WorldWideScience

Sample records for stronger predictive power

  1. Communal and Agentic Interpersonal and Intergroup Motives Predict Preferences for Status Versus Power.

    Science.gov (United States)

    Locke, Kenneth D; Heller, Sonja

    2017-01-01

    Seven studies involving 1,343 participants showed how circumplex models of social motives can help explain individual differences in preferences for status (having others' admiration) versus power (controlling valuable resources). Studies 1 to 3 and 7 concerned interpersonal motives in workplace contexts, and found that stronger communal motives (to have mutual trust, support, and cooperation) predicted being more attracted to status (but not power) and achieving more workplace status, while stronger agentic motives (to be firm, decisive, and influential) predicted being more attracted to and achieving more workplace power, and experiencing a stronger connection between workplace power and job satisfaction. Studies 4 to 6 found similar effects for intergroup motives: Stronger communal motives predicted wanting one's ingroup (e.g., country) to have status-but not power-relative to other groups. Finally, most people preferred status over power, and this was especially true for women, which was partially explained by women having stronger communal motives.

  2. The predictive power of Japanese candlestick charting in Chinese stock market

    Science.gov (United States)

    Chen, Shi; Bao, Si; Zhou, Yu

    2016-09-01

    This paper studies the predictive power of 4 popular pairs of two-day bullish and bearish Japanese candlestick patterns in Chinese stock market. Based on Morris' study, we give the quantitative details of definition of long candlestick, which is important in two-day candlestick pattern recognition but ignored by several previous researches, and we further give the quantitative definitions of these four pairs of two-day candlestick patterns. To test the predictive power of candlestick patterns on short-term price movement, we propose the definition of daily average return to alleviate the impact of correlation among stocks' overlap-time returns in statistical tests. To show the robustness of our result, two methods of trend definition are used for both the medium-market-value and large-market-value sample sets. We use Step-SPA test to correct for data snooping bias. Statistical results show that the predictive power differs from pattern to pattern, three of the eight patterns provide both short-term and relatively long-term prediction, another one pair only provide significant forecasting power within very short-term period, while the rest three patterns present contradictory results for different market value groups. For all the four pairs, the predictive power drops as predicting time increases, and forecasting power is stronger for stocks with medium market value than those with large market value.

  3. Female Psychology in August Strindberg's the Stronger

    OpenAIRE

    Sutandio, Anton; Apriliani, Erica

    2017-01-01

    This research aimed to offer interpretations of August Strindberg's The Stronger through the lens of female psychology. The Stronger is unique as it seemed very simple yet so intense and powerful with layers of interpretations. Written during 1888-1889, The Stronger, which only had two characters and only one speaking character, had become one of Strindberg's shortest yet important plays during his career. The female psychology approach used in the analysis would cover the discussion of gende...

  4. Rehabilitation after stroke: predictive power of Barthel Index versus a cognitive and a motor index

    DEFF Research Database (Denmark)

    Engberg, A; Bentzen, L; Garde, B

    1995-01-01

    The aim of the present study was to investigate the predictive power of ratings of Barthel Index at Day 40 post stroke, compared with and/or combined with simultaneous ratings from a mobility scale (EG motor index) and a rather simple cognitive test scale (CT50). The parameter to be individually...... predicted was the need for special living facilities and support at discharge from a rehabilitation hospital, as well as six months later; 53 stroke patients with age median 68 years were included in this prospective study. It was shown that a combination of Barthel Index and CT50 had a stronger predictive...

  5. Female Psychology in August Strindberg’s The Stronger

    Directory of Open Access Journals (Sweden)

    Anton Sutandio

    2017-11-01

    Full Text Available This research aimed to offer interpretations of August Strindberg’s The Stronger through the lens of female psychology. The Stronger is unique as it seemed very simple yet so intense and powerful with layers of interpretations. Written during 1888-1889, The Stronger, which only had two characters and only one speaking character, had become one of Strindberg’s shortest yet important plays during his career. The female psychology approach used in the analysis would cover the discussion of gender role, women’s self-esteem, competition for males, women’s friendships, ego style, and female psychology. It was an interdisciplinary research that combined structuralist, historical, biographical, and feminist approach to gain a better interpretation on the play. By referring to three different sources on the concept of female psychology, the analysis offered different and interesting interpretations on the nature and dynamics of the two female characters’ relationship. The Stronger has shown an enigmatic attraction in Strindberg’s authorship in which the readers could see the co-existence, collision, conflict, and merge of different paradigms concerning sex, gender, and sexuality.

  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. Predicting High-Power Performance in Professional Cyclists.

    Science.gov (United States)

    Sanders, Dajo; Heijboer, Mathieu; Akubat, Ibrahim; Meijer, Kenneth; Hesselink, Matthijs K

    2017-03-01

    To assess if short-duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model in professional cyclists. Data from 4 professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output, and an exponential constant describing the decrement in power over time, a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared with the predicted power output by the APR model. The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r = .88 ± .21, .92 ± .17, .95 ± .13, and .97 ± .09). Power output during the all-out trials remained within an average of 6.6% (53 W) of the predicted power output by the model. This preliminary pilot study presents 4 case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential-decay model describing the decrement in power vs increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.

  8. Short-term wind power prediction

    DEFF Research Database (Denmark)

    Joensen, Alfred K.

    2003-01-01

    , and to implement these models and methods in an on-line software application. The economical value of having predictions available is also briefly considered. The summary report outlines the background and motivation for developing wind power prediction models. The meteorological theory which is relevant......The present thesis consists of 10 research papers published during the period 1997-2002 together with a summary report. The objective of the work described in the thesis is to develop models and methods for calculation of high accuracy predictions of wind power generated electricity...

  9. Effect of accuracy of wind power prediction on power system operator

    Science.gov (United States)

    Schlueter, R. A.; Sigari, G.; Costi, T.

    1985-01-01

    This research project proposed a modified unit commitment that schedules connection and disconnection of generating units in response to load. A modified generation control is also proposed that controls steam units under automatic generation control, fast responding diesels, gas turbines and hydro units under a feedforward control, and wind turbine array output under a closed loop array control. This modified generation control and unit commitment require prediction of trend wind power variation one hour ahead and the prediction of error in this trend wind power prediction one half hour ahead. An improved meter for predicting trend wind speed variation is developed. Methods for accurately simulating the wind array power from a limited number of wind speed prediction records was developed. Finally, two methods for predicting the error in the trend wind power prediction were developed. This research provides a foundation for testing and evaluating the modified unit commitment and generation control that was developed to maintain operating reliability at a greatly reduced overall production cost for utilities with wind generation capacity.

  10. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  11. The wind power prediction research based on mind evolutionary algorithm

    Science.gov (United States)

    Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

    2018-04-01

    When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

  12. Exercise training raises daily activity stronger than predicted from exercise capacity in patients with COPD.

    Science.gov (United States)

    Behnke, Michaela; Wewel, Alexandra R; Kirsten, Detlef; Jörres, Rudolf A; Magnussen, Helgo

    2005-06-01

    The 6-min walking (6MWD) and 6-min treadmill distance (6MTD) are often used as measures of exercise performance in patients with COPD. The aim of our study was to assess their relationship to daily activity in the course of an exercise training program. Eighty-eight patients with stable COPD (71m/17f; mean +/- SD age, 60 +/-8 year; FEV1, 43+/-14% pred) were recruited, 66 of whom performed a hospital-based 10-day walking training, whereas 22 were treated as control. On day 16MTD, and on days 8 and 10, 6MTD and 6MWD were determined. In addition, patients used an accelerometer (TriTrac-R3D) to record 24 h-activity, whereby training sessions were excluded. In both groups there was a linear relationship (r > or = 0.84 and P daily activity did not markedly vary with exercise capacity under baseline conditions. Participation in a training program increased activity significantly stronger than predicted from the gain in exercise capacity. This underlines the importance of non-physiological, patient-centered factors associated with training in COPD.

  13. Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

    Directory of Open Access Journals (Sweden)

    Yagang Zhang

    2015-01-01

    Full Text Available This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind speed predictions. Finally we separately use the original and improved wind speed series to predict the related wind power. This proves that the corrected wind speed provides higher precision wind power predictions. This research presents a totally new direction in the wind prediction field and has profound theoretical research value and practical guiding significance.

  14. Potentiality Prediction of Electric Power Replacement Based on Power Market Development Strategy

    Science.gov (United States)

    Miao, Bo; Yang, Shuo; Liu, Qiang; Lin, Jingyi; Zhao, Le; Liu, Chang; Li, Bin

    2017-05-01

    The application of electric power replacement plays an important role in promoting the development of energy conservation and emission reduction in our country. To exploit the potentiality of regional electric power replacement, the regional GDP (gross domestic product) and energy consumption are taken as potentiality evaluation indicators. The principal component factors are extracted with PCA (principal component analysis), and the integral potentiality analysis is made to the potentiality of electric power replacement in the national various regions; a region is taken as a research object, and the potentiality of electric power replacement is defined and quantified. The analytical model for the potentiality of multi-scenario electric power replacement is developed, and prediction is made to the energy consumption with the grey prediction model. The relevant theoretical research is utilized to realize prediction analysis on the potentiality amount of multi-scenario electric power replacement.

  15. Improved techniques for predicting spacecraft power

    International Nuclear Information System (INIS)

    Chmielewski, A.B.

    1987-01-01

    Radioisotope Thermoelectric Generators (RTGs) are going to supply power for the NASA Galileo and Ulysses spacecraft now scheduled to be launched in 1989 and 1990. The duration of the Galileo mission is expected to be over 8 years. This brings the total RTG lifetime to 13 years. In 13 years, the RTG power drops more than 20 percent leaving a very small power margin over what is consumed by the spacecraft. Thus it is very important to accurately predict the RTG performance and be able to assess the magnitude of errors involved. The paper lists all the error sources involved in the RTG power predictions and describes a statistical method for calculating the tolerance

  16. Using meteorological forecasts in on-line predictions of wind power

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Nielsen, Henrik Aalborg; Madsen, Henrik

    1999-01-01

    This report describes a model investigation into wind power prediction model as well as a tool for predicting the power production from wind turbines in an area - the Wind Power Prediction Tool (WPPT). The predictions are based on on-line measurements of power production for a selected set...

  17. Power load prediction based on GM (1,1)

    Science.gov (United States)

    Wu, Di

    2017-05-01

    Currently, Chinese power load prediction is highly focused; the paper deeply studies grey prediction and applies it to Chinese electricity consumption during the recent 14 years; through after-test test, it obtains grey prediction which has good adaptability to medium and long-term power load.

  18. Prediction of lacking control power in power plants using statistical models

    DEFF Research Database (Denmark)

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

    2007-01-01

    Prediction of the performance of plants like power plants is of interest, since the plant operator can use these predictions to optimize the plant production. In this paper the focus is addressed on a special case where a combination of high coal moisture content and a high load limits the possible...... plant load, meaning that the requested plant load cannot be met. The available models are in this case uncertain. Instead statistical methods are used to predict upper and lower uncertainty bounds on the prediction. Two different methods are used. The first relies on statistics of recent prediction...... errors; the second uses operating point depending statistics of prediction errors. Using these methods on the previous mentioned case, it can be concluded that the second method can be used to predict the power plant performance, while the first method has problems predicting the uncertain performance...

  19. Conditional prediction intervals of wind power generation

    DEFF Research Database (Denmark)

    Pinson, Pierre; Kariniotakis, Georges

    2010-01-01

    A generic method for the providing of prediction intervals of wind power generation is described. Prediction intervals complement the more common wind power point forecasts, by giving a range of potential outcomes for a given probability, their so-called nominal coverage rate. Ideally they inform...... on the characteristics of prediction errors for providing conditional interval forecasts. By simultaneously generating prediction intervals with various nominal coverage rates, one obtains full predictive distributions of wind generation. Adapted resampling is applied here to the case of an onshore Danish wind farm...... to the case of a large number of wind farms in Europe and Australia among others is finally discussed....

