WorldWideScience

Sample records for models predict performance

  1. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-02-01

    Full Text Available Orientation: The article discussed the importance of rigour in credit risk assessment.Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  2. DKIST Polarization Modeling and Performance Predictions

    Science.gov (United States)

    Harrington, David

    2016-05-01

    Calibrating the Mueller matrices of large aperture telescopes and associated coude instrumentation requires astronomical sources and several modeling assumptions to predict the behavior of the system polarization with field of view, altitude, azimuth and wavelength. The Daniel K Inouye Solar Telescope (DKIST) polarimetric instrumentation requires very high accuracy calibration of a complex coude path with an off-axis f/2 primary mirror, time dependent optical configurations and substantial field of view. Polarization predictions across a diversity of optical configurations, tracking scenarios, slit geometries and vendor coating formulations are critical to both construction and contined operations efforts. Recent daytime sky based polarization calibrations of the 4m AEOS telescope and HiVIS spectropolarimeter on Haleakala have provided system Mueller matrices over full telescope articulation for a 15-reflection coude system. AEOS and HiVIS are a DKIST analog with a many-fold coude optical feed and similar mirror coatings creating 100% polarization cross-talk with altitude, azimuth and wavelength. Polarization modeling predictions using Zemax have successfully matched the altitude-azimuth-wavelength dependence on HiVIS with the few percent amplitude limitations of several instrument artifacts. Polarization predictions for coude beam paths depend greatly on modeling the angle-of-incidence dependences in powered optics and the mirror coating formulations. A 6 month HiVIS daytime sky calibration plan has been analyzed for accuracy under a wide range of sky conditions and data analysis algorithms. Predictions of polarimetric performance for the DKIST first-light instrumentation suite have been created under a range of configurations. These new modeling tools and polarization predictions have substantial impact for the design, fabrication and calibration process in the presence of manufacturing issues, science use-case requirements and ultimate system calibration

  3. Performance model to predict overall defect density

    Directory of Open Access Journals (Sweden)

    J Venkatesh

    2012-08-01

    Full Text Available Management by metrics is the expectation from the IT service providers to stay as a differentiator. Given a project, the associated parameters and dynamics, the behaviour and outcome need to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. In most of the cases, the actions taken are re-active. It is too late in the life cycle. Root cause analysis and corrective actions can be implemented only to the benefit of the next project. The focus has to shift left, towards the execution phase than waiting for lessons to be learnt post the implementation. How do we pro-actively predict defect metrics and have a preventive action plan in place. This paper illustrates the process performance model to predict overall defect density based on data from projects in an organization.

  4. Numerical modeling capabilities to predict repository performance

    Energy Technology Data Exchange (ETDEWEB)

    1979-09-01

    This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used.

  5. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  6. Performance Predictable ServiceBSP Model for Grid Computing

    Institute of Scientific and Technical Information of China (English)

    TONG Weiqin; MIAO Weikai

    2007-01-01

    This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model,the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP (integer program) approach, the Service Selection Agent selects the computation services with global optimized QoS (quality of service) consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.

  7. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “any fall” and “recurrent falls.” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  8. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  9. Assessment of performance of survival prediction models for cancer prognosis

    Directory of Open Access Journals (Sweden)

    Chen Hung-Chia

    2012-07-01

    Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in

  10. Hybrid Corporate Performance Prediction Model Considering Technical Capability

    Directory of Open Access Journals (Sweden)

    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.

  11. Performance modeling and prediction for linear algebra algorithms

    OpenAIRE

    Iakymchuk, Roman

    2012-01-01

    This dissertation incorporates two research projects: performance modeling and prediction for dense linear algebra algorithms, and high-performance computing on clouds. The first project is focused on dense matrix computations, which are often used as computational kernels for numerous scientific applications. To solve a particular mathematical operation, linear algebra libraries provide a variety of algorithms. The algorithm of choice depends, obviously, on its performance. Performance of su...

  12. A CHAID Based Performance Prediction Model in Educational Data Mining

    Directory of Open Access Journals (Sweden)

    R. Bhaskaran

    2010-01-01

    Full Text Available The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO. A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student records, which were used for CHAID prediction model construction. A set of prediction rules were extracted from CHIAD prediction model and the efficiency of the generated CHIAD prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory.

  13. Mantis: Predicting System Performance through Program Analysis and Modeling

    CERN Document Server

    Chun, Byung-Gon; Lee, Sangmin; Maniatis, Petros; Naik, Mayur

    2010-01-01

    We present Mantis, a new framework that automatically predicts program performance with high accuracy. Mantis integrates techniques from programming language and machine learning for performance modeling, and is a radical departure from traditional approaches. Mantis extracts program features, which are information about program execution runs, through program instrumentation. It uses machine learning techniques to select features relevant to performance and creates prediction models as a function of the selected features. Through program analysis, it then generates compact code slices that compute these feature values for prediction. Our evaluation shows that Mantis can achieve more than 93% accuracy with less than 10% training data set, which is a significant improvement over models that are oblivious to program features. The system generates code slices that are cheap to compute feature values.

  14. Neural Network Based Model for Predicting Housing Market Performance

    Institute of Scientific and Technical Information of China (English)

    Ahmed Khalafallah

    2008-01-01

    The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home develop-ers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neu-ral network based models to support real estate investors and home developers in this critical task. The pa-per describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to pre-dict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.

  15. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...

  16. The predictive performance and stability of six species distribution models.

    Science.gov (United States)

    Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao

    2014-01-01

    Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pMAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  17. The predictive performance and stability of six species distribution models.

    Directory of Open Access Journals (Sweden)

    Ren-Yan Duan

    Full Text Available Predicting species' potential geographical range by species distribution models (SDMs is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials. We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05, while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05, and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points.According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  18. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...

  19. Model for performance prediction in multi-axis machining

    CERN Document Server

    Lavernhe, Sylvain; Lartigue, Claire; 10.1007/s00170-007-1001-4

    2009-01-01

    This paper deals with a predictive model of kinematical performance in 5-axis milling within the context of High Speed Machining. Indeed, 5-axis high speed milling makes it possible to improve quality and productivity thanks to the degrees of freedom brought by the tool axis orientation. The tool axis orientation can be set efficiently in terms of productivity by considering kinematical constraints resulting from the set machine-tool/NC unit. Capacities of each axis as well as some NC unit functions can be expressed as limiting constraints. The proposed model relies on each axis displacement in the joint space of the machine-tool and predicts the most limiting axis for each trajectory segment. Thus, the calculation of the tool feedrate can be performed highlighting zones for which the programmed feedrate is not reached. This constitutes an indicator for trajectory optimization. The efficiency of the model is illustrated through examples. Finally, the model could be used for optimizing process planning.

  20. Tank System Integrated Model: A Cryogenic Tank Performance Prediction Program

    Science.gov (United States)

    Bolshinskiy, L. G.; Hedayat, A.; Hastings, L. J.; Sutherlin, S. G.; Schnell, A. R.; Moder, J. P.

    2017-01-01

    Accurate predictions of the thermodynamic state of the cryogenic propellants, pressurization rate, and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning for future space exploration missions. This Technical Memorandum (TM) presents the analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, mixing, and condensation on the tank wall. This TM also includes comparisons of TankSIM program predictions with the test data andexamples of multiphase mission calculations.

  1. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  2. Lightweight ZERODUR: Validation of Mirror Performance and Mirror Modeling Predictions

    Science.gov (United States)

    Hull, Tony; Stahl, H. Philip; Westerhoff, Thomas; Valente, Martin; Brooks, Thomas; Eng, Ron

    2017-01-01

    Upcoming spaceborne missions, both moderate and large in scale, require extreme dimensional stability while relying both upon established lightweight mirror materials, and also upon accurate modeling methods to predict performance under varying boundary conditions. We describe tests, recently performed at NASA's XRCF chambers and laboratories in Huntsville Alabama, during which a 1.2 m diameter, f/1.2988% lightweighted SCHOTT lightweighted ZERODUR(TradeMark) mirror was tested for thermal stability under static loads in steps down to 230K. Test results are compared to model predictions, based upon recently published data on ZERODUR(TradeMark). In addition to monitoring the mirror surface for thermal perturbations in XRCF Thermal Vacuum tests, static load gravity deformations have been measured and compared to model predictions. Also the Modal Response(dynamic disturbance) was measured and compared to model. We will discuss the fabrication approach and optomechanical design of the ZERODUR(TradeMark) mirror substrate by SCHOTT, its optical preparation for test by Arizona Optical Systems (AOS). Summarize the outcome of NASA's XRCF tests and model validations

  3. Planetary Suit Hip Bearing Model for Predicting Design vs. Performance

    Science.gov (United States)

    Cowley, Matthew S.; Margerum, Sarah; Harvil, Lauren; Rajulu, Sudhakar

    2011-01-01

    , the suited performance trends were comparable between the model and the suited subjects. With the three off-nominal bearing configurations compared to the nominal bearing configurations, human subjects showed decreases in hip flexion of 64%, 6%, and 13% and in hip abduction of 59%, 2%, and 20%. Likewise the solid model showed decreases in hip flexion of 58%, 1%, and 25% and in hip abduction of 56%, 0%, and 30%, under the same condition changes from the nominal configuration. Differences seen between the model predictions and the human subject performance data could be attributed to the model lacking dynamic elements and performing kinematic analysis only, the level of fit of the subjects with the suit, the levels of the subject s suit experience.

  4. Fuzzy regression modeling for tool performance prediction and degradation detection.

    Science.gov (United States)

    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.

  5. Performance and Prediction: Bayesian Modelling of Fallible Choice in Chess

    Science.gov (United States)

    Haworth, Guy; Regan, Ken; di Fatta, Giuseppe

    Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks.

  6. A CHAID Based Performance Prediction Model in Educational Data Mining

    CERN Document Server

    Ramaswami, M

    2010-01-01

    The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO). A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student r...

  7. Performance prediction model for distributed applications on multicore clusters

    CSIR Research Space (South Africa)

    Khanyile, NP

    2012-07-01

    Full Text Available Distributed processing offers a way of successfully dealing with computationally demanding applications such as scientific problems. Over the years, researchers have investigated ways to predict the performance of parallel algorithms. Amdahl’s law...

  8. Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model

    Directory of Open Access Journals (Sweden)

    Yu-Ri Kim

    2016-03-01

    Full Text Available Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology, a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance. Furthermore, using the new prediction model, a related mobile application was also developed.

  9. A unified tool for performance modelling and prediction

    Energy Technology Data Exchange (ETDEWEB)

    Gilmore, Stephen [Laboratory for Foundations of Computer Science, University of Edinburgh, King' s Buildings, Mayfield Road, Edinburgh, Scotland EH9 3JZ (United Kingdom)]. E-mail: stg@inf.ed.ac.uk; Kloul, Leila [Laboratory for Foundations of Computer Science, University of Edinburgh, King' s Buildings, Mayfield Road, Edinburgh, Scotland EH9 3JZ (United Kingdom)

    2005-07-01

    We describe a novel performability modelling approach, which facilitates the efficient solution of performance models extracted from high-level descriptions of systems. The notation which we use for our high-level designs is the Unified Modelling Language (UML) graphical modelling language. The technology which provides the efficient representation capability for the underlying performance model is the multi-terminal binary decision diagram (MTBDD)-based PRISM probabilistic model checker. The UML models are compiled through an intermediate language, the stochastic process algebra PEPA, before translation into MTBDDs for solution. We illustrate our approach on a real-world analysis problem from the domain of mobile telephony.

  10. Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

    Science.gov (United States)

    Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

    2014-01-01

    This presentation describes the capabilities of three-dimensional thermal power model of advanced stirling radioisotope generator (ASRG). The performance of the ASRG is presented for different scenario, such as Venus flyby with or without the auxiliary cooling system.

  11. A Formal Comparison of Model Variants for Performance Prediction

    Science.gov (United States)

    2009-12-01

    400 450 500 1 2 3 4 5 6 7 8 P e rf o rm a n c e S c o re s Mission Team Performance in UAS Predator Simulation CERI , 2005 Humans Model...Simulation CERI , 2005 Humans Model Team Performance in F-16 Simulator Missions DMO Testbd, Mesa Table 2. Cross-validation RMSD...Warfighter Readiness Research Division. The authors would like to thank the Cognitive Engineering Research Institute ( CERI ) and researchers from Mesa’s

  12. Predictive models for population performance on real biological fitness landscapes.

    Science.gov (United States)

    Rowe, William; Wedge, David C; Platt, Mark; Kell, Douglas B; Knowles, Joshua

    2010-09-01

    Directed evolution, in addition to its principal application of obtaining novel biomolecules, offers significant potential as a vehicle for obtaining useful information about the topologies of biomolecular fitness landscapes. In this article, we make use of a special type of model of fitness landscapes-based on finite state machines-which can be inferred from directed evolution experiments. Importantly, the model is constructed only from the fitness data and phylogeny, not sequence or structural information, which is often absent. The model, called a landscape state machine (LSM), has already been used successfully in the evolutionary computation literature to model the landscapes of artificial optimization problems. Here, we use the method for the first time to simulate a biological fitness landscape based on experimental evaluation. We demonstrate in this study that LSMs are capable not only of representing the structure of model fitness landscapes such as NK-landscapes, but also the fitness landscape of real DNA oligomers binding to a protein (allophycocyanin), data we derived from experimental evaluations on microarrays. The LSMs prove adept at modelling the progress of evolution as a function of various controlling parameters, as validated by evaluations on the real landscapes. Specifically, the ability of the model to 'predict' optimal mutation rates and other parameters of the evolution is demonstrated. A modification to the standard LSM also proves accurate at predicting the effects of recombination on the evolution.

  13. Comparison of Predictive Models for PV Module Performance (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Marion, B.

    2008-05-01

    This paper examines three models used to estimate the maximum power (P{sub m}) of PV modules when the irradiance and PV cell temperature are known: (1) the power temperature coefficient model, (2) the PVFORM model, and (3) the bilinear interpolation model. A variation of the power temperature coefficient model is also presented that improved model accuracy. For modeling values of P{sub m}, an 'effective' plane-of-array (POA) irradiance (E{sub e}) and the PV cell temperature (T) are used as model inputs. Using E{sub e} essentially removes the effects of variations in solar spectrum and reflectance losses, and permits the influence of irradiance and temperature on model performance for P{sub m} to be more easily studied. Eq. 1 is used to determine E{sub e} from T and the PV module's measured short-circuit current (I{sub sc}). Zero subscripts denote performance at Standard Reporting Conditions (SRC).

  14. Simplified Predictive Models for CO2 Sequestration Performance Assessment

    Science.gov (United States)

    Mishra, Srikanta; RaviGanesh, Priya; Schuetter, Jared; Mooney, Douglas; He, Jincong; Durlofsky, Louis

    2014-05-01

    We present results from an ongoing research project that seeks to develop and validate a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formation. The overall research goal is to provide tools for predicting: (a) injection well and formation pressure buildup, and (b) lateral and vertical CO2 plume migration. Simplified modeling approaches that are being developed in this research fall under three categories: (1) Simplified physics-based modeling (SPM), where only the most relevant physical processes are modeled, (2) Statistical-learning based modeling (SLM), where the simulator is replaced with a "response surface", and (3) Reduced-order method based modeling (RMM), where mathematical approximations reduce the computational burden. The system of interest is a single vertical well injecting supercritical CO2 into a 2-D layered reservoir-caprock system with variable layer permeabilities. In the first category (SPM), we use a set of well-designed full-physics compositional simulations to understand key processes and parameters affecting pressure propagation and buoyant plume migration. Based on these simulations, we have developed correlations for dimensionless injectivity as a function of the slope of fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. In the second category (SLM), we develop statistical "proxy models" using the simulation domain described previously with two different approaches: (a) classical Box-Behnken experimental design with a quadratic response surface fit, and (b) maximin Latin Hypercube sampling (LHS) based design with a Kriging metamodel fit using a quadratic trend and Gaussian correlation structure. For roughly the same number of

  15. PREDICTION VERSUS REALITY: THE USE OF MATHEMATICAL MODELS TO PREDICT ELITE PERFORMANCE IN SWIMMING AND ATHLETICS AT THE OLYMPIC GAMES

    Directory of Open Access Journals (Sweden)

    Timothy Heazlewood

    2006-12-01

    Full Text Available A number of studies have attempted to predict future Olympic performances in athletics and swimming based on trends displayed in previous Olympic Games. Some have utilised linear models to plot and predict change, whereas others have utilised multiple curve estimation methods based on inverse, sigmoidal, quadratic, cubic, compound, logistic, growth and exponential functions. The non linear models displayed closer fits to the actual data and were used to predict performance changes 10's, 100's and 1000's of years into the future. Some models predicted that in some events male and female times and distances would crossover and females would eventually display superior performance to males. Predictions using mathematical models based on pre-1996 athletics and pre-1998 swimming performances were evaluated based on how closely they predicted sprints and jumps, and freestyle swimming performances for both male and females at the 2000 and 2004 Olympic Games. The analyses revealed predictions were closer for the shorter swimming events where men's 50m and women's 50m and 100m actual times were almost identical to predicted times. For both men and women, as the swim distances increased the accuracy of the predictive model decreased, where predicted times were 4.5-7% faster than actual times achieved. The real trends in some events currently displaying performance declines were not foreseen by the mathematical models, which predicted consistent improvements across all athletic and swimming events selected for in this study

  16. Cognition and procedure representational requirements for predictive human performance models

    Science.gov (United States)

    Corker, K.

    1992-01-01

    Models and modeling environments for human performance are becoming significant contributors to early system design and analysis procedures. Issues of levels of automation, physical environment, informational environment, and manning requirements are being addressed by such man/machine analysis systems. The research reported here investigates the close interaction between models of human cognition and models that described procedural performance. We describe a methodology for the decomposition of aircrew procedures that supports interaction with models of cognition on the basis of procedures observed; that serves to identify cockpit/avionics information sources and crew information requirements; and that provides the structure to support methods for function allocation among crew and aiding systems. Our approach is to develop an object-oriented, modular, executable software representation of the aircrew, the aircraft, and the procedures necessary to satisfy flight-phase goals. We then encode in a time-based language, taxonomies of the conceptual, relational, and procedural constraints among the cockpit avionics and control system and the aircrew. We have designed and implemented a goals/procedures hierarchic representation sufficient to describe procedural flow in the cockpit. We then execute the procedural representation in simulation software and calculate the values of the flight instruments, aircraft state variables and crew resources using the constraints available from the relationship taxonomies. The system provides a flexible, extensible, manipulative and executable representation of aircrew and procedures that is generally applicable to crew/procedure task-analysis. The representation supports developed methods of intent inference, and is extensible to include issues of information requirements and functional allocation. We are attempting to link the procedural representation to models of cognitive functions to establish several intent inference methods

  17. An improved model for TPV performance predictions and optimization

    Science.gov (United States)

    Schroeder, K. L.; Rose, M. F.; Burkhalter, J. E.

    1997-03-01

    Previously a model has been presented for calculating the performance of a TPV system. This model has been revised into a general purpose algorithm, improved in fidelity, and is presented here. The basic model is an energy based formulation and evaluates both the radiant and heat source elements of a combustion based system. Improvements in the radiant calculations include the use of ray tracking formulations and view factors for evaluating various flat plate and cylindrical configurations. Calculation of photocell temperature and performance parameters as a function of position and incident power have also been incorporated. Heat source calculations have been fully integrated into the code by the incorporation of a modified version of the NASA Complex Chemical Equilibrium Compositions and Applications (CEA) code. Additionally, coding has been incorporated to allow optimization of various system parameters and configurations. Several examples cases are presented and compared, and an optimum flat plate emitter/filter/photovoltaic configuration is also described.

  18. Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

    Science.gov (United States)

    Manta, Asimina; Gresil, Matthieu; Soutis, Constantinos

    2017-04-01

    In this computational work, a new simulation tool on the graphene/polymer nanocomposites electrical response is developed based on the finite element method (FEM). This approach is built on the multi-scale multi-physics format, consisting of a unit cell and a representative volume element (RVE). The FE methodology is proven to be a reliable and flexible tool on the simulation of the electrical response without inducing the complexity of raw programming codes, while it is able to model any geometry, thus the response of any component. This characteristic is supported by its ability in preliminary stage to predict accurately the percolation threshold of experimental material structures and its sensitivity on the effect of different manufacturing methodologies. Especially, the percolation threshold of two material structures of the same constituents (PVDF/Graphene) prepared with different methods was predicted highlighting the effect of the material preparation on the filler distribution, percolation probability and percolation threshold. The assumption of the random filler distribution was proven to be efficient on modelling material structures obtained by solution methods, while the through-the -thickness normal particle distribution was more appropriate for nanocomposites constructed by film hot-pressing. Moreover, the parametrical analysis examine the effect of each parameter on the variables of the percolation law. These graphs could be used as a preliminary design tool for more effective material system manufacturing.

  19. Test of the classic model for predicting endurance running performance.

    Science.gov (United States)

    McLaughlin, James E; Howley, Edward T; Bassett, David R; Thompson, Dixie L; Fitzhugh, Eugene C

    2010-05-01

    To compare the classic physiological variables linked to endurance performance (VO2max, %VO2max at lactate threshold (LT), and running economy (RE)) with peak treadmill velocity (PTV) as predictors of performance in a 16-km time trial. Seventeen healthy, well-trained distance runners (10 males and 7 females) underwent laboratory testing to determine maximal oxygen uptake (VO2max), RE, percentage of maximal oxygen uptake at the LT (%VO2max at LT), running velocity at LT, and PTV. Velocity at VO2max (vVO2max) was calculated from RE and VO2max. Three stepwise regression models were used to determine the best predictors (classic vs treadmill performance protocols) for the 16-km running time trial. Simple Pearson correlations of the variables with 16-km performance showed vVO2max to have the highest correlation (r = -0.972) and %VO2max at the LT the lowest (r = 0.136). The correlation coefficients for LT, VO2max, and PTV were very similar in magnitude (r = -0.903 to r = -0.892). When VO2max, %VO2max at LT, RE, and PTV were entered into SPSS stepwise analysis, VO2max explained 81.3% of the total variance, and RE accounted for an additional 10.7%. vVO2max was shown to be the best predictor of the 16-km performance, accounting for 94.4% of the total variance. The measured velocity at VO2max (PTV) was highly correlated with the estimated velocity at vVO2max (r = 0.8867). Among well-trained subjects heterogeneous in VO2max and running performance, vVO2max is the best predictor of running performance because it integrates both maximal aerobic power and the economy of running. The PTV is linked to the same physiological variables that determine vVO2max.

  20. Predicting optimum vortex tube performance using a simplified CFD model

    Energy Technology Data Exchange (ETDEWEB)

    Karimi-Esfahani, M; Fartaj, A.; Rankin, G.W. [Univ. of Windsor, Dept. of Mechanical, Automotive and Materials Engineering, Windsor, Ontario (Canada)]. E-mail: mki_60@hotmail.com

    2004-07-01

    The Ranque-Hilsch tube is a particular type of vortex tube device. The flow enters the device tangentially near one end and exits from the open ends of the tube. The inlet air is of a uniform temperature throughout while the outputs are of different temperatures. One outlet is hotter and the other is colder than the inlet air. This device has no moving parts and does not require any additional power for its operation other than that supplied to the device to compress the inlet air. It has, however, not been widely used, mainly because of its low efficiency. In this paper, a simplified 2-dimensional computational fluid dynamics model for the flow in the vortex tube is developed using FLUENT. This model makes use of the assumption of axial symmetry throughout the entire flow domain. Compared to a three-dimensional computational solution, the simplified model requires significantly less computational time. This is important because the model is to be used for an optimization study. A user-defined function is generated to implement a modified version of the k-epsilon model to account for turbulence. This model is validated by comparing a particular solution with available experimental data. The variation of cold temperature drop and efficiency of the device with orifice diameter, inlet pressure and cold mass flow ratio qualitatively agree with experimental results. Variation of these performance indices with tube length did not agree with the experiments for small values of tube length. However, it did agree qualitatively for large values. (author)

  1. Using dynamical uncertainty models estimating uncertainty bounds on power plant performance prediction

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Mataji, B.

    2007-01-01

    Predicting the performance of large scale plants can be difficult due to model uncertainties etc, meaning that one can be almost certain that the prediction will diverge from the plant performance with time. In this paper output multiplicative uncertainty models are used as dynamical models of th...... models, is applied to two different sets of measured plant data. The computed uncertainty bounds cover the measured plant output, while the nominal prediction is outside these uncertainty bounds for some samples in these examples.  ......Predicting the performance of large scale plants can be difficult due to model uncertainties etc, meaning that one can be almost certain that the prediction will diverge from the plant performance with time. In this paper output multiplicative uncertainty models are used as dynamical models...... of the prediction error. These proposed dynamical uncertainty models result in an upper and lower bound on the predicted performance of the plant. The dynamical uncertainty models are used to estimate the uncertainty of the predicted performance of a coal-fired power plant. The proposed scheme, which uses dynamical...

  2. Prediction Model for Estimating Performance Impacts of Maintenance Stress

    Science.gov (United States)

    1988-06-01

    relationship woula turn Into a curvilinear one when extremely complex tasks were taken into, accout ..) Lquu’lly important to tn-e lack or indepenoent...somewhat unique. Consequcntly, preparati_ n cannot be as extensive for them as for the SMEs (constitutin9 the current uitabase) in the hazarauus chemical...prediction (CBP) of training device cost and etfectiveness (Klein Associates TR-84-43-9). Alexandria, VA: Army Research Institute for the Behavioral

  3. The European computer model for optronic system performance prediction (ECOMOS)

    Science.gov (United States)

    Repasi, Endre; Bijl, Piet; Labarre, Luc; Wittenstein, Wolfgang; Bürsing, Helge

    2017-05-01

    ECOMOS is a multinational effort within the framework of an EDA Project Arrangement. Its aim is to provide a generally accepted and harmonized European computer model for computing nominal Target Acquisition (TA) ranges of optronic imagers operating in the Visible or thermal Infrared (IR). The project involves close co-operation of defence and security industry and public research institutes from France, Germany, Italy, The Netherlands and Sweden. ECOMOS uses and combines well-accepted existing European tools to build up a strong competitive position. This includes two TA models: the analytical TRM4 model and the image-based TOD model. In addition, it uses the atmosphere model MATISSE. In this paper, the central idea of ECOMOS is exposed. The overall software structure and the underlying models are shown and elucidated. The status of the project development is given as well as a short outlook on validation tests and the future potential of simulation for sensor assessment.

  4. A Structural Equation Model for Predicting Business Student Performance

    Science.gov (United States)

    Pomykalski, James J.; Dion, Paul; Brock, James L.

    2008-01-01

    In this study, the authors developed a structural equation model that accounted for 79% of the variability of a student's final grade point average by using a sample size of 147 students. The model is based on student grades in 4 foundational business courses: introduction to business, macroeconomics, statistics, and using databases. Educators and…

  5. Predicting Transfer Performance: A Comparison of Competing Function Learning Models

    Science.gov (United States)

    McDaniel, Mark A.; Dimperio, Eric; Griego, Jacqueline A.; Busemeyer, Jerome R.

    2009-01-01

    The population of linear experts (POLE) model suggests that function learning and transfer are mediated by activation of a set of prestored linear functions that together approximate the given function (Kalish, Lewandowsky, & Kruschke, 2004). In the extrapolation-association (EXAM) model, an exemplar-based architecture associates trained input…

  6. Predicting Adaptive Performance in Multicultural Teams: A Causal Model

    Science.gov (United States)

    2008-02-01

    IPIP personality scale . Based on Matsumoto et al.’s (2001) results, only those items that exceeded their established criterion for factor loadings...adaptability as a predictor and adaptive performance as an outcome are operationalized as separate constructs, each with their own measurement scales ...capabilities (e.g., low need for cognitive structure) as well as conducive personality characteristics (e.g., high emotional stability). Scales from the

  7. Standardizing the performance evaluation of short-term wind prediction models

    DEFF Research Database (Denmark)

    Madsen, Henrik; Pinson, Pierre; Kariniotakis, G.

    2005-01-01

    Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective...... evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term wind-poser preciction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated using results...

  8. Algorithms and Methods for High-Performance Model Predictive Control

    DEFF Research Database (Denmark)

    Frison, Gianluca

    routines employed in the numerical tests. The main focus of this thesis is on linear MPC problems. In this thesis, both the algorithms and their implementation are equally important. About the implementation, a novel implementation strategy for the dense linear algebra routines in embedded optimization...... is proposed, aiming at improving the computational performance in case of small matrices. About the algorithms, they are built on top of the proposed linear algebra, and they are tailored to exploit the high-level structure of the MPC problems, with special care on reducing the computational complexity....

  9. An Enlisted Performance Prediction Model for Aviation Structural Mechanics.

    Science.gov (United States)

    1983-09-01

    8217" , * **’" ,’’’."’"’-"’ ’ ’,’’ ’ " "- ’ TABLE 27 DISCRIMINNT ANALYSIS OUTPUT AND VALIDATION FOR MODEL 1 (PRIOR PROBABILITIES .62 / .38) CLASSIFICATION MATRIZ 1 2 TOTAL 1 1508 147 1655 91.12...CRYKC!~ IF HYECull THIN CHYEC:18; IF HYBCu12 THEN CHTEC 20; IF H!EC=13 THIN CH!EC=11.5; Hy EC ;CRT BC * IFf(NOTRC~b-1) AND (NIUYTPA! GZ 4) AND

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

    Science.gov (United States)

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

    2016-04-01

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

  11. Assessing the performance of prediction models: a framework for traditional and novel measures

    DEFF Research Database (Denmark)

    Steyerberg, Ewout W; Vickers, Andrew J; Cook, Nancy R;

    2010-01-01

    The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver...

  12. The Targeting Task Performance (TTP) Metric A New Model for Predicting Target Acquisition Performance

    Science.gov (United States)

    2006-05-31

    of patterns by means of spatial-frequency filte Howe, James (1993), “Electro-Optical Imaging System Performance Prediction,” In Electro-Optic systems...with respect to the wer. iven in Table D.1. Using these characteristics, the coherent n n ons for t n w re created. m ameters. ensor To

  13. Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients.

    Science.gov (United States)

    Bloomfield, Celeste; Staatz, Christine E; Unwin, Sean; Hennig, Stefanie

    2016-06-01

    Several population pharmacokinetic models describe the dose-exposure relationship of tobramycin in pediatric patients. Before the implementation of these models in clinical practice for dosage adjustment, their predictive performance should be externally evaluated. This study tested the predictive performance of all published population pharmacokinetic models of tobramycin developed for pediatric patients with an independent patient cohort. A literature search was conducted to identify suitable models for testing. Demographic and pharmacokinetic data were collected retrospectively from the medical records of pediatric patients who had received intravenous tobramycin. Tobramycin exposure was predicted from each model. Predictive performance was assessed by visual comparison of predictions to observations, by calculation of bias and imprecision, and through the use of simulation-based diagnostics. Eight population pharmacokinetic models were identified. A total of 269 concentration-time points from 41 pediatric patients with cystic fibrosis were collected for external evaluation. Three models consistently performed best in all evaluations and had mean errors ranging from -0.4 to 1.8 mg/liter, relative mean errors ranging from 4.9 to 29.4%, and root mean square errors ranging from 47.8 to 66.9%. Simulation-based diagnostics supported these findings. Models that allowed a two-compartment disposition generally had better predictive performance than those that used a one-compartment disposition model. Several published models of the pharmacokinetics of tobramycin showed reasonable low levels of bias, although all models seemed to have some problems with imprecision. This suggests that knowledge of typical pharmacokinetic behavior and patient covariate values alone without feedback concentration measurements from individual patients is not sufficient to make precise predictions. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

  14. Effect of patient location on the performance of clinical models to predict pulmonary embolism.

    Science.gov (United States)

    Ollenberger, Glenn P; Worsley, Daniel F

    2006-01-01

    Current clinical likelihood models for predicting pulmonary embolism (PE) are used to categorize outpatients into low, intermediate and high clinical pre-test likelihood of PE. Since these clinical prediction rules were developed using outpatients it is not known if they can be applied universally to both inpatients and outpatients with suspected PE. Thus, the purpose of this study was to determine the effect of patient location on the performance of clinical models to predict PE. Two clinical models (Wells and Wicki) were applied to data from the multi-centered PIOPED study. The Wells score was applied to 1359 patients and the Wicki score was applied to 998 patients. 361 patients (27%) from the PIOPED study did not have arterial gas measurement and were excluded from the Wicki score patient group. Patients were stratified by their location at the time of entry into the PIOPED study as follows: outpatient/emergency, surgical ward, medicine/coronary care unit or intensive care unit. The diagnostic performance of the two clinical models was applied to the various patient locations and the performance was evaluated using the area under a fitted receiver operating characteristic curve (AUC). The prevalence of PE in the three clinical probability categories were similar for the two scoring methods. Both clinical models yielded the lowest diagnostic performance in patients referred from surgical wards. The AUC for both clinical prediction rules decreased significantly when applied to inpatients in comparison to outpatients. Current clinical prediction rules for determining the pre-test likelihood of PE yielded different diagnostic performances depending upon patient location. The performance of the clinical prediction rules decreased significantly when applied to inpatients. In particular, the rules performed least well when applied to patients referred from surgical wards suggesting these rules should not be used in this patient group. As expected the clinical

  15. A human capital predictive model for agent performance in contact centres

    Directory of Open Access Journals (Sweden)

    Chris Jacobs

    2011-03-01

    Full Text Available Orientation: Currently no integrative model exists that can explain the phenomena contributing to agent performance in the South African contact centre industry.Research purpose: The primary focus of this article was to develop a theoretically derived human capital predictive model for agent performance in contact centres and Business Process Outsourcing (BPO based on a review of current empirical research literature.Motivation for the study: The study was motivated by the need for a human capital predictive model that can predict agent and overall business performance.Research design: A nonempirical (theoretical research paradigm was adopted for this study and more specifically a theory or model-building approach was followed. A systematic review of published empirical research articles (for the period 2000–2009 in scholarly search portals was performed.Main findings: Eight building blocks of the human capital predictive model for agent performance in contact centres were identified. Forty-two of the human capital contact centre related articles are detailed in this study. Key empirical findings suggest that person– environment fit, job demands-resources, human resources management practices, engagement, agent well-being, agent competence; turnover intention; and agent performance are related to contact centre performance.Practical/managerial implications: The human capital predictive model serves as an operational management model that has performance implications for agents and ultimately influences the contact centre’s overall business performance.Contribution/value-add: This research can contribute to the fields of human resource management (HRM, human capital and performance management within the contact centre and BPO environment.

  16. A Wake Model for the Prediction of Propeller Performance at Low Advance Ratios

    Directory of Open Access Journals (Sweden)

    Ye Tian

    2012-01-01

    Full Text Available A low order panel method is used to predict the performance of propellers. A wake alignment model based on a pseudounsteady scheme is proposed and implemented. The results from this full wake alignment (FWA model are correlated with available experimental data, and results from RANS for some propellers at design and low advance ratios. Significant improvements have been found in the predicted integrated forces and pressure distributions.

  17. Predicting Examination Performance Using an Expanded Integrated Hierarchical Model of Test Emotions and Achievement Goals

    Science.gov (United States)

    Putwain, Dave; Deveney, Carolyn

    2009-01-01

    The aim of this study was to examine an expanded integrative hierarchical model of test emotions and achievement goal orientations in predicting the examination performance of undergraduate students. Achievement goals were theorised as mediating the relationship between test emotions and performance. 120 undergraduate students completed…

  18. Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

    Science.gov (United States)

    Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

    2011-01-01

    In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

  19. A target detection model predicting field observer performance in maritime scenes

    Science.gov (United States)

    Culpepper, Joanne B.; Wheaton, Vivienne C.

    2014-10-01

    The U.S. Army's target acquisition models, the ACQUIRE and Target Task Performance (TTP) models, have been employed for many years to assess the performance of thermal infrared sensors. In recent years, ACQUIRE and the TTP models have been adapted to assess the performance of visible sensors. These adaptations have been primarily focused on the performance of an observer viewing a display device. This paper describes an implementation of the TTP model to predict field observer performance in maritime scenes. Predictions of the TTP model implementation were compared to observations of a small watercraft taken in a field trial. In this field trial 11 Australian Navy observers viewed a small watercraft in an open ocean scene. Comparisons of the observed probability of detection to predictions of the TTP model implementation showed the normalised RSS metric overestimated the probability of detection. The normalised Pixel Contrast using a literature value for V50 yielded a correlation of 0.58 between the predicted and observed probability of detection. With a measured value of N50 or V50 for the small watercraft used in this investigation, this implementation of the TTP model may yield stronger correlation with observed probability of detection.

  20. Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

    Directory of Open Access Journals (Sweden)

    Barkhordari

    2016-01-01

    Full Text Available Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD. The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI and relative and absolute Integrated discriminatory improvement index (IDI for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

  1. Predictive performance of DSGE model for small open economy – the case study of Czech Republic

    Directory of Open Access Journals (Sweden)

    Tomáš Jeřábek

    2013-01-01

    Full Text Available Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of almost all macroeconomic analysis. From the point of view of political practice is appropriate to seek a model that reached a quality prediction performance for all the variables. As monitored variables were used GDP growth, inflation and interest rates. The paper focuses on performance prediction evaluation of the small open economy New Keynesian DSGE model for the Czech republic, where Bayesian method are used for their parameters estimation, against different types of Bayesian and naive random walk model. The performance of models is identified using historical dates including domestic economy and foreign economy, which is represented by countries of the Eurozone. The results indicate that the DSGE model generates estimates that are competitive with other models used in this paper.

  2. LogGPO: An accurate communication model for performance prediction of MPI programs

    Institute of Scientific and Technical Information of China (English)

    CHEN WenGuang; ZHAI JiDong; ZHANG Jin; ZHENG WeiMin

    2009-01-01

    Message passing interface (MPI) is the de facto standard in writing parallel scientific applications on distributed memory systems. Performance prediction of MPI programs on current or future parallel sys-terns can help to find system bottleneck or optimize programs. To effectively analyze and predict per-formance of a large and complex MPI program, an efficient and accurate communication model is highly needed. A series of communication models have been proposed, such as the LogP model family, which assume that the sending overhead, message transmission, and receiving overhead of a communication is not overlapped and there is a maximum overlap degree between computation and communication. However, this assumption does not always hold for MPI programs because either sending or receiving overhead introduced by MPI implementations can decrease potential overlap for large messages. In this paper, we present a new communication model, named LogGPO, which captures the potential overlap between computation with communication of MPI programs. We design and implement a trace-driven simulator to verify the LogGPO model by predicting performance of point-to-point communication and two real applications CG and Sweep3D. The average prediction errors of LogGPO model are 2.4% and 2.0% for these two applications respectively, while the average prediction errors of LogGP model are 38.3% and 9.1% respectively.

  3. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    Science.gov (United States)

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  4. Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance

    Directory of Open Access Journals (Sweden)

    Qasem A. Al-Radaideh

    2012-02-01

    Full Text Available Human capital is of a high concern for companies’ management where their most interest is in hiring the highly qualified personnel which are expected to perform highly as well. Recently, there has been a growing interest in the data mining area, where the objective is the discovery of knowledge that is correct and of high benefit for users. In this paper, data mining techniques were utilized to build a classification model to predict the performance of employees. To build the classification model the CRISP-DM data mining methodology was adopted. Decision tree was the main data mining tool used to build the classification model, where several classification rules were generated. To validate the generated model, several experiments were conducted using real data collected from several companies. The model is intended to be used for predicting new applicants’ performance.

  5. Standardizing the performance evaluation of short-term wind prediction models

    DEFF Research Database (Denmark)

    Madsen, Henrik; Pinson, Pierre; Kariniotakis, G.;

    2005-01-01

    evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term wind-poser preciction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated using results...... from both on-shore and off-shore wind forms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems....

  6. Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students

    Directory of Open Access Journals (Sweden)

    Osman Yildiz

    2013-12-01

    Full Text Available It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the development of a mathematical model intended to predict distance education students’ year-end academic performance using the first eight-week data on the learning management system. First, two fuzzy models were constructed, namely the classical fuzzy model and the expert fuzzy model, the latter being based on expert opinion. Afterwards, a gene-fuzzy model was developed optimizing membership functions through genetic algorithm. The data on distance education were collected through Moodle, an open source learning management system. The data were on a total of 218 students who enrolled in Basic Computer Sciences in 2012. The input data consisted of the following variables: When a student logged on to the system for the last time after the content of a lesson was uploaded, how often he/she logged on to the system, how long he/she stayed online in the last login, what score he/she got in the quiz taken in Week 4, and what score he/she got in the midterm exam taken in Week 8. A comparison was made among the predictions of the three models concerning the students’ year-end academic performance.

  7. Multi-Site Validation of the SWAT Model on the Bani Catchment: Model Performance and Predictive Uncertainty

    Directory of Open Access Journals (Sweden)

    Jamilatou Chaibou Begou

    2016-04-01

    Full Text Available The objective of this study was to assess the performance and predictive uncertainty of the Soil and Water Assessment Tool (SWAT model on the Bani River Basin, at catchment and subcatchment levels. The SWAT model was calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE approach. Potential Evapotranspiration (PET and biomass were considered in the verification of model outputs accuracy. Global Sensitivity Analysis (GSA was used for identifying important model parameters. Results indicated a good performance of the global model at daily as well as monthly time steps with adequate predictive uncertainty. PET was found to be overestimated but biomass was better predicted in agricultural land and forest. Surface runoff represents the dominant process on streamflow generation in that region. Individual calibration at subcatchment scale yielded better performance than when the global parameter sets were applied. These results are very useful and provide a support to further studies on regionalization to make prediction in ungauged basins.

  8. Does Statistical Significance Help to Evaluate Predictive Performance of Competing Models?

    Directory of Open Access Journals (Sweden)

    Levent Bulut

    2016-04-01

    Full Text Available In Monte Carlo experiment with simulated data, we show that as a point forecast criterion, the Clark and West's (2006 unconditional test of mean squared prediction errors does not reflect the relative performance of a superior model over a relatively weaker one. The simulation results show that even though the mean squared prediction errors of a constructed superior model is far below a weaker alternative, the Clark- West test does not reflect this in their test statistics. Therefore, studies that use this statistic in testing the predictive accuracy of alternative exchange rate models, stock return predictability, inflation forecasting, and unemployment forecasting should not weight too much on the magnitude of the statistically significant Clark-West tests statistics.

  9. Application of regression and neural models to predict competitive swimming performance.

    Science.gov (United States)

    Maszczyk, Adam; Roczniok, Robert; Waśkiewicz, Zbigniew; Czuba, Miłosz; Mikołajec, Kazimierz; Zajac, Adam; Stanula, Arkadiusz

    2012-04-01

    This research problem was indirectly but closely connected with the optimization of an athlete-selection process, based on predictions viewed as determinants of future successes. The research project involved a group of 249 competitive swimmers (age 12 yr., SD = 0.5) who trained and competed for four years. Measures involving fitness (e.g., lung capacity), strength (e.g., standing long jump), swimming technique (turn, glide, distance per stroke cycle), anthropometric variables (e.g., hand and foot size), as well as specific swimming measures (speeds in particular distances), were used. The participants (n = 189) trained from May 2008 to May 2009, which involved five days of swimming workouts per week, and three additional 45-min. sessions devoted to measurements necessary for this study. In June 2009, data from two groups of 30 swimmers each (n = 60) were used to identify predictor variables. Models were then constructed from these variables to predict final swimming performance in the 50 meter and 800 meter crawl events. Nonlinear regression models and neural models were built for the dependent variable of sport results (performance at 50m and 800m). In May 2010, the swimmers' actual race times for these events were compared to the predictions created a year prior to the beginning of the experiment. Results for the nonlinear regression models and perceptron networks structured as 8-4-1 and 4-3-1 indicated that the neural models overall more accurately predicted final swimming performance from initial training, strength, fitness, and body measurements. Differences in the sum of absolute error values were 4:11.96 (n = 30 for 800m) and 20.39 (n = 30 for 50m), for models structured as 8-4-1 and 4-3-1, respectively, with the neural models being more accurate. It seems possible that such models can be used to predict future performance, as well as in the process of recruiting athletes for specific styles and distances in swimming.

