-01
Prediction of hot spots in protein interfaces provides crucial information for the research on protein-protein interaction and drug design. Existing machine learning methods generally judge whether a given residue is likely to be a hot spot by extracting features only from the target residue. However, hot spots usually form a small cluster of residues which are tightly packed together at the center of protein interface. With this in mind, we present a novel method to extract hybrid features which incorporate a wide range of information of the target residue and its spatially neighboring residues, i.e. the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). We provide a novel random forest (RF) model to effectively integrate these hybrid features for predicting hot spots in protein interfaces. Our method can achieve accuracy (ACC) of 82.4% and Matthew's correlation coefficient (MCC) of 0.482 in Alanine Scanning Energetics Database, and ACC of 77.6% and MCC of 0.429 in Binding Interface Database. In a comparison study, performance of our RF model exceeds other existing methods, such as Robetta, FOLDEF, KFC, KFC2, MINERVA and HotPoint. Of our hybrid features, three physicochemical features of target residues (mass, polarizability and isoelectric point), the relative side-chain accessible surface area and the average depth index of mirror-contact residues are found to be the main discriminative features in hot spots prediction. We also confirm that hot spots tend to form large contact surface areas between two interacting proteins. Source data and code are available at: http://www.aporc.org/doc/wiki/HotSpot.
H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus.
Ali, Rahman; Hussain, Jamil; Siddiqi, Muhammad Hameed; Hussain, Maqbool; Lee, Sungyoung
2015-07-03
Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body's resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient's data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies.
Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches
Mohammed, E.; Wang, S.; Yu, J.
2017-05-01
Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.
Institute of Scientific and Technical Information of China (English)
Gurmanik KAUR‡; Ajat Shatru ARORA; Vijender Kumar JAIN
2015-01-01
Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and re-search studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of com-ponents, termed‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in com-parison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.
A hybrid convection scheme for use in non-hydrostatic numerical weather prediction models
Directory of Open Access Journals (Sweden)
Volker Kuell
2008-12-01
Full Text Available The correct representation of convection in numerical weather prediction (NWP models is essential for quantitative precipitation forecasts. Due to its small horizontal scale convection usually has to be parameterized, e.g. by mass flux convection schemes. Classical schemes originally developed for use in coarse grid NWP models assume zero net convective mass flux, because the whole circulation of a convective cell is confined to the local grid column and all convective mass fluxes cancel out. However, in contemporary NWP models with grid sizes of a few kilometers this assumption becomes questionable, because here convection is partially resolved on the grid. To overcome this conceptual problem we propose a hybrid mass flux convection scheme (HYMACS in which only the convective updrafts and downdrafts are parameterized. The generation of the larger scale environmental subsidence, which may cover several grid columns, is transferred to the grid scale equations. This means that the convection scheme now has to generate a net convective mass flux exerting a direct dynamical forcing to the grid scale model via pressure gradient forces. The hybrid convection scheme implemented into the COSMO model of Deutscher Wetterdienst (DWD is tested in an idealized simulation of a sea breeze circulation initiating convection in a realistic manner. The results are compared with analogous simulations with the classical Tiedtke and Kain-Fritsch convection schemes.
Directory of Open Access Journals (Sweden)
Tri-Vien Vu
2014-10-01
Full Text Available This study applied a model predictive control (MPC framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV. The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID controllers are used for cruising control.
Directory of Open Access Journals (Sweden)
M. Sudha
2015-12-01
Full Text Available Uncertain atmosphere is a prevalent factor affecting the existing prediction approaches. Rough set and fuzzy set theories as proposed by Pawlak and Zadeh have become an effective tool for handling vagueness and fuzziness in the real world scenarios. This research work describes the impact of Hybrid Intelligent System (HIS for strategic decision support in meteorology. In this research a novel exhaustive search based Rough set reduct Selection using Genetic Algorithm (RSGA is introduced to identify the significant input feature subset. The proposed model could identify the most effective weather parameters efficiently than other existing input techniques. In the model evaluation phase two adaptive techniques were constructed and investigated. The proposed Artificial Neural Network based on Back Propagation learning (ANN-BP and Adaptive Neuro Fuzzy Inference System (ANFIS was compared with existing Fuzzy Unordered Rule Induction Algorithm (FURIA, Structural Learning Algorithm on Vague Environment (SLAVE and Particle Swarm OPtimization (PSO. The proposed rainfall prediction models outperformed when trained with the input generated using RSGA. A meticulous comparison of the performance indicates ANN-BP model as a suitable HIS for effective rainfall prediction. The ANN-BP achieved 97.46% accuracy with a nominal misclassification rate of 0.0254 %.
Du, Kongchang; Zhao, Ying; Lei, Jiaqiang
2017-09-01
In hydrological time series prediction, singular spectrum analysis (SSA) and discrete wavelet transform (DWT) are widely used as preprocessing techniques for artificial neural network (ANN) and support vector machine (SVM) predictors. These hybrid or ensemble models seem to largely reduce the prediction error. In current literature researchers apply these techniques to the whole observed time series and then obtain a set of reconstructed or decomposed time series as inputs to ANN or SVM. However, through two comparative experiments and mathematical deduction we found the usage of SSA and DWT in building hybrid models is incorrect. Since SSA and DWT adopt 'future' values to perform the calculation, the series generated by SSA reconstruction or DWT decomposition contain information of 'future' values. These hybrid models caused incorrect 'high' prediction performance and may cause large errors in practice.
Genomic Prediction of Barley Hybrid Performance
Directory of Open Access Journals (Sweden)
Norman Philipp
2016-07-01
Full Text Available Hybrid breeding in barley ( L. offers great opportunities to accelerate the rate of genetic improvement and to boost yield stability. A crucial requirement consists of the efficient selection of superior hybrid combinations. We used comprehensive phenotypic and genomic data from a commercial breeding program with the goal of examining the potential to predict the hybrid performances. The phenotypic data were comprised of replicated grain yield trials for 385 two-way and 408 three-way hybrids evaluated in up to 47 environments. The parental lines were genotyped using a 3k single nucleotide polymorphism (SNP array based on an Illumina Infinium assay. We implemented ridge regression best linear unbiased prediction modeling for additive and dominance effects and evaluated the prediction ability using five-fold cross validations. The prediction ability of hybrid performances based on general combining ability (GCA effects was moderate, amounting to 0.56 and 0.48 for two- and three-way hybrids, respectively. The potential of GCA-based hybrid prediction requires that both parental components have been evaluated in a hybrid background. This is not necessary for genomic prediction for which we also observed moderate cross-validated prediction abilities of 0.51 and 0.58 for two- and three-way hybrids, respectively. This exemplifies the potential of genomic prediction in hybrid barley. Interestingly, prediction ability using the two-way hybrids as training population and the three-way hybrids as test population or vice versa was low, presumably, because of the different genetic makeup of the parental source populations. Consequently, further research is needed to optimize genomic prediction approaches combining different source populations in barley.
Wang, X; Li, L; Yang, Z; Zheng, X; Yu, S; Xu, C; Hu, Z
2017-03-01
Genomic selection (GS) is more efficient than traditional phenotype-based methods in hybrid breeding. The present study investigated the predictive ability of genomic best linear unbiased prediction models for rice hybrids based on the North Carolina mating design II, in which a total of 115 inbred rice lines were crossed with 5 male sterile lines. Using 8 traits of the 575 (115 × 5) hybrids from two environments, both univariate (UV) and multivariate (MV) prediction analyses, including additive and dominance effects, were performed. Using UV models, the prediction results of cross-validation indicated that including dominance effects could improve the predictive ability for some traits in rice hybrids. Additionally, we could take advantage of GS even for a low-heritability trait, such as grain yield per plant (GY), because a modest increase in the number of top selection could generate a higher, more stable mean phenotypic value for rice hybrids. Thus this strategy was used to select superior potential crosses between the 115 inbred lines and those between the 5 male sterile lines and other genotyped varieties. In our MV research, an MV model (MV-ADV) was developed utilizing a MV relationship matrix constructed with auxiliary variates. Based on joint analysis with multi-trait (MT) or with multi-environment, the prediction results confirmed the superiority of MV-ADV over an UV model, particularly in the MT scenario for a low-heritability target trait (such as GY), with highly correlated auxiliary traits. For a high-heritability trait (such as thousand-grain weight), MT prediction is unnecessary, and UV prediction is sufficient.
Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine
2017-06-01
The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.
Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele
2014-01-01
The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363
Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele
2014-01-01
The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.
Directory of Open Access Journals (Sweden)
Zhisheng Wu
2015-01-01
Full Text Available This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP = 0.492 mg·g−1, correlation coefficient RP=0.9221, and relative predictive determinant (RPD = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1, RP=0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.
Directory of Open Access Journals (Sweden)
Nicolas Sockeel
2017-04-01
Full Text Available Power management strategies have impacts on fuel economy, greenhouse gasses (GHG emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flows inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC based algorithm. Inside a MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC algorithms. The contribution of this manuscript consists of developing and evaluating a high-fidelity battery model of a plug-in hybrid electric vehicle (PHEV that has been used for MPC. Via empirical work and simulation, the impact of a high-fidelity battery model has been evaluated and compared to a simpler model in the context of MPC. It is proven that the new battery model reduces the absolute voltage, state of charge (SoC, and battery power loss error by a factor of 3.2, 1.9 and 2.1 on average respectively, compared to the simpler battery model.
Institute of Scientific and Technical Information of China (English)
Yuanyuan ZOU; Shaoyuan LI
2007-01-01
In this paper,a linear programming method is proposed to solve model predictive control for a class of hybrid systems.Firstly,using the(max,+)algebra,a typical subclass of hybrid systems called max-plus-linear(MPL)systems is obtained.And then,model predictive control(MPC)framework is extended to MPL systems.In general,the nonlinear optimization approach or extended linear complementarity problem(ELCP)were applied to solve the MPL-MPC optimization problem.A new optimization method based on canonical forms for max-min-plus-scaling(MMPS)functions (using the operations maximization,minimization,addition and scalar multiplication)with linear constraints on the inputs is presented.The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization.The validity of the algorithm is illustrated by an example.
Directory of Open Access Journals (Sweden)
Américo José dos Santos Reis
2005-01-01
Full Text Available By definition, the genetic effects obtained from a circulant diallel table are random. However, because of the methods of analysis, those effects have been considered as fixed. Two different statistical approaches were applied. One assumed the model to be fixed and obtained solutions through the ordinary least square (OLS method. The other assumed a mixed model and estimated the fixed effects (BLUE by generalized least squares (GLS and the best linear unbiased predictor (BLUP of the random effects. The goal of this study was to evaluate the consequences when considering these effects as fixed or random, using the coefficient of correlation between the responses of observed and non-observed hybrids. Crossings were made between S1 inbred lines from two maize populations developed at Universidade Federal de Goiás, the UFG-Samambaia "Dent" and UFG-Samambaia "Flint". A circulant inter-group design was applied, and there were five (s = 5 crossings for each parent. The predictions were made using a reduced model. Diallels with different sizes of s (from 2 to 5 were simulated, and the coefficients of correlation were obtained using two different approaches for each size of s. In the first approach, the observed hybrids were included in both the estimation of the genetic parameters and the coefficient of correlation, while in the second a cross-validation process was employed. In this process, the set of hybrids was divided in two groups: one group, comprising 75% of the original group, to estimate the genetic parameters, and a second one, consisting of the remaining 25%, to validate the predictions. In all cases, a bootstrap process with 200 resamplings was used to generate the empirical distribution of the correlation coefficient. This coefficient showed a decrease as the value of s decreased. The cross-validation method allowed to estimate the bias magnitude in evaluating the correlation coefficient using the same hybrids, to predict the genetic
ANALYSIS OF ASSEMBLY SUITABILITY OF THE HYBRID NODE BASED ON WELD DISTORTION PREDICTION MODELS
Directory of Open Access Journals (Sweden)
Tomasz Urbański
2015-08-01
Full Text Available The article presents an analysis of assembly suitability of the innovative hybrid node. Weld distortions are a factor that affects significantly the quality of a structure during its pre-fabrication stage, thus increasing manufacturing costs . For the purposes of this analysis, such distortion forms were chosen that are the highest-ranking ones in the technological hierarchy. The analysis was performed taking advantage of significant parameters in order to demonstrate the possibilities of using mathematical models determined on the basis of a designed experiment to modify the construction technology as early as during the stage of the hybrid node’s manufacture. It was shown that using the above-mentioned theoretical models a technological assessment of the structural component can be performed by selecting such system of parameters that will produce distortions at a level acceptable from the point of view of further assembly suitability.
Energy Technology Data Exchange (ETDEWEB)
Salcedo-Sanz, Sancho; Perez-Bellido, Angel M.; Ortiz-Garcia, Emilio G.; Portilla-Figueras, Antonio [Department of Signal Theory and Communications, Universidad de Alcala, Madrid (Spain); Prieto, Luis [Wind Resource Department, Iberdrola Renovables, Madrid (Spain); Paredes, Daniel [Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid (Spain)
2009-06-15
This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points. (author)
A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2.5
Michanowicz, Drew R.; Shmool, Jessie L. C.; Tunno, Brett J.; Tripathy, Sheila; Gillooly, Sara; Kinnee, Ellen; Clougherty, Jane E.
2016-04-01
Characterizing near-source spatio-temporal variation is a long -standing challenge in air pollution epidemiology, and common intra-urban modeling approaches [e.g., land use regression (LUR)], do not account for short-term meteorological variation. Atmospheric dispersion modeling approaches, such as AERMOD, can account for near-source pollutant behavior by capturing source-meteorological interactions, but requires external validation and resolved background concentrations. In this study, we integrate AERMOD-based predictions for source-specific fine particle (PM2.5) concentrations into LUR models derived from total ambient PM2.5 measured at 36 unique sites selected to represent different source and elevation profiles, during summer and winter, 2012-2013 in Pittsburgh, Pennsylvania (PA). We modeled PM2.5 emissions from 207 local stationary sources in AERMOD, utilizing the monitoring locations as receptors, and hourly meteorological information matching each sampling period. Finally, we compare results of the integrated LUR/AERMOD hybrid model to those of the AERMOD + background and standard LUR models, at the full domain scale and within a 5 km2 sub-domain surrounding a large industrial facility. The hybrid model improved out-of-sample prediction accuracy by 2-10% over LUR alone, though performance differed by season, in part due to within-season temporal variability. We found differences up to 10 μg/m3 in predicted concentrations, and observed the largest differences within the industrial sub-domain. LUR underestimated concentrations from 500 to 2500 m downwind of major sources. The hybrid modeling approach we developed may help to improve intra-urban exposure estimates, particularly in regions of large industrial sources, sharp elevation gradients, or complex meteorology (e.g., frequent inversion events), such as Pittsburgh, PA. More broadly, the approach may inform the development of spatio-temporal modeling frameworks for air pollution exposure assessment for
Directory of Open Access Journals (Sweden)
Banjanovic-Mehmedovic Lejla
2016-01-01
Full Text Available Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS, since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS. The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error, coefficient of determination (R2 and Pearson coefficient (r, were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.
Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang
2016-01-01
Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.
Institute of Scientific and Technical Information of China (English)
WANG Hao-ran; ZHENG Zhe; XIONG Hui; XU Bo; LI Li-huan; GAO Run-lin; HU Sheng-shou
2013-01-01
Background The hybrid procedure for coronary heart disease combines minimally invasive coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) and is an alternative to revascularization treatment.We sought to assess the predictive value of four risk-stratification models for risk assessment of major adverse cardiac and cerebrovascular events (MACCE) in patients with multivessel disease undergoing hybrid coronary revascularization.Methods The data of 120 patients were retrospectively collected and the SYNTAX score,EuroSCORE,SinoSCORE and the Global Risk Classification (GRC) calculated for each patient.The outcomes of interest were 2.7-year incidences of MACCE,including death,myocardial infarction,stroke,and any-vessel revascularization.Results During a mean of 2.7-year follow-up,actuarial survival was 99.17％,and no myocardial infarctions occurred.The discriminatory power (area under curve (AUC)) of the SYNTAX score,EuroSCORE,SinoSCORE and GRC for 2.7-year MACCE was 0.60 (95％ confidence interval 0.42-0.77),0.65 (0.47-0.82),0.57 (0.39-0.75) and 0.65 (0.46-0.83),respectively.The calibration characteristics of the SYNTAX score,EuroSCORE,SinoSCORE and GRC were 3.92 (P=0.86),5.39 (P=0.37),13.81 (P=0.32) and 0.02 (P=0.89),respectively.Conclusions In patients with multivessel disease undergoing a hybrid procedure,the SYNTAX score,EuroSCORE,SinoSCORE and GRC were inaccurate in predicting MACCE.Modifying risk-stratification models to improve the predictive value for a hybrid procedure is needed.
Directory of Open Access Journals (Sweden)
Jing Sun
2015-01-01
Full Text Available The torque coordination control during mode transition is a very important task for hybrid electric vehicle (HEV with a clutch serving as the key enabling actuator element. Poor coordination will deteriorate the drivability of the driver and lead to excessive wearing to the clutch friction plates. In this paper, a novel torque coordination control strategy for a single-shaft parallel hybrid electric vehicle is presented to coordinate the motor torque, engine torque, and clutch torque so that the seamless mode switching can be achieved. Different to the existing model predictive control (MPC methods, only one model predictive controller is needed and the clutch torque is taken as an optimized variable rather than a known parameter. Furthermore, the successful idea of model reference control (MRC is also used for reference to generate the set-point signal required by MPC. The parameter sensitivity is studied for better performance of the proposed model predictive controller. The simulation results validate that the proposed novel torque coordination control strategy has less vehicle jerk, less torque interruption, and smaller clutch frictional losses, compared with the baseline method. In addition, the sensitivity and adaptiveness of the proposed novel torque coordination control strategy are evaluated.
Abdelkrim Moussaoui; Yacine Selaimia; Hadj A. Abbassi
2006-01-01
The authors discuss the combination of an Artificial Neural Network (ANN) with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capa...
Institute of Scientific and Technical Information of China (English)
Abdallah; en Mosbah; Ruxandra Mihaela; otez; Thien My; ao
2016-01-01
A new approach for the prediction of lift, drag, and moment coefficients is presented. This approach is based on the support vector machines (SVMs) methodology and an optimization meta-heuristic algorithm called extended great deluge (EGD). The novelty of this approach is the hybridization between the SVM and the EGD algorithm. The EGD is used to optimize the SVM parameters. The training and validation of this new identification approach is realized using the aerodynamic coefficients of an ATR-42 wing model. The aerodynamic coefficients data are obtained with the XFoil software and experimental tests using the Price–Paıdoussis wind tunnel. The predicted results with our approach are compared with those from the XFoil software and experimental results for different flight cases of angles of attack and Mach numbers. The main pur-pose of this methodology is to rapidly predict aircraft aerodynamic coefficients.
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Abdallah Ben Mosbah
2016-02-01
Full Text Available A new approach for the prediction of lift, drag, and moment coefficients is presented. This approach is based on the support vector machines (SVMs methodology and an optimization meta-heuristic algorithm called extended great deluge (EGD. The novelty of this approach is the hybridization between the SVM and the EGD algorithm. The EGD is used to optimize the SVM parameters. The training and validation of this new identification approach is realized using the aerodynamic coefficients of an ATR-42 wing model. The aerodynamic coefficients data are obtained with the XFoil software and experimental tests using the Price–Païdoussis wind tunnel. The predicted results with our approach are compared with those from the XFoil software and experimental results for different flight cases of angles of attack and Mach numbers. The main purpose of this methodology is to rapidly predict aircraft aerodynamic coefficients.
U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model
2016-06-07
assimilation of sea surface height ( SSH ) from satellite altimeters, sea surface temperature (SST) and temperature (T)/salinity (S) profiles and the...and H.-S. Kang (U. Miami). 3DVAR is a planned upgrade to NCODA, which also includes advanced data QC. The primary data types are SSH from satellite...for downward projection of SSH and SST. 3. Ocean model and prediction system configurations: The primary model domain is a fully global HYCOM
Hybrid Model of Content Extraction
DEFF Research Database (Denmark)
Qureshi, Pir Abdul Rasool; Memon, Nasrullah
2012-01-01
We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict signi...
Energy Technology Data Exchange (ETDEWEB)
Beck, R. [RWTH Aachen (Germany). Inst. fuer Regelungstechnik; Saenger Zetina, S.; Neiss, K. [DaimlerChrysler Corp., Troy, MI (United States); Bollig, A. [Siemens AG, Muenchen (Germany)
2007-07-15
Hybrid electric vehicles increasingly gain importance mainly due to the potential fuel saving but also because of the additional agility achieved by the increased peak torques during boosting. An important aspect of a hybrid architecture is the transition between pure electric and hybrid operation, which is exemplarily investigated in a flexible hybrid transmission under a parallel engagement configuration. A clutch which separates the transmission input side (engine side) from the rest of the drivetrain is engaged in order to achieve the transition. A noticeable jerk during the engagement process or an interruption of the drive torque cannot be tolerated since the driving comfort must not be reduced. Special attention was given to the robustness of the controller, since the transition has to be carried out reliably and comfortably under varying vehicle properties and drive conditions. (orig.)
Ransom, Katherine M.; Nolan, Bernard T.; Traum, Jonathan A.; Faunt, Claudia; Bell, Andrew M.; Gronberg, Jo Ann M.; Wheeler, David C.; Zamora, Celia; Jurgens, Bryant; Schwarz, Gregory; Belitz, Kenneth; Eberts, Sandra; Kourakos, George; Harter, Thomas
2017-01-01
Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative
Jackson, Chris; Baguma, Peter; Furnham, Adrian
2009-01-01
Jackson developed a hybrid model of learning in personality, known as the Learning Styles Profiler (LSP), which seeks to explain personality in terms of biological, socio-cognitive and experiential processes. The hybrid model argues that functional learning outcomes can be understood in terms of how cognitions and experiences re-express sensation…
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Simeone Marino
2016-10-01
Full Text Available Tuberculosis (TB is a world-wide health problem with approximately 2 billion people infected with Mycobacterium tuberculosis (Mtb, the causative bacterium of TB. The pathologic hallmark of Mtb infection in humans and Non-Human Primates (NHPs is the formation of spherical structures, primarily in lungs, called granulomas. Infection occurs after inhalation of bacteria into lungs, where resident antigen-presenting cells (APCs, take up bacteria and initiate the immune response to Mtb infection. APCs traffic from the site of infection (lung to lung-draining lymph nodes (LNs where they prime T cells to recognize Mtb. These T cells, circulating back through blood, migrate back to lungs to perform their immune effector functions. We have previously developed a hybrid agent-based model (ABM, labeled GranSim describing in silico immune cell, bacterial (Mtb and molecular behaviors during tuberculosis infection and recently linked that model to operate across three physiological compartments: lung (infection site where granulomas form, lung draining lymph node (LN, site of generation of adaptive immunity and blood (a measurable compartment. Granuloma formation and function is captured by a spatio-temporal model (i.e., ABM, while LN and blood compartments represent temporal dynamics of the whole body in response to infection and are captured with ordinary differential equations (ODEs. In order to have a more mechanistic representation of APC trafficking from the lung to the lymph node, and to better capture antigen presentation in a draining LN, this current study incorporates the role of dendritic cells (DCs in a computational fashion into GranSim. Results: The model was calibrated using experimental data from the lungs and blood of NHPs. The addition of DCs allowed us to investigate in greater detail mechanisms of recruitment, trafficking and antigen presentation and their role in tuberculosis infection. Conclusion: The main conclusion of this study is
Lossless compression of hyperspectral images using hybrid context prediction.
Liang, Yuan; Li, Jianping; Guo, Ke
2012-03-26
In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.
Directory of Open Access Journals (Sweden)
Deyou Yang
2016-08-01
Full Text Available The application of large scale energy storage makes wind farms more dispatchable, which lowers operating risks to the grid from interconnected large scale wind farms. In order to make full use of the flexibility and controllability of energy storage to improve the schedulability of wind farms, this paper presents a rolling and dispatching control strategy with a battery energy storage system (BESS based on model predictive control (MPC. The proposed control scheme firstly plans expected output, i.e., dispatching order, of a wind/battery energy storage hybrid system based on the predicted output of the wind farm, then calculates the order in the predictive horizon with the receding horizon optimization and the limitations of energy storage such as state of charge and depth of charge/discharge to maintain the combination of active output of the wind farm and the BESS to track dispatching order at the extreme. The paper shows and analyses the effectiveness of the proposed strategy with different sizes of capacity of the BESS based on the actual output of a certain actual wind farm in the northeast of China. The results show that the proposed strategy that controls the BESS could improve the schedulability of the wind farm and maintain smooth output of wind/battery energy storage hybrid system while tracking the dispatching orders. When the capacity of the BESS is 20% or the rated capacity of the wind farm, the mean dispatching error is only 0.153% of the rated capacity of the wind farm.
Shadmand, Mohammad Bagher
Renewable energy sources continue to gain popularity. However, two major limitations exist that prevent widespread adoption: availability and variability of the electricity generated and the cost of the equipment. The focus of this dissertation is Model Predictive Control (MPC) for optimal sized photovoltaic (PV), DC Microgrid, and multi-sourced hybrid energy systems. The main considered applications are: maximum power point tracking (MPPT) by MPC, droop predictive control of DC microgrid, MPC of grid-interaction inverter, MPC of a capacitor-less VAR compensator based on matrix converter (MC). This dissertation firstly investigates a multi-objective optimization technique for a hybrid distribution system. The variability of a high-penetration PV scenario is also studied when incorporated into the microgrid concept. Emerging (PV) technologies have enabled the creation of contoured and conformal PV surfaces; the effect of using non-planar PV modules on variability is also analyzed. The proposed predictive control to achieve maximum power point for isolated and grid-tied PV systems speeds up the control loop since it predicts error before the switching signal is applied to the converter. The low conversion efficiency of PV cells means we want to ensure always operating at maximum possible power point to make the system economical. Thus the proposed MPPT technique can capture more energy compared to the conventional MPPT techniques from same amount of installed solar panel. Because of the MPPT requirement, the output voltage of the converter may vary. Therefore a droop control is needed to feed multiple arrays of photovoltaic systems to a DC bus in microgrid community. Development of a droop control technique by means of predictive control is another application of this dissertation. Reactive power, denoted as Volt Ampere Reactive (VAR), has several undesirable consequences on AC power system network such as reduction in power transfer capability and increase in
Directory of Open Access Journals (Sweden)
Jieqiong Su
2015-04-01
Full Text Available With decreasing water availability as a result of climate change and human activities, analysis of the influential factors and variation trends of chlorophyll a has become important to prevent reservoir eutrophication and ensure water supply safety. In this paper, a structurally simplified hybrid model of the genetic algorithm (GA and the support vector machine (SVM was developed for the prediction of monthly concentration of chlorophyll a in the Miyun Reservoir of northern China over the period from 2000 to 2010. Based on the influence factor analysis, the four most relevant influence factors of chlorophyll a (i.e., total phosphorus, total nitrogen, permanganate index, and reservoir storage were extracted using the method of feature selection with the GA, which simplified the model structure, making it more practical and efficient for environmental management. The results showed that the developed simplified GA-SVM model could solve nonlinear problems of complex system, and was suitable for the simulation and prediction of chlorophyll a with better performance in accuracy and efficiency in the Miyun Reservoir.
Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.
Directory of Open Access Journals (Sweden)
Ying-Wooi Wan
Full Text Available Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7 and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4 and DFCI (n = 82; log-rank P = 2.57e-4, using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04. The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46] than other commonly used clinical factors except tumor stage (III vs. I in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017. The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of
Hybrid Unifying Variable Supernetwork Model
Institute of Scientific and Technical Information of China (English)
LIU; Qiang; FANG; Jin-qing; LI; Yong
2015-01-01
In order to compare new phenomenon of topology change,evolution,hybrid ratio and network characteristics of unified hybrid network theoretical model with unified hybrid supernetwork model,this paper constructed unified hybrid variable supernetwork model(HUVSM).The first layer introduces a hybrid ratio dr,the
Wang, Dan; LeBauer, David; Dietze, Michael
2013-06-01
Hybrid poplar (Populus spp.) is an important biomass crop being evaluated for cellulosic ethanol production. Predictions of poplar growth, rotation period, and soil carbon sequestration under various growing conditions, soils, and climates are critical for farmers and managers planning to establish short-rotation forestry (SRF) plantations. In this study, we used an ecoinformatics workflow, the Predictive Ecosystem Analyzer (PEcAn), to integrate literature data and field measurements into the Ecosystem Demography 2 (ED2) model to estimate yield potential of poplar plantations. Within PEcAn 164 records of seven different traits from the literature were assimilated using a Bayesian meta-analysis. Next, variance decomposition identified seven variables for further constraint that contributed > 80% to the uncertainty in modeled yields: growth respiration, dark respiration, quantum efficiency, mortality coefficient, water conductance, fine-root allocation, and root turnover rate. Assimilation of observed yields further constrained uncertainty in model parameters (especially dark respiration and root turnover rate) and biomass estimates. Additional measurements of growth respiration, mortality, water conductance, and quantum efficiency would provide the most efficient path toward further constraint of modeled yields. Modeled validation demonstrated that ED2 successfully captured the interannual and spatial variability of poplar yield observed at nine independent sites. Site-level analyses were conducted to estimate the effect of land use change to SRF poplar on soil C sequestration compared to alternate land uses. These suggest that poplar plantations became a C sink within 18 years of conversion from corn production or existing forest. Finally, poplar yields were estimated for the contiguous United States at a half degree resolution in order to determine potential productivity, estimate the optimal rotation period, and compare poplar to perennial grass yields. This
Hybrid2 - The hybrid power system simulation model
Energy Technology Data Exchange (ETDEWEB)
Baring-Gould, E.I.; Green, H.J.; Dijk, V.A.P. van [National Renewable Energy Lab., Golden, CO (United States); Manwell, J.F. [Univ. of Massachusetts, Amherst, MA (United States)
1996-12-31
There is a large-scale need and desire for energy in remote communities, especially in the developing world; however the lack of a user friendly, flexible performance prediction model for hybrid power systems incorporating renewables hindered the analysis of hybrids as options to conventional solutions. A user friendly model was needed with the versatility to simulate the many system locations, widely varying hardware configurations, and differing control options for potential hybrid power systems. To meet these ends, researchers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) developed the Hybrid2 software. This paper provides an overview of the capabilities, features, and functionality of the Hybrid2 code, discusses its validation and future plans. Model availability and technical support provided to Hybrid2 users are also discussed. 12 refs., 3 figs., 4 tabs.
Model Predictive Control of Hybrid Thermal Energy Systems in Transport Refrigeration
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Alleyne, Andrew
2015-01-01
A predictive control scheme is designed to control a transport refrigeration system, such as a delivery truck, that includes a vapor compression cycle configured in parallel with a thermal energy storage (TES) unit. A novel approach to TES utilization is introduced and is based on the current...
US GODAE: Global Ocean Prediction with the Hybrid Coordinate Ocean Model (HYCOM)
2009-06-01
coastal application was to assess the impact of open boundary conditions from the HYCOM ocean prediction system on the dynamics and accuracy of a...near the Florida Keys when hurricane Katrina crossed South Florida in late August 2005. iMPact In summary, 25 institutions (nine from academia, nine...example, detailed surface current information derived from HYCOM is summarized by OCENS (Ocean and Coastal ENviromental Sensing, http
Baydaroğlu, Özlem; Koçak, Kasım; Duran, Kemal
2017-03-01
Prediction of water amount that will enter the reservoirs in the following month is of vital importance especially for semi-arid countries like Turkey. Climate projections emphasize that water scarcity will be one of the serious problems in the future. This study presents a methodology for predicting river flow for the subsequent month based on the time series of observed monthly river flow with hybrid models of support vector regression (SVR). Monthly river flow over the period 1940-2012 observed for the Kızılırmak River in Turkey has been used for training the method, which then has been applied for predictions over a period of 3 years. SVR is a specific implementation of support vector machines (SVMs), which transforms the observed input data time series into a high-dimensional feature space (input matrix) by way of a kernel function and performs a linear regression in this space. SVR requires a special input matrix. The input matrix was produced by wavelet transforms (WT), singular spectrum analysis (SSA), and a chaotic approach (CA) applied to the input time series. WT convolutes the original time series into a series of wavelets, and SSA decomposes the time series into a trend, an oscillatory and a noise component by singular value decomposition. CA uses a phase space formed by trajectories, which represent the dynamics producing the time series. These three methods for producing the input matrix for the SVR proved successful, while the SVR-WT combination resulted in the highest coefficient of determination and the lowest mean absolute error.
Free light fields can change the predictions of hybrid inflation
Energy Technology Data Exchange (ETDEWEB)
Matsuda, Tomohiro, E-mail: matsuda@sit.ac.jp [Department of Physics, Lancaster University, Lancaster LA1 4YB (United Kingdom)
2012-04-01
We show that the free light scalar fields that may exist in the inflationary Universe can change the predictions of the hybrid inflation model. Possible signatures are discussed, which can be used to discriminate the sources of the spectrum.
Hybrid Model of Content Extraction
DEFF Research Database (Denmark)
Qureshi, Pir Abdul Rasool; Memon, Nasrullah
2012-01-01
We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict...... model outperformed other existing content extraction models. We present a browser based implementation of the proposed model as proof of concept and compare the implementation strategy with various state of art implementations. We also discuss various applications of the proposed model with special...
Development of a hybrid model to predict construction and demolition waste: China as a case study.
Song, Yiliao; Wang, Yong; Liu, Feng; Zhang, Yixin
2017-01-01
Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers.
Large Unifying Hybrid Supernetwork Model
Institute of Scientific and Technical Information of China (English)
LIU; Qiang; FANG; Jin-qing; LI; Yong
2015-01-01
For depicting multi-hybrid process,large unifying hybrid network model(so called LUHNM)has two sub-hybrid ratios except dr.They are deterministic hybrid ratio(so called fd)and random hybrid ratio(so called gr),respectively.
A Hybrid Intelligent Method of Predicting Stock Returns
Directory of Open Access Journals (Sweden)
Akhter Mohiuddin Rather
2014-01-01
Full Text Available This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements.
Directory of Open Access Journals (Sweden)
Jing Lian
2017-01-01
Full Text Available Plug-in hybrid electric vehicles (PHEVs can be considered as a hybrid system (HS which includes the continuous state variable, discrete event, and operation constraint. Thus, a model predictive control (MPC strategy for PHEVs based on the mixed logical dynamical (MLD model and short-term vehicle speed prediction is proposed in this paper. Firstly, the mathematical model of the controlled PHEV is set-up to evaluate the energy consumption using the linearized models of core power components. Then, based on the recognition of driving intention and the past vehicle speed data, a nonlinear auto-regressive (NAR neural network structure is designed to predict the vehicle speed for known driving profiles of city buses and the predicted vehicle speed is used to calculate the total required torque. Next, a MLD model is established with appropriate constraints for six possible driving modes. By solving the objective function with the Mixed Integer Linear Programming (MILP algorithm, the optimal motor torque and the corresponding driving mode sequence within the speed prediction horizon can be obtained. Finally, the proposed energy control strategy shows substantial improvement in fuel economy in the simulation results.
Lombardo, Franco; Obach, R Scott; Dicapua, Frank M; Bakken, Gregory A; Lu, Jing; Potter, David M; Gao, Feng; Miller, Michael D; Zhang, Yao
2006-04-06
A computational approach is described that can predict the VD(ss) of new compounds in humans, with an accuracy of within 2-fold of the actual value. A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant analysis-random forest (MDA-RF) model using 31 computed descriptors. Descriptors included terms describing lipophilicity, ionization, molecular volume, and various molecular fragments. For a test set of 23 proprietary compounds not used in model construction, the geometric mean fold-error (GMFE) was 1.78-fold (+/-11.4%). The model was also tested using a leave-class out approach wherein subsets of drugs based on therapeutic class were removed from the training set of 384, the model was recast, and the VD(ss) values for each of the subsets were predicted. GMFE values ranged from 1.46 to 2.94-fold, depending on the subset. Finally, for an additional set of 74 compounds, VD(ss) predictions made using the computational model were compared to predictions made using previously described methods dependent on animal pharmacokinetic data. Computational VD(ss) predictions were, on average, 2.13-fold different from the VD(ss) predictions from animal data. The computational model described can predict human VD(ss) with an accuracy comparable to predictions requiring substantially greater effort and can be applied in place of animal experimentation.
Hybrid Predictive Control for Dynamic Transport Problems
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) ...
Prediction of properties of intraply hybrid composites
Chamis, C. C.; Sinclair, J. H.
1979-01-01
Equations based on the mixtures rule are presented for predicting the physical, thermal, hygral, and mechanical properties of unidirectional intraply hybrid composites (UIHC) from the corresponding properties of their constituent composites. Bounds were derived for uniaxial longitudinal strengths, tension, compression, and flexure of UIHC. The equations predict shear and flexural properties which agree with experimental data from UIHC. Use of these equations in a composites mechanics computer code predicted flexural moduli which agree with experimental data from various intraply hybrid angleplied laminates (IHAL). It is indicated, briefly, how these equations can be used in conjunction with composite mechanics and structural analysis during the analysis/design process.
A hybrid travel time prediction framework for planned motorway roadworks
Calvert, S.C.; Lint, J.W.C. van; Hoogendoorn, S.P.