  20. Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

    Energy Technology Data Exchange (ETDEWEB)

    Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng; Pota, Hemanshu; Gadh, Rajit

    2016-05-02

    This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA) models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.

  1. Development and design of photovoltaic power prediction system

    Science.gov (United States)

    Wang, Zhijia; Zhou, Hai; Cheng, Xu

    2018-02-01

    In order to reduce the impact of power grid safety caused by volatility and randomness of the energy produced in photovoltaic power plants, this paper puts forward a construction scheme on photovoltaic power generation prediction system, introducing the technical requirements, system configuration and function of each module, and discussing the main technical features of the platform software development. The scheme has been applied in many PV power plants in the northwest of China. It shows that the system can produce reasonable prediction results, providing a right guidance for dispatching and efficient running for PV power plant.

  2. Short-term wind power prediction based on LSSVM–GSA model

    International Nuclear Information System (INIS)

    Yuan, Xiaohui; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qingxiong

    2015-01-01

    Highlights: • A hybrid model is developed for short-term wind power prediction. • The model is based on LSSVM and gravitational search algorithm. • Gravitational search algorithm is used to optimize parameters of LSSVM. • Effect of different kernel function of LSSVM on wind power prediction is discussed. • Comparative studies show that prediction accuracy of wind power is improved. - Abstract: Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction

  3. Model Predictive Control of a Wave Energy Converter with Discrete Fluid Power Power Take-Off System

    Directory of Open Access Journals (Sweden)

    Anders Hedegaard Hansen

    2018-03-01

    Full Text Available Wave power extraction algorithms for wave energy converters are normally designed without taking system losses into account leading to suboptimal power extraction. In the current work, a model predictive power extraction algorithm is designed for a discretized power take of system. It is shown how the quantized nature of a discrete fluid power system may be included in a new model predictive control algorithm leading to a significant increase in the harvested power. A detailed investigation of the influence of the prediction horizon and the time step is reported. Furthermore, it is shown how the inclusion of a loss model may increase the energy output. Based on the presented results it is concluded that power extraction algorithms based on model predictive control principles are both feasible and favorable for use in a discrete fluid power power take-off system for point absorber wave energy converters.

  4. Model Predictive Control of a Wave Energy Converter with Discrete Fluid Power Power Take-Off System

    DEFF Research Database (Denmark)

    Hansen, Anders Hedegaard; Asmussen, Magnus Færing; Bech, Michael Møller

    2018-01-01

    Wave power extraction algorithms for wave energy converters are normally designed without taking system losses into account leading to suboptimal power extraction. In the current work, a model predictive power extraction algorithm is designed for a discretized power take of system. It is shown how...... the quantized nature of a discrete fluid power system may be included in a new model predictive control algorithm leading to a significant increase in the harvested power. A detailed investigation of the influence of the prediction horizon and the time step is reported. Furthermore, it is shown how...

  5. Stronger Schrödinger-like uncertainty relations

    International Nuclear Information System (INIS)

    Song, Qiu-Cheng; Qiao, Cong-Feng

    2016-01-01

    Highlights: • A stronger Schrödinger-like uncertainty relation in the sum of variances of two observables is obtained. • An improved Schrödinger-like uncertainty relation in the product of variances of two observables is obtained. • A stronger uncertainty relation in the sum of variances of three observables is proposed. - Abstract: Uncertainty relation is one of the fundamental building blocks of quantum theory. Nevertheless, the traditional uncertainty relations do not fully capture the concept of incompatible observables. Here we present a stronger Schrödinger-like uncertainty relation, which is stronger than the relation recently derived by Maccone and Pati (2014) [11]. Furthermore, we give an additive uncertainty relation which holds for three incompatible observables, which is stronger than the relation newly obtained by Kechrimparis and Weigert (2014) [12] and the simple extension of the Schrödinger uncertainty relation.

  6. Local Geomagnetic Indices and the Prediction of Auroral Power

    Science.gov (United States)

    Newell, P. T.; Gjerloev, J. W.

    2014-12-01

    As the number of magnetometer stations and data processing power increases, just how auroral power relates to geomagnetic observations becomes a quantitatively more tractable question. This paper compares Polar UVI auroral power observations during 1997 with a variety of geomagnetic indices. Local time (LT) versions of the SuperMAG auroral electojet (SME) are introduced and examined, along with the corresponding upper and lower envelopes (SMU and SML). Also, the East-West component, BE, is investigated. We also consider whether using any of the local indices is actually better at predicting local auroral power than a single global index. Each index is separated into 24 LT indices based on a sliding 3-h MLT window. The ability to predict - or better reconstruct - auroral power varies greatly with LT, peaking at 1900 MLT, where about 75% of the variance (r2) can be predicted at 1-min cadence. The aurora is fairly predictable from 1700 MLT - 0400 MLT, roughly the region in which substorms occur. Auroral power is poorly predicted from auroral electrojet indices from 0500 MLT - 1500 MLT, with the minima at 1000-1300 MLT. In the region of high predictability, the local variable which works best is BE, in contrast to long-standing expectations. However using global SME is better than any local variable. Auroral power is best predicted by combining global SME with a local index: BE from 1500-0200 MLT, and either SMU or SML from 0300-1400 MLT. In the region of the diffuse aurora, it is better to use a 30 min average than the cotemporaneous 1-min SME value, while from 1500-0200 MLT the cotemporaneous 1-min SME works best, suggesting a more direct physical relationship with the auroral circuit. These results suggest a significant role for discrete auroral currents closing locally with Pedersen currents.

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

    Science.gov (United States)

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

    2017-10-12

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

  8. Skill forecasting from ensemble predictions of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik

    2009-01-01

    Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...

  9. Stronger Fire-Resistant Epoxies

    Science.gov (United States)

    Fohlen, George M.; Parker, John A.; Kumar, Devendra

    1988-01-01

    New curing agent improves mechanical properties and works at lower temperature. Use of aminophenoxycyclotriphosphazene curing agents yields stronger, more heat- and fire-resistant epoxy resins. Used with solvent if necessary for coating fabrics or casting films.

  10. Prediction of future dispute concerning nuclear power generation

    International Nuclear Information System (INIS)

    1981-04-01

    This investigation is the third research on the public acceptance of nuclear power generation by the National Congress on Social Economics. In this study, how the energy dispute including that concerning nuclear power generation will develop in 1980s and 1990s, how the form of dispute and the point of controversy will change, were predicted. Though the maintenance of the concord of groups strongly regulates the behavior of people, recently they have become to exercise individual rights frequently. The transition to the society of dispute is the natural result of the modernization of society and the increase of richness. The proper prediction of social problems and the planning and execution of proper countermeasures are very important. The background, objective, basic viewpoint, range and procedure of this investigation, the change of social dispute, the history of the dispute concerning nuclear power generation, the basic viewpoint in the prediction of the dispute concerning nuclear power generation, the social situation in 1980s, the prediction and avoidance of the dispute in view of social and energy situations, and the fundamental strategy for seeking a clue to the solution in 1980s and 1990s are described. The establishment of neutral mediation organs and the flexible technologies of nuclear reactors are necessary. (Kako, I.)

  11. Power capability prediction for lithium-ion batteries based on multiple constraints analysis

    International Nuclear Information System (INIS)

    Pan, Rui; Wang, Yujie; Zhang, Xu; Yang, Duo; Chen, Zonghai

    2017-01-01

    Highlights: • Multiple constraints for peak power capability prediction are deeply analyzed. • Multi-limited method is proposed for the peak power capability prediction of LIBs. • The EKF is used for the model based peak power capability prediction. • The FUDS and UDDS profiles are executed to evaluate the proposed method. - Abstract: The power capability of the lithium-ion battery is a key performance indicator for electric vehicle, and it is intimately correlated with the acceleration, regenerative braking and gradient climbing power requirements. Therefore, an accurate power capability or state-of-power prediction is critical to a battery management system, which can help the battery to work in suitable area and prevent the battery from over-charging and over-discharging. However, the power capability is easily affected by dynamic load, voltage variation and temperature. In this paper, three different constraints in power capability prediction are introduced, and the advantages and disadvantages of the three methods are deeply analyzed. Furthermore, a multi-limited approach for the power capability prediction is proposed, which can overcome the drawbacks of the three methods. Subsequently, the extended Kalman filter algorithm is employed for model based state-of-power prediction. In order to verify the proposed method, diverse experiments are executed to explore the efficiency, robustness, and precision. The results indicate that the proposed method can improve the precision and robustness obviously.

  12. Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

    Science.gov (United States)

    Mohammed, E.; Wang, S.; Yu, J.

    2017-05-01

    This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

  13. Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures.

    Science.gov (United States)

    Lai, Ying-Cheng; Harrison, Mary Ann F; Frei, Mark G; Osorio, Ivan

    2004-09-01

    Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy. Copyright 2004 American Institute of Physics

  14. Using machine learning to predict wind turbine power output

    International Nuclear Information System (INIS)

    Clifton, A; Kilcher, L; Lundquist, J K; Fleming, P

    2013-01-01

    Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to rank atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that from the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data are required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of the different turbulence and shear at the new site, compared to the test site. (letter)

  15. Power Admission Control with Predictive Thermal Management in Smart Buildings

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  16. WPPT, a tool for on-line wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Skov Nielsen, T. [Dept. of Mathematical Modelling (IMM-DTU), Kgs. Lyngby (Denmark); Madsen, H. [Dept. of Mathematical Modelling (IMM-DTU) Kgs. Lyngby (Denmark); Toefting, J. [Elsam, Fredericia (Denmark)

    2004-07-01

    This paper dsecribes VPPT (Wind Power Prediction Tool), an application for assessing the future available wind power up to 36 hours ahead in time. WPPT has been installed in the Eltra/Elsam central dispatch center since October 1997. The paper describes the prediction model used, the actual implementation of WPPT as well as the experience gained by the operators in the dispatch center (au)

  17. On-line test of power distribution prediction system for boiling water reactors

    International Nuclear Information System (INIS)

    Nishizawa, Y.; Kiguchi, T.; Kobayashi, S.; Takumi, K.; Tanaka, H.; Tsutsumi, R.; Yokomi, M.

    1982-01-01

    A power distribution prediction system for boiling water reactors has been developed and its on-line performance test has proceeded at an operating commercial reactor. This system predicts the power distribution or thermal margin in advance of control rod operations and core flow rate change. This system consists of an on-line computer system, an operator's console with a color cathode-ray tube, and plant data input devices. The main functions of this system are present power distribution monitoring, power distribution prediction, and power-up trajectory prediction. The calculation method is based on a simplified nuclear thermal-hydraulic calculation, which is combined with a method of model identification to the actual reactor core state. It has been ascertained by the on-line test that the predicted power distribution (readings of traversing in-core probe) agrees with the measured data within 6% root-mean-square. The computing time required for one prediction calculation step is less than or equal to 1.5 min by an HIDIC-80 on-line computer

  18. Wind power prediction based on genetic neural network

    Science.gov (United States)

    Zhang, Suhan

    2017-04-01

    The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

  19. Power that Builds Others and Power that Breaks: Effects of Power and Humility on Altruism and Incivility in Female Employees.

    Science.gov (United States)

    Yoon, David J; Farmer, Steven M

    2018-01-02

    Building on the approach/inhibition theory of power and the situated focus theory of power, we examine the roles of positional and personal power on altruism and incivility in workplace dyads. Results from a field study in daycare centers showed that legitimate power (a dimension of positional power) was positively associated with incivility. In contrast, personal power-referent power and expert power-was positively associated altruism and was negatively associated with incivility. Referent power was a stronger predictor of both altruism and incivility for individuals with low humility than those with high humility. Coercive power was a stronger predictor of incivility for individuals with high humility than those with low humility.

  20. Prospects for stronger calandria tubes

    International Nuclear Information System (INIS)

    Ells, C.E.; Coleman, C.E.; Hosbons, R.R.; Ibrahim, E.F.; Doubt, G.L.