  10. Dynamic Model of Centrifugal Compressor for Prediction of Surge Evolution and Performance Variations

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Mooncheong; Han, Jaeyoung; Yu, Sangseok [Chungnam National Univ., Daejeon (Korea, Republic of)

    2016-05-15

    When a control algorithm is developed to protect automotive compressor surges, the simulation model typically selects an empirically determined look-up table. However, it is difficult for a control oriented empirical model to show surge characteristics of the super charger. In this study, a dynamic supercharger model is developed to predict the performance of a centrifugal compressor under dynamic load follow-up. The model is developed using Simulink® environment, and is composed of a compressor, throttle body, valves, and chamber. Greitzer’s compressor model is used, and the geometric parameters are achieved by the actual supercharger. The simulation model is validated with experimental data. It is shown that compressor surge is effectively predicted by this dynamic compressor model under various operating conditions.

  11. Predictive performance of eleven pharmacokinetic models for propofol infusion in children for long-duration anaesthesia

    NARCIS (Netherlands)

    Hara, M.; Masui, K.; Eleveld, D. J.; Struys, M. M. R. F.; Uchida, O.

    Background. Predictive performance of eleven published propofol pharmacokinetic models was evaluated for long-duration propofol infusion in children. Methods. Twenty-one aged three-11 yr ASA I-II patients were included. Anaesthesia was induced with propofol or sevoflurane, and maintained with

  12. Prediction models for performance and emissions of a dual fuel CI engine using ANFIS

    Indian Academy of Sciences (India)

    A Adarsh Rai; P Srinivasa Pai; B R Shrinivasa Rao

    2015-04-01

    Dual fuel engines are being used these days to overcome shortage of fossil fuels and fulfill stringent exhaust gas emission regulations. They have several advantages over conventional diesel engines. In this context, this paper makes use of experimental results obtained from a dual fuel engine for developing models to predict performance and emission parameters. Conventional modelling efforts to understand the relationships between the input and the output variables, requires thermodynamic analysis which is complex and time consuming. As a result, efforts have been made to use artificial intelligence modelling techniques like fuzzy logic, Artificial Neural Network (ANN), Genetic Algorithm (GA), etc. This paper uses a neuro fuzzy modelling technique, Adaptive Neuro Fuzzy Inference System (ANFIS) for developing prediction models for performance and emission parameter of a dual fuel engine. Percentage load, percentage Liquefied Petroleum Gas (LPG) and Injection Timing (IT) have been used as input parameters, whereas output parameters include Brake Specific Energy Consumption (BSEC), Brake Thermal Efficiency (BTE), Exhaust Gas Temperature (EGT) and smoke. In order to further improve the prediction accuracy of the model, GA has been used to optimize ANFIS. GA optimized ANFIS gives higher prediction accuracy of more than 90% for all parameters except for smoke, where there is a substantial improvement from 46.67% to 73.33%, when compared to conventional ANFIS model.

  13. Performance of several models for predicting budburst date of grapevine (Vitis vinifera L.).

    Science.gov (United States)

    García de Cortázar-Atauri, Iñaki; Brisson, Nadine; Gaudillere, Jean Pierre

    2009-07-01

    The budburst stage is a key phenological stage for grapevine (Vitis vinifera L.), with large site and cultivar variability. The objective of the present work was to provide a reliable agro-meteorological model for simulating grapevine budburst occurrence all over France. The study was conducted using data from ten cultivars of grapevine (Cabernet Sauvignon, Chasselas, Chardonnay, Grenache, Merlot, Pinot Noir, Riesling, Sauvignon, Syrah, Ugni Blanc) and five locations (Bordeaux, Colmar, Angers, Montpellier, Epernay). First, we tested two commonly used models that do not take into account dormancy: growing degree days with a base temperature of 10 degrees C (GDD(10)), and Riou's model (RIOU). The errors of predictions of these models ranged between 9 and 21 days. Second, a new model (BRIN) was studied relying on well-known formalisms for orchard trees and taking into account the dormancy period. The BRIN model showed better performance in predicting budburst date than previous grapevine models. Analysis of the components of BRIN formalisms (calculation of dormancy, use of hourly temperatures, base temperature) explained the better performances obtained with the BRIN model. Base temperature was the main driver, while dormancy period was not significant in simulating budburst date. For each cultivar, we provide the parameter estimates that showed the best performance for both the BRIN model and the GDD model with a base temperature of 5 degrees C.

  14. Performance of several models for predicting budburst date of grapevine ( Vitis vinifera L.)

    Science.gov (United States)

    García de Cortázar-Atauri, Iñaki; Brisson, Nadine; Gaudillere, Jean Pierre

    2009-07-01

    The budburst stage is a key phenological stage for grapevine ( Vitis vinifera L.), with large site and cultivar variability. The objective of the present work was to provide a reliable agro-meteorological model for simulating grapevine budburst occurrence all over France. The study was conducted using data from ten cultivars of grapevine (Cabernet Sauvignon, Chasselas, Chardonnay, Grenache, Merlot, Pinot Noir, Riesling, Sauvignon, Syrah, Ugni Blanc) and five locations (Bordeaux, Colmar, Angers, Montpellier, Epernay). First, we tested two commonly used models that do not take into account dormancy: growing degree days with a base temperature of 10°C (GDD10), and Riou’s model (RIOU). The errors of predictions of these models ranged between 9 and 21 days. Second, a new model (BRIN) was studied relying on well-known formalisms for orchard trees and taking into account the dormancy period. The BRIN model showed better performance in predicting budburst date than previous grapevine models. Analysis of the components of BRIN formalisms (calculation of dormancy, use of hourly temperatures, base temperature) explained the better performances obtained with the BRIN model. Base temperature was the main driver, while dormancy period was not significant in simulating budburst date. For each cultivar, we provide the parameter estimates that showed the best performance for both the BRIN model and the GDD model with a base temperature of 5°C.

  15. Using Predictive Uncertainty Analysis to Assess Hydrologic Model Performance for a Watershed in Oregon

    Science.gov (United States)

    Brannan, K. M.; Somor, A.

    2016-12-01

    A variety of statistics are used to assess watershed model performance but these statistics do not directly answer the question: what is the uncertainty of my prediction. Understanding predictive uncertainty is important when using a watershed model to develop a Total Maximum Daily Load (TMDL). TMDLs are a key component of the US Clean Water Act and specify the amount of a pollutant that can enter a waterbody when the waterbody meets water quality criteria. TMDL developers use watershed models to estimate pollutant loads from nonpoint sources of pollution. We are developing a TMDL for bacteria impairments in a watershed in the Coastal Range of Oregon. We setup an HSPF model of the watershed and used the calibration software PEST to estimate HSPF hydrologic parameters and then perform predictive uncertainty analysis of stream flow. We used Monte-Carlo simulation to run the model with 1,000 different parameter sets and assess predictive uncertainty. In order to reduce the chance of specious parameter sets, we accounted for the relationships among parameter values by using mathematically-based regularization techniques and an estimate of the parameter covariance when generating random parameter sets. We used a novel approach to select flow data for predictive uncertainty analysis. We set aside flow data that occurred on days that bacteria samples were collected. We did not use these flows in the estimation of the model parameters. We calculated a percent uncertainty for each flow observation based 1,000 model runs. We also used several methods to visualize results with an emphasis on making the data accessible to both technical and general audiences. We will use the predictive uncertainty estimates in the next phase of our work, simulating bacteria fate and transport in the watershed.

  16. Predicting photothermal field performance

    Science.gov (United States)

    Gonzalez, C. C.; Ross, R. G., Jr.

    1984-01-01

    Photothermal field performance in flat plate solar collectors was predicted. An analytical model which incorporates the measured dependency between transmittance loss and UV and temperature exposure levels was developed. The model uses SOLMET weather data extrapolated to 30 years for various sites and module mounting configurations. It is concluded that the temperature is the key to photothermally induced transmittance loss. The sensitivity of transmittance loss to UV level is nonlinear with minimum in curve near one sun. The ethylene vinyl acetate (EVA) results are consistent with 30 year life allocation.

  17. Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature

    Directory of Open Access Journals (Sweden)

    Jin Woo Moon

    2016-12-01

    Full Text Available The aim of this study was to develop an artificial neural network (ANN prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied to a target setpoint temperature (when a building was occupied. The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1 model development; (2 model optimization; and (3 performance evaluation. Two software programs—Matrix Laboratory (MATLAB and Transient Systems Simulation (TRNSYS—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature, presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9, hidden layers (3, moment (0.9, and learning rate (0.9. At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors.

  18. Performance Prediction of the NCAT Test Track Pavements Using Mechanistic Models

    Science.gov (United States)

    LaCroix, Andrew Thomas

    In the pavement industry in the United States of America, there is an increasing desire to improve the pavement construction quality and life for new and rehabilitated pavements. In order to improve the quality of the pavements, the Federal Highway Administration (FHWA) has pursued a performance-related specification (PRS) for over 20 years. The goal of PRS is to provide material and construction (M/C) properties that correlate well with pavement performance. In order to improve upon the PRS projects developed in WesTrack (NCHRP 9-20) and the MEPDG-based PRS (NCHRP 9-22), a set of PRS tests and models are proposed to provide a critical link between pavement performance and M/C properties. The PRS testing is done using the asphalt mixture performance tester (AMPT). The proposed PRS focuses on rutting and fatigue cracking of asphalt mixtures. The mixtures are characterized for their stiffness, fatigue behavior, and rutting resistance using a dynamic modulus (|E*|) test, a fatigue test, and a triaxial stress sweep (TSS) test, respectively. Information from the fatigue test characterizes the simplified viscoelastic continuum damage (S-VECD) model. Once the stiffness is reduced to a certain level, the material develops macro-cracks and fails. The TSS test is used to characterize a viscoplastic (VP) model. The VP model allows the prediction of the rut depth beneath the center of the wheel. The VECD and VP models are used within a layered viscoelastic (LVE) pavement model to predict fatigue and rutting performance of pavements. The PRS is evaluated by comparing the predictions to the field performance at the NCAT pavement test track in Opelika, Alabama. The test track sections evaluated are part of the 2009 test cycle group experiment, which focused on WMA, high RAP (50%), and a combination of both. The fatigue evaluation shows that all sections would last at least 18 years at the same traffic rate. The sections do not show any cracking, suggesting the sections are well

  19. Predicting the operation performance of condensate polishing plant using a mathematical kinetic model

    Energy Technology Data Exchange (ETDEWEB)

    Handy, B.J.; Greene, J.C. [NNC Solutions Ltd, Warrington (United Kingdom)

    2004-09-01

    NNC limited provides an ion exchange resin technology facility, which includes a resin testing service. A range of ion exchange resin properties is measured and this includes ion exchange capacity, resin bead particle sizes and anion kinetic performance in terms of mass transfer coefficients. It has long been considered by the authors that the experimental data for resins taken from operating condensate polishing plant (CPP) could be used to predict the expected plant performance. This has now been realised with the development of a mathematical model which predicts CPP behaviour using appropriate experimentally derived parameters and plant design data. Modelling methods for the separate anion and cation components of a mixed bed were initially developed before the mixed bed as a whole was addressed. Initially, an analytical approach was adopted, which proved successful for simple cases. For more complex examples a numerical approach was developed and found to be more suitable. The paper describes the development of anion and cation bed models, and a mixed bed model. In the latter model, the anion and cation components modelled earlier are combined, and used to model simultaneously typical concentrations of ammonia, sodium, chloride and sulphate. Examples of operation are given, and observations and points of interest are discussed with respect to the calculated concentration profiles. The experimental behaviour of a number of resin samples taken from operating plant was examined in a purpose-built ultrapure water recirculation loop equipped with a range of analytical instruments. This has permitted the observed experimental results to be compared with model predictions. The next stage of the model development is to identify plants suitable for testing the model against real plant performance and the authors are now seeking to identify plant managers interested in collaborating in this venture. (orig.)

  20. On the Performance of Alternate Conceptual Ecohydrological Models for Streamflow Prediction

    Science.gov (United States)

    Naseem, Bushra; Ajami, Hoori; Cordery, Ian; Sharma, Ashish

    2016-04-01

    A merging of a lumped conceptual hydrological model with two conceptual dynamic vegetation models is presented to assess the performance of these models for simultaneous simulations of streamflow and leaf area index (LAI). Two conceptual dynamic vegetation models with differing representation of ecological processes are merged with a lumped conceptual hydrological model (HYMOD) to predict catchment scale streamflow and LAI. The merged RR-LAI-I model computes relative leaf biomass based on transpiration rates while the RR-LAI-II model computes above ground green and dead biomass based on net primary productivity and water use efficiency in response to soil moisture dynamics. To assess the performance of these models, daily discharge and 8-day MODIS LAI product for 27 catchments of 90 - 1600km2 in size located in the Murray - Darling Basin in Australia are used. Our results illustrate that when single-objective optimisation was focussed on maximizing the objective function for streamflow or LAI, the other un-calibrated predicted outcome (LAI if streamflow is the focus) was consistently compromised. Thus, single-objective optimization cannot take into account the essence of all processes in the conceptual ecohydrological models. However, multi-objective optimisation showed great strength for streamflow and LAI predictions. Both response outputs were better simulated by RR-LAI-II than RR-LAI-I due to better representation of physical processes such as net primary productivity (NPP) in RR-LAI-II. Our results highlight that simultaneous calibration of streamflow and LAI using a multi-objective algorithm proves to be an attractive tool for improved streamflow predictions.

  1. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    constructed from geological and hydrological data. However, geophysical data are increasingly used to inform hydrogeologic models because they are collected at lower cost and much higher density than geological and hydrological data. Despite increased use of geophysics, it is still unclear whether......, ‘true’, hydrogeological and geophysical systems. The two types of ‘true’ systems can be used together with corresponding forward codes to generate hydrological and geophysical datasets, respectively. These synthetic datasets can be interpreted using any hydrogeophysical inversion scheme...

  2. A new model to predict roadheader performance using rock mass properties

    Institute of Scientific and Technical Information of China (English)

    Yazdani-Chamzini ABDOLREZA; SIAMAK Haji Yakhchali

    2013-01-01

    Prediction of roadheader performance plays a significant role in the plan of tunnel construction,which is influenced by different key parameters,including rock strength,discontinuity in rock mass,type and specifications of roadheader machine,and brittleness.The main aim of this study is to build a robust empirical equation based on rock mass properties for the roadheader performance prediction.For achieving the aim,a dataset composed of roadheader performance rate and rock properties is established using the dataset compiled from an underground coal mine located in a remote rugged desert environment some 85 km south of Tabas City in mid east Iran.By using gathered data,the statistical analyses are conducted between rock mass properties and roadheader performance to find whether there is a significant relationship between input variables and roadheader performance.The results show that rock mass properties have a considerable impact on the rate of the roadheader performance.It is demonstrated that the proposed model can accurately predict the roadheader performance as a function of rock mass properties.

  3. Corrosion models for predictions of performance of high-level radioactive-waste containers

    Energy Technology Data Exchange (ETDEWEB)

    Farmer, J.C.; McCright, R.D. [Lawrence Livermore National Lab., CA (United States); Gdowski, G.E. [KMI Energy Services, Livermore, CA (United States)

    1991-11-01

    The present plan for disposal of high-level radioactive waste in the US is to seal it in containers before emplacement in a geologic repository. A proposed site at Yucca Mountain, Nevada, is being evaluated for its suitability as a geologic repository. The containers will probably be made of either an austenitic or a copper-based alloy. Models of alloy degradation are being used to predict the long-term performance of the containers under repository conditions. The models are of uniform oxidation and corrosion, localized corrosion, and stress corrosion cracking, and are applicable to worst-case scenarios of container degradation. This paper reviews several of the models.

  4. RFID Equivalent Model for Prediction of Functional and EMC Performances in Complex Aeronautic Environments

    Science.gov (United States)

    Piche, Alexandre; Perraud, Richard; Peres, Gilles; Nguyen, Francois; Herlem, Yannick

    2016-05-01

    Wireless networks are widely used in urban or office environments and are increasingly considered as an attractive solution for various aeronautic applications. Current investigations focus in particular on RFID technologies because of their widespread use, low cost and ease of installation. The objective of this publication is to propose a concept of RFID equivalent model to predict functional and EMC performances in complex aeronautic areas.

  5. An Analytical Prediction Model of Time Diversity Performance for Earth-Space Fade Mitigation

    Directory of Open Access Journals (Sweden)

    Pantelis-Daniel M. Arapoglou

    2008-01-01

    Full Text Available Time diversity (TD has recently attracted attention as a promising and cost-efficient solution for high-frequency broadcast satellite applications. The present work proposes a general prediction model for the application of TD by approximating the time dynamics of rain attenuation through the use of the joint lognormal distribution. The proposed method is tested against experimental data and its performance is investigated with respect to the basic parameters of a satellite link.

  6. SOUTH CHINA REGIONAL SHORT RANGE CLIMATE PREDICTION MODEL AND ITS PERFORMANCE

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper, a newly established "South China Regional Short Range Climate Prediction Model System" is introduced and its performance is analyzed in real case simulation. It shows that the system has a good performance and suitable for short range climate modeling. The model simulates well the monthly mean, pentad mean and daily field, pentad mean and daily field and can depict more details than coarse resolution analyses. Weather systems and information can pass into and out of the model domain through lateral boundaries without notable damping. Almost all of the weather and climate changes can be reflected in the simulation, in which both the changing tendencies, amplitudes, speeds, and phases are consistent with the real cases. The simulated precipitation is much close to the observed one, both in the extent, position and in the intensity of rainfall. In addition, some smaller precipitation centers could also be reflected in the simulation.

  7. Relative performance of different numerical weather prediction models for short term predition of wind wnergy

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Landberg, L. [Risoe National Lab., Wind Energy and Atmospheric Physics Dept., Roskilde (Denmark); Moennich, K.; Waldl, H.P. [Carl con Ossietzky Univ., Faculty of Physics, Dept. of Energy and Semiconductor, Oldenburg (Germany)

    1999-03-01

    In several approaches presented in other papers in this conference, short term forecasting of wind power for a time horizon covering the next two days is done on the basis of Numerical Weather Prediction (NWP) models. This paper explores the relative merits of HIRLAM, which is the model used by the Danish Meteorological Institute, the Deutschlandmodell from the German Weather Service and the Nested Grid Model used in the US. The performance comparison will be mainly done for a site in Germany which is in the forecasting area of both the Deutschlandmodell and HIRLAM. In addition, a comparison of measured data with the forecasts made for one site in Iowa will be included, which allows conclusions on the merits of all three models. Differences in the relative performances could be due to a better tailoring of one model to its country, or to a tighter grid, or could be a function of the distance between the grid points and the measuring site. Also the amount, in which the performance can be enhanced by the use of model output statistics (topic of other papers in this conference) could give insights into the performance of the models. (au)

  8. Performance Prediction Modelling for Flexible Pavement on Low Volume Roads Using Multiple Linear Regression Analysis

    Directory of Open Access Journals (Sweden)

    C. Makendran

    2015-01-01

    Full Text Available Prediction models for low volume village roads in India are developed to evaluate the progression of different types of distress such as roughness, cracking, and potholes. Even though the Government of India is investing huge quantum of money on road construction every year, poor control over the quality of road construction and its subsequent maintenance is leading to the faster road deterioration. In this regard, it is essential that scientific maintenance procedures are to be evolved on the basis of performance of low volume flexible pavements. Considering the above, an attempt has been made in this research endeavor to develop prediction models to understand the progression of roughness, cracking, and potholes in flexible pavements exposed to least or nil routine maintenance. Distress data were collected from the low volume rural roads covering about 173 stretches spread across Tamil Nadu state in India. Based on the above collected data, distress prediction models have been developed using multiple linear regression analysis. Further, the models have been validated using independent field data. It can be concluded that the models developed in this study can serve as useful tools for the practicing engineers maintaining flexible pavements on low volume roads.

  9. In vitro models for the prediction of in vivo performance of oral dosage forms.

    Science.gov (United States)

    Kostewicz, Edmund S; Abrahamsson, Bertil; Brewster, Marcus; Brouwers, Joachim; Butler, James; Carlert, Sara; Dickinson, Paul A; Dressman, Jennifer; Holm, René; Klein, Sandra; Mann, James; McAllister, Mark; Minekus, Mans; Muenster, Uwe; Müllertz, Anette; Verwei, Miriam; Vertzoni, Maria; Weitschies, Werner; Augustijns, Patrick

    2014-06-16

    Accurate prediction of the in vivo biopharmaceutical performance of oral drug formulations is critical to efficient drug development. Traditionally, in vitro evaluation of oral drug formulations has focused on disintegration and dissolution testing for quality control (QC) purposes. The connection with in vivo biopharmaceutical performance has often been ignored. More recently, the switch to assessing drug products in a more biorelevant and mechanistic manner has advanced the understanding of drug formulation behavior. Notwithstanding this evolution, predicting the in vivo biopharmaceutical performance of formulations that rely on complex intraluminal processes (e.g. solubilization, supersaturation, precipitation…) remains extremely challenging. Concomitantly, the increasing demand for complex formulations to overcome low drug solubility or to control drug release rates urges the development of new in vitro tools. Development and optimizing innovative, predictive Oral Biopharmaceutical Tools is the main target of the OrBiTo project within the Innovative Medicines Initiative (IMI) framework. A combination of physico-chemical measurements, in vitro tests, in vivo methods, and physiology-based pharmacokinetic modeling is expected to create a unique knowledge platform, enabling the bottlenecks in drug development to be removed and the whole process of drug development to become more efficient. As part of the basis for the OrBiTo project, this review summarizes the current status of predictive in vitro assessment tools for formulation behavior. Both pharmacopoeia-listed apparatus and more advanced tools are discussed. Special attention is paid to major issues limiting the predictive power of traditional tools, including the simulation of dynamic changes in gastrointestinal conditions, the adequate reproduction of gastrointestinal motility, the simulation of supersaturation and precipitation, and the implementation of the solubility-permeability interplay. It is

  10. Evaluation of a Nutrition Model in Predicting Performance of Vietnamese Cattle

    Directory of Open Access Journals (Sweden)

    David Parsons

    2012-09-01

    Full Text Available The objective of this study was to evaluate the predictions of dry matter intake (DM and average daily gain (ADG of Vietnamese Yellow (Vang purebred and crossbred (Vang with Red Sindhi or Brahman bulls fed under Vietnamese conditions using two levels of solution (1 and 2 of the large ruminant nutrition system (LRNS model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW of the animals was 186, with standard deviation ±33.2 kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP, accuracy (Cb, coefficient of determination (r2, and mean bias (MB. When all treatment means were used, both levels of solution predicted DMI similarly with low precision (r2 of 0.389 and 0.45 for level 1 and 2, respectively and medium accuracy (Cb of 0.827 and 0.859, respectively. The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively, the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively. The accuracy (Cb was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively. The RMSEP indicated that both levels were on average under- or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the

  11. Effects of error covariance structure on estimation of model averaging weights and predictive performance

    Science.gov (United States)

    Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.

    2013-01-01

    obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

  12. Effects of error covariance structure on estimation of model averaging weights and predictive performance

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steven B.

    2013-07-23

    obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

  13. Effects of error covariance structure on estimation of model averaging weights and predictive performance

    Science.gov (United States)

    Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.

    2013-09-01

    obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

  14. Application and performance of two stroke outcome prediction models in a chinese population.

    Science.gov (United States)

    Li, Wen-Juan; Gao, Zhi-Yu; He, Yang; Liu, Guang-Zhi; Gao, Xu-Guang

    2012-02-01

    To apply and examine the performance of 2 acute stroke outcome prediction models, the Six Simple Variable Model (SSV model) and the One-Year Mortality Model (OYM model), in patients in China who had either a cerebral infarction or a cerebral hemorrhage. An observational study that used both retrospective and prospective study methods. A regional acute care facility in China. Two hundred and forty-eight consecutive patients who had an acute stroke who were admitted to the hospital between October 2007 and March 2009. Not applicable. Survival and daily activity independence 6 months after a stroke and 1-year mortality. The study sample had a mean age of 68.6 years (standard deviation, 11.1); 52.8% of the subjects were men, 66.5% had a cerebral infarction, and 33.5% had a cerebral hemorrhage. In the cohort, 107 patients (43.1%) achieved daily activity independence at 6-month follow-up, and 52 patients (21.0%) had died within 1 year. The area under the receiver operating characteristic curve (ROC) was 0.966 (0.935-0.998) for patients who had a cerebral infarction and 0.859 (0.766-0.952) for patients who had a cerebral hemorrhage in the prediction of 6-month survival and daily activity independence with use of the SSV model. The area under the ROC curve was 0.894 (0.846-0.965) for patients who had a cerebral infarction and 0.937 (0.904-0.988) for patients who had a cerebral hemorrhage in the prediction of 1-year mortality when the OYM model was used. Both the SSV and OYM prognostic models can be used for function and mortality outcome prediction for patients in China who have had a stroke. Variation existed in the precision of prediction between patients who had a cerebral infarction and those who had a cerebral hemorrhage. Other potential factors influencing functional recovery and mortality after stroke must be considered in outcome prediction. Copyright © 2012 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  15. Performance Prediction of Two-Phase Geothermal Reservoir using Lumped Parameter Model

    Science.gov (United States)

    Nurlaela, F.; Sutopo

    2016-09-01

    Many studies have been conducted to simulate performance of low-temperature geothermal reservoirs using lumped parameter method. Limited work had been done on applying non-isothermal lumped parameter models to higher temperature geothermal reservoirs. In this study, the lumped parameter method was applied to high-temperature two phase geothermal reservoirs. The model couples both energy and mass balance equations thus can predict temperature, pressure and fluid saturation changes in the reservoir as a result of production, reinjection of water, and/or natural recharge. This method was validated using reservoir simulation results of TOUGH2. As the results, the two phase lumped parameter model simulation without recharge shows good matching, however reservoir model with recharge condition show quite good conformity.

  16. Two-structured solid particle model for predicting and analyzing supercritical extraction performance.

    Science.gov (United States)

    Samadi, Sara; Vaziri, Behrooz Mahmoodzadeh

    2017-07-14

    Solid extraction process, using the supercritical fluid, is a modern science and technology, which has come in vogue regarding its considerable advantages. In the present article, a new and comprehensive model is presented for predicting the performance and separation yield of the supercritical extraction process. The base of process modeling is partial differential mass balances. In the proposed model, the solid particles are considered twofold: (a) particles with intact structure, (b) particles with destructed structure. A distinct mass transfer coefficient has been used for extraction of each part of solid particles to express different extraction regimes and to evaluate the process accurately (internal mass transfer coefficient was used for the intact-structure particles and external mass transfer coefficient was employed for the destructed-structure particles). In order to evaluate and validate the proposed model, the obtained results from simulations were compared with two series of available experimental data for extraction of chamomile extract with supercritical carbon dioxide, which had an excellent agreement. This is indicative of high potentiality of the model in predicting the extraction process, precisely. In the following, the effect of major parameters on supercritical extraction process, like pressure, temperature, supercritical fluid flow rate, and the size of solid particles was evaluated. The model can be used as a superb starting point for scientific and experimental applications. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. REVIEW OF MECHANISTIC UNDERSTANDING AND MODELING AND UNCERTAINTY ANALYSIS METHODS FOR PREDICTING CEMENTITIOUS BARRIER PERFORMANCE

    Energy Technology Data Exchange (ETDEWEB)

    Langton, C.; Kosson, D.

    2009-11-30

    Cementitious barriers for nuclear applications are one of the primary controls for preventing or limiting radionuclide release into the environment. At the present time, performance and risk assessments do not fully incorporate the effectiveness of engineered barriers because the processes that influence performance are coupled and complicated. Better understanding the behavior of cementitious barriers is necessary to evaluate and improve the design of materials and structures used for radioactive waste containment, life extension of current nuclear facilities, and design of future nuclear facilities, including those needed for nuclear fuel storage and processing, nuclear power production and waste management. The focus of the Cementitious Barriers Partnership (CBP) literature review is to document the current level of knowledge with respect to: (1) mechanisms and processes that directly influence the performance of cementitious materials (2) methodologies for modeling the performance of these mechanisms and processes and (3) approaches to addressing and quantifying uncertainties associated with performance predictions. This will serve as an important reference document for the professional community responsible for the design and performance assessment of cementitious materials in nuclear applications. This review also provides a multi-disciplinary foundation for identification, research, development and demonstration of improvements in conceptual understanding, measurements and performance modeling that would be lead to significant reductions in the uncertainties and improved confidence in the estimating the long-term performance of cementitious materials in nuclear applications. This report identifies: (1) technology gaps that may be filled by the CBP project and also (2) information and computational methods that are in currently being applied in related fields but have not yet been incorporated into performance assessments of cementitious barriers. The various

  18. PBPK models for the prediction of in vivo performance of oral dosage forms.

    Science.gov (United States)

    Kostewicz, Edmund S; Aarons, Leon; Bergstrand, Martin; Bolger, Michael B; Galetin, Aleksandra; Hatley, Oliver; Jamei, Masoud; Lloyd, Richard; Pepin, Xavier; Rostami-Hodjegan, Amin; Sjögren, Erik; Tannergren, Christer; Turner, David B; Wagner, Christian; Weitschies, Werner; Dressman, Jennifer

    2014-06-16

    Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data

  19. Distributed Model Predictive Control of the Multi-Agent Systems with Improving Control Performance

    Directory of Open Access Journals (Sweden)

    Wei Shanbi

    2012-01-01

    Full Text Available This paper addresses a distributed model predictive control (DMPC scheme for multiagent systems with improving control performance. In order to penalize the deviation of the computed state trajectory from the assumed state trajectory, the deviation punishment is involved in the local cost function of each agent. The closed-loop stability is guaranteed with a large weight for deviation punishment. However, this large weight leads to much loss of control performance. Hence, the time-varying compatibility constraints of each agent are designed to balance the closed-loop stability and the control performance, so that the closed-loop stability is achieved with a small weight for the deviation punishment. A numerical example is given to illustrate the effectiveness of the proposed scheme.

  20. A Family of High-Performance Solvers for Linear Model Predictive Control

    DEFF Research Database (Denmark)

    Frison, Gianluca; Sokoler, Leo Emil; Jørgensen, John Bagterp

    2014-01-01

    In Model Predictive Control (MPC), an optimization problem has to be solved at each sampling time, and this has traditionally limited the use of MPC to systems with slow dynamic. In this paper, we propose an e_cient solution strategy for the unconstrained sub-problems that give the search......, and techniques such as inexact search direction and mixed precision computation. Finally, we test our HPMPC toolbox, a family of high-performance solvers tailored for MPC and implemented using these techniques, that is shown to be several times faster than current state-of-the-art solvers for linear MPC....

  1. Lagrangian and Control Volume Models for Prediction of Cooling Lake Performance at SRP

    Energy Technology Data Exchange (ETDEWEB)

    Garrett, A.J.

    2001-06-26

    The model validation described in this document indicates that the methods described here and by Cooper (1984) for predicting the performance of the proposed L-Area cooling lake are reliable. Extensive observations from the Par Pond system show that lake surface temperatures exceeding 32.2 degrees C (90 degrees F) are attained occasionally in the summer in areas where there is little or no heating from the P-Area Reactor. Regulations which restrict lake surface temperatures to less than 32.2 degrees C should be structured to allow for these naturally-occurring thermal excursions.

  2. South African seasonal rainfall prediction performance by a coupled ocean-atmosphere model

    CSIR Research Space (South Africa)

    Landman, WA

    2010-12-01

    Full Text Available Evidence is presented that coupled ocean-atmosphere models can already outscore computationally less expensive atmospheric models. However, if the atmospheric models are forced with highly skillful SST predictions, they may still be a very strong...

  3. An Improved Methodology for Individualized Performance Prediction of Sleep-Deprived Individuals with the Two-Process Model

    Science.gov (United States)

    2009-01-01

    process model of sleep regulation for developing individualized biomathematical models that predict performance impairment for individuals subjected to total sleep loss. This new method advances our previous work in two important ways. First, it enables model customization to start as soon as the first performance measurement from an individual becomes available. This was achieved by optimally combining the performance information obtained from the individual’s performance measurements with a priori performance information using a Bayesian framework, while retaining

  4. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    Energy Technology Data Exchange (ETDEWEB)

    Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

    2011-07-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  5. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    Energy Technology Data Exchange (ETDEWEB)

    Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

    2011-07-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  6. In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation

    Energy Technology Data Exchange (ETDEWEB)

    G. R. Odette; G. E. Lucas

    2005-11-15

    This final report on "In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation" (DE-FG03-01ER54632) consists of a series of summaries of work that has been published, or presented at meetings, or both. It briefly describes results on the following topics: 1) A Transport and Fate Model for Helium and Helium Management; 2) Atomistic Studies of Point Defect Energetics, Dynamics and Interactions; 3) Multiscale Modeling of Fracture consisting of: 3a) A Micromechanical Model of the Master Curve (MC) Universal Fracture Toughness-Temperature Curve Relation, KJc(T - To), 3b) An Embrittlement DTo Prediction Model for the Irradiation Hardening Dominated Regime, 3c) Non-hardening Irradiation Assisted Thermal and Helium Embrittlement of 8Cr Tempered Martensitic Steels: Compilation and Analysis of Existing Data, 3d) A Model for the KJc(T) of a High Strength NFA MA957, 3e) Cracked Body Size and Geometry Effects of Measured and Effective Fracture Toughness-Model Based MC and To Evaluations of F82H and Eurofer 97, 3-f) Size and Geometry Effects on the Effective Toughness of Cracked Fusion Structures; 4) Modeling the Multiscale Mechanics of Flow Localization-Ductility Loss in Irradiation Damaged BCC Alloys; and 5) A Universal Relation Between Indentation Hardness and True Stress-Strain Constitutive Behavior. Further details can be found in the cited references or presentations that generally can be accessed on the internet, or provided upon request to the authors. Finally, it is noted that this effort was integrated with our base program in fusion materials, also funded by the DOE OFES.

  7. Correlation of Amine Swingbed On-Orbit CO2 Performance with a Hardware Independent Predictive Model

    Science.gov (United States)

    Papale, William; Sweterlitsch, Jeffery

    2015-01-01

    The Amine Swingbed Payload is an experimental system deployed on the International Space Station (ISS) that includes a two-bed, vacuum regenerated, amine-based carbon dioxide (CO2) removal subsystem as the principal item under investigation. The aminebased subsystem, also described previously in various publications as CAMRAS 3, was originally designed, fabricated and tested by Hamilton Sundstrand Space Systems International, Inc. (HSSSI) and delivered to NASA in November 2008. The CAMRAS 3 unit was subsequently designed into a flight payload experiment in 2010 and 2011, with flight test integration activities accomplished on-orbit between January 2012 and March 2013. Payload activation was accomplished in May 2013 followed by a 1000 hour experimental period. The experimental nature of the Payload and the interaction with the dynamic ISS environment present unique scientific and engineering challenges, in particular to the verification and validation of the expected Payload CO2 removal performance. A modeling and simulation approach that incorporates principles of chemical reaction engineering has been developed for the amine-based system to predict the dynamic cabin CO2 partial pressure with given inputs of sorbent bed size, process air flow, operating temperature, half-cycle time, CO2 generation rate, cabin volume and the magnitude of vacuum available. Simulation runs using the model to predict ambient CO2 concentrations show good correlation to on-orbit performance measurements and ISS dynamic concentrations for the assumed operating conditions. The dynamic predictive modelling could benefit operational planning to help ensure ISS CO2 concentrations are maintained below prescribed limits and for the Orion vehicle to simulate various operating conditions, scenarios and transients.

  8. Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students

    Science.gov (United States)

    Yildiz, Osman; Bal, Abdullah; Gulsecen, Sevinc

    2013-01-01

    It is essential to predict distance education students' year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the…

  9. Design and off-design thermodynamic model of a gas turbine for performance prediction

    Energy Technology Data Exchange (ETDEWEB)

    Monteiro, Ulisses A. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Lab. de Ensaios de Modelos de Engenharia (LEME)]. E-mail: ulisses@peno.coppe.ufrj.br; Belchior, Carlos Rodrigues Pereira [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE). Lab. de Maquinas Termicas (LMT)]. E-mail: belchior@peno.coppe.ufrj.br

    2008-07-01

    There are some types of faults that do not leave 'signatures' in the vibration spectrum of a gas turbine. These faults can only be detected by other analysis techniques. One of these techniques is the gas turbine performance analysis or gas path analysis which relates the efficiency, mass flow, temperature, pressure, fuel consumption and power to the gas turbine faults. In this paper the methodology used in the development of a thermodynamic model that simulates the design and off-design operation of a gas turbine with a free power turbine will be presented. The results obtained are used to predict the gas turbine performance in both design and off-design operation point, and also to simulate some types of faults. (author)

  10. Performance of WRF-ARW model in real-time prediction of Bay of Bengal cyclone `Phailin'

    Science.gov (United States)

    Mandal, M.; Singh, K. S.; Balaji, M.; Mohapatra, M.

    2016-05-01

    This study examines the performance of the Advanced Research core of Weather Research and Forecasting (ARW-WRF) model in prediction of the Bay of Bengal cyclone `Phailin'. The two-way interactive double-nested model at 27 and 9-km resolutions customized at Indian Institute of Technology Kharagpur (IITKGP) is used to predict the storm on real-time basis and five predictions are made with five different initial conditions. The initial and boundary conditions for the model are derived from the Global Forecasting System (GFS) analysis and forecast respectively. The track of storm is well predicted in all the five forecasts. In particular, the forecast with less initial positional error led to more accurate track and landfall prediction. It is observed that the predicted peak intensity and translation speed of the storm depends strongly on initial intensity error, vertical wind shear and vertical distribution of maximum potential vorticity. The trend of intensification and dissipation of the storm is well predicted by the model in terms of central sea level pressure (CSLP). The intensity in terms of maximum surface wind (MSW) is under-predicted by the model and it is suggested that the MSW estimated from predicted pressure drop may be used as prediction guideline. The storm intensified rapidly during its passage over the high Tropical Cyclone Heat Potential zone and is reasonably well predicted by the model. Though the magnitude of the precipitation is not well predicted, distribution of precipitation is fairly well predicted by the model. The track and intensity of the storm predicted by the customized WRF-ARW is better than that of other NWP models. The landfall (time and position) is also better predicted by the model compared to other NWP models if initialized at cyclonic storm stage. The results indicate that the customized model have good potential for real-time prediction of Bay of Bengal cyclones and encourage further investigation with larger number of cyclones.

  11. Performance of mesoscale modeling methods for predicting microstructure, mobility and rheology of charged suspensions.

    Energy Technology Data Exchange (ETDEWEB)

    Pierce, Flint; Grillet, Anne Mary; Grest, Gary Stephen; Lechman, Jeremy B.; Plimpton, Steven James; in' t Veld, Pieter J. (BASF Corporation Ludwigshafen, Germany); Schunk, Peter Randall; Heine, D. R. (Corning, Inc. Corning, NY); Stoltz, C. (Procter and Gamble Co. West Chester, OH); Weiss, Horst (BASF Corporation Ludwigshafen, Germany); Jendrejack, R. (3M Corporation St. Paul, MN); Petersen, Matthew K.

    2010-06-01

    In this presentation we examine the accuracy and performance of a suite of discrete-element-modeling approaches to predicting equilibrium and dynamic rheological properties of polystyrene suspensions. What distinguishes each approach presented is the methodology of handling the solvent hydrodynamics. Specifically, we compare stochastic rotation dynamics (SRD), fast lubrication dynamics (FLD) and dissipative particle dynamics (DPD). Method-to-method comparisons are made as well as comparisons with experimental data. Quantities examined are equilibrium structure properties (e.g. pair-distribution function), equilibrium dynamic properties (e.g. short- and long-time diffusivities), and dynamic response (e.g. steady shear viscosity). In all approaches we deploy the DLVO potential for colloid-colloid interactions. Comparisons are made over a range of volume fractions and salt concentrations. Our results reveal the utility of such methods for long-time diffusivity prediction can be dubious in certain ranges of volume fraction, and other discoveries regarding the best formulation to use in predicting rheological response.

  12. Progress in sensor performance testing, modeling and range prediction using the TOD method: an overview

    Science.gov (United States)

    Bijl, Piet; Hogervorst, Maarten A.; Toet, Alexander

    2017-05-01

    The Triangle Orientation Discrimination (TOD) methodology includes i) a widely applicable, accurate end-to-end EO/IR sensor test, ii) an image-based sensor system model and iii) a Target Acquisition (TA) range model. The method has been extensively validated against TA field performance for a wide variety of well- and under-sampled imagers, systems with advanced image processing techniques such as dynamic super resolution and local adaptive contrast enhancement, and sensors showing smear or noise drift, for both static and dynamic test stimuli and as a function of target contrast. Recently, significant progress has been made in various directions. Dedicated visual and NIR test charts for lab and field testing are available and thermal test benches are on the market. Automated sensor testing using an objective synthetic human observer is within reach. Both an analytical and an image-based TOD model have recently been developed and are being implemented in the European Target Acquisition model ECOMOS and in the EOSTAR TDA. Further, the methodology is being applied for design optimization of high-end security camera systems. Finally, results from a recent perception study suggest that DRI ranges for real targets can be predicted by replacing the relevant distinctive target features by TOD test patterns of the same characteristic size and contrast, enabling a new TA modeling approach. This paper provides an overview.

  13. Urban climate model MUKLIMO_3 in prediction mode - evaluation of model performance based on the case study of Vienna

    Science.gov (United States)

    Hollosi, Brigitta; Zuvela-Aloise, Maja

    2017-04-01

    To reduce negative health impacts of extreme heat load in urban areas is the application of early warning systems that use weather forecast models to predict forthcoming heat events of utmost importance. In the state-of-the-art operational heat warning systems the meteorological information relies on the weather forecast from the regional numerical models and monitoring stations that do not include details of urban structure. In this study, the dynamical urban climate model MUKLIMO3 (horizontal resolution of 100 - 200 m) is initialized with the vertical profiles from the archived daily forecast data of the ZAMG from the hydrostatic ALARO numerical weather prediction model run at 0600 UTC to simulate the development of the urban heat island in Vienna on a daily basis. The aim is to evaluate the performance of the urban climate model, so far applied only for climatological studies, in a weather prediction mode using the summer period 2011-2015 as a test period. The focus of the investigation is on assessment of the urban heat load during the day-time. The model output has been evaluated against the monitoring data at the weather stations in the area of the city. The model results for daily maximum temperature show good agreement with the observations, especially at the urban and suburban stations where the mean bias is low. The results are highly dependent on the input data from the meso-scale model that leads to larger deviation from observations if the prediction is not representative for the given day. This study can be used to support urban planning strategies and to improve existing practices to alert decision-makers and the public to impending dangers of excessive heat.

  14. In vitro models for the prediction of in vivo performance of oral dosage forms

    DEFF Research Database (Denmark)

    Kostewicz, Edmund S; Abrahamsson, Bertil; Brewster, Marcus

    2014-01-01

    Accurate prediction of the in vivo biopharmaceutical performance of oral drug formulations is critical to efficient drug development. Traditionally, in vitro evaluation of oral drug formulations has focused on disintegration and dissolution testing for quality control (QC) purposes. The connectio...

  15. High-performance small-scale solvers for linear Model Predictive Control

    DEFF Research Database (Denmark)

    Frison, Gianluca; Sørensen, Hans Henrik Brandenborg; Dammann, Bernd

    2014-01-01

    In Model Predictive Control (MPC), an optimization problem needs to be solved at each sampling time, and this has traditionally limited use of MPC to systems with slow dynamic. In recent years, there has been an increasing interest in the area of fast small-scale solvers for linear MPC......, with the two main research areas of explicit MPC and tailored on-line MPC. State-of-the-art solvers in this second class can outperform optimized linear-algebra libraries (BLAS) only for very small problems, and do not explicitly exploit the hardware capabilities, relying on compilers for that. This approach...... problems 2 to 8 times faster than the current state-of-the-art solver for this class of problems, and the high-performance is maintained for MPC problems with up to a few hundred states....