2010-01-01
In this paper we propose a hybrid motorway travel time prediction framework aimed at providing pre-trip travel information in case of roadworks. The framework utilises a first order macroscopic traffic flow model to predict the consequences in travel time of changes in both traffic demand and roadwa
Predicting bending stiffness of randomly oriented hybrid panels
Laura Moya; William T.Y. Tze; Jerrold E. Winandy
2010-01-01
This study was conducted to develop a simple model to predict the bending modulus of elasticity (MOE) of randomly oriented hybrid panels. The modeling process involved three modules: the behavior of a single layer was computed by applying micromechanics equations, layer properties were adjusted for densification effects, and the entire panel was modeled as a three-...
Vazquez-Anderson, Jorge; Mihailovic, Mia K.; Baldridge, Kevin C.; Reyes, Kristofer G.; Haning, Katie; Cho, Seung Hee; Amador, Paul; Powell, Warren B.
2017-01-01
Abstract Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA–RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA–RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA–mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs. PMID:28334800
Directory of Open Access Journals (Sweden)
Lan-Rong Dung
2016-01-01
Full Text Available A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.
Interior Noise Prediction of the Automobile Based on Hybrid FE-SEA Method
National Research Council Canada - National Science Library
Chen, S. M; Wang, D. F; Zan, J. M
2011-01-01
In order to predict the interior noise of the automobile in the low and middle frequency band in the design and development stage, the hybrid FE-SEA model of an automobile was created using hybrid FE-SEA method...
Improving personalized link prediction by hybrid diffusion
Liu, Jin-Hu; Zhou, Tao
2016-01-01
Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which each individual will get equal number of diversiform predictions. While the performances of many classical algorithms are not satisfactory under this framework, thus new algorithms are in urgent need. Motivated by previous researches in other fields, we generalize heat conduction process to the framework of personalized link prediction and find that this method outperforms many classical similarity-based algorithms, especially in the performance of diversity. In addition, we demonstrate that adding one ground node who is supposed to connect all the nodes in the system will greatly benefit the performance of heat conduction. Finally, better hybrid algorithms composed of local random walk and heat conduction have been proposed. Numerical results show that the hybrid algorithms can outperform other algorithms simultaneously in all four ...
Directory of Open Access Journals (Sweden)
Zhenya Ji
2016-11-01
Full Text Available A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs and electric vehicles (EVs. At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM on the condition of satisfying each EV user’s demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times.
Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation
Directory of Open Access Journals (Sweden)
Erdong Zhao
2015-12-01
Full Text Available Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA auto-regressive integrated moving average with exogenous variables (ARIMAX model that is optimized very-short-term by the chaotic particle swarm optimization (CPSO algorithm, known as the SA-ARIMA-CPSO approach, for wind speed prediction. The ARIMAX model chooses the wind speed result from the Weather Research and Forecasting (WRF simulation as an exogenous input variable. Further, an SA strategy is applied to the ARIMAX process. When new information is available, the model process can be updated adaptively with parameters optimized by the CPSO algorithm. The proposed SA-ARIMA-CPSO approach enables the forecasting process to update training information and model parameters intelligently and adaptively. As tested using the 15-min wind speed data collected from a wind farm in Northern China, the improved method has the best performance compared with several other models.
Body Fat Percentage Prediction Using Intelligent Hybrid Approaches
Directory of Open Access Journals (Sweden)
Yuehjen E. Shao
2014-01-01
Full Text Available Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP, the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR, artificial neural network (ANN, multivariate adaptive regression splines (MARS, and support vector regression (SVR techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models.
Casalderrey-Solana, Jorge; Milhano, José Guilherme; Pablos, Daniel; Rajagopal, Krishna
2016-01-01
We have previously introduced a hybrid strong/weak coupling model for jet quenching in heavy ion collisions that describes the production and fragmentation of jets at weak coupling, using PYTHIA, and describes the rate at which each parton in the jet shower loses energy as it propagates through the strongly coupled plasma, dE/dx, using an expression computed holographically at strong coupling. The model has a single free parameter that we fit to a single experimental measurement. We then confront our model with experimental data on many other jet observables, focusing here on boson-jet observables, finding that it provides a good description of present jet data. Next, we provide the predictions of our hybrid model for many measurements to come, including those for inclusive jet, dijet, photon-jet and Z-jet observables in heavy ion collisions with energy $\\sqrt{s}=5.02$ ATeV coming soon at the LHC. As the statistical uncertainties on near-future measurements of photon-jet observables are expected to be much sm...
He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong
2016-03-01
In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance.
Improving personalized link prediction by hybrid diffusion
Liu, Jin-Hu; Zhu, Yu-Xiao; Zhou, Tao
2016-04-01
Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which each individual will get equal number of diversiform predictions. While the performances of many classical algorithms are not satisfactory under this framework, thus new algorithms are in urgent need. Motivated by previous researches in other fields, we generalize heat conduction process to the framework of personalized link prediction and find that this method outperforms many classical similarity-based algorithms, especially in the performance of diversity. In addition, we demonstrate that adding one ground node that is supposed to connect all the nodes in the system will greatly benefit the performance of heat conduction. Finally, better hybrid algorithms composed of local random walk and heat conduction have been proposed. Numerical results show that the hybrid algorithms can outperform other algorithms simultaneously in all four adopted metrics: AUC, precision, recall and hamming distance. In a word, this work may shed some light on the in-depth understanding of the effect of physical processes in personalized link prediction.
Modeling lithium/hybrid-cathode batteries
Energy Technology Data Exchange (ETDEWEB)
Gomadam, Parthasarathy M.; Merritt, Don R.; Scott, Erik R.; Schmidt, Craig L.; Skarstad, Paul M. [Medtronic Energy and Component Center, 6700 Shingle Creek Pkwy, Brooklyn Center, MN 55430 (United States); Weidner, John W. [Center for Electrochemical Engineering, Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208 (United States)
2007-12-06
This document describes a first-principles-based mathematical model developed to predict the voltage-capacity behavior of batteries having hybrid cathodes comprising a mixture of carbon monofluoride (CF{sub x}) and silver vanadium oxide (SVO). These batteries typically operate at moderate rates of discharge, lasting several years. The model presented here is an accurate tool for design optimization and performance prediction of batteries under current drains that encompass both the application rate and accelerated testing. (author)
Prediction of popcorn hybrid and composite means
Directory of Open Access Journals (Sweden)
Ramon Macedo Rangel
2007-01-01
Full Text Available The objectives of this study were to evaluate the combining ability of 10 popcorn populations in a circulantdiallel; to test the viability of superior hybrids; and to identify genetic composites for intrapopulation breeding. In fourcontrasting environments, 30 treatments were evaluated for grain yield (GY, plant height (PLH and popping expansion(PE, in a random block design with four replications. Results indicated that the evaluation of treatments in a larger groupof environments favors the expression of variability in genotypes. The additive effects for PE and the dominance effects forGY and PLH were highest. GY and PE of the combinations UNB2U-C1 x Angela and Braskalb x Angela were outstanding.The predicted mean PE and GY were highest for hybrid UNB2U-C1 x Angela and the composite formed by these parents(26.54 mL g-1 and 1,446.09 kg ha-1 respectively.
A study of hybrid prediction method for ship parametric rolling
Institute of Scientific and Technical Information of China (English)
周耀华; 马宁; 鲁江; 顾民
2016-01-01
The parametric rolling (PR) in the head or following waves has been considered as one of the main stability failure modes in the development of the 2nd generation Intact Stability criterion by the International Maritime Organization (IMO). According to previous studies, the estimation methods of the roll damping affect the prediction of the PR significantly, and most of them are based on experiment data or Ikeda’s empirical formula. The accuracy of the estimation method for the roll damping could be a key aspect for the validity of its prediction for the full scale ship. In this research, a hybrid prediction method is developed for the numerical prediction of the parametric rolling when experiment data are not available for the roll damping. Comparison study is also carried out between the hybrid method and a nonlinear dynamics method, where the roll damping is estimated by the simplified Ikeda’s method and the direct CFD prediction method in a direct non-linear simulation based on the 3-D CFD approach in the model scale. It is shown that the results of the hybrid method are in satisfactory agreements with the model experiment results, and the method can be used for analysis especially at the early design stage where experiment data are often not available.
Directory of Open Access Journals (Sweden)
Izaskun Garrido
2016-08-01
Full Text Available Plasma stability is one of the obstacles in the path to the successful operation of fusion devices. Numerical control-oriented codes as it is the case of the widely accepted RZIp may be used within Tokamak simulations. The novelty of this article relies in the hierarchical development of a dynamic control loop. It is based on a current profile Model Predictive Control (MPC algorithm within a multiloop structure, where a MPC is developed at each step so as to improve the Proportional Integral Derivative (PID global scheme. The inner control loop is composed of a PID-based controller that acts over the Multiple Input Multiple Output (MIMO system resulting from the RZIp plasma model of the Tokamak à Configuration Variable (TCV. The coefficients of this PID controller are initially tuned using an eigenmode reduction over the passive structure model. The control action corresponding to the state of interest is then optimized in the outer MPC loop. For the sake of comparison, both the traditionally used PID global controller as well as the multiloop enhanced MPC are applied to the same TCV shot. The results show that the proposed control algorithm presents a superior performance over the conventional PID algorithm in terms of convergence. Furthermore, this enhanced MPC algorithm contributes to extend the discharge length and to overcome the limited power availability restrictions that hinder the performance of advanced tokamaks.
A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion
Institute of Scientific and Technical Information of China (English)
Wen-yang DUAN; Li-min HUANG; Yang HAN; Ya-hui ZHANG; Shuo HUANG
2015-01-01
题目：用于非线性非平稳船舶运动极短期预报的一种复合自回归经验模态分解支持向量机回归模型 目的：基于支持向量机回归（SVR）模型在非线时间序列的预测能力及经验模态分解（EMD）方法在处理非线性非平稳性的优势，提出一种复合自回归经验模态分解支持向量机回归（AR-EMD-SVR）模型，提高非线性非平稳船舶运动极短期预报精度。 创新点：1.研究非线性非平稳船舶运动的极短期预报问题，提出一种复合的预报方法；2.基于不同层次的预报模型和模型试验数据，分析非线性非平稳性对极短期预报精度的影响。 方法：1.在SVR模型中引入基于自回归（AR）预报端点延拓的 EMD 方法，形成复合的 AR-EMD-SVR 预报模型；2.基于集装箱船模水池试验运动数据将 AR-EMD-SVR 模型与 AR、SVR 和EMD-AR 三种模型进行比较，分析非线性非平稳性对极短期预报的影响以及不同模型的预报性能。 结论：1. AR-EMD 方法能够有效的克服非平稳对极短期预报模型（AR和 SVR）在精度上所带来的不良影响；2.基于船模试验数据的预报结果表明：相较于 AR、SVR 和 EMD-AR 三种预报模型，基于 AR-EMD-SVR模型的非线性非平稳船舶运动极短期预报结果具有更高的精度。%Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector re-gression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends
Directory of Open Access Journals (Sweden)
Alexey Solovyev
Full Text Available Pressure ulcers are costly and life-threatening complications for people with spinal cord injury (SCI. People with SCI also exhibit differential blood flow properties in non-ulcerated skin. We hypothesized that a computer simulation of the pressure ulcer formation process, informed by data regarding skin blood flow and reactive hyperemia in response to pressure, could provide insights into the pathogenesis and effective treatment of post-SCI pressure ulcers. Agent-Based Models (ABM are useful in settings such as pressure ulcers, in which spatial realism is important. Ordinary Differential Equation-based (ODE models are useful when modeling physiological phenomena such as reactive hyperemia. Accordingly, we constructed a hybrid model that combines ODEs related to blood flow along with an ABM of skin injury, inflammation, and ulcer formation. The relationship between pressure and the course of ulcer formation, as well as several other important characteristic patterns of pressure ulcer formation, was demonstrated in this model. The ODE portion of this model was calibrated to data related to blood flow following experimental pressure responses in non-injured human subjects or to data from people with SCI. This model predicted a higher propensity to form ulcers in response to pressure in people with SCI vs. non-injured control subjects, and thus may serve as novel diagnostic platform for post-SCI ulcer formation.
Unified Hybrid Network Theoretical Model Trilogy
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
The first of the unified hybrid network theoretical model trilogy (UHNTF) is the harmonious unification hybrid preferential model (HUHPM), seen in the inner loop of Fig. 1, the unified hybrid ratio is defined.
Model Reduction of Hybrid Systems
DEFF Research Database (Denmark)
Shaker, Hamid Reza
systems are derived in this thesis. The results are used for output feedback control of switched nonlinear systems. Model reduction of piecewise affine systems is also studied in this thesis. The proposed method is based on the reduction of linear subsystems inside the polytopes. The methods which......High-Technological solutions of today are characterized by complex dynamical models. A lot of these models have inherent hybrid/switching structure. Hybrid/switched systems are powerful models for distributed embedded systems design where discrete controls are applied to continuous processes...... of hybrid systems, designing controllers and implementations is very high so that the use of these models is limited in applications where the size of the state space is large. To cope with complexity, model reduction is a powerful technique. This thesis presents methods for model reduction and stability...
Directory of Open Access Journals (Sweden)
Meng Xiong
2015-08-01
Full Text Available Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.
Metabolic prediction of important agronomic traits in hybrid rice (Oryza sativa L.).
Dan, Zhiwu; Hu, Jun; Zhou, Wei; Yao, Guoxin; Zhu, Renshan; Zhu, Yingguo; Huang, Wenchao
2016-02-24
Hybrid crops have contributed greatly to improvements in global food and fodder production over the past several decades. Nevertheless, the growing population and changing climate have produced food crises and energy shortages. Breeding new elite hybrid varieties is currently an urgent task, but present breeding procedures are time-consuming and labour-intensive. In this study, parental metabolic information was utilized to predict three polygenic traits in hybrid rice. A complete diallel cross population consisting of eighteen rice inbred lines was constructed, and the hybrids' plant height, heading date and grain yield per plant were predicted using 525 metabolites. Metabolic prediction models were built using the partial least square regression method, with predictive abilities ranging from 0.858 to 0.977 for the hybrid phenotypes, relative heterosis, and specific combining ability. Only slight changes in predictive ability were observed between hybrid populations, and nearly no changes were detected between reciprocal hybrids. The outcomes of prediction of the three highly polygenic traits demonstrated that metabolic prediction was an accurate (high predictive abilities) and efficient (unaffected by population genetic structures) strategy for screening promising superior hybrid rice. Exploitation of this pre-hybridization strategy may contribute to rice production improvement and accelerate breeding programs.
Hybrid Prediction and Fractal Hyperspectral Image Compression
Directory of Open Access Journals (Sweden)
Shiping Zhu
2015-01-01
Full Text Available The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.
Hybrid models for complex fluids
Tronci, Cesare
2010-01-01
This paper formulates a new approach to complex fluid dynamics, which accounts for microscopic statistical effects in the micromotion. While the ordinary fluid variables (mass density and momentum) undergo usual dynamics, the order parameter field is replaced by a statistical distribution on the order parameter space. This distribution depends also on the point in physical space and its dynamics retains the usual fluid transport features while containing the statistical information on the order parameter space. This approach is based on a hybrid moment closure for Yang-Mills Vlasov plasmas, which replaces the usual cold-plasma assumption. After presenting the basic properties of the hybrid closure, such as momentum map features, singular solutions and Casimir invariants, the effect of Yang-Mills fields is considered and a direct application to ferromagnetic fluids is presented. Hybrid models are also formulated for complex fluids with symmetry breaking. For the special case of liquid crystals, a hybrid formul...
Modeling and Analysis of Hybrid Dynamic Systems Using Hybrid Petri Nets
GHOMRI Latefa; Alla, Hassane
2008-01-01
Some extensions of PNs permitting HDS modeling were presented here. The first models to be presented are continuous PNs. This model may be used for modeling either a continuous system or a discrete system. In this case, it is an approximation that is often satisfactory. Hybrid PNs combine in the same formalism a discrete PN and a continuous PN. Two hybrid PN models were considered in this chapter. The first, called the hybrid PN, has a deterministic behavior; this means that we can predict th...
Kim, Chan Moon; Parnichkun, Manukid
2017-02-01
Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system (k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.
Modeling and analysis using hybrid Petri nets
Ghomri, Latéfa
2007-01-01
This paper is devoted to the use of hybrid Petri nets (PNs) for modeling and control of hybrid dynamic systems (HDS). Modeling, analysis and control of HDS attract ever more of researchers' attention and several works have been devoted to these topics. We consider in this paper the extensions of the PN formalism (initially conceived for modeling and analysis of discrete event systems) in the direction of hybrid modeling. We present, first, the continuous PN models. These models are obtained from discrete PNs by the fluidification of the markings. They constitute the first steps in the extension of PNs toward hybrid modeling. Then, we present two hybrid PN models, which differ in the class of HDS they can deal with. The first one is used for deterministic HDS modeling, whereas the second one can deal with HDS with nondeterministic behavior. Keywords: Hybrid dynamic systems; D-elementary hybrid Petri nets; Hybrid automata; Controller synthesis
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.
Gao, Ya; Wang, Zhanyong; Lu, Qing-Chang; Liu, Chao; Peng, Zhong-Ren; Yu, Yue
2016-10-01
A study on vertical variation of PM2.5 concentrations was carried out in this paper. Field measurements were conducted at eight different floor heights outside a building alongside a typical elevated expressway in downtown Shanghai, China. Results show that PM2.5 concentration decreases significantly with the increase of height from the 3rd to 7th floor or the 8th to 15th floor, and increases suddenly from the 7th to 8th floor which is the same height as the elevated expressway. A non-parametric test indicates that the data of PM2.5 concentration is statistically different under the 7th floor and above the 8th floor at the 5% significance level. To investigate the relationships between PM2.5 concentration and influencing factors, the Pearson correlation analysis was performed and the results indicate that both traffic and meteorological factors have crucial impacts on the variation of PM2.5 concentration, but there is a rather large variation in correlation coefficients under the 7th floor and above the 8th floor. Furthermore, the back propagation neural network based on principal component analysis (PCA-BPNN), as well as generalized additive model (GAM), was applied to predict the vertical PM2.5 concentration and examined with the field measurement dataset. Experimental results indicated that both models can obtain accurate predictions, while PCA-BPNN model provides more reliable and accurate predictions as it can reduce the complexity and eliminate data co-linearity. These findings reveal the vertical distribution of PM2.5 concentration and the potential of the proposed model to be applicable to predict the vertical trends of air pollution in similar situations.
Customer churn prediction using a hybrid method and censored data
Directory of Open Access Journals (Sweden)
Reza Tavakkoli-Moghaddam
2013-05-01
Full Text Available Customers are believed to be the main part of any organization’s assets and customer retention as well as customer churn management are important responsibilities of organizations. In today’s competitive environment, organization must do their best to retain their existing customers since attracting new customers cost significantly more than taking care of existing ones. In this paper, we present a hybrid method based on neural network and Cox regression analysis where neural network is used for outlier data and Cox regression method is implemented for prediction of future events. The proposed model of this paper has been implemented on some data and the results are compared based on five criteria including prediction accuracy, errors’ type I and II, root mean square error and mean absolute deviation. The preliminary results indicate that the proposed model of this paper performs better than alternative methods.
Ichiba, Tomoyuki; Banner, Adrian; Karatzas, Ioannis; Fernholz, Robert
2009-01-01
We study Atlas-type models of equity markets with local characteristics that depend on both name and rank, and in ways that induce a stability of the capital distribution. Ergodic properties and rankings of processes are examined with reference to the theory of reflected Brownian motions in polyhedral domains. In the context of such models, we discuss properties of various investment strategies, including the so-called growth-optimal and universal portfolios.
Hybrid2: The hybrid system simulation model, Version 1.0, user manual
Energy Technology Data Exchange (ETDEWEB)
Baring-Gould, E.I.
1996-06-01
In light of the large scale desire for energy in remote communities, especially in the developing world, the need for a detailed long term performance prediction model for hybrid power systems was seen. To meet these ends, engineers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) have spent the last three years developing the Hybrid2 software. The Hybrid2 code provides a means to conduct long term, detailed simulations of the performance of a large array of hybrid power systems. This work acts as an introduction and users manual to the Hybrid2 software. The manual describes the Hybrid2 code, what is included with the software and instructs the user on the structure of the code. The manual also describes some of the major features of the Hybrid2 code as well as how to create projects and run hybrid system simulations. The Hybrid2 code test program is also discussed. Although every attempt has been made to make the Hybrid2 code easy to understand and use, this manual will allow many organizations to consider the long term advantages of using hybrid power systems instead of conventional petroleum based systems for remote power generation.
Hybrid model for QCD deconfining phase boundary
Srivastava, P. K.; Singh, C. P.
2012-06-01
Intensive search for a proper and realistic equations of state (EOS) is still continued for studying the phase diagram existing between quark gluon plasma (QGP) and hadron gas (HG) phases. Lattice calculations provide such EOS for the strongly interacting matter at finite temperature (T) and vanishing baryon chemical potential (μB). These calculations are of limited use at finite μB due to the appearance of notorious sign problem. In the recent past, we had constructed a hybrid model description for the QGP as well as HG phases where we make use of a new excluded-volume model for HG and a thermodynamically-consistent quasiparticle model for the QGP phase and used them further to get QCD phase boundary and a critical point. Since then many lattice calculations have appeared showing various thermal and transport properties of QCD matter at finite T and μB=0. We test our hybrid model by reproducing the entire data for strongly interacting matter and predict our results at finite μB so that they can be tested in future. Finally we demonstrate the utility of the model in fixing the precise location, the order of the phase transition and the nature of CP existing on the QCD phase diagram. We thus emphasize the suitability of the hybrid model as formulated here in providing a realistic EOS for the strongly interacting matter.
Energy Technology Data Exchange (ETDEWEB)
Gerlagh, Reyer [University of Manchester, Manchester (United Kingdom); Van der Zwaan, Bob [ECN Policy Studies, Petten (Netherlands)
2009-11-15
This insightful book explores the issue of sustainable development in its more operative and applied sense. Although a great deal of research has addressed potential interpretations and definitions of sustainable development, much of this work is too abstract to offer policy-makers and researchers the feasible and effective guidelines they require. This book redresses the balance. The authors highlight how various indicators and aggregate measures can be included in models that are used for decision-making support and sustainability assessment. They also demonstrate the importance of identifying practical means to assess whether policy proposals, specific decisions or targeted scenarios are sustainable. With discussions of basic concepts relevant to understanding applied sustainability analysis, such as definitions of costs and revenue recycling, this book provides policy-makers, researchers and graduate students with feasible and effective principles for measuring sustainable development.
Predictive cruise control in hybrid electric vehicles
Keulen, T. van; Naus, M.J.G.; Jager, B. de; Molengraft, G.J.L. van de; Steinbuch, M.; Aneke, N.P.I.
2009-01-01
Deceleration rates have considerable influence on the fuel economy of hybrid electric vehicles. Given the vehicle characteristics and actual/measured operating conditions, as well as upcoming route information, optimal velocity trajectories can be constructed that maximize energy recovery. To suppor
Directory of Open Access Journals (Sweden)
Kaijiang YU
2015-10-01
Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.
Institute of Scientific and Technical Information of China (English)
王宇红; 黄德先; 金以慧
2005-01-01
A hybrid approach using MLD (mixed logical dynamical) framework to handle infeasibility and constraint prioritization issues in MPC (model predictive control) based on input-output model is introduced. By expressing constraint priorities as propositional logics and by transforming the propositional logics into inequalities,the infeasibility and constraint prioritization issues are solved in the MPC. Constraints with higher priorities are met first, and then these with lower priorities are satisfied as much as possible. This new approach is illustrated in the control of a heavy oil fractionator-Shell column. The overall control performance has been significantly improved through the infeasibility and control priorities handling.
Improving longitudinal motion prediction of hybrid monohulls with the viscous effect
Institute of Scientific and Technical Information of China (English)
ZHANG Heng; LI Ji-de
2007-01-01
A new method improves prediction of the motion of a hybrid monohull in regular waves. Stem section hydrodynamic coefficients of a hybrid monohull with harmonic oscillation were computed using the Reynolds Averaged Navier-Stokes Equations (RANSE). The governing equations were solved using the finite volume method. The VOF method was used for free surface treatment, and RNGK-ε turbulence model was employed in viscous flow calculation. The whole computational domain was divided into many blocks each with structured grids, and the dynamic process was treated with moving grids. Using a 2-D strip method and 2.5D theory with the correction hydrodynamic coefficients allows consideration of the viscous effect when predicting longitudinal motion of a hybrid monohull in regular waves. The method is effective at predicting motion of a hybrid monohull, showing that the viscous effect on a semi-submerged body cannot be ignored.
Levy, R.; Mcginness, H.
1976-01-01
Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.
CSIR Research Space (South Africa)
Duma, M
2013-09-01
Full Text Available We propose a hybrid missing data imputation technique using positive selection and correlation-based feature selection for insurance data. The hybrid is used to help supervised learning methods improve their classification accuracy and resilience...
Directory of Open Access Journals (Sweden)
Jingwei Song
2014-01-01
Full Text Available A simulated annealing (SA based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN, and partial least square support vector machine (PLS-SVM to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model, 12.93% (ANN, and 12.94% (PLS-SVM to 9.38%. Five-week average has been raised from 13.02% (chaotic model, 15.69% (ANN, and 15.92% (PLS-SVM to 11.27%.
Travelling waves in hybrid chemotaxis models
Franz, Benjamin; Painter, Kevin J; Erban, Radek
2013-01-01
Hybrid models of chemotaxis combine agent-based models of cells with partial differential equation models of extracellular chemical signals. In this paper, travelling wave properties of hybrid models of bacterial chemotaxis are investigated. Bacteria are modelled using an agent-based (individual-based) approach with internal dynamics describing signal transduction. In addition to the chemotactic behaviour of the bacteria, the individual-based model also includes cell proliferation and death. Cells consume the extracellular nutrient field (chemoattractant) which is modelled using a partial differential equation. Mesoscopic and macroscopic equations representing the behaviour of the hybrid model are derived and the existence of travelling wave solutions for these models is established. It is shown that cell proliferation is necessary for the existence of non-transient (stationary) travelling waves in hybrid models. Additionally, a numerical comparison between the wave speeds of the continuum models and the hybr...
Time Series Prediction based on Hybrid Neural Networks
Directory of Open Access Journals (Sweden)
S. A. Yarushev
2016-01-01
Full Text Available In this paper, we suggest to use hybrid approach to time series forecasting problem. In first part of paper, we create a literature review of time series forecasting methods based on hybrid neural networks and neuro-fuzzy approaches. Hybrid neural networks especially effective for specific types of applications such as forecasting or classification problem, in contrast to traditional monolithic neural networks. These classes of problems include problems with different characteristics in different modules. The main part of paper create a detailed overview of hybrid networks benefits, its architectures and performance under traditional neural networks. Hybrid neural networks models for time series forecasting are discussed in the paper. Experiments with modular neural networks are given.
Hadron rapidity spectra within a hybrid model
Khvorostukhin, A S
2016-01-01
A 2-stage hybrid model is proposed that joins the fast initial state of interaction, described by the hadron string dynamics (HSD) model, to subsequent evolution of the expanding system at the second stage, treated within ideal hydrodynamics. The developed hybrid model is assigned to describe heavy-ion collisions in the energy range of the NICA collider under construction in Dubna. Generally, the model is in reasonable agreement with the available data on proton rapidity spectra. However, reproducing proton rapidity spectra, our hybrid model cannot describe the rapidity distributions of pions. The model should be improved by taking into consideration viscosity effects at the hydrodynamical stage of system evolution.
Statistical Model Checking for Stochastic Hybrid Systems
DEFF Research Database (Denmark)
David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand
2012-01-01
This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique ap...
A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries
Directory of Open Access Journals (Sweden)
Wen-An Yang
2016-01-01
Full Text Available Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs like the conventional backpropagation (BP algorithm and support vector machines (SVMs. In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.
Fluid and hybrid models for streamers
Bonaventura, Zdeněk
2016-09-01
Streamers are contracted ionizing waves with self-generated field enhancement that propagate into a low-ionized medium exposed to high electric field leaving filamentary trails of plasma behind. The widely used model to study streamer dynamics is based on drift-diffusion equations for electrons and ions, assuming local field approximation, coupled with Poisson's equation. For problems where presence of energetic electrons become important a fluid approach needs to be extended by a particle model, accompanied also with Monte Carlo Collision technique, that takes care of motion of these electrons. A combined fluid-particle approach is used to study an influence of surface emission processes on a fast-pulsed dielectric barrier discharge in air at atmospheric pressure. It is found that fluid-only model predicts substantially faster reignition dynamics compared to coupled fluid-particle model. Furthermore, a hybrid model can be created in which the population of electrons is divided in the energy space into two distinct groups: (1) low energy `bulk' electrons that are treated with fluid model, and (2) high energy `beam' electrons, followed as particles. The hybrid model is then capable not only to deal with streamer discharges in laboratory conditions, but also allows us to study electron acceleration in streamer zone of lighting leaders. There, the production of fast electrons from streamers is investigated, since these (runaway) electrons act as seeds for the relativistic runaway electron avalanche (RREA) mechanism, important for high-energy atmospheric physics phenomena. Results suggest that high energy electrons effect the streamer propagation, namely the velocity, the peak electric field, and thus also the production rate of runaway electrons. This work has been supported by the Czech Science Foundation research project 15-04023S.
Hybrid neural network models of transducers
Xie, Shilin; Zhang, Xinong; Chen, Shenglai; Zhu, Changchun
2011-10-01
A hybrid neural network (NN) approach is proposed and applied to modeling of transducers in the paper. The modeling procedures are also presented in detail. First, the simulated studies on the modeling of single input-single output and multi input-multi output transducers are conducted respectively by use of the developed hybrid NN scheme. Secondly, the hybrid NN modeling approach is utilized to characterize a six-axis force sensor prototype based on the measured data. The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method. In addition, the method is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.
Cestari, Andrea
2013-01-01
Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.
Evaluating the Pedagogical Potential of Hybrid Models
Levin, Tzur; Levin, Ilya
2013-01-01
The paper examines how the use of hybrid models--that consist of the interacting continuous and discrete processes--may assist in teaching system thinking. We report an experiment in which undergraduate students were asked to choose between a hybrid and a continuous solution for a number of control problems. A correlation has been found between…
Harmonious Unifying Hybrid Preferential Supernetwork Model
Institute of Scientific and Technical Information of China (English)
LIU; Qiang; FANG; Jin-qing; LI; Yong
2015-01-01
The basic concepts and methods for harmonious unifying hybrid preferential model(HUHPM)are based on random preferential attachment(RPA)mixed with deterministic preferential attachment(DPA),so there is only one unified hybrid ratio dr,which is defined as:
Constraining hybrid inflation models with WMAP three-year results
Cardoso, A
2006-01-01
We reconsider the original model of quadratic hybrid inflation in light of the WMAP three-year results and study the possibility of obtaining a spectral index of primordial density perturbations, $n_s$, smaller than one from this model. The original hybrid inflation model naturally predicts $n_s\\geq1$ in the false vacuum dominated regime but it is also possible to have $n_s<1$ when the quadratic term dominates. We therefore investigate whether there is also an intermediate regime compatible with the latest constraints, where the scalar field value during the last 50 e-folds of inflation is less than the Planck scale.
Hybrid Modeling Improves Health and Performance Monitoring
2007-01-01
Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.
基于风光混合模型的短期功率预测方法研究%Short term power prediction based on hybrid wind-PV forecasting model
Institute of Scientific and Technical Information of China (English)
孔波利; 崔丽艳; 丁钊; 李现伟; 王以笑
2015-01-01
Accurate wind power and photovoltaic power outputs forecasting is important to improve the scheduling quality of wind and solar hybrid generation system. The hybrid wind-PV forecasting model based on BP neural network is established to achieve the forecasting of the entire wind power and photovoltaic power station, using the same forecasting model instead of twice forecasting, it can simplify the prediction and improve the prediction precision effectively. The method is validated by wind farm and photovoltaic system data in a coastal islands and the forecast error is calculated and analyzed. The results show the method has high accuracy, which has good academic value and practical value to forecast power for hybrid wind-PV generation system.%准确地预测风力发电及光伏发电的输出功率对提高风光互补供电系统的调度质量具有重要意义.建立了基于BP神经网络的风光混合预测模型,将现有技术中分两次预测的风电功率和光伏功率采用同一个预测模型,同时实现整个区域风电场及光伏电站的输出功率预测,在简化预测方法的同时提高预测准确度.通过某海岛的风电及光伏电站的实际数据验证,计算分析了预测误差.结果表明该方法具有较高的预测精度,对风光混合的功率预测具有一定的学术价值和工程实用价值.
Towards Modelling of Hybrid Systems
DEFF Research Database (Denmark)
Wisniewski, Rafal
2006-01-01
The article is an attempt to use methods of category theory and topology for analysis of hybrid systems. We use the notion of a directed topological space; it is a topological space together with a set of privileged paths. Dynamical systems are examples of directed topological spaces. A hybrid...... system consists of a number of dynamical systems that are glued together according to information encoded in the discrete part of the system. We develop a definition of a hybrid system as a functor from the category generated by a transition system to the category of directed topological spaces. Its...... directed homotopy colimit (geometric realization) is a single directed topological space. The behavior of hybrid systems can be then understood in terms of the behavior of dynamical systems through the directed homotopy colimit....
Hybrid estimation technique for predicting butene concentration in polyethylene reactor
Mohd Ali, Jarinah; Hussain, M. A.
2016-03-01
A component of artificial intelligence (AI), which is fuzzy logic, is combined with the so-called conventional sliding mode observer (SMO) to establish a hybrid type estimator to predict the butene concentration in the polyethylene production reactor. Butene or co-monomer concentration is another significant parameter in the polymerization process since it will affect the molecular weight distribution of the polymer produced. The hybrid estimator offers straightforward formulation of SMO and its combination with the fuzzy logic rules. The error resulted from the SMO estimation will be manipulated using the fuzzy rules to enhance the performance, thus improved on the convergence rate. This hybrid estimation is able to estimate the butene concentration satisfactorily despite the present of noise in the process.
Indian Academy of Sciences (India)
Anirban Mukhopadhyay; Arun Mondal; Sandip Mukherjee; Dipam Khatua; Subhajit Ghosh; Debasish Mitra; Tuhin Ghosh
2014-08-01
In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020.
Modeling hybrid perovskites by molecular dynamics.
Mattoni, Alessandro; Filippetti, Alessio; Caddeo, Claudia
2017-02-01
The topical review describes the recent progress in the modeling of hybrid perovskites by molecular dynamics simulations. Hybrid perovskites and in particular methylammonium lead halide (MAPI) have a tremendous technological relevance representing the fastest-advancing solar material to date. They also represent the paradigm of an organic-inorganic crystalline material with some conceptual peculiarities: an inorganic semiconductor for what concerns the electronic and absorption properties with a hybrid and solution processable organic-inorganic body. After briefly explaining the basic concepts of ab initio and classical molecular dynamics, the model potential recently developed for hybrid perovskites is described together with its physical motivation as a simple ionic model able to reproduce the main dynamical properties of the material. Advantages and limits of the two strategies (either ab initio or classical) are discussed in comparison with the time and length scales (from pico to microsecond scale) necessary to comprehensively study the relevant properties of hybrid perovskites from molecular reorientations to electrocaloric effects. The state-of-the-art of the molecular dynamics modeling of hybrid perovskites is reviewed by focusing on a selection of showcase applications of methylammonium lead halide: molecular cations disorder; temperature evolution of vibrations; thermally activated defects diffusion; thermal transport. We finally discuss the perspectives in the modeling of hybrid perovskites by molecular dynamics.
Modeling hybrid perovskites by molecular dynamics
Mattoni, Alessandro; Filippetti, Alessio; Caddeo, Claudia
2017-02-01
The topical review describes the recent progress in the modeling of hybrid perovskites by molecular dynamics simulations. Hybrid perovskites and in particular methylammonium lead halide (MAPI) have a tremendous technological relevance representing the fastest-advancing solar material to date. They also represent the paradigm of an organic-inorganic crystalline material with some conceptual peculiarities: an inorganic semiconductor for what concerns the electronic and absorption properties with a hybrid and solution processable organic-inorganic body. After briefly explaining the basic concepts of ab initio and classical molecular dynamics, the model potential recently developed for hybrid perovskites is described together with its physical motivation as a simple ionic model able to reproduce the main dynamical properties of the material. Advantages and limits of the two strategies (either ab initio or classical) are discussed in comparison with the time and length scales (from pico to microsecond scale) necessary to comprehensively study the relevant properties of hybrid perovskites from molecular reorientations to electrocaloric effects. The state-of-the-art of the molecular dynamics modeling of hybrid perovskites is reviewed by focusing on a selection of showcase applications of methylammonium lead halide: molecular cations disorder; temperature evolution of vibrations; thermally activated defects diffusion; thermal transport. We finally discuss the perspectives in the modeling of hybrid perovskites by molecular dynamics.