    1990-12-01

    The CANDU calandria tubes, made of seam welded and annealed Zircaloy-2, have given exemplary service in-reactor. Although not designed as a system pressure containment, calandria tubes may remain intact even in the face of pressure tube rupture. One such incident at Pickering Unit 2 demonstrated the economic advantage of such an outcome, and a case can be made for increasing the probability that other calandria tubes would perform in a similar fashion. Various methods of obtaining stronger calandria tubes are available, and reviewed here. When the tubes are internally pressurized, the weld is the weak section of the tube. Increasing the oxygen concentration in the starting sheet, and thickening the weld, are promising routes to a stronger tube

  1. Predicting Output Power for Nearshore Wave Energy Harvesting

    Directory of Open Access Journals (Sweden)

    Henock Mamo Deberneh

    2018-04-01

    Full Text Available Energy harvested from a Wave Energy Converter (WEC varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In this paper, we present a novel approach to predicting the output power of a nearshore WEC by characterizing ocean waves using floating buoys. We monitored the movement of the buoys using an Arduino-based data collection module, including a gyro-accelerometer sensor and a wireless transceiver. The collected data were utilized to train and test prediction models. The models were developed using machine learning algorithms: SVM, RF and ANN. The results of the experiments showed that measurements from the data collection module can yield a reliable predictor of output power. Furthermore, we found that the predictors work better when the regressors are combined with a classifier. The accuracy of the proposed prediction model suggests that it could be extremely useful in both locating optimal placement for wave energy harvesting plants and designing the shape of the buoys used by them.

  2. Evaluation of peak power prediction equations in male basketball players.

    Science.gov (United States)

    Duncan, Michael J; Lyons, Mark; Nevill, Alan M

    2008-07-01

    This study compared peak power estimated using 4 commonly used regression equations with actual peak power derived from force platform data in a group of adolescent basketball players. Twenty-five elite junior male basketball players (age, 16.5 +/- 0.5 years; mass, 74.2 +/- 11.8 kg; height, 181.8 +/- 8.1 cm) volunteered to participate in the study. Actual peak power was determined using a countermovement vertical jump on a force platform. Estimated peak power was determined using countermovement jump height and body mass. All 4 prediction equations were significantly related to actual peak power (all p jump prediction equations, 12% for the Canavan and Vescovi equation, and 6% for the Sayers countermovement jump equation. In all cases peak power was underestimated.

  3. Model output statistics applied to wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

    1999-03-01

    Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

  4. Prediction of Wind Energy Resources (PoWER) Users Guide

    Science.gov (United States)

    2016-01-01

    ARL-TR-7573● JAN 2016 US Army Research Laboratory Prediction of Wind Energy Resources (PoWER) User’s Guide by David P Sauter...manufacturer’s or trade names does not constitute an official endorsement or approval of the use thereof. Destroy this report when it is no longer needed. Do...not return it to the originator. ARL-TR-7573 ● JAN 2016 US Army Research Laboratory Prediction of Wind Energy Resources (PoWER

  5. Operational results from a physical power prediction model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L [Risoe National Lab., Meteorology and Wind Energy Dept., Roskilde (Denmark)

    1999-03-01

    This paper will describe a prediction system which predicts the expected power output of a number of wind farms. The system is automatic and operates on-line. The paper will quantify the accuracy of the predictions and will also give examples of the performance for specific storm events. An actual implementation of the system will be described and the robustness demonstrated. (au) 11 refs.

  6. Stronger pack warnings predict quitting more than weaker ones: finding from the ITC Malaysia and Thailand surveys.

    Science.gov (United States)

    Fathelrahman, Ahmed I; Li, Lin; Borland, Ron; Yong, Hua-Hie; Omar, Maizurah; Awang, Rahmat; Sirirassamee, Buppha; Fong, Geoffrey T; Hammond, David

    2013-09-18

    mechanisms for influencing quitting regardless of warning strength. The larger and more informative Thai warnings were associated with higher levels of reactions predictive of quitting and stronger associations with subsequent quitting, demonstrating their greater potency.

  7. Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

    Energy Technology Data Exchange (ETDEWEB)

    Melin, Alexander M. [ORNL; Olama, Mohammed M. [ORNL; Dong, Jin [ORNL; Djouadi, Seddik M. [ORNL; Zhang, Yichen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

    2017-09-01

    The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.

  8. Skill forecasting from different wind power ensemble prediction methods

    International Nuclear Information System (INIS)

    Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

    2007-01-01

    This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

  9. Feature Selection and ANN Solar Power Prediction

    Directory of Open Access Journals (Sweden)

    Daniel O’Leary

    2017-01-01

    Full Text Available A novel method of solar power forecasting for individuals and small businesses is developed in this paper based on machine learning, image processing, and acoustic classification techniques. Increases in the production of solar power at the consumer level require automated forecasting systems to minimize loss, cost, and environmental impact for homes and businesses that produce and consume power (prosumers. These new participants in the energy market, prosumers, require new artificial neural network (ANN performance tuning techniques to create accurate ANN forecasts. Input masking, an ANN tuning technique developed for acoustic signal classification and image edge detection, is applied to prosumer solar data to improve prosumer forecast accuracy over traditional macrogrid ANN performance tuning techniques. ANN inputs tailor time-of-day masking based on error clustering in the time domain. Results show an improvement in prediction to target correlation, the R2 value, lowering inaccuracy of sample predictions by 14.4%, with corresponding drops in mean average error of 5.37% and root mean squared error of 6.83%.

  10. Using data-driven approach for wind power prediction: A comparative study

    International Nuclear Information System (INIS)

    Taslimi Renani, Ehsan; Elias, Mohamad Fathi Mohamad; Rahim, Nasrudin Abd.

    2016-01-01

    Highlights: • Double exponential smoothing is the most accurate model in wind speed prediction. • A two-stage feature selection method is proposed to select most important inputs. • Direct prediction illustrates better accuracy than indirect prediction. • Adaptive neuro fuzzy inference system outperforms data mining algorithms. • Random forest performs the worst compared to other data mining algorithm. - Abstract: Although wind energy is intermittent and stochastic in nature, it is increasingly important in the power generation due to its sustainability and pollution-free. Increased utilization of wind energy sources calls for more robust and efficient prediction models to mitigate uncertainties associated with wind power. This research compares two different approaches in wind power forecasting which are indirect and direct prediction methods. In indirect method, several times series are applied to forecast the wind speed, whereas the logistic function with five parameters is then used to forecast the wind power. In this study, backtracking search algorithm with novel crossover and mutation operators is employed to find the best parameters of five-parameter logistic function. A new feature selection technique, combining the mutual information and neural network is proposed in this paper to extract the most informative features with a maximum relevancy and minimum redundancy. From the comparative study, the results demonstrate that, in the direct prediction approach where the historical weather data are used to predict the wind power generation directly, adaptive neuro fuzzy inference system outperforms five data mining algorithms namely, random forest, M5Rules, k-nearest neighbor, support vector machine and multilayer perceptron. Moreover, it is also found that the mean absolute percentage error of the direct prediction method using adaptive neuro fuzzy inference system is 1.47% which is approximately less than half of the error obtained with the

  11. Testing the predictive power of nuclear mass models

    International Nuclear Information System (INIS)

    Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

    2008-01-01

    A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool

  12. Sludge pipe flow pressure drop prediction using composite power ...

    African Journals Online (AJOL)

    Sludge pipe flow pressure drop prediction using composite power-law friction ... Water SA. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue ... When predicting pressure gradients for the flow of sludges in pipes, the ...

  13. Synchrophasor-Assisted Prediction of Stability/Instability of a Power System

    Science.gov (United States)

    Saha Roy, Biman Kumar; Sinha, Avinash Kumar; Pradhan, Ashok Kumar

    2013-05-01

    This paper presents a technique for real-time prediction of stability/instability of a power system based on synchrophasor measurements obtained from phasor measurement units (PMUs) at generator buses. For stability assessment the technique makes use of system severity indices developed using bus voltage magnitude obtained from PMUs and generator electrical power. Generator power is computed using system information and PMU information like voltage and current phasors obtained from PMU. System stability/instability is predicted when the indices exceeds a threshold value. A case study is carried out on New England 10-generator, 39-bus system to validate the performance of the technique.

  14. 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...

  15. Hybrid robust predictive optimization method of power system dispatch

    Science.gov (United States)

    Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

    2011-08-02

    A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

  16. Wind Power Plant Prediction by Using Neural Networks: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Z.; Gao, W.; Wan, Y. H.; Muljadi, E.

    2012-08-01

    This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-06-03

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

  18. Pre-stimulus thalamic theta power predicts human memory formation.

    Science.gov (United States)

    Sweeney-Reed, Catherine M; Zaehle, Tino; Voges, Jürgen; Schmitt, Friedhelm C; Buentjen, Lars; Kopitzki, Klaus; Richardson-Klavehn, Alan; Hinrichs, Hermann; Heinze, Hans-Jochen; Knight, Robert T; Rugg, Michael D

    2016-09-01

    Pre-stimulus theta (4-8Hz) power in the hippocampus and neocortex predicts whether a memory for a subsequent event will be formed. Anatomical studies reveal thalamus-hippocampal connectivity, and lesion, neuroimaging, and electrophysiological studies show that memory processing involves the dorsomedial (DMTN) and anterior thalamic nuclei (ATN). The small size and deep location of these nuclei have limited real-time study of their activity, however, and it is unknown whether pre-stimulus theta power predictive of successful memory formation is also found in these subcortical structures. We recorded human electrophysiological data from the DMTN and ATN of 7 patients receiving deep brain stimulation for refractory epilepsy. We found that greater pre-stimulus theta power in the right DMTN was associated with successful memory encoding, predicting both behavioral outcome and post-stimulus correlates of successful memory formation. In particular, significant correlations were observed between right DMTN theta power and both frontal theta and right ATN gamma (32-50Hz) phase alignment, and frontal-ATN theta-gamma cross-frequency coupling. We draw the following primary conclusions. Our results provide direct electrophysiological evidence in humans of a role for the DMTN as well as the ATN in memory formation. Furthermore, prediction of subsequent memory performance by pre-stimulus thalamic oscillations provides evidence that post-stimulus differences in thalamic activity that index successful and unsuccessful encoding reflect brain processes specifically underpinning memory formation. Finally, the findings broaden the understanding of brain states that facilitate memory encoding to include subcortical as well as cortical structures. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Uncertainties in predicting solar panel power output

    Science.gov (United States)

    Anspaugh, B.

    1974-01-01

    The problem of calculating solar panel power output at launch and during a space mission is considered. The major sources of uncertainty and error in predicting the post launch electrical performance of the panel are considered. A general discussion of error analysis is given. Examples of uncertainty calculations are included. A general method of calculating the effect on the panel of various degrading environments is presented, with references supplied for specific methods. A technique for sizing a solar panel for a required mission power profile is developed.

  20. Predictive Maintenance: One key to improved power plant availability

    International Nuclear Information System (INIS)

    Mobley; Allen, J.W.

    1986-01-01

    Recent developments in microprocessor technology has provided the ability to routinely monitor the actual mechanical condition of all rotating and reciprocating machinery and process variables (i.e. pressure, temperature, flow, etc.) of other process equipment within an operating electric power generating plant. This direct correlation between frequency domain vibration and actual mechanical condition of machinery and trending process variables of non-rotating equipment can provide the ''key'' to improving the availability and reliability, thermal efficiency and provide the baseline information necessary for developing a realistic plan for extending the useful life of power plants. The premise of utilizing microprocessor-based Predictive Maintenance to improve power plant operation has been proven by a number of utilities. This paper provides a comprehensive discussion of the TEC approach to Predictive Maintenance and examples of successful programs

  1. Stronger multilayer acrylic dielectric elastomer actuators with silicone gel coatings

    Science.gov (United States)

    Lau, Gih-Keong; La, Thanh-Giang; Sheng-Wei Foong, Ervin; Shrestha, Milan

    2016-12-01

    Multilayer dielectric elastomer actuators (DEA) perform worst off than single-layer DEAs due to higher susceptibility to electro-thermal breakdown. This paper presents a hot-spot model to predict the electro-thermal breakdown field of DEAs and its dependence on thermal insulation. To inhibit the electrothermal breakdown, silicone gel coating was applied as barrier coating to multilayer acrylic DEA. The gel coating helps suppress the electro-thermally induced puncturing of DEA membrane at the hot spot. As a result, the gel-coated DEAs, in either a single layer or a multilayer stack, can produce 30% more isometric stress change as compared to those none-coated. These gel-coated acrylic DEAs show great potential to make stronger artificial muscles.