  16. Performance prediction and validation of equilibrium modeling for gasification of cashew nut shell char

    Directory of Open Access Journals (Sweden)

    M. Venkata Ramanan

    2008-09-01

    Full Text Available Cashew nut shell, a waste product obtained during deshelling of cashew kernels, had in the past been deemed unfit as a fuel for gasification owing to its high occluded oil content. The oil, a source of natural phenol, oozes upon gasification, thereby clogging the gasifier throat, downstream equipment and associated utilities with oil, resulting in ineffective gasification and premature failure of utilities due to its corrosive characteristics. To overcome this drawback, the cashew shells were de-oiled by charring in closed chambers and were subsequently gasified in an autothermal downdraft gasifier. Equilibrium modeling was carried out to predict the producer gas composition under varying performance influencing parameters, viz., equivalence ratio (ER, reaction temperature (RT and moisture content (MC. The results were compared with the experimental output and are presented in this paper. The model is quite satisfactory with the experimental outcome at the ER applicable to gasification systems, i.e., 0.15 to 0.30. The results show that the mole fraction of (i H2, CO and CH4 decreases while (N2 + H2O and CO2 increases with ER, (ii H2 and CO increases while CH4, (N2 + H2O and CO2 decreases with reaction temperature, (iii H2, CH4, CO2 and (N2 + H2O increases while CO decreases with moisture content. However at an equivalence ratio less than 0.15, the model predicts an unrealistic composition and is observed to be non valid below this ER.

  17. Modeling and Performance Prediction of Induction Motor Drive System for Electric Drive Tracked Vehicles

    Institute of Scientific and Technical Information of China (English)

    CHEN Shu-yong; CHEN Quan-shi; SUN Feng-chun

    2007-01-01

    The principle of rotor flux-orientation vector control on 100/150 kW three-phase AC induction motor for electric drive tracked vehicles is analyzed, and the mathematic model is deduced. The drive system of induction motor is modeled and simulated by Matlab/Simulink. The characteristics of motor and drive system are analyzed and evaluated by practical bench test. The simulation and bench test results show that the model is valid, and the driving control system has constant torque under rated speed, constant torque above rated speed, widely variable speed range and better dynamic characteristics. In order to evaluate the practical applications of high power induction motor driving system in electric drive tracked vehicles, a collaborative simulation based on interface technology of Matlab/Simulink and multi-body dynamic analysis software known as RecurDyn is done, the vehicle performances are predicted in the acceleration time (0-32 km/h) and turning characteristic (v=10 km/h, R=B).

  18. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

    Science.gov (United States)

    Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

    2016-01-01

    Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

  19. Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test

    DEFF Research Database (Denmark)

    Møller, Jonas Bech; Overgaard, R.V.; Madsen, Henrik

    2010-01-01

    Several articles have investigated stochastic differential equations (SDEs) in PK/PD models, but few have quantitatively investigated the benefits to predictive performance of models based on real data. Estimation of first phase insulin secretion which reflects beta-cell function using models...

  20. Performance of predictive models in phase equilibria of complex associating systems: PC-SAFT and CEOS/GE

    OpenAIRE

    Bender, N.; P. B. Staudt; Soares, R.P.; Cardozo,N. S. M.

    2013-01-01

    Cubic equations of state combined with excess Gibbs energy predictive models (like UNIFAC) and equations of state based on applied statistical mechanics are among the main alternatives for phase equilibria prediction involving polar substances in wide temperature and pressure ranges. In this work, the predictive performances of the PC-SAFT with association contribution and Peng-Robinson (PR) combined with UNIFAC (Do) through mixing rules are compared. Binary and multi-component systems involv...

  1. Performance of statistical models to predict mental health and substance abuse cost

    Directory of Open Access Journals (Sweden)

    Ettner Susan L

    2006-10-01

    Full Text Available Abstract Background Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice. Methods Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH or substance abuse (SA diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS; Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE, mean absolute prediction error (MAPE, and predictive ratios of predicted to observed cost (PR among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample. Results The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples. Conclusion Models with square-root transformation or link fit the data best. This function

  2. Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration

    Directory of Open Access Journals (Sweden)

    Minal Patel

    2016-01-01

    Full Text Available Service can be delivered anywhere and anytime in cloud computing using virtualization. The main issue to handle virtualized resources is to balance ongoing workloads. The migration of virtual machines has two major techniques: (i reducing dirty pages using CPU scheduling and (ii compressing memory pages. The available techniques for live migration are not able to predict dirty pages in advance. In the proposed framework, time series based prediction techniques are developed using historical analysis of past data. The time series is generated with transferring of memory pages iteratively. Here, two different regression based models of time series are proposed. The first model is developed using statistical probability based regression model and it is based on ARIMA (autoregressive integrated moving average model. The second one is developed using statistical learning based regression model and it uses SVR (support vector regression model. These models are tested on real data set of Xen to compute downtime, total number of pages transferred, and total migration time. The ARIMA model is able to predict dirty pages with 91.74% accuracy and the SVR model is able to predict dirty pages with 94.61% accuracy that is higher than ARIMA.

  3. Significance of uncertainties derived from settling tank model structure and parameters on predicting WWTP performance - A global sensitivity analysis study

    DEFF Research Database (Denmark)

    Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen

    2011-01-01

    Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based....... The outcome of this study contributes to a better understanding of uncertainty in WWTPs, and explicitly demonstrates the significance of secondary settling processes that are crucial elements of model prediction under dry and wet-weather loading conditions....

  4. Mortality prediction models for pediatric intensive care : comparison of overall and subgroup specific performance

    NARCIS (Netherlands)

    Visser, Idse H. E.; Hazelzet, Jan A.; Albers, Marcel J. I. J.; Verlaat, Carin W. M.; Hogenbirk, Karin; van Woensel, Job B.; van Heerde, Marc; van Waardenburg, Dick A.; Jansen, Nicolaas J. G.; Steyerberg, Ewout W.

    2013-01-01

    To validate paediatric index of mortality (PIM) and pediatric risk of mortality (PRISM) models within the overall population as well as in specific subgroups in pediatric intensive care units (PICUs). Variants of PIM and PRISM prediction models were compared with respect to calibration (agreement be

  5. Mortality prediction models for pediatric intensive care : comparison of overall and subgroup specific performance

    NARCIS (Netherlands)

    Visser, Idse H. E.; Hazelzet, Jan A.; Albers, Marcel J. I. J.; Verlaat, Carin W. M.; Hogenbirk, Karin; van Woensel, Job B.; van Heerde, Marc; van Waardenburg, Dick A.; Jansen, Nicolaas J. G.; Steyerberg, Ewout W.

    2013-01-01

    To validate paediatric index of mortality (PIM) and pediatric risk of mortality (PRISM) models within the overall population as well as in specific subgroups in pediatric intensive care units (PICUs). Variants of PIM and PRISM prediction models were compared with respect to calibration (agreement be

  6. Wind power prediction models

    Science.gov (United States)

    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.

  7. Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

    Science.gov (United States)

    Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

    2017-08-28

    The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical

  8. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

    Science.gov (United States)

    Syfert, Mindy M; Smith, Matthew J; Coomes, David A

    2013-01-01

    Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

  9. A Model-Based Approach to Predicting Graduate-Level Performance Using Indicators of Undergraduate-Level Performance

    Science.gov (United States)

    Zimmermann, Judith; Brodersen, Kay H.; Heinimann, Hans R.; Buhmann, Joachim M.

    2015-01-01

    The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…

  10. Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

    Science.gov (United States)

    Bondi, Robert W; Igne, Benoît; Drennen, James K; Anderson, Carl A

    2012-12-01

    Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

  11. Initial Cognitive Performance Predicts Longitudinal Aviator Performance

    Science.gov (United States)

    Jo, Booil; Adamson, Maheen M.; Kennedy, Quinn; Noda, Art; Hernandez, Beatriz; Zeitzer, Jamie M.; Friedman, Leah F.; Fairchild, Kaci; Scanlon, Blake K.; Murphy, Greer M.; Taylor, Joy L.

    2011-01-01

    Objectives. The goal of the study was to improve prediction of longitudinal flight simulator performance by studying cognitive factors that may moderate the influence of chronological age. Method. We examined age-related change in aviation performance in aircraft pilots in relation to baseline cognitive ability measures and aviation expertise. Participants were aircraft pilots (N = 276) aged 40–77.9. Flight simulator performance and cognition were tested yearly; there were an average of 4.3 (± 2.7; range 1–13) data points per participant. Each participant was classified into one of the three levels of aviation expertise based on Federal Aviation Administration pilot proficiency ratings: least, moderate, or high expertise. Results. Addition of measures of cognitive processing speed and executive function to a model of age-related change in aviation performance significantly improved the model. Processing speed and executive function performance interacted such that the slowest rate of decline in flight simulator performance was found in aviators with the highest scores on tests of these abilities. Expertise was beneficial to pilots across the age range studied; however, expertise did not show evidence of reducing the effect of age. Discussion. These data suggest that longitudinal performance on an important real-world activity can be predicted by initial assessment of relevant cognitive abilities. PMID:21586627

  12. The Real World Significance of Performance Prediction

    Science.gov (United States)

    Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

    2012-01-01

    In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…

  13. A cycle simulation model for predicting the performance of a diesel engine fuelled by diesel and biodiesel blends

    Energy Technology Data Exchange (ETDEWEB)

    Gogoi, T.K. [Mechanical Engineering Department, Tezpur University, Napaam, Tezpur, Assam 784028 (India); Baruah, D.C. [Energy Department, Tezpur University, Napaam, Tezpur, Assam 784028 (India)

    2010-03-15

    Among the alternative fuels, biodiesel and its blends are considered suitable and the most promising fuel for diesel engine. The properties of biodiesel are found similar to that of diesel. Many researchers have experimentally evaluated the performance characteristics of conventional diesel engines fuelled by biodiesel and its blends. However, experiments require enormous effort, money and time. Hence, a cycle simulation model incorporating a thermodynamic based single zone combustion model is developed to predict the performance of diesel engine. The effect of engine speed and compression ratio on brake power and brake thermal efficiency is analysed through the model. The fuel considered for the analysis are diesel, 20%, 40%, 60% blending of diesel and biodiesel derived from Karanja oil (Pongamia Glabra). The model predicts similar performance with diesel, 20% and 40% blending. However, with 60% blending, it reveals better performance in terms of brake power and brake thermal efficiency. (author)

  14. Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN model for exchange rate prediction

    Directory of Open Access Journals (Sweden)

    Pradyot Ranjan Jena

    2015-10-01

    Full Text Available This paper presents a new adaptive forecasting model using a knowledge guided artificial neural network (KGANN structure for efficient prediction of exchange rate. The new structure has two parallel systems. The first system is a least mean square (LMS trained adaptive linear combiner, whereas the second system employs an adaptive FLANN model to supplement the knowledge base with an objective to improve its performance value. The output of a trained LMS model is added to an adaptive FLANN model to provide a more accurate exchange rate compared to that predicted by either a simple LMS or a FLANN model. This finding has been demonstrated through an exhausting computer simulation study and using real life data. Thus the proposed KGANN is an efficient forecasting model for exchange rate prediction.

  15. Performance Of Bathymetric Lidar On Flow Properties Predicted With A 2-Dimensional Hydraulic Model

    Science.gov (United States)

    Tonina, D.; McKean, J. A.; Wright, C. W.

    2014-12-01

    Increased computer processing speeds and new computational fluid dynamics codes have significantly improved numerical modeling of flow and sediment transport over large domains of streams, up to several kilometers in length. Recent developments in remote sensing technologies have also greatly improved our ability to map the morphology of streams over similar spatial extents. However, limited information is available on whether the remote sensing methods can map channel topography with sufficient accuracy to define the flow boundary necessary for a fluid dynamics model. We assessed the ability of a second generation airborne bathymetric sensor, the Experimental Advanced Airborne Research Lidar (EAARL-B), to support a two dimensional fluid dynamics model of a small morphologically-complex mountain stream. We compared flow model predictions using the lidar bathymetry with those made using a total station field survey of the channel. In this riverscape, results suggest EAARL bathymetric lidar can map channel topography with sufficient accuracy to support a two dimensional computational flow model.

  16. An injector design model for predicting rocket engine performance and heat transfer

    Science.gov (United States)

    Calhoon, D. F.; Kors, D. L.; Gordon, L. H.

    1973-01-01

    A model is formulated for estimating the performance and chamber heat transfer in rocket injectors/chambers operating with gaseous H2-O2 propellants. The model quantifies the combustion performance and chamber heat flux for variables such as chamber length, element type, element area ratio, impingement angle, thrust/element, mixture ratio, moment ratio, element spacing, and physical size. Design equations are given and curves are plotted for evaluation of combustion performance in injectors comprised of F-O-F triplet, premix, coaxial and swirl coaxial element types. Curve plots and equations are also included for estimation of the chamber wall heat fluxes generated by these element types.

  17. Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts.

    Science.gov (United States)

    Chakravarti, Suman K; Saiakhov, Roustem D; Klopman, Gilles

    2012-10-22

    Fragment based expert system models of toxicological end points are primarily comprised of a set of substructures that are statistically related to the toxic property in question. These special substructures are often referred to as toxicity alerts, toxicophores, or biophores. They are the main building blocks/classifying units of the model, and it is important to define the chemical structural space within which the alerts are expected to produce reliable predictions. Furthermore, defining an appropriate applicability domain is required as part of the OECD guidelines for the validation of quantitative structure-activity relationships (QSARs). In this respect, this paper describes a method to construct applicability domains for individual toxicity alerts that are part of the CASE Ultra expert system models. Defining applicability domain for individual alerts was necessary because each CASE Ultra model is comprised of multiple alerts, and different alerts of a model usually represent different toxicity mechanisms and cover different structural space; the use of an applicability domain for the overall model is often not adequate. The domain for each alert was constructed using a set of fragments that were found to be statistically related to the end point in question as opposed to using overall structural similarity or physicochemical properties. Use of the applicability domains in reducing false positive predictions is demonstrated. It is now possible to obtain ROC (receiver operating characteristic) profiles of CASE Ultra models by applying domain adherence cutoffs on the alerts identified in test chemicals. This helps in optimizing the performance of a model based on their true positive-false positive prediction trade-offs and reduce drastic effects on the predictive performance caused by the active/inactive ratio of the model's training set. None of the major currently available commercial expert systems for toxicity prediction offer the possibility to explore a

  18. Evaluation of Blast-Resistant Performance Predicted by Damaged Plasticity Model for Concrete

    Institute of Scientific and Technical Information of China (English)

    HUAN Yi; FANG Qin; CHEN Li; ZHANG Yadong

    2008-01-01

    In order to evaluate the capacity of reinforced concrete (RC) structures subjected to blast Ioadings, the damaged plasticity model for concrete was used in the analysis of the dynamic responses of blast-loaded RC structures, and all three failure modes were numerically simulated by the finite element software ABAQUS.Simulation results agree with the experimental observations.It is demonstrated that the damaged plasticity model for concrete in the finite element software ABAQUS can predict dynamic responses and typical flexure, flexure-shear and direct shear failure modes of the blast-loaded RC structures.

  19. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

    NARCIS (Netherlands)

    K.I.E. Snell (Kym I.E.); H. Hua (Harry); T.P. Debray (Thomas P.A.); J. Ensor (Joie); M.P. Look (Maxime); K.G.M. Moons (Karel G.M.); R.D. Riley (Richard D.)

    2016-01-01

    textabstractObjectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing c

  20. Assessment of three turbulence model performances in predicting water jet flow plunging into a liquid pool

    Directory of Open Access Journals (Sweden)

    Zidouni Kendil Faiza

    2010-01-01

    Full Text Available The main purpose of the current study is to numerically investigate, through computational fluid dynamics modeling, a water jet injected vertically downward through a straight circular pipe into a water bath. The study also aims to obtain a better understanding of jet behavior, air entrainment and the dispersion of bubbles in the developing flow region. For these purposes, three dimensional air and water flows were modeled using the volume of fluid technique. The equations in question were formulated using the density and viscosity of a 'gas-liquid mixture', described in terms of the phase volume fraction. Three turbulence models with a high Reynolds number have been considered i. e. the standard k-e model, realizable k-e model, and Reynolds stress model. The predicted flow patterns for the realizable k-e model match well with experimental measurements found in available literature. Nevertheless, some discrepancies regarding velocity relaxation and turbulent momentum distribution in the pool are still observed for both the standard k-e and the Reynolds stress model.

  1. Performance of predictive models in phase equilibria of complex associating systems: PC-SAFT and CEOS/GE

    Directory of Open Access Journals (Sweden)

    N. Bender

    2013-03-01

    Full Text Available Cubic equations of state combined with excess Gibbs energy predictive models (like UNIFAC and equations of state based on applied statistical mechanics are among the main alternatives for phase equilibria prediction involving polar substances in wide temperature and pressure ranges. In this work, the predictive performances of the PC-SAFT with association contribution and Peng-Robinson (PR combined with UNIFAC (Do through mixing rules are compared. Binary and multi-component systems involving polar and non-polar substances were analyzed. Results were also compared to experimental data available in the literature. Results show a similar predictive performance for PC-SAFT with association and cubic equations combined with UNIFAC (Do through mixing rules. Although PC-SAFT with association requires less parameters, it is more complex and requires more computation time.

  2. Do Dual-Route Models Accurately Predict Reading and Spelling Performance in Individuals with Acquired Alexia and Agraphia?

    Science.gov (United States)

    Rapcsak, Steven Z.; Henry, Maya L.; Teague, Sommer L.; Carnahan, Susan D.; Beeson, Pélagie M.

    2007-01-01

    Coltheart and colleagues (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Castles, Bates, & Coltheart, 2006) have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing. PMID:17482218

  3. A fully unsteady prescribed wake model for HAWT performance prediction in yawed flow

    Energy Technology Data Exchange (ETDEWEB)

    Coton, F.N.; Tongguang, Wang; Galbraith, R.A.M.; Lee, D. [Univ. of Glasgow (United Kingdom)

    1997-12-31

    This paper describes the development of a fast, accurate, aerodynamic prediction scheme for yawed flow on horizontal axis wind turbines (HAWTs). The method is a fully unsteady three-dimensional model which has been developed over several years and is still being enhanced in a number of key areas. The paper illustrates the current ability of the method by comparison with field data from the NREL combined experiment and also describes the developmental work in progress. In particular, an experimental test programme designed to yield quantitative wake convection information is summarised together with modifications to the numerical model which are necessary for meaningful comparison with the experiments. Finally, current and future work on aspects such as tower-shadow and improved unsteady aerodynamic modelling are discussed.

  4. A new modeling and simulation method for important statistical performance prediction of single photon avalanche diode detectors

    Science.gov (United States)

    Xu, Yue; Xiang, Ping; Xie, Xiaopeng; Huang, Yang

    2016-06-01

    This paper presents a new modeling and simulation method to predict the important statistical performance of single photon avalanche diode (SPAD) detectors, including photon detection efficiency (PDE), dark count rate (DCR) and afterpulsing probability (AP). Three local electric field models are derived for the PDE, DCR and AP calculations, which show analytical dependence of key parameters such as avalanche triggering probability, impact ionization rate and electric field distributions that can be directly obtained from Geiger mode Technology Computer Aided Design (TCAD) simulation. The model calculation results are proven to be in good agreement with the reported experimental data in the open literature, suggesting that the proposed modeling and simulation method is very suitable for the prediction of SPAD statistical performance.

  5. Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

    Science.gov (United States)

    Wu, Yenan; Zhong, Ping-an; Xu, Bin; Zhu, Feilin; Fu, Jisi

    2017-06-01

    Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

  6. An evaluation of 1D loss model collections for the off-design performance prediction of automotive turbocharger compressors

    Science.gov (United States)

    Harley, P.; Spence, S.; Early, J.; Filsinger, D.; Dietrich, M.

    2013-12-01

    Single-zone modelling is used to assess different collections of impeller 1D loss models. Three collections of loss models have been identified in literature, and the background to each of these collections is discussed. Each collection is evaluated using three modern automotive turbocharger style centrifugal compressors; comparisons of performance for each of the collections are made. An empirical data set taken from standard hot gas stand tests for each turbocharger is used as a baseline for comparison. Compressor range is predicted in this study; impeller diffusion ratio is shown to be a useful method of predicting compressor surge in 1D, and choke is predicted using basic compressible flow theory. The compressor designer can use this as a guide to identify the most compatible collection of losses for turbocharger compressor design applications. The analysis indicates the most appropriate collection for the design of automotive turbocharger centrifugal compressors.

  7. Predicting the Consequences of Workload Management Strategies with Human Performance Modeling

    Science.gov (United States)

    Mitchell, Diane Kuhl; Samma, Charneta

    2011-01-01

    Human performance modelers at the US Army Research Laboratory have developed an approach for establishing Soldier high workload that can be used for analyses of proposed system designs. Their technique includes three key components. To implement the approach in an experiment, the researcher would create two experimental conditions: a baseline and a design alternative. Next they would identify a scenario in which the test participants perform all their representative concurrent interactions with the system. This scenario should include any events that would trigger a different set of goals for the human operators. They would collect workload values during both the control and alternative design condition to see if the alternative increased workload and decreased performance. They have successfully implemented this approach for military vehicle. designs using the human performance modeling tool, IMPRINT. Although ARL researches use IMPRINT to implement their approach, it can be applied to any workload analysis. Researchers using other modeling and simulations tools or conducting experiments or field tests can use the same approach.

  8. Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.

    Science.gov (United States)

    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.

  9. Numerical modelling of the proposed WFIRST-AFTA coronagraphs and their predicted optical performances

    CERN Document Server

    Krist, John; Mennesson, Bertrand

    2015-01-01

    The WFIRST-AFTA 2.4 m telescope will provide in the next decade the opportunity to host a coronagraph for the imaging and spectroscopy of planets and disks. The telescope, however, is not ideal, given its obscured aperture. Only recently have coronagraph designs been thoroughly investigated that can efficiently work with this configuration. Three coronagraph designs, the hybrid Lyot, the shaped pupil, and the phase-induced amplitude-apodization complex mask coronagraph (PIAA-CMC) have been selected for further development by the AFTA project. Real-world testbed demonstrations of these have just begun, so for now the most reliable means of evaluating their potential performance comes from numerical modeling incorporating diffraction propagation, realistic system models, and simulated wavefront sensing and control. Here we present the methods of performance evaluation and results for the current coronagraph designs.

  10. Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance

    Energy Technology Data Exchange (ETDEWEB)

    Atamturktur, Sez [Clemson Univ., SC (United States); Unal, Cetin [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Hemez, Francois [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Williams, Brian [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Tome, Carlos [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-03-16

    The project proposed to provide a Predictive Maturity Framework with its companion metrics that (1) introduce a formalized, quantitative means to communicate information between interested parties, (2) provide scientifically dependable means to claim completion of Validation and Uncertainty Quantification (VU) activities, and (3) guide the decision makers in the allocation of Nuclear Energy’s resources for code development and physical experiments. The project team proposed to develop this framework based on two complimentary criteria: (1) the extent of experimental evidence available for the calibration of simulation models and (2) the sophistication of the physics incorporated in simulation models. The proposed framework is capable of quantifying the interaction between the required number of physical experiments and degree of physics sophistication. The project team has developed this framework and implemented it with a multi-scale model for simulating creep of a core reactor cladding. The multi-scale model is composed of the viscoplastic self-consistent (VPSC) code at the meso-scale, which represents the visco-plastic behavior and changing properties of a highly anisotropic material and a Finite Element (FE) code at the macro-scale to represent the elastic behavior and apply the loading. The framework developed takes advantage of the transparency provided by partitioned analysis, where independent constituent codes are coupled in an iterative manner. This transparency allows model developers to better understand and remedy the source of biases and uncertainties, whether they stem from the constituents or the coupling interface by exploiting separate-effect experiments conducted within the constituent domain and integral-effect experiments conducted within the full-system domain. The project team has implemented this procedure with the multi- scale VPSC-FE model and demonstrated its ability to improve the predictive capability of the model. Within this

  11. Comparison of vacuum glazing thermal performance predicted using two- and three-dimensional models and their experimental validation

    Energy Technology Data Exchange (ETDEWEB)

    Fang, Yueping; Hyde, Trevor; Hewitt, Neil [Centre for Sustainable Technologies, School of the Built Environment, University of Ulster, Newtownabbey, BT37 0QB Northern Ireland (United Kingdom); Eames, Philip C. [Centre for Research in Renewable Energy Science and Technology, University of Loughborough (United Kingdom); Norton, Brian [Dublin Energy Lab, Dublin Institute of Technology, Aungier Street, Dublin 2 (Ireland)

    2009-09-15

    Thermal performance of vacuum glazing predicted by using two-dimensional (2-D) finite element and three-dimensional (3-D) finite volume models are presented. In the 2-D model, the vacuum space, including the pillar arrays, was represented by a material whose effective thermal conductivity was determined from the specified vacuum space width, the heat conduction through the pillar array and the calculated radiation heat transfer between the two interior glass surfaces within the vacuum gap. In the 3-D model, the support pillar array was incorporated and modelled within the glazing unit directly. The predicted difference in overall heat transfer coefficients between the two models for the vacuum window simulated was less than 3%. A guarded hot box calorimeter was used to determine the experimental thermal performance of vacuum glazing. The experimentally determined overall heat transfer coefficient and temperature profiles along the central line of the vacuum glazing are in very good agreement with the predictions made using the 2-D and 3-D models. (author)

  12. Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

    DEFF Research Database (Denmark)

    Salo, T J; Palosuo, T; Kersebaum, K C

    2016-01-01

    , Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area...... index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields...... mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any...

  13. Simplified predictive models for CO2 sequestration performance assessment

    Energy Technology Data Exchange (ETDEWEB)

    Mishra, Srikanta [Battelle Memorial Inst., Columbus, OH (United States); Ganesh, Priya [Battelle Memorial Inst., Columbus, OH (United States); Schuetter, Jared [Battelle Memorial Inst., Columbus, OH (United States); He, Jincong [Battelle Memorial Inst., Columbus, OH (United States); Jin, Zhaoyang [Battelle Memorial Inst., Columbus, OH (United States); Durlofsky, Louis J. [Battelle Memorial Inst., Columbus, OH (United States)

    2015-09-30

    Latin Hypercube sampling (LHS) based design with a multidimensional kriging metamodel fit. For roughly the same number of simulations, the LHS-based metamodel yields a more robust predictive model, as verified by a k-fold cross-validation approach (with data split into training and test sets) as well by validation with an independent dataset. In the third category, a reduced-order modeling procedure is utilized that combines proper orthogonal decomposition (POD) for reducing problem dimensionality with trajectory-piecewise linearization (TPWL) in order to represent system response at new control settings from a limited number of training runs. Significant savings in computational time are observed with reasonable accuracy from the PODTPWL reduced-order model for both vertical and horizontal well problems – which could be important in the context of history matching, uncertainty quantification and optimization problems. The simplified physics and statistical learning based models are also validated using an uncertainty analysis framework. Reference cumulative distribution functions of key model outcomes (i.e., plume radius and reservoir pressure buildup) generated using a 97-run full-physics simulation are successfully validated against the CDF from 10,000 sample probabilistic simulations using the simplified models. The main contribution of this research project is the development and validation of a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formations.

  14. State-Space Modeling and Performance Analysis of Variable-Speed Wind Turbine Based on a Model Predictive Control Approach

    Directory of Open Access Journals (Sweden)

    H. Bassi

    2017-04-01

    Full Text Available Advancements in wind energy technologies have led wind turbines from fixed speed to variable speed operation. This paper introduces an innovative version of a variable-speed wind turbine based on a model predictive control (MPC approach. The proposed approach provides maximum power point tracking (MPPT, whose main objective is to capture the maximum wind energy in spite of the variable nature of the wind’s speed. The proposed MPC approach also reduces the constraints of the two main functional parts of the wind turbine: the full load and partial load segments. The pitch angle for full load and the rotating force for the partial load have been fixed concurrently in order to balance power generation as well as to reduce the operations of the pitch angle. A mathematical analysis of the proposed system using state-space approach is introduced. The simulation results using MATLAB/SIMULINK show that the performance of the wind turbine with the MPC approach is improved compared to the traditional PID controller in both low and high wind speeds.

  15. Effect of Time Step Size and Turbulence Model on the Open Water Hydrodynamic Performance Prediction of Contra-Rotating Propellers

    Institute of Scientific and Technical Information of China (English)

    WANG Zhan-zhi; XIONG Ying

    2013-01-01

    A growing interest has been devoted to the contra-rotating propellers (CRPs) due to their high propulsive efficiency,torque balance,low fuel consumption,low cavitations,low noise performance and low hull vibration.Compared with the single-screw system,it is more difficult for the open water performance prediction because forward and aft propellers interact with each other and generate a more complicated flow field around the CRPs system.The current work focuses on the open water performance prediction of contra-rotating propellers by RANS and sliding mesh method considering the effect of computational time step size and turbulence model.The validation study has been performed on two sets of contra-rotating propellers developed by David W Taylor Naval Ship R & D center.Compared with the experimental data,it shows that RANS with sliding mesh method and SST k-ω turbulence model has a good precision in the open water performance prediction of contra-rotating propellers,and small time step size can improve the level of accuracy for CRPs with the same blade number of forward and aft propellers,while a relatively large time step size is a better choice for CRPs with different blade numbers.

  16. Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan

    Directory of Open Access Journals (Sweden)

    Motomura Noboru

    2008-11-01

    Full Text Available Abstract Objective In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan. Methods Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002 were randomly split into preliminary (179,156 records and test (45,051 records groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances. Results In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay. Conclusion Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.

  17. Impact of Pilot Light Modeling on the Predicted Annual Performance of Residential Gas Water Heaters: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Maguire, J.; Burch, J.

    2013-08-01

    Modeling residential water heaters with dynamic simulation models can provide accurate estimates of their annual energy consumption, if the units? characteristics and use conditions are known. Most gas storage water heaters (GSWHs) include a standing pilot light. It is generally assumed that the pilot light energy will help make up standby losses and have no impact on the predicted annual energy consumption. However, that is not always the case. The gas input rate and conversion efficiency of a pilot light for a GSWH were determined from laboratory data. The data were used in simulations of a typical GSWH with and without a pilot light, for two cases: 1) the GSWH is used alone; and 2) the GSWH is the second tank in a solar water heating (SWH) system. The sensitivity of wasted pilot light energy to annual hot water use, climate, and installation location was examined. The GSWH used alone in unconditioned space in a hot climate had a slight increase in energy consumption. The GSWH with a pilot light used as a backup to an SWH used up to 80% more auxiliary energy than one without in hot, sunny locations, from increased tank losses.

  18. Physiological models to understand exercise fatigue and the adaptations that predict or enhance athletic performance.

    Science.gov (United States)

    Noakes, T D

    2000-06-01

    A popular concept in the exercise sciences holds that fatigue develops during exercise of moderate to high intensity, when the capacity of the cardiorespiratory system to provide oxygen to the exercising muscles falls behind their demand inducing "anaerobic" metabolism. But this cardiovascular/anaerobic model is unsatisfactory because (i) a more rigorous analysis indicates that the first organ to be affected by anaerobiosis during maximal exercise would likely be the heart, not the skeletal muscles. This probability was fully appreciated by the pioneering exercise physiologists, A. V Hill, A. Bock and D. B. Dill, but has been systematically ignored by modern exercise physiologists; (ii) no study has yet definitely established the presence of either anaerobiosis, hypoxia or ischaemia in skeletal muscle during maximal exercise; (iii) the model is unable to explain why exercise terminates in a variety of conditions including prolonged exercise, exercise in the heat and at altitude, and in those with chronic diseases of the heart and lungs, without any evidence for skeletal muscle anaerobiosis, hypoxia or ischaemia, and before there is full activation of the total skeletal muscle mass, and (iv) cardiovascular and other measures believed to relate to skeletal muscle anaerobiosis, including the maximum oxygen consumption (VO2 max) and the "anaerobic threshold", are indifferent predictors of exercise capacity in athletes with similar abilities. This review considers four additional models that need to be considered when factors limiting either short duration, maximal or prolonged submaximal exercise are evaluated. These additional models are: (i) the energy supply/energy depletion model; (ii) the muscle power/muscle recruitment model; (iii) the biomechanical model and (iv) the psychological model. By reviewing features of these models, this review provides a broad overview of the physiological, metabolic and biomechanical factors that may limit exercise performance under

  19. Geochemical modelling for predicting the long-term performance of zeolite-PRB to treat lead contaminated groundwater

    Science.gov (United States)

    Obiri-Nyarko, Franklin; Kwiatkowska-Malina, Jolanta; Malina, Grzegorz; Kasela, Tomasz

    2015-06-01

    The feasibility of using geochemical modelling to predict the performance of a zeolite-permeable reactive barrier (PRB) for treating lead (Pb2 +) contaminated water was investigated in this study. A short-term laboratory column experiment was first performed with the zeolite (clinoptilolite) until the elution of 50 PV (1 PV = ca. 283 mL). Geochemical simulations of the one-dimensional transport of the Pb2+, considering removal processes including: ion-exchange, adsorption and complexation; the concomitant release of exchangeable cations (Ca2 +, Mg2 +, Na+, and K+) and the changes in pH were subsequently performed using the geochemical model PHREEQC. The results showed a reasonable agreement between the experimental results and the numerical simulations, with the exception of Ca2 + for which a great discrepancy was observed. The model also indicated the formation of secondary mineral precipitates such as goethite and hematite throughout the experiment, of which the effect on the hydraulic conductivity was found to be negligible. The results were further used to extrapolate the long-term performance of the zeolite. We found the capacity would be completely exhausted at PV = 250 (ca. 3 days). The study, thus, generally demonstrates the applicability of PHREEQC to predict the short and long-term performance of zeolite-PRBs. Therefore, it can be used to assist in the design and for management purposes of such barriers.

  20. Geochemical modelling for predicting the long-term performance of zeolite-PRB to treat lead contaminated groundwater.

    Science.gov (United States)

    Obiri-Nyarko, Franklin; Kwiatkowska-Malina, Jolanta; Malina, Grzegorz; Kasela, Tomasz

    2015-01-01

    The feasibility of using geochemical modelling to predict the performance of a zeolite-permeable reactive barrier (PRB) for treating lead (Pb(2+)) contaminated water was investigated in this study. A short-term laboratory column experiment was first performed with the zeolite (clinoptilolite) until the elution of 50 PV (1 PV=ca. 283 mL). Geochemical simulations of the one-dimensional transport of the Pb(2+), considering removal processes including: ion-exchange, adsorption and complexation; the concomitant release of exchangeable cations (Ca(2+), Mg(2+), Na(+), and K(+)) and the changes in pH were subsequently performed using the geochemical model PHREEQC. The results showed a reasonable agreement between the experimental results and the numerical simulations, with the exception of Ca(2+) for which a great discrepancy was observed. The model also indicated the formation of secondary mineral precipitates such as goethite and hematite throughout the experiment, of which the effect on the hydraulic conductivity was found to be negligible. The results were further used to extrapolate the long-term performance of the zeolite. We found the capacity would be completely exhausted at PV=250 (ca. 3 days). The study, thus, generally demonstrates the applicability of PHREEQC to predict the short and long-term performance of zeolite-PRBs. Therefore, it can be used to assist in the design and for management purposes of such barriers.

  1. Predicting Electrochromic Smart Window Performance

    Energy Technology Data Exchange (ETDEWEB)

    Degerman Engfeldt, Johnny

    2012-07-01

    The building sector is one of the largest consumers of energy, where the cooling of buildings accounts for a large portion of the total energy consumption. Electrochromic (EC) smart windows have a great potential for increasing indoor comfort and saving large amounts of energy for buildings. An EC device can be viewed as a thin-film electrical battery whose charging state is manifested in optical absorption, i.e. the optical absorption increases with increased state-of-charge (SOC) and decreases with decreased state-of-charge. It is the EC technology's unique ability to control the absorption (transmittance) of solar energy and visible light in windows with small energy effort that can reduce buildings' cooling needs. Today, the EC technology is used to produce small windows and car rearview mirrors, and to reach the construction market it is crucial to be able to produce large area EC devices with satisfactory performance. A challenge with up-scaling is to design the EC device system with a rapid and uniform coloration (charging) and bleaching (discharging). In addition, up-scaling the EC technology is a large economic risk due to its expensive production equipment, thus making the choice of EC material and system extremely critical. Although this is a well-known issue, little work has been done to address and solve these problems. This thesis introduces a cost-efficient methodology, validated with experimental data, capable of predicting and optimizing EC device systems' performance in large area applications, such as EC smart windows. This methodology consists of an experimental set-up, experimental procedures and a two dimensional current distribution model. The experimental set-up, based on camera vision, is used in performing experimental procedures to develop and validate the model and methodology. The two-dimensional current distribution model takes secondary current distribution with charge transfer resistance, ohmic and time

  2. Predictive performance of three multivariate difficult tracheal intubation models: a double-blind, case-controlled study.

    Science.gov (United States)

    Naguib, Mohamed; Scamman, Franklin L; O'Sullivan, Cormac; Aker, John; Ross, Alan F; Kosmach, Steven; Ensor, Joe E

    2006-03-01

    We performed a case-controlled, double-blind study to examine the performance of three multivariate clinical models (Wilson, Arné, and Naguib models) in the prediction of unanticipated difficult intubation. The study group consisted of 97 patients in whom an unanticipated difficult intubation had occurred. For each difficult intubation patient, a matched control patient was selected in whom tracheal intubation had been easily accomplished. Postoperatively, a blinded investigator evaluated both patients. The clinical assessment included the patient's weight, height, age, Mallampati score, interincisor gap, thyromental distance, thyrosternal distance, neck circumference, Wilson risk sum score, history of previous difficult intubation, and diseases associated with difficult laryngoscopy or intubation. The Naguib model was significantly more sensitive (81.4%; P thyromental distance, Mallampati score, interincisor gap, and height. This model is 82.5% sensitive and 85.6% specific with an area under the receiver operating characteristic curve of 0.90.

  3. Nominal model predictive control

    OpenAIRE

    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...

  4. Nominal Model Predictive Control

    OpenAIRE

    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...

  5. The Ahuachapan geothermal field, El Salvador: Exploitation model, performance predictions, economic analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ripperda, M.; Bodvarsson, G.S.; Lippmann, M.J.; Witherspoon, P.A.; Goranson, C.

    1991-05-01

    The Earth Sciences Division of Lawrence Berkeley Laboratory (LBL) is conducting a reservoir evaluation study of the Ahuachapan geothermal field in El Salvador. This work is being performed in cooperation with the Comision Ejecutiva Hidroelectrica del Rio Lempa (CEL) and the Los Alamos National Laboratory (LANL) with funding from the US Agency for International Development (USAID). This report describes the work done during the second year of the study (FY89--90). The first year's report included (1) the development of geological and conceptual models of the field, (2) the evaluation of the reservoir's initial thermodynamic and chemical conditions and their changes during exploitation, (3) the evaluation of interference test data and the observed reservoir pressure decline and (4) the development of a natural state model for the field. In the present report the results of reservoir engineering studies to evaluate different production-injection scenarios for the Ahuachapan geothermal field are discussed. The purpose of the work was to evaluate possible reservoir management options to enhance as well as to maintain the productivity of the field during a 30-year period (1990--2020). The ultimate objective was to determine the feasibility of increasing the electrical power output at Ahuachapan from the current level of about 50 MW{sub e} to the total installed capacity of 95 MW{sub e}. 20 refs., 75 figs., 10 tabs.

  6. Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater

    Science.gov (United States)

    Manu, D. S.; Thalla, Arun Kumar

    2017-01-01

    The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, real-time and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014-September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the inter-relationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP.

  7. The effects of gas models on the predicted performance and flow of a centrifugal refrigeration compressor stage

    Institute of Scientific and Technical Information of China (English)

    WANG ZhiHeng; XI Guang

    2008-01-01

    In this paper three perfect gas models with constant specific heat or with variable specific heat and one real gas model based on the gas property tables are respec-tively considered to implement into the three-dimensional CFD (computational fluid dynamics) analysis of a centrifugal refrigeration compressor stage. The results show that the gas models applied to the CFD code have significant influences on the performance of stage and the flow structures in the stage. Although the ther-modynamics operating condition of evolving fluid in the centrifugal refrigeration compressor has a significant deviation from the perfect gas, the perfect gas model with the modified value of gas constant and the variable specific heat offers a good prediction of stage performance. To predict some basic fluid flow parameters and flow structure accurately, the real gas effects should be considered and the rea-sonably accurate thermodynamic properties based on the analytical gas equation of state or numerical interpolation of gas tables should be applied to the CFD code.

  8. The effects of gas models on the predicted performance and flow of a centrifugal refrigeration compressor stage

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In this paper three perfect gas models with constant specific heat or with variable specific heat and one real gas model based on the gas property tables are respec- tively considered to implement into the three-dimensional CFD (computational fluid dynamics) analysis of a centrifugal refrigeration compressor stage. The results show that the gas models applied to the CFD code have significant influences on the performance of stage and the flow structures in the stage. Although the ther- modynamics operating condition of evolving fluid in the centrifugal refrigeration compressor has a significant deviation from the perfect gas, the perfect gas model with the modified value of gas constant and the variable specific heat offers a good prediction of stage performance. To predict some basic fluid flow parameters and flow structure accurately, the real gas effects should be considered and the rea- sonably accurate thermodynamic properties based on the analytical gas equation of state or numerical interpolation of gas tables should be applied to the CFD code.

  9. Development of database and mathematical models for predicting engine performance parameters using biodiesel

    National Research Council Canada - National Science Library

    P K Pranav; Thaneswer Patel; K Padmeshore Singh

    2017-01-01

    ... properties, engine performance parameters and emission characteristics. The comparisons of fuel properties among the biodiesel, its blends and engine performance parameters are one of the most attractive features of the developed database...

  10. South African mid-summer seasonal rainfall prediction performance by a coupled ocean-atmosphere model

    CSIR Research Space (South Africa)

    Landman, WA

    2011-01-01

    Full Text Available . 2000; Goddard and Mason, 2002). Such a so-called two-tiered procedure to predict the outcome of the rainfall season has been employed in South Africa for a number of years already (e.g., Landman et al., 2001). The advent of fully coupled ocean...

  11. Vmax estimate from three-parameter critical velocity models: validity and impact on 800 m running performance prediction.

    Science.gov (United States)

    Bosquet, Laurent; Duchene, Antoine; Lecot, François; Dupont, Grégory; Leger, Luc

    2006-05-01

    The purpose of this study was to evaluate the validity of maximal velocity (Vmax) estimated from three-parameter systems models, and to compare the predictive value of two- and three-parameter models for the 800 m. Seventeen trained male subjects (VO2max=66.54+/-7.29 ml min(-1) kg(-1)) performed five randomly ordered constant velocity tests (CVT), a maximal velocity test (mean velocity over the last 10 m portion of a 40 m sprint) and a 800 m time trial (V 800 m). Five systems models (two three-parameter and three two-parameter) were used to compute V max (three-parameter models), critical velocity (CV), anaerobic running capacity (ARC) and V800m from times to exhaustion during CVT. Vmax estimates were significantly lower than (0.19Critical velocity (CV) alone explained 40-62% of the variance in V800m. Combining CV with other parameters of each model to produce a calculated V800m resulted in a clear improvement of this relationship (0.83models had a better association (0.93models (0.83models appear to have a better predictive value for short duration events such as the 800 m, the fact the Vmax is not associated with the ability it is supposed to reflect suggests that they are more empirical than systems models.

  12. Model Predictions and Observed Performance of JWST's Cryogenic Position Metrology System

    Science.gov (United States)

    Lunt, Sharon R.; Rhodes, David; DiAntonio, Andrew; Boland, John; Wells, Conrad; Gigliotti, Trevis; Johanning, Gary

    2016-01-01

    The James Webb Space Telescope cryogenic testing requires measurement systems that both obtain a very high degree of accuracy and can function in that environment. Close-range photogrammetry was identified as meeting those criteria. Testing the capability of a close-range photogrammetric system prior to its existence is a challenging problem. Computer simulation was chosen over building a scaled mock-up to allow for increased flexibility in testing various configurations. Extensive validation work was done to ensure that the actual as-built system meet accuracy and repeatability requirements. The simulated image data predicted the uncertainty in measurement to be within specification and this prediction was borne out experimentally. Uncertainty at all levels was verified experimentally to be less than 0.1 millimeters.