Institute of Scientific and Technical Information of China (English)
李明伟; 康海贵; 周鹏飞
2012-01-01
To improve urban passenger prediction accuracy with parametric projection pursuit regression, acceleration genetic algorithm was improved with cat map, Gaussian distribution and local searching. A new chaos accelerating genetic algorithm (NCAGA) was presented, used to optimize the best projection direction a of PPPR model. A hybrid algorithm of NCAGA-PPPR urban passenger forecasting model was proposed, in which the best projection direction was hybrid optimized inner by the NCAGA at the time of optimizing outer the number of ridge functions M. The simulation prediction was made with observed data, compared with BP neural network model, traditional PPR model and PPPR model optimized by acceleration genetic algorithm. The urban passenger forecasting accuracy is higher than the others, which the mean absolute relative error is less than 3.1%. The new hybrid algorithm can improve prediction accuracy of urban passenger and can be used efficaciously to forecast the urban passenger.%为了提高参数投影寻踪回归(parameter projection pursuit regression,PPPR)模型对城市客运量的预测精度,基于cat映射、高斯分布和精英局部搜索对加速遗传算法进行改进.提出了新的混沌加速遗传算法(new chaos accelerating genetic algorithm,NCAGA),用于对PPPR模型的最佳投影方向a的优选.建立了在外层优化岭函数个数M的同时,内层利用NCAGA优化最佳投影方向a的NCAGA-PPPR混合优化城市客运量预测模型,结合某市统计资料进行了仿真预测.结果表明该方法的预测精度优于BP神经网络模型、传统PPR模型和基于加速遗传优选的PPPR模型,平均绝对相对误差小于3.1％,提高了城市客运量的预测精度,可有效应用于城市客运量的预测.
A hybrid random field model for scalable statistical learning.
Freno, A; Trentin, E; Gori, M
2009-01-01
This paper introduces hybrid random fields, which are a class of probabilistic graphical models aimed at allowing for efficient structure learning in high-dimensional domains. Hybrid random fields, along with the learning algorithm we develop for them, are especially useful as a pseudo-likelihood estimation technique (rather than a technique for estimating strict joint probability distributions). In order to assess the generality of the proposed model, we prove that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by Bayesian networks. Once we establish this result, we develop a scalable algorithm for learning the structure of hybrid random fields, which we call 'Markov Blanket Merging'. On the one hand, we characterize some complexity properties of Markov Blanket Merging both from a theoretical and from the experimental point of view, using a series of synthetic benchmarks. On the other hand, we evaluate the accuracy of hybrid random fields (as learned via Markov Blanket Merging) by comparing them to various alternative statistical models in a number of pattern classification and link-prediction applications. As the results show, learning hybrid random fields by the Markov Blanket Merging algorithm not only reduces significantly the computational cost of structure learning with respect to several considered alternatives, but it also leads to models that are highly accurate as compared to the alternative ones.
MODEL PREDICTIVE CONTROL FUNDAMENTALS
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... paper, we will present an introduction to the theory and application of MPC with Matlab codes written to ... model predictive control, linear systems, discrete-time systems, ... and then compute very rapidly for this open-loop con-.
Travelling Waves in Hybrid Chemotaxis Models
Franz, Benjamin
2013-12-18
Hybrid models of chemotaxis combine agent-based models of cells with partial differential equation models of extracellular chemical signals. In this paper, travelling wave properties of hybrid models of bacterial chemotaxis are investigated. Bacteria are modelled using an agent-based (individual-based) approach with internal dynamics describing signal transduction. In addition to the chemotactic behaviour of the bacteria, the individual-based model also includes cell proliferation and death. Cells consume the extracellular nutrient field (chemoattractant), which is modelled using a partial differential equation. Mesoscopic and macroscopic equations representing the behaviour of the hybrid model are derived and the existence of travelling wave solutions for these models is established. It is shown that cell proliferation is necessary for the existence of non-transient (stationary) travelling waves in hybrid models. Additionally, a numerical comparison between the wave speeds of the continuum models and the hybrid models shows good agreement in the case of weak chemotaxis and qualitative agreement for the strong chemotaxis case. In the case of slow cell adaptation, we detect oscillating behaviour of the wave, which cannot be explained by mean-field approximations. © 2013 Society for Mathematical Biology.
HYbrid Coordinate Ocean Model (HYCOM): Global
National Oceanic and Atmospheric Administration, Department of Commerce — Global HYbrid Coordinate Ocean Model (HYCOM) and U.S. Navy Coupled Ocean Data Assimilation (NCODA) 3-day, daily forecast at approximately 9-km (1/12-degree)...
Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting
Indian Academy of Sciences (India)
C NARENDRA BABU; PALLAVIRAM SURE
2016-07-01
Time series data (TSD) originating from different applications have dissimilar characteristics. Hence for prediction of TSD, diversified varieties of prediction models exist. In many applications, hybrid models provide more accurate predictions than individual models. One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ARIMA–ANN model is proposed, which combines the concepts of the recently developed moving average (MA) filter based hybrid ARIMA–ANN model, with a processing technique involving a partitioning–interpolation (PI) step. The improved prediction accuracy of the proposed PI based hybrid ARIMA–ANN model is justified using a simulation experiment.Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all the others, and hence is a promising model for TSD prediction
Boltzmann Transport in Hybrid PIC HET Modeling
2015-07-01
Paper 3. DATES COVERED (From - To) July 2015-July 2015 4. TITLE AND SUBTITLE Boltzmann transport in hybrid PIC HET modeling 5a. CONTRACT NUMBER In...produced a variety of self-consistent electron swarm codes, such as the Magboltz code, focused on directly solving the steady Boltzmann trans-port...Std. 239.18 Boltzmann transport in hybrid PIC HET modeling IEPC-2015- /ISTS-2015-b- Presented at Joint Conference of 30th International
Statistical Model Checking for Stochastic Hybrid Systems
DEFF Research Database (Denmark)
David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand
2012-01-01
This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique...... applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings....
Prediction of daily rainfall by a hybrid wavelet-season-neuro technique
Altunkaynak, Abdusselam; Nigussie, Tewodros Assefa
2015-10-01
Accurate daily rainfall prediction is required for accurate streamflow prediction, flooding risk analysis, constructing a reliable flood control and early warning system. However, because of its nonlinearity, prediction of daily rainfall with high accuracy and long prediction lead time is difficult. There are many daily rainfall prediction methods in the literature, but they are known to yield inaccurate predictions with short lead time, require many physical parameters and involve complicated mathematical equations with huge computational burden. Recently, artificial neural network has been used for predicting rainfall with the objective of addressing the above mentioned problems. But still, the accuracy has not been satisfactory and predictions are with short lead time. In this study, two methods called combined season-multilayer perceptron (SAS-MP) and hybrid wavelet-season-multilayer perceptron (W-SAS-MP) were developed to enhance prediction accuracy and extend prediction lead time of daily rainfall up to 5 days by using data from two stations in Turkey. These two models were compared with the stand-alone multilayer perceptron and another most commonly used method called combined wavelet-multilayer perceptron (W-MP). The performances of the models were evaluated by using coefficient of determination, coefficient of efficiency and root mean squared error. The SAS-MP model was found to be better than W-MP in most cases, except lead time day 1, where W-MP performed better. Throughout all the lead times, however, the hybrid W-SAS-MP model performed best with CE values of 0.911 and 0.909, respectively, for prediction lead time of 1 day and 0.588 and 0.570, respectively, for prediction lead time of 5 days at Stations 17836 and 17837, respectively, at the model testing (validation) phase. Therefore, W-SAS-MP can be an appropriate tool for enhancing daily rainfall prediction accuracy and extend prediction lead time.
Dissolved oxygen content prediction in crab culture using a hybrid intelligent method
Yu, Huihui; Chen, Yingyi; Hassan, Shahbazgul; Li, Daoliang
2016-06-01
A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds.
Gas emission prediction model of hybrid pi-sigma fuzzy neural network%瓦斯涌出量的混合pi-sigma模糊神经网络预测模型
Institute of Scientific and Technical Information of China (English)
潘玉民; 赵立永; 张全柱
2012-01-01
提出了一种利用混合pi-sigma模糊神经推理方法建立瓦斯涌出量的预测模型.该模型采用高斯基函数作为模糊子集的隶属度函数,可在线动态调整隶属度函数和结论参数.与神经网络预测模型比较,该模型具有物理意义明确、原理清晰、收敛速度快、预测精度高等特点,在对某矿瓦斯涌出量数据的仿真结果表明,该方法预测准确度高、速度快,并且结果具有可重复性,证明该方法是有效的.为便于工程实际应用,在Matlab环境中开发了基于图形用户界面(GUI)的仿真应用界面,给出了使用方法和预测结果.实验同时表明,对所采用的数据,模型的训练精度设置为0.001时网络的泛化能力最好,网络训练精度和预测精度之间不具有正比关系.%A gas emission prediction model established by using reasoning method of hybrid pi-sigma fuzzy neural networks was proposed. The model adopted Gaussian function as a fuzzy membership function, and the membership functions and conclusions parameters of the model could be adjusted online dynamically. Compared with the neural network prediction model, the method has characteristics of clear physical meaning, clear principle, fast convergence, high prediction accuracy and so on. The gas emission data of a coal mine simulation results show that the prediction has a high accuracy, fast convergence and the prediction results can be repeated, it is proved that the method is effective. In order to facilitate the practical application, the authors developed a Graphical User Interface (GUI) application interface in the Matlab environment, and gave the method and prediction results. The experiments also show that, for the data, the generalization ability of the model is best when the training accuracy is set 0. 001, and the training accuracy and the prediction accuracy of the model do not have positive relationship.
Predictive control strategy for power management in parallel hybrid-electric vehicle
DEFF Research Database (Denmark)
Nodeh, Mohammad Taqi; Gholizade, Hossein; Hajizadeh, Amin
2016-01-01
In this paper, a hybrid model-based nonlinear optimal control method is used to compute the optimal power distribution and power management in parallel hybrid electric vehicles. In the power management strategy, for optimal power distribution between the internal combustion engine, electrical...... system and the other subsystems, nonlinear predictive control is applied. In order to achieve this goal, a hierarchical control structure is utilized. This type of control structure consists of three levels of monitoring, coordinating and local controllers. Nonlinear modeling and performance index...... in the proposed method should be formulated at the regulatory level of the controller. Discrete dynamic mode of operation (motor-generator) in hybrid electric vehicle requires to use a dual-mode switch model and to define an alternative expression of performance index for the optimal control problem...
Nominal model predictive control
Grüne, Lars
2013-01-01
5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...
Nominal Model Predictive Control
Grüne, Lars
2014-01-01
5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...
Candidate Prediction Models and Methods
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2005-01-01
This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....
A Mathematical Model for Suppression Subtractive Hybridization
2002-01-01
Suppression subtractive hybridization (SSH) is frequently used to unearth differentially expressed genes on a whole-genome scale. Its versatility is based on combining cDNA library subtraction and normalization, which allows the isolation of sequences of varying degrees of abundance and differential expression. SSH is a complex process with many adjustable parameters that affect the outcome of gene isolation.We present a mathematical model of SSH based on DNA hybridization kinetics for assess...
A Hybrid 3D Indoor Space Model
Jamali, Ali; Rahman, Alias Abdul; Boguslawski, Pawel
2016-10-01
GIS integrates spatial information and spatial analysis. An important example of such integration is for emergency response which requires route planning inside and outside of a building. Route planning requires detailed information related to indoor and outdoor environment. Indoor navigation network models including Geometric Network Model (GNM), Navigable Space Model, sub-division model and regular-grid model lack indoor data sources and abstraction methods. In this paper, a hybrid indoor space model is proposed. In the proposed method, 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. This research proposes a method of indoor space modeling for the buildings which do not have proper 2D/3D geometrical models or they lack semantic or topological information. The proposed hybrid model consists of topological, geometrical and semantical space.
A Hybrid 3D Indoor Space Model
Directory of Open Access Journals (Sweden)
A. Jamali
2016-10-01
Full Text Available GIS integrates spatial information and spatial analysis. An important example of such integration is for emergency response which requires route planning inside and outside of a building. Route planning requires detailed information related to indoor and outdoor environment. Indoor navigation network models including Geometric Network Model (GNM, Navigable Space Model, sub-division model and regular-grid model lack indoor data sources and abstraction methods. In this paper, a hybrid indoor space model is proposed. In the proposed method, 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. This research proposes a method of indoor space modeling for the buildings which do not have proper 2D/3D geometrical models or they lack semantic or topological information. The proposed hybrid model consists of topological, geometrical and semantical space.
Institute of Scientific and Technical Information of China (English)
唐朝晖; 杜金芳; 陈青
2012-01-01
矿物浮选过程中,矿浆pH值作为影响浮选效果的一个重要因素,是实现浮选过程监视及优化控制的一个重要参量.目前的pH值测定仪存在交叉污染、测量滞后等问题,难以获得实时准确的pH值.为使浮选运行在最优状态,在泡沫图像特征提取的基础上,提出一种基于自适应遗传混合神经网络的预测模型,该模型首先利用主元分析(PCA)方法对提取的多个图像特征进行降维,然后采用自适应遗传混合神经网络(AGA-HNN)建立PH值预测模型.最后将该模型应用于浮选现场,预测结果能够实时跟踪实际值,根据预测值实时调整工况条件,改善了浮选效果,提高了浮选效率.%In mineral flotation process, pH value is one of the flotation elements which affect the flotation performance significantly. It is very important for flotation process monitoring and optimized controtl. At present, pH determinator has the problem of cross contamination , measurement lag, and so on. So it is difficult to obtain real lime and accurate pH value. To make flotation limning in an optimal slate, a novel prediction model is proposed in this paper based on adaptive genetic hybrid neural network after extracting several image features. Firstly, feature dimension reduction is done by principal component analysia( PCA). Then prediction model is built through a-daptive genetic hybrid neural network( AGA-HNN). Finally, the model is applied to flotation field. Predicted value can well trace the actual value. At the same time, working condition is adjusted according to the predicted value. As a result, the flotation performance and efficiency are improved obviously.
Predictive Surface Complexation Modeling
Energy Technology Data Exchange (ETDEWEB)
Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences
2016-11-29
Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO_{2} and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.
Directory of Open Access Journals (Sweden)
C. Mahesh
2013-01-01
Full Text Available Finite element method is effectively used to homogenize the thermal conductivity of FRP composites consisting of hybrid materials and fibre-matrix debonds at some of the fibres. The homogenized result at microlevel is used to determine the property of the layer using macromechanics principles; thereby, it is possible to minimize the computational efforts required to solve the problem as in state through only micromechanics approach. The working of the proposed procedure is verified for three different problems: (i hybrid composite having two different fibres in alternate layers, (ii fibre-matrix interface debond in alternate layers, and (iii fibre-matrix interface debond at one fibre in a group of four fibres in one unit cell. It is observed that the results are in good agreement with those obtained through pure micro-mechanics approach.
Multiview coding mode decision with hybrid optimal stopping model.
Zhao, Tiesong; Kwong, Sam; Wang, Hanli; Wang, Zhou; Pan, Zhaoqing; Kuo, C-C Jay
2013-04-01
In a generic decision process, optimal stopping theory aims to achieve a good tradeoff between decision performance and time consumed, with the advantages of theoretical decision-making and predictable decision performance. In this paper, optimal stopping theory is employed to develop an effective hybrid model for the mode decision problem, which aims to theoretically achieve a good tradeoff between the two interrelated measurements in mode decision, as computational complexity reduction and rate-distortion degradation. The proposed hybrid model is implemented and examined with a multiview encoder. To support the model and further promote coding performance, the multiview coding mode characteristics, including predicted mode probability and estimated coding time, are jointly investigated with inter-view correlations. Exhaustive experimental results with a wide range of video resolutions reveal the efficiency and robustness of our method, with high decision accuracy, negligible computational overhead, and almost intact rate-distortion performance compared to the original encoder.
Hybrid simulation models of production networks
Kouikoglou, Vassilis S
2001-01-01
This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.
Hybrid Models in Loop Quantum Cosmology
Navascués, B Elizaga; Marugán, G A Mena
2016-01-01
In the framework of Loop Quantum Cosmology, inhomogeneous models are usually quantized by means of a hybrid approach that combines loop quantization techniques with standard quantum field theory methods. This approach is based on a splitting of the phase space in a homogeneous sector, formed by global, zero-modes, and an inhomogeneous sector, formed by the remaining, infinite number of modes, that describe the local degrees of freedom. Then, the hybrid quantization is attained by adopting a loop representation for the homogeneous gravitational sector, while a Fock representation is used for the inhomogeneities. The zero-mode of the Hamiltonian constraint operator couples the homogeneous and inhomogeneous sectors. The hybrid approach, therefore, is expected to provide a suitable quantum theory in regimes where the main quantum effects of the geometry are those affecting the zero-modes, while the inhomogeneities, still being quantum, can be treated in a more conventional way. This hybrid strategy was first prop...
Hybrid modelling of anaerobic wastewater treatment processes.
Karama, A; Bernard, O; Genovesi, A; Dochain, D; Benhammou, A; Steyer, J P
2001-01-01
This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feed-forward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data.
Madadgar, Shahrbanou; AghaKouchak, Amir; Shukla, Shraddhanand; Wood, Andrew W.; Cheng, Linyin; Hsu, Kou-Lin; Svoboda, Mark
2016-07-01
Improving water management in water stressed-regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical-dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi-Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian-based model that relates precipitation to atmosphere-ocean teleconnections (also known as an analog-year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so-called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog-year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3-5 month lead time) by 5-60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10-60% improvement over NMME) than positive precipitation anomalies (5-25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012-2014 event in the western United States that resulted in significant socioeconomic impacts.
Candidate Prediction Models and Methods
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2005-01-01
This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...
A hybrid wind farm parameterization for mesoscale and climate models
Pan, Y.; Archer, C. L.
2016-12-01
To better understand the potential impacts of wind farms on weather and climate at the local to regional scale, a new hybrid wind farm parameterization is proposed here for mesoscale models, such as the Weather Research and Forecasting Model (WRF), or climate models, such as the Community Atmosphere Model (CAM). All previous wind farm parameterizations treat all the wind turbines in the same grid cell as identical (i.e., they all share the same upstream wind velocity) and ignore the effect of wind direction. By contrast, the new hybrid model considers each individual wind turbine, based on its position in the layout and on wind direction. The new parameterization is developed starting from large eddy simulations (LES) of existing wind farms, in which the local flow around each wind turbine is directly simulated at high spatial ( 3.5 m) and temporal ( 0.1 s) resolutions and the effects of subgrid-scale processes are modeled. Based on analytic and statistical relationships between the LES results and several geometric properties of the wind farm layout (such as blockage ratio and blocking distance), the new hybrid parameterization predicts the local upstream wind speed of each individual wind turbine in the same grid cell, and thus successfully account for the effects of layout and wind direction with little computational cost. With the newly predicted upstream velocity, the turbine-induced forces and added turbulence kinetic energy (TKE) in the atmosphere are derived analytically. The wind speed, wind speed deficit, and TKE profiles and power production obtained with the hybrid parameterization for the test case (the 48-turbine Lillgrund wind farm in Sweden) are in better agreement with the LES results than previous parameterizations. Future work includes the insertion of the hybrid parameterization into the WRF code to assess impacts on near-surface properties, such as temperature and heat and momentum fluxes, in the region surrounding the wind farm.
Weather forecasting based on hybrid neural model
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-02-01
Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.
MODA - A hybrid atmospheric pollutant dispersion model
Energy Technology Data Exchange (ETDEWEB)
Favaron, M.; Oliveti Selmi, O. [Servizi Territorio srl, Milan (Italy); Sozzi, R. [Agenzia Regionale Protezione Ambiente (ARPA) Lazio, Rieti (Italy)
2004-07-01
MODA is a Gaussian-hybrid atmospheric dispersion model, intended for regulatory applications, and designed to meet the following requirements: ability to operate in complex terrain, standard use of a refined description of turbulence, operational efficiency (in terms of both speed and ease to change simulation parameters), ease of integration in modelling interfaces, output compatibility with the widely-used ISC3. MODA can operate in two modes: a standard mode, in which the pollutant dispersion is treated as Gaussian, and an advanced mode, in which the hybrid relations are used to compute the pollutant concentrations. (orig.)
Electrical test prediction using hybrid metrology and machine learning
Breton, Mary; Chao, Robin; Muthinti, Gangadhara Raja; de la Peña, Abraham A.; Simon, Jacques; Cepler, Aron J.; Sendelbach, Matthew; Gaudiello, John; Emans, Susan; Shifrin, Michael; Etzioni, Yoav; Urenski, Ronen; Lee, Wei Ti
2017-03-01
Electrical test measurement in the back-end of line (BEOL) is crucial for wafer and die sorting as well as comparing intended process splits. Any in-line, nondestructive technique in the process flow to accurately predict these measurements can significantly improve mean-time-to-detect (MTTD) of defects and improve cycle times for yield and process learning. Measuring after BEOL metallization is commonly done for process control and learning, particularly with scatterometry (also called OCD (Optical Critical Dimension)), which can solve for multiple profile parameters such as metal line height or sidewall angle and does so within patterned regions. This gives scatterometry an advantage over inline microscopy-based techniques, which provide top-down information, since such techniques can be insensitive to sidewall variations hidden under the metal fill of the trench. But when faced with correlation to electrical test measurements that are specific to the BEOL processing, both techniques face the additional challenge of sampling. Microscopy-based techniques are sampling-limited by their small probe size, while scatterometry is traditionally limited (for microprocessors) to scribe targets that mimic device ground rules but are not necessarily designed to be electrically testable. A solution to this sampling challenge lies in a fast reference-based machine learning capability that allows for OCD measurement directly of the electrically-testable structures, even when they are not OCD-compatible. By incorporating such direct OCD measurements, correlation to, and therefore prediction of, resistance of BEOL electrical test structures is significantly improved. Improvements in prediction capability for multiple types of in-die electrically-testable device structures is demonstrated. To further improve the quality of the prediction of the electrical resistance measurements, hybrid metrology using the OCD measurements as well as X-ray metrology (XRF) is used. Hybrid metrology
Bifurcation of resistive wall mode dynamics predicted by magnetohydrodynamic-kinetic hybrid theory
Energy Technology Data Exchange (ETDEWEB)
Yang, S. X.; Wang, Z. X., E-mail: zxwang@dlut.edu.cn [Key Laboratory of Materials Modification by Beams of the Ministry of Education, School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian 116024 (China); Wang, S.; Hao, G. Z., E-mail: haogz@swip.ac.cn; Song, X. M.; Wang, A. K. [Southwestern Institute of Physics, P.O.Box 432, Chengdu 610041 (China); Liu, Y. Q. [Culham Centre for Fusion Energy, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Southwestern Institute of Physics, P.O.Box 432, Chengdu 610041 (China)
2015-09-15
The magnetohydrodynamic-kinetic hybrid theory has been extensively and successfully applied for interpreting experimental observations of macroscopic, low frequency instabilities, such as the resistive wall mode, in fusion plasmas. In this work, it is discovered that an analytic version of the hybrid formulation predicts a bifurcation of the mode dynamics while varying certain physical parameters of the plasma, such as the thermal particle collisionality or the ratio of the thermal ion to electron temperatures. This bifurcation can robustly occur under reasonably large parameter spaces as well as with different assumptions, for instance, on the particle collision model. Qualitatively similar bifurcation features are also observed in full toroidal computations presented in this work, based on a non-perturbative hybrid formulation.
SCAN-based hybrid and double-hybrid density functionals from models without fitted parameters
Hui, Kerwin; Chai, Jeng-Da
2015-01-01
By incorporating the nonempirical SCAN semilocal density functional [Sun, Ruzsinszky, and Perdew, Phys. Rev. Lett. 115, 036402 (2015)] in the underlying expression of four existing hybrid and double-hybrid models, we propose one hybrid (SCAN0) and three double-hybrid (SCAN0-DH, SCAN-QIDH, and SCAN0-2) density functionals, which are free from any fitted parameters. The SCAN-based double-hybrid functionals consistently outperform their parent SCAN semilocal functional for self-interaction probl...
Hybrid models in loop quantum cosmology
Elizaga Navascués, Beatriz; Martín-Benito, Mercedes; Mena Marugán, Guillermo A.
2016-06-01
In the framework of Loop Quantum Cosmology (LQC), inhomogeneous models are usually quantized by means of a hybrid approach that combines loop quantization techniques with standard quantum field theory methods. This approach is based on a splitting of the phase space in a homogeneous sector, formed by global, zero-modes and an inhomogeneous sector, formed by the remaining, infinite number of modes, that describe the local degrees of freedom. Then, the hybrid quantization is attained by adopting a loop representation for the homogeneous gravitational sector, while a Fock representation is used for the inhomogeneities. The zero-mode of the Hamiltonian constraint operator couples the homogeneous and inhomogeneous sectors. The hybrid approach, therefore, is expected to provide a suitable quantum theory in regimes where the main quantum effects of the geometry are those affecting the zero-modes, while the inhomogeneities, still being quantum, can be treated in a more conventional way. This hybrid strategy was first proposed for the simplest cosmological midisuperspaces: the Gowdy models, and it has been later applied to the case of cosmological perturbations. This paper reviews the construction and main applications of hybrid LQC.
Melanoma risk prediction models
Directory of Open Access Journals (Sweden)
Nikolić Jelena
2014-01-01
Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were
QCD Phase Transition in a new Hybrid Model Formulation
Srivastava, P K
2013-01-01
Search of a proper and realistic equations of state (EOS) for strongly interacting matter used in the study of QCD phase diagram still appears as a challenging task. Recently, we have constructed a hybrid model description for the quark gluon plasma (QGP) as well as hadron gas (HG) phases where we use a new excluded-volume model for HG and a thermodynamically-consistent quasiparticle model for the QGP phase. We attempt to use them to get a QCD phase boundary and a critical point. We test our hybrid model by reproducing the entire lattice QCD data for strongly interacting matter at zero baryon chemical potential ($\\mu_{B}$)and predict the results at finite $\\mu_{B}$ and $T$.
Modelling of data uncertainties on hybrid computers
Energy Technology Data Exchange (ETDEWEB)
Schneider, Anke (ed.)
2016-06-15
The codes d{sup 3}f and r{sup 3}t are well established for modelling density-driven flow and nuclide transport in the far field of repositories for hazardous material in deep geological formations. They are applicable in porous media as well as in fractured rock or mudstone, for modelling salt- and heat transport as well as a free groundwater surface. Development of the basic framework of d{sup 3}f and r{sup 3}t had begun more than 20 years ago. Since that time significant advancements took place in the requirements for safety assessment as well as for computer hardware development. The period of safety assessment for a repository of high-level radioactive waste was extended to 1 million years, and the complexity of the models is steadily growing. Concurrently, the demands on accuracy increase. Additionally, model and parameter uncertainties become more and more important for an increased understanding of prediction reliability. All this leads to a growing demand for computational power that requires a considerable software speed-up. An effective way to achieve this is the use of modern, hybrid computer architectures which requires basically the set-up of new data structures and a corresponding code revision but offers a potential speed-up by several orders of magnitude. The original codes d{sup 3}f and r{sup 3}t were applications of the software platform UG /BAS 94/ whose development had begun in the early nineteennineties. However, UG had recently been advanced to the C++ based, substantially revised version UG4 /VOG 13/. To benefit also in the future from state-of-the-art numerical algorithms and to use hybrid computer architectures, the codes d{sup 3}f and r{sup 3}t were transferred to this new code platform. Making use of the fact that coupling between different sets of equations is natively supported in UG4, d{sup 3}f and r{sup 3}t were combined to one conjoint code d{sup 3}f++. A direct estimation of uncertainties for complex groundwater flow models with the
Hybrid quantum teleportation: A theoretical model
Energy Technology Data Exchange (ETDEWEB)
Takeda, Shuntaro; Mizuta, Takahiro; Fuwa, Maria; Yoshikawa, Jun-ichi; Yonezawa, Hidehiro; Furusawa, Akira [Department of Applied Physics, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan)
2014-12-04
Hybrid quantum teleportation – continuous-variable teleportation of qubits – is a promising approach for deterministically teleporting photonic qubits. We propose how to implement it with current technology. Our theoretical model shows that faithful qubit transfer can be achieved for this teleportation by choosing an optimal gain for the teleporter’s classical channel.
Prasetyo Utomo, Chandra
2011-06-01
Permeability is an important parameter connected with oil reservoir. Predicting the permeability could save millions of dollars. Unfortunately, petroleum engineers have faced numerous challenges arriving at cost-efficient predictions. Much work has been carried out to solve this problem. The main challenge is to handle the high range of permeability in each reservoir. For about a hundred year, mathematicians and engineers have tried to deliver best prediction models. However, none of them have produced satisfying results. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. In this proposal, the system combines classification and regression models to predict the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion and thereby avoids the complexity of all previous models. Finally, it is important to note that the ELM is used as a final predictor. Results demonstrate that this proposed hybrid model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers concerning this important process and has wider implications for oil reservoir management efficiency.
Novel Hybrid Model: Integrating Scrum and XP
Directory of Open Access Journals (Sweden)
Zaigham Mushtaq
2012-06-01
Full Text Available Scrum does not provide any direction about how to engineer a software product. The project team has to adopt suitable agile process model for the engineering of software. XP process model is mainly focused on engineering practices rather than management practices. The design of XP process makes it suitable for simple and small size projects and not appropriate for medium and large projects. A fine integration of management and engineering practices is desperately required to build quality product to make it valuable for customers. In this research a novel framework hybrid model is proposed to achieve this integration. The proposed hybrid model is actually an express version of Scrum model. It possesses features of engineering practices that are necessary to develop quality software as per customer requirements and company objectives. A case study is conducted to validate the proposal of hybrid model. The results of the case study reveal that proposed model is an improved version of XP and Scrum model.
Marano, Rachel M; Mercurio, Laura; Kanter, Rebecca; Doyle, Richard; Abuelo, Dianne; Morrow, Eric M; Shur, Natasha
2013-03-01
Array comparative genomic hybridization (aCGH) testing can diagnose chromosomal microdeletions and duplications too small to be detected by conventional cytogenetic techniques. We need to consider which patients are more likely to receive a diagnosis from aCGH testing versus patients that have lower likelihood and may benefit from broader genome wide scanning. We retrospectively reviewed charts of a population of 200 patients, 117 boys and 83 girls, who underwent aCGH testing in Genetics Clinic at Rhode Island hospital between 1 January/2008 and 31 December 2010. Data collected included sex, age at initial clinical presentation, aCGH result, history of seizures, autism, dysmorphic features, global developmental delay/intellectual disability, hypotonia and failure to thrive. aCGH analysis revealed abnormal results in 34 (17%) and variants of unknown significance in 24 (12%). Patients with three or more clinical diagnoses had a 25.0% incidence of abnormal aCGH findings, while patients with two or fewer clinical diagnoses had a 12.5% incidence of abnormal aCGH findings. Currently, we provide families with a range of 10-30% of a diagnosis with aCGH testing. With increased clinical complexity, patients have an increased probability of having an abnormal aCGH result. With this, we can provide individualized risk estimates for each patient.
A Hybrid Method to Predict Success of Dental Implants
Reyhaneh Sadat Moayeri; Mehdi Khalili; Mahsa Nazari
2016-01-01
Background/Objectives: The market demand for dental implants is growing at a significant pace. Results obtained from real cases shows that some dental implants do not lead to success. Hence, the main problem is whether machine learning techniques can be successful in prediction of success of dental implants. Methods/Statistical Analysis: This paper presents a combined predictive model to evaluate the success of dental implants. The classifiers used in this model are W-J48, SVM, Neural Network...
CORSICA modelling of ITER hybrid operation scenarios
Kim, S. H.; Bulmer, R. H.; Campbell, D. J.; Casper, T. A.; LoDestro, L. L.; Meyer, W. H.; Pearlstein, L. D.; Snipes, J. A.
2016-12-01
The hybrid operating mode observed in several tokamaks is characterized by further enhancement over the high plasma confinement (H-mode) associated with reduced magneto-hydro-dynamic (MHD) instabilities linked to a stationary flat safety factor (q ) profile in the core region. The proposed ITER hybrid operation is currently aiming at operating for a long burn duration (>1000 s) with a moderate fusion power multiplication factor, Q , of at least 5. This paper presents candidate ITER hybrid operation scenarios developed using a free-boundary transport modelling code, CORSICA, taking all relevant physics and engineering constraints into account. The ITER hybrid operation scenarios have been developed by tailoring the 15 MA baseline ITER inductive H-mode scenario. Accessible operation conditions for ITER hybrid operation and achievable range of plasma parameters have been investigated considering uncertainties on the plasma confinement and transport. ITER operation capability for avoiding the poloidal field coil current, field and force limits has been examined by applying different current ramp rates, flat-top plasma currents and densities, and pre-magnetization of the poloidal field coils. Various combinations of heating and current drive (H&CD) schemes have been applied to study several physics issues, such as the plasma current density profile tailoring, enhancement of the plasma energy confinement and fusion power generation. A parameterized edge pedestal model based on EPED1 added to the CORSICA code has been applied to hybrid operation scenarios. Finally, fully self-consistent free-boundary transport simulations have been performed to provide information on the poloidal field coil voltage demands and to study the controllability with the ITER controllers. Extended from Proc. 24th Int. Conf. on Fusion Energy (San Diego, 2012) IT/P1-13.
Influence of Deterministic Attachments for Large Unifying Hybrid Network Model
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
Large unifying hybrid network model (LUHPM) introduced the deterministic mixing ratio fd on the basis of the harmonious unification hybrid preferential model, to describe the influence of deterministic attachment to the network topology characteristics,
Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles.
Toropova, Alla P; Toropov, Andrey A
2017-06-05
Skin sensitization (allergic contact dermatitis) is a widespread problem arising from the contact of chemicals with the skin. The detection of molecular features with undesired effect for skin is complex task owing to unclear biochemical mechanisms and unclearness of conditions of action of chemicals to skin. The development of computational methods for estimation of this endpoint in order to reduce animal testing is recommended (Cosmetics Directive EC regulation 1907/2006; EU Regulation, Regulation, 1223/2009). The CORAL software (http://www.insilico.eu/coral) gives good predictive models for the skin sensitization. Simplified molecular input-line entry system (SMILES) together with molecular graph are used to represent the molecular structure for these models. So-called hybrid optimal descriptors are used to establish quantitative structure-activity relationships (QSARs). The aim of this study is the estimation of the predictive potential of the hybrid descriptors. Three different distributions into the training (≈70%), calibration (≈15%), and validation (≈15%) sets are studied. QSAR for these three distributions are built up with using the Monte Carlo technique. The statistical characteristics of these models for external validation set are used as a measure of predictive potential of these models. The best model, according to the above criterion, is characterized by nvalidation=29, r(2)validation=0.8596, RMSEvalidation=0.489. Mechanistic interpretation and domain of applicability for these models are defined. Copyright © 2017 Elsevier B.V. All rights reserved.
Liu, Yebin; Dai, Qionghai; You, Zhixiang; Xu, Wenli
2007-01-01
Efficient exploitation of the temporal and inter-view correlation is critical to multi-view video coding (MVC), and the key to it relies on the design of prediction chain structure according to the various pattern of correlations. In this paper, we propose a novel prediction structure model to design optimal MVC coding schemes along with tradeoff analysis in depth between compression efficiency and prediction structure complexity for certain standard functionalities. Focusing on the representation of the entire set of possible chain structures rather than certain typical ones, the proposed model can given efficient MVC schemes that adaptively vary with the requirements of structure complexity and video source characteristics (the number of views, the degrees of temporal and interview correlations). To handle large scale problem in model optimization, we deploy a hybrid genetic algorithm which yields satisfactory results shown in the simulations.
Hybrid Energy System Modeling in Modelica
Energy Technology Data Exchange (ETDEWEB)
William R. Binder; Christiaan J. J. Paredis; Humberto E. Garcia
2014-03-01
In this paper, a Hybrid Energy System (HES) configuration is modeled in Modelica. Hybrid Energy Systems (HES) have as their defining characteristic the use of one or more energy inputs, combined with the potential for multiple energy outputs. Compared to traditional energy systems, HES provide additional operational flexibility so that high variability in both energy production and consumption levels can be absorbed more effectively. This is particularly important when including renewable energy sources, whose output levels are inherently variable, determined by nature. The specific HES configuration modeled in this paper include two energy inputs: a nuclear plant, and a series of wind turbines. In addition, the system produces two energy outputs: electricity and synthetic fuel. The models are verified through simulations of the individual components, and the system as a whole. The simulations are performed for a range of component sizes, operating conditions, and control schemes.
Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed
2017-05-01
Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.
Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed
2016-02-01
Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.
Directory of Open Access Journals (Sweden)
Göran Ståhl
2016-02-01
Full Text Available This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Keywords: Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National forest inventory, Remote sensing, Sampling
Experimental Validation of a Thermoelastic Model for SMA Hybrid Composites
Turner, Travis L.
2001-01-01
This study presents results from experimental validation of a recently developed model for predicting the thermomechanical behavior of shape memory alloy hybrid composite (SMAHC) structures, composite structures with an embedded SMA constituent. The model captures the material nonlinearity of the material system with temperature and is capable of modeling constrained, restrained, or free recovery behavior from experimental measurement of fundamental engineering properties. A brief description of the model and analysis procedures is given, followed by an overview of a parallel effort to fabricate and characterize the material system of SMAHC specimens. Static and dynamic experimental configurations for the SMAHC specimens are described and experimental results for thermal post-buckling and random response are presented. Excellent agreement is achieved between the measured and predicted results, fully validating the theoretical model for constrained recovery behavior of SMAHC structures.