  2. Predicting Power Outages Using Multi-Model Ensemble Forecasts

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

    2017-12-01

    Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

  3. Thermal Storage Power Balancing with Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

    2013-01-01

    The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination....... The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates...

  4. The impact of explicit and implicit power motivation on educational choices

    Directory of Open Access Journals (Sweden)

    Maliszewski Norbert

    2014-09-01

    Full Text Available The aim of three studies was to examine the differences between business majors and non-business majors, in their level of implicit (measured by an Implicit Association Test [IAT], Greenwald, McGhee, & Schwarz, 1998 and explicit power motivation (measured by Power Motivation and Helping Power Motivation scales, Frieze & Boneva, 2001.It was predicted that there are no differences between these two groups in the general (implicit level of power motivation, but that differences exist in the way it is explicitly expressed: through desire for leadership and prominence vs. desire for helping. Results of Study 1 indicated that business majors (management, N=79 declared a higher leadership motive and a lower helping motive than non-business majors (history, psychology, linguistics, N=62.Study 2 addressed question whether the above differences in power motivation stem from socialization at the university level or from pre-selection. The relationship between high school students’ (N=134 academic major preferences and their power motivation was tested. It was found that the more they were business-oriented, the higher their scores were on leadership, and lower on helping scales. In Study 3, business majors (economics, N=75 and non-business majors (psychology, N=82 completed the same questionnaire as participants in previous studies, as well as performed the IAT. Non-business majors declared stronger explicit helping motive, while business majors expressed stronger prominence and leadership motives. Furthermore, for non-business majors, IAT results could be predicted by their helping score. Implications and possible limitations of the presented results are discussed.

  5. Predicting Harmonic Distortion of Multiple Converters in a Power System

    Directory of Open Access Journals (Sweden)

    P. M. Ivry

    2017-01-01

    Full Text Available Various uncertainties arise in the operation and management of power systems containing Renewable Energy Sources (RES that affect the systems power quality. These uncertainties may arise due to system parameter changes or design parameter choice. In this work, the impact of uncertainties on the prediction of harmonics in a power system containing multiple Voltage Source Converters (VSCs is investigated. The study focuses on the prediction of harmonic distortion level in multiple VSCs when some system or design parameters are only known within certain constraints. The Univariate Dimension Reduction (UDR method was utilized in this study as an efficient predictive tool for the level of harmonic distortion of the VSCs measured at the Point of Common Coupling (PCC to the grid. Two case studies were considered and the UDR technique was also experimentally validated. The obtained results were compared with that of the Monte Carlo Simulation (MCS results.

  6. VT Predicted Mean Wind Power - 50 meter height

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) Wind power predictions at 50m are generated by a numerical model that simulates weather conditions over a 15-year period, taking into account...

  7. Critical power prediction by CATHARE2 of the OECD/NRC BFBT benchmark

    Energy Technology Data Exchange (ETDEWEB)

    Lutsanych, Sergii, E-mail: s.lutsanych@ing.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122, San Piero a Grado, Pisa (Italy); Sabotinov, Luben, E-mail: luben.sabotinov@irsn.fr [Institut for Radiological Protection and Nuclear Safety (IRSN), 31 avenue de la Division Leclerc, 92262 Fontenay-aux-Roses (France); D’Auria, Francesco, E-mail: francesco.dauria@dimnp.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122, San Piero a Grado, Pisa (Italy)

    2015-03-15

    Highlights: • We used CATHARE code to calculate the critical power exercises of the OECD/NRC BFBT benchmark. • We considered both steady-state and transient critical power tests of the benchmark. • We used both the 1D and 3D features of the CATHARE code to simulate the experiments. • Acceptable prediction of the critical power and its location in the bundle is obtained using appropriate modelling. - Abstract: This paper presents an application of the French best estimate thermal-hydraulic code CATHARE 2 to calculate the critical power and departure from nucleate boiling (DNB) exercises of the International OECD/NRC BWR Fuel Bundle Test (BFBT) benchmark. The assessment activity is performed comparing the code calculation results with available in the framework of the benchmark experimental data from Japanese Nuclear Power Engineering Corporation (NUPEC). Two-phase flow calculations on prediction of the critical power have been carried out both in steady state and transient cases, using one-dimensional and three-dimensional modelling. Results of the steady-state critical power tests calculation have shown the ability of CATHARE code to predict reasonably the critical power and its location, using appropriate modelling.

  8. The Educational Program "Zajedno Jaci" (Stronger Together) in Croatia

    Science.gov (United States)

    Spanja, Sanja

    2011-01-01

    In this paper, we explore intercultural learning undertaken through the educational program "Stronger Together." The program "Stronger Together" was created in 1998 in order to support and educate teachers working with children in post-war regions of Croatia using intercultural education and cooperative learning as tools for…

  9. Prediction of power-ramp defects in CANDU fuel

    International Nuclear Information System (INIS)

    Gillespie, P.; Wadsworth, S.; Daniels, T.

    2010-01-01

    Power ramps result in fuel pellet expansion and can lead to fuel sheath failures by fission product induced stress corrosion cracking (SCC). Historically, empirical models fit to experimental test data were used to predict the onset of power-ramp failures in CANDU fuel. In 1988, a power-ramped fuel defect event at PNGS-1 led to the refinement of these empirical models. This defect event has recently been re-analyzed and the empirical model updated. The empirical model is supported by a physically based model which can be used to extrapolate to fuel conditions (density, burnup) outside of the 1988 data set. (author)

  10. Stronger synergies

    CERN Multimedia

    Antonella Del Rosso

    2012-01-01

    CERN was founded 58 years ago under the auspices of UNESCO. Since then, both organisations have grown to become world leaders in their respective fields. The links between the two have always existed but today they are even stronger, with new projects under way to develop a more efficient way of exchanging information and devise a common strategy on topics of mutual interest.   CERN and UNESCO are a perfect example of natural partners: their common field is science and education is one of the pillars on which both are built. Historically, they share a common heritage. Both UNESCO and CERN were born of the desire to use scientific cooperation to rebuild peace and security in the aftermath of the Second World War. "Recently, building on our common roots and in close collaboration with UNESCO, we have been developing more structured links to ensure the continuity of the actions taken over the years," says Maurizio Bona, who is in charge of CERN relations with international orga...

  11. Spectral intensity dependence an isotropy of sources stronger than 0.1 Jy at 2700 MHz

    International Nuclear Information System (INIS)

    Balonek, T.J.; Broderick, J.J.; Condon, J.J.; Crawford, D.F.; Jauncey, D.L.

    1975-01-01

    The 1000-foot (305 m) telescope of the National Astronomy and Ionosphere Center was used to measure 430 MHz flux densities of sources stronger than 0.1 Jy at 2700 MHz. Distributions of the resulting two-point spectral indices α (430, 2700) of sources in the intensity range 0.1less than or equal toS<0.35 Jy were compared with α (318, 2700) distributions of sources stronger than 0.35 Jy at 2700 MHz. The median normal-component spectral index and fraction of flat-spectrum sources in the faintest sample do not continue the previously discovered trend toward increased spectral steepening of faint sources. This result differs from the prediction of simple evolutionary cosmological models and therefore favors the alternative explanation that local source-density inhomogeneities are responsible for the observed intensity dependence of spectral indices

  12. Fuzzy-predictive direct power control implementation of a grid connected photovoltaic system, associated with an active power filter

    International Nuclear Information System (INIS)

    Ouchen, Sabir; Betka, Achour; Abdeddaim, Sabrina; Menadi, Abdelkrim

    2016-01-01

    Highlights: • An implementation on dSPACE 1104 of a double stage grid connected photovoltaic system, associated with an active power filter. • A fuzzy logic controller for maximum power point tracking of photovoltaic generator using a boost converter. • Predictive direct power control almost eliminates the effect of harmonics under a unite power factor. • The robustness of control strategies was examined in different irradiance level conditions. - Abstract: The present paper proposes a real time implementation of an optimal operation of a double stage grid connected photovoltaic system, associated with a shunt active power filter. On the photovoltaic side, a fuzzy logic based maximum power point taking control is proposed to track permanently the optimum point through an adequate tuning of a boost converter regardless the solar irradiance variations; whereas, on the grid side, a model predictive direct power control is applied, to ensure both supplying a part of the load demand with the extracted photovoltaic power, and a compensation of undesirable harmonic contents of the grid current, under a unity power factor operation. The implementation of the control strategies is conducted on a small scale photovoltaic system, controlled via a dSPACE 1104 single card. The obtained experimental results show on one hand, that the proposed Fuzzy logic based maximum power taking point technique provides fast and high performances under different irradiance levels while compared with a sliding mode control, and ensures 1.57% more in efficiency. On the other hand, the predictive power control ensures a flexible settlement of active power amounts exchanges with the grid, under a unity power functioning. Furthermore, the grid current presents a sinusoidal shape with a tolerable total harmonic distortion coefficient 4.71%.

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

    Energy Technology Data Exchange (ETDEWEB)

    Abbas, Nikhar; Tom, Nathan

    2017-09-01

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

  14. Magnetic storm effects in electric power systems and prediction needs

    Science.gov (United States)

    Albertson, V. D.; Kappenman, J. G.

    1979-01-01

    Geomagnetic field fluctuations produce spurious currents in electric power systems. These currents enter and exit through points remote from each other. The fundamental period of these currents is on the order of several minutes which is quasi-dc compared to the normal 60 Hz or 50 Hz power system frequency. Nearly all of the power systems problems caused by the geomagnetically induced currents result from the half-cycle saturation of power transformers due to simultaneous ac and dc excitation. The effects produced in power systems are presented, current research activity is discussed, and magnetic storm prediction needs of the power industry are listed.

  15. Predictive power of the grace score in population with diabetes.

    Science.gov (United States)

    Baeza-Román, Anna; de Miguel-Balsa, Eva; Latour-Pérez, Jaime; Carrillo-López, Andrés

    2017-12-01

    Current clinical practice guidelines recommend risk stratification in patients with acute coronary syndrome (ACS) upon admission to hospital. Diabetes mellitus (DM) is widely recognized as an independent predictor of mortality in these patients, although it is not included in the GRACE risk score. The objective of this study is to validate the GRACE risk score in a contemporary population and particularly in the subgroup of patients with diabetes, and to test the effects of including the DM variable in the model. Retrospective cohort study in patients included in the ARIAM-SEMICYUC registry, with a diagnosis of ACS and with available in-hospital mortality data. We tested the predictive power of the GRACE score, calculating the area under the ROC curve. We assessed the calibration of the score and the predictive ability based on type of ACS and the presence of DM. Finally, we evaluated the effect of including the DM variable in the model by calculating the net reclassification improvement. The GRACE score shows good predictive power for hospital mortality in the study population, with a moderate degree of calibration and no significant differences based on ACS type or the presence of DM. Including DM as a variable did not add any predictive value to the GRACE model. The GRACE score has an appropriate predictive power, with good calibration and clinical applicability in the subgroup of diabetic patients. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

  16. Model predictive control for Z-source power converter

    DEFF Research Database (Denmark)

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

    2011-01-01

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

  17. When Power Shapes Interpersonal Behavior: Low Relationship Power Predicts Men’s Aggressive Responses to Low Situational Power

    Science.gov (United States)

    Overall, Nickola C.; Hammond, Matthew D.; McNulty, James K.; Finkel, Eli J.