  13. Model predictions and observed performance of JWST's cryogenic position metrology system

    Science.gov (United States)

    Lunt, Sharon R.; Rhodes, David; DiAntonio, Andrew; Boland, John; Wells, Conrad; Gigliotti, Trevis; Johanning, Gary

    2016-07-01

    The James Webb Space Telescope (JWST) cryogenic testing requires measurement systems that both obtain a very high degree of accuracy and can function in that environment. Close-range photogrammetry was identified as meeting those criteria. Testing the capability of a close-range photogrammetric system prior to its existence is a challenging problem. Computer simulation was chosen over building a scaled mock-up to allow for increased flexibility in testing various configurations. Extensive validation work was done to ensure that the actual as-built system meets accuracy and repeatability requirements. The simulated image data predicted the uncertainty in measurement to be within specification and this prediction was borne out experimentally. Uncertainty at all levels was verified experimentally to be <0.1 mm.

  14. High involvement work programs (HIWP measurement model validation and its capacity to predict perceived performance

    Directory of Open Access Journals (Sweden)

    Amable Juarez-Tarraga

    2016-11-01

    Originality/value: The findings are important for practitioners and researchers because quantifies the relationship between the use of HIWP and performance in two countries with important cultural differences, and also identifies practices that contribute most to this positive relationship.

  15. A RANS modelling approach for predicting powering performance of ships in waves

    Directory of Open Access Journals (Sweden)

    Windén Björn

    2014-06-01

    Full Text Available In this paper, a modelling technique for simulating self-propelled ships in waves is presented. The flow is modelled using a RANS solver coupled with an actuator disk model for the propeller. The motion of the ship is taken into consideration in the definition of the actuator disk region as well as the advance ratio of the propeller. The RPM of the propeller is controlled using a PID-controller with constraints added on the maximum permissible RPM increase rate. Results are presented for a freely surging model in regular waves with different constraints put on the PID-controller. The described method shows promising results and allows for the studying of several factors relating to selfpropulsion. However, more validation data is needed to judge the accuracy of the model.

  16. Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models

    Directory of Open Access Journals (Sweden)

    LeDonne Norman C

    2011-02-01

    Full Text Available Abstract Background Standard approaches to address the performance of predictive models that used common statistical measurements for the entire data set provide an overview of the average performance of the models across the entire predictive space, but give little insight into applicability of the model across the prediction space. Guha and Van Drie recently proposed the use of structure-activity landscape index (SALI curves via the SALI curve integral (SCI as a means to map the predictive power of computational models within the predictive space. This approach evaluates model performance by assessing the accuracy of pairwise predictions, comparing compound pairs in a manner similar to that done by medicinal chemists. Results The SALI approach was used to evaluate the performance of continuous prediction models for MDR1-MDCK in vitro efflux potential. Efflux models were built with ADMET Predictor neural net, support vector machine, kernel partial least squares, and multiple linear regression engines, as well as SIMCA-P+ partial least squares, and random forest from Pipeline Pilot as implemented by AstraZeneca, using molecular descriptors from SimulationsPlus and AstraZeneca. Conclusion The results indicate that the choice of training sets used to build the prediction models is of great importance in the resulting model quality and that the SCI values calculated for these models were very similar to their Kendall τ values, leading to our suggestion of an approach to use this SALI/SCI paradigm to evaluate predictive model performance that will allow more informed decisions regarding model utility. The use of SALI graphs and curves provides an additional level of quality assessment for predictive models.

  17. Multiphase porous media modelling: A novel approach to predicting food processing performance.

    Science.gov (United States)

    Khan, Md Imran H; Joardder, M U H; Kumar, Chandan; Karim, M A

    2016-07-20

    The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

  18. Study of the Correlation between the Performances of Lunar Vehicle Wheels Predicted by the Nepean Wheeled Vehicle Performance Model and Test Data

    Science.gov (United States)

    Wong, J. Y.; Asnani, V. M.

    2008-01-01

    This paper describes the results of a study of the correlation between the performances of wheels for lunar vehicles predicted using the Nepean wheeled vehicle performance model (NWVPM), developed under the auspices of Vehicle Systems Development Corporation, Ottawa, Canada, and the corresponding test data presented in Performance evaluation of wheels for lunar vehicles , Technical Report M-70-2, prepared for George C. Marshall Space Flight Center, National Aeronautics and Space Administration (NASA), USA, by the US Army Engineer Waterways Experiment Station (WES). The NWVPM was originally developed for design and performance evaluation of terrestrial off-road wheeled vehicles. The purpose of this study is to assess the potential of the NWVPM for evaluating wheel candidates for the new generation of extra-terrestrial vehicles. Two versions of a wire-mesh wheel and a hoop-spring wheel, which were considered as candidates for lunar roving vehicles for the NASA Apollo program in the late 1960s, together with a pneumatic wheel were examined in this study. The tractive performances of these wheels and of a 464 test vehicle with the pneumatic wheels on air-dry sand were predicted using the NWVPM and compared with the corresponding test data obtained under Earth s gravity and previously documented in the above-named report. While test data on wheel or vehicle performances obtained under Earth s gravity may not necessarily be representative of those on extra-terrestrial bodies, because of the differences in gravity and in environmental conditions, such as atmospheric pressure, it is still a valid approach to use test data obtained under Earth s gravity to evaluate the predictive capability of the NWVPM and its potential applications to predicting wheel or wheeled rover performances on extra-terrestrial bodies. Results of this study show that, using the ratio (P20/W) of the drawbar pull to normal load at 20 per cent slip as a performance indicator, there is a reasonable

  19. The application of Nonlinear Local Lyapunov Vectors to the Zebiak-Cane Model and their performance in the Ensemble Prediction

    Science.gov (United States)

    Hou, Zhaolu; Li, Jianping; Ding, Ruiqiang; Feng, Jie

    2017-04-01

    Nonlinear local Lyapunov vectors (NLLVs) have been developed to indicate orthogonal directions in phase space with different error growth rates. Comparing to the breeding vectors (BVs), NLLVs can span the fast-growing perturbation subspace efficiently and may gasp more components in analysis errors than the BVs in the nonlinear dynamical system. Here, NLLVs are employed in the Zebiak-Cane (ZC) atmosphere-ocean coupled model and represent a nonlinear, finite-time extension of the local Lyapunov vectors of the ZC model. The statistical properties of NLLVs is not very sensitive to the choice of the breeding parameter. However, the non-leading NLLVs have some randomness, which increase the diversity of NLLVs. Not only the leading NLLV but also the non-leading NLLVs are flow-dependent and related to the background ENSO evolution of the ZC model in the aspect of spatial structure and error growth rate. the non-leading NLLVs also are the instability direction related to the ENSO process in the ZC model. Due to the non-leading NLLVs, the subspace of the first few NLLVs can describe better the analysis error than that of the same number BVs in the ZC model. NLLVs as initial ensemble perturbations are applied to the ensemble prediction of ENSO and the performance are systematically compared to those of the random perturbation (RP) technique, and the BV method in the prefect environment. The results demonstrate that the RP technique has the worst performance and the NLLVs method is the best in the ensemble forecasts. In particular, the NLLV technique can reduce the "spring barrier" for ENSO prediction further than the other ensemble method.

  20. A Performance Prediction Method for Pumps as Turbines (PAT Using a Computational Fluid Dynamics (CFD Modeling Approach

    Directory of Open Access Journals (Sweden)

    Emma Frosina

    2017-01-01

    Full Text Available Small and micro hydropower systems represent an attractive solution for generating electricity at low cost and with low environmental impact. The pump-as-turbine (PAT approach has promise in this application due to its low purchase and maintenance costs. In this paper, a new method to predict the inverse characteristic of industrial centrifugal pumps is presented. This method is based on results of simulations performed with commercial three-dimensional Computational Fluid Dynamics (CFD software. Model results have been first validated in pumping mode using data supplied by pump manufacturers. Then, the results have been compared to experimental data for a pump running in reverse. Experimentation has been performed on a dedicated test bench installed in the Department of Civil Construction and Environmental Engineering of the University of Naples Federico II. Three different pumps, with different specific speeds, have been analyzed. Using the model results, the inverse characteristic and the best efficiency point have been evaluated. Finally, results have been compared to prediction methods available in the literature.

  1. Use of statistical modeling to predict the effect of formulation composition on conditioning shampoo performance.

    Science.gov (United States)

    Lepilleur, Carole; Giovannitti-Jensen, Ann; Kyer, Carol

    2013-01-01

    Formulation composition has a dramatic influence on the performance of conditioning shampoos. The purpose of this study is to determine the factors affecting the performance of various cationic polymers in those systems. An experiment was conducted by varying the levels of three surfactants (sodium lauryl ether sulfate, sodium lauryl sulfate, and cocamidopropyl betaine) in formulations containing various cationic polymers such as cationic cassia derivatives of different cationic charge densities (1.9, 2.3, and 3.0 mEq/g), cationic guar (0.98 mEq/g), and cationic hydroxyethyl cellulose (1.03 mEq/g). The results show the formulation composition dramatically affects silicone and cationic polymer deposition. In particular, three parameters are of importance in determining deposition efficiency: ionic strength, surfactant (micelle) charge, and total amount of surfactant. The cationic polymer composition, molecular weight, and charge density are also important in determining which of the previous three parameters influence the performance most.

  2. Assessment of three turbulence model performances in predicting water jet flow plunging into a liquid pool

    OpenAIRE

    Zidouni Kendil Faiza; Bousbia Salah Anis; Mataoui Amina

    2010-01-01

    The main purpose of the current study is to numerically investigate, through computational fluid dynamics modeling, a water jet injected vertically downward through a straight circular pipe into a water bath. The study also aims to obtain a better understanding of jet behavior, air entrainment and the dispersion of bubbles in the developing flow region. For these purposes, three dimensional air and water flows were modeled using the volume of fluid technique. The equations in question were fo...

  3. Green building performance prediction/assessment

    Energy Technology Data Exchange (ETDEWEB)

    Papamichael, Konstantinos

    2000-02-01

    To make decisions, building designers need to predict and assess the performance of their ideas with respect to various criteria, such as comfort, esthetics, energy, environmental impact, economics, etc. Performance prediction with respect to environmental impact requires complicated models and massive computations, which are usually possible only through computer-based tools. This paper focuses on the use of computer-based tools for predicting and assessing building performance with respect to environmental impact criteria for the design of green buildings. It contains analyses of green performance prediction/assessment and descriptions of available tools, along with discussions on their use by different types of users. Finally, it includes analyses of the cost and benefits of green performance prediction and assessment.

  4. SIMPLIFIED PREDICTIVE MODELS FOR CO₂ SEQUESTRATION PERFORMANCE ASSESSMENT RESEARCH TOPICAL REPORT ON TASK #3 STATISTICAL LEARNING BASED MODELS

    Energy Technology Data Exchange (ETDEWEB)

    Mishra, Srikanta; Schuetter, Jared

    2014-11-01

    We compare two approaches for building a statistical proxy model (metamodel) for CO₂ geologic sequestration from the results of full-physics compositional simulations. The first approach involves a classical Box-Behnken or Augmented Pairs experimental design with a quadratic polynomial response surface. The second approach used a space-filling maxmin Latin Hypercube sampling or maximum entropy design with the choice of five different meta-modeling techniques: quadratic polynomial, kriging with constant and quadratic trend terms, multivariate adaptive regression spline (MARS) and additivity and variance stabilization (AVAS). Simulations results for CO₂ injection into a reservoir-caprock system with 9 design variables (and 97 samples) were used to generate the data for developing the proxy models. The fitted models were validated with using an independent data set and a cross-validation approach for three different performance metrics: total storage efficiency, CO₂ plume radius and average reservoir pressure. The Box-Behnken–quadratic polynomial metamodel performed the best, followed closely by the maximin LHS–kriging metamodel.

  5. Prediction of the lifetime productive and reproductive performance of Holstein cows managed for different lactation durations, using a model of lifetime nutrient partitioning

    DEFF Research Database (Denmark)

    Gaillard, Charlotte; Martin, O; Blavy, P

    2016-01-01

    The GARUNS model is a lifetime performance model taking into account the changing physiological priorities of an animal during its life and through repeated reproduction cycles. This dynamic and stochastic model has been previously used to predict the productive and reproductive performance of va...

  6. Development of a CSP plant energy yield calculation tool applying predictive models to analyze plant performance sensitivities

    Science.gov (United States)

    Haack, Lukas; Peniche, Ricardo; Sommer, Lutz; Kather, Alfons

    2017-06-01

    At early project stages, the main CSP plant design parameters such as turbine capacity, solar field size, and thermal storage capacity are varied during the techno-economic optimization to determine most suitable plant configurations. In general, a typical meteorological year with at least hourly time resolution is used to analyze each plant configuration. Different software tools are available to simulate the annual energy yield. Software tools offering a thermodynamic modeling approach of the power block and the CSP thermal cycle, such as EBSILONProfessional®, allow a flexible definition of plant topologies. In EBSILON, the thermodynamic equilibrium for each time step is calculated iteratively (quasi steady state), which requires approximately 45 minutes to process one year with hourly time resolution. For better presentation of gradients, 10 min time resolution is recommended, which increases processing time by a factor of 5. Therefore, analyzing a large number of plant sensitivities, as required during the techno-economic optimization procedure, the detailed thermodynamic simulation approach becomes impracticable. Suntrace has developed an in-house CSP-Simulation tool (CSPsim), based on EBSILON and applying predictive models, to approximate the CSP plant performance for central receiver and parabolic trough technology. CSPsim significantly increases the speed of energy yield calculations by factor ≥ 35 and has automated the simulation run of all predefined design configurations in sequential order during the optimization procedure. To develop the predictive models, multiple linear regression techniques and Design of Experiment methods are applied. The annual energy yield and derived LCOE calculated by the predictive model deviates less than ±1.5 % from the thermodynamic simulation in EBSILON and effectively identifies the optimal range of main design parameters for further, more specific analysis.

  7. In vitro dissolution models for the prediction of in vivo performance of an oral mesoporous silica formulation.

    Science.gov (United States)

    McCarthy, Carol A; Faisal, Waleed; O'Shea, Joseph P; Murphy, Colm; Ahern, Robert J; Ryan, Katie B; Griffin, Brendan T; Crean, Abina M

    2017-03-28

    Drug release from mesoporous silica systems has been widely investigated in vitro using USP Type II (paddle) dissolution apparatus. However, it is not clear if the observed enhanced in vitro dissolution can forecast drug bioavailability in vivo. In this study, the ability of different in vitro dissolution models to predict in vivo oral bioavailability in a pig model was examined. The fenofibrate-loaded mesoporous silica formulation was compared directly to a commercial reference product, Lipantil Supra®. Three in vitro dissolution methods were considered; USP Type II (paddle) apparatus, USP Type IV (flow-through cell) apparatus and a USP IV Transfer model (incorporating a SGF to FaSSIF-V2 media transfer). In silico modelling, using a physiologically based pharmacokinetic modelling and simulation software package (Gastroplus™), to generate in vitro/in vivo relationships, was also investigated. The study demonstrates that the in vitro dissolution performance of a mesoporous silica formulation varies depending on the dissolution apparatus utilised and experimental design. The findings show that the USP IV transfer model was the best predictor of in vivo bioavailability. The USP Type II (paddle) apparatus was not effective at forecasting in vivo behaviour. This observation is likely due to hydrodynamic differences between the two apparatus and the ability of the transfer model to better simulate gastrointestinal transit. The transfer model is advantageous in forecasting in vivo behaviour for formulations which promote drug supersaturation and as a result are prone to precipitation to a more energetically favourable, less soluble form. The USP IV transfer model could prove useful in future mesoporous silica formulation development. In silico modelling has the potential to assist in this process. However, further investigation is required to overcome the limitations of the model for solubility enhancing formulations. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. A Bayesian model for predicting face recognition performance using image quality

    NARCIS (Netherlands)

    Dutta, A.; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan

    2014-01-01

    Quality of a pair of facial images is a strong indicator of the uncertainty in decision about identity based on that image pair. In this paper, we describe a Bayesian approach to model the relation between image quality (like pose, illumination, noise, sharpness, etc) and corresponding face

  9. Predictive Performance of a Busulfan Pharmacokinetic Model in Children and Young Adults

    NARCIS (Netherlands)

    Bartelink, Imke H.; van Kesteren, Charlotte; Boelens, Jaap J.; Egberts, Toine C. G.; Bierings, Marc B.; Cuvelier, Geoff D. E.; Wynn, Robert F.; Slatter, Mary A.; Chiesa, Robert; Danhof, Meindert; Knibbe, Catherijne A. J.

    2012-01-01

    Background: Recently a pediatric pharmacokinetic (PK) model was developed for busulfan to explain the wide variability in PK of busulfan in children, as this variability is known to influence the outcome of hematopoietic stem cell transplantation in terms of toxicity and event free survival. This st

  10. Development of Simple Drying Model for Performance Prediction of Solar Dryer: Theoretical Analysis

    DEFF Research Database (Denmark)

    Singh, Shobhana; Kumar, Subodh

    2012-01-01

    of experimental drying parameters. A laboratory model of mixed-mode solar dryer system is tested with cylindrical potato samples of thickness 5 and 18 mm under simulated indoor conditions. The potato samples were dried at a constant absorbed thermal energy of 750 W/m2 and air mass flow rate of 0.011 kg...

  11. Paint Pavement Marking Performance Prediction Model That Includes the Impacts of Snow Removal Operations

    Science.gov (United States)

    2011-03-01

    Hypothesized that snow plows wear down mountain road pavement markings. 2007 Craig et al. -Edge lines degrade slower than center/skip lines 2007...retroreflectivity to create the models. They discovered that paint pavement markings last 80% longer on Portland Cement Concrete than Asphalt Concrete at low AADT...retroreflectivity, while yellow markings lost 21%. Lu and Barter attributed the sizable degradation to snow removal, sand application, and studded

  12. Performance of a New Model for Predicting End of Flowering Date (bbch 69) of Grapevine (Vitis Vinifera L.)

    Science.gov (United States)

    Gentilucci, Matteo

    2017-04-01

    The end of flowering date (BBCH 69) is an important phenological stage for grapevine (Vitis Vinifera L.), in fact up to this date the growth is focused on the plant and gradually passes on the berries through fruit set. The aim of this study is to perform a model to predict the date of the end of flowering (BBCH69) for some grapevine varieties. This research carried out using three cultivars of grapevine (Maceratino, Montepulciano, Sangiovese) in three different locations (Macerata, Morrovalle and Potenza Picena), places of an equal number of wine farms for the time interval between 2006 and 2013. In order to have reliable temperatures for each location, the data of 6 weather stations near these farms have been interpolated using cokriging methods with elevation as independent variable. The procedure to predict the end of flowering date starts with an investigation of cardinal temperatures typical of each grapevine cultivar. In fact the analysis is characterized by four temperature thresholds (cardinals): minimum activity temperature (TCmin = below this temperature there is no growth for the plant), lower optimal temperature (TLopt = above this temperature there is maximum growth), upper optimal temperature (TUopt = below this temperature there is maximum growth) and maximum activity temperature (TC max = above this temperature there is no growth). Thus this model take into consideration maximum, mean and minimum daily temperatures of each location, relating them with the four above mentioned cultivar temperature thresholds. In this way it has been obtained some possible cases (32) corresponding to as many equations, depending on the position of temperatures compared with the thresholds, in order to calculate the amount of growing degree units (GDU) for each day. Several iterative tests (about 1000 for each cultivar) have been performed, changing the values of temperature thresholds and GDU in order to find the best possible combination which minimizes error

  13. Impacts of solar activity on performance of the IRI-2012 model predictions from low to mid latitudes

    Science.gov (United States)

    Kumar, Sanjay; Tan, Eng Leong; Murti, Dhimas Sentanu

    2015-03-01

    This study investigates the impacts of solar activity on the performance of the latest release of International Reference Ionosphere (IRI) model version 2012 (IRI-2012) predictions during the ascending phase of solar activity from 2009 to 2013. The study is based on the data of total electron content (TEC) retrieved from the Global Positioning System (GPS) at Singapore (NTUS) (geographic latitude 01.34°N, longitude 103.67°E, geomagnetic latitude 8.4°S), Thailand (CUSV) (geographic latitude 13.73°N, longitude 100.54°E, geomagnetic latitude 3.96°N), China (KUNM) (geographic latitude 25.02°N, longitude 102.79°E, geomagnetic latitude 15.15°N), Mongolia (ULAB) (geographic latitude 47.67°N, longitude 107.05°E, geomagnetic latitude 37.73°S), and Russia (IRKM) (geographic latitude 52.21°N, 104.31°E, geomagnetic latitude 42.28°S). The GPS-TEC has been compared with the IRI-2012 model TEC for three different options, namely, IRI-NeQ, IRI01-corr, and IRI-2001, for topside Ne over all the above five stations lying at different latitudes from equatorial-equatorial ionization anomaly (EIA) to mid-latitude regions but at around the same longitude line (104° ± 3°E). The study showed that the IRI model predictions for different topside options are different and significant in low-latitude region but insignificant in mid-latitude regions (except during winter season of high solar activity year 2012). During the period from 2009 to 2013, upon moving from low to high solar activity, the prediction nature (overestimation/underestimation) of IRI-2012 model changes significantly at EIA station KUNM of low-latitude region. The discrepancy in IRI-2012 model TEC as compared to GPS-TEC in low-latitude region is found to be larger and significant than in mid-latitude region (Mongolia and Russia). The discrepancy in the IRI-2012 model TEC with IRI-2001 topside is found to be maximum at equatorial station CUSV (RMSD 99%) during the solar minimum year 2009 and decreases moving

  14. Encoding of vowel-like sounds in the auditory nerve: Model predictions of discrimination performance

    Science.gov (United States)

    Tan, Qing; Carney, Laurel H.

    2005-03-01

    The sensitivity of listeners to changes in the center frequency of vowel-like harmonic complexes as a function of the center frequency of the complex cannot be explained by changes in the level of the stimulus [Lyzenga and Horst, J. Acoust. Soc. Am. 98, 1943-1955 (1995)]. Rather, a complex pattern of sensitivity is seen; for a spectrum with a triangular envelope, the greatest sensitivity occurs when the center frequency falls between harmonics, whereas for a spectrum with a trapezoidal envelope, greatest sensitivity occurs when the center frequency is aligned with a harmonic. In this study, the thresholds of a population model of auditory-nerve (AN) fibers were quantitatively compared to these trends in psychophysical thresholds. Single-fiber and population model responses were evaluated in terms of both average discharge rate and the combination of rate and timing information. Results indicate that phase-locked responses of AN fibers encode phase transitions associated with minima in these amplitude-modulated stimuli. The temporal response properties of a single AN fiber, tuned to a frequency slightly above the center frequency of the harmonic complex, were able to explain the trends in thresholds for both triangular- and trapezoidal-shaped spectra. .

  15. Performance of Various Models in Predicting Vital Capacity Changes Caused by Breathing High Oxygen Partial Pressures

    Science.gov (United States)

    2007-10-01

    average data is moderately high at 0.43. 70 >0 8A m m I I " IO~~C C? nIf _ ~ ~ c > , i𔃺 I " g i0 i0 04 (WIB CO (U13 CO 0 00 0 0 I oBueqo % coo 0 0 C...B e o LU o w 0. 0 0I L CD 0 74 00 CDE 00 00 0-0 I 0) 00 o 0 0 0 C6 CI (D a 0. 0E c\\l E * 0- ’U.)C C .. 0* ** C\\l Cf GOB40L% cmJ 75 Model 9. Delayed

  16. Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment

    Directory of Open Access Journals (Sweden)

    Leila Ismail

    2012-05-01

    Full Text Available Cloud computing is an emerging technology where IT resources are virtualized to users as a set of a unified computing resources on a pay per use basis. The resources are dynamically chosen to satisfy a user Service Level Agreement and a required level of performance. Divisible load applications occur in many scientific and engineering applications and can easily be mapped to a Cloud using a master-worker pattern. However, those applications pose challenges to obtain the required performance. We model divisible load applications tasks processing on a set of cloud resources. We derive a novel model and formulas for computing the blocking probability in the system. The formulas are useful to analyze and predict the behavior of a divisible load application on a chosen set of resources to satisfy a Service Level Agreement before the implementation phase, thus saving time and platform energy. They are also useful as a dynamic feedback to a cloud scheduler for optimal scheduling. We evaluate the model in a set of illustrative scenarios.

  17. Midtemperature solar systems test facility predictions for thermal performance based on test data. Polisolar model POL solar collector with glass reflector surface

    Science.gov (United States)

    Harrison, T. D.

    1981-05-01

    Thermal performance predictions based on test data are presented for the Polisolar Model POL sola collector, with glass reflector surfaces, for three output temperatures at five cities in the United States.

  18. Using high-performance mathematical modelling tools to predict erosion and sediment fluxes in peri-urban catchments

    Science.gov (United States)

    Pereira, André; Conde, Daniel; Ferreira, Carla S. S.; Walsh, Rory; Ferreira, Rui M. L.

    2017-04-01

    Deforestation and urbanization generally lead to increased soil erosion andthrough the indirect effect of increased overland flow and peak flood discharges. Mathematical modelling tools can be helpful for predicting the spatial distribution of erosion and the morphological changes on the channel network. This is especially useful to predict the impacts of land-use changes in parts of the watershed, namely due to urbanization. However, given the size of the computational domain (normally the watershed itself), the need for high spatial resolution data to model accurately sediment transport processes and possible need to model transcritical flows, the computational cost is high and requires high-performance computing techniques. The aim of this work is to present the latest developments of the hydrodynamic and morphological model STAV2D and its applicability to predict runoff and erosion at watershed scale. STAV2D was developed at CEris - Instituto Superior Técnico, Universidade de Lisboa - as a tool particularly appropriated to model strong transient flows in complex and dynamic geometries. It is based on an explicit, first-order 2DH finite-volume discretization scheme for unstructured triangular meshes, in which a flux-splitting technique is paired with a reviewed Roe-Riemann solver, yielding a model applicable to discontinuous flows over time-evolving geometries. STAV2D features solid transport in both Euleran and Lagrangian forms, with the aim of describing the transport of fine natural sediments and then the large individual debris. The model has been validated with theoretical solutions and laboratory experiments (Canelas et al., 2013 & Conde et al., 2015). STAV-2D now supports fully distributed and heterogeneous simulations where multiple different hardware devices can be used to accelerate computation time within a unified Object-Oriented approach: the source code for CPU and GPU has the same compilation units and requires no device specific branches, like

  19. An improved model for predicting performance of finned tube heat exchanger under frosting condition, with frost thickness variation along fin

    Energy Technology Data Exchange (ETDEWEB)

    Tso, C.P. [Multimedia University, Jalan Ayer Keroh Lama, Melaka (Malaysia). Faculty of Engineering and Technology; Cheng, Y.C.; Lai, A.C.K. [Nanyang Technological University, Singapore (Singapore). School of Mechanical and Aerospace Engineering

    2006-01-15

    Frost accumulation on a heat exchanger, a direct result of combined heat and mass transfer between the moist air flowing across a cold surface, causes heat transfer performance degradation due to the insulating effect of frost layer and the coil blockage as the frost grows. The complex geometry of finned tube heat exchangers leads to uneven wall and air temperature distribution inside the coil, and causes variations of frost growth rate and densification along the coil. In this study, a general distributed model with frost formation was developed. The equations for finned tube heat exchanger were derived in non-steady-state manner and quasi-steady state in the frost model. In order to make the model more realistic, the variation of frost along fin due to uneven temperature distribution was included. The presented model is able to predict the dynamic behavior of an air cooler both under non-frost and frost condition. Comparisons were made based on the frost mass accumulation, pressure drop across coil and energy transfer coefficient, and results were found to agree well with reported experimental results. (author)

  20. Predicting Radiation Induced Performance Decrements of AH-1 Helicopter Crews. Volume 2. Evaluation of Modeling and Simulation Techniques for Predicting Radiation Induced Performance Decrements

    Science.gov (United States)

    1993-03-01

    X E-2 jerk joule 1J) 1.000 0 XOOX E#9 jouleikilogram IJ/Kgl (radiation dose absorbed) Gray IGyv 1.000000 kilotons teraJoules 4.183 kip 11000 Ibfl...newton (N) 4.448 222 X E*3 kip /tnch 2 (ksti kilo pascal tkPa) 6.894 757 X E+3 ktap newton-secondim 2 IN-s/M 2) 1.000 000 X E-2 micron meter (mI 1.000 000...designed as a research tool for following performance changes over time, treatments, dosages or levels ( Thorne , Genser, Sing & Hegge, 1985). The WRPAB

  1. Predictive performance of the Domino, Hijazi, and Clements models during low-dose target-controlled ketamine infusions in healthy volunteers

    NARCIS (Netherlands)

    Absalom, A. R.; Lee, M.; Menon, D. K.; Sharar, S. R.; De Smet, T.; Halliday, J.; Ogden, M.; Corlett, P.; Honey, G. D.; Fletcher, P. C.

    2007-01-01

    Background. Healthy volunteers received low-dose target-controlled infusions (TCI) of ketamine controlled by the Domino model while cognitive function tests and functional neuroimaging were performed. The aim of the current study was to assess the predictive performance of the Domino model during th

  2. Predictive performance of the Domino, Hijazi, and Clements models during low-dose target-controlled ketamine infusions in healthy volunteers

    NARCIS (Netherlands)

    Absalom, A. R.; Lee, M.; Menon, D. K.; Sharar, S. R.; De Smet, T.; Halliday, J.; Ogden, M.; Corlett, P.; Honey, G. D.; Fletcher, P. C.

    2007-01-01

    Background. Healthy volunteers received low-dose target-controlled infusions (TCI) of ketamine controlled by the Domino model while cognitive function tests and functional neuroimaging were performed. The aim of the current study was to assess the predictive performance of the Domino model during th

  3. 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...

  4. Performance assessment of the COAMPS numerical weather prediction model in precise GPS positioning: EUPOS network case study

    Science.gov (United States)

    Wielgosz, Pawel; Paziewski, Jacek; Krankowski, Andrzej; Kroszczynski, Krzyszfof; Figurski, Mariusz

    2010-05-01

    The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) represents a complete three-dimensional data assimilation system comprised of data quality control, analysis, initialization, and forecast model components. COAMPS has been developed by the Marine Meteorology Division (MMD) of the Naval Research Laboratory (NRL). The U.S. Navy uses the system for short-term numerical weather predictions for various regions of the world. Currently COAMPS ver.3.1 is also operated and tested at the Department of Civil Engineering and Geodesy of the Military University of Technology, Warsaw, Poland (MUT). It is primarily used for military applications, but also a new module has been developed to provide tropospheric zenith total delays (ZTD) for stations of the Polish part of the European Position Determination System (EUPOS). ZTDs can be obtained in both near-real time and several hours ahead. In the highest-precision GPS applications tropospheric delays are usually estimated from satellite observables. When processing long baselines the common practice is to derive the hydrostatic component from any troposphere model and use it as a priori information. The non-hydrostatic part is estimated in the adjustment along with station coordinates. The change of satellite geometry during the observational session allows overcome high correlation between the tropospheric delays and the station height components. However, when processing very short sessions and medium baselines, this change is too small and does not allow estimating reliable ZTDs. Hence, ZTD are derived from troposphere models and used for correction of GPS data in the processing. This contribution presents the application of COAMPS-derived ZTDs in precise GPS positioning when using short data spans (1-5 minutes) and processing medium baselines (50-80 km). The presented tests were performed in two areas: Wielkopolska Lowland (all stations located at similar heights), and Carpathian Mountains (where station

  5. 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...

  6. Performance modeling of Beamlet

    Energy Technology Data Exchange (ETDEWEB)

    Auerbach, J.M.; Lawson, J.K.; Rotter, M.D.; Sacks, R.A.; Van Wonterghem, B.W.; Williams, W.H.

    1995-06-27

    Detailed modeling of beam propagation in Beamlet has been made to predict system performance. New software allows extensive use of optical component characteristics. This inclusion of real optical component characteristics has resulted in close agreement between calculated and measured beam distributions.

  7. Quadratic Prediction Models for the Performance Comparison of a Marine Engine Fuelled with Biodiesels B5 and B20

    Directory of Open Access Journals (Sweden)

    Chedthawut Poompipatpong

    2014-01-01

    Full Text Available According to Thailand’s renewable energy development plan, biodiesel is one of the interesting alternative energies. In this research, biodiesels B5 and B20 are tested in a marine engine. The experimental results are then compared by using three different techniques including (1 the conventional technique, (2 average of the point-to-point comparisons, and (3 a comparison by using quadratic prediction models. This research aims to present the procedures of these techniques in-depth. The results show that the comparison by using quadratic prediction models can accurately predict ample amounts of results and make the comparison more logical. The results are compatible with those of the conventional technique, while the average of the point-to-point comparisons shows diverse results. These results are also explained on the fuel property basis, confirming that the quadratic prediction model and the conventional technique are practical, but the average of the point-to-point comparison technique presents an inaccurate result. The benefit of this research shows that the quadratic prediction model is more flexible for future science and engineering experimental design, thus reducing cost and time usage. The details of the calculation, results, and discussion are presented in the paper.

  8. Academic Performance of First-Year Students at a College of Pharmacy in East Tennessee: Models for Prediction

    Science.gov (United States)

    Clavier, Cheri Whitehead

    2013-01-01

    With the increase of students applying to pharmacy programs, it is imperative that admissions committees choose appropriate measures to analyze student readiness. The purpose of this research was to identify significant factors that predict the academic performance, defined as grade point average (GPA) at the end of the first professional year, of…

  9. Academic Performance of First-Year Students at a College of Pharmacy in East Tennessee: Models for Prediction

    Science.gov (United States)

    Clavier, Cheri Whitehead

    2013-01-01

    With the increase of students applying to pharmacy programs, it is imperative that admissions committees choose appropriate measures to analyze student readiness. The purpose of this research was to identify significant factors that predict the academic performance, defined as grade point average (GPA) at the end of the first professional year, of…

  10. 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...

  11. Performance of a coupled lagged ensemble weather and river runoff prediction model system for the Alpine Ammer River catchment

    Science.gov (United States)

    Smiatek, G.; Kunstmann, H.; Werhahn, J.

    2012-04-01

    The Ammer River catchment located in the Bavarian Ammergau Alps and alpine forelands, Germany, represents with elevations reaching 2185 m and annual mean precipitation between1100 and 2000 mm a very demanding test ground for a river runoff prediction system. Large flooding events in 1999 and 2005 motivated the development of a physically based prediction tool in this area. Such a tool is the coupled high resolution numerical weather and river runoff forecasting system AM-POE that is being studied in several configurations in various experiments starting from the year 2005. Corner stones of the coupled system are the hydrological water balance model WaSiM-ETH run at 100 m grid resolution, the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution and the Perl Object Environment (POE) framework. POE implements the input data download from various sources, the input data provision via SOAP based WEB services as well as the runs of the hydrology model both with observed and with NWP predicted meteorology input. The one way coupled system utilizes a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. Results obtained in the years 2005-2011 reveal that river runoff simulations depict high correlation with observed runoff when driven with monitored observations in hindcast experiments. The ability to runoff forecasts is depending on lead times in the lagged ensemble prediction and shows still limitations resulting from errors in timing and total amount of the predicted precipitation in the complex mountainous area. The presentation describes the system implementation, and demonstrates the application of the POE framework in networking, distributed computing and in the setup of various experiments as well as long term results of the system application in the years 2005 - 2011.

  12. Testing the near field/far field model performance for prediction of particulate matter emissions in a paint factory

    DEFF Research Database (Denmark)

    Koivisto, A.J.; Jensen, A.C.Ø.; Levin, Marcus

    2015-01-01

    A Near Field/Far Field (NF/FF) model is a well-accepted tool for precautionary exposure assessment but its capability to estimate particulate matter (PM) concentrations is not well studied. The main concern is related to emission source characterization which is not as well defined for PM emitters...... compared to e.g. for solvents. One way to characterize PM emission source strength is by using the material dustiness index which is scaled to correspond to industrial use by using modifying factors, such as handling energy factors. In this study we investigate how well the NF/FF model predicts PM...... concentration levels in a paint factory. PM concentration levels were measured during big bag and small bag powder pouring. Rotating drum dustiness indices were determined for the specific powders used and applied in the NF/FF model to predict mass concentrations. Modeled process specific concentration levels...

  13. Predictive models in urology.

    Science.gov (United States)

    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.

  14. A Framework for Simplified Residential Energy Consumption Assessment towards Developing Performance Prediction Models for Retrofit Decision-Making

    OpenAIRE

    Durak, Tolga

    2011-01-01

    This research proposes to simplify the energy consumption assessment for residential homes while building the foundation towards the development of prediction tools that can achieve a credible level of accuracy for confident decision making. The energy consumption assessment is based on simplified energy consumption models. The energy consumption analysis uses a reduced number of energy model equations utilizing a critical, limited set of parameters. The results of the analysis are used to de...

  15. Prediction of aerodynamic performance for MEXICO rotor

    DEFF Research Database (Denmark)

    Hong, Zedong; Yang, Hua; Xu, Haoran

    2013-01-01

    The aerodynamic performance of the MEXICO (Model EXperiments In Controlled cOnditions) rotor at five tunnel wind speeds is predicted by making use of BEM and CFD methods, respectively, using commercial MATLAB and CFD software. Due to the pressure differences on both sides of the blade, the tip...... the reliability of the MEXICO data. Second, the SST turbulence model can better capture the flow separation on the blade and has high aerodynamic performance prediction accuracy for a horizontal axis wind turbine in axial inflow conditions. Finally, the comparisons of the axial and tangential forces as well...... as the contrast of the angle of attack indicate that the prediction accuracy of BEM method is high when the blade is not in the stall condition. However, the airfoil characteristic becomes unstable in the stall condition, and the maximum relative error of tangential force calculated by BEM is -0.471. As a result...

  16. Analysis of DoD inkjet printhead performance for printable electronics fabrication using dynamic lumped element modeling and swarm intelligence based optimal prediction

    Institute of Scientific and Technical Information of China (English)

    何茂伟; 孙丽玲; 胡琨元; 朱云龙; 陈瀚宁

    2015-01-01

    The major challenge in printable electronics fabrication is to effectively and accurately control a drop-on-demand (DoD) inkjet printhead for high printing quality. In this work, an optimal prediction model, constructed with the lumped element modeling (LEM) and the artificial bee colony (ABC) algorithm, was proposed to efficiently predict the combination of waveform parameters for obtaining the desired droplet properties. For acquiring higher simulation accuracy, a modified dynamic lumped element model (DLEM) was proposed with time-varying equivalent circuits, which can characterize the nonlinear behaviors of piezoelectric printhead. The proposed method was then applied to investigate the influences of various waveform parameters on droplet volume and velocity of nano-silver ink, and to predict the printing quality using nano-silver ink. Experimental results show that, compared with two-dimension manual search, the proposed optimal prediction model perform efficiently and accurately in searching the appropriate combination of waveform parameters for printable electronics fabrication.

  17. Educational Data Mining & Students’ Performance Prediction

    Directory of Open Access Journals (Sweden)

    Amjad Abu Saa

    2016-05-01

    Full Text Available It is important to study and analyse educational data especially students’ performance. Educational Data Mining (EDM is the field of study concerned with mining educational data to find out interesting patterns and knowledge in educational organizations. This study is equally concerned with this subject, specifically, the students’ performance. This study explores multiple factors theoretically assumed to affect students’ performance in higher education, and finds a qualitative model which best classifies and predicts the students’ performance based on related personal and social factors.

  18. Long-term behaviour of concrete: development of operational model to predict the evolution of its containment performance. Application to cemented waste packages

    Energy Technology Data Exchange (ETDEWEB)

    Peycelon, H.; Le Bescop, P.; Richet, C. [CEA Saclay, Dept. de Physico-Chimie, DPC, 91 - Gif-sur-Yvette (France); Adenot, F. [CEA Cadarache, 13 - Saint Paul lez Durance (France). Dept. d' Entreposage et de Stockage des Dechets; Blanc, V. [Cogema, 78 - Saint Quentin en Yvelines (France)

    2001-07-01

    In order to describe the main phenomena during different stages of cement waste packages life-time and to predict the long-term behaviour (containment performance) of concrete, coupled experiments and modelling studies are achieved. With respect to logical methodology, improvement of these studies is accomplished. Degradation of concrete in low mineralized, carbonated and sulfated water lead to an evolution of chemical characteristics (dissolution/precipitation of solid phases) and of transport properties which must be included or coupled in retention/transport modelling of radio nuclides to predict containment performance. (author)

  19. Principles of Sonar Performance Modeling

    NARCIS (Netherlands)

    Ainslie, M.A.

    2010-01-01

    Sonar performance modelling (SPM) is concerned with the prediction of quantitative measures of sonar performance, such as probability of detection. It is a multidisciplinary subject, requiring knowledge and expertise in the disparate fields of underwater acoustics, acoustical oceanography, sonar sig

  20. Deliberate practice predicts performance throughout time in adolescent chess players and dropouts: A linear mixed models analysis.

    NARCIS (Netherlands)

    Bruin, de A.B.H.; Smits, N.; Rikers, R.M.J.P.; Schmidt, H.G.

    2008-01-01

    In this study, the longitudinal relation between deliberate practice and performance in chess was examined using a linear mixed models analysis. The practice activities and performance ratings of young elite chess players, who were either in, or had dropped out of the Dutch national chess training,

  1. Deliberate practice predicts performance throughout time in adolescent chess players and dropouts: A linear mixed models analysis.

    NARCIS (Netherlands)

    Bruin, de A.B.H.; Smits, N.; Rikers, R.M.J.P.; Schmidt, H.G.

    2008-01-01

    In this study, the longitudinal relation between deliberate practice and performance in chess was examined using a linear mixed models analysis. The practice activities and performance ratings of young elite chess players, who were either in, or had dropped out of the Dutch national chess training,

  2. 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.

  3. 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-.

  4. Predictive Model Assessment for Count Data

    Science.gov (United States)

    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

  5. Prediction of GPR performance in landmine detection

    Science.gov (United States)

    Riahi, Mohammad; Tavangar, Amirhossein

    2009-03-01

    The contrast in the dielectric constant between a landmine and the surrounding soil is one of the most important parameters to be considered when using ground penetrating radar (GPR) for landmine detection. In this paper, we discuss available models for the prediction of the dielectric constant from soil physical properties including bulk density, particles density soil texture, and water content. We predict the effects of such properties on the antipersonnel (AP) landmine detection performance of GPR in an application in Iran. Initially, available soil geophysical information was used from four types of soil selected from Iranian mine-affected areas. Subsequently, a pedotransfer model was developed to predict whether or not field conditions are appropriate for use of GPR instruments. The predictions outcome obtained through usage of this model was based on different soil textures at various soil water contents. Knowledge of soil texture, dry bulk density, and water content are necessary to determine whether soil conditions are suitable for utilization of GPR mine detection. The developed model presented here can be useful for making this determination. Finally, the graphical user interface (GUI) of the pedotransfer model was calculated and presented herein. This software package facilitates the analysis of complex dielectric constant of soil as well as attenuation of GPR signals. The developed package is also capable of plotting the complex dielectric constant of soil coupled with attenuation of GPR signals versus soil physical properties.

  6. A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

    Directory of Open Access Journals (Sweden)

    Sergio Briguglio

    2003-01-01

    Full Text Available A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage and OpenMP (at the intra-node one. The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

  7. Predicting sample size required for classification performance

    Directory of Open Access Journals (Sweden)

    Figueroa Rosa L

    2012-02-01

    Full Text Available Abstract Background Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target. Methods We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method. Results A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p Conclusions This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.