Mathematical Modeling of Hybrid Electrical Engineering Systems
Directory of Open Access Journals (Sweden)
A. A. Lobaty
2016-01-01
Full Text Available A large class of systems that have found application in various industries and households, electrified transportation facilities and energy sector has been classified as electrical engineering systems. Their characteristic feature is a combination of continuous and discontinuous modes of operation, which is reflected in the appearance of a relatively new term “hybrid systems”. A wide class of hybrid systems is pulsed DC converters operating in a pulse width modulation, which are non-linear systems with variable structure. Using various methods for linearization it is possible to obtain linear mathematical models that rather accurately simulate behavior of such systems. However, the presence in the mathematical models of exponential nonlinearities creates considerable difficulties in the implementation of digital hardware. The solution can be found while using an approximation of exponential functions by polynomials of the first order, that, however, violates the rigor accordance of the analytical model with characteristics of a real object. There are two practical approaches to synthesize algorithms for control of hybrid systems. The first approach is based on the representation of the whole system by a discrete model which is described by difference equations that makes it possible to synthesize discrete algorithms. The second approach is based on description of the system by differential equations. The equations describe synthesis of continuous algorithms and their further implementation in a digital computer included in the control loop system. The paper considers modeling of a hybrid electrical engineering system using differential equations. Neglecting the pulse duration, it has been proposed to describe behavior of vector components in phase coordinates of the hybrid system by stochastic differential equations containing generally non-linear differentiable random functions. A stochastic vector-matrix equation describing dynamics of the
Institute of Scientific and Technical Information of China (English)
舒红; 潘文军; 袁景敏; 蒋勇
2011-01-01
基于车载导航系统(GPS/CIS等)所提供的未来一段预测路线上的汽车运行状态信息,建立中度混合动力汽车再生制动能量回收的全局优化动态规划模型;采用模型预测控制方法,将动态规划的全局优化控制策略转化成预测视距内的局部优化算法,实现滚动优化控制;为解决动态规划中的维数灾问题,确定了电池荷电状态和温度的可达区域;对模型预测控制策略、全局优化控制策略和瞬时优化控制策略进行了计算比较,在不同坡度、不同坡长的匀速下坡工况下的仿真表明:模型预测算法的计算效率显著高于全局优化策略的;应用模型预测控制策略的再生制动能量回收效率明显高于瞬时优化控制策略的,相比全局优化策略的降低不到1.31%,且采用档位提示的模型预测控制策略能量回收效果更好.%Based on the driving states of vehicles built by GPS/GIS on board in the future predictive route, a global optimal dynamic programming model of regenerative braking energy recovery for a medium hybrid electric vehicle was established. In order to realize a receding horizon optimal control, the global optimal dynamic programming algorithm was converted into a local optimal algorithm within prediction horizon using the model predictive control method. To overcome the curse of dimensionality of dynamic programming, the reachable ranges of SOC and temperature for battery were determined. The calculation comparison among the control strategies of model prediction, global optimization and instantaneous optimization was carried out. The simulation under the conditions of different gradients and slope lengths, and cruising downhill was performed.The results show that ( 1 ) the computational efficiency of model predictive control strategy is higher than that of global optimal control strategy; (2) energy recovery efficiency of regenerative braking of model predictive control strategy is greater than
Energy Technology Data Exchange (ETDEWEB)
Stursberg, Olaf; Paschedag, Tina; Rungger, Matthias; Ding, Hao [Kassel Univ. (Germany). Fachgebiet Regelungs- und Systemtheorie
2010-08-15
While hybrid dynamic models are, to a certain degree, established for modeling systems with heterogeneous dynamics, most approaches for design and analysis of hybrid systems are restricted to monolithic models without hierarchy. This contribution first shows, how modular hybrid systems with two layers of decision, as appropriate for representing manufacturing systems for example, can be modeled systematically. The second part proposes a technique for fixing discrete inputs (for coordinating control) and continuous inputs (for embedded continuous controllers) in combination. The method uses a graph-based search on the upper decision layer, while principles of predictive control are used on the lower layer. The procedure of modeling and control is illustrated for a manufacturing process. (orig.)
Hybrid optimization model of product concepts
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the "evolution - evaluation" iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the proposed method and associated algorithms.
Hybrid perturbation methods based on statistical time series models
San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario
2016-04-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
2011-01-01
Numerical Modeling of the Global Atmosphere in the Climate System. Kluwer Academic Press. Bi.mk.irt. J.M.. Testut , C.E.. Brasseur, P., Verron. J...2001JC001198. Itr.ink.irt, J.M., Testut , C.E., Parent, L, 2003b. An integrated system of sequential assimilation modules. sesam3.2 reference manual. MEOM...Fukumori. I.. Kamachi. M.. Martin. M.J., Mogensen, K., Oke. P.. Testut . C.E., Verroa J.. Weaver, A., 2009. Ocean data assimilation systems for GODAE
Paiement, Jean-François; Grandvalet, Yves; Bengio, Samy
2008-01-01
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce generative models for melodies. We decompose melodic modeling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modeling sequences of Narmour featur...
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR mcta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.
Reverse engineering cellular decisions for hybrid reconfigurable network modeling
Blair, Howard A.; Saranak, Jureepan; Foster, Kenneth W.
2011-06-01
Cells as microorganisms and within multicellular organisms make robust decisions. Knowing how these complex cells make decisions is essential to explain, predict or mimic their behavior. The discovery of multi-layer multiple feedback loops in the signaling pathways of these modular hybrid systems suggests their decision making is sophisticated. Hybrid systems coordinate and integrate signals of various kinds: discrete on/off signals, continuous sensory signals, and stochastic and continuous fluctuations to regulate chemical concentrations. Such signaling networks can form reconfigurable networks of attractors and repellors giving them an extra level of organization that has resilient decision making built in. Work on generic attractor and repellor networks and on the already identified feedback networks and dynamic reconfigurable regulatory topologies in biological cells suggests that biological systems probably exploit such dynamic capabilities. We present a simple behavior of the swimming unicellular alga Chlamydomonas that involves interdependent discrete and continuous signals in feedback loops. We show how to rigorously verify a hybrid dynamical model of a biological system with respect to a declarative description of a cell's behavior. The hybrid dynamical systems we use are based on a unification of discrete structures and continuous topologies developed in prior work on convergence spaces. They involve variables of discrete and continuous types, in the sense of type theory in mathematical logic. A unification such as afforded by convergence spaces is necessary if one wants to take account of the affect of the structural relationships within each type on the dynamics of the system.
Hamiltonian approach to hybrid plasma models
Tronci, Cesare
2010-01-01
The Hamiltonian structures of several hybrid kinetic-fluid models are identified explicitly, upon considering collisionless Vlasov dynamics for the hot particles interacting with a bulk fluid. After presenting different pressure-coupling schemes for an ordinary fluid interacting with a hot gas, the paper extends the treatment to account for a fluid plasma interacting with an energetic ion species. Both current-coupling and pressure-coupling MHD schemes are treated extensively. In particular, pressure-coupling schemes are shown to require a transport-like term in the Vlasov kinetic equation, in order for the Hamiltonian structure to be preserved. The last part of the paper is devoted to studying the more general case of an energetic ion species interacting with a neutralizing electron background (hybrid Hall-MHD). Circulation laws and Casimir functionals are presented explicitly in each case.
Hybrid Perturbation methods based on Statistical Time Series models
San-Juan, Juan Félix; Pérez, Iván; López, Rosario
2016-01-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of a...
Zephyr - the prediction models
DEFF Research Database (Denmark)
Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg
2001-01-01
This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Dani...
All-atom/coarse-grained hybrid predictions of distribution coefficients in SAMPL5
Genheden, Samuel; Essex, Jonathan W.
2016-11-01
We present blind predictions submitted to the SAMPL5 challenge on calculating distribution coefficients. The predictions were based on estimating the solvation free energies in water and cyclohexane of the 53 compounds in the challenge. These free energies were computed using alchemical free energy simulations based on a hybrid all-atom/coarse-grained model. The compounds were treated with the general Amber force field, whereas the solvent molecules were treated with the Elba coarse-grained model. Considering the simplicity of the solvent model and that we approximate the distribution coefficient with the partition coefficient of the neutral species, the predictions are of good accuracy. The correlation coefficient, R is 0.64, 82 % of the predictions have the correct sign and the mean absolute deviation is 1.8 log units. This is on a par with or better than the other simulation-based predictions in the challenge. We present an analysis of the deviations to experiments and compare the predictions to another submission that used all-atom solvent.
Low speed hybrid generalized predictive control of a gasoline-propelled car.
Romero, M; de Madrid, A P; Mañoso, C; Milanés, V
2015-07-01
Low-speed driving in traffic jams causes significant pollution and wasted time for commuters. Additionally, from the passengers׳ standpoint, this is an uncomfortable, stressful and tedious scene that is suitable to be automated. The highly nonlinear dynamics of car engines at low-speed turn its automation in a complex problem that still remains as unsolved. Considering the hybrid nature of the vehicle longitudinal control at low-speed, constantly switching between throttle and brake pedal actions, hybrid control is a good candidate to solve this problem. This work presents the analytical formulation of a hybrid predictive controller for automated low-speed driving. It takes advantage of valuable characteristics supplied by predictive control strategies both for compensating un-modeled dynamics and for keeping passengers security and comfort analytically by means of the treatment of constraints. The proposed controller was implemented in a gas-propelled vehicle to experimentally validate the adopted solution. To this end, different scenarios were analyzed varying road layouts and vehicle speeds within a private test track. The production vehicle is a commercial Citroën C3 Pluriel which has been modified to automatically act over its throttle and brake pedals.
Directory of Open Access Journals (Sweden)
Yuehjen E. Shao
2013-01-01
Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.
Schrag, Tobias A; Möhring, Jens; Maurer, Hans Peter; Dhillon, Baldev S; Melchinger, Albrecht E; Piepho, Hans-Peter; Sørensen, Anker P; Frisch, Matthias
2009-02-01
In hybrid breeding, the prediction of hybrid performance (HP) is extremely important as it is difficult to evaluate inbred lines in numerous cross combinations. Recent developments such as doubled haploid production and molecular marker technologies have enhanced the prospects of marker-based HP prediction to accelerate the breeding process. Our objectives were to (1) predict HP using a combined analysis of hybrids and parental lines from a breeding program, (2) evaluate the use of molecular markers in addition to phenotypic and pedigree data, (3) evaluate the combination of line per se data with marker-based estimates, (4) study the effect of the number of tested parents, and (5) assess the advantage of haplotype blocks. An unbalanced dataset of 400 hybrids from 9 factorial crosses tested in different experiments and data of 79 inbred parents were subjected to combined analyses with a mixed linear model. Marker data of the inbreds were obtained with 20 AFLP primer-enzyme combinations. Cross-validation was used to assess the performance prediction of hybrids of which no or only one parental line was testcross evaluated. For HP prediction, the highest proportion of explained variance (R (2)), 46% for grain yield (GY) and 70% for grain dry matter content (GDMC), was obtained from line per se best linear unbiased prediction (BLUP) estimates plus marker effects associated with mid-parent heterosis (TEAM-LM). Our study demonstrated that HP was efficiently predicted using molecular markers even for GY when testcross data of both parents are not available. This can help in improving greatly the efficiency of commercial hybrid breeding programs.
Polarization of Inclusive $\\Lambda_{c}$'s in a Hybrid Model
Goldstein, G R
2000-01-01
A hybrid model is presented for hyperon polarization that is based on perturbative QCD subprocesses and the recombination of polarized quarks with scalar diquarks. The updated hybrid model is applied to $p+p\\to \\Lambda +X$ and successfully reproduces the detailed kinematic dependence shown by the data. The hybrid model is extended to include pion beams and polarized $\\Lambda_c$'s. The resulting polarization is found to be in fair agreement with recent experiments. Predictions for the polarization dependence on $x_F$ and $p_T$ is given.
Zhan, Liwei; Li, Chengwei
2017-02-01
A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.
Infectious disease modeling a hybrid system approach
Liu, Xinzhi
2017-01-01
This volume presents infectious diseases modeled mathematically, taking seasonality and changes in population behavior into account, using a switched and hybrid systems framework. The scope of coverage includes background on mathematical epidemiology, including classical formulations and results; a motivation for seasonal effects and changes in population behavior, an investigation into term-time forced epidemic models with switching parameters, and a detailed account of several different control strategies. The main goal is to study these models theoretically and to establish conditions under which eradication or persistence of the disease is guaranteed. In doing so, the long-term behavior of the models is determined through mathematical techniques from switched systems theory. Numerical simulations are also given to augment and illustrate the theoretical results and to help study the efficacy of the control schemes.
A Hybrid Ground-Motion Prediction Equation for Earthquakes in Western Alberta
Spriggs, N.; Yenier, E.; Law, A.; Moores, A. O.
2015-12-01
Estimation of ground-motion amplitudes that may be produced by future earthquakes constitutes the foundation of seismic hazard assessment and earthquake-resistant structural design. This is typically done by using a prediction equation that quantifies amplitudes as a function of key seismological variables such as magnitude, distance and site condition. In this study, we develop a hybrid empirical prediction equation for earthquakes in western Alberta, where evaluation of seismic hazard associated with induced seismicity is of particular interest. We use peak ground motions and response spectra from recorded seismic events to model the regional source and attenuation attributes. The available empirical data is limited in the magnitude range of engineering interest (M>4). Therefore, we combine empirical data with a simulation-based model in order to obtain seismologically informed predictions for moderate-to-large magnitude events. The methodology is two-fold. First, we investigate the shape of geometrical spreading in Alberta. We supplement the seismic data with ground motions obtained from mining/quarry blasts, in order to gain insights into the regional attenuation over a wide distance range. A comparison of ground-motion amplitudes for earthquakes and mining/quarry blasts show that both event types decay at similar rates with distance and demonstrate a significant Moho-bounce effect. In the second stage, we calibrate the source and attenuation parameters of a simulation-based prediction equation to match the available amplitude data from seismic events. We model the geometrical spreading using a trilinear function with attenuation rates obtained from the first stage, and calculate coefficients of anelastic attenuation and site amplification via regression analysis. This provides a hybrid ground-motion prediction equation that is calibrated for observed motions in western Alberta and is applicable to moderate-to-large magnitude events.
Confidence scores for prediction models
DEFF Research Database (Denmark)
Gerds, Thomas Alexander; van de Wiel, MA
2011-01-01
modelling strategy is applied to different training sets. For each modelling strategy we estimate a confidence score based on the same repeated bootstraps. A new decomposition of the expected Brier score is obtained, as well as the estimates of population average confidence scores. The latter can be used...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...
Physiological sex predicts hybrid sterility regardless of genotype.
Malone, John H; Michalak, P
2008-01-01
Reproductive isolation between biological species is characterized by Haldane's rule, which states that the heterogametic sex (XY or ZW) suffers the most dysfunctional effects of hybridization. It has been hypothesized that, in addition to X-linked recessive genes, Haldane's rule also reflects the faster evolution of genes related to male reproduction (faster-male evolution). We used sex-reversed hybrid Xenopus to test faster-male evolution by examining the fertility of sex-reversed individuals with the genotype of the inverse phenotypic sex. Hybrid males are sterile and hybrid females are fertile irrespective of their genotypic sex. Gene expression profiles match these adult phenotypes, and our results demonstrate that faster-male evolution is the most likely mechanism producing hybrid male sterility.
Modelling, controlling, predicting blackouts
Wang, Chengwei; Baptista, Murilo S
2016-01-01
The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids, and another one for smart grids. The control strategie...
Melanoma Risk Prediction Models
Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Sign prediction and volatility dynamics with hybrid neurofuzzy approaches.
Bekiros, Stelios D
2011-12-01
Reliable forecasting techniques for financial applications are important for investors either to make profit by trading or hedge against potential market risks. In this paper the efficiency of a trading strategy based on the utilization of a neurofuzzy model is investigated, in order to predict the direction of the market in case of FTSE100 and New York stock exchange returns. Moreover, it is demonstrated that the incorporation of the estimates of the conditional volatility changes, according to the theory of Bekaert and Wu (2000), strongly enhances the predictability of the neurofuzzy model, as it provides valid information for a potential turning point on the next trading day. The total return of the proposed volatility-based neurofuzzy model including transaction costs is consistently superior to that of a Markov-switching model, a feedforward neural network as well as of a buy & hold strategy. The findings can be justified by invoking either the "volatility feedback" theory or the existence of portfolio insurance schemes in the equity markets and are also consistent with the view that volatility dependence produces sign dependence. Thus, a trading strategy based on the proposed neurofuzzy model might allow investors to earn higher returns than the passive portfolio management strategy.
New hybrid model of proton exchange membrane fuel cell
Institute of Scientific and Technical Information of China (English)
WANG Rui-min; CAO Guang-yi; ZHU Xin-jian
2007-01-01
Model and simulation are good tools for design optimization of fuel cell systems. This paper proposes a new hybrid model of proton exchange membrane fuel cell (PEMFC). The hybrid model includes physical component and black-box component. The physical component represents the well-known part of PEMFC, while artificial neural network (ANN) component estimates the poorly known part of PEMFC. The ANN model can compensate the performance of the physical model. This hybrid model is implemented on Matlab/Simulink software. The hybrid model shows better accuracy than that of the physical model and ANN model. Simulation results suggest that the hybrid model can be used as a suitable and accurate model for PEMFC.
Hybrid Modeling for Soft Sensing of Molten Steel Temperature in LF
Institute of Scientific and Technical Information of China (English)
TIAN Hui-xin; MAO Zhi-zhong; WANG An-na
2009-01-01
Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM (extreme learning machine) for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.
Araújo, Ricardo de A
2010-12-01
This paper presents a hybrid intelligent methodology to design increasing translation invariant morphological operators applied to Brazilian stock market prediction (overcoming the random walk dilemma). The proposed Translation Invariant Morphological Robust Automatic phase-Adjustment (TIMRAA) method consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best time lags to reconstruct the phase space of the time series generator phenomenon and determines the initial (sub-optimal) parameters of the MMNN. Each individual of the QIEA population is further trained by the Back Propagation (BP) algorithm to improve the MMNN parameters supplied by the QIEA. Also, for each prediction model generated, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in stock market time series. Furthermore, an experimental analysis is conducted with the proposed method through four Brazilian stock market time series, and the achieved results are discussed and compared to results found with random walk models and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) and Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) methods.
A four-stage hybrid model for hydrological time series forecasting.
Di, Chongli; Yang, Xiaohua; Wang, Xiaochao
2014-01-01
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.
Modeling of renewable hybrid energy sources
Directory of Open Access Journals (Sweden)
Dumitru Cristian Dragos
2009-12-01
Full Text Available Recent developments and trends in the electric power consumption indicate an increasing use of renewable energy. Renewable energy technologies offer the promise of clean, abundant energy gathered from self-renewing resources such as the sun, wind, earth and plants. Virtually all regions of the world have renewable resources of one type or another. By this point of view studies on renewable energies focuses more and more attention. The present paper intends to present different mathematical models related to different types of renewable energy sources such as: solar energy and wind energy. It is also presented the validation and adaptation of such models to hybrid systems working in geographical and meteorological conditions specific to central part of Transylvania region. The conclusions based on validation of such models are also shown.
Prediction models in complex terrain
DEFF Research Database (Denmark)
Marti, I.; Nielsen, Torben Skov; Madsen, Henrik
2001-01-01
are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production......The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... and HIRLAM predictions. The statistical models belong to the class of conditional parametric models. The models are estimated using local polynomial regression, but the estimation method is here extended to be adaptive in order to allow for slow changes in the system e.g. caused by the annual variations...
Ionocovalency and Applications 1. Ionocovalency Model and Orbital Hybrid Scales
Directory of Open Access Journals (Sweden)
Yonghe Zhang
2010-11-01
Full Text Available Ionocovalency (IC, a quantitative dual nature of the atom, is defined and correlated with quantum-mechanical potential to describe quantitatively the dual properties of the bond. Orbiotal hybrid IC model scale, IC, and IC electronegativity scale, XIC, are proposed, wherein the ionicity and the covalent radius are determined by spectroscopy. Being composed of the ionic function I and the covalent function C, the model describes quantitatively the dual properties of bond strengths, charge density and ionic potential. Based on the atomic electron configuration and the various quantum-mechanical built-up dual parameters, the model formed a Dual Method of the multiple-functional prediction, which has much more versatile and exceptional applications than traditional electronegativity scales and molecular properties. Hydrogen has unconventional values of IC and XIC, lower than that of boron. The IC model can agree fairly well with the data of bond properties and satisfactorily explain chemical observations of elements throughout the Periodic Table.
Nonlinear Thermoelastic Model for SMAs and SMA Hybrid Composites
Turner, Travis L.
2004-01-01
A constitutive mathematical model has been developed that predicts the nonlinear thermomechanical behaviors of shape-memory-alloys (SMAs) and of shape-memory-alloy hybrid composite (SMAHC) structures, which are composite-material structures that contain embedded SMA actuators. SMAHC structures have been investigated for their potential utility in a variety of applications in which there are requirements for static or dynamic control of the shapes of structures, control of the thermoelastic responses of structures, or control of noise and vibrations. The present model overcomes deficiencies of prior, overly simplistic or qualitative models that have proven ineffective or intractable for engineering of SMAHC structures. The model is sophisticated enough to capture the essential features of the mechanics of SMAHC structures yet simple enough to accommodate input from fundamental engineering measurements and is in a form that is amenable to implementation in general-purpose structural analysis environments.
Interior Noise Prediction of the Automobile Based on Hybrid FE-SEA Method
Directory of Open Access Journals (Sweden)
S. M. Chen
2011-01-01
created using hybrid FE-SEA method. The modal density was calculated using analytical method and finite element method; the damping loss factors of the structural and acoustic cavity subsystems were also calculated with analytical method; the coupling loss factors between structure and structure, structure and acoustic cavity were both calculated. Four different kinds of excitations including road excitations, engine mount excitations, sound radiation excitations of the engine, and wind excitations are exerted on the body of automobile when the automobile is running on the road. All the excitations were calculated using virtual prototype technology, computational fluid dynamics (CFD, and experiments realized in the design and development stage. The interior noise of the automobile was predicted and verified at speed of 120 km/h. The predicted and tested overall SPLs of the interior noise were 73.79 and 74.44 dB(A respectively. The comparison results also show that the prediction precision is satisfied, and the effectiveness and reliability of the hybrid FE-SEA model of the automobile is verified.
Multiscale Modeling of Graphite/CNT/Epoxy Hybrid Composites
2016-03-09
AFRL-AFOSR-VA-TR-2016-0154 Multiscale Modeling of Graphite/CNT/Epoxy Hybrid Composites Gregory Odegard MICHIGAN TECHNOLOGICAL UNIVERSITY Final Report...SUBTITLE Multiscale Modeling of Graphite/CNT/Epoxy Hybrid Composites 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-13-1-0030 5c. PROGRAM ELEMENT NUMBER...DISTRIBUTION A: Distribution approved for public release. Final Report Multiscale Modeling of Graphite/CNT/Epoxy Hybrid Composites Grant FA9550-13-1-0030 PI
Hybrid Models of Alternative Current Filter for Hvdc
Directory of Open Access Journals (Sweden)
Ufa Ruslan A.
2017-01-01
Full Text Available Based on a hybrid simulation concept of HVDC, the developed hybrid AC filter models, providing the sufficiently full and adequate modeling of all single continuous spectrum of quasi-steady-state and transient processes in the filter, are presented. The obtained results suggest that usage of the hybrid simulation approach is carried out a methodically accurate with guaranteed instrumental error solution of differential equation systems of mathematical models of HVDC.
Directory of Open Access Journals (Sweden)
Mostafa Gandomi
2016-01-01
Full Text Available A new model is derived to predict the peak ground acceleration (PGA utilizing a hybrid method coupling artificial neural network (ANN and simulated annealing (SA, called SA-ANN. The proposed model relates PGA to earthquake source to site distance, earthquake magnitude, average shear-wave velocity, faulting mechanisms, and focal depth. A database of strong ground-motion recordings of 36 earthquakes, which happened in Iran's tectonic regions, is used to establish the model. For more validity verification, the SA-ANN model is employed to predict the PGA of a part of the database beyond the training data domain. The proposed SA-ANN model is compared with the simple ANN in addition to 10 well-known models proposed in the literature. The proposed model performance is superior to the single ANN and other existing attenuation models. The SA-ANN model is highly correlated to the actual records (R = 0.835 and ρ = 0.0908 and it is subsequently converted into a tractable design equation.
Prediction of monthly regional groundwater levels through hybrid soft-computing techniques
Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei; Kao, I.-Feng
2016-10-01
Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input-output patterns of basin-wide groundwater-aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.
Analysis of chromosome aberration data by hybrid-scale models
Energy Technology Data Exchange (ETDEWEB)
Indrawati, Iwiq [Research and Development on Radiation and Nuclear Biomedical Center, National Nuclear Energy Agency (Indonesia); Kumazawa, Shigeru [Nuclear Technology and Education Center, Japan Atomic Energy Research Institute, Honkomagome, Tokyo (Japan)
2000-02-01
This paper presents a new methodology for analyzing data of chromosome aberrations, which is useful to understand the characteristics of dose-response relationships and to construct the calibration curves for the biological dosimetry. The hybrid scale of linear and logarithmic scales brings a particular plotting paper, where the normal section paper, two types of semi-log papers and the log-log paper are continuously connected. The hybrid-hybrid plotting paper may contain nine kinds of linear relationships, and these are conveniently called hybrid scale models. One can systematically select the best-fit model among the nine models by among the conditions for a straight line of data points. A biological interpretation is possible with some hybrid-scale models. In this report, the hybrid scale models were applied to separately reported data on chromosome aberrations in human lymphocytes as well as on chromosome breaks in Tradescantia. The results proved that the proposed models fit the data better than the linear-quadratic model, despite the demerit of the increased number of model parameters. We showed that the hybrid-hybrid model (both variables of dose and response using the hybrid scale) provides the best-fit straight lines to be used as the reliable and readable calibration curves of chromosome aberrations. (author)
Hybrid Prediction Method for Aircraft Interior Noise Project
National Aeronautics and Space Administration — The goal of the project is research and development of methods for application of the Hybrid FE-SEA method to aircraft vibro-acoustic problems. This proposal...
Modelling supervisory controller for hybrid power systems
Energy Technology Data Exchange (ETDEWEB)
Pereira, A.; Bindner, H.; Lundsager, P. [Risoe National Lab., Roskilde (Denmark); Jannerup, O. [Technical Univ. of Denmark, Dept. of Automation, Lyngby (Denmark)
1999-03-01
Supervisory controllers are important to achieve optimal operation of hybrid power systems. The performance and economics of such systems depend mainly on the control strategy for switching on/off components. The modular concept described in this paper is an attempt to design standard supervisory controllers that could be used in different applications, such as village power and telecommunication applications. This paper presents some basic aspects of modelling and design of modular supervisory controllers using the object-oriented modelling technique. The functional abstraction hierarchy technique is used to formulate the control requirements and identify the functions of the control system. The modular algorithm is generic and flexible enough to be used with any system configuration and several goals (different applications). The modularity includes accepting modification of system configuration and goals during operation with minor or no changes in the supervisory controller. (au)
A Hybrid Teaching and Learning Model
Juhary, Jowati Binti
This paper aims at analysing the needs for a specific teaching and learning model for the National Defence University of Malaysia (NDUM). The main argument is that whether there are differences between teaching and learning for academic component versus military component at the university. It is further argued that in order to achieve excellence, there should be one teaching and learning culture. Data were collected through interviews with military cadets. It is found that there are variations of teaching and learning strategies for academic courses, in comparison to a dominant teaching and learning style for military courses. Thus, in the interest of delivering quality education and training for students at the university, the paper argues that possibly a hybrid model for teaching and learning is fundamental in order to generate a one culture of academic and military excellence for the NDUM.
Hybrid adaptive control of a dragonfly model
Couceiro, Micael S.; Ferreira, Nuno M. F.; Machado, J. A. Tenreiro
2012-02-01
Dragonflies show unique and superior flight performances than most of other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper, it is presented an adaptive scheme controlling a nonlinear model inspired in a dragonfly-like robot. It is proposed a hybrid adaptive ( HA) law for adjusting the parameters analyzing the tracking error. At the current stage of the project it is considered essential the development of computational simulation models based in the dynamics to test whether strategies or algorithms of control, parts of the system (such as different wing configurations, tail) as well as the complete system. The performance analysis proves the superiority of the HA law over the direct adaptive ( DA) method in terms of faster and improved tracking and parameter convergence.
A Hybrid Method to Predict Success of Dental Implants
Directory of Open Access Journals (Sweden)
Reyhaneh Sadat Moayeri
2016-05-01
Full Text Available Background/Objectives: The market demand for dental implants is growing at a significant pace. Results obtained from real cases shows that some dental implants do not lead to success. Hence, the main problem is whether machine learning techniques can be successful in prediction of success of dental implants. Methods/Statistical Analysis: This paper presents a combined predictive model to evaluate the success of dental implants. The classifiers used in this model are W-J48, SVM, Neural Network, K-NN and Naïve Bayes. All internal parameters of each classifier are optimized. These classifiers are combined in a way that results in the highest possible accuracies. Results: The performance of the proposed method is compared with single classifiers. Results of our study show that the combinative approach can achieve higher performance than the best of the single classifiers. Using the combinative approach improves the sensitivity indicator by up to 13.3%. Conclusion/Application: Since diagnosis of patients whose implant does not lead to success is very important in implant surgery, the presented model can help surgeons to make a more reliable decision on level of success of implant operation prior to surgery.
Battery thermal models for hybrid vehicle simulations
Pesaran, Ahmad A.
This paper summarizes battery thermal modeling capabilities for: (1) an advanced vehicle simulator (ADVISOR); and (2) battery module and pack thermal design. The National Renewable Energy Laboratory's (NREL's) ADVISOR is developed in the Matlab/Simulink environment. There are several battery models in ADVISOR for various chemistry types. Each one of these models requires a thermal model to predict the temperature change that could affect battery performance parameters, such as resistance, capacity and state of charges. A lumped capacitance battery thermal model in the Matlab/Simulink environment was developed that included the ADVISOR battery performance models. For thermal evaluation and design of battery modules and packs, NREL has been using various computer aided engineering tools including commercial finite element analysis software. This paper will discuss the thermal ADVISOR battery model and its results, along with the results of finite element modeling that were presented at the workshop on "Development of Advanced Battery Engineering Models" in August 2001.
A hybrid neural network system for prediction and recognition of promoter regions in human genome
Institute of Scientific and Technical Information of China (English)
CHEN Chuan-bo; LI Tao
2005-01-01
This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence,feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm.Evaluation on Human chromosome 22 was ～66% in sensitivity and ～48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.
Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns
Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto
2017-09-01
Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.
Prediction models in complex terrain
DEFF Research Database (Denmark)
Marti, I.; Nielsen, Torben Skov; Madsen, Henrik
2001-01-01
The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...
A Hybrid Model for QCD Deconfining Phase Boundary
Srivastava, P K
2012-01-01
Intensive search for a proper and realistic equations of state (EOS) is still continued for studying the phase diagram existing between quark gluon plasma (QGP) and hadron gas (HG) phases. Lattice calculations provide such EOS for the strongly interacting matter at finite temperature ($T$) and vanishing baryon chemical potential ($\\mu_{B}$). These calculations are of limited use at finite $\\mu_{B}$ due to the appearance of notorious sign problem. In the recent past, we had constructed a hybrid model description for the QGP as well as HG phases where we make use of a new excluded-volume model for HG and a thermodynamically-consistent quasiparticle model for the QGP phase and used them further to get QCD phase boundary and a critical point. Since then many lattice calculations have appeared showing various thermal and transport properties of QCD matter at finite $T$ and $\\mu_{B}=0$. We test our hybrid model by reproducing the entire data for strongly interacting matter and predict our results at finite $\\mu_{B}...
Interval forecasts of a novelty hybrid model for wind speeds
Directory of Open Access Journals (Sweden)
Shanshan Qin
2015-11-01
Full Text Available The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO-based Back Propagation Neural Network (BPNN is proposed to establish wind speed interval forecasts (IFs by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP and IFs normalized average width (IFNAW. Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW and validity (IFCP of IFs is examined by coverage width-based criteria (CWC. As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.
A site dependent top height growth model for hybrid aspen
Institute of Scientific and Technical Information of China (English)
Tord Johansson
2013-01-01
In this study height growth models for hybrid aspen were developed using three growth equations. The mean age of the hybrid aspen was 21 years (range 15−51 years) with a mean stand density of 946 stems ha-1 (87−2374) and a mean diameter at breast height (over bark) of 19.6 cm (8.5−40.8 cm). Site index was also examined in relation to soil type. Multiple samples were collected for three types of soil: light clay, medium clay and till. Site index curves were constructed using the col-lected data and compared with published reports. A number of dynamic equations were assessed for modeling top-height growth from total age. A Generalized Algebraic Difference Approach model derived by Cieszewski (2001) performed the best. This model explained 99% of the observed variation in tree height growth and exhibited no apparent bias across the range of predicted site indices. There were no significant differences between the soil types and site indices.
Directory of Open Access Journals (Sweden)
Shibiao Wan
Full Text Available Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.
Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan
2014-01-01
Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/. PMID:24647341
Adaptive control using a hybrid-neural model: application to a polymerisation reactor
Directory of Open Access Journals (Sweden)
Cubillos F.
2001-01-01
Full Text Available This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM is based on fundamental conservation laws associated with a neural network (NN used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.
A muscle model for hybrid muscle activation
Directory of Open Access Journals (Sweden)
Klauer Christian
2015-09-01
Full Text Available To develop model-based control strategies for Functional Electrical Stimulation (FES in order to support weak voluntary muscle contractions, a hybrid model for describing joint motions induced by concurrent voluntary-and FES induced muscle activation is proposed. It is based on a Hammerstein model – as commonly used in feedback controlled FES – and exemplarily applied to describe the shoulder abduction joint angle. Main component of a Hammerstein muscle model is usually a static input nonlinearity depending on the stimulation intensity. To additionally incorporate voluntary contributions, we extended the static non-linearity by a second input describing the intensity of the voluntary contribution that is estimated by electromyography (EMG measurements – even during active FES. An Artificial Neural Network (ANN is used to describe the static input non-linearity. The output of the ANN drives a second-order linear dynamical system that describes the combined muscle activation and joint angle dynamics. The tunable parameters are adapted to the individual subject by a system identification approach using previously recorded I/O-data. The model has been validated in two healthy subjects yielding RMS values for the joint angle error of 3.56° and 3.44°, respectively.
A Hybrid LDA+gCCA Model for fMRI Data Classification and Visualization.
Afshin-Pour, Babak; Shams, Seyed-Mohammad; Strother, Stephen
2015-05-01
Linear predictive models are applied to functional MRI (fMRI) data to estimate boundaries that predict experimental task states for scans. These boundaries are visualized as statistical parametric maps (SPMs) and range from low to high spatial reproducibility across subjects (e.g., Strother , 2004; LaConte , 2003). Such inter-subject pattern reproducibility is an essential characteristic of interpretable SPMs that generalize across subjects. Therefore, we introduce a flexible hybrid model that optimizes reproducibility by simultaneously enhancing the prediction power and reproducibility. This hybrid model is formed by a weighted summation of the optimization functions of a linear discriminate analysis (LDA) model and a generalized canonical correlation (gCCA) model (Afshin-Pour , 2012). LDA preserves the model's ability to discriminate the fMRI scans of multiple brain states while gCCA finds a linear combination for each subject's scans such that the estimated boundary map is reproducible. The hybrid model is implemented in a split-half resampling framework (Strother , 2010) which provides reproducibility (r) and prediction (p) quality metrics. Then the model was compared with LDA, and Gaussian Naive Bayes (GNB). For simulated fMRI data, the hybrid model outperforms the other two techniques in terms of receiver operating characteristic (ROC) curves, particularly for detecting less predictable but spatially reproducible networks. These techniques were applied to real fMRI data to estimate the maps for two task contrasts. Our results indicate that compared to LDA and GNB, the hybrid model can provide maps with large increases in reproducibility for small reductions in prediction, which are jointly closer to the ideal performance point of (p=1, r=1).
Davis, Brian; Turner, Travis L.; Seelecke, Stefan
2005-01-01
Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.
Modelling of Natural and Hybrid Ventilation
DEFF Research Database (Denmark)
Heiselberg, Per
be installed in existing buildings after a few modifications. In contrast, ventilation systems using only natural forces such as wind and thermal buoyancy need to be designed together with the building, since the building itself and its components are the elements that can reduce or increase air movement...... as well as influence the air content (dust, pollution etc.). Architects and engineers need to acquire qualitative and quantitative information about the interactions between building characteristics and natural ventilation in order to design buildings and systems consistent with a passive low......-energy approach. These lecture notes focus on modelling of natural and hybrid ventilation driven by thermal buoyancy, wind and/or mechanical driving forces for a single zone with one, two or several openings....