    2016-01-01

    When does power in intimate relationships shape important interpersonal behaviors, such as psychological aggression? Five studies tested whether possessing low relationship power was associated with aggressive responses, but (1) only within power-relevant relationship interactions when situational power was low, and (2) only by men because masculinity (but not femininity) involves the possession and demonstration of power. In Studies 1 and 2, men lower in relationship power exhibited greater aggressive communication during couples’ observed conflict discussions, but only when they experienced low situational power because they were unable to influence their partner. In Study 3, men lower in relationship power reported greater daily aggressive responses toward their partner, but only on days when they experienced low situational power because they were either (a) unable to influence their partner or (b) dependent on their partner for support. In Study 4, men who possessed lower relationship power exhibited greater aggressive responses during couples’ support-relevant discussions, but only when they had low situational power because they needed high levels of support. Study 5 provided evidence for the theoretical mechanism underlying men’s aggressive responses to low relationship power. Men who possessed lower relationship power felt less manly on days they faced low situational power because their partner was unwilling to change to resolve relationship problems, which in turn predicted greater aggressive responses to their partner. These results demonstrate that fully understanding when and why power is associated with interpersonal behavior requires differentiating between relationship and situational power. PMID:27442766

  18. Quantitative Prediction of Power Loss for Damaged Photovoltaic Modules Using Electroluminescence

    Directory of Open Access Journals (Sweden)

    Timo Kropp

    2018-05-01

    Full Text Available Electroluminescence (EL is a powerful tool for the qualitative mapping of the electronic properties of solar modules, where electronic and electrical defects are easily detected. However, a direct quantitative prediction of electrical module performance purely based on electroluminescence images has yet to be accomplished. Our novel approach, called “EL power prediction of modules” (ELMO as presented here, used just two electroluminescence images to predict the electrical loss of mechanically damaged modules when compared to their original (data sheet power. First, using this method, two EL images taken at different excitation currents were converted into locally resolved (relative series resistance images. From the known, total applied voltage to the module, we were then able to calculate absolute series resistance values and the real distribution of voltages and currents. Then, we reconstructed the complete current/voltage curve of the damaged module. We experimentally validated and confirmed the simulation model via the characterization of a commercially available photovoltaic module containing 60 multicrystalline silicon cells, which were mechanically damaged by hail. Deviation between the directly measured and predicted current/voltage curve was less than 4.3% at the maximum power point. For multiple modules of the same type, the level of error dropped below 1% by calibrating the simulation. We approximated the ideality factor from a module with a known current/voltage curve and then expand the application to modules of the same type. In addition to yielding series resistance mapping, our new ELMO method was also capable of yielding parallel resistance mapping. We analyzed the electrical properties of a commercially available module, containing 72 monocrystalline high-efficiency back contact solar cells, which suffered from potential induced degradation. For this module, we predicted electrical performance with an accuracy of better

  19. On the universality of power laws for tokamak plasma predictions

    Science.gov (United States)

    Garcia, J.; Cambon, D.; Contributors, JET

    2018-02-01

    Significant deviations from well established power laws for the thermal energy confinement time, obtained from extensive databases analysis as the IPB98(y,2), have been recently reported in dedicated power scans. In order to illuminate the adequacy, validity and universality of power laws as tools for predicting plasma performance, a simplified analysis has been carried out in the framework of a minimal modeling for heat transport which is, however, able to account for the interplay between turbulence and collinear effects with the input power known to play a role in experiments with significant deviations from such power laws. Whereas at low powers, the usual scaling laws are recovered with little influence of other plasma parameters, resulting in a robust power low exponent, at high power it is shown how the exponents obtained are extremely sensitive to the heating deposition, the q-profile or even the sampling or the number of points considered due to highly non-linear behavior of the heat transport. In particular circumstances, even a minimum of the thermal energy confinement time with the input power can be obtained, which means that the approach of the energy confinement time as a power law might be intrinsically invalid. Therefore plasma predictions with a power law approximation with a constant exponent obtained from a regression of a broad range of powers and other plasma parameters which can non-linearly affect and suppress heat transport, can lead to misleading results suggesting that this approach should be taken cautiously and its results continuously compared with modeling which can properly capture the underline physics, as gyrokinetic simulations.

  20. Empirical Information Metrics for Prediction Power and Experiment Planning

    Directory of Open Access Journals (Sweden)

    Christopher Lee

    2011-01-01

    Full Text Available In principle, information theory could provide useful metrics for statistical inference. In practice this is impeded by divergent assumptions: Information theory assumes the joint distribution of variables of interest is known, whereas in statistical inference it is hidden and is the goal of inference. To integrate these approaches we note a common theme they share, namely the measurement of prediction power. We generalize this concept as an information metric, subject to several requirements: Calculation of the metric must be objective or model-free; unbiased; convergent; probabilistically bounded; and low in computational complexity. Unfortunately, widely used model selection metrics such as Maximum Likelihood, the Akaike Information Criterion and Bayesian Information Criterion do not necessarily meet all these requirements. We define four distinct empirical information metrics measured via sampling, with explicit Law of Large Numbers convergence guarantees, which meet these requirements: Ie, the empirical information, a measure of average prediction power; Ib, the overfitting bias information, which measures selection bias in the modeling procedure; Ip, the potential information, which measures the total remaining information in the observations not yet discovered by the model; and Im, the model information, which measures the model’s extrapolation prediction power. Finally, we show that Ip + Ie, Ip + Im, and Ie — Im are fixed constants for a given observed dataset (i.e. prediction target, independent of the model, and thus represent a fundamental subdivision of the total information contained in the observations. We discuss the application of these metrics to modeling and experiment planning.    

  1. Which cue to ‘want’? Opioid stimulation of central amygdala makes goal-trackers show stronger goal-tracking, just as sign-trackers show stronger sign-tracking

    Science.gov (United States)

    DiFeliceantonio, Alexandra G.; Berridge, Kent C.

    2012-01-01

    Pavlovian cues that have been paired with reward can gain incentive salience. Drug addicts find drug cues motivationally attractive and binge eaters are attracted by food cues. But the level of incentive salience elicited by a cue re-encounter still varies across time and brain states. In an animal model, cues become attractive and ‘wanted’ in an ‘autoshaping’ paradigm, where different targets of incentive salience emerge for different individuals. Some individuals (sign-trackers) find a predictive discrete cue attractive while others find a reward contiguous and goal cue more attractive (location where reward arrives: goal-trackers). Here we assessed whether central amygdala mu opioid receptor stimulation enhances the phasic incentive salience of the goal-cue for goal-trackers during moments of predictive cue presence (expressed in both approach and consummatory behaviors to goal cue), just as it enhances the attractiveness of the predictive cue target for sign-trackers. Using detailed video analysis we measured the approaches, nibbles, sniffs, and bites directed at their preferred target for both sign-trackers and goal-trackers. We report that DAMGO microinjections in central amygdala made goal-trackers, like sign-trackers, show phasic increases in appetitive nibbles and sniffs directed at the goal-cue expressed selectively whenever the predictive cue was present. This indicates enhancement of incentive salience attributed by both goal trackers and sign-trackers, but attributed in different directions: each to their own target cue. For both phenotypes, amygdala opioid stimulation makes the individual’s prepotent cue into a stronger motivational magnet at phasic moments triggered by a CS that predicts the reward UCS. PMID:22391118

  2. A variable capacitance based modeling and power capability predicting method for ultracapacitor

    Science.gov (United States)

    Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

    2018-01-01

    Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

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

    International Nuclear Information System (INIS)

    Su Jie

    2012-01-01

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

  4. K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

    Directory of Open Access Journals (Sweden)

    Lv Tao

    2017-01-01

    Full Text Available Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1 the predictive power of a pattern varies a great deal for different shapes and (2 each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.

  5. Brief implicit association test: Validity and utility in prediction of voting behavior

    Directory of Open Access Journals (Sweden)

    Pavlović Maša D.

    2013-01-01

    Full Text Available We employed the Brief Implicit Association Test (a recently developed short version of IAT to measure implicit political attitudes toward four political parties running for Serbian parliament. To test its criterion validity, we measured voting intention and actual voting behavior. In addition, we introduced political involvement as a potential moderator of the BIAT’s predictive and incremental validity. The BIAT demonstrated good internal and predictive validity, but lacked incremental validity over self-report measures. Predictive power of the BIAT was moderated by political involvement - the BIAT scores were stronger predictors of voting intention and behavior among voters highly involved in politics. [Projekat Ministarstva nauke Republike Srbije, br. 179018

  6. Predicting Power Output of Upper Body using the OMNI-RES Scale

    Directory of Open Access Journals (Sweden)

    Bautista Iker J.

    2014-12-01

    Full Text Available The main aim of this study was to determine the optimal training zone for maximum power output. This was to be achieved through estimating mean bar velocity of the concentric phase of a bench press using a prediction equation. The values for the prediction equation would be obtained using OMNI-RES scale values of different loads of the bench press exercise. Sixty males ( voluntarily participated in the study and were tested using an incremental protocol on a Smith machine to determine one repetition maximum (1RM in the bench press exercise. A linear regression analysis produced a strong correlation (r = -0.94 between rating of perceived exertion (RPE and mean bar velocity (Velmean. The Pearson correlation analysis between real power output (PotReal and estimated power (PotEst showed a strong correlation coefficient of r = 0.77, significant at a level of p = 0.01. Therefore, the OMNI-RES scale can be used to predict Velmean in the bench press exercise to control the intensity of the exercise. The positive relationship between PotReal and PotEst allowed for the identification of a maximum power-training zone.

  7. Error analysis of short term wind power prediction models

    International Nuclear Information System (INIS)

    De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco

    2011-01-01

    The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)

  8. Error analysis of short term wind power prediction models

    Energy Technology Data Exchange (ETDEWEB)

    De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco [Dipartimento di Ingegneria dell' Innovazione, Universita del Salento, Via per Monteroni, 73100 Lecce (Italy)

    2011-04-15

    The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)

  9. Model predictive control for power flows in networks with limited capacity

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Bendtsen, Jan Dimon

    2012-01-01

    this problem can be formulated as an optimization problem, leading directly to the design of a model predictive controller. Using this scheme, we are able to incorporate predictions of future consumption and exploit knowledge of link limitations such that the intelligent consumers are utilized ahead of time......We consider an interconnected network of consumers powered through an electrical grid of limited capacity. A subset of the consumers are intelligent consumers and have the ability to store energy in a controllable fashion; they can be filled and emptied as desired under power and capacity...... limitations. We address the problem of maintaining power balance between production and consumption using the intelligent consumers to ensure smooth power consumption from the grid. Further, certain capacity limitations to the links interconnecting the consumers must be honored. In this paper, we show how...

  10. Power maximization of a point absorber wave energy converter using improved model predictive control

    Science.gov (United States)

    Milani, Farideh; Moghaddam, Reihaneh Kardehi

    2017-08-01

    This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

  11. COSMIC EMULATION: FAST PREDICTIONS FOR THE GALAXY POWER SPECTRUM

    Energy Technology Data Exchange (ETDEWEB)

    Kwan, Juliana; Heitmann, Katrin; Habib, Salman; Frontiere, Nicholas; Pope, Adrian [High Energy Physics Division, Argonne National Laboratory, Lemont, IL 60439 (United States); Padmanabhan, Nikhil [Department of Physics, Yale University, 260 Whitney Ave., New Haven, CT 06520 (United States); Lawrence, Earl [Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Finkel, Hal [Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL 60439 (United States)

    2015-09-01

    The halo occupation distribution (HOD) approach has proven to be an effective method for modeling galaxy clustering and bias. In this approach, galaxies of a given type are probabilistically assigned to individual halos in N-body simulations. In this paper, we present a fast emulator for predicting the fully nonlinear galaxy–galaxy auto and galaxy–dark matter cross power spectrum and correlation function over a range of freely specifiable HOD modeling parameters. The emulator is constructed using results from 100 HOD models run on a large ΛCDM N-body simulation, with Gaussian Process interpolation applied to a PCA-based representation of the galaxy power spectrum. The total error is currently ∼1% in the auto correlations and ∼2% in the cross correlations from z = 1 to z = 0, over the considered parameter range. We use the emulator to investigate the accuracy of various analytic prescriptions for the galaxy power spectrum, parametric dependencies in the HOD model, and the behavior of galaxy bias as a function of HOD parameters. Additionally, we obtain fully nonlinear predictions for tangential shear correlations induced by galaxy–galaxy lensing from our galaxy–dark matter cross power spectrum emulator. All emulation products are publicly available at http://www.hep.anl.gov/cosmology/CosmicEmu/emu.html.