  8. An effective simulation model to predict and optimize the performance of single and double glaze flat-plate solar collector designs

    OpenAIRE

    Kaplanis, S.; Kaplani, E.

    2012-01-01

    This paper outlines and formulates a compact and effective simulation model, which predicts the performance of single and double glaze flat-plate collector. The model uses an elaborated iterative simulation algorithm and provides the collector top losses, the glass covers temperatures, the collector absorber temperature, the collector fluid outlet temperature, the system efficiency, and the thermal gain for any operational and environmental conditions. It is a numerical approach based on simu...

  9. 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....

  10. Performance of two predictive uncertainty estimation approaches for conceptual Rainfall-Runoff Model: Bayesian Joint Inference and Hydrologic Uncertainty Post-processing

    Science.gov (United States)

    Hernández-López, Mario R.; Romero-Cuéllar, Jonathan; Camilo Múnera-Estrada, Juan; Coccia, Gabriele; Francés, Félix

    2017-04-01

    It is noticeably important to emphasize the role of uncertainty particularly when the model forecasts are used to support decision-making and water management. This research compares two approaches for the evaluation of the predictive uncertainty in hydrological modeling. First approach is the Bayesian Joint Inference of hydrological and error models. Second approach is carried out through the Model Conditional Processor using the Truncated Normal Distribution in the transformed space. This comparison is focused on the predictive distribution reliability. The case study is applied to two basins included in the Model Parameter Estimation Experiment (MOPEX). These two basins, which have different hydrological complexity, are the French Broad River (North Carolina) and the Guadalupe River (Texas). The results indicate that generally, both approaches are able to provide similar predictive performances. However, the differences between them can arise in basins with complex hydrology (e.g. ephemeral basins). This is because obtained results with Bayesian Joint Inference are strongly dependent on the suitability of the hypothesized error model. Similarly, the results in the case of the Model Conditional Processor are mainly influenced by the selected model of tails or even by the selected full probability distribution model of the data in the real space, and by the definition of the Truncated Normal Distribution in the transformed space. In summary, the different hypotheses that the modeler choose on each of the two approaches are the main cause of the different results. This research also explores a proper combination of both methodologies which could be useful to achieve less biased hydrological parameter estimation. For this approach, firstly the predictive distribution is obtained through the Model Conditional Processor. Secondly, this predictive distribution is used to derive the corresponding additive error model which is employed for the hydrological parameter

  11. STUDY ON TURBOMACHINERY PERFORMANCE PREDICTION WITH NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Traditional methods for performance prediction of a turbomachinery are usually based on certain computations from a set of data obtained in limited experiment measurements of the machine, or the machinemodels. Since the computational (mathematical) models used in such performance prediction are often crude,most of the predicted results are only correct in very small ranges around the known data points. Beyond the limited ranges,the accuracy of the resultant predictions decrease abruptly. Therefore, an alternative approach,neural network technique,is studied for performance prediction of turbomachinery. The new approach has been applied to two typical performance prediction cases to verify its feasibility and reliability.

  12. The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

    Science.gov (United States)

    Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

    2017-04-01

    Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

  13. 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 CO2 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.

  14. Axial-Flow Compressor Performance Prediction in Design and Off-Design Conditions through 1-D and 3-D Modeling and Experimental Study

    Directory of Open Access Journals (Sweden)

    Ahmad Peyvan

    2016-01-01

    Full Text Available In this study, the main objective is to develop a one dimensional model to predict design and off design performance of an operational axial flow compressor by considering the whole gas turbine assembly. The design and off-design performance of a single stage axial compressor are predicted through 1D and 3D modeling. In one dimensional model the mass, momentum and energy conservation equations and ideal gas equation of state are solved in mean line at three axial stations including rotor inlet, rotor outlet and stator outlet. The total to total efficiency and pressure ratio are forecasted using the compressor geometry, inlet stagnation temperature and stagnation pressure, the mass flow rate and the rotational speed of the rotor, and the available empirical correlation predicting the losses. By changing the mass flow rate while the rotational speed is fixed, characteristic curves of the compressor are obtained. The 3D modeling is accomplished with CFD method to verify one dimensional code at non-running line conditions. By defining the three-dimensional geometry of the compressor and the boundary conditions coinciding with one dimensional model for the numerical solver, axial compressor behavior is predicted for various mass flow rates in different rotational speeds. Experimental data are obtained from tests of the axial compressor of a gas turbine engine in Sharif University gas turbine laboratory and consequently the running line is attained. As a result, the two important extremities of compressor performance including surge and choking conditions are obtained through 1D and 3D modeling. Moreover, by comparing the results of one-dimensional and three-dimensional models with experimental results, good agreement is observed. The maximum differences of pressure ratio and isentropic efficiency of one dimensional modeling with experimental results are 2.1 and 3.4 percent, respectively.

  15. Outcome Prediction after Traumatic Brain Injury: Comparison of the Performance of Routinely Used Severity Scores and Multivariable Prognostic Models

    Science.gov (United States)

    Majdan, Marek; Brazinova, Alexandra; Rusnak, Martin; Leitgeb, Johannes

    2017-01-01

    Objectives: Prognosis of outcome after traumatic brain injury (TBI) is important in the assessment of quality of care and can help improve treatment and outcome. The aim of this study was to compare the prognostic value of relatively simple injury severity scores between each other and against a gold standard model – the IMPACT-extended (IMP-E) multivariable prognostic model. Materials and Methods: For this study, 866 patients with moderate/severe TBI from Austria were analyzed. The prognostic performances of the Glasgow coma scale (GCS), GCS motor (GCSM) score, abbreviated injury scale for the head region, Marshall computed tomographic (CT) classification, and Rotterdam CT score were compared side-by-side and against the IMP-E score. The area under the receiver operating characteristics curve (AUC) and Nagelkerke's R2 were used to assess the prognostic performance. Outcomes at the Intensive Care Unit, at hospital discharge, and at 6 months (mortality and unfavorable outcome) were used as end-points. Results: Comparing AUCs and R2s of the same model across four outcomes, only little variation was apparent. A similar pattern is observed when comparing the models between each other: Variation of AUCs 0.83 and R2 > 0.42 for all outcomes): AUCs were worse by 0.10–0.22 (P prognosis. However, it is confirmed that well-developed multivariable prognostic models outperform these scores significantly and should be used for prognosis in patients after TBI wherever possible.

  16. Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa.

    Directory of Open Access Journals (Sweden)

    Katya L Masconi

    Full Text Available Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imputation methods, and assess the effect of these techniques on the performance of undiagnosed diabetes risk prediction models during external validation.Data from the Cape Town Bellville-South cohort served as the basis for this study. Imputation methods and models were identified via recent systematic reviews. Models' discrimination was assessed and compared using C-statistic and non-parametric methods, before and after recalibration through simple intercept adjustment.The study sample consisted of 1256 individuals, of whom 173 were excluded due to previously diagnosed diabetes. Of the final 1083 individuals, 329 (30.4% had missing data. Family history had the highest proportion of missing data (25%. Imputation of the outcome, undiagnosed diabetes, was highest in stochastic regression imputation (163 individuals. Overall, deletion resulted in the lowest model performances while simple imputation yielded the highest C-statistic for the Cambridge Diabetes Risk model, Kuwaiti Risk model, Omani Diabetes Risk model and Rotterdam Predictive model. Multiple imputation only yielded the highest C-statistic for the Rotterdam Predictive model, which were matched by simpler imputation methods.Deletion was confirmed as a poor technique for handling missing data. However, despite the emphasized disadvantages of simpler imputation methods, this study showed that implementing these methods results in similar predictive utility for undiagnosed diabetes when compared to multiple imputation.

  17. Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?

    Science.gov (United States)

    Snell, Kym Ie; Ensor, Joie; Debray, Thomas Pa; Moons, Karel Gm; Riley, Richard D

    2017-01-01

    If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model's discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of 'true' performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.

  18. 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...

  19. Outcome prediction after traumatic brain injury: Comparison of the performance of routinely used severity scores and multivariable prognostic models

    Directory of Open Access Journals (Sweden)

    Marek Majdan

    2017-01-01

    Full Text Available Objectives: Prognosis of outcome after traumatic brain injury (TBI is important in the assessment of quality of care and can help improve treatment and outcome. The aim of this study was to compare the prognostic value of relatively simple injury severity scores between each other and against a gold standard model – the IMPACT-extended (IMP-E multivariable prognostic model. Materials and Methods: For this study, 866 patients with moderate/severe TBI from Austria were analyzed. The prognostic performances of the Glasgow coma scale (GCS, GCS motor (GCSM score, abbreviated injury scale for the head region, Marshall computed tomographic (CT classification, and Rotterdam CT score were compared side-by-side and against the IMP-E score. The area under the receiver operating characteristics curve (AUC and Nagelkerke's R2 were used to assess the prognostic performance. Outcomes at the Intensive Care Unit, at hospital discharge, and at 6 months (mortality and unfavorable outcome were used as end-points. Results: Comparing AUCs and R2s of the same model across four outcomes, only little variation was apparent. A similar pattern is observed when comparing the models between each other: Variation of AUCs 0.83 and R2 > 0.42 for all outcomes: AUCs were worse by 0.10–0.22 (P < 0.05 and R2s were worse by 0.22–0.39 points. Conclusions: All tested simple scores can provide reasonably valid prognosis. However, it is confirmed that well-developed multivariable prognostic models outperform these scores significantly and should be used for prognosis in patients after TBI wherever possible.

  20. On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices

    Directory of Open Access Journals (Sweden)

    Babita Majhi

    2014-09-01

    Full Text Available This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer and the output mean square error (MSE of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500 and Dow Jones Industrial Average (DJIA stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF-based forecasting model in terms of the mean average percentage error (MAPE, directional accuracy (DA, Thelis’ U and average relative variance (ARV values.

  1. e-Dairy: a dynamic and stochastic whole-farm model that predicts biophysical and economic performance of grazing dairy systems.

    Science.gov (United States)

    Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N

    2013-05-01

    A whole-farm, stochastic and dynamic simulation model was developed to predict biophysical and economic performance of grazing dairy systems. Several whole-farm models simulate grazing dairy systems, but most of them work at a herd level. This model, named e-Dairy, differs from the few models that work at an animal level, because it allows stochastic behaviour of the genetic merit of individual cows for several traits, namely, yields of milk, fat and protein, live weight (LW) and body condition score (BCS) within a whole-farm model. This model accounts for genetic differences between cows, is sensitive to genotype × environment interactions at an animal level and allows pasture growth, milk and supplements price to behave stochastically. The model includes an energy-based animal module that predicts intake at grazing, mammary gland functioning and body lipid change. This whole-farm model simulates a 365-day period for individual cows within a herd, with cow parameters randomly generated on the basis of the mean parameter values, defined as input and variance and co-variances from experimental data sets. The main inputs of e-Dairy are farm area, use of land, type of pasture, type of crops, monthly pasture growth rate, supplements offered, nutritional quality of feeds, herd description including herd size, age structure, calving pattern, BCS and LW at calving, probabilities of pregnancy, average genetic merit and economic values for items of income and costs. The model allows to set management policies to define: dry-off cows (ceasing of lactation), target pre- and post-grazing herbage mass and feed supplementation. The main outputs are herbage dry matter intake, annual pasture utilisation, milk yield, changes in BCS and LW, economic farm profit and return on assets. The model showed satisfactory accuracy of prediction when validated against two data sets from farmlet system experiments. Relative prediction errors were profit and the associated risk.

  2. 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

  3. Back propagation neural network model for predicting the performance of immobilized cell biofilters handling gas-phase hydrogen sulphide and ammonia.

    Science.gov (United States)

    Rene, Eldon R; López, M Estefanía; Kim, Jung Hoon; Park, Hung Suck

    2013-01-01

    Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m(3) ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m(3) ·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R (2)) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently.

  4. Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

    Directory of Open Access Journals (Sweden)

    Eldon R. Rene

    2013-01-01

    Full Text Available Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs for removing hydrogen sulphide (H2S and ammonia (NH3 from gas phase. The removal efficiencies (REs of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs varying up to 13 g H2S/m3·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m3·h. An application of the back propagation neural network (BPNN to predict the performance parameter, namely, RE (% using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min and inlet concentrations (ppmv, respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R2 values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently.

  5. Predicting edge seal performance from accelerated testing

    Science.gov (United States)

    Hardikar, Kedar; Vitkavage, Dan; Saproo, Ajay; Krajewski, Todd

    2014-10-01

    Degradation in performance of a PV module attributable to moisture ingress has received significant attention in PV reliability research. Assessment of field performance of PV modules against moisture ingress through product-level testing in temperature-humidity control chambers poses challenges. Development of a meaningful acceleration factor model is challenging due to different rates of degradation of components embedded in a PV module, when exposed to moisture. Test results are typically a convolution of moisture barrier performance of the edge seal and degradation of laminated components when exposed to moisture. It is desirable to have an alternate method by which moisture barrier performance of the edge seal in its end product form can be assessed in any given field conditions, independent of particular cell design. In this work, a relatively inexpensive test technique was developed to test the edge seal in its end product form in a manner that is decoupled from other components of the PV module. A theoretical framework was developed to assess moisture barrier performance of edge seal with desiccants subjected to different conditions. This framework enables the analysis of test results from accelerated tests and prediction of the field performance of the edge seal. Results from this study lead to the conclusion that the edge seal on certain Miasole glass-glass modules studied is effective for the most aggressive weather conditions examined, beyond the intended service.

  6. Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds.

    Science.gov (United States)

    Goryński, Krzysztof; Bojko, Barbara; Nowaczyk, Alicja; Buciński, Adam; Pawliszyn, Janusz; Kaliszan, Roman

    2013-10-01

    Quantitative structure-retention relationship (QSRR) is a technique capable of improving the identification of analytes by predicting their retention time on a liquid chromatography column (LC) and/or their properties. This approach is particularly useful when LC is coupled with a high-resolution mass spectrometry (HRMS) platform. The main aim of the present study was to develop and describe appropriate QSRR models that provide usable predictive capability, allowing false positive identification to be removed during the interpretation of metabolomics data, while additionally increasing confidence of experimental results in doping control area. For this purpose, a dataset consisting of 146 drugs, metabolites and banned compounds from World Anti-Doping Agency (WADA) lists, was used. A QSRR study was carried out separately on high quality retention data determined by reversed-phase (RP-LC-HRMS) and hydrophilic interaction chromatography (HILIC-LC-HRMS) systems, employing a single protocol for each system. Multiple linear regression (MLR) was applied to construct the linear QSRR models based on a variety of theoretical molecular descriptors. The regression equations included a set of three descriptors for each model: ALogP, BELe6, R2p and ALogP(2), FDI, BLTA96, were used in the analysis of reversed-phase and HILIC column models, respectively. Statistically significant QSRR models (squared correlation coefficient for model fitting, R(2)=0.95 for RP and R(2)=0.84 for HILIC) indicate a strong correlation between retention time and the molecular descriptors. An evaluation of the best correlation models, performed by validation of each model using three tests (leave-one-out, leave-many-out, external tests), demonstrated the reliability of the models. This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.

  7. Deep lithography with protons Modelling and predicting the performances of a novel fabrication technology for micro-optical components

    CERN Document Server

    Volckaerts, B; Veretennicoff, I; Thienpont, H

    2002-01-01

    We developed a simulation package that predicts 3D-dose distributions in proton irradiated poly(methylmetacrylate) samples considering primary energy transfer and scattering phenomena. In this paper, we apply this code to predict the surface flatness and maximum thickness of micro-optical and mechanical structures fabricated with deep lithography with protons (DLP). We compare these simulation results with experimental data and highlight the fundamental differences between DLP and deep X-ray lithography.

  8. The Prediction Performance of Asset Pricing Models and Their Capability of Capturing the Effects of Economic Crises: The Case of Istanbul Stock Exchange

    Directory of Open Access Journals (Sweden)

    Erol Muzır

    2010-09-01

    Full Text Available This paper is prepared to test the common opinion that the multifactor asset pricing models produce superior predictions as compared to the single factor models and to evaluate the performance of Arbitrage Pricing Theory (APT and Capital Asset Pricing Model (CAPM. For this purpose, the monthly return data from January 1996 and December 2004 of the stocks of 45 firms listed at Istanbul Stock Exchange were used. Our factor analysis results show that 68,3 % of the return variation can be explained by five factors. Although the APT model has generated a low coefficient of determination, 28,3 %, it proves to be more competent in explaining stock return changes when compared to CAPM which has an inferior explanation power, 5,4 %. Furthermore, we have observed that APT is more robust also in capturing the effects of any economic crisis on return variations.

  9. Predicting performance : relative importance of students' background and past performance

    NARCIS (Netherlands)

    Stegers-Jager, Karen M.; Themmen, Axel P. N.; Cohen-Schotanus, Janke; Steyerberg, Ewout W.

    2015-01-01

    ContextDespite evidence for the predictive value of both pre-admission characteristics and past performance at medical school, their relative contribution to predicting medical school performance has not been thoroughly investigated. ObjectivesThis study was designed to determine the relative import

  10. Predicting performance : relative importance of students' background and past performance

    NARCIS (Netherlands)

    Stegers-Jager, Karen M.; Themmen, Axel P. N.; Cohen-Schotanus, Janke; Steyerberg, Ewout W.

    ContextDespite evidence for the predictive value of both pre-admission characteristics and past performance at medical school, their relative contribution to predicting medical school performance has not been thoroughly investigated. ObjectivesThis study was designed to determine the relative

  11. Hierarchical representations of the five-factor model of personality in predicting job performance: integrating three organizing frameworks with two theoretical perspectives.

    Science.gov (United States)

    Judge, Timothy A; Rodell, Jessica B; Klinger, Ryan L; Simon, Lauren S; Crawford, Eean R

    2013-11-01

    Integrating 2 theoretical perspectives on predictor-criterion relationships, the present study developed and tested a hierarchical framework in which each five-factor model (FFM) personality trait comprises 2 DeYoung, Quilty, and Peterson (2007) facets, which in turn comprise 6 Costa and McCrae (1992) NEO facets. Both theoretical perspectives-the bandwidth-fidelity dilemma and construct correspondence-suggest that lower order traits would better predict facets of job performance (task performance and contextual performance). They differ, however, as to the relative merits of broad and narrow traits in predicting a broad criterion (overall job performance). We first meta-analyzed the relationship of the 30 NEO facets to overall job performance and its facets. Overall, 1,176 correlations from 410 independent samples (combined N = 406,029) were coded and meta-analyzed. We then formed the 10 DeYoung et al. facets from the NEO facets, and 5 broad traits from those facets. Overall, results provided support for the 6-2-1 framework in general and the importance of the NEO facets in particular. (c) 2013 APA, all rights reserved.

  12. Statistical modeling of program performance

    Directory of Open Access Journals (Sweden)

    A. P. Karpenko

    2014-01-01

    Full Text Available A task of evaluation of program performance often occurs in the process of design of computer systems or during iterative compilation. A traditional way to solve this problem is emulation of program execution on the target system. A modern alternative approach to evaluation of program performance is based on statistical modeling of program performance on a computer under investigation. This statistical method of modeling program performance called Velocitas was introduced in this work. The method and its implementation in the Adaptor framework were presented. Investigation of the method's effectiveness showed high adequacy of program performance prediction.

  13. Predictive Models for Music

    OpenAIRE

    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...

  14. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

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

  15. Beef Species Symposium: an assessment of the 1996 Beef NRC: metabolizable protein supply and demand and effectiveness of model performance prediction of beef females within extensive grazing systems.

    Science.gov (United States)

    Waterman, R C; Caton, J S; Löest, C A; Petersen, M K; Roberts, A J

    2014-07-01

    Interannual variation of forage quantity and quality driven by precipitation events influence beef livestock production systems within the Southern and Northern Plains and Pacific West, which combined represent 60% (approximately 17.5 million) of the total beef cows in the United States. The beef cattle requirements published by the NRC are an important tool and excellent resource for both professionals and producers to use when implementing feeding practices and nutritional programs within the various production systems. The objectives of this paper include evaluation of the 1996 Beef NRC model in terms of effectiveness in predicting extensive range beef cow performance within arid and semiarid environments using available data sets, identifying model inefficiencies that could be refined to improve the precision of predicting protein supply and demand for range beef cows, and last, providing recommendations for future areas of research. An important addition to the current Beef NRC model would be to allow users to provide region-specific forage characteristics and the ability to describe supplement composition, amount, and delivery frequency. Beef NRC models would then need to be modified to account for the N recycling that occurs throughout a supplementation interval and the impact that this would have on microbial efficiency and microbial protein supply. The Beef NRC should also consider the role of ruminal and postruminal supply and demand of specific limiting AA. Additional considerations should include the partitioning effects of nitrogenous compounds under different physiological production stages (e.g., lactation, pregnancy, and periods of BW loss). The intent of information provided is to aid revision of the Beef NRC by providing supporting material for changes and identifying gaps in existing scientific literature where future research is needed to enhance the predictive precision and application of the Beef NRC models.

  16. Evaluation of the performance of four chemical transport models in predicting the aerosol chemical composition in Europe in 2005

    NARCIS (Netherlands)

    Prank, M.; Sofiev, M.; Tsyro, S.; Hendriks, C.; Semeena, V.; Francis, X.V.; Butler, T.; Gon, H.D. van der; Friedrich, R.; Hendricks, J.; Kong, X.; Lawrence, M.; Righi, M.; Samaras, Z.; Sausen, R.; Kukkonen, J.; Sokhi, R.

    2016-01-01

    Four regional chemistry transport models were applied to simulate the concentration and composition of particulate matter (PM) in Europe for 2005 with horizontal resolution 20 km. The modelled concentrations were compared with the measurements of PM chemical composition by the European Monitoring

  17. Proactive Supply Chain Performance Management with Predictive Analytics

    Directory of Open Access Journals (Sweden)

    Nenad Stefanovic

    2014-01-01

    Full Text Available Today’s business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators. Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

  18. Proactive supply chain performance management with predictive analytics.

    Science.gov (United States)

    Stefanovic, Nenad

    2014-01-01

    Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

  19. Proactive Supply Chain Performance Management with Predictive Analytics

    Science.gov (United States)

    Stefanovic, Nenad

    2014-01-01

    Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

  20. Development of a model capable of predicting the performance of piston ring-cylinder liner-like tribological interfaces

    DEFF Research Database (Denmark)

    Felter, C.L.; Vølund, A.; Imran, Tajammal

    2010-01-01

    on a measured temperature only; thus, it is not necessary to include the energy equation. Conservation of oil is ensured throughout the domain by considering the amount of oil outside the lubricated interface. A model for hard contact through asperities is also included. Second, a laboratory-scale test rig....... The work described in this article addresses the subject from both an experimental and a theoretical perspective. First, a one-dimensional numerical model based on the Reynolds equation is presented. It uses a pressure-density relation for the modelling of cavitation. The viscosity is assumed to depend...

  1. Effect of water depth on the performance of intelligent computing models in predicting wave transmission of floating pipe breakwater.

    Digital Repository Service at National Institute of Oceanography (India)

    Patil, S.G.; Mandal, S.; Hegde, A.V.

    Understanding the physics of complex system plays an important role in selection of data for training intelligent computing models. Based on the physics of the wave transmission of Horizontally Interlaced Multilayer Moored Floating Pipe Breakwater...

  2. Predicting Students’ Academic Performance in Iranian Schools

    Directory of Open Access Journals (Sweden)

    Mostafa Namjoo

    2014-05-01

    Full Text Available Data mining is the process of extracting valuable and novel knowledge from large data. It is analysis of data sets for finding patterns, relationships and help to summarize the knowledge for various goals. This investigation is motivated to study the students’ academic performance in high schools during 4 years which are collected from the department of education, Shiraz, Iran. Since one of the main challenges in Iranian schools is, prediction of students’ academic performance and their success in university entrance exam, therefore, we applied different classification and prediction algorithms on students’ data for discovering the possibility of predicting students’ scores before examination. Our results show that, it is possible to predict students’ gender, marks with applying classification and prediction algorithms and verifying some factors which are mentioned in this paper

  3. 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...

  4. Influence of simulation time-step (temporal-scale) on optimal parameter estimation and runoff prediction performance in hydrological modeling

    Science.gov (United States)

    Loizu, Javier; Álvarez-Mozos, Jesús; Casalí, Javier; Goñi, Mikel

    2015-04-01

    Nowadays, most hydrological catchment models are designed to allow their use for streamflow simulation at different time-scales. While this permits models to be applied for broader purposes, it can also be a source of error in hydrological processes simulation at catchment scale. Those errors seem not to affect significantly simple conceptual models, but this flexibility may lead to large behavior errors in physically based models. Equations used in processes such as those related to soil moisture time-variation are usually representative at certain time-scales but they may not characterize properly water transfer in soil layers at larger scales. This effect is especially relevant as we move from detailed hourly scale to daily time-step, which are common time scales used at catchment streamflow simulation for different research and management practices purposes. This study aims to provide an objective methodology to identify the degree of similarity of optimal parameter values when hydrological catchment model calibration is developed at different time-scales. Thus, providing information for an informed discussion of physical parameter significance on hydrological models. In this research, we analyze the influence of time scale simulation on: 1) the optimal values of six highly sensitive parameters of the TOPLATS model and 2) the streamflow simulation efficiency, while optimization is carried out at different time scales. TOPLATS (TOPMODEL-based Land-Atmosphere Transfer Scheme) has been applied on its lumped version on three catchments of varying size located in northern Spain. The model has its basis on shallow groundwater gradients (related to local topography) that set up spatial patterns of soil moisture and are assumed to control infiltration and runoff during storm events and evaporation and drainage in between storm events. The model calculates the saturated portion of the catchment at each time step based on Topographical Index (TI) intervals. Surface

  5. Massive Predictive Modeling using Oracle R Enterprise

    CERN Document Server

    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...

  6. Predictive Bias and Sensitivity in NRC Fuel Performance Codes

    Energy Technology Data Exchange (ETDEWEB)

    Geelhood, Kenneth J.; Luscher, Walter G.; Senor, David J.; Cunningham, Mitchel E.; Lanning, Donald D.; Adkins, Harold E.

    2009-10-01

    The latest versions of the fuel performance codes, FRAPCON-3 and FRAPTRAN were examined to determine if the codes are intrinsically conservative. Each individual model and type of code prediction was examined and compared to the data that was used to develop the model. In addition, a brief literature search was performed to determine if more recent data have become available since the original model development for model comparison.

  7. Predictive Models to Estimate Probabilities of Injuries and Adverse Performance Outcomes in U.S. Army Basic Combat Training

    Science.gov (United States)

    2014-03-01

    Height (inches) was measured in stocking feet using a stadiometer (Seca, Hamburg , Germany). Body mass (lbs) was measured in stocking feet, ACU pants...undergarments and t-shirts using a digital scale (Seca model 770, Hamburg , Germany). 7 APFT Data Collection The 1-1-1 initial physical fitness test...physical damage to the. body as a result of an energy exchange .14 Recruit attrition was defined as failure to successfully complete a cycle of

  8. Modeling typical performance measures

    NARCIS (Netherlands)

    Weekers, Anke Martine

    2009-01-01

    In the educational, employment, and clinical context, attitude and personality inventories are used to measure typical performance traits. Statistical models are applied to obtain latent trait estimates. Often the same statistical models as the models used in maximum performance measurement are appl

  9. An empirical/theoretical model with dimensionless numbers to predict the performance of electrodialysis systems on the basis of operating conditions.

    Science.gov (United States)

    Karimi, Leila; Ghassemi, Abbas

    2016-07-01

    Among the different technologies developed for desalination, the electrodialysis/electrodialysis reversal (ED/EDR) process is one of the most promising for treating brackish water with low salinity when there is high risk of scaling. Multiple researchers have investigated ED/EDR to optimize the process, determine the effects of operating parameters, and develop theoretical/empirical models. Previously published empirical/theoretical models have evaluated the effect of the hydraulic conditions of the ED/EDR on the limiting current density using dimensionless numbers. The reason for previous studies' emphasis on limiting current density is twofold: 1) to maximize ion removal, most ED/EDR systems are operated close to limiting current conditions if there is not a scaling potential in the concentrate chamber due to a high concentration of less-soluble salts; and 2) for modeling the ED/EDR system with dimensionless numbers, it is more accurate and convenient to use limiting current density, where the boundary layer's characteristics are known at constant electrical conditions. To improve knowledge of ED/EDR systems, ED/EDR models should be also developed for the Ohmic region, where operation reduces energy consumption, facilitates targeted ion removal, and prolongs membrane life compared to limiting current conditions. In this paper, theoretical/empirical models were developed for ED/EDR performance in a wide range of operating conditions. The presented ion removal and selectivity models were developed for the removal of monovalent ions and divalent ions utilizing the dominant dimensionless numbers obtained from laboratory scale electrodialysis experiments. At any system scale, these models can predict ED/EDR performance in terms of monovalent and divalent ion removal.

  10. Childhood asthma prediction models: a systematic review.

    Science.gov (United States)

    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.

  11. Photovoltaic array performance model.

    Energy Technology Data Exchange (ETDEWEB)

    Kratochvil, Jay A.; Boyson, William Earl; King, David L.

    2004-08-01

    This document summarizes the equations and applications associated with the photovoltaic array performance model developed at Sandia National Laboratories over the last twelve years. Electrical, thermal, and optical characteristics for photovoltaic modules are included in the model, and the model is designed to use hourly solar resource and meteorological data. The versatility and accuracy of the model has been validated for flat-plate modules (all technologies) and for concentrator modules, as well as for large arrays of modules. Applications include system design and sizing, 'translation' of field performance measurements to standard reporting conditions, system performance optimization, and real-time comparison of measured versus expected system performance.

  12. Reliable predictions of waste performance in a geologic repository

    Energy Technology Data Exchange (ETDEWEB)

    Pigford, T.H.; Chambre, P.L.

    1985-08-01

    Establishing reliable estimates of long-term performance of a waste repository requires emphasis upon valid theories to predict performance. Predicting rates that radionuclides are released from waste packages cannot rest upon empirical extrapolations of laboratory leach data. Reliable predictions can be based on simple bounding theoretical models, such as solubility-limited bulk-flow, if the assumed parameters are reliably known or defensibly conservative. Wherever possible, performance analysis should proceed beyond simple bounding calculations to obtain more realistic - and usually more favorable - estimates of expected performance. Desire for greater realism must be balanced against increasing uncertainties in prediction and loss of reliability. Theoretical predictions of release rate based on mass-transfer analysis are bounding and the theory can be verified. Postulated repository analogues to simulate laboratory leach experiments introduce arbitrary and fictitious repository parameters and are shown not to agree with well-established theory. 34 refs., 3 figs., 2 tabs.

  13. Predicting residents' performance: A prospective study

    Directory of Open Access Journals (Sweden)

    Ozuah Philip O

    2002-07-01

    Full Text Available Abstract Background Objective criteria for predicting residents' performance do not exist. The purpose of this study was to test the hypothesis that global assessment by an intern selection committee (ISC would correlate with the future performance of residents. Methods A prospective study of 277 residents between 1992 and 1999. Global assessment at the time of interview was compared to subsequent clinical (assessed by chief residents and cognitive performance (assessed by the American Board of Pediatrics in-service training examination. Results ISC ratings correlated significantly with clinical performance at 24 and 36 months of training (r = 0.58, P st, 2nd, and 3rd years of training (r = 0.35, P = .0016; r = 0.39, P = 0.0003; r = 0.50, P = 0.005 respectively. Conclusions Global assessment by an ISC predicted residents' clinical and cognitive performances.

  14. Modelling, controlling, predicting blackouts

    CERN Document Server

    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...

  15. Melanoma Risk Prediction Models

    Science.gov (United States)

    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.

  16. Computer modeling and prediction of multi-cylinder turbo-compound adiabatic diesel processes and performances; Une simulation mathematique des processus et prediction des performances du diesel multicylindrique turbocompound adiabatique

    Energy Technology Data Exchange (ETDEWEB)

    Abdelhakem, B.; Aleksey, K. [Centre Universitaire de Bechar (Algeria); Serguevich, A. [Institut d`Automobiles et de Routes de Moscou (Russian Federation)

    1997-12-31

    Using a differential equation of the first law of thermodynamic, the volumetric balance equation and the equation of state a mathematical model is developed. It makes possible to calculate the performance parameters of turbo-compound adiabatic Diesel engine and the instantaneous parameters of working fluid in the cylinder, in the volumes ahead of the turbine and ahead the power turbine, in the volumes behind the compressor and the air cooler. The heat release to cycle and the heat transfer are calculated by the Wibe`s law and by Woschni`s equation. The computer program is used to evaluated the improving on the performance parameters of automotive Diesel engine by compounding and by heat loss restriction. It was found that the program is very useful both in teaching and research. (authors) 20 refs.

  17. Data Mining: A prediction for performance improvement using classification

    CERN Document Server

    Bhardwaj, Brijesh Kumar

    2012-01-01

    Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.

  18. What predicts performance during clinical psychology training?

    Science.gov (United States)

    Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

    2014-06-01

    While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Longitudinal cross-sectional study using prospective and retrospective data. Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. © 2013 The Authors. British Journal of Clinical Psychology published by John Wiley & Sons Ltd on behalf of the British Psychological Society.

  19. Event rate and reaction time performance in ADHD: Testing predictions from the state regulation deficit hypothesis using an ex-Gaussian model.

    Science.gov (United States)

    Metin, Baris; Wiersema, Jan R; Verguts, Tom; Gasthuys, Roos; van Der Meere, Jacob J; Roeyers, Herbert; Sonuga-Barke, Edmund

    2014-12-06

    According to the state regulation deficit (SRD) account, ADHD is associated with a problem using effort to maintain an optimal activation state under demanding task settings such as very fast or very slow event rates. This leads to a prediction of disrupted performance at event rate extremes reflected in higher Gaussian response variability that is a putative marker of activation during motor preparation. In the current study, we tested this hypothesis using ex-Gaussian modeling, which distinguishes Gaussian from non-Gaussian variability. Twenty-five children with ADHD and 29 typically developing controls performed a simple Go/No-Go task under four different event-rate conditions. There was an accentuated quadratic relationship between event rate and Gaussian variability in the ADHD group compared to the controls. The children with ADHD had greater Gaussian variability at very fast and very slow event rates but not at moderate event rates. The results provide evidence for the SRD account of ADHD. However, given that this effect did not explain all group differences (some of which were independent of event rate) other cognitive and/or motivational processes are also likely implicated in ADHD performance deficits.

  20. Why Do Spatial Abilities Predict Mathematical Performance?

    Science.gov (United States)

    Tosto, Maria Grazia; Hanscombe, Ken B.; Haworth, Claire M. A.; Davis, Oliver S. P.; Petrill, Stephen A.; Dale, Philip S.; Malykh, Sergey; Plomin, Robert; Kovas, Yulia

    2014-01-01

    Spatial ability predicts performance in mathematics and eventual expertise in science, technology and engineering. Spatial skills have also been shown to rely on neuronal networks partially shared with mathematics. Understanding the nature of this association can inform educational practices and intervention for mathematical underperformance.…

  1. Which Social Skills Predict Academic Performance of Elementary School Students

    Science.gov (United States)

    Sung, Youngji Y.; Chang, Mido

    2010-01-01

    The study explored various aspects of students' social skills in an attempt to identify specific aspect that has significance in predicting their academic performance and examined the longitudinal relationship of these social skills with academic performance. The study used two models that applied advanced statistical tools to a nationally…

  2. 基于衰变-Markov模型的沥青路面性能预测研究%Pavement Performance Prediction of HighWay Based on the Decay-Markov Prediction Model

    Institute of Scientific and Technical Information of China (English)

    武昭融; 李秀君; 李梦晨; 许光孝

    2016-01-01

    In the forecasting process of pavement performance indicators,there is usually the error fluctuation between the predicted value and measured value because of using different maintenance methods.In order to solve this problem,the prediction model based on the standard decay equation suggested by Professsor Sun Lijun of Tongji University was taken as the basis of prediction model for forecasting the performance variation of a certain number of sections on the Jiaxing part of S101 provincial road.According to the data about the pavement structure,its thickness and the volume of traffic,the weight values of pavement surface condition index (PCI)and riding quality index (RQI)which are the most dominant among the commonly used pavement quality indices (PQI)were predicted.The predicted data of decay equation were further revised by adopting the Markov transfer matrix method,which provides a reliable basis for the next maintenance decision.%为解决在路面使用性能各项指标的预测过程中,预测值与实测值间误差波动较大这一问题,采取同济大学孙立军教授提出的标准衰变方程作为预测模型的基础,充分调查浙江省 S101省道嘉兴段现有路况,根据收集的公路路面结构、厚度和交通量轴载数据等资料,对其沥青路面性能变化进行预测,即对路面损坏状况指数 PCI 和行驶质量指数 RQI 这两个在路面使用性能指数 PQI 中占较大权重的指标进行预测,继而采用 Markov 模型对其预测结果进行修正,建立与养护策略相对应的 Markov 转移矩阵而得到衰变-Markov 预测模型,为之后能准确掌握沥青路面使用性能变化趋势并得到合理的养护决策提供可靠依据。

  3. Hadoop Performance Models

    OpenAIRE

    Herodotou, Herodotos

    2011-01-01

    Hadoop MapReduce is now a popular choice for performing large-scale data analytics. This technical report describes a detailed set of mathematical performance models for describing the execution of a MapReduce job on Hadoop. The models describe dataflow and cost information at the fine granularity of phases within the map and reduce tasks of a job execution. The models can be used to estimate the performance of MapReduce jobs as well as to find the optimal configuration settings to use when r...

  4. Hadoop Performance Models

    CERN Document Server

    Herodotou, Herodotos

    2011-01-01

    Hadoop MapReduce is now a popular choice for performing large-scale data analytics. This technical report describes a detailed set of mathematical performance models for describing the execution of a MapReduce job on Hadoop. The models describe dataflow and cost information at the fine granularity of phases within the map and reduce tasks of a job execution. The models can be used to estimate the performance of MapReduce jobs as well as to find the optimal configuration settings to use when running the jobs.

  5. Does safety climate predict safety performance in Italy and the USA? Cross-cultural validation of a theoretical model of safety climate.

    Science.gov (United States)

    Barbaranelli, Claudio; Petitta, Laura; Probst, Tahira M

    2015-04-01

    Previous studies have acknowledged the relevance of assessing the measurement equivalence of safety related measures across different groups, and demonstrating whether the existence of disparities in safety perceptions might impair direct group comparisons. The Griffin and Neal (2000) model of safety climate, and the accompanying measure (Neal et al. [NGH], 2000), are both widely cited and utilized. Yet neither the model in its entirety nor the measure have been previously validated across different national contexts. The current study is the first to examine the NGH measurement equivalence by testing whether their model of safety climate predicting safety performance is tenable in both English speaking and non-English speaking countries. The study involved 616 employees from 21 organizations in the US, and 738 employees from 20 organizations in Italy. A multi-group confirmatory factor analytic approach was used to assess the equivalence of the measures across the two countries. Similarly, the structural model of relations among the NGH variables was examined in order to demonstrate its cross-country invariance. Results substantially support strict invariance across groups for the NGH safety scales. Moreover, the invariance across countries is also demonstrated for the effects of safety climate on safety knowledge and motivation, which in turn positively relate to both compliance and participation. Our findings have relevant theoretical implications by establishing measurement and relational equivalence of the NGH model. Practical implications are discussed for managers and practitioners dealing with multi-national organizational contexts. Future research should continue to investigate potential differences in safety related perceptions across additional non-English speaking countries.

  6. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...

  7. Evaluating the Performance of a New Model for Predicting the Growth of Clostridium perfringens in Cooked, Uncured Meat and Poultry Products under Isothermal, Heating, and Dynamically Cooling Conditions.

    Science.gov (United States)

    Huang, Lihan

    2016-07-01

    Clostridium perfringens type A is a significant public health threat and its spores may germinate, outgrow, and multiply during cooling of cooked meats. This study applies a new C. perfringens growth model in the USDA Integrated Pathogen Modeling Program-Dynamic Prediction (IPMP Dynamic Prediction) Dynamic Prediction to predict the growth from spores of C. perfringens in cooked uncured meat and poultry products using isothermal, dynamic heating, and cooling data reported in the literature. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square error (RMSE) calculated. For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors (MRE) of predictions range from -0.40 to 0.02 Log colony forming units (CFU)/g, with a RMSE of approximately 0.6 Log CFU/g. For cooling, the end point predictions are conservative in nature, with an MRE of -1.16 Log CFU/g for single-rate cooling and -0.66 Log CFU/g for dual-rate cooling. The RMSE is between 0.6 and 0.7 Log CFU/g. Compared with other models reported in the literature, this model makes more accurate and fail-safe predictions. For cooling, the percentage for accurate and fail-safe predictions is between 97.6% and 100%. Under criterion 1, the percentage of accurate predictions is 47.5% for single-rate cooling and 66.7% for dual-rate cooling, while the fail-dangerous predictions are between 0% and 2.4%. This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to predict the growth of C. perfringens in uncured cooked meats and evaluate the safety of cooked or heat-treated uncured meat and poultry products exposed to cooling deviations or to develop customized cooling schedules. This study also demonstrates the need for more accurate data collection during cooling. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

  8. NIF capsule performance modeling

    OpenAIRE

    Weber S.; Callahan D.; Cerjan C.; Edwards M.; Haan S.; Hicks D.; Jones O.; Kyrala G.; Meezan N.; Olson R; Robey H.; Spears B.; Springer P.; Town R.

    2013-01-01

    Post-shot modeling of NIF capsule implosions was performed in order to validate our physical and numerical models. Cryogenic layered target implosions and experiments with surrogate targets produce an abundance of capsule performance data including implosion velocity, remaining ablator mass, times of peak x-ray and neutron emission, core image size, core symmetry, neutron yield, and x-ray spectra. We have attempted to match the integrated data set with capsule-only simulations by adjusting th...

  9. ARES:Autoregressive Emotion-Sensitive Model for Predicting Sales Performance%ARES:用于预测的情感感知自回归模型

    Institute of Scientific and Technical Information of China (English)

    李雪妮; 张绍武; 杨亮; 林鸿飞

    2013-01-01

    Along with the vigorous development of Web 2.0,lots of comments that represent the voices of customers appeared on the Internet,and the general public's sentiments toward products are increasingly influenced by the underlying viewpoints.Therefore mining the sentiment information from reviews would produce practical values for predicting sales performance and adjusting market strategy.Aiming at this problem,based on the result of the analysis on the characteristics of online book reviews,it proposes a sentiment analysis method.First,a polarity word dictionary is automatically constructed by the part of speech list and the prefix list.Afterwards the sentiments in the reviews can be extracted based on the polarity dictionary.Finally,the paper presents an ARES (autoregressive emotion-sensitive model),to utilize the emotion information acquired by the sentiment analysis method for predicting sales performance.Experiments are conducted on a book data set.By comparing the ARES with alternative models that do not take sentiment information into consideration,as well as a model with a different sentiment analysis method,the results,on the one hand,indicate that our sentiment analysis approach could generate a well summary of the review itself,and on the other hand,confirm the effectiveness of the proposed prediction model.%随着Web2.0的蓬勃发展,互联网上产生了大量由用户发表的评论,其中表达的观点看法对大众消费的影响越来越大,因此分析评论中蕴含的情感信息对产品销量的预测以及市场战略的调整有实际意义.针对这一问题,在分析图书销售领域网络评论特点的基础上,提出了相应的情感分析方法,首先利用词性列表及前缀词典完成极性词词典的自动抽取与构建,然后采用基于词典的方法对图书的评论内容进行情感分析及量化,最后通过将抽取的情感因素融合到自回归模型中,建立了新的预测模型——

  10. Validation of a zero-dimensional model for prediction of NOx and engine performance for electronically controlled marine two-stroke diesel engines

    DEFF Research Database (Denmark)

    Scappin, Fabio; Stefansson, Sigurður H.; Haglind, Fredrik

    2012-01-01

    combustion model using ideal gas law equations over a complete crank cycle. The combustion process is divided into intervals, and the product composition and flame temperature are calculated in each interval. The NOx emissions are predicted using the extended Zeldovich mechanism. The model is validated using...