A hybrid Fermi-Ulam-bouncer model
Energy Technology Data Exchange (ETDEWEB)
Leonel, Edson D; McClintock, P V E [Department of Physics, Lancaster University, Lancaster LA1 4YB (United Kingdom)
2005-01-28
Some dynamical and chaotic properties are studied for a classical particle bouncing between two rigid walls, one of which is fixed and the other moves in time, in the presence of an external field. The system is a hybrid, behaving not as a purely Fermi-Ulam model, nor as a bouncer, but as a combination of the two. We consider two different kinds of motion of the moving wall: (i) periodic and (ii) random. The dynamics of the model is studied via a two-dimensional nonlinear area-preserving map. We confirm that, for periodic oscillations, our model recovers the well-known results of the Fermi-Ulam model in the limit of zero external field. For intense external fields, we establish the range of control parameters values within which invariant spanning curves are observed below the chaotic sea in the low energy domain. We characterize this chaotic low energy region in terms of Lyapunov exponents. We also show that the velocity of the particle, and hence also its kinetic energy, grow according to a power law when the wall moves randomly, yielding clear evidence of Fermi acceleration.
IJER@2014 Page 21 Prediction of Mechanical Properties of Hybrid Fiber Reinforced Polymer Composites
Directory of Open Access Journals (Sweden)
P Sivaraj
2014-03-01
Full Text Available Abstract— This work presents a systematic approach to evaluate and study the effect of process parameters on tensile flexural and impact strength of coir and bagasse fiber reinforced polyester-based hybrid composites and also predicts the properties of random oriented hybrid composites. The composite panel was fabricated using hand lay-up method to the size of 300mmx200mmx3mm with various weight percentage of natural fibers namely coir (10, 20 and 30 wt % and bagasse (10, 20 and 30 wt % combined with polyester resin. The mechanical properties testing such as tensile, flexural and impact strength were carried out for the samples cut from the fabricated composite panel to the dimensions as per ASTM standard. The significant contribution of mixing of fiber was determined by analysis of variance. The second-order polynomial curve fitting equations are modelled to predict the mechanical properties such as tensile, flexural and impact strength. Also scanning electron microscopy testing was conducted on tensile test specimen to find the fiber matrix interfacial adhesion.
Kawai, Ronald T. (Compiler)
2011-01-01
This investigation was conducted to: (1) Develop a hybrid wing body subsonic transport configuration with noise prediction methods to meet the circa 2007 NASA Subsonic Fixed Wing (SFW) N+2 noise goal of -52 dB cum relative to FAR 36 Stage 3 (-42 dB cum re: Stage 4) while achieving a -25% fuel burned compared to current transports (re :B737/B767); (2) Develop improved noise prediction methods for ANOPP2 for use in predicting FAR 36 noise; (3) Design and fabricate a wind tunnel model for testing in the LaRC 14 x 22 ft low speed wind tunnel to validate noise predictions and determine low speed aero characteristics for an efficient low noise Hybrid Wing Body configuration. A medium wide body cargo freighter was selected to represent a logical need for an initial operational capability in the 2020 time frame. The Efficient Low Noise Hybrid Wing Body (ELNHWB) configuration N2A-EXTE was evolved meeting the circa 2007 NRA N+2 fuel burn and noise goals. The noise estimates were made using improvements in jet noise shielding and noise shielding prediction methods developed by UC Irvine and MIT. From this the Quiet Ultra Integrated Efficient Test Research Aircraft #1 (QUIET-R1) 5.8% wind tunnel model was designed and fabricated.
Field Test of a Hybrid Finite-Difference and Analytic Element Regional Model.
Abrams, D B; Haitjema, H M; Feinstein, D T; Hunt, R J
2016-01-01
Regional finite-difference models often have cell sizes that are too large to sufficiently model well-stream interactions. Here, a steady-state hybrid model is applied whereby the upper layer or layers of a coarse MODFLOW model are replaced by the analytic element model GFLOW, which represents surface waters and wells as line and point sinks. The two models are coupled by transferring cell-by-cell leakage obtained from the original MODFLOW model to the bottom of the GFLOW model. A real-world test of the hybrid model approach is applied on a subdomain of an existing model of the Lake Michigan Basin. The original (coarse) MODFLOW model consists of six layers, the top four of which are aggregated into GFLOW as a single layer, while the bottom two layers remain part of MODFLOW in the hybrid model. The hybrid model and a refined "benchmark" MODFLOW model simulate similar baseflows. The hybrid and benchmark models also simulate similar baseflow reductions due to nearby pumping when the well is located within the layers represented by GFLOW. However, the benchmark model requires refinement of the model grid in the local area of interest, while the hybrid approach uses a gridless top layer and is thus unaffected by grid discretization errors. The hybrid approach is well suited to facilitate cost-effective retrofitting of existing coarse grid MODFLOW models commonly used for regional studies because it leverages the strengths of both finite-difference and analytic element methods for predictions in mildly heterogeneous systems that can be simulated with steady-state conditions.
Translational Modeling in Schizophrenia : Predicting Human Dopamine D2 Receptor Occupancy
Johnson, Martin; Kozielska, Magdalena; Pilla Reddy, Venkatesh; Vermeulen, An; Barton, Hugh A; Grimwood, Sarah; de Greef, Rik; Groothuis, Geny M M; Danhof, Meindert; Proost, Johannes H
2015-01-01
OBJECTIVES: To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. METHODS: A hybrid PBPKPD model, previousl
A hybrid neural network model for noisy data regression.
Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M
2004-04-01
A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.
Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models
Güreşen, Erkam; Kayakutlu, Gülgün
Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).
Predictive models of forest dynamics.
Purves, Drew; Pacala, Stephen
2008-06-13
Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.
Directory of Open Access Journals (Sweden)
Kaisheng Zhang
2016-12-01
Full Text Available Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and base stations (BS. With the hybrid model, the Log Distance Path Loss (LDPL model was used to estimate the pedestrian density from raw network data, and retrieve information with the Gaussian Progress (GP through supervised learning. Temporal-spatial prediction of the pedestrian data was carried out with Machine Learning (ML approaches. Finally, a case study of a real Central Business District (CBD scenario in Shanghai, China using records of millions of cell phone users was conducted. The results showed that the new approach significantly increases the utility and capacity of the mobile network. A more reasonable overcrowding detection and alert system can be developed to improve safety in subway lines and other hotspot landmark areas, such as the Bundle, People’s Square or Disneyland, where a large passenger flow generally exists.
A Hybrid Model of a Brushless DC Motor
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Hansen, Hans Brink; Kallesøe, Carsten Skovmose
2007-01-01
This paper presents a novel approach to modeling of a Brush-Less Direct Current Motor (BLDCM) driven by an inverter using hybrid systems theory. Hybrid systems combine continuous and discrete (event-based) dynamics, which is exactly the case in an inverter-driven BLDCM. The model presented in thi...
Hybrid modelling of a sugar boiling process
Lauret, Alfred Jean Philippe; Gatina, Jean Claude
2012-01-01
The first and maybe the most important step in designing a model-based predictive controller is to develop a model that is as accurate as possible and that is valid under a wide range of operating conditions. The sugar boiling process is a strongly nonlinear and nonstationary process. The main process nonlinearities are represented by the crystal growth rate. This paper addresses the development of the crystal growth rate model according to two approaches. The first approach is classical and consists of determining the parameters of the empirical expressions of the growth rate through the use of a nonlinear programming optimization technique. The second is a novel modeling strategy that combines an artificial neural network (ANN) as an approximator of the growth rate with prior knowledge represented by the mass balance of sucrose crystals. The first results show that the first type of model performs local fitting while the second offers a greater flexibility. The two models were developed with industrial data...
Davis, Brian; Turner, Travis L.; Seelecke, Stefan
2008-01-01
An experimental and numerical investigation into the static and dynamic responses of shape memory alloy hybrid composite (SMAHC) beams is performed to provide quantitative validation of a recently commercialized numerical analysis/design tool for SMAHC structures. The SMAHC beam specimens consist of a composite matrix with embedded pre-strained SMA actuators, which act against the mechanical boundaries of the structure when thermally activated to adaptively stiffen the structure. Numerical results are produced from the numerical model as implemented into the commercial finite element code ABAQUS. A rigorous experimental investigation is undertaken to acquire high fidelity measurements including infrared thermography and projection moire interferometry for full-field temperature and displacement measurements, respectively. High fidelity numerical results are also obtained from the numerical model and include measured parameters, such as geometric imperfection and thermal load. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.
Davis, Brian; Turner, Travis L.; Seelecke, Stefan
2008-01-01
An experimental and numerical investigation into the static and dynamic responses of shape memory alloy hybrid composite (SMAHC) beams is performed to provide quantitative validation of a recently commercialized numerical analysis/design tool for SMAHC structures. The SMAHC beam specimens consist of a composite matrix with embedded pre-strained SMA actuators, which act against the mechanical boundaries of the structure when thermally activated to adaptively stiffen the structure. Numerical results are produced from the numerical model as implemented into the commercial finite element code ABAQUS. A rigorous experimental investigation is undertaken to acquire high fidelity measurements including infrared thermography and projection moire interferometry for full-field temperature and displacement measurements, respectively. High fidelity numerical results are also obtained from the numerical model and include measured parameters, such as geometric imperfection and thermal load. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.
Energy management in hybrid electric vehicles: benefit of prediction
Keulen, T. van; Jager, B. de; Kessels, J.T.B.A.; Steinbuch, M.
2010-01-01
Hybrid vehicles require a supervisory algorithm, often referred to as energy management strategy, which governs the drivetrain components. In general the energy management strategy objective is to minimize the fuel consumption subject to constraints on the components, vehicle performance and driver
Directory of Open Access Journals (Sweden)
Xiaoxia Yang
Full Text Available Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.
Hybrid Dynamical Systems Modeling, Stability, and Robustness
Goebel, Rafal; Teel, Andrew R
2012-01-01
Hybrid dynamical systems exhibit continuous and instantaneous changes, having features of continuous-time and discrete-time dynamical systems. Filled with a wealth of examples to illustrate concepts, this book presents a complete theory of robust asymptotic stability for hybrid dynamical systems that is applicable to the design of hybrid control algorithms--algorithms that feature logic, timers, or combinations of digital and analog components. With the tools of modern mathematical analysis, Hybrid Dynamical Systems unifies and generalizes earlier developments in continuous-time and discret
Comparative study of hybrid RANS-LES models for separated flows
Kumar, G.; Lakshmanan, S. K.; Gopalan, H.; De, A.
2016-06-01
Hybrid RANS-LES models are proven to be capable of predicting massively separated flows with reasonable computation cost. In this paper, Spalart-Allmaras (S-A) based detached eddy simulation (DES) model and three SST based hybrid models with different RANS to LES switching criteriaare investigated. The flow over periodic hill at Re = 10,595 is chosen as the benchmark for comparing the performance of the different models due to the complex flow physics and reasonablecomputational cost. The model performances are evaluated based on their prediction capabilities of velocity and stress profiles, and separation and reattachment point. The simulated results are validatedagainst experimental and numerical results available in literature. The S-A DES model predicted separation bubble accurately at the top of the hill, as reported earlier in experiments and other numerical results. This model also correctly predicted velocity and stress profiles in recirculation region. However, the performance of this model was poor in the post reattachment region. On the other hand, the k-ω SST based hybrid models performed poorly in recirculation region, but it fairly predicted stress profiles in post reattachment region.
Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
Reeve, Dominic E.; Karunarathna, Harshinie; Pan, Shunqi; Horrillo-Caraballo, Jose M.; Różyński, Grzegorz; Ranasinghe, Roshanka
2016-03-01
It is now common for coastal planning to anticipate changes anywhere from 70 to 100 years into the future. The process models developed and used for scheme design or for large-scale oceanography are currently inadequate for this task. This has prompted the development of a plethora of alternative methods. Some, such as reduced complexity or hybrid models simplify the governing equations retaining processes that are considered to govern observed morphological behaviour. The computational cost of these models is low and they have proven effective in exploring morphodynamic trends and improving our understanding of mesoscale behaviour. One drawback is that there is no generally agreed set of principles on which to make the simplifying assumptions and predictions can vary considerably between models. An alternative approach is data-driven techniques that are based entirely on analysis and extrapolation of observations. Here, we discuss the application of some of the better known and emerging methods in this category to argue that with the increasing availability of observations from coastal monitoring programmes and the development of more sophisticated statistical analysis techniques data-driven models provide a valuable addition to the armoury of methods available for mesoscale prediction. The continuation of established monitoring programmes is paramount, and those that provide contemporaneous records of the driving forces and the shoreline response are the most valuable in this regard. In the second part of the paper we discuss some recent research that combining some of the hybrid techniques with data analysis methods in order to synthesise a more consistent means of predicting mesoscale coastal morphological evolution. While encouraging in certain applications a universally applicable approach has yet to be found. The route to linking different model types is highlighted as a major challenge and requires further research to establish its viability. We argue that
Mekonnen, B.; Nazemi, A.; Elshorbagy, A.; Mazurek, K.; Putz, G.
2012-04-01
Modeling the hydrological response in prairie regions, characterized by flat and undulating terrain, and thus, large non-contributing areas, is a known challenge. The hydrological response (runoff) is the combination of the traditional runoff from the hydrologically contributing area and the occasional overflow from the non-contributing area. This study provides a unique opportunity to analyze the issue of fusing the Soil and Water Assessment Tool (SWAT) and Artificial Neural Networks (ANNs) in a hybrid structure to model the hydrological response in prairie regions. A hybrid SWAT-ANN model is proposed, where the SWAT component and the ANN module deal with the effective (contributing) area and the non-contributing area, respectively. The hybrid model is applied to the case study of Moose Jaw watershed, located in southern Saskatchewan, Canada. As an initial exploration, a comparison between ANN and SWAT models is established based on addressing the daily runoff (streamflow) prediction accuracy using multiple error measures. This is done to identify the merits and drawbacks of each modeling approach. It has been found out that the SWAT model has better performance during the low flow periods but with degraded efficiency during periods of high flows. The case is different for the ANN model as ANNs exhibit improved simulation during high flow periods but with biased estimates during low flow periods. The modelling results show that the new hybrid SWAT-ANN model is capable of exploiting the strengths of both SWAT and ANN models in an integrated framrwork. The new hybrid SWAT-ANN model simulates daily runoff quite satisfactorily with NSE measures of 0.80 and 0.83 during calibration and validation periods, respectively. Furthermore, an experimental assessment was performed to identify the effects of the ANN training method on the performance of the hybrid model as well as the parametric identifiability. Overall, the results obtained in this study suggest that the fusion
A Probability-Based Hybrid User Model for Recommendation System
Directory of Open Access Journals (Sweden)
Jia Hao
2016-01-01
Full Text Available With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.
Synchronizability Analysis of Harmonious Unification Hybrid Preferential Model
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
The harmonious unification hybrid preferential model uses the dr ratio to adjust the proportion of deterministic preferential attachment and random preferential attachment, enriched the only deterministic preferential network model,
Hybrid Information Retrieval Model For Web Images
Bassil, Youssef
2012-01-01
The Bing Bang of the Internet in the early 90's increased dramatically the number of images being distributed and shared over the web. As a result, image information retrieval systems were developed to index and retrieve image files spread over the Internet. Most of these systems are keyword-based which search for images based on their textual metadata; and thus, they are imprecise as it is vague to describe an image with a human language. Besides, there exist the content-based image retrieval systems which search for images based on their visual information. However, content-based type systems are still immature and not that effective as they suffer from low retrieval recall/precision rate. This paper proposes a new hybrid image information retrieval model for indexing and retrieving web images published in HTML documents. The distinguishing mark of the proposed model is that it is based on both graphical content and textual metadata. The graphical content is denoted by color features and color histogram of ...
Estimating hybrid choice models with the new version of Biogeme
Bierlaire, Michel
2010-01-01
Hybrid choice models integrate many types of discrete choice modeling methods, including latent classes and latent variables, in order to capture concepts such as perceptions, attitudes, preferences, and motivatio (Ben-Akiva et al., 2002). Although they provide an excellent framework to capture complex behavior patterns, their use in applications remains rare in the literature due to the difficulty of estimating the models. In this talk, we provide a short introduction to hybrid choice model...
Hybrids of Gibbs Point Process Models and Their Implementation
Directory of Open Access Journals (Sweden)
Adrian Baddeley
2013-11-01
Full Text Available We describe a simple way to construct new statistical models for spatial point pattern data. Taking two or more existing models (finite Gibbs spatial point processes we multiply the probability densities together and renormalise to obtain a new probability density. We call the resulting model a hybrid. We discuss stochastic properties of hybrids, their statistical implications, statistical inference, computational strategies and software implementation in the R package spatstat. Hybrids are particularly useful for constructing models which exhibit interaction at different spatial scales. The methods are demonstrated on a real data set on human social interaction. Software and data are provided.
Almeida Filho, J E; Tardin, F D; Guimarães, J F R; Resende, M D V; Silva, F F; Simeone, M L; Menezes, C B; Queiroz, V A V
2016-02-26
The breeding of sorghum, Sorghum bicolor (L.) Moench, aimed at improving its nutritional quality, is of great interest, since it can be used as a highly nutritive alternative food source and can possibly be cultivated in regions with low rainfall. The objective of the present study was to evaluate the potential and genetic diversity of grain-sorghum hybrids for traits of agronomic and nutritional interest. To this end, the traits grain yield and flowering, and concentrations of protein, potassium, calcium, magnesium, sulfur, iron, manganese, and zinc in the grain were evaluated in 25 grain-sorghum hybrids, comprising 18 experimental hybrids of Embrapa Milho e Sorgo and seven commercial hybrids. The genetic potential was analyzed by a multi-trait best linear unbiased prediction (BLUP) model, and cluster analysis was accomplished by squared Mahalanobis distance using the predicted genotypic values. Hybrids 0306037 and 0306034 stood out in the agronomic evaluation. The hybrids with agronomic prominence, however, did not stand out for the traits related to the nutritional quality of the grain. Three clusters were formed from the dendrogram obtained with the unweighted pair group method with arithmetic mean method. From the results of the genotypic BLUP and the analysis of the dendrogram, hybrids 0577337, 0441347, 0307651, and 0306037 were identified as having the potential to establish a population that can aggregate alleles for all the evaluated traits of interest.
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning
Stephenson, David; Lockerby, Duncan A
2016-01-01
We present a hybrid continuum-atomistic scheme which combines molecular dynamics (MD) simulations with on-the-fly machine learning techniques for the accurate and efficient prediction of multiscale fluidic systems. By using a Gaussian process as a surrogate model for the computationally expensive MD simulations, we use Bayesian inference to predict the system behaviour at the atomistic scale, purely by consideration of the macroscopic inputs and outputs. Whenever the uncertainty of this prediction is greater than a predetermined acceptable threshold, a new MD simulation is performed to continually augment the database, which is never required to be complete. This provides a substantial enhancement to the current generation of hybrid methods, which often require many similar atomistic simulations to be performed, discarding information after it is used once. We apply our hybrid scheme to nano-confined unsteady flow through a high-aspect-ratio converging-diverging channel, and make comparisons between the new s...
Investigation of a Hybrid Winding Concept for Toroidal Inductors using 3D Finite Element Modeling
DEFF Research Database (Denmark)
Schneider, Henrik; Andersen, Thomas; Mønster, Jakob Døllner;
2013-01-01
This paper investigates a hybrid winding concept for a toroidal inductor by simulating the winding resistance as a function of frequency. The problem of predicting the resistance of a non-uniform and complex winding shape is solved using 3D Finite Element Modeling. A prototype is built and tested...
Modelling of hybrid scenario: from present-day experiments towards ITER
Litaudon, X.; Voitsekhovitch, I.; Artaud, J. F.; Belo, P.; Bizarro, João P. S.; Casper, T.; Citrin, J.; Fable, E.; Ferreira, J.; Garcia, J.; Garzotti, L.; Giruzzi, G.; Hobirk, J.; Hogeweij, G. M. D.; Imbeaux, F.; Joffrin, E.; Koechl, F.; Liu, F.; Lönnroth, J.; Moreau, D.; Parail, V.; Schneider, M.; Snyder, P. B.; the ASDEX-Upgrade Team; Contributors, JET-EFDA; the EU-ITM ITER Scenario Modelling Group
2013-07-01
The ‘hybrid’ scenario is an attractive operating scenario for ITER since it combines long plasma duration with the reliability of the reference H-mode regime. We review the recent European modelling effort carried out within the Integrated Scenario Modelling group which aims at (i) understanding the underlying physics of the hybrid regime in ASDEX-Upgrade and JET and (ii) extrapolating them towards ITER. JET and ASDEX-Upgrade hybrid scenarios performed under different experimental conditions have been simulated in an interpretative and predictive way in order to address the current profile dynamics and its link with core confinement, the relative importance of magnetic shear, s, and E × B flow shear on the core turbulence, pedestal stability and H-L transition. The correlation of the improved confinement with an increased s/q at outer radii observed in JET and ASDEX-Upgrade discharges is consistent with the predictions based on the GLF23 model applied in the simulations of the ion and electron kinetic profiles. Projections to ITER hybrid scenarios have been carried out focusing on optimization of the heating/current drive schemes to reach and ultimately control the desired plasma equilibrium using ITER actuators. Firstly, access condition to the hybrid-like q-profiles during the current ramp-up phase has been investigated. Secondly, from the interpreted role of the s/q ratio, ITER hybrid scenario flat-top performance has been optimized through tailoring the q-profile shape and pedestal conditions. EPED predictions of pedestal pressure and width have been used as constraints in the interpretative modelling while the core heat transport is predicted by GLF23. Finally, model-based approach for real-time control of advanced tokamak scenarios has been applied to ITER hybrid regime for simultaneous magnetic and kinetic profile control.
Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie
2015-08-01
The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.
Hybrid nonlinear model of the angular vestibulo-ocular reflex.
Ranjbaran, Mina; Galiana, Henrietta L
2013-01-01
A hybrid nonlinear bilateral model for the horizontal angular vestibulo-ocular reflex (AVOR) is presented in this paper. The model relies on known interconnections between saccadic burst circuits in the brainstem and ocular premotor areas in the vestibular nuclei during slow and fast phase intervals. A viable switching strategy for the timing of nystagmus events is proposed. Simulations show that this hybrid model replicates AVOR nystagmus patterns that are observed in experimentally recorded data.
Wu, Xingfu
2013-12-01
In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore supercomputers: IBM POWER4, POWER5+ and BlueGene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks and Intel\\'s MPI benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore supercomputers because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyrokinetic Toroidal Code (GTC) in magnetic fusion to validate our performance model of the hybrid application on these multicore supercomputers. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore supercomputers. © 2013 Elsevier Inc.
Wu, Guang; Dong, Zuomin
2017-09-01
Hybrid electric vehicles are widely accepted as a promising short to mid-term technical solution due to noticeably improved efficiency and lower emissions at competitive costs. In recent years, various hybrid powertrain systems were proposed and implemented based on different types of conventional transmission. Power-split system, including Toyota Hybrid System and Ford Hybrid System, are well-known examples. However, their relatively low torque capacity, and the drive of alternative and more advanced designs encouraged other innovative hybrid system designs. In this work, a new type of hybrid powertrain system based hybridized automated manual transmission (HAMT) is proposed. By using the concept of torque gap filler (TGF), this new hybrid powertrain type has the potential to overcome issue of torque gap during gearshift. The HAMT design (patent pending) is described in details, from gear layout and design of gear ratios (EV mode and HEV mode) to torque paths at different gears. As an analytical tool, mutli-body model of vehicle equipped with this HAMT was built to analyze powertrain dynamics at various steady and transient modes. A gearshift was decomposed and analyzed based basic modes. Furthermore, a Simulink-SimDriveline hybrid vehicle model was built for the new transmission, driveline and vehicle modular. Control strategy has also been built to harmonically coordinate different powertrain components to realize TGF function. A vehicle launch simulation test has been completed under 30% of accelerator pedal position to reveal details during gearshift. Simulation results showed that this HAMT can eliminate most torque gap that has been persistent issue of traditional AMT, improving both drivability and performance. This work demonstrated a new type of transmission that features high torque capacity, high efficiency and improved drivability.
A model updating method for hybrid composite/aluminum bolted joints using modal test data
Adel, Farhad; Shokrollahi, Saeed; Jamal-Omidi, Majid; Ahmadian, Hamid
2017-05-01
The aim of this paper is to present a simple and applicable model for predicting the dynamic behavior of bolted joints in hybrid aluminum/composite structures and its model updating using modal test data. In this regards, after investigations on bolted joints in metallic structures which led to a new concept called joint affected region (JAR) published in Shokrollahi and Adel (2016), now, a doubly connective layer is established in order to simulate the bolted joint interfaces in hybrid structures. Using the proposed model, the natural frequencies of the hybrid bolted joint structure are computed and compared to the modal test results in order to evaluate and verify the new model predictions. Because of differences in the results of two approaches, the finite element (FE) model is updated based on the genetic algorithm (GA) by minimizing the differences between analytical model and test results. This is done by identifying the parameters at the JAR including isotropic Young's modulus in metallic substructure and that of anisotropic composite substructure. The updated model compared to the initial model simulates experimental results more properly. Therefore, the proposed model can be used for modal analysis of the hybrid joint interfaces in complex and large structures.
Bond graph model-based fault diagnosis of hybrid systems
Borutzky, Wolfgang
2015-01-01
This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...
A hybrid model of a subminiature helicopter in horizontal turn
Institute of Scientific and Technical Information of China (English)
Chen Li; Gong Zhenbang; Liu Liang
2007-01-01
A hybrid model of a subminiature helicopter in horizontal turn is presented. This model is based on a mechanism model and its compensated neural network (NN). First, the nonlinear dynamics of a subminiature helicopter is established. Through the linearization of the nonlinear dynamics on a trim point, the linear time-invariant mechanism model in horizontal turn is obtained. Then a diagonal recursive neural network is used to compensate the model error between the mechanism model and the nonlinear model, thus the hybrid model of a subminiature helicopter in horizontal turn is achieved. Simulation results show that the hybrid model has higher accuracy than the mechanism model and the obtained compensated-NN has good generalization capability.
Liu, Guozheng
2016-07-06
Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.
Directory of Open Access Journals (Sweden)
Guozheng Liu
Full Text Available Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1 examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2 explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3 investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L. and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs, but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.
Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises.
Borrajo, M Lourdes; Baruque, Bruno; Corchado, Emilio; Bajo, Javier; Corchado, Juan M
2011-08-01
During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.
Simulation of hybrid vehicle propulsion with an advanced battery model
Energy Technology Data Exchange (ETDEWEB)
Nallabolu, S.; Kostetzer, L.; Rudnyi, E. [CADFEM GmbH, Grafing (Germany); Geppert, M.; Quinger, D. [LION Smart GmbH, Frieding (Germany)
2011-07-01
In the recent years there has been observed an increasing concern about global warming and greenhouse gas emissions. In addition to the environmental issues the predicted scarcity of oil supplies and the dramatic increase in oil price puts new demands on vehicle design. As a result energy efficiency and reduced emission have become one of main selling point for automobiles. Hybrid electric vehicles (HEV) have therefore become an interesting technology for the governments and automotive industries. HEV are more complicated compared to conventional vehicles due to the fact that these vehicles contain more electrical components such as electric machines, power electronics, electronic continuously variable transmissions (CVT), and embedded powertrain controllers. Advanced energy storage devices and energy converters, such as Li-ion batteries, ultracapacitors, and fuel cells are also considered. A detailed vehicle model used for an energy flow analysis and vehicle performance simulation is necessary. Computer simulation is indispensible to facilitate the examination of the vast hybrid electric vehicle design space with the aim to predict the vehicle performance over driving profiles, estimate fuel consumption and the pollution emissions. There are various types of mathematical models and simulators available to perform system simulation of vehicle propulsion. One of the standard methods to model the complete vehicle powertrain is ''backward quasistatic modeling''. In this method vehicle subsystems are defined based on experiential models in the form of look-up tables and efficiency maps. The interaction between adjacent subsystems of the vehicle is defined through the amount of power flow. Modeling the vehicle subsystems like motor, engine, gearbox and battery is under this technique is based on block diagrams. The vehicle model is applied in two case studies to evaluate the vehicle performance and fuel consumption. In the first case study the affect
Institute of Scientific and Technical Information of China (English)
许冬梅; 黄向宇; 于洪利; 闵锦忠
2015-01-01
The impacts of AMSU-A and IASI (Infrared Atmospheric Sounding Interferometer) radiances assimila-tion on the prediction of typhoons Vicente and Saola (2012) are studied by using the ensemble transform Kalman fi lter/three-dimensional variational (ETKF/3DVAR) Hybrid system for the Weather Research and Forecasting (WRF) model. The experiment without assimilating radiance data in 3DVAR is compared with two experiments using the 3DVAR and ETKF/3DVAR hybrid systems to assimilate AMSU-A radiance, respectively. The results show that AMSU-A radiance data have slight positive impacts on track forecasts of the 3DVAR system. When the ETKF/3DVAR hybrid system is employed, typhoon track forecast skills are greatly improved. For 36-h forecasts, the hybrid system has a lower root-mean-square error for wind and temperature at most levels, and specifi c humidity at low levels, compared to 3DVAR. It is also found that, on average, the use of the IASI radiance data along with AMSU-A radiance data in the hybrid system further increases the track, wind, and specifi c humidity forecast accuracy compared to the experiment without IASI radiance assimilation.
Dynamic Modeling and Simulation of a Switched Reluctance Motor in a Series Hybrid Electric Vehicle
Directory of Open Access Journals (Sweden)
Siavash Sadeghi
2010-04-01
Full Text Available Dynamic behavior analysis of electric motors is required in order to accuratelyevaluate the performance, energy consumption and pollution level of hybrid electricvehicles. Simulation tools for hybrid electric vehicles are divided into steady state anddynamic models. Tools with steady-state models are useful for system-level analysiswhereas tools that utilize dynamic models give in-depth information about the behavior ofsublevel components. For the accurate prediction of hybrid electric vehicle performance,dynamic modeling of the motor and other components is necessary. Whereas the switchedreluctance machine is well suited for electric and hybrid electric vehicles, due to the simpleand rugged construction, low cost, and ability to operate over a wide speed range atconstant power, in this paper dynamic performance of the switched reluctance motor for eseries hybrid electric vehicles is investigated. For this purpose a switched reluctance motorwith its electrical drive is modeld and simulated first, and then the other components of aseries hybrid electric vehicle, such as battery, generator, internal combusion engine, andgearbox, are designed and linked with the electric motor. Finally a typical series hybridelectric vehicle is simulated for different drive cycles. The extensive simulation results showthe dynamic performance of SRM, battery, fuel consumption, and emissions.
Hybrid Modelling of Individual Movement and Collective Behaviour
Franz, Benjamin
2013-01-01
Mathematical models of dispersal in biological systems are often written in terms of partial differential equations (PDEs) which describe the time evolution of population-level variables (concentrations, densities). A more detailed modelling approach is given by individual-based (agent-based) models which describe the behaviour of each organism. In recent years, an intermediate modelling methodology - hybrid modelling - has been applied to a number of biological systems. These hybrid models couple an individual-based description of cells/animals with a PDE-model of their environment. In this chapter, we overview hybrid models in the literature with the focus on the mathematical challenges of this modelling approach. The detailed analysis is presented using the example of chemotaxis, where cells move according to extracellular chemicals that can be altered by the cells themselves. In this case, individual-based models of cells are coupled with PDEs for extracellular chemical signals. Travelling waves in these hybrid models are investigated. In particular, we show that in contrary to the PDEs, hybrid chemotaxis models only develop a transient travelling wave. © 2013 Springer-Verlag Berlin Heidelberg.
Hybrid ODE/SSA methods and the cell cycle model
Wang, S.; Chen, M.; Cao, Y.
2017-07-01
Stochastic effect in cellular systems has been an important topic in systems biology. Stochastic modeling and simulation methods are important tools to study stochastic effect. Given the low efficiency of stochastic simulation algorithms, the hybrid method, which combines an ordinary differential equation (ODE) system with a stochastic chemically reacting system, shows its unique advantages in the modeling and simulation of biochemical systems. The efficiency of hybrid method is usually limited by reactions in the stochastic subsystem, which are modeled and simulated using Gillespie's framework and frequently interrupt the integration of the ODE subsystem. In this paper we develop an efficient implementation approach for the hybrid method coupled with traditional ODE solvers. We also compare the efficiency of hybrid methods with three widely used ODE solvers RADAU5, DASSL, and DLSODAR. Numerical experiments with three biochemical models are presented. A detailed discussion is presented for the performances of three ODE solvers.
A Structural Model Decomposition Framework for Hybrid Systems Diagnosis
Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil
2015-01-01
Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.
Hybrid Computational Model for High-Altitude Aeroassist Vehicles Project
National Aeronautics and Space Administration — A hybrid continuum/noncontinuum computational model will be developed for analyzing the aerodynamics and heating on aeroassist vehicles. Unique features of this...
Coupled thermal model of photovoltaic-thermoelectric hybrid panel for sample cities in Europe
DEFF Research Database (Denmark)
Rezaniakolaei, Alireza; Sera, Dezso; Rosendahl, Lasse Aistrup
2016-01-01
generation by the TEG is insignificant compared to electrical output by the PV panel, and the TEG plays only a small role on power generation in the hybrid PV/TEG panel. However, contribution of the TEG in the power generation can be improved via higher ZT thermoelectric materials and geometry optimization......In general, modeling of photovoltaic-thermoelectric (PV/TEG) hybrid panels have been mostly simplified and disconnected from the actual ambient conditions and thermal losses from the panel. In this study, a thermally coupled model of PV/TEG panel is established to precisely predict performance...... of the hybrid system under different weather conditions. The model takes into account solar irradiation, wind speed and ambient temperature as well as convective and radiated heat losses from the front and rear surfaces of the panel. The model is developed for three sample cities in Europe with different...
Mathematical Model and Its Hybrid Dynamic Mechanism in Intelligent Control of Ironmaking
Institute of Scientific and Technical Information of China (English)
LIU Xiang-guan; ZENG Jiu-sun; ZHAO Min
2007-01-01
A hybrid dynamic model was proposed, which considered both the hydrokinetic and the chaotic properties of the blast furnace ironmaking process; and great emphasis was put on its mechanism. The new model took the high complexity of the blast furnace as well as the effects of main parameters of the model into account, and the predicted results were in very good agreement with actual data.
Nuclear Hybrid Energy System Modeling: RELAP5 Dynamic Coupling Capabilities
Energy Technology Data Exchange (ETDEWEB)
Piyush Sabharwall; Nolan Anderson; Haihua Zhao; Shannon Bragg-Sitton; George Mesina
2012-09-01
The nuclear hybrid energy systems (NHES) research team is currently developing a dynamic simulation of an integrated hybrid energy system. A detailed simulation of proposed NHES architectures will allow initial computational demonstration of a tightly coupled NHES to identify key reactor subsystem requirements, identify candidate reactor technologies for a hybrid system, and identify key challenges to operation of the coupled system. This work will provide a baseline for later coupling of design-specific reactor models through industry collaboration. The modeling capability addressed in this report focuses on the reactor subsystem simulation.
Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach
Directory of Open Access Journals (Sweden)
Oliveira Rui
2010-09-01
Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.
DEVELOPMENT OF A HYBRID MODEL FOR THREE-DIMENSIONAL GIS
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper presents a hybrid model for three-dimensional Geographical Information Systems which is an integration of surface- and volume-based models. The Triangulat ed Irregular Network (TIN) and octree models are integrated in this hybrid model. The TIN model works as a surface-based model which mainly serves for surface presentation and visualization. On the other hand, the octree encoding supports volumetric analysis. The designed data structure brings a major advantage in the three-dimensional selective retrieval. This technique increases the efficiency of three-dimensional data operation.
Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility
Tuba, Zoltán; Bottyán, Zsolt
2017-02-01
Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.
Two-compartment model for competitive hybridization on molecular biochips
Chechetkin, V. R.
2007-01-01
During competitive hybridization the specific and non-specific fractions of tested biomolecules in solution bind jointly with the specific probes immobilized in a separate cell of a microchip. The application of two-compartment model to the two-component hybridization allows analytically investigating the underlying kinetics. It is shown that the behaviour with the non-monotonous growth of complexes formed by the non-specific fraction on a probe cell is a typical feature of competitive hybridization for both diffusion-limited and reaction-limited kinetics. The physical reason behind such an evolution consists in the fact that the characteristic hybridization time for the perfect complexes turns out longer with respect to that for the mismatch complexes. This behaviour should be taken into account for the choice of optimum hybridization and washing conditions for the analysis of specific fraction.
Two-compartment model for competitive hybridization on molecular biochips
Energy Technology Data Exchange (ETDEWEB)
Chechetkin, V.R. [Theoretical Department of Division for Perspective Investigations, Troitsk Institute of Innovation and Thermonuclear Investigations (TRINITI), Troitsk, 142190 Moscow Region (Russian Federation)]. E-mail: chechet@biochip.ru
2007-01-08
During competitive hybridization the specific and non-specific fractions of tested biomolecules in solution bind jointly with the specific probes immobilized in a separate cell of a microchip. The application of two-compartment model to the two-component hybridization allows analytically investigating the underlying kinetics. It is shown that the behaviour with the non-monotonous growth of complexes formed by the non-specific fraction on a probe cell is a typical feature of competitive hybridization for both diffusion-limited and reaction-limited kinetics. The physical reason behind such an evolution consists in the fact that the characteristic hybridization time for the perfect complexes turns out longer with respect to that for the mismatch complexes. This behaviour should be taken into account for the choice of optimum hybridization and washing conditions for the analysis of specific fraction.