  12. Analysis of variability and predictability challenges of wind and solar power

    NARCIS (Netherlands)

    Haan, de J.E.S.; Virag, A.; Kling, W.L.

    2013-01-01

    In power systems, reserves are essential to ensure system security, certainly when challenges of predictability (inaccurate forecast) and variability (imperfect correlation of renewable generation and system load) are causing power imbalances. Different techniques can be used to size and allocate

  13. Does Spontaneous Favorability to Power (vs. Universalism) Values Predict Spontaneous Prejudice and Discrimination?

    Science.gov (United States)

    Souchon, Nicolas; Maio, Gregory R; Hanel, Paul H P; Bardin, Brigitte

    2017-10-01

    We conducted five studies testing whether an implicit measure of favorability toward power over universalism values predicts spontaneous prejudice and discrimination. Studies 1 (N = 192) and 2 (N = 86) examined correlations between spontaneous favorability toward power (vs. universalism) values, achievement (vs. benevolence) values, and a spontaneous measure of prejudice toward ethnic minorities. Study 3 (N = 159) tested whether conditioning participants to associate power values with positive adjectives and universalism values with negative adjectives (or inversely) affects spontaneous prejudice. Study 4 (N = 95) tested whether decision bias toward female handball players could be predicted by spontaneous attitude toward power (vs. universalism) values. Study 5 (N = 123) examined correlations between spontaneous attitude toward power (vs. universalism) values, spontaneous importance toward power (vs. universalism) values, and spontaneous prejudice toward Black African people. Spontaneous positivity toward power (vs. universalism) values was associated with spontaneous negativity toward minorities and predicted gender bias in a decision task, whereas the explicit measures did not. These results indicate that the implicit assessment of evaluative responses attached to human values helps to model value-attitude-behavior relations. © 2016 The Authors. Journal of Personality Published by Wiley Periodicals, Inc.

  14. A new wind power prediction method based on chaotic theory and Bernstein Neural Network

    International Nuclear Information System (INIS)

    Wang, Cong; Zhang, Hongli; Fan, Wenhui; Fan, Xiaochao

    2016-01-01

    The accuracy of wind power prediction is important for assessing the security and economy of the system operation when wind power connects to the grids. However, multiple factors cause a long delay and large errors in wind power prediction. Hence, efficient wind power forecasting approaches are still required for practical applications. In this paper, a new wind power forecasting method based on Chaos Theory and Bernstein Neural Network (BNN) is proposed. Firstly, the largest Lyapunov exponent as a judgment for wind power system's chaotic behavior is made. Secondly, Phase Space Reconstruction (PSR) is used to reconstruct the wind power series' phase space. Thirdly, the prediction model is constructed using the Bernstein polynomial and neural network. Finally, the weights and thresholds of the model are optimized by Primal Dual State Transition Algorithm (PDSTA). The practical hourly data of wind power generation in Xinjiang is used to test this forecaster. The proposed forecaster is compared with several current prominent research findings. Analytical results indicate that the forecasting error of PDSTA + BNN is 3.893% for 24 look-ahead hours, and has lower errors obtained compared with the other forecast methods discussed in this paper. The results of all cases studying confirm the validity of the new forecast method. - Highlights: • Lyapunov exponent is used to verify chaotic behavior of wind power series. • Phase Space Reconstruction is used to reconstruct chaotic wind power series. • A new Bernstein Neural Network to predict wind power series is proposed. • Primal dual state transition algorithm is chosen as the training strategy of BNN.

  15. Reduction of wind power induced reserve requirements by advanced shortest-term forecasts and prediction intervals

    Energy Technology Data Exchange (ETDEWEB)

    Dobschinski, Jan; Wessel, Arne; Lange, Bernhard; Bremen, Lueder von [Fraunhofer Institut fuer Windenergie und Energiesystemtechnik (IWES), Kassel (Germany)

    2009-07-01

    In electricity systems with large penetration of wind power, the limited predictability of the wind power generation leads to an increase in reserve and balancing requirements. At first the present study concentrates on the capability of dynamic day-ahead prediction intervals to reduce the wind power induced reserve and balancing requirements. Alternatively the reduction of large forecast errors of the German wind power generation by using advanced shortest-term predictions has been evaluated in a second approach. With focus on the allocation of minute reserve power the aim is to estimate the maximal remaining uncertainty after trading activities on the intraday market. Finally both approaches were used in a case study concerning the reserve requirements induced by the total German wind power expansion in 2007. (orig.)

  16. Predicting the emissive power of hydrocarbon pool fires

    International Nuclear Information System (INIS)

    Munoz, Miguel; Planas, Eulalia; Ferrero, Fabio; Casal, Joaquim

    2007-01-01

    The emissive power (E) of a flame depends on the size of the fire and the type of fuel. In fact, it changes significantly over the flame surface: the zones of luminous flame have high emittance, while those covered by smoke have low E values. The emissive power of each zone (that is, the luminous or clear flame and the non-luminous or smoky flame) and the portion of total flame area they occupy must be assessed when a two-zone model is used. In this study, data obtained from an experimental set-up were used to estimate the emissive power of fires and its behaviour as a function of pool size. The experiments were performed using gasoline and diesel oil as fuel. Five concentric circular pools (1.5, 3, 4, 5 and 6 m in diameter) were used. Appropriate instruments were employed to determine the main features of the fires. By superimposing IR and VHS images it was possible to accurately identify the luminous and non-luminous zones of the fire. Mathematical expressions were obtained that give a more accurate prediction of E lum , E soot and the average emissive power of a fire as a function of its luminous and smoky zones. These expressions can be used in a two-zone model to obtain a better prediction of the thermal radiation. The value of the radiative fraction was determined from the thermal flux measured with radiometers. An expression is also proposed for estimating the radiative fraction

  17. A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction

    International Nuclear Information System (INIS)

    Yesilbudak, Mehmet; Sagiroglu, Seref; Colak, Ilhami

    2017-01-01

    Highlights: • An accurate wind power prediction model is proposed for very short-term horizon. • The k-nearest neighbor classifier is implemented based on the multi-tupled inputs. • The variation of wind power prediction errors is evaluated in various aspects. • Our approach shows the superior prediction performance over the persistence method. - Abstract: With the growing share of wind power production in the electric power grids, many critical challenges to the grid operators have been emerged in terms of the power balance, power quality, voltage support, frequency stability, load scheduling, unit commitment and spinning reserve calculations. To overcome such problems, numerous studies have been conducted to predict the wind power production, but a small number of them have attempted to improve the prediction accuracy by employing the multidimensional meteorological input data. The novelties of this study lie in the proposal of an efficient and easy to implement very short-term wind power prediction model based on the k-nearest neighbor classifier (kNN), in the usage of wind speed, wind direction, barometric pressure and air temperature parameters as the multi-tupled meteorological inputs and in the comparison of wind power prediction results with respect to the persistence reference model. As a result of the achieved patterns, we characterize the variation of wind power prediction errors according to the input tuples, distance measures and neighbor numbers, and uncover the most influential and the most ineffective meteorological parameters on the optimization of wind power prediction results.

  18. Predicting Power Output of Upper Body using the OMNI-RES Scale.

    Science.gov (United States)

    Bautista, Iker J; Chirosa, Ignacio J; Tamayo, Ignacio Martín; González, Andrés; Robinson, Joseph E; Chirosa, Luis J; Robertson, Robert J

    2014-12-09

    The main aim of this study was to determine the optimal training zone for maximum power output. This was to be achieved through estimating mean bar velocity of the concentric phase of a bench press using a prediction equation. The values for the prediction equation would be obtained using OMNI-RES scale values of different loads of the bench press exercise. Sixty males (age 23.61 2.81 year; body height 176.29 6.73 cm; body mass 73.28 4.75 kg) voluntarily participated in the study and were tested using an incremental protocol on a Smith machine to determine one repetition maximum (1RM) in the bench press exercise. A linear regression analysis produced a strong correlation (r = -0.94) between rating of perceived exertion (RPE) and mean bar velocity (Velmean). The Pearson correlation analysis between real power output (PotReal) and estimated power (PotEst) showed a strong correlation coefficient of r = 0.77, significant at a level of p = 0.01. Therefore, the OMNI-RES scale can be used to predict Velmean in the bench press exercise to control the intensity of the exercise. The positive relationship between PotReal and PotEst allowed for the identification of a maximum power-training zone.

  19. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

    International Nuclear Information System (INIS)

    Pousinho, H.M.I.; Mendes, V.M.F.; Catalao, J.P.S.

    2011-01-01

    The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

  20. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...

  1. Optimization of maintenance for power system equipment using a predictive health model

    NARCIS (Netherlands)

    Bajracharya, G.; Koltunowicz, T.; Negenborn, R.R.; Papp, Z.; Djairam, D.; Schutter, B.D. de; Smit, J.J.

    2009-01-01

    In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based

  2. A novel method for predicting the power outputs of wave energy converters

    Science.gov (United States)

    Wang, Yingguang

    2018-03-01

    This paper focuses on realistically predicting the power outputs of wave energy converters operating in shallow water nonlinear waves. A heaving two-body point absorber is utilized as a specific calculation example, and the generated power of the point absorber has been predicted by using a novel method (a nonlinear simulation method) that incorporates a second order random wave model into a nonlinear dynamic filter. It is demonstrated that the second order random wave model in this article can be utilized to generate irregular waves with realistic crest-trough asymmetries, and consequently, more accurate generated power can be predicted by subsequently solving the nonlinear dynamic filter equation with the nonlinearly simulated second order waves as inputs. The research findings demonstrate that the novel nonlinear simulation method in this article can be utilized as a robust tool for ocean engineers in their design, analysis and optimization of wave energy converters.

  3. ESB-Based Sensor Web Integration for the Prediction of Electric Power Supply System Vulnerability

    Directory of Open Access Journals (Sweden)

    Milos Bogdanovic

    2013-08-01

    Full Text Available Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

  4. ESB-based Sensor Web integration for the prediction of electric power supply system vulnerability.

    Science.gov (United States)

    Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

    2013-08-15

    Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

  5. ESB-Based Sensor Web Integration for the Prediction of Electric Power Supply System Vulnerability

    Science.gov (United States)

    Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

    2013-01-01

    Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application. PMID:23955435

  6. Predictive Smart Grid Control with Exact Aggregated Power Constraints

    DEFF Research Database (Denmark)

    Trangbæk, K; Petersen, Mette Højgaard; Bendtsen, Jan Dimon

    2012-01-01

    of autonomous consumers. The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the flexibility of a large number of power producing and/or power consuming units. The load variations on the grid arise on one hand from varying......This chapter deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high-level MPC controller, a second level of so-called aggregators,which reduces the computational and communication related load on the high-level control, and a lower level...... consumption, and on the other hand from natural variations in power production from e.g. wind turbines. The consumers represent energy-consuming units such as heat pumps, car batteries etc. These units obviously have limits on how much power and energy they can consume at any given time, which impose...