  11. 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...

  12. Computer Program Predicts Turbine-Stage Performance

    Science.gov (United States)

    Boyle, Robert J.; Haas, Jeffrey E.; Katsanis, Theodore

    1988-01-01

    MTSBL updated version of flow-analysis programs MERIDL and TSONIC coupled to boundary-layer program BLAYER. Method uses quasi-three-dimensional, inviscid, stream-function flow analysis iteratively coupled to calculated losses so changes in losses result in changes in flow distribution. Manner effects both configuration on flow distribution and flow distribution on losses taken into account in prediction of performance of stage. Written in FORTRAN IV.

  13. Simplified Predictive Models for CO2 Sequestration Performance Assessment: Research Topical Report on Task #4 - Reduced-Order Method (ROM) Based Models

    Energy Technology Data Exchange (ETDEWEB)

    Mishra, Srikanta; Jin, Larry; He, Jincong; Durlofsky, Louis

    2015-06-30

    Reduced-order models provide a means for greatly accelerating the detailed simulations that will be required to manage CO2 storage operations. In this work, we investigate the use of one such method, POD-TPWL, which has previously been shown to be effective in oil reservoir simulation problems. This method combines trajectory piecewise linearization (TPWL), in which the solution to a new (test) problem is represented through a linearization around the solution to a previously-simulated (training) problem, with proper orthogonal decomposition (POD), which enables solution states to be expressed in terms of a relatively small number of parameters. We describe the application of POD-TPWL for CO2-water systems simulated using a compositional procedure. Stanford’s Automatic Differentiation-based General Purpose Research Simulator (AD-GPRS) performs the full-order training simulations and provides the output (derivative matrices and system states) required by the POD-TPWL method. A new POD-TPWL capability introduced in this work is the use of horizontal injection wells that operate under rate (rather than bottom-hole pressure) control. Simulation results are presented for CO2 injection into a synthetic aquifer and into a simplified model of the Mount Simon formation. Test cases involve the use of time-varying well controls that differ from those used in training runs. Results of reasonable accuracy are consistently achieved for relevant well quantities. Runtime speedups of around a factor of 370 relative to full- order AD-GPRS simulations are achieved, though the preprocessing needed for POD-TPWL model construction corresponds to the computational requirements for about 2.3 full-order simulation runs. A preliminary treatment for POD-TPWL modeling in which test cases differ from training runs in terms of geological parameters (rather than well controls) is also presented. Results in this case involve only small differences between

  14. Educational Data Mining & Students’ Performance Prediction

    OpenAIRE

    Amjad Abu Saa

    2016-01-01

    It is important to study and analyse educational data especially students’ performance. Educational Data Mining (EDM) is the field of study concerned with mining educational data to find out interesting patterns and knowledge in educational organizations. This study is equally concerned with this subject, specifically, the students’ performance. This study explores multiple factors theoretically assumed to affect students’ performance in higher education, and finds a qualitative model which b...

  15. Predictive models of forest dynamics.

    Science.gov (United States)

    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.

  16. Hybrid modeling and prediction of dynamical systems

    Science.gov (United States)

    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

  17. Comparison of prediction performance using statistical postprocessing methods

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2016-11-01

    As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.

  18. Modeling road-cycling performance.

    Science.gov (United States)

    Olds, T S; Norton, K I; Lowe, E L; Olive, S; Reay, F; Ly, S

    1995-04-01

    This paper presents a complete set of equations for a "first principles" mathematical model of road-cycling performance, including corrections for the effect of winds, tire pressure and wheel radius, altitude, relative humidity, rotational kinetic energy, drafting, and changed drag. The relevant physiological, biophysical, and environmental variables were measured in 41 experienced cyclists completing a 26-km road time trial. The correlation between actual and predicted times was 0.89 (P road-cycling performance are maximal O2 consumption, fractional utilization of maximal O2 consumption, mechanical efficiency, and projected frontal area. The model is then applied to some practical problems in road cycling: the effect of drafting, the advantage of using smaller front wheels, the effects of added mass, the importance of rotational kinetic energy, the effect of changes in drag due to changes in bicycle configuration, the normalization of performances under different conditions, and the limits of human performance.

  19. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  20. Biomarker case-detection and prediction with potential for functional psychosis screening: development and validation of a model related to biochemistry, sensory neural timing and end organ performance.

    Directory of Open Access Journals (Sweden)

    Stephanie eFryar-Williams

    2016-04-01

    Full Text Available The Mental Health Biomarker Project aimed to discover case-predictive biomarkers for functional psychosis. In a retrospective, cross-sectional study, candidate marker results from 67, highly-characterized symptomatic participants were compared with results from 67 gender and age matched controls. Urine samples were analysed for catecholamines, their metabolites and hydroxylpyrolline-2-one, an oxidative stress marker. Blood samples were analyzed for vitamin and trace element cofactors of enzymes in the catecholamine synthesis and metabolism pathways. Cognitive, auditory and visual processing measures were assessed using a simple 45 minute, office-based procedure. Receiver Operating Curve (ROC and Odds Ratio analysis discovered biomarkers for deficits in folate, vitamin D and B6 and elevations in free copper to zinc ratio, catecholamines and the oxidative stress marker. Deficits were discovered in peripheral visual and auditory end-organ function, intra-cerebral auditory and visual processing speed and dichotic-listening performance. 15 ROC biomarker variables were divided into 5 functional domains. Through a repeated ROC process, individual ROC variables, followed by domains and finally the overall 15 set model, were dichotomously scored and tallied for abnormal results upon which it was found that ≥ 3 out of 5 abnormal domains achieved an AUC of 0.952 with a sensitivity of 84 per cent and a specificity of 90 percent. Six additional middle ear biomarkers in a 21 biomarker set increased sensitivity to 94% percent. Fivefold cross-validation yielded a mean sensitivity of 85% for the 15 biomarker set. Non-parametric regression analysis confirmed that ≥ 3 out of 5 abnormally scored domains predicted > 50% risk of case-ness whilst 4 abnormally-scored domains predicted 88% risk of case-ness and 100% diagnostic certainty was reached when all 5 domains were abnormally scored. These findings require validation in prospective cohorts and other mental

  1. NIF capsule performance modeling

    Directory of Open Access Journals (Sweden)

    Weber S.

    2013-11-01

    Full Text Available Post-shot modeling of NIF capsule implosions was performed in order to validate our physical and numerical models. Cryogenic layered target implosions and experiments with surrogate targets produce an abundance of capsule performance data including implosion velocity, remaining ablator mass, times of peak x-ray and neutron emission, core image size, core symmetry, neutron yield, and x-ray spectra. We have attempted to match the integrated data set with capsule-only simulations by adjusting the drive and other physics parameters within expected uncertainties. The simulations include interface roughness, time-dependent symmetry, and a model of mix. We were able to match many of the measured performance parameters for a selection of shots.

  2. Predicting the performance of fingerprint similarity searching.

    Science.gov (United States)

    Vogt, Martin; Bajorath, Jürgen

    2011-01-01

    Fingerprints are bit string representations of molecular structure that typically encode structural fragments, topological features, or pharmacophore patterns. Various fingerprint designs are utilized in virtual screening and their search performance essentially depends on three parameters: the nature of the fingerprint, the active compounds serving as reference molecules, and the composition of the screening database. It is of considerable interest and practical relevance to predict the performance of fingerprint similarity searching. A quantitative assessment of the potential that a fingerprint search might successfully retrieve active compounds, if available in the screening database, would substantially help to select the type of fingerprint most suitable for a given search problem. The method presented herein utilizes concepts from information theory to relate the fingerprint feature distributions of reference compounds to screening libraries. If these feature distributions do not sufficiently differ, active database compounds that are similar to reference molecules cannot be retrieved because they disappear in the "background." By quantifying the difference in feature distribution using the Kullback-Leibler divergence and relating the divergence to compound recovery rates obtained for different benchmark classes, fingerprint search performance can be quantitatively predicted.

  3. Prediction of Cone Crusher Performance Considering Liner Wear

    Directory of Open Access Journals (Sweden)

    Yanjun Ma

    2016-12-01

    Full Text Available Cone crushers are used in the aggregates and mining industries to crush rock material. The pressure on cone crusher liners is the key factor that influences the hydraulic pressure, power draw and liner wear. In order to dynamically analyze and calculate cone crusher performance along with liner wear, a series of experiments are performed to obtain the crushed rock material samples from a crushing plant at different time intervals. In this study, piston die tests are carried out and a model relating compression coefficient, compression ratio and particle size distribution to a corresponding pressure is presented. On this basis, a new wear prediction model is proposed combining the empirical model for predicting liner wear with time parameter. A simple and practical model, based on the wear model and interparticle breakage, is presented for calculating compression ratio of each crushing zone along with liner wear. Furthermore, the size distribution of the product is calculated based on existing size reduction process model. A method of analysis of product size distribution and shape in the crushing process considering liner wear is proposed. Finally, the validity of the wear model is verified via testing. The result shows that there is a significant improvement of the prediction of cone crusher performance considering liner wear as compared to the previous model.

  4. Autonomous prediction of performance-based standards for heavy vehicles

    CSIR Research Space (South Africa)

    Berman, R

    2015-11-01

    Full Text Available determined by physical testing or detailed vehicle simulations, both of which are costly and time consuming processes. This paper presents a data driven, detailed model to predict the low-speed performance of an articulated vehicle, given only the vehicle...

  5. Performance Evaluation of FAO Model for Prediction of Yield Production, Soil Water and Solute Balance under Environmental Stresses (Case Study Winter Wheat

    Directory of Open Access Journals (Sweden)

    V. Rezaverdinejad

    2014-11-01

    Full Text Available In this study, the FAO agro-hydrological model was investigated and evaluated to predict of yield production, soil water and solute balance by winter wheat field data under water and salt stresses. For this purpose, a field experimental was conducted with three salinity levels of irrigation water include: S1, S2 and S3 corresponding to 1.4, 4.5 and 9.6 dS/m, respectively, and four irrigation depth levels include: I1, I2, I3 and I4 corresponding to 50, 75, 100 and 125% of crop water requirement, respectively, for two varieties of winter wheat: Roshan and Ghods, with three replications in an experimental farm of Birjand University for 1384-85 period. Based on results, the mean relative error of the model in yield prediction for Roshan and Ghods were obtained 9.2 and 26.1%, respectively. The maximum error of yield prediction in both of the Roshan and Ghods varieties, were obtained for S1I1, S2I1 and S3I1 treatments. The relative error of Roshan yield prediction for S1I1, S2I1 and S3I1 were calculated 20.0, 28.1 and 26.6%, respectively and for Ghods variety were calculated 61, 94.5 and 99.9%, respectively, that indicated a significant over estimate error under higher water stress. The mean relative error of model for all treatments, in prediction of soil water depletion and electrical conductivity of soil saturation extract, were calculated 7.1 and 5.8%, respectively, that indicated proper accuracy of model in prediction of soil water content and soil salinity.

  6. Evaluating the performance of a new model for predicting the growth of Clostridium perfringens in cooked, uncured meat and poultry products under isothermal, heating, and dynamically cooling conditions

    Science.gov (United States)

    Clostridium perfringens Type A is a significant public health threat and may germinate, outgrow, and multiply during cooling of cooked meats. This study evaluates a new C. perfringens growth model in IPMP Dynamic Prediction using the same criteria and cooling data in Mohr and others (2015), but inc...

  7. Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis%基于统计分析的多变量预测控制性能检测与诊断

    Institute of Scientific and Technical Information of China (English)

    张强; 李少远

    2006-01-01

    A statistic-based benchmark was proposed for performance assessment and monitoring of model predictive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user's selection. Principal component model was built and an autoregressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the performance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.

  8. Reading Performance Is Predicted by More Than Phonological Processing

    Directory of Open Access Journals (Sweden)

    Michelle Y. Kibby

    2014-09-01

    Full Text Available We compared three phonological processing components (phonological awareness, rapid automatized naming and phonological memory, verbal working memory, and attention control in terms of how well they predict the various aspects of reading: word recognition, pseudoword decoding, fluency and comprehension, in a mixed sample of 182 children ages 8-12 years. Participants displayed a wide range of reading ability and attention control. Multiple regression was used to determine how well the phonological processing components, verbal working memory, and attention control predict reading performance. All equations were highly significant. Phonological memory predicted word identification and decoding. In addition, phonological awareness and rapid automatized naming predicted every aspect of reading assessed, supporting the notion that phonological processing is a core contributor to reading ability. Nonetheless, phonological processing was not the only predictor of reading performance. Verbal working memory predicted fluency, decoding and comprehension, and attention control predicted fluency. Based upon our results, when using Baddeley’s model of working memory it appears that the phonological loop contributes to basic reading ability, whereas the central executive contributes to fluency and comprehension, along with decoding. Attention control was of interest as some children with ADHD have poor reading ability even if it is not sufficiently impaired to warrant diagnosis. Our finding that attention control predicts reading fluency is consistent with prior research which showed sustained attention plays a role in fluency. Taken together, our results suggest that reading is a highly complex skill that entails more than phonological processing to perform well.

  9. Specialized Language Models using Dialogue Predictions

    CERN Document Server

    Popovici, C; Popovici, Cosmin; Baggia, Paolo

    1996-01-01

    This paper analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason several models have been created, each one for a specific system question, such as the request or the confirmation of a parameter. The use of dialogue-dependent language models increases the performance both at the recognition and at the understanding level, especially on answers to system requests. Moreover other methods to increase performance, like automatic clustering of vocabulary words or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models. The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues coll...

  10. Students Performance Prediction System Using Multi Agent Data Mining Technique

    Directory of Open Access Journals (Sweden)

    Abdullah AL-Malaise

    2014-09-01

    Full Text Available A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making. Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost is a generation of boosting algorithm. It is used for the binary classification and not applicable to multiclass classification directly. SAMME boosting technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binary classification. In this paper, students’ performance prediction system using Multi Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide help to the low students by optimization rules. The proposed system has been implemented and evaluated by investigate the prediction accuracy of Adaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed C4.5 single classifier and LogitBoost.

  11. The Search Performance Evaluation and Prediction in Exploratory Search

    OpenAIRE

    2016-01-01

    The exploratory search for complex search tasks requires an effective search behavior model to evaluate and predict user search performance. Few studies have investigated the relationship between user search behavior and search performance in exploratory search. This research adopts a mixed approach combining search system development, user search experiment, search query log analysis, and multivariate regression analysis to resolve the knowledge gap. Through this study, it is shown that expl...

  12. The Search Performance Evaluation and Prediction in Exploratory Search

    OpenAIRE

    Liu, Fei

    2016-01-01

    The exploratory search for complex search tasks requires an effective search behavior model to evaluate and predict user search performance. Few studies have investigated the relationship between user search behavior and search performance in exploratory search. This research adopts a mixed approach combining search system development, user search experiment, search query log analysis, and multivariate regression analysis to resolve the knowledge gap. Through this study, it is shown that expl...

  13. Hydrodynamic properties of fin whale flippers predict maximum rolling performance.

    Science.gov (United States)

    Segre, Paolo S; Cade, David E; Fish, Frank E; Potvin, Jean; Allen, Ann N; Calambokidis, John; Friedlaender, Ari S; Goldbogen, Jeremy A

    2016-11-01

    Maneuverability is one of the most important and least understood aspects of animal locomotion. The hydrofoil-like flippers of cetaceans are thought to function as control surfaces that effect maneuvers, but quantitative tests of this hypothesis have been lacking. Here, we constructed a simple hydrodynamic model to predict the longitudinal-axis roll performance of fin whales, and we tested its predictions against kinematic data recorded by on-board movement sensors from 27 free-swimming fin whales. We found that for a given swimming speed and roll excursion, the roll velocity of fin whales calculated from our field data agrees well with that predicted by our hydrodynamic model. Although fluke and body torsion may further influence performance, our results indicate that lift generated by the flippers is sufficient to drive most of the longitudinal-axis rolls used by fin whales for feeding and maneuvering.

  14. Introduction of the conditional correlated Bernoulli model of similarity value distributions and its application to the prospective prediction of fingerprint search performance.

    Science.gov (United States)

    Vogt, Martin; Bajorath, Jürgen

    2011-10-24

    A statistical approach named the conditional correlated Bernoulli model is introduced for modeling of similarity scores and predicting the potential of fingerprint search calculations to identify active compounds. Fingerprint features are rationalized as dependent Bernoulli variables and conditional distributions of Tanimoto similarity values of database compounds given a reference molecule are assessed. The conditional correlated Bernoulli model is utilized in the context of virtual screening to estimate the position of a compound obtaining a certain similarity value in a database ranking. Through the generation of receiver operating characteristic curves from cumulative distribution functions of conditional similarity values for known active and random database compounds, one can predict how successful a fingerprint search might be. The comparison of curves for different fingerprints makes it possible to identify fingerprints that are most likely to identify new active molecules in a database search given a set of known reference molecules.

  15. Model Predictive Control of Sewer Networks

    Science.gov (United States)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  16. ATR performance modeling concepts

    Science.gov (United States)

    Ross, Timothy D.; Baker, Hyatt B.; Nolan, Adam R.; McGinnis, Ryan E.; Paulson, Christopher R.

    2016-05-01

    Performance models are needed for automatic target recognition (ATR) development and use. ATRs consume sensor data and produce decisions about the scene observed. ATR performance models (APMs) on the other hand consume operating conditions (OCs) and produce probabilities about what the ATR will produce. APMs are needed for many modeling roles of many kinds of ATRs (each with different sensing modality and exploitation functionality combinations); moreover, there are different approaches to constructing the APMs. Therefore, although many APMs have been developed, there is rarely one that fits a particular need. Clarified APM concepts may allow us to recognize new uses of existing APMs and identify new APM technologies and components that better support coverage of the needed APMs. The concepts begin with thinking of ATRs as mapping OCs of the real scene (including the sensor data) to reports. An APM is then a mapping from explicit quantized OCs (represented with less resolution than the real OCs) and latent OC distributions to report distributions. The roles of APMs can be distinguished by the explicit OCs they consume. APMs used in simulations consume the true state that the ATR is attempting to report. APMs used online with the exploitation consume the sensor signal and derivatives, such as match scores. APMs used in sensor management consume neither of those, but estimate performance from other OCs. This paper will summarize the major building blocks for APMs, including knowledge sources, OC models, look-up tables, analytical and learned mappings, and tools for signal synthesis and exploitation.

  17. A performance prediction approach to enhance collaborative filtering performance

    OpenAIRE

    Bellogín, Alejandro; Castells, Pablo

    2010-01-01

    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-12275-0_34 Proceedings of 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010. Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. The present work restates the problem in the area of Collaborative Filtering (CF), where it has barely been researched so fa...

  18. Can cycle power predict sprint running performance?

    Science.gov (United States)

    van Ingen Schenau, G J; Jacobs, R; de Koning, J J

    1991-01-01

    A major criticism of present models of the energetics and mechanics of sprint running concerns the application of estimates of parameters which seem to be adapted from measurements of running during actual competitions. This study presents a model which does not perpetuate this solecism. Using data obtained during supra-maximal cycle ergometer tests of highly trained athletes, the kinetics of the anaerobic and aerobic pathways were modelled. Internal power wasted in the acceleration and deceleration of body limbs and the power necessary to overcome air friction was calculated from data in the literature. Assuming a mechanical efficiency as found during submaximal cycling, a power equation was constructed which also included the power necessary to accelerate the body at the start of movement. The differential equation thus obtained was solved through simulation. The model appeared to predict realistic times at 100 m (10.47 s), 200 m (19.63 s) and 400 m (42.99 s) distances. By comparison with other methods it is argued that power equations of locomotion should include the concept of mechanical efficiency.

  19. Prediction and Quantification of Individual Athletic Performance of Runners.

    Science.gov (United States)

    Blythe, Duncan A J; Király, Franz J

    2016-01-01

    We present a novel, quantitative view on the human athletic performance of individual runners. We obtain a predictor for running performance, a parsimonious model and a training state summary consisting of three numbers by application of modern validation techniques and recent advances in machine learning to the thepowerof10 database of British runners' performances (164,746 individuals, 1,417,432 performances). Our predictor achieves an average prediction error (out-of-sample) of e.g. 3.6 min on elite Marathon performances and 0.3 seconds on 100 metres performances, and a lower error than the state-of-the-art in performance prediction (30% improvement, RMSE) over a range of distances. We are also the first to report on a systematic comparison of predictors for running performance. Our model has three parameters per runner, and three components which are the same for all runners. The first component of the model corresponds to a power law with exponent dependent on the runner which achieves a better goodness-of-fit than known power laws in the study of running. Many documented phenomena in quantitative sports science, such as the form of scoring tables, the success of existing prediction methods including Riegel's formula, the Purdy points scheme, the power law for world records performances and the broken power law for world record speeds may be explained on the basis of our findings in a unified way. We provide strong evidence that the three parameters per runner are related to physiological and behavioural parameters, such as training state, event specialization and age, which allows us to derive novel physiological hypotheses relating to athletic performance. We conjecture on this basis that our findings will be vital in exercise physiology, race planning, the study of aging and training regime design.

  20. The performance of the EU-Rotate_N model in predicting the growth and nitrogen uptake of rotations of field vegetable crops in a Mediterranean environment

    OpenAIRE

    Nendel, Claas; Venezia, A.; Piro, F.; Ren, T; Lillywhite, Robert; Rahn, C. (Clive)

    2013-01-01

    The EU-Rotate_N model was developed as a tool to estimate the growth and nitrogen (N) uptake of vegetable crop rotations across a wide range of European climatic conditions and to assess the economic and environmental consequences of alternative management strategies. The model has been evaluated under field conditions in Germany and Norway and under greenhouse conditions in China. The present work evaluated the model using Italian data to evaluate its performance in a warm and dry environmen...

  1. Performance Evaluation and Relative Predictive Model of Parallel File System%面向并行文件系统的性能评估及相对预测模型

    Institute of Scientific and Technical Information of China (English)

    赵铁柱; 董守斌; VerdiMARCH; SimonSEE

    2011-01-01

    基于Lustre文件系统,对并行文件系统的性能评估和性能建模进行了研究.通过对性能因子的调研,进行了一系列性能评估实验,并提出性能相关性模型(PRModel).在实验评估和PRModel分析中发现,在不同的性能因子之间存在着紧密的性能相关性.为了挖掘并利用这种相关性信息,提出了一种相对性能预测模型(RPPModel)来预测不同性能因子条件下的性能.为了验证RPPModel的有效性,设计了大量实验用例.结果表明,预测结果的平均相对误差能够控制在l7%~28%的范围内,易于使用且具有较好的预测准确度.%In this paper, the performance evaluation and modeling of parallel file system based on Lustre file system is studied. After performing a survey on performance factors, a series of performance evaluations via experimental approaches and propose a performance relational model (PRModel). In the experimental and PRModel analysis, it is found that different performance factors have closed performance correlations. In order to mime the relational information, a novel relative performance predictive model (RPPModel) is proposed. This model can be used to predict the overhead over different performance factors. The model is validated through a series of experiments over a variety of performance factors. The experimental results show that the average relative errors results can be controlled within 17%~28%. This model is easy to use and can obtain better prediction accuracy.

  2. Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models

    Directory of Open Access Journals (Sweden)

    Cheng-Hung Hsieh

    2007-09-01

    Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.

  3. A Study On Distributed Model Predictive Consensus

    CERN Document Server

    Keviczky, Tamas

    2008-01-01

    We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.

  4. Testing performance of CIECAM02 in predicting perceptual contrast

    Institute of Scientific and Technical Information of China (English)

    Weige Lü; Haisong Xu; M.Ronnier Luo

    2012-01-01

    A psychophysical experiment is performed on two large-size liquid crystal displays under three viewing conditions to assess perceptual contrast.Based on the visual data,the performance of CIECAM02 in predicting perceptual contrast under different viewing conditions is tested and compared with other models by F-test.Results show that the perceptual contrast models in the form of Weber contrast using CIECAM02 brightness Q agreed better with the contrast perception of human visual system compared to the models using luminance,CIELAB lightness L*,and CIECAM02 lightness J.%A psychophysical experiment is performed on two large-size liquid crystal displays under three viewing conditions to assess perceptual contrast. Based on the visual data, the performance of CIECAM02 in predicting perceptual contrast under different viewing conditions is tested and compared with other models by F-test. Results show that the perceptual contrast models in the form of Weber contrast using CIECAM02 brightness Q agreed better with the contrast perception of human visual system compared to the models using luminance, CIELAB lightness U, and CIECAM02 lightness J.

  5. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    R. G. SILVA

    1999-03-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  6. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  7. Genetic predictions of racing performance in quarter horses.

    Science.gov (United States)

    Willham, R L; Wilson, D E

    1991-09-01

    Research on the racing performance of quarter horses has been used to develop genetic prediction summaries on all horses with at least one start on record at the American Quarter Horse Association. In the 1987 summary, records from a total of 212,065 horses were used to give genetic predictions on stallions, mares, geldings, fillies, and colts. A reduced animal model was used that incorporated the repeated records of individuals. The individual race was the contemporary group after the data were adjusted for distance, sex, and age. Estimates of heritability of .24 and repeatability of .32 suggest that increased racing performance can be achieved if the predictions are used by breeders. Continued research in variance component estimation includes the genetic covariances among the several distances, maternal influence, and genetic parameters for racing longevity.

  8. Calibrated predictions for multivariate competing risks models.

    Science.gov (United States)

    Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni

    2014-04-01

    Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

  9. DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE

    Directory of Open Access Journals (Sweden)

    Mirza Suljić

    2012-05-01

    Full Text Available Although data mining has been successfully implemented in the business world for some time now, its use in higher education is still relatively new, i.e. its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was evaluated with the passing grade at the exam. The impact of students' social demographic variables, achieved results from high school and from the entrance exam, and attitudes towards studying which can have an affect on success, were all investigated. In future investigations, with identifying and evaluating variables associated with process of studying, and with the sample increase, it would be possible to produce a model which would stand as a foundation for the development of decision support system in higher education.

  10. Numerical Prediction of Cold Season Fog Events over Complex Terrain: the Performance of the WRF Model During MATERHORN-Fog and Early Evaluation

    Science.gov (United States)

    Pu, Zhaoxia; Chachere, Catherine N.; Hoch, Sebastian W.; Pardyjak, Eric; Gultepe, Ismail

    2016-09-01

    A field campaign to study cold season fog in complex terrain was conducted as a component of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program from 07 January to 01 February 2015 in Salt Lake City and Heber City, Utah, United States. To support the field campaign, an advanced research version of the Weather Research and Forecasting (WRF) model was used to produce real-time forecasts and model evaluation. This paper summarizes the model performance and preliminary evaluation of the model against the observations. Results indicate that accurately forecasting fog is challenging for the WRF model, which produces large errors in the near-surface variables, such as relative humidity, temperature, and wind fields in the model forecasts. Specifically, compared with observations, the WRF model overpredicted fog events with extended duration in Salt Lake City because it produced higher moisture, lower wind speeds, and colder temperatures near the surface. In contrast, the WRF model missed all fog events in Heber City, as it reproduced lower moisture, higher wind speeds, and warmer temperatures against observations at the near-surface level. The inability of the model to produce proper levels of near-surface atmospheric conditions under fog conditions reflects uncertainties in model physical parameterizations, such as the surface layer, boundary layer, and microphysical schemes.

  11. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...

  12. Predicting Performance of a Face Recognition System Based on Image Quality

    NARCIS (Netherlands)

    Dutta, A.

    2015-01-01

    In this dissertation, we focus on several aspects of models that aim to predict performance of a face recognition system. Performance prediction models are commonly based on the following two types of performance predictor features: a) image quality features; and b) features derived solely from

  13. PREDICT : model for prediction of survival in localized prostate cancer

    NARCIS (Netherlands)

    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

  14. Predicting Students' Academic Performance Using Artificial Neural Networks: A Case Study

    OpenAIRE

    Ghaleb A. El-Refae; Qeethara Kadhim Al-Shayea

    2010-01-01

    Predicting students’ academic performance is critical for universities because strategic programs can be planned in improving or maintaining students’ performance. The goal of this study is to predict the factors affecting the university students' performance using Artificial Neural Networks (ANN) model. Various factors that may likely influence the performance of a student were identified. Generalized Regression Neural Network (GRNN) is used to predict the university students' performance. I...

  15. Numerical simulation of a twin screw expander for performance prediction

    Science.gov (United States)

    Papes, Iva; Degroote, Joris; Vierendeels, Jan

    2015-08-01

    With the increasing use of twin screw expanders in waste heat recovery applications, the performance prediction of these machines plays an important role. This paper presents a mathematical model for calculating the performance of a twin screw expander. From the mass and energy conservation laws, differential equations are derived which are then solved together with the appropriate Equation of State in the instantaneous control volumes. Different flow processes that occur inside the screw expander such as filling (accompanied by a substantial pressure loss) and leakage flows through the clearances are accounted for in the model. The mathematical model employs all geometrical parameters such as chamber volume, suction and leakage areas. With R245fa as working fluid, the Aungier Redlich-Kwong Equation of State has been used in order to include real gas effects. To calculate the mass flow rates through the leakage paths formed inside the screw expander, flow coefficients are considered as constant and they are derived from 3D Computational Fluid Dynamic calculations at given working conditions and applied to all other working conditions. The outcome of the mathematical model is the P-V indicator diagram which is compared to CFD results of the same twin screw expander. Since CFD calculations require significant computational time, developed mathematical model can be used for the faster performance prediction.

  16. Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

    Energy Technology Data Exchange (ETDEWEB)

    Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au [School of Physics, University of Western Australia, Western Australia 6009, Australia and School of Health Sciences, National University of Malaysia, Bangi 43600 (Malaysia); Ebert, Martin A. [School of Physics, University of Western Australia, Western Australia 6009, Australia and Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008 (Australia); Bulsara, Max [Institute for Health Research, University of Notre Dame, Fremantle, Western Australia 6959 (Australia); House, Michael J. [School of Physics, University of Western Australia, Western Australia 6009 (Australia); Kennedy, Angel [Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008 (Australia); Joseph, David J. [Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia and School of Surgery, University of Western Australia, Western Australia 6009 (Australia); Denham, James W. [School of Medicine and Public Health, University of Newcastle, New South Wales 2308 (Australia)

    2016-05-15

    Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥ 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions

  17. Performance and wake predictions of HAWTs in wind farms

    Energy Technology Data Exchange (ETDEWEB)

    Leclerc, C.; Masson, C.; Paraschivoiu, I. [Ecole Polytechnique, Montreal (Canada)

    1997-12-31

    The present contribution proposes and describes a promising way towards performance prediction of an arbitrary array of turbines. It is based on the solution of the time-averaged, steady-state, incompressible Navier-Stokes equations with an appropriate turbulence closure model. The turbines are represented by distributions of momentum sources in the Navier-Stokes equations. In this paper, the applicability and viability of the proposed methodology is demonstrated using an axisymmetric implementation. The k-{epsilon} model has been chosen for the closure of the time-averaged, turbulent flow equations and the properties of the incident flow correspond to those of a neutral atmospheric boundary layer. The proposed mathematical model is solved using a Control-Volume Finite Element Method (CVFEM). Detailed results have been obtained using the proposed method for an isolated wind turbine and for two turbines one behind another. In the case of an isolated turbine, accurate wake velocity deficit predictions are obtained and an increase in power due to atmospheric turbulence is found in agreement with measurements. In the case of two turbines, the proposed methodology provides an appropriate modelling of the wind-turbine wake and a realistic prediction of the performance degradation of the downstream turbine.

  18. ENSO Prediction using Vector Autoregressive Models

    Science.gov (United States)

    Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.

    2013-12-01

    A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.

  19. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  20. Gas explosion prediction using CFD models

    Energy Technology Data Exchange (ETDEWEB)

    Niemann-Delius, C.; Okafor, E. [RWTH Aachen Univ. (Germany); Buhrow, C. [TU Bergakademie Freiberg Univ. (Germany)

    2006-07-15

    A number of CFD models are currently available to model gaseous explosions in complex geometries. Some of these tools allow the representation of complex environments within hydrocarbon production plants. In certain explosion scenarios, a correction is usually made for the presence of buildings and other complexities by using crude approximations to obtain realistic estimates of explosion behaviour as can be found when predicting the strength of blast waves resulting from initial explosions. With the advance of computational technology, and greater availability of computing power, computational fluid dynamics (CFD) tools are becoming increasingly available for solving such a wide range of explosion problems. A CFD-based explosion code - FLACS can, for instance, be confidently used to understand the impact of blast overpressures in a plant environment consisting of obstacles such as buildings, structures, and pipes. With its porosity concept representing geometry details smaller than the grid, FLACS can represent geometry well, even when using coarse grid resolutions. The performance of FLACS has been evaluated using a wide range of field data. In the present paper, the concept of computational fluid dynamics (CFD) and its application to gas explosion prediction is presented. Furthermore, the predictive capabilities of CFD-based gaseous explosion simulators are demonstrated using FLACS. Details about the FLACS-code, some extensions made to FLACS, model validation exercises, application, and some results from blast load prediction within an industrial facility are presented. (orig.)

  1. Seasonal Predictability in a Model Atmosphere.

    Science.gov (United States)

    Lin, Hai

    2001-07-01

    The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

  2. Optimal feedback scheduling of model predictive controllers

    Institute of Scientific and Technical Information of China (English)

    Pingfang ZHOU; Jianying XIE; Xiaolong DENG

    2006-01-01

    Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.

  3. Predictive Modeling of Cardiac Ischemia

    Science.gov (United States)

    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.

  4. The applicability and limitations of the geochemical models and tools used in simulating radionuclide behaviour in natural waters. Lessons learned from the Blind Predictive Modelling exercises performed in conjunction with Natural Analogue studies

    Energy Technology Data Exchange (ETDEWEB)

    Bruno, J.; Duro, L.; Grive, M. [QuantiSci SL, Parc Tecnologic del Valles (Spain)

    2001-07-01

    One of the key applications of Natural Analogue studies to the Performance Assessment (PA) of nuclear waste disposal has been the possibility to test the geochemical models and tools to be used in describing the migration of radionuclides in a future radioactive waste repository system. To this end, several geochemical modelling testing exercises (commonly denoted as Blind Predictive Modelling), have formed an integral part of Natural Analogue Studies over the last decade. Consequently, we thought that this is a timely occasion to make an evaluation of the experience gained and lessons learnt. We have reviewed, discussed and compared the results obtained from the Blind Prediction Modelling (BPM) exercises carried out within 7 Natural Analogue Studies: Oman, Pocos de Caldas, Cigar Lake, Maqarin, El Berrocal, Oklo and Palmottu. To make this comparison meaningful, we present the main geochemical characteristics of each site in order to highlight the most relevant mineralogical and hydrochemical differences. From the complete list of elements studied at all the investigated sites we have made a selection based on the relevance of a given element from a PA viewpoint and on the frequency this element has been included in the BPM exercises. The elements selected for discussion are: Sr, Ba, Sn, Pb, Se, Ni, Zn, REEs, Th and U. We have based our discussion on the results obtained from the speciation as well as solubility calculations. From the comparison of the results it is concluded that we can differentiate between three element categories: 1. Elements whose geochemical behaviour can be fairly well described by assuming solubility control exerted by pure solid phases of the given element (i.e. Th, U under reducing conditions and U in some sites under oxidising conditions); 2. Elements for which the association to major geochemical components of the system must be considered in order to explain their concentrations in groundwaters (i.e. Sr, Ba, Zn, Se, REEs and U under

  5. A predictive analytic model for high-performance tunneling field-effect transistors approaching non-equilibrium Green's function simulations

    Energy Technology Data Exchange (ETDEWEB)

    Salazar, Ramon B., E-mail: ramon@purdue.edu, E-mail: hilatikh@purdue.edu; Appenzeller, Joerg [Birck Nanotechnology Center, Purdue University, 1205 W. State Street, West Lafayette, Indiana 47907 (United States); Ilatikhameneh, Hesameddin, E-mail: ramon@purdue.edu, E-mail: hilatikh@purdue.edu; Rahman, Rajib; Klimeck, Gerhard [Network for Computational Nanotechnology, 207 S. Martin Jischke Drive, West Lafayette, Indiana 47907 (United States)

    2015-10-28

    A new compact modeling approach is presented which describes the full current-voltage (I-V) characteristic of high-performance (aggressively scaled-down) tunneling field-effect-transistors (TFETs) based on homojunction direct-bandgap semiconductors. The model is based on an analytic description of two key features, which capture the main physical phenomena related to TFETs: (1) the potential profile from source to channel and (2) the elliptic curvature of the complex bands in the bandgap region. It is proposed to use 1D Poisson's equations in the source and the channel to describe the potential profile in homojunction TFETs. This allows to quantify the impact of source/drain doping on device performance, an aspect usually ignored in TFET modeling but highly relevant in ultra-scaled devices. The compact model is validated by comparison with state-of-the-art quantum transport simulations using a 3D full band atomistic approach based on non-equilibrium Green's functions. It is shown that the model reproduces with good accuracy the data obtained from the simulations in all regions of operation: the on/off states and the n/p branches of conduction. This approach allows calculation of energy-dependent band-to-band tunneling currents in TFETs, a feature that allows gaining deep insights into the underlying device physics. The simplicity and accuracy of the approach provide a powerful tool to explore in a quantitatively manner how a wide variety of parameters (material-, size-, and/or geometry-dependent) impact the TFET performance under any bias conditions. The proposed model presents thus a practical complement to computationally expensive simulations such as the 3D NEGF approach.

  6. Distributional Analysis for Model Predictive Deferrable Load Control

    OpenAIRE

    Chen, Niangjun; Gan, Lingwen; Low, Steven H.; Wierman, Adam

    2014-01-01

    Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. In particular, previous work has analyzed the average-case performance of model predictive deferrable load control. However, to this point, distributional analysis of model predictive deferrable load control has been elusive. In ...

  7. Evaluating the performance of an integrated CALPUFF-MM5 modeling system for predicting SO{sub 2} emission from a refinery

    Energy Technology Data Exchange (ETDEWEB)

    Abdul-Wahab, Sabah Ahmed [Sultan Qaboos University, Department of Mechanical and Industrial Engineering, College of Engineering, Muscat (Oman); Ali, Sappurd [National Engineering and Scientific Commission (NESCOM), Islamabad (Pakistan); Sardar, Sabir; Irfan, Naseem [Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad (Pakistan); Al-Damkhi, Ali [Public Authority for Applied Education and Training (PAAET), Department of Environmental Sciences College of Health Sciences, Salmiyah (Kuwait)

    2011-12-15

    Oil refineries are one of the proven sources of environmental pollution as they emit more than 100 chemicals into the atmosphere including sulfur dioxide (SO{sub 2}). The dispersion patterns of SO{sub 2} from emissions of Sohar refinery was simulated by employing California Puff (CALPUFF) model integrated with state of the art meteorological Mesoscale Model (MM5). The results of this simulation were used to quantify the ground level concentrations of SO{sub 2} in and around the refinery. The evaluation of the CALPUFF and MM5 modeling system was carried out by comparing the estimated results with that of observed data of the same area. The predicted concentrations of SO{sub 2} agreed well with the observed data, with minor differences in magnitudes. In addition, the ambient air quality of the area was checked by comparing the model results with the regulatory limits for SO{sub 2} set by the Ministry of Environment and Climate Affairs (MECA) in Oman. From the analysis of results, it was found that the concentration of SO{sub 2} in the nearby communities of Sohar refinery is well within the regulatory limits specified by MECA. Based on these results, it was concluded that no health risk, due to SO{sub 2} emissions, is present in areas adjacent to the refinery. (orig.)

  8. Numerical weather prediction model tuning via ensemble prediction system

    Science.gov (United States)

    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.

  9. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  10. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;

    2016-01-01

    The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  11. Use of statistical modeling to predict the effect of formulation composition on coacervation, silicone deposition, and conditioning sensory performance of cationic cassia polymers.

    Science.gov (United States)

    Lepilleur, Carole; Mullay, John; Kyer, Carol; McCalister, Pam; Clifford, Ted

    2011-01-01

    Formulation composition has a dramatic influence on coacervate formation in conditioning shampoo. The purpose of this study is to correlate the amount of coacervate formation of novel cationic cassia polymers to the corresponding conditioning profiles on European brown hair using silicone deposition, cationic polymer deposition and sensory evaluation. A design of experiments was conducted by varying the levels of three surfactants (sodium lauryl ether sulfate, sodium lauryl sulfate, and cocamidopropyl betaine) in formulations containing cationic cassia polymers of different cationic charge density (1.7 and 3.0m Eq/g). The results show formulation composition dramatically affects physical properties, coacervation, silicone deposition, cationic polymer deposition and hair sensory attributes. Particularly, three parameters are of importance in determining silicone deposition: polymer charge, surfactant (micelle) charge and total amount of surfactant (micelle aspect ratio). Both sensory panel testing and silicone deposition results can be predicted with a high confidence level using statistical models that incorporate these parameters.

  12. Genomic Prediction of Testcross Performance in Canola (Brassica napus.

    Directory of Open Access Journals (Sweden)

    Habib U Jan

    Full Text Available Genomic selection (GS is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81 followed by oil yield (0.75 and lowest for seedling emergence (0.29. For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF, prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows

  13. Measuring reflective-band imaging systems for performance prediction

    Science.gov (United States)

    Slonopas, Andre; Preece, Bradley L.; Haefner, David P.

    2017-05-01

    An objective performance of the reflective-band imaging systems is required in order to provide the warfighter with the right technology for a specific task. Various methods to measure and model performance in the visible (Vis) spectral regions have been proposed in the literature. This correspondence shows the influence of the spectral region averaging on the monochromatic modulation transfer function (MTF). This works unequivocally shows that the illumination source plays a crucial role in the accurate predictive analysis of the system performance. For accurate analysis the illumination sources need to be carefully considered for the atmospheric conditions. This work shows the possibility of using an LED configuration in the system performance analysis. Such configurations need rigorous calibration in order to become a valuable asset in system characterization.

  14. 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...

  15. Predictive modeling and reducing cyclic variability in autoignition engines

    Energy Technology Data Exchange (ETDEWEB)

    Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

    2016-08-30

    Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

  16. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  17. Performance Prediction of Mechanical Pump in STELLA-1

    Energy Technology Data Exchange (ETDEWEB)

    Han, Ji-Woong; Cho, Chungho; Jeong, Ji-Young [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2014-10-15

    Under a mid- and long-term nuclear R-D program, STELLA (Sodium Integral Effect Test Loop for Safety Simulation and Assessment) project is in progress in KAERI (Korea Atomic Energy Research Institute). In STELLA-1, the experiments for the evaluation of heat exchangers such as DHX (Decay heat exchanger) and AHX (Air heat exchanger) are being performed, and those for PHTS (Primary heat transport system) mechanical pump are being prepared. The detailed design of each component is based on that of a 600MWe demonstration reactor. The model pump installed in STELLA-1 was scaled down based on the scaling law. Since the reference reactor of STELLA-1 is a 600MWe pool type demonstration reactor, some design modifications were inevitable between pool type prototype pump and loop type model pump, such as outer case and inlet pipe. In this study performance evaluation on the model pump has been done by CFD methods. The Design modeler in ANSYS Workbench was utilized in modeling process. The computations were performed using the commercial code ANSYS CFX. The overall hydraulic behaviors in the model pump have been predicted at a steady state condition.

  18. Developing predictive models for return to work using the Military Power, Performance and Prevention (MP3) musculoskeletal injury risk algorithm: a study protocol for an injury risk assessment programme.