A hybrid Scatter/Transform cloaking model
Directory of Open Access Journals (Sweden)
Gad Licht
2015-01-01
Full Text Available A new Scatter/Transform cloak is developed that combines the light bending of refraction characteristic of a Transform cloak with the scatter cancellation characteristic of a Scatter cloak. The hybrid cloak incorporates both Transform’s variable index of refraction with modified linear intrusions to maximize the Scatter cloak effect. Scatter/Transform improved the scattering cross-section of cloaking in a 2-dimensional space to 51.7% compared to only 39.6% or 45.1% respectively with either Scatter or Transform alone. Metamaterials developed with characteristics based on the new ST hybrid cloak will exhibit superior cloaking capabilities.
The development of a mathematical model of a hybrid airship
Abdul Ghaffar, Alia Farhana
The mathematical model of a winged hybrid airship is developed for the analysis of its dynamic stability characteristics. A full nonlinear equation of motion that describes the dynamics of the hybrid airship is determined and for completeness, some of the components in the equations are estimated using the appropriate methods that has been established and used in the past. Adequate assumptions are made in order to apply any relevant computation and estimation methods. While this hybrid airship design is unique, its modeling and stability analysis were done according to the typical procedure of conventional airships and aircrafts. All computations pertaining to the hybrid airship's equation of motion are carried out and any issues related to the integration of the wing to the conventional airship design are discussed in this thesis. The design of the hybrid airship is also slightly modified to suit the demanding requirement of a complete and feasible mathematical model. Then, linearization is performed under a chosen trim condition, and eigenvalue analysis is carried out to determine the general dynamic stability characteristics of the winged hybrid airship. The result shows that the winged hybrid airship possesses dynamic instability in longitudinal pitch motion and lateral-directional slow roll motion. This is due to the strong coupling between the aerostatic lift from the buoyant gas and aerodynamic lift from the wing.
Exploratory Topology Modelling of Form-Active Hybrid Structures
DEFF Research Database (Denmark)
Holden Deleuran, Anders; Pauly, Mark; Tamke, Martin;
2016-01-01
The development of novel form-active hybrid structures (FAHS) is impeded by a lack of modelling tools that allow for exploratory topology modelling of shaped assemblies. We present a flexible and real-time computational design modelling pipeline developed for the exploratory modelling of FAHS tha...
Data assimilation using a hybrid ice flow model
Directory of Open Access Journals (Sweden)
D. N. Goldberg
2010-10-01
Full Text Available Hybrid models, or depth-integrated flow models that include the effect of both longitudinal stresses and vertical shearing, are becoming more prevalent in dynamical ice modeling. Under a wide range of conditions they closely approximate the well-known First Order stress balance, yet are of computationally lower dimension, and thus require less intensive resources. Concomitant with the development and use of these models is the need to perform inversions of observed data. Here, an inverse control method is extended to use a hybrid flow model as a forward model. We derive an adjoint of a hybrid model and use it for inversion of ice-stream basal traction from observed surface velocities. A novel aspect of the adjoint derivation is a retention of non-linearities in Glen's flow law. Experiments show that including those nonlinearities is advantageous in minimization of the cost function, yielding a more efficient inversion procedure.
Hybrid modeling of xanthan gum bioproduction in batch bioreactor.
Zabot, Giovani L; Mecca, Jaqueline; Mesomo, Michele; Silva, Marceli F; Prá, Valéria Dal; de Oliveira, Débora; Oliveira, J Vladimir; Castilhos, Fernanda; Treichel, Helen; Mazutti, Marcio A
2011-10-01
This work is focused on hybrid modeling of xanthan gum bioproduction process by Xanthomonas campestris pv. mangiferaeindicae. Experiments were carried out to evaluate the effects of stirred speed and superficial gas velocity on the kinetics of cell growth, lactose consumption and xanthan gum production in a batch bioreactor using cheese whey as substrate. A hybrid model was employed to simulate the bio-process making use of an artificial neural network (ANN) as a kinetic parameter estimator for the phenomenological model. The hybrid modeling of the process provided a satisfactory fitting quality of the experimental data, since this approach makes possible the incorporation of the effects of operational variables on model parameters. The applicability of the validated model was investigated, using the model as a process simulator to evaluate the effects of initial cell and lactose concentration in the xanthan gum production.
Hybrid reliability model for fatigue reliability analysis of steel bridges
Institute of Scientific and Technical Information of China (English)
曹珊珊; 雷俊卿
2016-01-01
A kind of hybrid reliability model is presented to solve the fatigue reliability problems of steel bridges. The cumulative damage model is one kind of the models used in fatigue reliability analysis. The parameter characteristics of the model can be described as probabilistic and interval. The two-stage hybrid reliability model is given with a theoretical foundation and a solving algorithm to solve the hybrid reliability problems. The theoretical foundation is established by the consistency relationships of interval reliability model and probability reliability model with normally distributed variables in theory. The solving process is combined with the definition of interval reliability index and the probabilistic algorithm. With the consideration of the parameter characteristics of theS−N curve, the cumulative damage model with hybrid variables is given based on the standards from different countries. Lastly, a case of steel structure in the Neville Island Bridge is analyzed to verify the applicability of the hybrid reliability model in fatigue reliability analysis based on the AASHTO.
Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS
Jemili, Farah; Ahmed, Mohamed Ben
2009-01-01
This paper proposes an intrusion detection and prediction system based on uncertain and imprecise inference networks and its implementation. Giving a historic of sessions, it is about proposing a method of supervised learning doubled of a classifier permitting to extract the necessary knowledge in order to identify the presence or not of an intrusion in a session and in the positive case to recognize its type and to predict the possible intrusions that will follow it. The proposed system takes into account the uncertainty and imprecision that can affect the statistical data of the historic. The systematic utilization of an unique probability distribution to represent this type of knowledge supposes a too rich subjective information and risk to be in part arbitrary. One of the first objectives of this work was therefore to permit the consistency between the manner of which we represent information and information which we really dispose.
Modeling and design of a high-performance hybrid actuator
Aloufi, Badr; Behdinan, Kamran; Zu, Jean
2016-12-01
This paper presents the model and design of a novel hybrid piezoelectric actuator which provides high active and passive performances for smart structural systems. The actuator is composed of a pair of curved pre-stressed piezoelectric actuators, so-called commercially THUNDER actuators, installed opposite each other using two clamping mechanisms constructed of in-plane fixable hinges, grippers and solid links. A fully mathematical model is developed to describe the active and passive dynamics of the actuator and investigate the effects of its geometrical parameters on the dynamic stiffness, free displacement and blocked force properties. Among the literature that deals with piezoelectric actuators in which THUNDER elements are used as a source of electromechanical power, the proposed study is unique in that it presents a mathematical model that has the ability to predict the actuator characteristics and achieve other phenomena, such as resonances, mode shapes, phase shifts, dips, etc. For model validation, the measurements of the free dynamic response per unit voltage and passive acceleration transmissibility of a particular actuator design are used to check the accuracy of the results predicted by the model. The results reveal that there is a good agreement between the model and experiment. Another experiment is performed to teste the linearity of the actuator system by examining the variation of the output dynamic responses with varying forces and voltages at different frequencies. From the results, it can be concluded that the actuator acts approximately as a linear system at frequencies up to 1000 Hz. A parametric study is achieved here by applying the developed model to analyze the influence of the geometrical parameters of the fixable hinges on the active and passive actuator properties. The model predictions in the frequency range of 0-1000 Hz show that the hinge thickness, radius, and opening angle parameters have great effects on the frequency dynamic
2015-01-01
Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algori...
PREDICT : model for prediction of survival in localized prostate cancer
Kerkmeijer, Linda G W; Monninkhof, Evelyn M.; van Oort, Inge M.; van der Poel, Henk G.; de Meerleer, Gert; van Vulpen, Marco
2016-01-01
Purpose: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed.Methods: From 1989 to 2008, 3383 patients were treated with I
Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks
DEFF Research Database (Denmark)
Hagen, Espen; Dahmen, David; Stavrinou, Maria L
2016-01-01
and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...... on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network...... model for a ∼1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its...
Modeling Hybridization Kinetics of Gene Probes in a DNA Biochip Using FEMLAB
Directory of Open Access Journals (Sweden)
Ahsan Munir
2017-05-01
Full Text Available Microfluidic DNA biochips capable of detecting specific DNA sequences are useful in medical diagnostics, drug discovery, food safety monitoring and agriculture. They are used as miniaturized platforms for analysis of nucleic acids-based biomarkers. Binding kinetics between immobilized single stranded DNA on the surface and its complementary strand present in the sample are of interest. To achieve optimal sensitivity with minimum sample size and rapid hybridization, ability to predict the kinetics of hybridization based on the thermodynamic characteristics of the probe is crucial. In this study, a computer aided numerical model for the design and optimization of a flow-through biochip was developed using a finite element technique packaged software tool (FEMLAB; package included in COMSOL Multiphysics to simulate the transport of DNA through a microfluidic chamber to the reaction surface. The model accounts for fluid flow, convection and diffusion in the channel and on the reaction surface. Concentration, association rate constant, dissociation rate constant, recirculation flow rate, and temperature were key parameters affecting the rate of hybridization. The model predicted the kinetic profile and signal intensities of eighteen 20-mer probes targeting vancomycin resistance genes (VRGs. Predicted signal intensities and hybridization kinetics strongly correlated with experimental data in the biochip (R2 = 0.8131.
Fluid Survival Tool: A Model Checker for Hybrid Petri Nets
Postema, Björn; Remke, Anne; Haverkort, Boudewijn R.; Ghasemieh, Hamed
2014-01-01
Recently, algorithms for model checking Stochastic Time Logic (STL) on Hybrid Petri nets with a single general one-shot transition (HPNG) have been introduced. This paper presents a tool for model checking HPNG models against STL formulas. A graphical user interface (GUI) not only helps to demonstra
Nuclear Hybrid Energy System Model Stability Testing
Energy Technology Data Exchange (ETDEWEB)
Greenwood, Michael Scott [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cetiner, Sacit M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fugate, David W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2017-04-01
A Nuclear Hybrid Energy System (NHES) uses a nuclear reactor as the basic power generation unit, and the power generated is used by multiple customers as combinations of thermal power or electrical power. The definition and architecture of a particular NHES can be adapted based on the needs and opportunities of different localities and markets. For example, locations in need of potable water may be best served by coupling a desalination plant to the NHES. Similarly, a location near oil refineries may have a need for emission-free hydrogen production. Using the flexible, multi-domain capabilities of Modelica, Argonne National Laboratory, Idaho National Laboratory, and Oak Ridge National Laboratory are investigating the dynamics (e.g., thermal hydraulics and electrical generation/consumption) and cost of a hybrid system. This paper examines the NHES work underway, emphasizing the control system developed for individual subsystems and the overall supervisory control system.
Nonlinear lower hybrid modeling in tokamak plasmas
Energy Technology Data Exchange (ETDEWEB)
Napoli, F.; Schettini, G. [Università Roma Tre, Dipartimento di Ingegneria, Roma (Italy); Castaldo, C.; Cesario, R. [Associazione EURATOM/ENEA sulla Fusione, Centro Ricerche Frascati (Italy)
2014-02-12
We present here new results concerning the nonlinear mechanism underlying the observed spectral broadening produced by parametric instabilities occurring at the edge of tokamak plasmas in present day LHCD (lower hybrid current drive) experiments. Low frequency (LF) ion-sound evanescent modes (quasi-modes) are the main parametric decay channel which drives a nonlinear mode coupling of lower hybrid (LH) waves. The spectrum of the LF fluctuations is calculated here considering the beating of the launched LH wave at the radiofrequency (RF) operating line frequency (pump wave) with the noisy background of the RF power generator. This spectrum is calculated in the frame of the kinetic theory, following a perturbative approach. Numerical solutions of the nonlinear LH wave equation show the evolution of the nonlinear mode coupling in condition of a finite depletion of the pump power. The role of the presence of heavy ions in a Deuterium plasma in mitigating the nonlinear effects is analyzed.
Benvenuto, Chiara; Tabone, Elisabeth; Vercken, Elodie; Sorbier, Nathalie; Colombel, Etty; Warot, Sylvie; Fauvergue, Xavier; Ris, Nicolas
2012-07-01
In the framework of biological control, the selection of effective natural enemies determines the final pest control. Thus, the genetic improvement of biocontrol agents could enhance the efficiency of biocontrol programs. Although promising, this approach has rarely been applied in this field. At the intraspecific level, hybridization between divergent populations of biocontrol agents is expected to promote hybrid vigor (heterosis), but it is not clear to what extent. An even more difficult task is the ability to predict the fitness of hybrids from the biological characteristics of their parents. We investigated these general questions by crossing seven populations of the parasitoid wasp Trichogramma chilonis (Hymenoptera: Trichogrammatidae). Our results show different levels of mating compatibilities among populations, including asymmetric or almost complete reproductive isolation. Hybrids' performance (fitness of the F(1) generation) ranges from inbreeding depression to heterosis. It was possible, to some extent, to predict hybrid fitness from pairwise genetic and phenotypic distances among parents, in accordance with the 'dominance' hypothesis. This may provide general guidelines for the genetic improvement of biological control agents.
Pseudospectral Model for Hybrid PIC Hall-effect Thruster Simulation
2015-07-01
1149. 8Goebel, D. M. and Katz, I., Fundamentals of Electric Propulsion : Ion and Hall Thrusters, John Wiley & Sons, Inc., 2008. 9Martin, R., J.W., K...Bilyeu, D., and Tran, J., “Dynamic Particle Weight Remapping in Hybrid PIC Hall -effect Thruster Simulation,” 34th Int. Electric Propulsion Conf...Paper 3. DATES COVERED (From - To) July 2015-July 2015 4. TITLE AND SUBTITLE Pseudospectral model for hybrid PIC Hall -effect thruster simulationect
Hybrid Modeling and Optimization of Yogurt Starter Culture Continuous Fermentation
Directory of Open Access Journals (Sweden)
Silviya Popova
2009-10-01
Full Text Available The present paper presents a hybrid model of yogurt starter mixed culture fermentation. The main nonlinearities within a classical structure of continuous process model are replaced by neural networks. The new hybrid model accounts for the dependence of the two microorganisms' kinetics from the on-line measured characteristics of the culture medium - pH. Then the model was used further for calculation of the optimal time profile of pH. The obtained results are with agreement with the experimental once.
A Hybrid FEM-ANN Approach for Slope Instability Prediction
Verma, A. K.; Singh, T. N.; Chauhan, Nikhil Kumar; Sarkar, K.
2016-08-01
Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope.
A Hybrid FEM-ANN Approach for Slope Instability Prediction
Verma, A. K.; Singh, T. N.; Chauhan, Nikhil Kumar; Sarkar, K.
2016-09-01
Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope.
Development of Hybrid Models for a Vapor-Phase Fungi Bioreactor
Directory of Open Access Journals (Sweden)
Giorgia Spigno
2015-01-01
Full Text Available This study is aimed at the development of a model for an experimental vapour-phase fungi bioreactor, which could be derived in a simple way using the available measurements of a pilot-plant reactor, without the development of ad hoc experiments for the evaluation of fungi kinetics and the estimation of parameters related to biofilm characteristics. The proposed approach is based on hybrid models, obtained by the connection of the mass balance equation (used in traditional phenomenological models with a feedforward neural network (used in black-box modelling, and the proper use of statistical tools for the model assessment and system understanding. Two different hybrid models were developed and compared by proper performance indexes, and their capability to predict the biological complex phenomena was demonstrated and compared to that of a first-principle model.
Mechanisms Underlying Mammalian Hybrid Sterility in Two Feline Interspecies Models.
Davis, Brian W; Seabury, Christopher M; Brashear, Wesley A; Li, Gang; Roelke-Parker, Melody; Murphy, William J
2015-10-01
The phenomenon of male sterility in interspecies hybrids has been observed for over a century, however, few genes influencing this recurrent phenotype have been identified. Genetic investigations have been primarily limited to a small number of model organisms, thus limiting our understanding of the underlying molecular basis of this well-documented "rule of speciation." We utilized two interspecies hybrid cat breeds in a genome-wide association study employing the Illumina 63 K single-nucleotide polymorphism array. Collectively, we identified eight autosomal genes/gene regions underlying associations with hybrid male sterility (HMS) involved in the function of the blood-testis barrier, gamete structural development, and transcriptional regulation. We also identified several candidate hybrid sterility regions on the X chromosome, with most residing in close proximity to complex duplicated regions. Differential gene expression analyses revealed significant chromosome-wide upregulation of X chromosome transcripts in testes of sterile hybrids, which were enriched for genes involved in chromatin regulation of gene expression. Our expression results parallel those reported in Mus hybrids, supporting the "Large X-Effect" in mammalian HMS and the potential epigenetic basis for this phenomenon. These results support the value of the interspecies feline model as a powerful tool for comparison to rodent models of HMS, demonstrating unique aspects and potential commonalities that underpin mammalian reproductive isolation.
Predictive Modeling of Cardiac Ischemia
Anderson, Gary T.
1996-01-01
The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.
Institute of Scientific and Technical Information of China (English)
李飞; 张冬冬; 赵启林; 邓安仲
2015-01-01
A novel hybrid FRP-aluminum space truss was employed in a two-rut modular bridge superstructure, which is composed of standard structural units. The main objective of this work was to obtain a simple analytical solution that can conveniently predict the deflection of the proposed hybrid space truss bridge. The analytical formulae are expected to possess a straightforward format and simple calculation process. A simple description of the proposed bridge was introduced. The design formulae of the deflection were derived based on a simplified analytical plane truss model, which possessed hinge nodes and was subsequently simplified as two solid web beams during the theoretical derivation process. To validate the analytical model and formulae, numerical and experimental works were conducted and compared with the theoretical solutions. The results indicate that the analytical formulae provide higher deflection magnitudes with a difference of <1.5% compared with the experiments performed and <4.5% compared with the FE model used; the simplified plane truss is thus shown to be an effective analytical model for the derivation of deflection design formulae, which can conveniently calculate the deflection of the hybrid space truss bridge with satisfactory accuracy.
Tamma, Kumar K.; Railkar, Sudhir B.
1988-01-01
This paper represents an attempt to apply extensions of a hybrid transfinite element computational approach for accurately predicting thermoelastic stress waves. The applicability of the present formulations for capturing the thermal stress waves induced by boundary heating for the well known Danilovskaya problems is demonstrated. A unique feature of the proposed formulations for applicability to the Danilovskaya problem of thermal stress waves in elastic solids lies in the hybrid nature of the unified formulations and the development of special purpose transfinite elements in conjunction with the classical Galerkin techniques and transformation concepts. Numerical test cases validate the applicability and superior capability to capture the thermal stress waves induced due to boundary heating.
A hybrid simulation model for a stable auroral arc
Directory of Open Access Journals (Sweden)
P. Janhunen
Full Text Available We present a new type of hybrid simulation model, intended to simulate a single stable auroral arc in the latitude/altitude plane. The ionospheric ions are treated as particles, the electrons are assumed to follow a Boltzmann response and the magnetospheric ions are assumed to be so hot that they form a background population unaffected by the electric fields that arise. The system is driven by assumed parallel electron energisation causing a primary negative charge cloud and an associated potential structure to build up. The results show how a closed potential structure and density depletion of an auroral arc build up and how they decay after the driver is turned off. The model also produces upgoing energetic ion beams and predicts strong static perpendicular electric fields to be found in a relatively narrow altitude range (~ 5000–11 000 km.
Key words. Magnetospheric physics (magnetosphere-ionosphere interactions; auroral phenomena – Space plasma physics (numerical simulation studies
Numerical weather prediction model tuning via ensemble prediction system
Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.
2011-12-01
This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.
A New Hybrid Method for Improving the Performance of Myocardial Infarction Prediction
Directory of Open Access Journals (Sweden)
Hojatollah Hamidi
2016-06-01
Full Text Available Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method that includes Forward Selection and Genetic Algorithm. Materials & Methods: The Myocardial Infarction data set used in this study contains the information related to 519 visitors to Shahid Madani Specialized Hospital of Khorramabad, Iran. This data set includes 33 features. The proposed method includes a hybrid feature selection method in order to enhance the performance of classification algorithms. The first step of this method selects the features using Forward Selection. At the second step, the selected features were given to a genetic algorithm, in order to select the best features. Classification algorithms entail Ada Boost, Naïve Bayes, J48 decision tree and simpleCART are applied to the data set with selected features, for predicting Myocardial Infarction. Results: The best results have been achieved after applying the proposed feature selection method, which were obtained via simpleCART and J48 algorithms with the accuracies of 96.53% and 96.34%, respectively. Conclusion: Based on the results, the performances of classification algorithms are improved. So, applying the proposed feature selection method, along with classification algorithms seem to be considered as a confident method with respect to predicting the Myocardial Infarction.
Diagnosing Hybrid Systems: a Bayesian Model Selection Approach
McIlraith, Sheila A.
2005-01-01
In this paper we examine the problem of monitoring and diagnosing noisy complex dynamical systems that are modeled as hybrid systems-models of continuous behavior, interleaved by discrete transitions. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. Building on our previous work in this area (MBCG99;MBCG00), our specific focus in this paper ins on the mathematical formulation of the hybrid monitoring and diagnosis task as a Bayesian model tracking algorithm. The nonlinear dynamics of many hybrid systems present challenges to probabilistic tracking. Further, probabilistic tracking of a system for the purposes of diagnosis is problematic because the models of the system corresponding to failure modes are numerous and generally very unlikely. To focus tracking on these unlikely models and to reduce the number of potential models under consideration, we exploit logic-based techniques for qualitative model-based diagnosis to conjecture a limited initial set of consistent candidate models. In this paper we discuss alternative tracking techniques that are relevant to different classes of hybrid systems, focusing specifically on a method for tracking multiple models of nonlinear behavior simultaneously using factored sampling and conditional density propagation. To illustrate and motivate the approach described in this paper we examine the problem of monitoring and diganosing NASA's Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.
Hybrid Modeling of Flotation Height in Air Flotation Oven Based on Selective Bagging Ensemble Method
Directory of Open Access Journals (Sweden)
Shuai Hou
2013-01-01
Full Text Available The accurate prediction of the flotation height is very necessary for the precise control of the air flotation oven process, therefore, avoiding the scratch and improving production quality. In this paper, a hybrid flotation height prediction model is developed. Firstly, a simplified mechanism model is introduced for capturing the main dynamic behavior of the process. Thereafter, for compensation of the modeling errors existing between actual system and mechanism model, an error compensation model which is established based on the proposed selective bagging ensemble method is proposed for boosting prediction accuracy. In the framework of the selective bagging ensemble method, negative correlation learning and genetic algorithm are imposed on bagging ensemble method for promoting cooperation property between based learners. As a result, a subset of base learners can be selected from the original bagging ensemble for composing a selective bagging ensemble which can outperform the original one in prediction accuracy with a compact ensemble size. Simulation results indicate that the proposed hybrid model has a better prediction performance in flotation height than other algorithms’ performance.
Hybrid continuum-atomistic approach to model electrokinetics in nanofluidics
Energy Technology Data Exchange (ETDEWEB)
Amani, Ehsan, E-mail: eamani@aut.ac.ir; Movahed, Saeid, E-mail: smovahed@aut.ac.ir
2016-06-07
In this study, for the first time, a hybrid continuum-atomistic based model is proposed for electrokinetics, electroosmosis and electrophoresis, through nanochannels. Although continuum based methods are accurate enough to model fluid flow and electric potential in nanofluidics (in dimensions larger than 4 nm), ionic concentration is too low in nanochannels for the continuum assumption to be valid. On the other hand, the non-continuum based approaches are too time-consuming and therefore is limited to simple geometries, in practice. Here, to propose an efficient hybrid continuum-atomistic method of modelling the electrokinetics in nanochannels; the fluid flow and electric potential are computed based on continuum hypothesis coupled with an atomistic Lagrangian approach for the ionic transport. The results of the model are compared to and validated by the results of the molecular dynamics technique for a couple of case studies. Then, the influences of bulk ionic concentration, external electric field, size of nanochannel, and surface electric charge on the electrokinetic flow and ionic mass transfer are investigated, carefully. The hybrid continuum-atomistic method is a promising approach to model more complicated geometries and investigate more details of the electrokinetics in nanofluidics. - Highlights: • A hybrid continuum-atomistic model is proposed for electrokinetics in nanochannels. • The model is validated by molecular dynamics. • This is a promising approach to model more complicated geometries and physics.
Feller Property for a Special Hybrid Jump-Diffusion Model
Directory of Open Access Journals (Sweden)
Jinying Tong
2014-01-01
Full Text Available We consider the stochastic stability for a hybrid jump-diffusion model, where the switching here is a phase semi-Markovian process. We first transform the process into a corresponding jump-diffusion with Markovian switching by the supplementary variable technique. Then we prove the Feller and strong Feller properties of the model under some assumptions.
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R
2012-08-01
compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.
Hybrid programming model for implicit PDE simulations on multicore architectures
Kaushik, Dinesh K.
2011-01-01
The complexity of programming modern multicore processor based clusters is rapidly rising, with GPUs adding further demand for fine-grained parallelism. This paper analyzes the performance of the hybrid (MPI+OpenMP) programming model in the context of an implicit unstructured mesh CFD code. At the implementation level, the effects of cache locality, update management, work division, and synchronization frequency are studied. The hybrid model presents interesting algorithmic opportunities as well: the convergence of linear system solver is quicker than the pure MPI case since the parallel preconditioner stays stronger when hybrid model is used. This implies significant savings in the cost of communication and synchronization (explicit and implicit). Even though OpenMP based parallelism is easier to implement (with in a subdomain assigned to one MPI process for simplicity), getting good performance needs attention to data partitioning issues similar to those in the message-passing case. © 2011 Springer-Verlag.
Return Predictability, Model Uncertainty, and Robust Investment
DEFF Research Database (Denmark)
Lukas, Manuel
Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...
NewGOA: predicting new GO annotations of proteins by bi-random walks on a hybrid graph.
Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhao, Yingwen
2017-06-15
A remaining key challenge of modern biology is annotating the functional roles of proteins. Various computational models have been proposed for this challenge. Most of them assume the annotations of annotated proteins are complete. But in fact, many of them are incomplete. We proposed a method called NewGOA to predict new Gene Ontology (GO) annotations for incompletely annotated proteins and for completely un-annotated ones. NewGOA employs a hybrid graph, composed of two types of nodes (proteins and GO terms), to encode interactions between proteins, hierarchical relationships between terms and available annotations of proteins. To account for structural difference between the terms subgraph and the proteins subgraph, NewGOA applies a bi-random walks algorithm, which executes asynchronous random walks on the hybrid graph, to predict new GO annotations of proteins. Experimental study on archived GO annotations of two model species (H. Sapiens and S. cerevisiae) shows that NewGOA can more accurately and efficiently predict new annotations of proteins than other related methods. Experimental results also indicate the bi-random walks can explore and further exploit the structural difference between terms subgraph and proteins subgraph. The supplementary files and codes of NewGOA are available at: http://mlda.swu.edu.cn/codes.php?name=NewGO.
Energy Technology Data Exchange (ETDEWEB)
Cardelli, E.; Faba, A. [Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia (Italy); Laudani, A.; Lozito, G.M.; Riganti Fulginei, F.; Salvini, A. [Department of Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome (Italy)
2016-04-01
This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.
Parida, M K; Satpathy, Rajesh; Awasthi, Ram Lal
2016-01-01
We discuss gauge coupling unification of the SM descending directly from SO(10) while providing solutions to the three outstanding problems: neutrino masses, dark matter, and the baryon asymmetry of the universe. Conservation of matter parity as gauged discrete symmetry in the model calls for high-scale spontaneous symmetry breaking through ${126}_H$ Higgs representation. This naturally leads to the hybrid seesaw formula for neutrino masses mediated by heavy scalar triplet and right-handed neutrinos. The seesaw formula predicts two distinct patterns of RH$\
A structured modeling approach for dynamic hybrid fuzzy-first principles models
Lith, van Pascal F.; Betlem, Ben H.L.; Roffel, Brian
2002-01-01
Hybrid fuzzy-first principles models can be attractive if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented with fuzzy submodels describing additional equations, such as mass transformation and transfer rate
Predictive Model Assessment for Count Data
2007-09-05
critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts...the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. We consider a recent suggestion by Baker and...Figure 5. Boxplots for various scores for patent data count regressions. 11 Table 1 Four predictive models for larynx cancer counts in Germany, 1998–2002
Nuclear Hybrid Energy Systems FY16 Modeling Efforts at ORNL
Energy Technology Data Exchange (ETDEWEB)
Cetiner, Sacit M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Greenwood, Michael Scott [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Harrison, Thomas J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Qualls, A. L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Guler Yigitoglu, Askin [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fugate, David W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2016-09-01
A nuclear hybrid system uses a nuclear reactor as the basic power generation unit. The power generated by the nuclear reactor is utilized by one or more power customers as either thermal power, electrical power, or both. In general, a nuclear hybrid system will couple the nuclear reactor to at least one thermal power user in addition to the power conversion system. The definition and architecture of a particular nuclear hybrid system is flexible depending on local markets needs and opportunities. For example, locations in need of potable water may be best served by coupling a desalination plant to the nuclear system. Similarly, an area near oil refineries may have a need for emission free hydrogen production. A nuclear hybrid system expands the nuclear power plant from its more familiar central power station role by diversifying its immediately and directly connected customer base. The definition, design, analysis, and optimization work currently performed with respect to the nuclear hybrid systems represents the work of three national laboratories. Idaho National Laboratory (INL) is the lead lab working with Argonne National Laboratory (ANL) and Oak Ridge National Laboratory. Each laboratory is providing modeling and simulation expertise for the integration of the hybrid system.
Predictive Model of Energy Consumption in Beer Production
Directory of Open Access Journals (Sweden)
Tiecheng Pu
2013-07-01
Full Text Available The predictive model of energy consumption is presented based on subtractive clustering and Adaptive-Network-Based Fuzzy Inference System (for short ANFIS in the beer production. Using the subtractive clustering on the historical data of energy consumption, the limit of artificial experience is conquered while confirming the number of fuzzy rules. The parameters of the fuzzy inference system are acquired by the structure of adaptive network and hybrid on-line learning algorithm. The method can predict and guide the energy consumption of the factual production process. The reducing consumption scheme is provided based on the actual situation of the enterprise. Finally, using concrete examples verified the feasibility of this method comparing with the Radial Basis Functions (for short RBF neural network predictive model.
MODEL OF LASER-TIG HYBRID WELDING HEAT SOURCE
Institute of Scientific and Technical Information of China (English)
Chen Yanbin; Li Liqun; Feng Xiaosong; Fang Junfei
2004-01-01
The welding mechanism of laser-TIG hybrid welding process is analyzed. With the variation of arc current, the welding process is divided into two patterns: deep-penetration welding and heat conductive welding. The heat flow model of hybrid welding is presented. As to deep-penetration welding, the heat source includes a surface heat flux and a volume heat flux. The heat source of heat conductive welding is composed of two Gaussian distribute surface heat sources. With this heat source model, a temperature field is calculated. The finite element code MARC is employed for this purpose. The calculation results show a good agreement with the experimental data.
REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES
Directory of Open Access Journals (Sweden)
R. Chitra
2013-07-01
Full Text Available The Healthcare industry generally clinical diagnosis is done mostly by doctor’s expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.
Strength Modeling of High-Strength Concrete with Hybrid Fibre Reinforcement
Directory of Open Access Journals (Sweden)
A. Ravichandran
2009-01-01
Full Text Available The low tensile strength and limited ductility, the unavoidable deficiency, of concrete can be overcome by the addition of fibres. High strength concrete (HSC of 60 MPa containing hybrid fibres, combination of steel and polyolefin fibres, at different volume fraction of 0.5, 1.0, 1.5 and 2.0% were compared in terms of compressive, splitting tensile strength and flexural properties with HSC containing no fibres. Test results showed that the fibres when used in hybrid form could result in enhanced flexural toughness compared to steel fibre reinforced concrete [HSFRC]. The compressive strength of the fibre-reinforced concrete reached maximum at 1.5% volume fractions and the splitting tensile strength and modulus of rupture improved with increasing volume fraction. Strength models were established to predict the compressive and splitting tensile strength and modulus of rupture of the fibre-reinforced concrete. The models give prediction matching the measurements.
Modeling of hybrid vehicle fuel economy and fuel engine efficiency
Wu, Wei
"Near-CV" (i.e., near-conventional vehicle) hybrid vehicles, with an internal combustion engine, and a supplementary storage with low-weight, low-energy but high-power capacity, are analyzed. This design avoids the shortcoming of the "near-EV" and the "dual-mode" hybrid vehicles that need a large energy storage system (in terms of energy capacity and weight). The small storage is used to optimize engine energy management and can provide power when needed. The energy advantage of the "near-CV" design is to reduce reliance on the engine at low power, to enable regenerative braking, and to provide good performance with a small engine. The fuel consumption of internal combustion engines, which might be applied to hybrid vehicles, is analyzed by building simple analytical models that reflect the engines' energy loss characteristics. Both diesel and gasoline engines are modeled. The simple analytical models describe engine fuel consumption at any speed and load point by describing the engine's indicated efficiency and friction. The engine's indicated efficiency and heat loss are described in terms of several easy-to-obtain engine parameters, e.g., compression ratio, displacement, bore and stroke. Engine friction is described in terms of parameters obtained by fitting available fuel measurements on several diesel and spark-ignition engines. The engine models developed are shown to conform closely to experimental fuel consumption and motored friction data. A model of the energy use of "near-CV" hybrid vehicles with different storage mechanism is created, based on simple algebraic description of the components. With powertrain downsizing and hybridization, a "near-CV" hybrid vehicle can obtain a factor of approximately two in overall fuel efficiency (mpg) improvement, without considering reductions in the vehicle load.
Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier
Directory of Open Access Journals (Sweden)
Wasi Haider Butt
2014-01-01
attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network.
Baurle, R. A.
2015-01-01
Steady-state and scale-resolving simulations have been performed for flow in and around a model scramjet combustor flameholder. The cases simulated corresponded to those used to examine this flowfield experimentally using particle image velocimetry. A variety of turbulence models were used for the steady-state Reynolds-averaged simulations which included both linear and non-linear eddy viscosity models. The scale-resolving simulations used a hybrid Reynolds-averaged / large eddy simulation strategy that is designed to be a large eddy simulation everywhere except in the inner portion (log layer and below) of the boundary layer. Hence, this formulation can be regarded as a wall-modeled large eddy simulation. This effort was undertaken to formally assess the performance of the hybrid Reynolds-averaged / large eddy simulation modeling approach in a flowfield of interest to the scramjet research community. The numerical errors were quantified for both the steady-state and scale-resolving simulations prior to making any claims of predictive accuracy relative to the measurements. The steady-state Reynolds-averaged results showed a high degree of variability when comparing the predictions obtained from each turbulence model, with the non-linear eddy viscosity model (an explicit algebraic stress model) providing the most accurate prediction of the measured values. The hybrid Reynolds-averaged/large eddy simulation results were carefully scrutinized to ensure that even the coarsest grid had an acceptable level of resolution for large eddy simulation, and that the time-averaged statistics were acceptably accurate. The autocorrelation and its Fourier transform were the primary tools used for this assessment. The statistics extracted from the hybrid simulation strategy proved to be more accurate than the Reynolds-averaged results obtained using the linear eddy viscosity models. However, there was no predictive improvement noted over the results obtained from the explicit
Hybrid SPR algorithm to select predictive genes for effectual cancer classification
2012-01-01
Designing an automated system for classifying DNA microarray data is an extremely challenging problem because of its high dimension and low amount of sample data. In this paper, a hybrid statistical pattern recognition algorithm is proposed to reduce the dimensionality and select the predictive genes for the classification of cancer. Colon cancer gene expression profiles having 62 samples of 2000 genes were used for the experiment. A gene subset of 6 highly informative genes was selecte...
Tandem cylinder flow and noise predictions using a hybrid RANS/LES approach
M. Weinmann; Sandberg, R.D.; Doolan, C.
2014-01-01
The performance of a novel hybrid RANS/LES methodology for accurate flow and noise predictions of the NASA Tandem Cylinder Experiment is investigated. The proposed approach, the modified Flow Simulation Methodology (FSM), is based on scaling the turbulence viscosity and the turbulence kinetic energy dissipation rate with a damping function. This damping function consists of three individual components, a function based on the Kolmogorov length-scale ensuring correct behaviour in the direct nu...
CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications.
Lei, Guoqing; Dou, Yong; Wan, Wen; Xia, Fei; Li, Rongchun; Ma, Meng; Zou, Dan
2012-01-01
Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.
The innovative concept of three-dimensional hybrid receptor modeling
Stojić, A.; Stanišić Stojić, S.