  7. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

    Energy Technology Data Exchange (ETDEWEB)

    Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal); Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)

    2011-01-15

    The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches. (author)

  8. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  9. Wind Turbine Generator Efficiency Based on Powertrain Combination and Annual Power Generation Prediction

    Directory of Open Access Journals (Sweden)

    Dongmyung Kim

    2018-05-01

    Full Text Available Wind turbine generators are eco-friendly generators that produce electric energy using wind energy. In this study, wind turbine generator efficiency is examined using a powertrain combination and annual power generation prediction, by employing an analysis model. Performance testing was conducted in order to analyze the efficiency of a hydraulic pump and a motor, which are key components, and so as to verify the analysis model. The annual wind speed occurrence frequency for the expected installation areas was used to predict the annual power generation of the wind turbine generators. It was found that the parallel combination of the induction motors exhibited a higher efficiency when the wind speed was low and the serial combination showed higher efficiency when wind speed was high. The results of predicting the annual power generation considering the regional characteristics showed that the power generation was the highest when the hydraulic motors were designed in parallel and the induction motors were designed in series.

  10. Prediction on corrosion rate of pipe in nuclear power system based on optimized grey theory

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Chen Dengke; Jiang Wei

    2007-01-01

    For the prediction of corrosion rate of pipe in nuclear power system, the pre- diction error from the grey theory is greater, so a new method, optimized grey theory was presented in the paper. A comparison among predicted results from present and other methods was carried out, and it is seem that optimized grey theory is correct and effective for the prediction of corrosion rate of pipe in nuclear power system, and it provides a fundamental basis for the maintenance of pipe in nuclear power system. (authors)

  11. Power system dynamic state estimation using prediction based evolutionary technique

    International Nuclear Information System (INIS)

    Basetti, Vedik; Chandel, Ashwani K.; Chandel, Rajeevan

    2016-01-01

    In this paper, a new robust LWS (least winsorized square) estimator is proposed for dynamic state estimation of a power system. One of the main advantages of this estimator is that it has an inbuilt bad data rejection property and is less sensitive to bad data measurements. In the proposed approach, Brown's double exponential smoothing technique has been utilised for its reliable performance at the prediction step. The state estimation problem is solved as an optimisation problem using a new jDE-self adaptive differential evolution with prediction based population re-initialisation technique at the filtering step. This new stochastic search technique has been embedded with different state scenarios using the predicted state. The effectiveness of the proposed LWS technique is validated under different conditions, namely normal operation, bad data, sudden load change, and loss of transmission line conditions on three different IEEE test bus systems. The performance of the proposed approach is compared with the conventional extended Kalman filter. On the basis of various performance indices, the results thus obtained show that the proposed technique increases the accuracy and robustness of power system dynamic state estimation performance. - Highlights: • To estimate the states of the power system under dynamic environment. • The performance of the EKF method is degraded during anomaly conditions. • The proposed method remains robust towards anomalies. • The proposed method provides precise state estimates even in the presence of anomalies. • The results show that prediction accuracy is enhanced by using the proposed model.

  12. 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...

  13. A configural dominant account of contextual cueing: Configural cues are stronger than colour cues.

    Science.gov (United States)

    Kunar, Melina A; John, Rebecca; Sweetman, Hollie

    2014-01-01

    Previous work has shown that reaction times to find a target in displays that have been repeated are faster than those for displays that have never been seen before. This learning effect, termed "contextual cueing" (CC), has been shown using contexts such as the configuration of the distractors in the display and the background colour. However, it is not clear how these two contexts interact to facilitate search. We investigated this here by comparing the strengths of these two cues when they appeared together. In Experiment 1, participants searched for a target that was cued by both colour and distractor configural cues, compared with when the target was only predicted by configural information. The results showed that the addition of a colour cue did not increase contextual cueing. In Experiment 2, participants searched for a target that was cued by both colour and distractor configuration compared with when the target was only cued by colour. The results showed that adding a predictive configural cue led to a stronger CC benefit. Experiments 3 and 4 tested the disruptive effects of removing either a learned colour cue or a learned configural cue and whether there was cue competition when colour and configural cues were presented together. Removing the configural cue was more disruptive to CC than removing colour, and configural learning was shown to overshadow the learning of colour cues. The data support a configural dominant account of CC, where configural cues act as the stronger cue in comparison to colour when they are presented together.

  14. Synapse:neural network for predict power consumption: users guide

    Energy Technology Data Exchange (ETDEWEB)

    Muller, C; Mangeas, M; Perrot, N

    1994-08-01

    SYNAPSE is forecasting tool designed to predict power consumption in metropolitan France on the half hour time scale. Some characteristics distinguish this forecasting model from those which already exist. In particular, it is composed of numerous neural networks. The idea for using many neural networks arises from past tests. These tests showed us that a single neural network is not able to solve the problem correctly. From this result, we decided to perform unsupervised classification of the 24 consumption curves. From this classification, six classes appeared, linked with the weekdays: Mondays, Tuesdays, Wednesdays, Thursdays, Fridays, Saturdays, Sundays, holidays and bridge days. For each class and for each half hour, two multilayer perceptrons are built. The two of them forecast the power for one particular half hour, and for a day including one of the determined class. The input of these two network are different: the first one (short time forecasting) includes the powers for the most recent half hour and relative power of the previous day; the second (medium time forecasting) includes only the relative power of the previous day. A process connects the results of every networks and allows one to forecast more than one half-hour in advance. In this process, short time forecasting networks and medium time forecasting networks are used differently. The first kind of neural networks gives good results on the scale of one day. The second one gives good forecasts for the next predicted powers. In this note, the organization of the SYNAPSE program is detailed, and the user`s menu is described. This first version of synapse works and should allow the APC group to evaluate its utility. (authors). 6 refs., 2 appends.

  15. Design and implementation of predictive filtering system for current reference generation of active power filter

    Energy Technology Data Exchange (ETDEWEB)

    Kilic, Tomislav; Milun, Stanko; Petrovic, Goran [FESB University of Split, Faculty of Electrical Engineering, Machine Engineering and Naval Architecture, R. Boskovica bb, 21000, Split (Croatia)

    2007-02-15

    The shunt active power filters are used to attenuate the harmonic currents in power systems by injecting equal but opposite compensating currents. Successful control of the active filters requires an accurate current reference. In this paper the current reference determination based on predictive filtering structure is presented. Current reference was obtained by taking the difference of load current and its fundamental harmonic. For fundamental harmonic determination with no time delay a combination of digital predictive filter and low pass filter is used. The proposed method was implemented on a laboratory prototype of a three-phase active power filter. The algorithm for current reference determination was adapted and implemented on DSP controller. Simulation and experimental results show that the active power filter with implemented predictive filtering structure gives satisfactory performance in power system harmonic attenuation. (author)

  16. Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

  17. Automatic Power Control for Daily Load-following Operation using Model Predictive Control Method

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Keuk Jong; Kim, Han Gon [KH, Daejeon (Korea, Republic of)

    2009-10-15

    Under the circumstances that nuclear power occupies more than 50%, nuclear power plants are required to be operated on load-following operation in order to make the effective management of electric grid system and enhanced responsiveness to rapid changes in power demand. Conventional reactors such as the OPR1000 and APR1400 have a regulating system that controls the average temperature of the reactor core relation to the reference temperature. This conventional method has the advantages of proven technology and ease of implementation. However, this method is unsuitable for controlling the axial power shape, particularly the load following operation. Accordingly, this paper reports on the development of a model predictive control method which is able to control the reactor power and the axial shape index. The purpose of this study is to analyze the behavior of nuclear reactor power and the axial power shape by using a model predictive control method when the power is increased and decreased for a daily load following operation. The study confirms that deviations in the axial shape index (ASI) are within the operating limit.

  18. A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems

    Directory of Open Access Journals (Sweden)

    Yanzi Wang

    2016-01-01

    Full Text Available Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power management strategy based on Markov random prediction.

  19. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

    2013-01-01

    A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time...

  20. Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

    Directory of Open Access Journals (Sweden)

    Laura Cornejo-Bueno

    2017-11-01

    Full Text Available Wind Power Ramp Events (WPREs are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains. Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

  1. Electrical predictive maintenance at Trillo I Nuclear Power Plant

    International Nuclear Information System (INIS)

    Vicente, L. R.; Fernandez de la Mata, R.; Cano Gonzalez, J. C.

    1998-01-01

    An electrical predictive maintenance plan is currently being put into effect at Trillo I Nuclear Power Plant which is initially being applied to three types of equipment: motors, transformers and motor-driven valves. This paper describes the different phases considered in the implementation of the Predictive Maintenance Plan: study of existing techniques for such equipment (tangoδ, spectral analysis of stator current, chromatographic analysis of gases, spectral analysis of the axial stray magnetic flux, etc), study of the special characteristics of the electrical equipment at Trillo NPP, analysis of applicable techniques (characteristic parameters, alert-alarm values, experience with such techniques, etc), analysis of machine history records, study of the optimum preventive-predictive case, study of applicable frequencies and definition of the computerised predictive maintenance management tool. With the exception of the computerised predictive maintenance management applications which are presently being implemented, all the activities described above have been carried out on the three types of equipment mentioned. (Author)

  2. Integrated Solid Oxide Fuel Cell Power System Characteristics Prediction

    Directory of Open Access Journals (Sweden)

    Marian GAICEANU

    2009-07-01

    Full Text Available The main objective of this paper is to deduce the specific characteristics of the CHP 100kWe Solid Oxide Fuel Cell (SOFC Power System from the steady state experimental data. From the experimental data, the authors have been developed and validated the steady state mathematical model. From the control room the steady state experimental data of the SOFC power conditioning are available and using the developed steady state mathematical model, the authors have been obtained the characteristic curves of the system performed by Siemens-Westinghouse Power Corporation. As a methodology the backward and forward power flow analysis has been employed. The backward power flow makes possible to obtain the SOFC power system operating point at different load levels, resulting as the load characteristic. By knowing the fuel cell output characteristic, the forward power flow analysis is used to predict the power system efficiency in different operating points, to choose the adequate control decision in order to obtain the high efficiency operation of the SOFC power system at different load levels. The CHP 100kWe power system is located at Gas Turbine Technologies Company (a Siemens Subsidiary, TurboCare brand in Turin, Italy. The work was carried out through the Energia da Ossidi Solidi (EOS Project. The SOFC stack delivers constant power permanently in order to supply the electric and thermal power both to the TurboCare Company and to the national grid.

  3. Evaluation of a Trapezoidal Predictive Controller for a Four-Wire Active Power Filter for Utility Equipment of Metro Railway, Power-Land Substations

    Directory of Open Access Journals (Sweden)

    Sergio Salas-Duarte

    2016-01-01

    Full Text Available The realization of an improved predictive current controller based on a trapezoidal model is described, and the impact of this technique is assessed on the performance of a 2 kW, 21.6 kHz, four-wire, Active Power Filter for utility equipment of Metro Railway, Power-Land Substations. The operation of the trapezoidal predictive current controller is contrasted with that of a typical predictive control technique, based on a single Euler approximation, which has demonstrated generation of high-quality line currents, each using a 400 V DC link to improve the power quality of an unbalanced nonlinear load of Metro Railway. The results show that the supply current waveforms become virtually sinusoidal waves, reducing the current ripple by 50% and improving its power factor from 0.8 to 0.989 when the active filter is operated with a 1.6 kW load. The principle of operation of the trapezoidal predictive controller is analysed together with a description of its practical development, showing experimental results obtained with a 2 kW prototype.

  4. Method for Prediction of the Power Output from Photovoltaic Power Plant under Actual Operating Conditions

    Science.gov (United States)

    Obukhov, S. G.; Plotnikov, I. A.; Surzhikova, O. A.; Savkin, K. D.

    2017-04-01

    Solar photovoltaic technology is one of the most rapidly growing renewable sources of electricity that has practical application in various fields of human activity due to its high availability, huge potential and environmental compatibility. The original simulation model of the photovoltaic power plant has been developed to simulate and investigate the plant operating modes under actual operating conditions. The proposed model considers the impact of the external climatic factors on the solar panel energy characteristics that improves accuracy in the power output prediction. The data obtained through the photovoltaic power plant operation simulation enable a well-reasoned choice of the required capacity for storage devices and determination of the rational algorithms to control the energy complex.

  5. Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

    Science.gov (United States)

    Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi

    2016-09-01

    We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.