    Science.gov (United States)

    Rhon, Daniel I; Teyhen, Deydre S; Shaffer, Scott W; Goffar, Stephen L; Kiesel, Kyle; Plisky, Phil P

    2016-11-24

    Musculoskeletal injuries are a primary source of disability in the US Military, and low back pain and lower extremity injuries account for over 44% of limited work days annually. History of prior musculoskeletal injury increases the risk for future injury. This study aims to determine the risk of injury after returning to work from a previous injury. The objective is to identify criteria that can help predict likelihood for future injury or re-injury. There will be 480 active duty soldiers recruited from across four medical centres. These will be patients who have sustained a musculoskeletal injury in the lower extremity or lumbar/thoracic spine, and have now been cleared to return back to work without any limitations. Subjects will undergo a battery of physical performance tests and fill out sociodemographic surveys. They will be followed for a year to identify any musculoskeletal injuries that occur. Prediction algorithms will be derived using regression analysis from performance and sociodemographic variables found to be significantly different between injured and non-injured subjects. Due to the high rates of injuries, injury prevention and prediction initiatives are growing. This is the first study looking at predicting re-injury rates after an initial musculoskeletal injury. In addition, multivariate prediction models appear to have move value than models based on only one variable. This approach aims to validate a multivariate model used in healthy non-injured individuals to help improve variables that best predict the ability to return to work with lower risk of injury, after a recent musculoskeletal injury. NCT02776930. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  19. A Model Performance

    Science.gov (United States)

    Thornton, Bradley D.; Smalley, Robert A.

    2008-01-01

    Building information modeling (BIM) uses three-dimensional modeling concepts, information technology and interoperable software to design, construct and operate a facility. However, BIM can be more than a tool for virtual modeling--it can provide schools with a 3-D walkthrough of a project while it still is on the electronic drawing board. BIM can…

  20. Predictions of H-mode performance in ITER

    Energy Technology Data Exchange (ETDEWEB)

    Budny, R. V.; Andre, R.; Bateman, G.; Halpern, F.; Kessel, C. E.; Kritz, A.; McCune, D.

    2008-03-03

    Time-dependent integrated predictive modeling is carried out using the PTRANSP code to predict fusion power and parameters such as alpha particle density and pressure in ITER H-mode plasmas. Auxiliary heating by negative ion neutral beam injection and ion cyclotron heating of He3 minority ions are modeled, and the GLF23 transport model is used in the prediction of the evolution of plasma temperature profiles. Effects of beam steering, beam torque, plasma rotation, beam current drive, pedestal temperatures, sawtooth oscillations, magnetic diffusion, and accumulation of He ash are treated self-consistently. Variations in assumptions associated with physics uncertainties for standard base-line DT H-mode plasmas (with Ip=15 MA, BTF=5.3 T, and Greenwald fraction=0.86) lead to a range of predictions for DT fusion power PDT and quasi-steady state fusion QDT (≡ PDT/Paux). Typical predictions assuming Paux = 50-53 MW yield PDT = 250- 720 MW and QDT = 5 - 14. In some cases where Paux is ramped down or shut off after initial flat-top conditions, quasi-steady QDT can be considerably higher, even infinite. Adverse physics assumptions such as existence of an inward pinch of the helium ash and an ash recycling coefficient approaching unity lead to very low values for PDT. Alternative scenarios with different heating and reduced performance regimes are also considered including plasmas with only H or D isotopes, DT plasmas with toroidal field reduced 10 or 20%, and discharges with reduced beam voltage. In full-performance D-only discharges, tritium burn-up is predicted to generate central tritium densities up to 1016/m3 and DT neutron rates up to 5×1016/s, compared with the DD neutron rates of 6×1017/s. Predictions with the toroidal field reduced 10 or 20% below the planned 5.3 T and keeping the same q98, Greenwald fraction, and Βη indicate that the fusion yield PDT and QDT will be lower by about a factor of two (scaling as B3.5).

  1. Prediction of College Performance of Superior Students.

    Science.gov (United States)

    Roberts, Roy J.

    1965-01-01

    Using 857 male National Merit Finalists and Commended Students, scales to predict 1st year college grades and science, writing, art, music, speech, and leadership achievement were developed by analysis of 906 pre-college questionnaire items. Two item analysis strategies were used: responses of achieving subjects (S's) and general samples of…

  2. Predicting work Performance through selection interview ratings and Psychological assessment

    Directory of Open Access Journals (Sweden)

    Liziwe Nzama

    2008-12-01

    Full Text Available The aim of the study was to establish whether selection interviews used in conjunction with psychological assessments of personality traits and cognitive functioning contribute to predicting work performance. The sample consisted of 102 managers who were appointed recently in a retail organisation. The independent variables were selection interview ratings obtained on the basis of structured competency-based interview schedules by interviewing panels, fve broad dimensions of personality defned by the Five Factor Model as measured by the 15 Factor Questionnaire (15FQ+, and cognitive processing variables (current level of work, potential level of work, and 12 processing competencies measured by the Cognitive Process Profle (CPP. Work performance was measured through annual performance ratings that focused on measurable outputs of performance objectives. Only two predictor variables correlated statistically signifcantly with the criterion variable, namely interview ratings (r = 0.31 and CPP Verbal Abstraction (r = 0.34. Following multiple regression, only these variables contributed signifcantly to predicting work performance, but only 17.8% of the variance of the criterion was accounted for.

  3. Active diagnosis of hybrid systems - A model predictive approach

    OpenAIRE

    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...

  4. Probabilistic prediction models for aggregate quarry siting

    Science.gov (United States)

    Robinson, G.R.; Larkins, P.M.

    2007-01-01

    Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.

  5. Data Mining Applications: A comparative Study for Predicting Student's performance

    CERN Document Server

    Yadav, Surjeet Kumar; Pal, Saurabh

    2012-01-01

    Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.

  6. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  7. Erosive Burning and its Applications for Performance Prediction

    Directory of Open Access Journals (Sweden)

    A. R. Kulkarni

    1993-04-01

    Full Text Available A modified method for prediction of performance of large motors based on erosion constant obtained by partial burning technique is discussed. Erosion constants for two different double base compositions have been determined by partial burning technique. These constraints have been used to predict the performance of the large scale motors developed for Defence applications. The predicted performance compares well with the experimental values.

  8. PERFORM 60 - Prediction of the effects of radiation for reactor pressure vessel and in-core materials using multi-scale modelling - 60 years foreseen plant lifetime

    Energy Technology Data Exchange (ETDEWEB)

    Leclercq, Sylvain, E-mail: sylvain.leclercq@edf.f [EDF R and D, Materials and Mechanics of Components, Avenue des Renardieres - Ecuelles, 77818 Moret sur Loing Cedex (France); Lidbury, David [SERCO Assurance - Walton House, 404 Faraday Street, Birchwood Park, Warrington, Cheshire WA3 6GA (United Kingdom); Van Dyck, Steven [SCK-CEN, Nuclear Material Science, Boeretang 200, BE, 2400 Mol (Belgium); Moinereau, Dominique [EDF R and D, Materials and Mechanics of Components, Avenue des Renardieres - Ecuelles, 77818 Moret sur Loing Cedex (France); Alamo, Ana [CEA Saclay, DEN/DSOE, 91191 Gif-sur-Yvette (France); Mazouzi, Abdou Al [EDF R and D, Materials and Mechanics of Components, Avenue des Renardieres - Ecuelles, 77818 Moret sur Loing Cedex (France)

    2010-11-01

    In nuclear power plants, materials may undergo degradation due to severe irradiation conditions that may limit their operational life. Utilities that operate these reactors need to quantify the ageing and the potential degradations of some essential structures of the power plant to ensure safe and reliable plant operation. So far, the material databases needed to take account of these degradations in the design and safe operation of installations mainly rely on long-term irradiation programs in test reactors as well as on mechanical or corrosion testing in specialized hot cells. Continuous progress in the physical understanding of the phenomena involved in irradiation damage and continuous progress in computer sciences have now made possible the development of multi-scale numerical tools able to simulate the effects of irradiation on materials microstructure. A first step towards this goal has been successfully reached through the development of the RPV-2 and Toughness Module numerical tools by the scientific community created around the FP6 PERFECT project. These tools allow to simulate irradiation effects on the constitutive behaviour of the reactor pressure vessel low alloy steel, and also on its failure properties. Relying on the existing PERFECT Roadmap, the 4 years Collaborative Project PERFORM 60 has mainly for objective to develop multi-scale tools aimed at predicting the combined effects of irradiation and corrosion on internals (austenitic stainless steels) and also to improve existing ones on RPV (bainitic steels). PERFORM 60 is based on two technical sub-projects: (i) RPV and (ii) internals. In addition to these technical sub-projects, the Users' Group and Training sub-project shall allow representatives of constructors, utilities, research organizations... from Europe, USA and Japan to receive the information and training to get their own appraisal on limits and potentialities of the developed tools. An important effort will also be made to teach

  9. PERFORM 60 - Prediction of the effects of radiation for reactor pressure vessel and in-core materials using multi-scale modelling - 60 years foreseen plant lifetime

    Science.gov (United States)

    Leclercq, Sylvain; Lidbury, David; Van Dyck, Steven; Moinereau, Dominique; Alamo, Ana; Mazouzi, Abdou Al

    2010-11-01

    In nuclear power plants, materials may undergo degradation due to severe irradiation conditions that may limit their operational life. Utilities that operate these reactors need to quantify the ageing and the potential degradations of some essential structures of the power plant to ensure safe and reliable plant operation. So far, the material databases needed to take account of these degradations in the design and safe operation of installations mainly rely on long-term irradiation programs in test reactors as well as on mechanical or corrosion testing in specialized hot cells. Continuous progress in the physical understanding of the phenomena involved in irradiation damage and continuous progress in computer sciences have now made possible the development of multi-scale numerical tools able to simulate the effects of irradiation on materials microstructure. A first step towards this goal has been successfully reached through the development of the RPV-2 and Toughness Module numerical tools by the scientific community created around the FP6 PERFECT project. These tools allow to simulate irradiation effects on the constitutive behaviour of the reactor pressure vessel low alloy steel, and also on its failure properties. Relying on the existing PERFECT Roadmap, the 4 years Collaborative Project PERFORM 60 has mainly for objective to develop multi-scale tools aimed at predicting the combined effects of irradiation and corrosion on internals (austenitic stainless steels) and also to improve existing ones on RPV (bainitic steels). PERFORM 60 is based on two technical sub-projects: (i) RPV and (ii) internals. In addition to these technical sub-projects, the Users' Group and Training sub-project shall allow representatives of constructors, utilities, research organizations… from Europe, USA and Japan to receive the information and training to get their own appraisal on limits and potentialities of the developed tools. An important effort will also be made to teach young

  10. The componential model of reading: predicting first grade reading performance of culturally diverse students from ecological, psychological, and cognitive factors assessed at kindergarten entry.

    Science.gov (United States)

    Ortiz, Miriam; Folsom, Jessica S; Al Otaiba, Stephanie; Greulich, Luana; Thomas-Tate, Shurita; Connor, Carol M

    2012-01-01

    This study, framed by the component model of reading (CMR), examined the relative importance of kindergarten-entry predictors of first grade reading performance. Specifically, elements within the ecological domain included dialect, maternal education, amount of preschool, and home literacy; elements within the psychological domain included teacher-reported academic competence, social skills, and behavior; and elements within the cognitive domain included initial vocabulary, phonological, and morpho-syntactic skills, and alphabetic and word recognition skills. Data were obtained for 224 culturally diverse kindergarteners (58% Black, 34% White, and 8% Hispanic or other; 58% received free or reduced-price lunch) from a larger study conducted in seven predominantly high poverty schools (n = 20 classrooms) in a midsized city school district in northern Florida. Results from a hierarchical multiple regression (with variables in the ecological domain entered first, followed by the psychological and cognitive domains) revealed a model that explained roughly 56% of the variance in first grade reading achievement, using fall-of-kindergarten predictors. Letter-word reading and morpho-syntactic skill were the strongest significant predictors. The findings largely support the CMR model as a means to understand individual differences in reading acquisition and, in turn, to support data-based instructional decisions for a wider range of children.

  11. Predicting Product Performance with Social Media

    Directory of Open Access Journals (Sweden)

    Liviu LICA

    2011-01-01

    Full Text Available Last 20 years brought massive growth in IT&C world. Mobile solutions such as netbooks, laptops, mobile phones, tablets enable the wireless connection to the Internet. Anyone can ac-cess it anytime and anywhere. In this context, a part of the activities from the real world have a correspondence in the online discussions. Social media in general and social networks in particular have turned into marketing tools for organizations and a place where people can express their opinions and attitudes about products.The paper shows how social media can be used for predicting the success of a product or service. To showcase this, two case studies are presented; a test to prove that the conversations that take place in social media are a good indicator of success and the second is an exercise to predict the winner of the Oscar for best picture in 2011.

  12. Specific Mindfulness Skills Differentially Predict Creative Performance.

    Science.gov (United States)

    Baas, Matthijs; Nevicka, Barbara; Ten Velden, Femke S

    2014-05-23

    Past work has linked mindfulness to improved emotion regulation, interpersonal skills, and basic cognitive abilities, but is unclear about the relation between mindfulness and creativity. Studies examining effects of mindfulness on factors pertinent to creativity suggest a uniform and positive relation, whereas work on specific mindfulness skills suggests that mindfulness skills may differentially predict creativity. To test whether the relation between mindfulness and creativity is positive and uniform (the uniform hypothesis) or differentially depends on particular components of mindfulness (the differential hypothesis), we conducted four studies in which mindfulness skills were measured, extensively trained, or manipulated with a short, incidental meditation session. Results supported a differential relation between mindfulness and creativity: Only the ability to observe and attend to various stimuli consistently and positively predicted creativity. Results regarding other mindfulness skills were less consistent. Implications for theory and practice are discussed.

  13. Physical Activity Predicts Performance in an Unpracticed Bimanual Coordination Task

    Science.gov (United States)

    Boisgontier, Matthieu P.; Serbruyns, Leen; Swinnen, Stephan P.

    2017-01-01

    Practice of a given physical activity is known to improve the motor skills related to this activity. However, whether unrelated skills are also improved is still unclear. To test the impact of physical activity on an unpracticed motor task, 26 young adults completed the international physical activity questionnaire and performed a bimanual coordination task they had never practiced before. Results showed that higher total physical activity predicted higher performance in the bimanual task, controlling for multiple factors such as age, physical inactivity, music practice, and computer games practice. Linear mixed models allowed this effect of physical activity to be generalized to a large population of bimanual coordination conditions. This finding runs counter to the notion that generalized motor abilities do not exist and supports the existence of a “learning to learn” skill that could be improved through physical activity and that impacts performance in tasks that are not necessarily related to the practiced activity. PMID:28265253

  14. 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.

  15. Nonlinear chaotic model for predicting storm surges

    NARCIS (Netherlands)

    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.

  16. Performance samples on academic tasks : improving prediction of academic performance

    NARCIS (Netherlands)

    Tanilon, Jenny

    2011-01-01

    This thesis is about the development and validation of a performance-based test, labeled as Performance Samples on academic tasks in Education and Child Studies (PSEd). PSEd is designed to identify students who are most able to perform the academic tasks involved in an Education and Child Studies br

  17. PREDICTION OF GAS INJECTION PERFORMANCE FOR HETEROGENEOUS RESERVOIRS

    Energy Technology Data Exchange (ETDEWEB)

    Martin J. Blunt; Franklin M. Orr Jr

    2000-06-01

    This final report describes research carried out in the Department of Petroleum Engineering at Stanford University from September 1996--May 2000 under a three-year grant from the Department of Energy on the ''Prediction of Gas Injection Performance for Heterogeneous Reservoirs''. The advances from the research include: new tools for streamline-based simulation including the effects of gravity, changing well conditions, and compositional displacements; analytical solutions to 1D compositional displacements which can speed-up gas injection simulation still further; and modeling and experiments that delineate the physics that is unique to three-phase flow.

  18. Methodologies for predicting the part-load performance of aero-derivative gas turbines

    DEFF Research Database (Denmark)

    Haglind, Fredrik; Elmegaard, Brian

    2009-01-01

    on methodologies for predicting part-load performance of aero-derivative gas turbines. Two different design models – one simple and one more complex – are created. Subsequently, for each of these models, the part-load performance is predicted using component maps and turbine constants, respectively. Comparisons...... with manufacturer data are made. With respect to the design models, the simple model, featuring a compressor, combustor and turbines, results in equally good performance prediction in terms of thermal efficiency and exhaust temperature as does a more complex model. As for part-load predictions, the results suggest...... that the mass flow and pressure ratio characteristics can be well predicted with both methods. The thermal efficiency and exhaust temperature, however, are not well predicted below 60–70% load when using turbine constants and assuming constant efficiencies for turbomachinery....

  19. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    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,...

  20. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  1. Performance analysis and prediction in triathlon.

    Science.gov (United States)

    Ofoghi, Bahadorreza; Zeleznikow, John; Macmahon, Clare; Rehula, Jan; Dwyer, Dan B

    2016-01-01

    Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008-2012). The analysis reveals patterns of performance in five components of triathlon (three race "legs" and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.

  2. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  3. 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.

  4. Elbow Extension Predicts Motor Impairment and Performance after Stroke

    Directory of Open Access Journals (Sweden)

    Crystal L. Massie

    2011-01-01

    Full Text Available Background and Purpose. Kinematic motion analysis has helped to characterize poststroke reaching strategies with the hemiparetic arm. However, the relationships between reaching strategy and performance on common functional outcome measures remain unclear. Methods. Thirty-five participants were tested for motor performance and motor impairment using the Wolf Motor Function Test (time and functional ability measure and Fugl-Meyer assessment, respectively. Kinematic motion analysis of a forward reaching paradigm provided potential predictors of reaching strategy including shoulder flexion, elbow extension, and trunk displacement. A stepwise linear regression model with three potential predictors was used in addition to Pearson-product moment correlations. Results. Kinematic analysis of elbow extension predicted performance on both the Wolf Motor Function Test and Fugl-Meyer assessment. Shoulder flexion and trunk displacement did not significantly predict functional or reaching time outcomes. The Wolf Motor Function Test and the Fugl-Meyer assessment were highly correlated. Conclusions. The ability to incorporate elbow extension during reach is a significant predictor of motor performance and hemiparetic arm motor capacity after stroke.

  5. Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

    Science.gov (United States)

    Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

    2017-05-01

    In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

  6. Predicting gifted foreign language learning and performance

    OpenAIRE

    Faulkner, Hilary

    2003-01-01

    This thesis examines individual learner characteristics in order to identify those useful as predictors of gifted foreign language learning performance and creativity in secondary school pupil learners. An individual learner might possess a range of learner characteristics which combine to support his or her gifted foreign language performance. Foreign language learning in England is examined in the opening chapter, providing an historical and educational context within which to explore in...

  7. High performance liquid chromatography of substituted aromatics with the metal-organic framework MIL-100(Fe): Mechanism analysis and model-based prediction.

    Science.gov (United States)

    Qin, Weiwei; Silvestre, Martin Eduardo; Li, Yongli; Franzreb, Matthias

    2016-02-05

    Metal-organic framework (MOF) MIL-100(Fe) with well-defined thickness was homogenously coated onto the outer surface of magnetic microparticles via a liquid-phase epitaxy method. The as-synthesized MIL-100(Fe) was used as stationary phase for high-performance liquid chromatography (HPLC) and separations of two groups of mixed aromatic hydrocarbons (toluene, styrene and p-xylene; acetanilide, 2-nirtoaniline and 1-naphthylamine) using methanol/water as mobile phase were performed to evaluate its performance. Increasing water content of the mobile phase composition can greatly improve the separations on the expense of a longer elution time. Stepwise elution significantly shortens the elution time of acetanilide, 2-nirtoaniline and 1-naphthylamine mixtures, while still achieving a baseline separation. Combining the experimental results and in-depth modeling using a recently developed chromatographic software (ChromX), adsorption equilibrium parameters, including the affinities and maximum capacities, for each analyte toward the MIL-100(Fe) are obtained. In addition, the pore diffusivity of aromatic hydrocarbons within MIL-100(Fe) was determined to be 5×10(-12)m(2)s(-1). While the affinities of MIL-100(Fe) toward the analyte molecules differs much, the maximum capacities of the analytes are in a narrow range with q*MOFmax,toluene=3.55molL(-1), q*MOFmax,styrene or p-xylene=3.53molL(-1), and q*MOFmax,anilines=3.12molL(-1) corresponding to approximately 842 toluene and 838 styrene or p-xylene, and 740 aniline molecules per MIL-100(Fe) unit cell, respectively.

  8. 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....

  9. Cognitive performance modeling based on general systems performance theory.

    Science.gov (United States)

    Kondraske, George V

    2010-01-01

    General Systems Performance Theory (GSPT) was initially motivated by problems associated with quantifying different aspects of human performance. It has proved to be invaluable for measurement development and understanding quantitative relationships between human subsystem capacities and performance in complex tasks. It is now desired to bring focus to the application of GSPT to modeling of cognitive system performance. Previous studies involving two complex tasks (i.e., driving and performing laparoscopic surgery) and incorporating measures that are clearly related to cognitive performance (information processing speed and short-term memory capacity) were revisited. A GSPT-derived method of task analysis and performance prediction termed Nonlinear Causal Resource Analysis (NCRA) was employed to determine the demand on basic cognitive performance resources required to support different levels of complex task performance. This approach is presented as a means to determine a cognitive workload profile and the subsequent computation of a single number measure of cognitive workload (CW). Computation of CW may be a viable alternative to measuring it. Various possible "more basic" performance resources that contribute to cognitive system performance are discussed. It is concluded from this preliminary exploration that a GSPT-based approach can contribute to defining cognitive performance models that are useful for both individual subjects and specific groups (e.g., military pilots).

  10. Modeling Performance of Plant Growth Regulators

    Directory of Open Access Journals (Sweden)

    W. C. Kreuser

    2017-03-01

    Full Text Available Growing degree day (GDD models can predict the performance of plant growth regulators (PGRs applied to creeping bentgrass ( L.. The goal of this letter is to describe experimental design strategies and modeling approaches to create PGR models for different PGRs, application rates, and turf species. Results from testing the models indicate that clipping yield should be measured until the growth response has diminished. This is in contrast to reapplication of a PGR at preselected intervals. During modeling, inclusion of an amplitude-dampening coefficient in the sinewave model allows the PGR effect to dissipate with time.

  11. Dichotic listening performance predicts language comprehension.

    Science.gov (United States)

    Asbjørnsen, Arve E; Helland, Turid

    2006-05-01

    Dichotic listening performance is considered a reliable and valid procedure for the assessment of language lateralisation in the brain. However, the documentation of a relationship between language functions and dichotic listening performance is sparse, although it is accepted that dichotic listening measures language perception. In particular, language comprehension should show close correspondence to perception of language stimuli. In the present study, we tested samples of reading-impaired and normally achieving children between 10 and 13 years of age with tests of reading skills, language comprehension, and dichotic listening to consonant-vowel (CV) syllables. A high correlation between the language scores and the dichotic listening performance was expected. However, since the left ear score is believed to be an error when assessing language laterality, covariation was expected for the right ear scores only. In addition, directing attention to one ear input was believed to reduce the influence of random factors, and thus show a more concise estimate of left hemisphere language capacity. Thus, a stronger correlation between language comprehension skills and the dichotic listening performance when attending to the right ear was expected. The analyses yielded a positive correlation between the right ear score in DL and language comprehension, an effect that was stronger when attending to the right ear. The present results confirm the assumption that dichotic listening with CV syllables measures an aspect of language perception and language skills that is related to general language comprehension.

  12. Accurate torque-speed performance prediction for brushless dc motors

    Science.gov (United States)

    Gipper, Patrick D.

    Desirable characteristics of the brushless dc motor (BLDCM) have resulted in their application for electrohydrostatic (EH) and electromechanical (EM) actuation systems. But to effectively apply the BLDCM requires accurate prediction of performance. The minimum necessary performance characteristics are motor torque versus speed, peak and average supply current and efficiency. BLDCM nonlinear simulation software specifically adapted for torque-speed prediction is presented. The capability of the software to quickly and accurately predict performance has been verified on fractional to integral HP motor sizes, and is presented. Additionally, the capability of torque-speed prediction with commutation angle advance is demonstrated.

  13. A comparison of user and system query performance predictions

    NARCIS (Netherlands)

    Hauff, C.; Kelly, Diane; Azzopardi, Leif

    2010-01-01

    Query performance prediction methods are usually applied to estimate the retrieval effectiveness of queries, where the evaluation is largely system sided. However, little work has been conducted to understand query performance prediction from the user's perspective. The question we consider is,

  14. Goal Setting and Expectancy Theory Predictions of Effort and Performance.

    Science.gov (United States)

    Dossett, Dennis L.; Luce, Helen E.

    Neither expectancy (VIE) theory nor goal setting alone are effective determinants of individual effort and task performance. To test the combined ability of VIE and goal setting to predict effort and performance, 44 real estate agents and their managers completed questionnaires. Quarterly income goals predicted managers' ratings of agents' effort,…

  15. Digital Troposcatter Performance Model

    Science.gov (United States)

    1983-12-01

    D,.-iD out ’e Pthr ~ These performance measures require a complete statistical de- scription of the components of the detection variable, which we...BER threshold Pthr " Let us denote by r the region of the 5-dimensional space (y,y) in which the BER exceeds Pthr : r = (Yy I): Pe(Y,!i) > Pthrl (A.46...y) by solving the nonlinear equation ... Pe ( Y,_21)= Pthr . A closed form expression for Pout(y) cannot be - obtained. Instead we developed an

  16. Predicting sales performance: Strengthening the personality – job performance linkage

    NARCIS (Netherlands)

    T.B. Sitser (Thomas)

    2014-01-01

    markdownabstract__Abstract__ Many organizations worldwide use personality measures to select applicants for sales jobs or to assess incumbent sales employees. In the present dissertation, consisting of four independent studies, five approaches to strengthen the personality-sales performance linkage

  17. RFI modeling and prediction approach for SATOP applications: RFI prediction models

    Science.gov (United States)

    Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang

    2016-05-01

    This paper describes a technical approach for the development of RFI prediction models using carrier synchronization loop when calculating Bit or Carrier SNR degradation due to interferences for (i) detecting narrow-band and wideband RFI signals, and (ii) estimating and predicting the behavior of the RFI signals. The paper presents analytical and simulation models and provides both analytical and simulation results on the performance of USB (Unified S-Band) waveforms in the presence of narrow-band and wideband RFI signals. The models presented in this paper will allow the future USB command systems to detect the RFI presence, estimate the RFI characteristics and predict the RFI behavior in real-time for accurate assessment of the impacts of RFI on the command Bit Error Rate (BER) performance. The command BER degradation model presented in this paper also allows the ground system operator to estimate the optimum transmitted SNR to maintain a required command BER level in the presence of both friendly and un-friendly RFI sources.

  18. Performance results of HESP physical model

    Science.gov (United States)

    Chanumolu, Anantha; Thirupathi, Sivarani; Jones, Damien; Giridhar, Sunetra; Grobler, Deon; Jakobsson, Robert

    2017-02-01

    As a continuation to the published work on model based calibration technique with HESP(Hanle Echelle Spectrograph) as a case study, in this paper we present the performance results of the technique. We also describe how the open parameters were chosen in the model for optimization, the glass data accuracy and handling the discrepancies. It is observed through simulations that the discrepancies in glass data can be identified but not quantifiable. So having an accurate glass data is important which is possible to obtain from the glass manufacturers. The model's performance in various aspects is presented using the ThAr calibration frames from HESP during its pre-shipment tests. Accuracy of model predictions and its wave length calibration comparison with conventional empirical fitting, the behaviour of open parameters in optimization, model's ability to track instrumental drifts in the spectrum and the double fibres performance were discussed. It is observed that the optimized model is able to predict to a high accuracy the drifts in the spectrum from environmental fluctuations. It is also observed that the pattern in the spectral drifts across the 2D spectrum which vary from image to image is predictable with the optimized model. We will also discuss the possible science cases where the model can contribute.

  19. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    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

  20. Prediction of Gas Injection Performance for Heterogeneous Reservoirs

    Energy Technology Data Exchange (ETDEWEB)

    Blunt, Martin J.; Orr, Jr., Franklin M.

    1999-12-20

    This report describes research carried out in the Department of Petroleum Engineering at Stanford University from September 1998 - September 1998 under the third year of a three-year Department of Energy (DOE) grant on the ''Prediction of Gas Injection Performance for Heterogeneous Reservoirs''. The research effort is an integrated study of the factors affecting gas injection, from the pore scale to the field scale, and involves theoretical analysis, laboratory experiments and numerical simulation. The research is divided into four main areas: (1) Pore scale modeling of three-phase flow in porous media; (2) Laboratory experiments and analysis of factors influencing gas injection performance at the core scale with an emphasis on the fundamentals of three-phase flow; (3) Benchmark simulations of gas injection at the field scale; and (4) Development of streamline-based reservoir simulator.

  1. Using the detectability index to predict P300 speller performance

    Science.gov (United States)

    Mainsah, B. O.; Collins, L. M.; Throckmorton, C. S.

    2016-12-01

    Objective. The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data. The discriminability between target and non-target EEG responses defines a user’s performance with the system. A previous P300 speller model has been proposed to estimate system accuracy given a certain amount of data collection. However, the approach was limited to a static stopping algorithm, i.e. averaging over a fixed number of measurements, and the row-column paradigm. A generalized method that is also applicable to dynamic stopping (DS) algorithms and other stimulus paradigms is desirable. Approach. We developed a new probabilistic model-based approach to predicting BCI performance, where performance functions can be derived analytically or via Monte Carlo methods. Within this framework, we introduce a new model for the P300 speller with the Bayesian DS algorithm, by simplifying a multi-hypothesis to a binary hypothesis problem using the likelihood ratio test. Under a normality assumption, the performance functions for the Bayesian algorithm can be parameterized with the detectability index, a measure which quantifies the discriminability between target and non-target EEG responses. Main results. Simulations with synthetic and empirical data provided initial verification of the proposed method of estimating performance with Bayesian DS using the detectability index. Analysis of results from previous online studies validated the proposed method. Significance. The proposed method could serve as a useful tool to initially assess BCI performance

  2. 基于分布参数模型的水平管式降膜蒸发器模拟%Prediction of the Performance of Falling Film Evaporator with Horizontal Tube Bundle Based on a Distributed Parameter Model

    Institute of Scientific and Technical Information of China (English)

    翟玉燕; 黄兴华

    2009-01-01

    A distributed parameter model is developed for predicting the performance of a horizontal-tube falling-film evaporator. In this model, the variation of heat transfer performance along the tube length and array, as well as the effect of the dry patch on the performance are considered. The model is applied to predicting the performance of a commercial falling film evaporator, and the influences of bundle layout, pass layout, refrigerant mass flow rate and the flooded level of refrigerant on the evaporator performances are studied. The results show that the simulation result agrees well with the experimental data, and it is possible to decrease or avoid the dry patch area on the tube bundle and therefore improve the evaporator performance by rationallly designing the layout of the tube bundle and the flooded level of the refrigerant.%建立水平管式降膜蒸发器蒸发换热的分布参数模型,考虑换热性能沿管子轴向、管排方向的变化,以及传热管发生干斑现象时对降膜蒸发的影响.对一降膜蒸发器的性能进行模拟分析,并考察管束布置、制冷剂液膜质量流量、管程布置以及满液位置对降膜蒸发器性能的影响.结果表明,计算结果和试验结果吻合良好,通过合理的设计管排方式和满液位置,可以减少或避免干斑现象的发生,提高降膜蒸发器性能.

  3. Design and Performance Analysis of Incremental Networked Predictive Control Systems.

    Science.gov (United States)

    Pang, Zhong-Hua; Liu, Guo-Ping; Zhou, Donghua

    2016-06-01

    This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

  4. Analysis on Fuel Thermal Conductivity Model of the Computer Code for Performance Prediction of Fuel Rods%燃料元件性能分析程序中的燃料热导率模型分析

    Institute of Scientific and Technical Information of China (English)

    李海; 黄晨; 杜爱兵; 徐宝玉

    2014-01-01

    The thermal conductivity is one of the most important parameters in the computer code for performance prediction for fuel rods.Several fuel thermal conductivity models used in foreign computer code,including thermal conductivity models for MOX fuel and UO2 fuel were introduced in this paper. Thermal conductivities were calculated by using these models, and the results were compared and analyzed.Finally, the thermal conductivity model for the native computer code for performance prediction for fuel rods in fast reactor was recommended.%热导率是燃料元件性能分析程序最重要的参数之一,本文介绍了各国部分性能分析程序的燃料热导率模型,按照 MOX和 UO2燃料分类,给出了这些性能分析程序热导率模型的计算结果,并进行分析对比,给出了国产快堆性能分析程序的热导率推荐模型。

  5. Predicting students' intention to use stimulants for academic performance enhancement.

    Science.gov (United States)

    Ponnet, Koen; Wouters, Edwin; Walrave, Michel; Heirman, Wannes; Van Hal, Guido

    2015-02-01

    The non-medical use of stimulants for academic performance enhancement is becoming a more common practice among college and university students. The objective of this study is to gain a better understanding of students' intention to use stimulant medication for the purpose of enhancing their academic performance. Based on an extended model of Ajzen's theory of planned behavior, we examined the predictive value of attitude, subjective norm, perceived behavioral control, psychological distress, procrastination, substance use, and alcohol use on students' intention to use stimulants to improve their academic performance. The sample consisted of 3,589 Flemish university and college students (mean age: 21.59, SD: 4.09), who participated anonymously in an online survey conducted in March and April 2013. Structural equation modeling was used to investigate the relationships among the study variables. Our results indicate that subjective norm is the strongest predictor of students' intention to use stimulant medication, followed by attitude and perceived behavioral control. To a lesser extent, procrastinating tendencies, psychological distress, and substance abuse contribute to students' intention. Conclusions/ Importance: Based on these findings, we provide several recommendations on how to curtail students' intention to use stimulant medication for the purpose of improving their academic performance. In addition, we urge researchers to identify other psychological variables that might be related to students' intention.

  6. Predicting performance of a face recognition system based on image quality

    NARCIS (Netherlands)

    Dutta, Abhishek

    2015-01-01

    In this dissertation, we present a generative model to capture the relation between facial image quality features (like pose, illumination direction, etc) and face recognition performance. Such a model can be used to predict the performance of a face recognition system. Since the model is based sole

  7. A Performance Prediction Model for a Piezoresistive Transducer Pressure Sensor%压阻变换压力传感器的性能预测模型

    Institute of Scientific and Technical Information of China (English)

    宋续; 刘胜

    2004-01-01

    Performance for a piezoresistive transducer pressure sensor to thermal and pressure environments can be predic ted by finite element method.A simplified 1/8 model,considering silicon dioxide and nitride process as well as stack anod ic bonding and adhesive bonding processes,was developed.The FEM results were found to be comparable to experimental data.Case studies suggested that Pyrex stack induces certain amount of non-linearity,while it isolates hard epoxy nonlinear effect.Flexible epoxy bonding or soft adhesive bonding is preferred to the packaging process.The viscoelasticity and visco plasticity of bonding material will result in hysteresis and drift errors to sensor output.However,soft adhesive' s influence on sensor can be ignored under relative stable environments.More over,detailed design and process information will help to improve modeling application.%热、压环境下压阻变换压力传感器的性能可以通过有限元方法预测.这里研究了简化的1/8模型,模型考虑了二氧化硅和氮化硅生成过程及堆阳极键合和胶粘结合过程.结果发现有限元预测结果和实验数据具有可比性.范例研究表明,硼硅堆导致产生一定的非线性,但它隔离了硬环氧树脂的非线性.在包装过程中最好使用柔性环氧黏合或软黏胶性结合.黏合材料的黏弹性和黏塑性将会导致传感器输出的滞后和漂移误差.然而,在相对稳定的环境下,软黏合剂对传感器的影响可以忽略.此外,详细的设计和过程信息有助于提高模型的适用性.

  8. Cold-Blooded Attention: Finger Temperature Predicts Attentional Performance

    Directory of Open Access Journals (Sweden)

    Rodrigo C. Vergara

    2017-09-01

    Full Text Available Thermal stress has been shown to increase the chances of unsafe behavior during industrial and driving performances due to reductions in mental and attentional resources. Nonetheless, establishing appropriate safety standards regarding environmental temperature has been a major problem, as modulations are also be affected by the task type, complexity, workload, duration, and previous experience with the task. To bypass this attentional and thermoregulatory problem, we focused on the body rather than environmental temperature. Specifically, we measured tympanic, forehead, finger and environmental temperatures accompanied by a battery of attentional tasks. We considered a 10 min baseline period wherein subjects were instructed to sit and relax, followed by three attentional tasks: a continuous performance task (CPT, a flanker task (FT and a counting task (CT. Using multiple linear regression models, we evaluated which variable(s were the best predictors of performance. The results showed a decrement in finger temperature due to instruction and task engagement that was absent when the subject was instructed to relax. No changes were observed in tympanic or forehead temperatures, while the environmental temperature remained almost constant for each subject. Specifically, the magnitude of the change in finger temperature was the best predictor of performance in all three attentional tasks. The results presented here suggest that finger temperature can be used as a predictor of alertness, as it predicted performance in attentional tasks better than environmental temperature. These findings strongly support that peripheral temperature can be used as a tool to prevent unsafe behaviors and accidents.

  9. PREDICTION OF HYDRODYNAMIC PERFORMANCE OF THE FLAP RUDDER

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The paper presents a new method for predicting the hydrodynamic performance of the flap rudder behind a propeller. The hydrodynamics of the rudder was calculated by the panel method and the performance of the propeller was predicted by the simplified propeller theoty. The interaction between the rudder and propeller was determined by iterative procedure. The pressure distribution on rudder surface and the hydrodynamic performance of the flap rudder are discussed in the paper.

  10. Predicting the protein targets for athletic performance-enhancing substances.

    Science.gov (United States)

    Mavridis, Lazaros; Mitchell, John Bo

    2013-06-25

    The World Anti-Doping Agency (WADA) publishes the Prohibited List, a manually compiled international standard of substances and methods prohibited in-competition, out-of-competition and in particular sports. It would be ideal to be able to identify all substances that have one or more performance-enhancing pharmacological actions in an automated, fast and cost effective way. Here, we use experimental data derived from the ChEMBL database (~7,000,000 activity records for 1,300,000 compounds) to build a database model that takes into account both structure and experimental information, and use this database to predict both on-target and off-target interactions between these molecules and targets relevant to doping in sport. The ChEMBL database was screened and eight well populated categories of activities (Ki, Kd, EC50, ED50, activity, potency, inhibition and IC50) were used for a rule-based filtering process to define the labels "active" or "inactive". The "active" compounds for each of the ChEMBL families were thereby defined and these populated our bioactivity-based filtered families. A structure-based clustering step was subsequently performed in order to split families with more than one distinct chemical scaffold. This produced refined families, whose members share both a common chemical scaffold and bioactivity against a common target in ChEMBL. We have used the Parzen-Rosenblatt machine learning approach to test whether compounds in ChEMBL can be correctly predicted to belong to their appropriate refined families. Validation tests using the refined families gave a significant increase in predictivity compared with the filtered or with the original families. Out of 61,660 queries in our Monte Carlo cross-validation, belonging to 19,639 refined families, 41,300 (66.98%) had the parent family as the top prediction and 53,797 (87.25%) had the parent family in the top four hits. Having thus validated our approach, we used it to identify the protein targets

  11. A multivariable approach toward predicting dental motor skill performance.

    Science.gov (United States)

    Wilson, S G; Husak, W S

    1988-08-01

    The purpose of the present study was to examine the potential of a multivariable approach in predicting dental motor skill performance. Variables measuring cognitive knowledge, motor abilities, educational background, and family demographics were examined. Data were obtained from 33 first-year dental students. Scaling and root planing tests were administered to each student at the beginning and end of a 14-week preclinical periodontal course. Correlations were low and no variable significantly predicted pre- or posttest scaling and root planing performance. Results are discussed in terms of the problems associated with predicting motor performance.

  12. System Advisor Model: Flat Plate Photovoltaic Performance Modeling Validation Report

    Energy Technology Data Exchange (ETDEWEB)

    Freeman, J.; Whitmore, J.; Kaffine, L.; Blair, N.; Dobos, A. P.

    2013-12-01

    The System Advisor Model (SAM) is a free software tool that performs detailed analysis of both system performance and system financing for a variety of renewable energy technologies. This report provides detailed validation of the SAM flat plate photovoltaic performance model by comparing SAM-modeled PV system generation data to actual measured production data for nine PV systems ranging from 75 kW to greater than 25 MW in size. The results show strong agreement between SAM predictions and field data, with annualized prediction error below 3% for all fixed tilt cases and below 8% for all one axis tracked cases. The analysis concludes that snow cover and system outages are the primary sources of disagreement, and other deviations resulting from seasonal biases in the irradiation models and one axis tracking issues are discussed in detail.

  13. 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...

  14. Prediction method of equipment performance parameter based on improved wavelet neural network and grey model%基于改进小波神经网络和灰色模型的装备性能参数预测

    Institute of Scientific and Technical Information of China (English)

    李梦妍; 于文震

    2016-01-01

    Prediction of equipment performance parameter is an important part of PHM (prognostics and health management) ,which has great significance for improving the efficiency of equipment support . This dissertation proposed a combined model ,which is based on grey model and improved wavelet neural network .The residual of grey prediction is trained in the wavelet neural network to correct the consequence of prediction .Also the learning efficiency is enhanced by the improvement of wavelet neural network .By analyzing the output frequency data of VCO in a intermediate frequency unit for a certain radar ,the experimental results shows that this combined model has higher prediction accuracy and generalization ability .Therefore it’s feasible to apply the combined model in the prediction of equipment performance parameter .%装备性能参数预测是装备系统故障预测与健康管理的重要组成部分,对于提高装备保障效能有重大意义。本文提出了一种基于灰色模型和改进小波神经网络的组合预测模型。在灰色预测的基础上,训练小波神经网络进行灰色预测的残差修正,并通过对小波神经网络的改进提高了网络学习效率。对某型雷达中频接受单元的压控振荡器输出频率进行预测,实验证明,该组合模型结合了灰色预测和改进小波神经网络的优点,有较高预测精度和泛化能力。将该组合模型应用于装备状态参数预测具有可行性。

  15. Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance

    Directory of Open Access Journals (Sweden)

    Jing Zhang

    2015-09-01

    Full Text Available Figural reasoning as an indicator of fluid intelligence and the domains of the Five Factor Model were explored as predictors of scholastic performance. A total of 836 Chinese secondary school students (406 girls from grades 7 to 11 participated. Figural reasoning, as measured by Raven’s Standard Progressive Matrices, predicted performance in Math, Chinese, and English, and also for a composite score. Among the personality domains, Openness had a positive effect on performance for all subjects after controlling for all the other variables. For Conscientiousness, the effects were smaller and only significant for Math. Neuroticism had a negative effect on Math grades. The effects of Extraversion on all grades were very small and not significant. Most importantly, hierarchical latent regression analyses indicated that all interaction effects between Openness and figural reasoning were significant, revealing a compensatory interaction. Our results further suggest that scholastic performance basically relies on the same traits through the secondary school years. However, importance is given to interaction effects between ability and personality. Implications along with limitations and suggestions for future research are discussed.