2017-09-01
The aim of this study was to improve the current understanding of air pollution transport processes at regional and long-range scale. For this purpose, three-dimensional (3D) potential source contribution function and concentration weighted trajectory models, as well as new hybrid receptor model, concentration weighted boundary layer (CWBL), which uses a two-dimensional grid and a planetary boundary layer height as a frame of reference, are presented. The refined approach to hybrid receptor modeling has two advantages. At first, it considers whether each trajectory endpoint meets the inclusion criteria based on planetary boundary layer height, which is expected to provide a more realistic representation of the spatial distribution of emission sources and pollutant transport pathways. Secondly, it includes pollutant time series preprocessing to make hybrid receptor models more applicable for suburban and urban locations. The 3D hybrid receptor models presented herein are designed to identify altitude distribution of potential sources, whereas CWBL can be used for analyzing the vertical distribution of pollutant concentrations along the transport pathway.
Fatigue reliability based on residual strength model with hybrid uncertain parameters
Institute of Scientific and Technical Information of China (English)
Jun Wang; Zhi-Ping Qiu
2012-01-01
The aim of this paper is to evaluate the fatigue reliability with hybrid uncertain parameters based on a residual strength model.By solving the non-probabilistic setbased reliability problem and analyzing the reliability with randomness,the fatigue reliability with hybrid parameters can be obtained.The presented hybrid model can adequately consider all uncertainties affecting the fatigue reliability with hybrid uncertain parameters.A comparison among the presented hybrid model,non-probabilistic set-theoretic model and the conventional random model is made through two typical numerical examples.The results show that the presented hybrid model,which can ensure structural security,is effective and practical.
Nonlinear chaotic model for predicting storm surges
Directory of Open Access Journals (Sweden)
M. Siek
2010-09-01
Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.
Nonlinear chaotic model for predicting storm surges
Siek, M.; Solomatine, D.P.
This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables.
Validating predictions from climate envelope models
Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.
2013-01-01
Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.
Theoretical prediction of regression rates in swirl-injection hybrid rocket engines
Ozawa, K.; Shimada, T.
2016-07-01
The authors theoretically and analytically predict what times regression rates of swirl injection hybrid rocket engines increase higher than the axial injection ones by estimating heat flux from boundary layer combustion to the fuel port. The schematic of engines is assumed as ones whose oxidizer is injected from the opposite side of the nozzle such as ones of Yuasa et al. propose. To simplify the estimation, we assume some hypotheses such as three-dimensional (3D) axisymmetric flows have been assumed. The results of this prediction method are largely consistent with Yuasa's experiments data in the range of high swirl numbers.
EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH
Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.
2014-01-01
The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain,...
Hybrid Scheduling Model for Independent Grid Tasks
Directory of Open Access Journals (Sweden)
J. Shanthini
2015-01-01
Full Text Available Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG search and Apparent Tardiness Cost (ATC indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms.
Hybrid Scheduling Model for Independent Grid Tasks.
Shanthini, J; Kalaikumaran, T; Karthik, S
2015-01-01
Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms.
Li, Zhong; Wang, Jing; Zhang, Shunpu; Zhang, Qifeng; Wu, Wuming
2017-03-16
The coding pattern of protein can greatly affect the prediction accuracy of protein secondary structure. In this paper, a novel hybrid coding method based on the physicochemical properties of amino acids and tendency factors is proposed for the prediction of protein secondary structure. The principal component analysis (PCA) is first applied to the physicochemical properties of amino acids to construct a 3-bit-code, and then the 3 tendency factors of amino acids are calculated to generate another 3-bit-code. Two 3-bit-codes are fused to form a novel hybrid 6-bit-code. Furthermore, we make a geometry-based similarity comparison of the protein primary structure between the reference set and the test set before the secondary structure prediction. We finally use the support vector machine (SVM) to predict those amino acids which are not detected by the primary structure similarity comparison. Experimental results show that our method achieves a satisfactory improvement in accuracy in the prediction of protein secondary structure.
Multi-model ensemble hydrologic prediction and uncertainties analysis
Directory of Open Access Journals (Sweden)
S. Jiang
2014-09-01
Full Text Available Modelling uncertainties (i.e. input errors, parameter uncertainties and model structural errors inevitably exist in hydrological prediction. A lot of recent attention has focused on these, of which input error modelling, parameter optimization and multi-model ensemble strategies are the three most popular methods to demonstrate the impacts of modelling uncertainties. In this paper the Xinanjiang model, the Hybrid rainfall–runoff model and the HYMOD model were applied to the Mishui Basin, south China, for daily streamflow ensemble simulation and uncertainty analysis. The three models were first calibrated by two parameter optimization algorithms, namely, the Shuffled Complex Evolution method (SCE-UA and the Shuffled Complex Evolution Metropolis method (SCEM-UA; next, the input uncertainty was accounted for by introducing a normally-distributed error multiplier; then, the simulation sets calculated from the three models were combined by Bayesian model averaging (BMA. The results show that both these parameter optimization algorithms generate good streamflow simulations; specifically the SCEM-UA can imply parameter uncertainty and give the posterior distribution of the parameters. Considering the precipitation input uncertainty, the streamflow simulation precision does not improve very much. While the BMA combination not only improves the streamflow prediction precision, it also gives quantitative uncertainty bounds for the simulation sets. The SCEM-UA calculated prediction interval is better than the SCE-UA calculated one. These results suggest that considering the model parameters' uncertainties and doing multi-model ensemble simulations are very practical for streamflow prediction and flood forecasting, from which more precision prediction and more reliable uncertainty bounds can be generated.
How to Establish Clinical Prediction Models
Directory of Open Access Journals (Sweden)
Yong-ho Lee
2016-03-01
Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.
Institute of Scientific and Technical Information of China (English)
Mostafa Gandomi; Mohsen Soltanpour; Mohammad R. Zolfaghari; Amir H. Gandomi
2016-01-01
A new model is derived to predict the peak ground acceleration (PGA) utilizing a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called SA-ANN. The proposed model re-lates PGA to earthquake source to site distance, earthquake magnitude, average shear-wave velocity, faulting mechanisms, and focal depth. A database of strong ground-motion recordings of 36 earthquakes, which happened in Iran’s tectonic regions, is used to establish the model. For more validity verification, the SA-ANN model is employed to predict the PGA of a part of the database beyond the training data domain. The proposed SA-ANN model is compared with the simple ANN in addition to 10 well-known models proposed in the literature. The proposed model performance is superior to the single ANN and other existing attenuation models. The SA-ANN model is highly correlated to the actual records (R ¼ 0.835 and r ¼ 0.0908) and it is subsequently converted into a tractable design equation.
Hybrid Clustering-GWO-NARX neural network technique in predicting stock price
Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.
2017-09-01
Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.
Efficient Proof Engines for Bounded Model Checking of Hybrid Systems
DEFF Research Database (Denmark)
Fränzle, Martin; Herde, Christian
2005-01-01
In this paper we present HySat, a new bounded model checker for linear hybrid systems, incorporating a tight integration of a DPLL-based pseudo-Boolean SAT solver and a linear programming routine as core engine. In contrast to related tools like MathSAT, ICS, or CVC, our tool exploits all...
A novel Monte Carlo approach to hybrid local volatility models
A.W. van der Stoep (Anton); L.A. Grzelak (Lech Aleksander); C.W. Oosterlee (Cornelis)
2017-01-01
textabstractWe present in a Monte Carlo simulation framework, a novel approach for the evaluation of hybrid local volatility [Risk, 1994, 7, 18–20], [Int. J. Theor. Appl. Finance, 1998, 1, 61–110] models. In particular, we consider the stochastic local volatility model—see e.g. Lipton et al. [Quant.
(Hybrid) Baryons in the Flux-Tube Model
Page, P R
1999-01-01
We construct baryons and hybrid baryons in the non-relativistic flux-tube model of Isgur and Paton. The motion of the flux-tube with the three quark positions fixed, except for centre of mass corrections, is discussed. It is shown that the problem can to an excellent approximation be reduced to the independent motion of a junction and strings.
New Models of Hybrid Leadership in Global Higher Education
Tonini, Donna C.; Burbules, Nicholas C.; Gunsalus, C. K.
2016-01-01
This manuscript highlights the development of a leadership preparation program known as the Nanyang Technological University Leadership Academy (NTULA), exploring the leadership challenges unique to a university undergoing rapid growth in a highly multicultural context, and the hybrid model of leadership it developed in response to globalization.…
Incorporating RTI in a Hybrid Model of Reading Disability
Spencer, Mercedes; Wagner, Richard K.; Schatschneider, Christopher; Quinn, Jamie M.; Lopez, Danielle; Petscher, Yaacov
2014-01-01
The present study seeks to evaluate a hybrid model of identification that incorporates response to instruction and intervention (RTI) as one of the key symptoms of reading disability. The 1-year stability of alternative operational definitions of reading disability was examined in a large-scale sample of students who were followed longitudinally…
A hybrid modelling approach to simulating foot-and-mouth disease outbreaks in Australian livestock
Directory of Open Access Journals (Sweden)
Richard A Bradhurst
2015-03-01
Full Text Available Foot-and-mouth disease (FMD is a highly contagious and economically important viral disease of cloven-hoofed animals. Australia's freedom from FMD underpins a valuable trade in live animals and animal products. An outbreak of FMD would result in the loss of export markets and cause severe disruption to domestic markets. The prevention of, and contingency planning for, FMD are of key importance to government, industry, producers and the community. The spread and control of FMD is complex and dynamic due to a highly contagious multi-host pathogen operating in a heterogeneous environment across multiple jurisdictions. Epidemiological modelling is increasingly being recognized as a valuable tool for investigating the spread of disease under different conditions and the effectiveness of control strategies. Models of infectious disease can be broadly classified as: population-based models that are formulated from the top-down and employ population-level relationships to describe individual-level behaviour, individual-based models that are formulated from the bottom-up and aggregate individual-level behaviour to reveal population-level relationships, or hybrid models which combine the two approaches into a single model.The Australian Animal Disease Spread (AADIS hybrid model employs a deterministic equation-based model (EBM to model within-herd spread of FMD, and a stochastic, spatially-explicit agent-based model (ABM to model between-herd spread and control. The EBM provides concise and computationally efficient predictions of herd prevalence and clinical signs over time. The ABM captures the complex, stochastic and heterogeneous environment in which an FMD epidemic operates. The AADIS event-driven hybrid EBM/ABM architecture is a flexible, efficient and extensible framework for modelling the spread and control of disease in livestock on a national scale. We present an overview of the AADIS hybrid approach and a description of the model
Advances in modeling of lower hybrid current drive
Peysson, Y.; Decker, J.; Nilsson, E.; Artaud, J.-F.; Ekedahl, A.; Goniche, M.; Hillairet, J.; Ding, B.; Li, M.; Bonoli, P. T.; Shiraiwa, S.; Madi, M.
2016-04-01
First principle modeling of the lower hybrid (LH) current drive in tokamak plasmas is a longstanding activity, which is gradually gaining in accuracy thanks to quantitative comparisons with experimental observations. The ability to reproduce simulatenously the plasma current and the non-thermal bremsstrahlung radial profiles in the hard x-ray (HXR) photon energy range represents in this context a significant achievement. Though subject to limitations, ray tracing calculations are commonly used for describing wave propagation in conjunction with Fokker-Planck codes, as it can capture prominent features of the LH wave dynamics in a tokamak plasma-like toroidal refraction. This tool has been validated on several machines when the full absorption of the LH wave requires the transfer of a small fraction of power from the main lobes of the launched power spectrum to a tail at a higher parallel refractive index. Conversely, standard modeling based on toroidal refraction only becomes more challenging when the spectral gap is large, except if other physical mechanisms may dominate to bridge it, like parametric instabilities, as suggested for JET LH discharges (Cesario et al 2004 Phys. Rev. Lett. 92 175002), or fast fluctuations of the launched power spectrum or ‘tail’ LH model, as shown for Tore Supra (Decker et al 2014 Phys. Plasma 21 092504). The applicability of the heuristic ‘tail’ LH model is investigated for a broader range of plasma parameters as compared to the Tore Supra study and with different LH wave characteristics. Discrepancies and agreements between simulations and experiments depending upon the different models used are discussed. The existence of a ‘tail’ in the launched power spectrum significantly improves the agreement between modeling and experiments in plasma conditions for which the spectral gap is large in EAST and Alcator C-Mod tokamaks. For the Alcator C-Mod tokamak, the experimental evolution of the HXR profiles with density suggests
Institute of Scientific and Technical Information of China (English)
SUN Li-li; LAN Yu-kun; ZHOU Li-ping; YU Yu; LI Zhi-liang
2007-01-01
A set of novel structural descriptors (molecular hybridization electronegativity-distance vector, VMEDh) was put forward, and the quantitative structure-activity relationship (QSAR) of a series of 17α-Acetoxyprogesterones (Aps) was investigated. Taking into account the effect of various hybridized orbits on atomic electronegativities, we developed the structure descriptors with amended electronegativities to build a QSAR model. The 10-parameter model based on VMEDh yields a correlation coefficient R=0.972 and standard deviation SD=0.262, which are more desirable than those of the previous molecular electonegativity-distance vector (MEDV-4) (R=0.969, SD=0.275). By stepwise multiple linear regression, several parameters are selected to construct optimal models. The 7-parameter model based on VMEDh has R=0.960 and SD=0.276; its correlation coefficient (RCV) and standard deviation (SDCV) for leave-one-out procedure crossvalidation are respectively RCV=0.890 and SDCV=0.445. The 6-parameter MEDV-4 model has R=0.946, SD=0.304, RCV=0.903 and SDCV=0.406. It is demonstrated that VMEDh has desirable estimation performance and good predictive capability for this series of chemical compounds.
Brain anatomical structure segmentation by hybrid discriminative/generative models.
Tu, Z; Narr, K L; Dollar, P; Dinov, I; Thompson, P M; Toga, A W
2008-04-01
In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multiclass discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus, low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3-D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3-D magnetic resonance imaging (MRI) volumes and the results obtained are encouraging.
Prediction of genetic gain from selection indices for disease resistance in papaya hybrids
Directory of Open Access Journals (Sweden)
Marcelo Vivas
2012-12-01
Full Text Available In order to select superior hybrids for the concentration of favorable alleles for resistance to papaya black spot, powdery mildew and phoma spot, 67 hybrids were evaluated in two seasons, in 2007, in a randomized block design with two replications. Genetic gains were estimated from the selection indices of Smith & Hazel, Pesek & Baker, Williams, Mulamba & Mock, with selection intensity of 22.39%, corresponding to 15 hybrids. The index of Mulamba & Mock showed gains more suitable for the five traits assessed when it was used the criterion of economic weight tentatively assigned. Together, severity of black spot on leaves and on fruits, characteristics considered most relevant to the selection of resistant materials, expressed percentage gain of -44.15%. In addition, there were gains for other characteristics, with negative predicted selective percentage gain. The results showed that the index of Mulamba & Mock is the most efficient procedure for simultaneous selection of papaya hybrid resistant to black spot, powdery mildew and phoma spot.
Comparison of Prediction-Error-Modelling Criteria
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
is a realization of a continuous-discrete multivariate stochastic transfer function model. The proposed prediction error-methods are demonstrated for a SISO system parameterized by the transfer functions with time delays of a continuous-discrete-time linear stochastic system. The simulations for this case suggest......Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which...... computational resources. The identification method is suitable for predictive control....
Hybrid Sludge Modeling in Water Treatment Processes
Brenda, Marian
2015-01-01
Sludge occurs in many waste water and drinking water treatment processes. The numeric modeling of sludge is therefore crucial for developing and optimizing water treatment processes. Numeric single-phase sludge models mainly include settling and viscoplastic behavior. Even though many investigators emphasize the importance of modeling the rheology of sludge for good simulation results, it is difficult to measure, because of settling and the viscoplastic behavior. In this thesis, a new method ...
Mass Predictions of Open-Flavour Hybrid Mesons from QCD Sum Rules
Ho, Jason; Steele, Tom
2016-01-01
Within QCD, colourless states may be constructed corresponding to exotic matter outside of the traditional quark model. Experiments have recently observed tetraquark and pentaquark states, but no definitive hybrid meson signals have been observed. With the construction of the PANDA experiment at FAIR, and with full commissioning of the GlueX experiment at JLab expected to be completed this year, the opportunity for the observation of hybrid mesons has greatly increased. However, theoretical calculations are necessary to ascertain the identity of any experimental resonances that may be observed. We present selected QCD sum rule results from a full range of quantum numbers for open-flavour hybrid mesons with heavy valence quark content, including non-perturbative condensate contributions up to six-dimensions.
A hybrid numerical prediction scheme for solar radiation estimation in un-gauged catchments.
Shamim, M. A.; Bray, M.; Ishak, A. M.; Remesan, R.; Han, D.
2009-09-01
The importance of solar radiation on earth's surface is depicted in its wide range of applications in the fields of meteorology, agricultural sciences, engineering, hydrology, crop water requirements, climatic changes and energy assessment. It is quite random in nature as it has to go through different processes of assimilation and dispersion while on its way to earth. Compared to other meteorological parameters, solar radiation is quite infrequently measured, for example, the worldwide ratio of stations collecting solar radiation to those collecting temperature is 1:500 (Badescu, 2008). Researchers, therefore, have to rely on indirect techniques of estimation that include nonlinear models, artificial intelligence (e.g. neural networks), remote sensing and numerical weather predictions (NWP). This study proposes a hybrid numerical prediction scheme for solar radiation estimation in un-gauged catchments. It uses the PSU/NCAR's Mesoscale Modelling system (MM5) (Grell et al., 1995) to parameterise the cloud effect on extraterrestrial radiation by dividing the atmosphere into four layers of very high (6-12 km), high (3-6 km), medium (1.5-3) and low (0-1.5) altitudes from earth. It is believed that various cloud forms exist within each of these layers. An hourly time series of upper air pressure and relative humidity data sets corresponding to all of these layers is determined for the Brue catchment, southwest UK, using MM5. Cloud Index (CI) was then determined using (Yang and Koike, 2002): 1 p?bi [ (Rh - Rh )] ci =------- max 0.0,---------cri dp pbi - ptipti (1- Rhcri) where, pbi and pti represent the air pressure at the top and bottom of each layer and Rhcri is the critical value of relative humidity at which a certain cloud type is formed. Output from a global clear sky solar radiation model (MRM v-5) (Kambezidis and Psiloglu, 2008) is used along with meteorological datasets of temperature and precipitation and astronomical information. The analysis is aided by the
Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model
Energy Technology Data Exchange (ETDEWEB)
Cadenas, Erasmo [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Rivera, Wilfrido [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)
2010-12-15
In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites. (author)
Modelling and analysis of real-time and hybrid systems
Energy Technology Data Exchange (ETDEWEB)
Olivero, A.
1994-09-29
This work deals with the modelling and analysis of real-time and hybrid systems. We first present the timed-graphs as model for the real-time systems and we recall the basic notions of the analysis of real-time systems. We describe the temporal properties on the timed-graphs using TCTL formulas. We consider two methods for property verification: in one hand we study the symbolic model-checking (based on backward analysis) and in the other hand we propose a verification method derived of the construction of the simulation graph (based on forward analysis). Both methods have been implemented within the KRONOS verification tool. Their application for the automatic verification on several real-time systems confirms the practical interest of our approach. In a second part we study the hybrid systems, systems combining discrete components with continuous ones. As in the general case the analysis of this king of systems is not decidable, we identify two sub-classes of hybrid systems and we give a construction based method for the generation of a timed-graph from an element into the sub-classes. We prove that in one case the timed-graph obtained is bi-similar with the considered system and that there exists a simulation in the other case. These relationships allow the application of the described technics on the hybrid systems into the defined sub-classes. (authors). 60 refs., 43 figs., 8 tabs., 2 annexes.
A hybrid parallel framework for the cellular Potts model simulations
Energy Technology Data Exchange (ETDEWEB)
Jiang, Yi [Los Alamos National Laboratory; He, Kejing [SOUTH CHINA UNIV; Dong, Shoubin [SOUTH CHINA UNIV
2009-01-01
The Cellular Potts Model (CPM) has been widely used for biological simulations. However, most current implementations are either sequential or approximated, which can't be used for large scale complex 3D simulation. In this paper we present a hybrid parallel framework for CPM simulations. The time-consuming POE solving, cell division, and cell reaction operation are distributed to clusters using the Message Passing Interface (MPI). The Monte Carlo lattice update is parallelized on shared-memory SMP system using OpenMP. Because the Monte Carlo lattice update is much faster than the POE solving and SMP systems are more and more common, this hybrid approach achieves good performance and high accuracy at the same time. Based on the parallel Cellular Potts Model, we studied the avascular tumor growth using a multiscale model. The application and performance analysis show that the hybrid parallel framework is quite efficient. The hybrid parallel CPM can be used for the large scale simulation ({approx}10{sup 8} sites) of complex collective behavior of numerous cells ({approx}10{sup 6}).
Directory of Open Access Journals (Sweden)
Charles M. Reinke
2011-12-01
Full Text Available Recent work has demonstrated that nanostructuring of a semiconductor material to form a phononic crystal (PnC can significantly reduce its thermal conductivity. In this paper, we present a classical method that combines atomic-level information with the application of Bloch theory at the continuum level for the prediction of the thermal conductivity of finite-thickness PnCs with unit cells sized in the micron scale. Lattice dynamics calculations are done at the bulk material level, and the plane-wave expansion method is implemented at the macrosale PnC unit cell level. The combination of the lattice dynamics-based and continuum mechanics-based dispersion information is then used in the Callaway-Holland model to calculate the thermal transport properties of the PnC. We demonstrate that this hybrid approach provides both accurate and efficient predictions of the thermal conductivity.
Reinke, Charles M.; Su, Mehmet F.; Davis, Bruce L.; Kim, Bongsang; Hussein, Mahmoud I.; Leseman, Zayd C.; Olsson-III, Roy H.; El-Kady, Ihab
2011-12-01
Recent work has demonstrated that nanostructuring of a semiconductor material to form a phononic crystal (PnC) can significantly reduce its thermal conductivity. In this paper, we present a classical method that combines atomic-level information with the application of Bloch theory at the continuum level for the prediction of the thermal conductivity of finite-thickness PnCs with unit cells sized in the micron scale. Lattice dynamics calculations are done at the bulk material level, and the plane-wave expansion method is implemented at the macrosale PnC unit cell level. The combination of the lattice dynamics-based and continuum mechanics-based dispersion information is then used in the Callaway-Holland model to calculate the thermal transport properties of the PnC. We demonstrate that this hybrid approach provides both accurate and efficient predictions of the thermal conductivity.
Strongly Interacting Matter at Finite Chemical Potential: Hybrid Model Approach
Srivastava, P. K.; Singh, C. P.
2013-06-01
Search for a proper and realistic equation of state (EOS) for strongly interacting matter used in the study of the QCD phase diagram still appears as a challenging problem. Recently, we constructed a hybrid model description for the quark-gluon plasma (QGP) as well as hadron gas (HG) phases where we used an excluded volume model for HG and a thermodynamically consistent quasiparticle model for the QGP phase. The hybrid model suitably describes the recent lattice results of various thermodynamical as well as transport properties of the QCD matter at zero baryon chemical potential (μB). In this paper, we extend our investigations further in obtaining the properties of QCD matter at finite value of μB and compare our results with the most recent results of lattice QCD calculation.
Predicting human walking gaits with a simple planar model.
Martin, Anne E; Schmiedeler, James P
2014-04-11
Models of human walking with moderate complexity have the potential to accurately capture both joint kinematics and whole body energetics, thereby offering more simultaneous information than very simple models and less computational cost than very complex models. This work examines four- and six-link planar biped models with knees and rigid circular feet. The two differ in that the six-link model includes ankle joints. Stable periodic walking gaits are generated for both models using a hybrid zero dynamics-based control approach. To establish a baseline of how well the models can approximate normal human walking, gaits were optimized to match experimental human walking data, ranging in speed from very slow to very fast. The six-link model well matched the experimental step length, speed, and mean absolute power, while the four-link model did not, indicating that ankle work is a critical element in human walking models of this type. Beyond simply matching human data, the six-link model can be used in an optimization framework to predict normal human walking using a torque-squared objective function. The model well predicted experimental step length, joint motions, and mean absolute power over the full range of speeds.
A hybrid phenomenological model for ferroelectroelastic ceramics. Part II: Morphotropic PZT ceramics
Stark, S.; Neumeister, P.; Balke, H.
2016-10-01
In this part II of a two part series, the rate-independent hybrid phenomenological constitutive model introduced in part I is modified to account for the material behavior of morphotropic lead zirconate titanate ceramics (PZT ceramics). The modifications are based on a discussion of the available literature results regarding the micro-structure of these materials. In particular, a monoclinic phase and a highly simplified representation of the hierarchical structure of micro-domains and nano-domains observed experimentally are incorporated into the model. It is shown that experimental data for the commercially available morphotropic PZT material PIC151 (PI Ceramic GmbH, Lederhose, Germany) can be reproduced and predicted based on the modified hybrid model.
An hybrid RANS/LES model for simulation of complex turbulent flow
Institute of Scientific and Technical Information of China (English)
魏群; 陈红勋; 马峥
2016-01-01
A non-linear eddy viscosity model (NLEVM) and a scalable hybrid Reynolds averaged Navier-Stokes/ large eddy simula- tion (RANS/LES) strategy are developed to improve the capability of the eddy viscosity model (EVM) to simulate complex flows featuring separations and unsteady motions. To study the performance of the NLEVM, numerical simulations around S809 airfoil are carried out and the results show that the NLEVM performs much better when a large separation occurs. Calculated results of the flow around a triangular cylinder show that the NLEVM can improve the precision of the flow fields to some extents, but the error is still considerable, and the small turbulence structures can not be clearly captured as the EVM. Whereas the scalable hybrid RANS/LES model based on the NLEVM is fairly effective on resolving the turbulent structures and can give more satisfactory predictions of the flow fields.
Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model
Directory of Open Access Journals (Sweden)
Bogdan Gliwa
2011-01-01
Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.
Noise propagation in hybrid models of nonlinear systems: The Ginzburg–Landau equation
Energy Technology Data Exchange (ETDEWEB)
Taverniers, Søren [Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093 (United States); Alexander, Francis J. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Tartakovsky, Daniel M. [Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093 (United States)
2014-04-01
Every physical phenomenon can be described by multiple models with varying degrees of fidelity. The computational cost of higher fidelity models (e.g., molecular dynamics simulations) is invariably higher than that of their lower fidelity counterparts (e.g., a continuum model based on differential equations). While the former might not be suitable for large-scale simulations, the latter are not universally valid. Hybrid algorithms provide a compromise between the computational efficiency of a coarse-scale model and the representational accuracy of a fine-scale description. This is achieved by conducting a fine-scale computation in subdomains where it is absolutely required (e.g., due to a local breakdown of a continuum model) and coupling it with a coarse-scale computation in the rest of a computational domain. We analyze the effects of random fluctuations generated by the fine-scale component of a nonlinear hybrid on the hybrid's overall accuracy and stability. Two variants of the time-dependent Ginzburg–Landau equation (GLE) and their discrete representations provided by a nearest-neighbor Ising model serve as a computational testbed. Our analysis shows that coupling these descriptions in a one-dimensional simulation leads to erroneous results. Adding a random source term to the GLE provides accurate prediction of the mean behavior of the quantity of interest (magnetization). It also allows the two GLE variants to correctly capture the strength of the microscale fluctuations. Our work demonstrates the importance of fine-scale noise in hybrid simulations, and suggests the need for replacing an otherwise deterministic coarse-scale component of the hybrid with its stochastic counterpart.
An Approach of Bio-inspired Hybrid Model for Financial Markets
Simić, Dragan; Gajić, Vladeta; Simić, Svetlana
Biological systems are inspiration for the design of optimisation and classification models. Applying various forms of bio-inspired algorithms may be a very high-complex system. Modelling of financial markets is challenging for several reasons, because many plausible factors impact on it. An automated trading on financial market is not a new phenomenon. The model of bio-inspired hybrid adaptive trading system based on technical indicators usage by grammatical evolution and moving window is presented in this paper. The proposed system is just one of possible bio-inspired system which can be used in financial forecast, corporate failure prediction or bond rating company.
Case studies in archaeological predictive modelling
Verhagen, Jacobus Wilhelmus Hermanus Philippus
2007-01-01
In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing p
Advanced Geometric Modeler with Hybrid Representation
Institute of Scientific and Technical Information of China (English)
杨长贵; 陈玉健; 等
1996-01-01
An advanced geometric modeler GEMS4.0 has been developed,in which feature representation is used at the highest level abstraction of a product model.Boundary representation is used at the bottom level,while CSG model is adopted at the median level.A BRep data structure capable of modeling non-manifold is adopted.UNRBS representation is used for all curved surfaces,Quadric surfaces have dual representations consisting of their geometric data such as radius,center point,and center axis.Boundary representation of free form surfaces is easily built by sweeping and skinning method with NURBS geometry.Set operations on curved solids with boundary representation are performed by an evaluation process consisting of four steps.A file exchange facility is provided for the conversion between product data described by STEP and product information generated by GEMS4.0.
Childhood asthma prediction models: a systematic review.
Smit, Henriette A; Pinart, Mariona; Antó, Josep M; Keil, Thomas; Bousquet, Jean; Carlsen, Kai H; Moons, Karel G M; Hooft, Lotty; Carlsen, Karin C Lødrup
2015-12-01
Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.
A biofilm model for prediction of pollutant transformation in sewers.
Jiang, Feng; Leung, Derek Hoi-Wai; Li, Shiyu; Chen, Guang-Hao; Okabe, Satoshi; van Loosdrecht, Mark C M
2009-07-01
This study developed a new sewer biofilm model to simulate the pollutant transformation and biofilm variation in sewers under aerobic, anoxic and anaerobic conditions. The biofilm model can describe the activities of heterotrophic, autotrophic, and sulfate-reducing bacteria (SRB) in the biofilm as well as the variations in biofilm thickness, the spatial profiles of SRB population and biofilm density. The model can describe dynamic biofilm growth, multiple biomass evolution and competitions among organic oxidation, denitrification, nitrification, sulfate reduction and sulfide oxidation in a heterogeneous biofilm growing in a sewer. The model has been extensively verified by three different approaches, including direct verification by measurement of the spatial concentration profiles of dissolved oxygen, nitrate, ammonia, and hydrogen sulfide in sewer biofilm. The spatial distribution profile of SRB in sewer biofilm was determined from the fluorescent in situ hybridization (FISH) images taken by a confocal laser scanning microscope (CLSM) and were predicted well by the model.
Tulchinsky, Alexander Y; Johnson, Norman A; Watt, Ward B; Porter, Adam H
2014-11-01
hybrid incompatibility than F1's to the extent that the bioenergetic properties favored dominant regulatory interactions. The present model is a mechanistically explicit case of the Bateson-Dobzhansky-Muller model, connecting environmental selective pressure to hybrid incompatibility through the molecular mechanism of regulatory divergence. The bioenergetic parameters that determine expression represent measurable properties of transcriptional regulation, providing a predictive framework for empirical studies of how phenotypic evolution results in epistatic incompatibility at the molecular level in hybrids.
Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong
2017-01-01
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.
Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong
2017-01-01
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889
A hybrid model for mapping simplified seismic response via a GIS-metamodel approach
Directory of Open Access Journals (Sweden)
G. Grelle
2014-02-01
Full Text Available An hybrid model, consisting of GIS and metamodel (model of model procedures, was introduced with the aim of estimating the 1-D spatial seismic site response. Inputs and outputs are provided and processed by means of an appropriate GIS model, named GIS Cubic Model (GCM. This discretizes the seismic underground half-space in a pseudo-tridimensional way. GCM consists of a layered parametric structure aimed at resolving a predicted metamodel by means of pixel to pixel vertical computing. The metamodel leading to the determination of a bilinear-polynomial function is able to design the classic shape of the spectral acceleration response in relation to the main physical parameters that characterize the spectrum itself. The main physical parameters consist of (i the average shear wave velocity of the shallow layer, (ii the fundamental period and, (iii the period where the spatial spectral response is required. The metamodel is calibrated on theoretical spectral accelerations regarding the local likely Vs-profiles, which are obtained using the Monte Carlo simulation technique on the basis of the GCM information. Therefore, via the GCM structure and the metamodel, the hybrid model provides maps of normalized acceleration response spectra. The hybrid model was applied and tested on the built-up area of the San Giorgio del Sannio village, located in a high-risk seismic zone of Southern Italy.
Li, Ben Q; Liu, Changhong
2011-01-15
A hybridization model for the localized surface plasmon resonance of a nanoshell is developed within the framework of long-wave approximation. Compared with the existing hybridization model derived from the hydrodynamic simulation of free electron gas, this approach is much simpler and gives identical results for a concentric nanoshell. Also, with this approach, the limitations associated with the original hybridization model are succinctly stated. Extension of this approach to hybridization modeling of more complicated structures such as multiplayered nanoshells is straightforward.
Stochastic Residual-Error Analysis For Estimating Hydrologic Model Predictive Uncertainty
A hybrid time series-nonparametric sampling approach, referred to herein as semiparametric, is presented for the estimation of model predictive uncertainty. The methodology is a two-step procedure whereby a distributed hydrologic model is first calibrated, then followed by brute ...
Modeling and control of a hybrid-electric vehicle for drivability and fuel economy improvements
Koprubasi, Kerem
The gradual decline of oil reserves and the increasing demand for energy over the past decades has resulted in automotive manufacturers seeking alternative solutions to reduce the dependency on fossil-based fuels for transportation. A viable technology that enables significant improvements in the overall tank-to-wheel vehicle energy conversion efficiencies is the hybridization of electrical and conventional drive systems. Sophisticated hybrid powertrain configurations require careful coordination of the actuators and the onboard energy sources for optimum use of the energy saving benefits. The term optimality is often associated with fuel economy, although other measures such as drivability and exhaust emissions are also equally important. This dissertation focuses on the design of hybrid-electric vehicle (HEV) control strategies that aim to minimize fuel consumption while maintaining good vehicle drivability. In order to facilitate the design of controllers based on mathematical models of the HEV system, a dynamic model that is capable of predicting longitudinal vehicle responses in the low-to-mid frequency region (up to 10 Hz) is developed for a parallel HEV configuration. The model is validated using experimental data from various driving modes including electric only, engine only and hybrid. The high fidelity of the model makes it possible to accurately identify critical drivability issues such as time lags, shunt, shuffle, torque holes and hesitation. Using the information derived from the vehicle model, an energy management strategy is developed and implemented on a test vehicle. The resulting control strategy has a hybrid structure in the sense that the main mode of operation (the hybrid mode) is occasionally interrupted by event-based rules to enable the use of the engine start-stop function. The changes in the driveline dynamics during this transition further contribute to the hybrid nature of the system. To address the unique characteristics of the HEV
Hybrid grey model to forecast monitoring series with seasonality
Institute of Scientific and Technical Information of China (English)
WANG Qi-jie; LIAO Xin-hao; ZHOU Yong-hong; ZOU Zheng-rong; ZHU Jian-jun; PENG Yue
2005-01-01
The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
Energy Technology Data Exchange (ETDEWEB)
Na, Cang Ju; Kim, Jeong Hun; Choi, Se Hun; Han, Yeon Hee; Jeong, Hwan Jeong; Sohn, Myung Hee; Youn, Hyun Jo; Lim, Seok Tae [Chonbuk National University Medical School and Hospital, Jeonju (Korea, Republic of)
2015-03-15
Hybrid imaging techniques can provide functional and anatomical information about sentinel lymph nodes in breast cancer. Our aim in this study was to evaluate which imaging parameters on hybrid sentinel lymphoscintigraphy predicted metastatic involvement of sentinel lymph nodes (SLNs) in patients with breast cancer. Among 56 patients who underwent conventional sentinel lymphoscintigraphy, 45 patients (age, 53.1 ± 9.5 years) underwent hybrid sentinel lymphoscintigraphy using a single-photon emission computed tomography (SPECT)/computed tomography (CT) gamma camera. On hybrid SPECT/CT images, we compared the shape and size (long-to-short axis [L/S] ratio) of the SLN, and SLN/periareolar injection site (S/P) count ratio between metastatic and non-metastatic SLNs. Metastatic involvement of sentinel lymph nodes was confirmed by pathological biopsy. Pathological biopsy revealed that 21 patients (46.7 %) had metastatic SLNs, while 24 (53.3 %) had non-metastatic SLNs. In the 21 patients with metastatic SLNs, the SLN was mostly round (57.1 %) or had an eccentric cortical rim (38.1 %). Of 24 patients with non-metastatic SLNs, 13 patients (54.1 %) had an SLN with a C-shape rim or eccentric cortex. L/S ratio was 2.04 for metastatic SLNs and 2.38 for non-metastatic SLNs. Seven (33 %) patients had T1 primary tumors and 14 (66 %) had T2 primary tumors in the metastatic SLN group. In contrast, 18 (75 %) patients had T1 primary tumors and six (25 %) had T2 tumors in the non-metastatic SLN group. S/P count ratio was significantly lower in the metastatic SLN group than the non-metastatic SLN group for those patients with a T1 primary tumor (p = 0.007). Hybrid SPECT/CT offers the physiologic data of SPECT together with the anatomic data of CT in a single image. This hybrid imaging improved the anatomic localization of SLNs in breast cancer patients and predicted the metastatic involvement of SLNs in the subgroup of breast cancer patients with T1 primary tumors.
Whispered speaker identification based on feature and model hybrid compensation
Institute of Scientific and Technical Information of China (English)
GU Xiaojiang; ZHAO Heming; Lu Gang
2012-01-01
In order to increase short time whispered speaker recognition rate in variable chan- nel conditions, the hybrid compensation in model and feature domains was proposed. This method is based on joint factor analysis in training model stage. It extracts speaker factor and eliminates channel factor by estimating training speech speaker and channel spaces. Then in the test stage, the test speech channel factor is projected into feature space to engage in feature compensation, so it can remove channel information both in model and feature domains in order to improve recognition rate. The experiment result shows that the hybrid compensation can obtain the similar recognition rate in the three different training channel conditions and this method is more effective than joint factor analysis in the test of short whispered speech.
Directory of Open Access Journals (Sweden)
Lihua Yang
2015-04-01
Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.
Credit Scoring Model Hybridizing Artificial Intelligence with Logistic Regression
Directory of Open Access Journals (Sweden)
Han Lu
2013-01-01
Full Text Available Today the most commonly used techniques for credit scoring are artificial intelligence and statistics. In this paper, we started a new way to use these two kinds of models. Through logistic regression filters the variables with a high degree of correlation, artificial intelligence models reduce complexity and accelerate convergence, while these models hybridizing logistic regression have better explanations in statistically significance, thus improve the effect of artificial intelligence models. With experiments on German data set, we find an interesting phenomenon defined as ‘Dimensional interference’ with support vector machine and from cross validation it can be seen that the new method gives a lot of help with credit scoring.
A Hybrid Tool for User Interface Modeling and Prototyping
Trætteberg, Hallvard
Although many methods have been proposed, model-based development methods have only to some extent been adopted for UI design. In particular, they are not easy to combine with user-centered design methods. In this paper, we present a hybrid UI modeling and GUI prototyping tool, which is designed to fit better with IS development and UI design traditions. The tool includes a diagram editor for domain and UI models and an execution engine that integrates UI behavior, live UI components and sample data. Thus, both model-based user interface design and prototyping-based iterative design are supported
IMPLICIT REPRESENTATION FOR THE MODELLING OF HYBRID DYNAMIC SYSTEMS
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Hybrid systems can be represented by a discrete event model interacting with a continuous model, and the interface by ideal switching components which modify the topology of a system at the switching time. This paper deals with the modelling of such systems using the bond graph approach. The paper shows the interest of the implicit representation: to derive a unique state equation with jumping parameters, to derive the implicit state equation with index of nilpotency one corresponding to each configuration, to analyze the properties of those models and to compute the discontinuity.
Directory of Open Access Journals (Sweden)
Kang-Wook Lee
2017-05-01
Full Text Available An important issue for international businesses and academia is selecting countries in which to expand in order to achieve entrepreneurial sustainability. This study develops a country selection model for sustainable construction businesses using both objective and subjective information. The objective information consists of 14 variables related to country risk and project performance in 32 countries over 25 years. This hybrid model applies subjective weighting from industrial experts to objective information using a fuzzy LinPreRa-based Analytic Hierarchy Process. The hybrid model yields a more accurate country selection compared to a purely objective information-based model in experienced countries. Interestingly, the hybrid model provides some different predictions with only subjective opinions in unexperienced countries, which implies that expert opinion is not always reliable. In addition, feedback from five experts in top international companies is used to validate the model’s completeness, effectiveness, generality, and applicability. The model is expected to aid decision makers in selecting better candidate countries that lead to sustainable business success.
HYBRID TRUST MODEL FOR INTERNET ROUTING
Directory of Open Access Journals (Sweden)
Pekka Rantala
2011-05-01
Full Text Available The current Internet is based on a fundamental assumption of reliability and good intent among actors inthe network. Unfortunately, unreliable and malicious behaviour is becoming a major obstacle forInternet communication. In order to improve the trustworthiness and reliability of the networkinfrastructure, we propose a novel trust model to be incorporated into BGP routing. In our approach,trust model is defined by combining voting and recommendation to direct trust estimation for neighbourrouters located in different autonomous systems. We illustrate the impact of our approach with cases thatdemonstrate the indication of distrusted paths beyond the nearest neighbours and the detection of adistrusted neighbour advertising a trusted path. We simulated the impact of weighting voted and directtrust in a rectangular grid of 15*15 nodes (autonomous systems with a randomly connected topology.
Hybrid Trust Model for Internet Routing
Rantala, Pekka; Isoaho, Jouni
2011-01-01
The current Internet is based on a fundamental assumption of reliability and good intent among actors in the network. Unfortunately, unreliable and malicious behaviour is becoming a major obstacle for Internet communication. In order to improve the trustworthiness and reliability of the network infrastructure, we propose a novel trust model to be incorporated into BGP routing. In our approach, trust model is defined by combining voting and recommendation to direct trust estimation for neighbour routers located in different autonomous systems. We illustrate the impact of our approach with cases that demonstrate the indication of distrusted paths beyond the nearest neighbours and the detection of a distrusted neighbour advertising a trusted path. We simulated the impact of weighting voted and direct trust in a rectangular grid of 15*15 nodes (autonomous systems) with a randomly connected topology.
Model predictive control classical, robust and stochastic
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...
Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling
Srivastav, Roshan; Srinivasan, K.; Sudheer, K. P.
2016-11-01
able to predict the characteristics of the multi-site multi-season streamflows under uncertain future. Also, the AMHMABB model is found to perform better than the linear multi-site disaggregation model (MDM) in preserving the statistical as well as the multi-site critical deficit run characteristics of the observed flows. However, a major drawback of the hybrid models persists in case of the AMHMABB model as well, of not being able to synthetically generate enough number of flows beyond the observed extreme flows, and not being able to generate values that are quite different from the observed flows.
A New Hybrid Model Rotor Flux Observer and Its Application
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A new hybrid model rotor flux observer, based on a new voltage model, is presented. In the first place, the voltage model of an induction machine was constructed by using the modeling method discussed in this paper and then the current model using a flux feedback was adopted in this flux observer. Secondly, the two models were combined via a filter and then the rotor flux observer was established. In the M-T synchronous coordinate, the observer was analyzed theoretically and several important functions were derived. A comparison between the observer and the traditional models was made using Matlab software. The simulation results show that the observer model had a better performance than the traditional model.
Modeling level change in Lake Urmia using hybrid artificial intelligence approaches
Esbati, M.; Ahmadieh Khanesar, M.; Shahzadi, Ali
2017-06-01
The investigation of water level fluctuations in lakes for protecting them regarding the importance of these water complexes in national and regional scales has found a special place among countries in recent years. The importance of the prediction of water level balance in Lake Urmia is necessary due to several-meter fluctuations in the last decade which help the prevention from possible future losses. For this purpose, in this paper, the performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the lake water level balance has been studied. In addition, for the training of the adaptive neuro-fuzzy inference system, particle swarm optimization (PSO) and hybrid backpropagation-recursive least square method algorithm have been used. Moreover, a hybrid method based on particle swarm optimization and recursive least square (PSO-RLS) training algorithm for the training of ANFIS structure is introduced. In order to have a more fare comparison, hybrid particle swarm optimization and gradient descent are also applied. The models have been trained, tested, and validated based on lake level data between 1991 and 2014. For performance evaluation, a comparison is made between these methods. Numerical results obtained show that the proposed methods with a reasonable error have a good performance in water level balance prediction. It is also clear that with continuing the current trend, Lake Urmia will experience more drop in the water level balance in the upcoming years.
A Secured Hybrid Architecture Model for Internet Banking (e - Banking
Directory of Open Access Journals (Sweden)
Ganesan R
2009-05-01
Full Text Available Internet banking has made it easy to carry out the personal or business financial trans action without going to bank and at any suitable time. This facility enables to transfer money to other accounts and checking current balance alongside the status of any financial transaction made in the account. However, in order to maintain privacy and t o avoid any misuse of transactions, it is necessary to follow a secured architecture model which ensures the privacy and integrity of the transactions and provides confidence on internet banking is stable. In this research paper, a secured hybrid architect ure model for the internet banking using Hyperelliptic curve cryptosystem and MD5 is described. This hybrid model is implemented with the hyperelliptic curve cryptosystem and it performs the encryption and decryption processes in an efficient way merely wi th an 80 - bit key size. The various screen shots given in this contribution shows that the hybrid model which encompasses HECC and MD5 can be considered in the internet banking environment to enrich the privacy and integrity of the sensitive data transmitte d between the clients and the application server
Wu, Xingfu
2011-08-01
In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore clusters: IBM POWER4, POWER5+ and Blue Gene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore clusters because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyro kinetic Toroidal Code in magnetic fusion to validate our performance model of the hybrid application on these multicore clusters. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore clusters. © 2011 IEEE.
Directory of Open Access Journals (Sweden)
Xiaomin Xu
2015-01-01
Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.
A Hybrid Ant Colony Optimization for the Prediction of Protein Secondary Structure
Institute of Scientific and Technical Information of China (English)
Chao CHEN; Yuan Xin TIAN; Xiao Yong ZOU; Pei Xiang CAI; Jin Yuan MO
2005-01-01
Based on the concept of ant colony optimization and the idea of population in genetic algorithm, a novel global optimization algorithm, called the hybrid ant colony optimization (HACO), is proposed in this paper to tackle continuous-space optimization problems. It was compared with other well-known stochastic methods in the optimization of the benchmark functions and was also used to solve the problem of selecting appropriate dilation efficiently by optimizing the wavelet power spectrum of the hydrophobic sequence of protein, which is thc key step on using continuous wavelet transform (CWT) to predict a-helices and connecting peptides.
Directory of Open Access Journals (Sweden)
Montri Inthachot
2016-01-01
Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.
Modelling hybrid stars in quark-hadron approaches
Energy Technology Data Exchange (ETDEWEB)
Schramm, S. [FIAS, Frankfurt am Main (Germany); Dexheimer, V. [Kent State University, Department of Physics, Kent, OH (United States); Negreiros, R. [Federal Fluminense University, Gragoata, Niteroi (Brazil)
2016-01-15
The density in the core of neutron stars can reach values of about 5 to 10 times nuclear matter saturation density. It is, therefore, a natural assumption that hadrons may have dissolved into quarks under such conditions, forming a hybrid star. This star will have an outer region of hadronic matter and a core of quark matter or even a mixed state of hadrons and quarks. In order to investigate such phases, we discuss different model approaches that can be used in the study of compact stars as well as being applicable to a wider range of temperatures and densities. One major model ingredient, the role of quark interactions in the stability of massive hybrid stars is discussed. In this context, possible conflicts with lattice QCD simulations are investigated. (orig.)
Hybrid Modeling of Elastic Wave Scattering in a Welded Cylinder
Mahmoud, A.; Shah, A. H.; Popplewell, N.
2003-03-01
In the present study, a 3D hybrid method, which couples the finite element region with guided elastic wave modes, is formulated to investigate the scattering by a non-axisymmetric crack in a welded steel pipe. The algorithm is implemented on a parallel computing platform. Implementation is facilitated by the dynamic memory allocation capabilities of Fortran 90™ and the parallel processing directives of OpenMp™. The algorithm is validated against available numerical results. The agreement with a previous 2D hybrid model is excellent. Novel results are presented for the scattering of the first longitudinal mode from different non-axisymmetric cracks. The trend of the new results is consistent with the previous findings for the axisymmetric case. The developed model has potential application in ultrasonic nondestructive evaluation of welded steel pipes.
A hybrid neural network model for consciousness
Institute of Scientific and Technical Information of China (English)
蔺杰; 金小刚; 杨建刚
2004-01-01
A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers,physical mnemonic layer and abstract thinking layer,which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness:(1)the reception process whereby cerebral subsystems group distributed signals into coherent object patterns;(2)the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and(3)the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework,various sorts of human actions can be explained,leading to a general approach for analyzing brain functions.
A hybrid neural network model for consciousness
Institute of Scientific and Technical Information of China (English)
蔺杰; 金小刚; 杨建刚
2004-01-01
A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (l) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.
Recent progress in battery models for hybrid wind power systems
Energy Technology Data Exchange (ETDEWEB)
Manwell, J.F.; McGowan, J.G.; Baring-Gould, I.; Stein, W. [Univ. of Massachusetts, Amherst, MA (United States)
1995-12-31
This paper summarizes the latest University of Massachusetts work on the analytical modeling and experimental testing of battery component models for hybrid power systems. An extension of the Kinetic Battery Model (KiBaM), developed at the University of Massachusetts is presented. The original model was based on a combination of phenomenological and physical considerations. As described in this paper, the modified KiBaM can now model the sharp increase in voltage near the end of charging, and the sharp drop in voltage when the battery is nearly empty. This model may readily be coupled with a DC load or charging source (such as a DC wind turbine or photovoltaic panels) to determine the corresponding DC bus voltage. For example, it is now an integral part of the DC bus section of the University of Massachusetts HYBRID simulation models. The paper describes the development of the extensions to the KiBaM model and the method of determining the constants from test data. On the experimental/applications side, it includes an illustration of how the constants are obtained from representative data (using a specially developed testing apparatus), and an example of how the model can be used.
Energy based prediction models for building acoustics
DEFF Research Database (Denmark)
Brunskog, Jonas
2012-01-01
In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...
A light neutralino in hybrid models of supersymmetry breaking
Dudas, Emilian; Parmentier, Jeanne; 10.1016
2008-01-01
We show that in gauge mediation models where heavy messenger masses are provided by the adjoint Higgs field of an underlying SU(5) theory, a generalized gauge mediation spectrum arises with the characteristic feature of having a neutralino much lighter than in the standard gauge or gravity mediation schemes. This naturally fits in a hybrid scenario where gravity mediation, while subdominant with respect to gauge mediation, provides mu and B mu parameters in the TeV range.
A Novel of Hybrid Maintenance Management Models for Industrial Applications
Tahir, Zulkifli
2010-01-01
It is observed through empirical studies that the effectiveness of industrial process have been increased by a well organized of machines maintenance structure. In current research, a novel of maintenance concept has been designed by hybrid several maintenance management models with Decision Making Grid (DMG), Analytic Hierarchy Process (AHP) and Fuzzy Logic. The concept is designed for maintenance personnel to evaluate and benchmark the maintenance operations and to reveal important maintena...
Controllability in hybrid kinetic equations modeling nonequilibrium multicellular systems.
Bianca, Carlo
2013-01-01
This paper is concerned with the derivation of hybrid kinetic partial integrodifferential equations that can be proposed for the mathematical modeling of multicellular systems subjected to external force fields and characterized by nonconservative interactions. In order to prevent an uncontrolled time evolution of the moments of the solution, a control operator is introduced which is based on the Gaussian thermostat. Specifically, the analysis shows that the moments are solution of a Riccati-type differential equation.
Incorporating RTI in a Hybrid Model of Reading Disability
2014-01-01
The present study seeks to evaluate a hybrid model of identification that incorporates response-to-intervention (RTI) as a one of the key symptoms of reading disability. The one-year stability of alternative operational definitions of reading disability was examined in a large scale sample of students who were followed longitudinally from first to second grade. The results confirmed previous findings of limited stability for single-criterion based operational definitions of reading disability...
Treatment of early and late reflections in a hybrid computer model for room acoustics
DEFF Research Database (Denmark)
Naylor, Graham
1992-01-01
The ODEON computer model for acoustics in large rooms is intended for use both in design (by predicting room acoustical indices quickly and easily) and in research (by forming the basis of an auralization system and allowing study of various room acoustical phenomena). These conflicting demands...... preclude the use of both ``pure'' image source and ``pure'' particle tracing methods. A hybrid model has been developed, in which rays discover potential image sources up to a specified order. Thereafter, the same ray tracing process is used in a different way to rapidly generate a dense reverberant decay...
Massive Predictive Modeling using Oracle R Enterprise
CERN. Geneva
2014-01-01
R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...
Kaisheng Zhang; Mei Wang; Bangyang Wei; Daniel(Jian) Sun
2016-01-01
Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and...
Kaisheng Zhang; Mei Wang; Bangyang Wei; Daniel (Jian) Sun
2016-01-01
Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and...
Statics of levitated vehicle model with hybrid magnets
Institute of Scientific and Technical Information of China (English)
Desheng LI; Zhiyuan LU; Tianwu DONG
2009-01-01
By studying the special characteristics of permanent and electronic magnets, a levitated vehicle model with hybrid magnets is established. The mathematical model of the vehicle is built based on its dynamics equation by studying its machine structure and working principle. Based on the model, the basic characteristics and the effect between the excluding forces from permanent magnets in three different spatial directions are analyzed, statics characteristics of the interference forces in three different spatial directions are studied, and self-adjusting equilibrium characteristics and stabilization are analyzed. Based on the structure above, the vehicle can levitate steadily by control system adjustment.
Hybrid Surface Mesh Adaptation for Climate Modeling
Institute of Scientific and Technical Information of China (English)
Ahmed Khamayseh; Valmor de Almeida; Glen Hansen
2008-01-01
Solution-driven mesh adaptation is becoming quite popular for spatial error control in the numerical simulation of complex computational physics applications, such as climate modeling. Typically, spatial adaptation is achieved by element subdivision (h adaptation) with a primary goal of resolving the local length scales of interest. A second, lesspopular method of spatial adaptivity is called "mesh motion" (r adaptation); the smooth repositioning of mesh node points aimed at resizing existing elements to capture the local length scales. This paper proposes an adaptation method based on a combination of both element subdivision and node point repositioning (rh adaptation). By combining these two methods using the notion of a mobility function, the proposed approach seeks to increase the flexibility and extensibility of mesh motion algorithms while providing a somewhat smoother transition between refined regions than is pro-duced by element subdivision alone. Further, in an attempt to support the requirements of a very general class of climate simulation applications, the proposed method is de-signed to accommodate unstructured, polygonal mesh topologies in addition to the most popular mesh types.
Kim, Ok-Yeon; Kim, Hye-Mi; Lee, Myong-In; Min, Young-Mi
2017-01-01
This study aims at predicting the seasonal number of typhoons (TY) over the western North Pacific with an Asia-Pacific Climate Center (APCC) multi-model ensemble (MME)-based dynamical-statistical hybrid model. The hybrid model uses the statistical relationship between the number of TY during the typhoon season (July-October) and the large-scale key predictors forecasted by APCC MME for the same season. The cross validation result from the MME hybrid model demonstrates high prediction skill, with a correlation of 0.67 between the hindcasts and observation for 1982-2008. The cross validation from the hybrid model with individual models participating in MME indicates that there is no single model which consistently outperforms the other models in predicting typhoon number. Although the forecast skill of MME is not always the highest compared to that of each individual model, the skill of MME presents rather higher averaged correlations and small variance of correlations. Given large set of ensemble members from multi-models, a relative operating characteristic score reveals an 82 % (above-) and 78 % (below-normal) improvement for the probabilistic prediction of the number of TY. It implies that there is 82 % (78 %) probability that the forecasts can successfully discriminate between above normal (below-normal) from other years. The forecast skill of the hybrid model for the past 7 years (2002-2008) is more skillful than the forecast from the Tropical Storm Risk consortium. Using large set of ensemble members from multi-models, the APCC MME could provide useful deterministic and probabilistic seasonal typhoon forecasts to the end-users in particular, the residents of tropical cyclone-prone areas in the Asia-Pacific region.
Magnetic equivalent circuit model for unipolar hybrid excitation synchronous machine
Directory of Open Access Journals (Sweden)
Kupiec Emil
2015-03-01
Full Text Available Lately, there has been increased interest in hybrid excitation electrical machines. Hybrid excitation is a construction that combines permanent magnet excitation with wound field excitation. Within the general classification, these machines can be classified as modified synchronous machines or inductor machines. These machines may be applied as motors and generators. The complexity of electromagnetic phenomena which occur as a result of coupling of magnetic fluxes of separate excitation systems with perpendicular magnetic axis is a motivation to formulate various mathematical models of these machines. The presented paper discusses the construction of a unipolar hybrid excitation synchronous machine. The magnetic equivalent circuit model including nonlinear magnetization curves is presented. Based on this model, it is possible to determine the multi-parameter relationships between the induced voltage and magnetomotive force in the excitation winding. Particular attention has been paid to the analysis of the impact of additional stator and rotor yokes on above relationship. Induced voltage determines the remaining operating parameters of the machine, both in the motor and generator mode of operation. The analysis of chosen correlations results in an identification of the effective control range of electromotive force of the machine.
A hybrid double-observer sightability model for aerial surveys
Griffin, Paul C.; Lubow, Bruce C.; Jenkins, Kurt J.; Vales, David J.; Moeller, Barbara J.; Reid, Mason; Happe, Patricia J.; Mccorquodale, Scott M.; Tirhi, Michelle J.; Schaberi, Jim P.; Beirne, Katherine
2013-01-01
Raw counts from aerial surveys make no correction for undetected animals and provide no estimate of precision with which to judge the utility of the counts. Sightability modeling and double-observer (DO) modeling are 2 commonly used approaches to account for detection bias and to estimate precision in aerial surveys. We developed a hybrid DO sightability model (model MH) that uses the strength of each approach to overcome the weakness in the other, for aerial surveys of elk (Cervus elaphus). The hybrid approach uses detection patterns of 2 independent observer pairs in a helicopter and telemetry-based detections of collared elk groups. Candidate MH models reflected hypotheses about effects of recorded covariates and unmodeled heterogeneity on the separate front-seat observer pair and back-seat observer pair detection probabilities. Group size and concealing vegetation cover strongly influenced detection probabilities. The pilot's previous experience participating in aerial surveys influenced detection by the front pair of observers if the elk group was on the pilot's side of the helicopter flight path. In 9 surveys in Mount Rainier National Park, the raw number of elk counted was approximately 80–93% of the abundance estimated by model MH. Uncorrected ratios of bulls per 100 cows generally were low compared to estimates adjusted for detection bias, but ratios of calves per 100 cows were comparable whether based on raw survey counts or adjusted estimates. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to DO modeling.
Predicting future trends in stock market by decision tree rough-set based hybrid system with HHMM
Directory of Open Access Journals (Sweden)
Shweta Tiwari
2012-06-01
Full Text Available Around the world, trading in the stock market has gained huge attractiveness as a means through which, one can obtain vast profits. Attempting to profitably and precisely predict the financial market has long engrossed the interests and attention of bankers, economists and scientists alike. Stock market prediction is the act of trying, to determine the future value of a company’s stock or other financial instrument traded on a financial exchange. Accurate stock market predictions are important for many reasons. Chief among all is the need for investors, to hedge against potential market risks and the opportunities for arbitrators and speculators, to make profits by trading indexes. Stock Market is a place, where shares are issued and traded. These shares are either traded through Stock exchanges or Overthe-Counter in physical or electronic form. Data mining, as a process of discovering useful patterns, correlations has its own role in financial modeling. Data mining is a discipline in computational intelligence that deals with knowledge discovery, data analysis and full and semi-autonomous decision making. Prediction of stock market by data mining techniques has been receiving a lot of attention recently. This paper presents a hybrid system based on decision tree- rough set, for predicting the trends in the Bombay Stock Exchange (BSESENSEX with the combination of Hierarchical Hidden Markov Model. In this paper we present future trends on the bases of price earnings and dividend. The data on accounting earnings when averaged over many years help to predict the present value of future dividends.
Liver Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Colorectal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Cervical Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Prostate Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Pancreatic Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Colorectal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Bladder Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Esophageal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Lung Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Breast Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Ovarian Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Testicular Cancer Risk Prediction Models
Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Fuzzy regression modeling for tool performance prediction and degradation detection.
Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L
2010-10-01
In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.
A predictive standard model for heavy electron systems
Energy Technology Data Exchange (ETDEWEB)
Yang, Yifeng [Los Alamos National Laboratory; Curro, N J [UC DAVIS; Fisk, Z [UC DAVIS; Pines, D [UC DAVIS
2010-01-01
We propose a predictive standard model for heavy electron systems based on a detailed phenomenological two-fluid description of existing experimental data. It leads to a new phase diagram that replaces the Doniach picture, describes the emergent anomalous scaling behavior of the heavy electron (Kondo) liquid measured below the lattice coherence temperature, T*, seen by many different experimental probes, that marks the onset of collective hybridization, and enables one to obtain important information on quantum criticality and the superconducting/antiferromagnetic states at low temperatures. Because T* is {approx} J{sup 2} {rho}/2, the nearest neighbor RKKY interaction, a knowledge of the single-ion Kondo coupling, J, to the background conduction electron density of states, {rho}, makes it possible to predict Kondo liquid behavior, and to estimate its maximum superconducting transition temperature in both existing and newly discovered heavy electron families.
Modeling and predicting historical volatility in exchange rate markets
Lahmiri, Salim
2017-04-01
Volatility modeling and forecasting of currency exchange rate is an important task in several business risk management tasks; including treasury risk management, derivatives pricing, and portfolio risk evaluation. The purpose of this study is to present a simple and effective approach for predicting historical volatility of currency exchange rate. The approach is based on a limited set of technical indicators as inputs to the artificial neural networks (ANN). To show the effectiveness of the proposed approach, it was applied to forecast US/Canada and US/Euro exchange rates volatilities. The forecasting results show that our simple approach outperformed the conventional GARCH and EGARCH with different distribution assumptions, and also the hybrid GARCH and EGARCH with ANN in terms of mean absolute error, mean of squared errors, and Theil's inequality coefficient. Because of the simplicity and effectiveness of the approach, it is promising for US currency volatility prediction tasks.
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
Hybrid and adaptive meta-model-based global optimization
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
A Course in... Model Predictive Control.
Arkun, Yaman; And Others
1988-01-01
Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)
Ontology-based tools to expedite predictive model construction.
Haug, Peter; Holmen, John; Wu, Xinzi; Mynam, Kumar; Ebert, Matthew; Ferraro, Jeffrey
2014-01-01
Large amounts of medical data are collected electronically during the course of caring for patients using modern medical information systems. This data presents an opportunity to develop clinically useful tools through data mining and observational research studies. However, the work necessary to make sense of this data and to integrate it into a research initiative can require substantial effort from medical experts as well as from experts in medical terminology, data extraction, and data analysis. This slows the process of medical research. To reduce the effort required for the construction of computable, diagnostic predictive models, we have developed a system that hybridizes a medical ontology with a large clinical data warehouse. Here we describe components of this system designed to automate the development of preliminary diagnostic models and to provide visual clues that can assist the researcher in planning for further analysis of the data behind these models.
Uccellini, L. W.; Johnson, D. R.; Schlesinger, R. E.
1979-01-01
A solution is presented for matching boundary conditions across the interface of an isentropic and sigma coordinate hybrid model. A hybrid model based on the flux form of the primitive equations is developed which allows direct vertical exchange between the model domains, satisfies conservation principles with respect to transport processes, and maintains a smooth transition across the interface without need for artificial adjustment or parameterization schemes. The initial hybrid model simulations of a jet streak propagating in a zonal channel are used to test the feasibility of the hybrid model approach. High efficiency of the hybrid model is demonstrated.
An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate
Directory of Open Access Journals (Sweden)
Milad Jajarmizadeh
2014-01-01
Full Text Available Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers—input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the network’s optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict stream flow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff relations.
Two-solar-mass hybrid stars: a two model description with the Nambu-Jona-Lasinio quark model
Pereira, Renan Câmara; Providência, Constança
2016-01-01
Hybrid stars with a quark phase described by the Nambu$-$Jona-Lasinio model are studied. The hadron-quark model used to determine the stellar matter equation of state favors the appearance of quark matter: the coincidence of the deconfinement and chiral transitions and a low vacuum constituent quark mass. These two properties are essential to build equations of state that predict pure quark matter in the center of neutron stars. The effect of vector-isoscalar and vector-isovector terms is discussed, and it is shown that the vector-isoscalar terms are necessary to describe 2$M_\\odot$ hybrid stars, and the vector-isovector terms result in larger quark cores and a smaller deconfinement density.
Equivalency and unbiasedness of grey prediction models
Institute of Scientific and Technical Information of China (English)
Bo Zeng; Chuan Li; Guo Chen; Xianjun Long
2015-01-01
In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction mo-dels, the equivalence and unbiasedness of grey prediction mo-dels are analyzed and verified. The results show that al the grey prediction models that are strictly derived from x(0)(k) +az(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homoge-neous exponential sequence can be accomplished. However, the models derived from dx(1)/dt+ax(1) =b are only close to those derived from x(0)(k)+az(1)(k)=b provided that|a|has to satisfy|a| < 0.1; neither could the unbiased simulation for the homoge-neous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.
Predictability of extreme values in geophysical models
Directory of Open Access Journals (Sweden)
A. E. Sterk
2012-09-01
Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.
Directory of Open Access Journals (Sweden)
Hahnbeom Park
Full Text Available Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.
A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction
Institute of Scientific and Technical Information of China (English)
Duksan Ryu; Jong-In Jang; Jongmoon Baik; Member; ACM; IEEE
2015-01-01
Software defect prediction (SDP) is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires suffcient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction (CPDP) using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor (HISNN) method that performs a hybrid classification selectively learning local knowledge (via k-nearest neighbor) and global knowledge (via na¨ıve Bayes). Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD (probability of detection) or high PF (probability of false alarm) which is impractical to use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.
Risk terrain modeling predicts child maltreatment.
Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye
2016-12-01
As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.
A Novel Software Simulator Model Based on Active Hybrid Architecture
Directory of Open Access Journals (Sweden)
Amr AbdElHamid
2015-01-01
Full Text Available The simulated training is an important issue for any type of missions such as aerial, ground, sea, or even space missions. In this paper, a new flexible aerial simulator based on active hybrid architecture is introduced. The simulator infrastructure is applicable to any type of training missions and research activities. This software-based simulator is tested on aerial missions to prove its applicability within time critical systems. The proposed active hybrid architecture is introduced via using the VB.NET and MATLAB in the same simulation loop. It exploits the remarkable computational power of MATLAB as a backbone aircraft model, and such mathematical model provides realistic dynamics to the trainee. Meanwhile, the Human-Machine Interface (HMI, the mission planning, the hardware interfacing, data logging, and MATLAB interfacing are developed using VB.NET. The proposed simulator is flexible enough to perform navigation and obstacle avoidance training missions. The active hybrid architecture is used during the simulated training, and also through postmission activities (like the generation of signals playback reports for evaluation purposes. The results show the ability of the proposed architecture to fulfill the aerial simulator demands and to provide a flexible infrastructure for different simulated mission requirements. Finally, a comparison with some existing simulators is introduced.
Property predictions using microstructural modeling
Energy Technology Data Exchange (ETDEWEB)
Wang, K.G. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)]. E-mail: wangk2@rpi.edu; Guo, Z. [Sente Software Ltd., Surrey Technology Centre, 40 Occam Road, Guildford GU2 7YG (United Kingdom); Sha, W. [Metals Research Group, School of Civil Engineering, Architecture and Planning, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom); Glicksman, M.E. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States); Rajan, K. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)
2005-07-15
Precipitation hardening in an Fe-12Ni-6Mn maraging steel during overaging is quantified. First, applying our recent kinetic model of coarsening [Phys. Rev. E, 69 (2004) 061507], and incorporating the Ashby-Orowan relationship, we link quantifiable aspects of the microstructures of these steels to their mechanical properties, including especially the hardness. Specifically, hardness measurements allow calculation of the precipitate size as a function of time and temperature through the Ashby-Orowan relationship. Second, calculated precipitate sizes and thermodynamic data determined with Thermo-Calc[copyright] are used with our recent kinetic coarsening model to extract diffusion coefficients during overaging from hardness measurements. Finally, employing more accurate diffusion parameters, we determined the hardness of these alloys independently from theory, and found agreement with experimental hardness data. Diffusion coefficients determined during overaging of these steels are notably higher than those found during the aging - an observation suggesting that precipitate growth during aging and precipitate coarsening during overaging are not controlled by the same diffusion mechanism.
MODEL APLIKASI FIKIH MUAMALAH PADA FORMULASI HYBRID CONTRACT
Directory of Open Access Journals (Sweden)
Ali Murtadho
2013-10-01
Full Text Available Modern literatures of fiqh mu’āmalah talk alot about various contract formulation with capability of maximizing profit in shariah finance industry. This new contract modification is the synthesis among existing contracts which is formulated in such a way to be an integrated contract. This formulation is known as a hybrid contract or multicontract (al-'uqūd al-murakkabah. Some of them are, bay' bi thaman 'ājil, Ijārah muntahiyah bi ’l-tamlīk dan mushārakah mutanāqiṣah. This study intends to further describe models of hybrid contract, and explore the shari'ah principles in modern financial institutions. This study found a potential shift from the ideal values of the spirit of shari'ah into the spirit of competition based shari'ah formally.
A Hybrid Windkessel Model of Blood Flow in Arterial Tree Using Velocity Profile Method
Aboelkassem, Yasser; Virag, Zdravko
2016-11-01
For the study of pulsatile blood flow in the arterial system, we derived a coupled Windkessel-Womersley mathematical model. Initially, a 6-elements Windkessel model is proposed to describe the hemodynamics transport in terms of constant resistance, inductance and capacitance. This model can be seen as a two compartment model, in which the compartments are connected by a rigid pipe, modeled by one inductor and resistor. The first viscoelastic compartment models proximal part of the aorta, the second elastic compartment represents the rest of the arterial tree and aorta can be seen as the connection pipe. Although the proposed 6-elements lumped model was able to accurately reconstruct the aortic pressure, it can't be used to predict the axial velocity distribution in the aorta and the wall shear stress and consequently, proper time varying pressure drop. We then modified this lumped model by replacing the connection pipe circuit elements with a vessel having a radius R and a length L. The pulsatile flow motions in the vessel are resolved instantaneously along with the Windkessel like model enable not only accurate prediction of the aortic pressure but also wall shear stress and frictional pressure drop. The proposed hybrid model has been validated using several in-vivo aortic pressure and flow rate data acquired from different species such as, humans, dogs and pigs. The method accurately predicts the time variation of wall shear stress and frictional pressure drop. Institute for Computational Medicine, Dept. Biomedical Engineering.
Spatial Economics Model Predicting Transport Volume
Directory of Open Access Journals (Sweden)
Lu Bo
2016-10-01
Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.
System Modeling and Diagnostics for Liquefying-Fuel Hybrid Rockets
Poll, Scott; Iverson, David; Ou, Jeremy; Sanderfer, Dwight; Patterson-Hine, Ann
2003-01-01
A Hybrid Combustion Facility (HCF) was recently built at NASA Ames Research Center to study the combustion properties of a new fuel formulation that burns approximately three times faster than conventional hybrid fuels. Researchers at Ames working in the area of Integrated Vehicle Health Management recognized a good opportunity to apply IVHM techniques to a candidate technology for next generation launch systems. Five tools were selected to examine various IVHM techniques for the HCF. Three of the tools, TEAMS (Testability Engineering and Maintenance System), L2 (Livingstone2), and RODON, are model-based reasoning (or diagnostic) systems. Two other tools in this study, ICS (Interval Constraint Simulator) and IMS (Inductive Monitoring System) do not attempt to isolate the cause of the failure but may be used for fault detection. Models of varying scope and completeness were created, both qualitative and quantitative. In each of the models, the structure and behavior of the physical system are captured. In the qualitative models, the temporal aspects of the system behavior and the abstraction of sensor data are handled outside of the model and require the development of additional code. In the quantitative model, less extensive processing code is also necessary. Examples of fault diagnoses are given.
A Simple Hybrid Model for Short-Term Load Forecasting
Directory of Open Access Journals (Sweden)
Suseelatha Annamareddi
2013-01-01
Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.
Hybrid Modeling of Intra-DCT Coefficients for Real-Time Video Encoding
Directory of Open Access Journals (Sweden)
Li Jin
2008-01-01
Full Text Available Abstract The two-dimensional discrete cosine transform (2-D DCT and its subsequent quantization are widely used in standard video encoders. However, since most DCT coefficients become zeros after quantization, a number of redundant computations are performed. This paper proposes a hybrid statistical model used to predict the zeroquantized DCT (ZQDCT coefficients for intratransform and to achieve better real-time performance. First, each pixel block at the input of DCT is decomposed into a series of mean values and a residual block. Subsequently, a statistical model based on Gaussian distribution is used to predict the ZQDCT coefficients of the residual block. Then, a sufficient condition under which each quantized coefficient becomes zero is derived from the mean values. Finally, a hybrid model to speed up the DCT and quantization calculations is proposed. Experimental results show that the proposed model can reduce more redundant computations and achieve better real-time performance than the reference in the literature at the cost of negligible video quality degradation. Experiments also show that the proposed model significantly reduces multiplications for DCT and quantization. This is particularly suitable for processors in portable devices where multiplications consume more power than additions. Computational reduction implies longer battery lifetime and energy economy.
Modeling and Prediction Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp
2016-01-01
Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...
Precision Plate Plan View Pattern Predictive Model
Institute of Scientific and Technical Information of China (English)
ZHAO Yang; YANG Quan; HE An-rui; WANG Xiao-chen; ZHANG Yun
2011-01-01
According to the rolling features of plate mill, a 3D elastic-plastic FEM （finite element model） based on full restart method of ANSYS/LS-DYNA was established to study the inhomogeneous plastic deformation of multipass plate rolling. By analyzing the simulation results, the difference of head and tail ends predictive models was found and modified. According to the numerical simulation results of 120 different kinds of conditions, precision plate plan view pattern predictive model was established. Based on these models, the sizing MAS （mizushima automatic plan view pattern control system） method was designed and used on a 2 800 mm plate mill. Comparing the rolled plates with and without PVPP （plan view pattern predictive） model, the reduced width deviation indicates that the olate !olan view Dattern predictive model is preeise.