  16. 基于LQG性能基准的预测控制经济性能评估算法%Economic performance assessment of model predictive control (MPC) based on LQG benchmarking

    Institute of Scientific and Technical Information of China (English)

    赵超; 张登峰; 许巧玲; 李学来

    2012-01-01

    With the goals of optimal performance, energy conservation and cost effectiveness of process operations in industry, economic performance assessment of advanced process control have received great attention in both academia and industry. Controller performance monitoring and assessment are necessary to assure effectiveness of model predictive control systems and consequently safe and profitable plant operation. An approach to economic performance assessment of model predictive control system is presented. The method builds on steady-state economic optimization techniques and uses the linear quadratic gaussian (LQG) benchmark other than conventional minimum variance control (MVC) to estimate the potential of reduction in variance. The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance, and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction. Combining the LQG benchmark directly with benefit potential of MPC control system, both the economic benefits and the optimal operation condition can be obtained by solving the economic optimization problem. The proposed algorithm is illustrated by a simulated example of Shell standard problem.%针对已有经济性能评估算法大多采用最小方差控制(Minimum Variance Control,MVC)性能基准,存在对预测控制系统(Model Predictive Control,MPC)性能评估结果可靠性不高的问题,提出了基于线性二次高斯控制(Linear Quadratic Gaussian,LQG)性能基准的经济性能评估算法.通过数值计算方法确定LQG性能基准曲线,避免了复杂交互矩阵的计算.算法以基于模型的稳态经济优化技术为基础,将LQG基准和预测控制系统的经济性能估计相结合,并通过建立一系列稳态优化问题来描述控制系统在不同控制策略下的经济性能.与已有评估算法相比,本

  17. Predicting Student Performance in a Collaborative Learning Environment

    Science.gov (United States)

    Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

    2015-01-01

    Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

  18. The regional prediction model of PM10 concentrations for Turkey

    Science.gov (United States)

    Güler, Nevin; Güneri İşçi, Öznur

    2016-11-01

    This study is aimed to predict a regional model for weekly PM10 concentrations measured air pollution monitoring stations in Turkey. There are seven geographical regions in Turkey and numerous monitoring stations at each region. Predicting a model conventionally for each monitoring station requires a lot of labor and time and it may lead to degradation in quality of prediction when the number of measurements obtained from any õmonitoring station is small. Besides, prediction models obtained by this way only reflect the air pollutant behavior of a small area. This study uses Fuzzy C-Auto Regressive Model (FCARM) in order to find a prediction model to be reflected the regional behavior of weekly PM10 concentrations. The superiority of FCARM is to have the ability of considering simultaneously PM10 concentrations measured monitoring stations in the specified region. Besides, it also works even if the number of measurements obtained from the monitoring stations is different or small. In order to evaluate the performance of FCARM, FCARM is executed for all regions in Turkey and prediction results are compared to statistical Autoregressive (AR) Models predicted for each station separately. According to Mean Absolute Percentage Error (MAPE) criteria, it is observed that FCARM provides the better predictions with a less number of models.

  19. A statistical study of the performance of the Hakamada-Akasofu-Fry version 2 numerical model in predicting solar shock arrival times at Earth during different phases of solar cycle 23

    Directory of Open Access Journals (Sweden)

    S. M. P. McKenna-Lawlor

    2012-02-01

    Full Text Available The performance of the Hakamada Akasofu-Fry, version 2 (HAFv.2 numerical model, which provides predictions of solar shock arrival times at Earth, was subjected to a statistical study to investigate those solar/interplanetary circumstances under which the model performed well/poorly during key phases (rise/maximum/decay of solar cycle 23. In addition to analyzing elements of the overall data set (584 selected events associated with particular cycle phases, subsets were formed such that those events making up a particular sub-set showed common characteristics. The statistical significance of the results obtained using the various sets/subsets was generally very low and these results were not significant as compared with the hit by chance rate (50%. This implies a low level of confidence in the predictions of the model with no compelling result encouraging its use. However, the data suggested that the success rates of HAFv.2 were higher when the background solar wind speed at the time of shock initiation was relatively fast. Thus, in scenarios where the background solar wind speed is elevated and the calculated success rate significantly exceeds the rate by chance, the forecasts could provide potential value to the customer. With the composite statistics available for solar cycle 23, the calculated success rate at high solar wind speed, although clearly above 50%, was indicative rather than conclusive. The RMS error estimated for shock arrival times for every cycle phase and for the composite sample was in each case significantly better than would be expected for a random data set. Also, the parameter "Probability of Detection, yes" (PODy which presents the Proportion of Yes observations that were correctly forecast (i.e. the ratio between the shocks correctly predicted and all the shocks observed, yielded values for the rise/maximum/decay phases of the cycle and using the composite sample of 0.85, 0.64, 0.79 and 0.77, respectively. The statistical

  20. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  1. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  2. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  3. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  4. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  5. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  6. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  7. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  8. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  9. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  10. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  11. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    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.

  12. Predicting expatriate job performance for selection purposes: A quantitative review

    NARCIS (Netherlands)

    H.T. van der Molen (Henk); M.Ph. Born (Marise); M.E. Willemsen (Madde)

    2005-01-01

    textabstractThis article meta-analytically reviews empirical studies on the prediction of expatriate job performance. Using 30 primary studies (total N=4,046), it was found that predictive validities of the Big Five were similar to Big Five validities reported for domestic employees. Extraversion, e

  13. Predicting Expatriate Job Performance for Selection Purposes: A Quantitative Review

    NARCIS (Netherlands)

    S.T. Mol (Stefan); M.Ph. Born (Marise); M.E. Willemsen (Madde); H.T. van der Molen (Henk)

    2005-01-01

    textabstractThis article meta-analytically reviews empirical studies on the prediction of expatriate job performance. Using 30 primary studies (total N=4046), it was found that predictive validities of the big five were similar to big five validities reported for domestic employees (Barrick & Mount,

  14. Genetic predictions of racing performance in quarter horses

    National Research Council Canada - National Science Library

    Willham, R. L; Wilson, D. E

    1991-01-01

    .... Research on the racing performance of quarter horses has been used to develop genetic prediction summaries on all horses with at least one start on record at the American Quarter Horse Association...

  15. Prediction of Student Performance Through Pretesting in Food and Nutrition

    Science.gov (United States)

    Carruth, Betty Ruth; Lamb, Mina W.

    1971-01-01

    Attempts to develop an objective pretest for identifying students' levels of knowledge in food and nutrition prior to class instruction and for predicting student performance on the final examination. (Editor/MU)

  16. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    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…

  17. Critical review of glass performance modeling

    Energy Technology Data Exchange (ETDEWEB)

    Bourcier, W.L. [Lawrence Livermore National Lab., CA (United States)

    1994-07-01

    Borosilicate glass is to be used for permanent disposal of high-level nuclear waste in a geologic repository. Mechanistic chemical models are used to predict the rate at which radionuclides will be released from the glass under repository conditions. The most successful and useful of these models link reaction path geochemical modeling programs with a glass dissolution rate law that is consistent with transition state theory. These models have been used to simulate several types of short-term laboratory tests of glass dissolution and to predict the long-term performance of the glass in a repository. Although mechanistically based, the current models are limited by a lack of unambiguous experimental support for some of their assumptions. The most severe problem of this type is the lack of an existing validated mechanism that controls long-term glass dissolution rates. Current models can be improved by performing carefully designed experiments and using the experimental results to validate the rate-controlling mechanisms implicit in the models. These models should be supported with long-term experiments to be used for model validation. The mechanistic basis of the models should be explored by using modern molecular simulations such as molecular orbital and molecular dynamics to investigate both the glass structure and its dissolution process.

  18. A Course in... Model Predictive Control.

    Science.gov (United States)

    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)

  19. Applying Neural Network in Evaporative Cooler Performance Prediction

    Institute of Scientific and Technical Information of China (English)

    QIANG Tian-wei; SHEN Heng-gen; HUANG Xiang; XUAN Yong-mei

    2007-01-01

    The back-propagation (BP) neural network is created to predict the performance of a direct evaporative cooling (DEC) air conditioner with GLASdek pads. The experiment data about the performance of the DEC air conditioner are obtained. Some experiment data are used to train the network until these data can approximate a function, then, simulate the network with the remanent data. The predicted result shows satisfying effects.

  20. 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.

  1. 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.

  2. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... connectivity approach. The development of these models requires measured property data and based on them, the regression of model parameters is performed. Although this class of models is empirical by nature, they do allow extrapolation from the regressed model parameters to predict properties of chemicals...... not included in the measured data-set. Therefore, they are also considered as predictive models. The paper will highlight different issues/challenges related to the role of the databases and the mathematical and thermodynamic consistency of the measured/estimated data, the predictive nature of the developed...

  3. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    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.

  4. Experience-based model predictive control using reinforcement learning

    NARCIS (Netherlands)

    Negenborn, R.R.; De Schutter, B.; Wiering, M.A.; Hellendoorn, J.

    2004-01-01

    Model predictive control (MPC) is becoming an increasingly popular method to select actions for controlling dynamic systems. TraditionallyMPC uses a model of the system to be controlled and a performance function to characterize the desired behavior of the system. The MPC agent finds actions over a

  5. Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat

    NARCIS (Netherlands)

    Fels, van der H.J.

    2014-01-01

    The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields th

  6. Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat

    NARCIS (Netherlands)

    Fels, van der H.J.

    2014-01-01

    The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields

  7. 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.

  8. Predictive Performance Tuning of OpenACC Accelerated Applications

    KAUST Repository

    Siddiqui, Shahzeb

    2014-05-04

    Graphics Processing Units (GPUs) are gradually becoming mainstream in supercomputing as their capabilities to significantly accelerate a large spectrum of scientific applications have been clearly identified and proven. Moreover, with the introduction of high level programming models such as OpenACC [1] and OpenMP 4.0 [2], these devices are becoming more accessible and practical to use by a larger scientific community. However, performance optimization of OpenACC accelerated applications usually requires an in-depth knowledge of the hardware and software specifications. We suggest a prediction-based performance tuning mechanism [3] to quickly tune OpenACC parameters for a given application to dynamically adapt to the execution environment on a given system. This approach is applied to a finite difference kernel to tune the OpenACC gang and vector clauses for mapping the compute kernels into the underlying accelerator architecture. Our experiments show a significant performance improvement against the default compiler parameters and a faster tuning by an order of magnitude compared to the brute force search tuning.

  9. Orientation toward humans predicts cognitive performance in orang-utans

    Science.gov (United States)

    Damerius, Laura A.; Forss, Sofia I. F.; Kosonen, Zaida K.; Willems, Erik P.; Burkart, Judith M.; Call, Josep; Galdikas, Birute M. F.; Liebal, Katja; Haun, Daniel B. M.; van Schaik, Carel P.

    2017-01-01

    Non-human animals sometimes show marked intraspecific variation in their cognitive abilities that may reflect variation in external inputs and experience during the developmental period. We examined variation in exploration and cognitive performance on a problem-solving task in a large sample of captive orang-utans (Pongo abelii & P. pygmaeus, N = 103) that had experienced different rearing and housing conditions during ontogeny, including human exposure. In addition to measuring exploration and cognitive performance, we also conducted a set of assays of the subjects’ psychological orientation, including reactions towards an unfamiliar human, summarized in the human orientation index (HOI), and towards novel food and objects. Using generalized linear mixed models we found that the HOI, rather than rearing background, best predicted both exploration and problem-solving success. Our results suggest a cascade of processes: human orientation was accompanied by a change in motivation towards problem-solving, expressed in reduced neophobia and increased exploration variety, which led to greater experience, and thus eventually to higher performance in the task. We propose that different experiences with humans caused individuals to vary in curiosity and understanding of the physical problem-solving task. We discuss the implications of these findings for comparative studies of cognitive ability. PMID:28067260

  10. 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.

  11. Performance Evaluation of Different Data Value Prediction Schemes

    Institute of Scientific and Technical Information of China (English)

    Yong Xiao; Xing-Ming Zhou

    2005-01-01

    Data value prediction has been widely accepted as an effective mechanism to break data hazards for high performance processor design. Several works have reported promising performance potential. However, there is hardly enough information that is presented in a clear way about performance comparison of these prediction mechanisms. This paper investigates the performance impact of four previously proposed value predictors, namely last value predictor, stride value predictor, two-level value predictor and hybrid (stride+two-level) predictor. The impact of misprediction penalty,which has been frequently ignored, is discussed in detail. Several other implementation issues, including instruction window size, issue width and branch predictor are also addressed and simulated. Simulation results indicate that data value predictors act differently under different configurations. In some cases, simpler schemes may be more beneficial than complicated ones.In some particular cases, value prediction may have negative impact on performance.

  12. Predicting the performance and innovativeness of scientists and engineers.

    Science.gov (United States)

    Keller, Robert T

    2012-01-01

    A study of 644 scientists and engineers from 5 corporate research and development organizations investigated hypotheses generated from an interactionist framework of 4 individual characteristics as longitudinal predictors of performance and innovativeness. An innovative orientation predicted 1-year-later and 5-years-later supervisory job performance ratings and 5-years-later counts of patents and publications. An internal locus of control predicted 5-years-later patents and publications, and self-esteem predicted performance ratings for both times and patents. Team-level nonroutine tasks moderated the individual-level relationships between an innovative orientation and performance ratings and patents such that the relationships were stronger in a nonroutine task environment. Implications for an interactionist framework of performance and innovativeness for knowledge workers are discussed.

  13. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  14. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  15. New Approaches for Channel Prediction Based on Sinusoidal Modeling

    Directory of Open Access Journals (Sweden)

    Ekman Torbjörn

    2007-01-01

    Full Text Available Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS prediction model and the associated joint least-squares (LS predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.

  16. Advanced Performance Modeling with Combined Passive and Active Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Dovrolis, Constantine [Georgia Inst. of Technology, Atlanta, GA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-04-15

    To improve the efficiency of resource utilization and scheduling of scientific data transfers on high-speed networks, the "Advanced Performance Modeling with combined passive and active monitoring" (APM) project investigates and models a general-purpose, reusable and expandable network performance estimation framework. The predictive estimation model and the framework will be helpful in optimizing the performance and utilization of networks as well as sharing resources with predictable performance for scientific collaborations, especially in data intensive applications. Our prediction model utilizes historical network performance information from various network activity logs as well as live streaming measurements from network peering devices. Historical network performance information is used without putting extra load on the resources by active measurement collection. Performance measurements collected by active probing is used judiciously for improving the accuracy of predictions.

  17. 不同湍流模型对MEXICO风力机气动性能预测精度的研究%Research on the Effect of Different Turbulence Models on the Aerodynamic Performance Prediction Accuracy of MEXICO Wind Turbines

    Institute of Scientific and Technical Information of China (English)

    徐浩然; 杨华; 刘超; 洪泽东

    2013-01-01

    In order to validate the aerodynamic prediction accuracy of different turbulence models for rotating wind turbines, the commercial CFD software ANSYS CFX 14.0 was used to predict the aerodynamic performance of MEXICO wind turbine at three different inflow velocities under non-yawed condition. Two turbulence models, which included one equation Spalart-Allmaras (S-A) model and two-equation SST model, were employed to analyze the effect of calculation grid number on the calculated power of MEXICO rotor. Then, by comparing the calculated data of pressure coefficients, loads applied on blade, lift and drag coefficients of airfoils with the experimental data, this paper finds that the results calculated by two turbulence models show good agreement with the relevant experimental results under attached flow condition, SST model gives more accurate prediction about the pressure coefficients on suction side of blades under separated flow condition. By using both S-A and SST turbulence models to predict the performance of MEXICO wind turbine, certain accuracy can be obtained when flow separates, while SST model has higher prediction precision than S-A model under large flow separation condition.%为了验证不同湍流模型对旋转风力机气动性能的预测精度,采用计算流体动力学软件ANSYS CFX 14.0,选用一方程 Spalart-Allmaras(S-A)模型和两方程剪切应力输运(shear stress transport,SST)模型2种湍流模型对非偏航工况3种不同来流风速下的MEXICO实验风力机进行数值计算。分析了计算网格数对计算结果的影响,然后把2种湍流模型计算得到的叶片表面压力系数、叶片所受气动载荷以及三维翼型特性与实验结果对比发现:2种湍流模型对附着流动情况都有较高的预测精度,SST模型在分离流动工况下对叶片吸力面压力系数预测精度高于 S-A 模型;且2种湍流模型对分离流动都有一定的预测精度,但对大分离流动而言SST

  18. 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.

  19. Prediction of Rowing Ergometer Performance from Functional Anaerobic Power, Strength and Anthropometric Components

    Directory of Open Access Journals (Sweden)

    Akça Firat

    2014-07-01

    Full Text Available The aim of this research was to develop different regression models to predict 2000 m rowing ergometer performance with the use of anthropometric, anaerobic and strength variables and to determine how precisely the prediction models constituted by different variables predict performance, when conducted together in the same equation or individually. 38 male collegiate rowers (20.17 ± 1.22 years participated in this study. Anthropometric, strength, 2000 m maximal rowing ergometer and rowing anaerobic power tests were applied. Multiple linear regression procedures were employed in SPSS 16 to constitute five different regression formulas using a different group of variables. The reliability of the regression models was expressed by R2 and the standard error of estimate (SEE. Relationships of all parameters with performance were investigated through Pearson correlation coefficients. The prediction model using a combination of anaerobic, strength and anthropometric variables was found to be the most reliable equation to predict 2000 m rowing ergometer performance (R2 = 0.92, SEE= 3.11 s. Besides, the equation that used rowing anaerobic and strength test results also provided a reliable prediction (R2 = 0.85, SEE= 4.27 s. As a conclusion, it seems clear that physiological determinants which are affected by anaerobic energy pathways should also get involved in the processes and models used for performance prediction and talent identification in rowing.

  20. 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...

  1. NBC Hazard Prediction Model Capability Analysis

    Science.gov (United States)

    1999-09-01

    Puff( SCIPUFF ) Model Verification and Evaluation Study, Air Resources Laboratory, NOAA, May 1998. Based on the NOAA review, the VLSTRACK developers...TO SUBSTANTIAL DIFFERENCES IN PREDICTIONS HPAC uses a transport and dispersion (T&D) model called SCIPUFF and an associated mean wind field model... SCIPUFF is a model for atmospheric dispersion that uses the Gaussian puff method - an arbitrary time-dependent concentration field is represented

  2. Research on Drag Torque Prediction Model for the Wet Clutches

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Considering the surface tension effect and centrifugal effect, a mathematical model based on Reynolds equation for predicting the drag torque of disengage wet clutches is presented. The model indicates that the equivalent radius is a function of clutch speed and flow rate. The drag torque achieves its peak at a critical speed. Above this speed, drag torque drops due to the shrinking of the oil film. The model also points out that viscosity and flow rate effects on drag torque. Experimental results indicate that the model is reasonable and it performs well for predicting the drag torque peak.

  3. QSPR Models for Octane Number Prediction

    Directory of Open Access Journals (Sweden)

    Jabir H. Al-Fahemi

    2014-01-01

    Full Text Available Quantitative structure-property relationship (QSPR is performed as a means to predict octane number of hydrocarbons via correlating properties to parameters calculated from molecular structure; such parameters are molecular mass M, hydration energy EH, boiling point BP, octanol/water distribution coefficient logP, molar refractivity MR, critical pressure CP, critical volume CV, and critical temperature CT. Principal component analysis (PCA and multiple linear regression technique (MLR were performed to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The results of PCA explain the interrelationships between octane number and different variables. Correlation coefficients were calculated using M.S. Excel to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The data set was split into training of 40 hydrocarbons and validation set of 25 hydrocarbons. The linear relationship between the selected descriptors and the octane number has coefficient of determination (R2=0.932, statistical significance (F=53.21, and standard errors (s =7.7. The obtained QSPR model was applied on the validation set of octane number for hydrocarbons giving RCV2=0.942 and s=6.328.

  4. MODELING SUPPLY CHAIN PERFORMANCE VARIABLES

    Directory of Open Access Journals (Sweden)

    Ashish Agarwal

    2005-01-01

    Full Text Available In order to understand the dynamic behavior of the variables that can play a major role in the performance improvement in a supply chain, a System Dynamics-based model is proposed. The model provides an effective framework for analyzing different variables affecting supply chain performance. Among different variables, a causal relationship among different variables has been identified. Variables emanating from performance measures such as gaps in customer satisfaction, cost minimization, lead-time reduction, service level improvement and quality improvement have been identified as goal-seeking loops. The proposed System Dynamics-based model analyzes the affect of dynamic behavior of variables for a period of 10 years on performance of case supply chain in auto business.

  5. Predicting Academic Performance Based on Students' Blog and Microblog Posts

    NARCIS (Netherlands)

    Dascalu, Mihai; Popescu, Elvira; Becheru, Alexandru; Crossley, Scott; Trausan-Matu, Stefan

    2016-01-01

    This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in th

  6. Analyzing Log Files to Predict Students' Problem Solving Performance in a Computer-Based Physics Tutor

    Science.gov (United States)

    Lee, Young-Jin

    2015-01-01

    This study investigates whether information saved in the log files of a computer-based tutor can be used to predict the problem solving performance of students. The log files of a computer-based physics tutoring environment called Andes Physics Tutor was analyzed to build a logistic regression model that predicted success and failure of students'…

  7. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  8. The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations

    Directory of Open Access Journals (Sweden)

    Chin Calvin

    2012-04-01

    Full Text Available Abstract Background Identification of high-risk individuals is crucial for effective implementation of type 2 diabetes mellitus prevention programs. Several studies have shown that multivariable predictive functions perform as well as the 2-hour post-challenge glucose in identifying these high-risk individuals. The performance of these functions in Asian populations, where the rise in prevalence of type 2 diabetes mellitus is expected to be the greatest in the next several decades, is relatively unknown. Methods Using data from three Asian populations in Singapore, we compared the performance of three multivariate predictive models in terms of their discriminatory power and calibration quality: the San Antonio Health Study model, Atherosclerosis Risk in Communities model and the Framingham model. Results The San Antonio Health Study and Atherosclerosis Risk in Communities models had better discriminative powers than using only fasting plasma glucose or the 2-hour post-challenge glucose. However, the Framingham model did not perform significantly better than fasting glucose or the 2-hour post-challenge glucose. All published models suffered from poor calibration. After recalibration, the Atherosclerosis Risk in Communities model achieved good calibration, the San Antonio Health Study model showed a significant lack of fit in females and the Framingham model showed a significant lack of fit in both females and males. Conclusions We conclude that adoption of the ARIC model for Asian populations is feasible and highly recommended when local prospective data is unavailable.

  9. Application of artificial neural network for prediction of marine diesel engine performance

    Science.gov (United States)

    Mohd Noor, C. W.; Mamat, R.; Najafi, G.; Nik, W. B. Wan; Fadhil, M.

    2015-12-01

    This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the brake power, output torque, brake specific fuel consumption, brake thermal efficiency and volumetric efficiency. The input data for network training was gathered from engine laboratory testing running at various engine speed. The prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data. Results showed that the ANN model provided good agreement with the experimental data with high accuracy.

  10. Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach.

    Science.gov (United States)

    Kneifel, Joshua; Webb, David

    2016-09-01

    Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the

  11. Air Conditioner Compressor Performance Model

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Ning; Xie, YuLong; Huang, Zhenyu

    2008-09-05

    During the past three years, the Western Electricity Coordinating Council (WECC) Load Modeling Task Force (LMTF) has led the effort to develop the new modeling approach. As part of this effort, the Bonneville Power Administration (BPA), Southern California Edison (SCE), and Electric Power Research Institute (EPRI) Solutions tested 27 residential air-conditioning units to assess their response to delayed voltage recovery transients. After completing these tests, different modeling approaches were proposed, among them a performance modeling approach that proved to be one of the three favored for its simplicity and ability to recreate different SVR events satisfactorily. Funded by the California Energy Commission (CEC) under its load modeling project, researchers at Pacific Northwest National Laboratory (PNNL) led the follow-on task to analyze the motor testing data to derive the parameters needed to develop a performance models for the single-phase air-conditioning (SPAC) unit. To derive the performance model, PNNL researchers first used the motor voltage and frequency ramping test data to obtain the real (P) and reactive (Q) power versus voltage (V) and frequency (f) curves. Then, curve fitting was used to develop the P-V, Q-V, P-f, and Q-f relationships for motor running and stalling states. The resulting performance model ignores the dynamic response of the air-conditioning motor. Because the inertia of the air-conditioning motor is very small (H<0.05), the motor reaches from one steady state to another in a few cycles. So, the performance model is a fair representation of the motor behaviors in both running and stalling states.

  12. Modelling Chemical Reasoning to Predict Reactions

    CERN Document Server

    Segler, Marwin H S

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically ac...

  13. 基于机制模型与数据驱动的超临界锅炉性能在线预测方法%Performance Online Prediction of Supercritical Boilers Based on Mechanism and Data-driven Model

    Institute of Scientific and Technical Information of China (English)

    仝营; 钟崴; 童水光

    2015-01-01

    The topology of the boiler heating system was described by the correlation matrix, and the mass conservation equations, energy conservation equations and momentum conservation equations of the flue gas, air and water flows were provided, as well as the solving method. Combined with the established algorithm model of the boiler heat exchanger components, the mechanistic model of the boiler heating system was obtained. Basing on the mechanism analysis and historical data, soft sensing models were established in order to achieve the measurement of the boiler thermal parameters not easily to be measured directly. The soft sensing results of data were as inputs of the mechanistic model of the boiler heating system to establish boiler performance analysis, calculation and prediction models. The browser/server architecture model was used to develop the utility boiler performance analysis and prediction system supporting online monitoring. Thermal performance of a supercritical power plant boiler was calculated and predicted. The results verify the validity of the model and method. And the steam temperature was controlled by spraying water according to the predicted value.%基于关联矩阵描述锅炉热力系统拓扑结构,给出锅炉烟气、空气和水流程的质量守恒方程、能量守恒方程和动量守恒方程及其求解方法;结合建立的锅炉换热部件算法模型,得到锅炉热力系统的机制模型。基于机制分析和历史数据建立软测量模型,实现对不易直接测量的锅炉热工参数的间接测量。将软测量结果数据再次作为锅炉热力系统机制模型的输入,从而建立锅炉性能分析计算和预测模型。在系统实现技术上,采用基于浏览器/服务器的架构模式,研发支持在线监测的电站锅炉性能分析与预测系统。对一台超临界电站锅炉的性能进行预测计算,验证文中模型和方法的有效性,并根据预测值,实现蒸汽温度的喷水减温控制。

  14. 基于灰色Verhulst和EVM模型的项目进度-成本绩效预测研究%Project Time-cost Performance Prediction Based on Grey Verhulst and EVM Model

    Institute of Scientific and Technical Information of China (English)

    欧阳红祥; 李欣; 陈伟伟

    2013-01-01

      传统挣值管理以进度和成本绩效指数为基础进行完工成本预测,在项目实施的早期其预测值与实际数据会存在较大偏差。因此,项目实施的早期,项目管理团队应侧重对进度和成本绩效趋势进行短期预测。在介绍挣值管理和灰色Verhulst模型的基础上,提出采用Verhulst模型对项目的实际成本和挣值进行预测,并计算进度和成本偏差,通过比较借此发现进度和成本绩效的发展趋势。最后结合具体案例详细介绍了建立灰色Verhulst模型以及利用此模型进行预测的步骤。案例表明该方法是可行和有效的。%  The traditional Earned Value Management is used to forecast the project complete cost based on the duration and cost performance indexes,and there could be large variance between the predicted value and the actual. This paper puts forward that in early stage of project implementation,project management team should focus on making a short-term prediction about the trend of time and cost performance. On the basis of introducing the principle of Earned Value Management and grey Verhulst model,the method of Verhulst model is proposed to predict the actual cost and earned value. So the variance between time and cost could be calculated. And then the developing trend of time and cost performance can be got. In the last section,combined with the specific case,the establishment of grey Verhulst model and the steps for forecasting are both introduced detailed. The case finally shows that the method is feasible and effective.

  15. Predicting Performance in Higher Education Using Proximal Predictors.

    Science.gov (United States)

    Niessen, A Susan M; Meijer, Rob R; Tendeiro, Jorge N

    2016-01-01

    We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach, and specific skills tests in English and math. Test scores were used to predict academic achievement and progress after the first year, achievement in specific course types, enrollment, and dropout after the first year. All tests showed positive significant correlations with the criteria. The trial-studying test was consistently the best predictor in the admission procedure. We found no significant differences between the predictive validity of the trial-studying test and prior educational performance, and substantial shared explained variance between the two predictors. Only applicants with lower trial-studying scores were significantly less likely to enroll in the program. In conclusion, the trial-studying test yielded predictive validities similar to that of prior educational performance and possibly enabled self-selection. In admissions aimed at student-program fit, or in admissions in which past educational performance is difficult to use, a trial-studying test is a good instrument to predict academic performance.

  16. Prediction of Gas Injection Performance for Heterogeneous Reservoirs

    Energy Technology Data Exchange (ETDEWEB)

    Blunt, Martin J.; Orr, Franklin M.

    1999-05-17

    This report describes research carried out in the Department of Petroleum Engineering at Stanford University from September 1997 - September 1998 under the second year of a three-year grant from the Department of Energy on the "Prediction of Gas Injection Performance for Heterogeneous Reservoirs." The research effort is an integrated study of the factors affecting gas injection, from the pore scale to the field scale, and involves theoretical analysis, laboratory experiments, and numerical simulation. The original proposal described research in four areas: (1) Pore scale modeling of three phase flow in porous media; (2) Laboratory experiments and analysis of factors influencing gas injection performance at the core scale with an emphasis on the fundamentals of three phase flow; (3) Benchmark simulations of gas injection at the field scale; and (4) Development of streamline-based reservoir simulator. Each state of the research is planned to provide input and insight into the next stage, such that at the end we should have an integrated understanding of the key factors affecting field scale displacements.

  17. Prediction of Gas Injection Performance for Heterogeneous Reservoirs

    Energy Technology Data Exchange (ETDEWEB)

    Blunt, Michael J.; Orr, Franklin M.

    1999-05-26

    This report describes research carried out in the Department of Petroleum Engineering at Stanford University from September 1996 - September 1997 under the first year of a three-year Department of Energy grant on the Prediction of Gas Injection Performance for Heterogeneous Reservoirs. The research effort is an integrated study of the factors affecting gas injection, from the pore scale to the field scale, and involves theoretical analysis, laboratory experiments and numerical simulation. The original proposal described research in four main areas; (1) Pore scale modeling of three phase flow in porous media; (2) Laboratory experiments and analysis of factors influencing gas injection performance at the core scale with an emphasis on the fundamentals of three phase flow; (3) Benchmark simulations of gas injection at the field scale; and (4) Development of streamline-based reservoir simulator. Each stage of the research is planned to provide input and insight into the next stage, such that at the end we should have an integrated understanding of the key factors affecting field scale displacements.

  18. PREDICTING THERMAL PERFORMANCE OF ROOFING SYSTEMS IN SURABAYA

    Directory of Open Access Journals (Sweden)

    MINTOROGO Danny Santoso

    2015-07-01

    Full Text Available Traditional roofing systems in the developing country likes Indonesia are still be dominated by the 30o, 45o, and more pitched angle roofs; the roofing cover materials are widely used to traditional clay roof tiles, then modern concrete roof tiles, and ceramic roof tiles. In the 90’s decay, shop houses are prosperous built with flat concrete roofs dominant. Green roofs and roof ponds are almost rarely built to meet the sustainable environmental issues. Some tested various roof systems in Surabaya were carried out to observe the roof thermal performances. Mathematical equation model from three references are also performed in order to compare with the real project tested. Calculated with equation (Kabre et al., the 30o pitched concrete-roof-tile, 30o clay-roof-tile, 45o pitched concrete-roof-tile are the worst thermal heat flux coming to room respectively. In contrast, the bare soil concrete roof and roof pond system are the least heat flux streamed onto room. Based on predicted calculation without insulation and cross-ventilation attic space, the roof pond and bare soil concrete roof (greenery roof are the appropriate roof systems for the Surabaya’s climate; meanwhile the most un-recommended roof is pitched 30o or 45o angle with concrete-roof tiles roofing systems.

  19. 一种数据驱动的预测控制器性能监控方法%A Data-Driven Approach for Model Predictive Control Performance Monitoring

    Institute of Scientific and Technical Information of China (English)

    张光明; 李柠; 李少远

    2011-01-01

    A data-driven approach for model predictive control performance monitoring was proposed.An overall performance index based on Mahalanobis distance was introduced with its benchmark deduced to achieve higher monitoring performance.To identify the root cause of performance degradation,Mahalanobis distance based performance diagnosis method was proposed.Performance signatures are extracted from both principal component and residual subspace,and a classifier is constructed to identify four common performance degradation patterns.The effectiveness of the proposed method was demonstrated in a case study of the Wood-Berry distillation system.%提出一种数据驱动的预测控制器性能监控方法.基于马氏距离的综合性能指标,推导了性能指标的基准,以实现对预测控制器性能下降的及时检测.考虑导致预测控制器性能下降的4种常见原因,提出了基于马氏距离性能指标的性能诊断方法,即通过提取过程变量中主元和误差子空间的马氏统计量作为性能特征,利用支持向量机构造分类器,实现了预测控制器的性能诊断.最后,通过Wood-Berry过程仿真,验证了所提方法在预测控制器性能监控中的有效性.

  20. Genetic models of homosexuality: generating testable predictions

    OpenAIRE

    Gavrilets, Sergey; Rice, William R.

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality inclu...

  1. Traffic Prediction Scheme based on Chaotic Models in Wireless Networks

    Directory of Open Access Journals (Sweden)

    Xiangrong Feng

    2013-09-01

    Full Text Available Based on the local support vector algorithm of chaotic time series analysis, the Hannan-Quinn information criterion and SAX symbolization are introduced. Then a novel prediction algorithm is proposed, which is successfully applied to the prediction of wireless network traffic. For the correct prediction problems of short-term flow with smaller data set size, the weakness of the algorithms during model construction is analyzed by study and comparison to LDK prediction algorithm. It is verified the Hannan-Quinn information principle can be used to calculate the number of neighbor points to replace pervious empirical method, which uses the number of neighbor points to acquire more accurate prediction model. Finally, actual flow data is applied to confirm the accuracy rate of the proposed algorithm LSDHQ. It is testified by our experiments that it also has higher performance in adaptability than that of LSDHQ algorithm.

  2. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)

    2002-06-01

    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  3. Selecting Optimal Subset of Features for Student Performance Model

    Directory of Open Access Journals (Sweden)

    Hany M. Harb

    2012-09-01

    Full Text Available Educational data mining (EDM is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the student behavior in the learning process. Classification methods like decision trees, rule mining, and Bayesian network, can be applied on the educational data for predicting the student behavior like performance in an examination. This prediction may help in student evaluation. As the feature selection influences the predictive accuracy of any performance model, it is essential to study elaborately the effectiveness of student performance model in connection with feature selection techniques. The main objective of this work is to achieve high predictive performance by adopting various feature selection techniques to increase the predictive accuracy with least number of features. The outcomes show a reduction in computational time and constructional cost in both training and classification phases of the student performance model.

  4. Generating Performance Models for Irregular Applications

    Energy Technology Data Exchange (ETDEWEB)

    Friese, Ryan D.; Tallent, Nathan R.; Vishnu, Abhinav; Kerbyson, Darren J.; Hoisie, Adolfy

    2017-05-30

    Many applications have irregular behavior --- non-uniform input data, input-dependent solvers, irregular memory accesses, unbiased branches --- that cannot be captured using today's automated performance modeling techniques. We describe new hierarchical critical path analyses for the \\Palm model generation tool. To create a model's structure, we capture tasks along representative MPI critical paths. We create a histogram of critical tasks with parameterized task arguments and instance counts. To model each task, we identify hot instruction-level sub-paths and model each sub-path based on data flow, instruction scheduling, and data locality. We describe application models that generate accurate predictions for strong scaling when varying CPU speed, cache speed, memory speed, and architecture. We present results for the Sweep3D neutron transport benchmark; Page Rank on multiple graphs; Support Vector Machine with pruning; and PFLOTRAN's reactive flow/transport solver with domain-induced load imbalance.

  5. Model predictive torque control with an extended prediction horizon for electrical drive systems

    Science.gov (United States)

    Wang, Fengxiang; Zhang, Zhenbin; Kennel, Ralph; Rodríguez, José

    2015-07-01

    This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.

  6. MJO prediction skill, predictability, and teleconnection impacts in the Beijing Climate Center Atmospheric General Circulation Model

    Science.gov (United States)

    Wu, Jie; Ren, Hong-Li; Zuo, Jinqing; Zhao, Chongbo; Chen, Lijuan; Li, Qiaoping

    2016-09-01

    This study evaluates performance of Madden-Julian oscillation (MJO) prediction in the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM2.2). By using the real-time multivariate MJO (RMM) indices, it is shown that the MJO prediction skill of BCC_AGCM2.2 extends to about 16-17 days before the bivariate anomaly correlation coefficient drops to 0.5 and the root-mean-square error increases to the level of the climatological prediction. The prediction skill showed a seasonal dependence, with the highest skill occurring in boreal autumn, and a phase dependence with higher skill for predictions initiated from phases 2-4. The results of the MJO predictability analysis showed that the upper bounds of the prediction skill can be extended to 26 days by using a single-member estimate, and to 42 days by using the ensemble-mean estimate, which also exhibited an initial amplitude and phase dependence. The observed relationship between the MJO and the North Atlantic Oscillation was accurately reproduced by BCC_AGCM2.2 for most initial phases of the MJO, accompanied with the Rossby wave trains in the Northern Hemisphere extratropics driven by MJO convection forcing. Overall, BCC_AGCM2.2 displayed a significant ability to predict the MJO and its teleconnections without interacting with the ocean, which provided a useful tool for fully extracting the predictability source of subseasonal prediction.

  7. Static and Transient Performance Prediction for CFB Boilers Using a Bayesian—Gaussian Neural Network

    Institute of Scientific and Technical Information of China (English)

    HaiwenYe; WeidouNi

    1997-01-01

    A bayesian-Gaussian Neural Network(BGNN)is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed(CFB) boilers.The advantages of this network over Back-Propagation Neural Networks(BPNNs),easier determination of topology,simpler and time saving in training process as well as self-organizing bility,make this network more practical in on-line performance prediction for complicatied processes,Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers.Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boilers,which are under research by the authors.

  8. Predator personality and prey behavioural predictability jointly determine foraging performance

    Science.gov (United States)

    Chang, Chia-chen; Teo, Huey Yee; Norma-Rashid, Y.; Li, Daiqin

    2017-01-01

    Predator-prey interactions play important roles in ecological communities. Personality, consistent inter-individual differences in behaviour, of predators, prey or both are known to influence inter-specific interactions. An individual may also behave differently under the same situation and the level of such variability may differ between individuals. Such intra-individual variability (IIV) or predictability may be a trait on which selection can also act. A few studies have revealed the joint effect of personality types of both predators and prey on predator foraging performance. However, how personality type and IIV of both predators and prey jointly influence predator foraging performance remains untested empirically. Here, we addressed this using a specialized spider-eating jumping spider, Portia labiata (Salticidae), as the predator, and a jumping spider, Cosmophasis umbratica, as the prey. We examined personality types and IIVs of both P. labiata and C. umbratica and used their inter- and intra-individual behavioural variation as predictors of foraging performance (i.e., number of attempts to capture prey). Personality type and predictability had a joint effect on predator foraging performance. Aggressive predators performed better in capturing unpredictable (high IIV) prey than predictable (low IIV) prey, while docile predators demonstrated better performance when encountering predictable prey. This study highlights the importance of the joint effect of both predator and prey personality types and IIVs on predator-prey interactions. PMID:28094288

  9. A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction.

    Science.gov (United States)

    Song, Jingwei; He, Jiaying

    2014-08-01

    In this study, a univariate local chaotic model is proposed to make one-step and multistep forecasts for daily municipal solid waste (MSW) generation in Seattle, Washington. For MSW generation prediction with long history data, this forecasting model was created based on a nonlinear dynamic method called phase-space reconstruction. Compared with other nonlinear predictive models, such as artificial neural network (ANN) and partial least square-support vector machine (PLS-SVM), and a commonly used linear seasonal autoregressive integrated moving average (sARIMA) model, this method has demonstrated better prediction accuracy from 1-step ahead prediction to 14-step ahead prediction assessed by both mean absolute percentage error (MAPE) and root mean square error (RMSE). Max error, MAPE, and RMSE show that chaotic models were more reliable than the other three models. As chaotic models do not involve random walk, their performance does not vary while ANN and PLS-SVM make different forecasts in each trial. Moreover, this chaotic model was less time consuming than ANN and PLS-SVM models.

  10. Using Pareto points for model identification in predictive toxicology.

    Science.gov (United States)

    Palczewska, Anna; Neagu, Daniel; Ridley, Mick

    2013-03-22

    : Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.

  11. Research on Application of Regression Least Squares Support Vector Machine on Performance Prediction of Hydraulic Excavator

    Directory of Open Access Journals (Sweden)

    Zhan-bo Chen

    2014-01-01

    Full Text Available In order to improve the performance prediction accuracy of hydraulic excavator, the regression least squares support vector machine is applied. First, the mathematical model of the regression least squares support vector machine is studied, and then the algorithm of the regression least squares support vector machine is designed. Finally, the performance prediction simulation of hydraulic excavator based on regression least squares support vector machine is carried out, and simulation results show that this method can predict the performance changing rules of hydraulic excavator correctly.

  12. ASSOCIATION RULE DISCOVERY FOR STUDENT PERFORMANCE PREDICTION USING METAHEURISTIC ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Roghayeh Saneifar

    2015-11-01

    Full Text Available According to the increase of using data mining techniques in improving educational systems operations, Educational Data Mining has been introduced as a new and fast growing research area. Educational Data Mining aims to analyze data in educational environments in order to solve educational research problems. In this paper a new associative classification technique has been proposed to predict students final performance. Despite of several machine learning approaches such as ANNs, SVMs, etc. associative classifiers maintain interpretability along with high accuracy. In this research work, we have employed Honeybee Colony Optimization and Particle Swarm Optimization to extract association rule for student performance prediction as a multi-objective classification problem. Results indicate that the proposed swarm based algorithm outperforms well-known classification techniques on student performance prediction classification problem.

  13. Predictive model for segmented poly(urea

    Directory of Open Access Journals (Sweden)

    Frankl P.

    2012-08-01

    Full Text Available Segmented poly(urea has been shown to be of significant benefit in protecting vehicles from blast and impact and there have been several experimental studies to determine the mechanisms by which this protective function might occur. One suggested route is by mechanical activation of the glass transition. In order to enable design of protective structures using this material a constitutive model and equation of state are needed for numerical simulation hydrocodes. Determination of such a predictive model may also help elucidate the beneficial mechanisms that occur in polyurea during high rate loading. The tool deployed to do this has been Group Interaction Modelling (GIM – a mean field technique that has been shown to predict the mechanical and physical properties of polymers from their structure alone. The structure of polyurea has been used to characterise the parameters in the GIM scheme without recourse to experimental data and the equation of state and constitutive model predicts response over a wide range of temperatures and strain rates. The shock Hugoniot has been predicted and validated against existing data. Mechanical response in tensile tests has also been predicted and validated.

  14. Prediction horizon effects on stochastic modelling hints for neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Drossu, R.; Obradovic, Z. [Washington State Univ., Pullman, WA (United States)

    1995-12-31

    The objective of this paper is to investigate the relationship between stochastic models and neural network (NN) approaches to time series modelling. Experiments on a complex real life prediction problem (entertainment video traffic) indicate that prior knowledge can be obtained through stochastic analysis both with respect to an appropriate NN architecture as well as to an appropriate sampling rate, in the case of a prediction horizon larger than one. An improvement of the obtained NN predictor is also proposed through a bias removal post-processing, resulting in much better performance than the best stochastic model.

  15. Determining the prediction limits of models and classifiers with applications for disruption prediction in JET

    Science.gov (United States)

    Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.; Contributors, JET

    2017-01-01

    Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.

  16. Performability Modelling Tools, Evaluation Techniques and Applications

    NARCIS (Netherlands)

    Haverkort, Boudewijn R.H.M.

    1990-01-01

    This thesis deals with three aspects of quantitative evaluation of fault-tolerant and distributed computer and communication systems: performability evaluation techniques, performability modelling tools, and performability modelling applications. Performability modelling is a relatively new

  17. A Fusion Model for CPU Load Prediction in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Dayu Xu

    2013-11-01

    Full Text Available Load prediction plays a key role in cost-optimal resource allocation and datacenter energy saving. In this paper, we use real-world traces from Cloud platform and propose a fusion model to forecast the future CPU loads. First, long CPU load time series data are divided into short sequences with same length from the historical data on the basis of cloud control cycle. Then we use kernel fuzzy c-means clustering algorithm to put the subsequences into different clusters. For each cluster, with current load sequence, a genetic algorithm optimized wavelet Elman neural network prediction model is exploited to predict the CPU load in next time interval. Finally, we obtain the optimal cloud computing CPU load prediction results from the cluster and its corresponding predictor with minimum forecasting error. Experimental results show that our algorithm performs better than other models reported in previous works.

  18. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  19. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  20. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    Science.gov (United States)

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. Copyright © 2011 Elsevier Inc. All rights reserved.