  6. A model to predict the power output from wind farms

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Riso National Lab., Roskilde (Denmark)

    1997-12-31

    This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

  7. “Swimming Ducks Forecast the Coming of Spring”—The predictability of aggregate insider trading on future market returns in the Chinese market

    Directory of Open Access Journals (Sweden)

    Chafen Zhu

    2014-09-01

    Full Text Available This study systematically examines the ability of aggregate insider trading to predict future market returns in the Chinese A-share market. After controlling for the contrarian investment strategy, aggregate executive (large shareholder trading conducted over the past six months can predict 66% (72.7% of market returns twelve months in advance. Aggregate insider trading predicts future market returns very accurately and is stronger for insiders who have a greater information advantage (e.g., executives and controlling shareholders. Corporate governance also affects the predictability of insider trading. The predictability of executive trading is weakest in central state-owned companies, probably because the “quasi-official” status of the executives in those companies effectively curbs their incentives to benefit from insider trading. The predictive power of large shareholder trading in private-owned companies is higher than that in state-owned companies, probably due to their stronger profit motivation and higher involvement in business operations. This study complements the literature by examining an emerging market and investigating how the institutional context and corporate governance affect insider trading.

  8. “Swimming Ducks Forecast the Coming of Spring”—The predictability of aggregate insider trading on future market returns in the Chinese market

    Institute of Scientific and Technical Information of China (English)

    Chafen; Zhu; Li; Wang; Tengfei; Yang

    2014-01-01

    This study systematically examines the ability of aggregate insider trading to predict future market returns in the Chinese A-share market. After controlling for the contrarian investment strategy, aggregate executive(large shareholder)trading conducted over the past six months can predict 66%(72.7%) of market returns twelve months in advance. Aggregate insider trading predicts future market returns very accurately and is stronger for insiders who have a greater information advantage(e.g., executives and controlling shareholders).Corporate governance also affects the predictability of insider trading. The predictability of executive trading is weakest in central state-owned companies,probably because the "quasi-official" status of the executives in those companies effectively curbs their incentives to benefit from insider trading.The predictive power of large shareholder trading in private-owned companies is higher than that in state-owned companies, probably due to their stronger profit motivation and higher involvement in business operations. This study complements the literature by examining an emerging market and investigating how the institutional context and corporate governance affect insider trading.

  9. Prediction of reflood behavior for tests with differing axial power shapes using WCOBRA/TRAC

    International Nuclear Information System (INIS)

    Bajorek, S.M.; Hochreiter, L.E.

    1991-01-01

    The rector core power shape can vary over the fuel cycle due to load follow, control rod movement, burnup effects and Xenon transients. a best estimate thermal-hydraulic code must be able to accurately predict the reflooding behavior for different axial power shapes in order to find the power shapes effects on the loss-of-coolant peak cladding temperature. Several different reflood heat transfer experiments have been performed at the same or similar PWR reflood conditions with different axial power shapes. These experiments have different rod diameters, were full length, 3.65 m (12 feet) in height, and had simple egg crate grids. The WCOBRA/TRAC code has been used to model several different tests from these three experiments to examine the code's capability to predict the reflood transient for different power shapes, with a consistent model and noding scheme. This paper describes these different experiments, their power shapes, and the test conditions. The WCOBRA/TRAC code is described as well as the noding scheme, and the calculated results will be compared in detail with the test data rod temperatures. An overall assessment of the code's predictions of these experiments is presented

  10. Robust Distributed Model Predictive Load Frequency Control of Interconnected Power System

    Directory of Open Access Journals (Sweden)

    Xiangjie Liu

    2013-01-01

    Full Text Available Considering the load frequency control (LFC of large-scale power system, a robust distributed model predictive control (RDMPC is presented. The system uncertainty according to power system parameter variation alone with the generation rate constraints (GRC is included in the synthesis procedure. The entire power system is composed of several control areas, and the problem is formulated as convex optimization problem with linear matrix inequalities (LMI that can be solved efficiently. It minimizes an upper bound on a robust performance objective for each subsystem. Simulation results show good dynamic response and robustness in the presence of power system dynamic uncertainties.

  11. Adaptive on-line prediction of the available power of lithium-ion batteries

    Science.gov (United States)

    Waag, Wladislaw; Fleischer, Christian; Sauer, Dirk Uwe

    2013-11-01

    In this paper a new approach for prediction of the available power of a lithium-ion battery pack is presented. It is based on a nonlinear battery model that includes current dependency of the battery resistance. It results in an accurate power prediction not only at room temperature, but also at lower temperatures at which the current dependency is substantial. The used model parameters are fully adaptable on-line to the given state of the battery (state of charge, state of health, temperature). This on-line adaption in combination with an explicit consideration of differences between characteristics of individual cells in a battery pack ensures an accurate power prediction under all possible conditions. The proposed trade-off between the number of used cell parameters and the total accuracy as well as the optimized algorithm results in a real-time capability of the method, which is demonstrated on a low-cost 16 bit microcontroller. The verification tests performed on a software-in-the-loop test bench system with four 40 Ah lithium-ion cells show promising results.

  12. PREDICTION OF POWER GENERATION OF SMALL SCALE VERTICAL AXIS WIND TURBINE USING FUZZY LOGIC

    Directory of Open Access Journals (Sweden)

    Altab Hossain

    2009-01-01

    Full Text Available Renewable energy from the wind turbine has been focused for the alternative source of power generation due to the following advances of the of the wind turbine. Firstly, the wind turbine is highly efficient and eco-friendly. Secondly, the turbine has the ability to response for the changeable power generation based on the wind velocity and structural framework. However, the competitive efficiency of the wind turbine is necessary to successfully alternate the conventional power sources. The most relevant factor which affects the overall efficiency of the wind turbine is the wind velocity and the relative turbine dimensions. Artificial intelligence systems are widely used technology that can learn from examples and are able to deal with non-linear problems. Compared with traditional approach, fuzzy logic approach is more efficient for the representation, manipulation and utilization. Therefore, the primary purpose of this work was to investigate the relationship between wind turbine power generation and wind velocity, and to illustrate how fuzzy expert system might play an important role in prediction of wind turbine power generation. The main purpose of the measurement over the small scaled prototype vertical axis wind turbine for the wind velocity is to predict the performance of full scaled H-type vertical axis wind turbine. Prediction of power generation at the different wind velocities has been tested at the Thermal Laboratory of Faculty of Engineering, Universiti Industri Selangor (UNISEL and results concerning the daily prediction have been obtained.

  13. PREDICTION OF POWER GENERATION OF SMALL SCALE VERTICAL AXIS WIND TURBINE USING FUZZY LOGIC

    Directory of Open Access Journals (Sweden)

    Altab Md. Hossain

    2009-12-01

    Full Text Available Renewable energy from the wind turbine has been focused for the alternative source of power generation due to the following advances of the of the wind turbine. Firstly, the wind turbine is highly efficient and eco-friendly. Secondly, the turbine has the ability to response for the changeable power generation based on the wind velocity and structural framework. However, the competitive efficiency of the wind turbine is necessary to successfully alternate the conventional power sources. The most relevant factor which affects the overall efficiency of the wind turbine is the wind velocity and the relative turbine dimensions. Artificial intelligence systems are widely used technology that can learn from examples and are able to deal with non-linear problems. Compared with traditional approach, fuzzy logic approach is more efficient for the representation, manipulation and utilization. Therefore, the primary purpose of this work was to investigate the relationship between wind turbine power generation and wind velocity, and to illustrate how fuzzy expert system might play an important role in prediction of wind turbine power generation. The main purpose of the measurement over the small scaled prototype vertical axis wind turbine for the wind velocity is to predict the performance of full scaled H-type vertical axis wind turbine. Prediction of power generation at the different wind velocities has been tested at the Thermal Laboratory of Faculty of Engineering, Universiti Industri Selangor (UNISEL and results concerning the daily prediction have been obtained.

  14. Plant interactions alter the predictions of metabolic scaling theory

    DEFF Research Database (Denmark)

    Lin, Yue; Berger, Uta; Grimm, Volker

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of 24/3 between mean individual biomass and density during densitydependent mortality (self-thinning). Empirical tests have...... processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive....... of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories...

  15. Power-Controlled MAC Protocols with Dynamic Neighbor Prediction for Ad hoc Networks

    Institute of Scientific and Technical Information of China (English)

    LI Meng; ZHANG Lin; XIAO Yong-kang; SHAN Xiu-ming

    2004-01-01

    Energy and bandwidth are the scarce resources in ad hoc networks because most of the mobile nodes are battery-supplied and share the exclusive wireless medium. Integrating the power control into MAC protocol is a promising technique to fully exploit these precious resources of ad hoc wireless networks. In this paper, a new intelligent power-controlled Medium Access Control (MAC) (iMAC) protocol with dynamic neighbor prediction is proposed. Through the elaborate design of the distributed transmit-receive strategy of mobile nodes, iMAC greatly outperforms the prevailing IEEE 802.11 MAC protocols in not only energy conservation but also network throughput. Using the Dynamic Neighbor Prediction (DNP), iMAC performs well in mobile scenes. To the best of our knowledge, iMAC is the first protocol that considers the performance deterioration of power-controlled MAC protocols in mobile scenes and then proposes a solution. Simulation results indicate that DNP is important and necessary for power-controlled MAC protocols in mobile ad hoc networks.

  16. Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

    Directory of Open Access Journals (Sweden)

    Yanjun Zhang

    2015-01-01

    Full Text Available A new optimized extreme learning machine- (ELM- based method for power system transient stability prediction (TSP using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

  17. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  18. On the impact of NWP model resolution and power source disaggregation on photovoltaic power prediction

    Czech Academy of Sciences Publication Activity Database

    Eben, Kryštof; Juruš, Pavel; Resler, Jaroslav; Pelikán, Emil; Krč, Pavel

    2011-01-01

    Roč. 8, - (2011), EMS2011-667-4 [EMS Annual Meeting /11./ and European Conference on Applications of Meteorology /10./. 12.09.2011-16.09.2011, Berlin] Institutional research plan: CEZ:AV0Z10300504 Keywords : photovoltaic power prediction * NWP * numerical model parameterization Subject RIV: DG - Athmosphere Sciences, Meteorology

  19. Women's political participation leads to stronger local economies ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2016-06-08

    Jun 8, 2016 ... Under changes to India's constitution, Indian women are gaining a stronger ... Legal reforms are encouraging women to contribute to economic growth ... on a panel on empowering women entrepreneurs at IDRC in Ottawa.

  20. Chemical reaction due to stronger Ramachandran interaction

    Indian Academy of Sciences (India)

    The origin of a chemical reaction between two reactant atoms is associated with the activation energy, on the assumption that, high-energy collisions between these atoms, are the ones that overcome the activation energy. Here, we show that a stronger attractive van der Waals (vdW) and electron-ion Coulomb interactions ...

  1. The prediction and prevention of voltage collapse in the Finnish power system

    Energy Technology Data Exchange (ETDEWEB)

    Bastman, J; Lakervi, E [Tampere Univ. of Tech. (Finland); Hirvonen, R; Kuronen, P; Hagman, E [IVO Group (Finland)

    1994-12-31

    The Finnish power system is a part of the Nordic power system (NORDEL), which includes Finland, Sweden, Norway and the eastern part of Denmark. In NORDEL the transmission distances are long, which implies that the power transmission capacities are determined by stability criteria . The methods to prevent and predict the voltage collapse during severe disturbances are studied using advances simulation program. Results are presented. (author) 10 figs., 1 tab.

  2. Prediction of Chiller Power Consumption: An Entropy Generation Approach

    KAUST Repository

    Saththasivam, Jayaprakash

    2016-06-21

    Irreversibilities in each component of vapor compression chillers contribute to additional power consumption in chillers. In this study, chiller power consumption was predicted by computing the Carnot reversible work and entropy generated in every component of the chiller. Thermodynamic properties namely enthalpy and entropy of the entire refrigerant cycle were obtained by measuring the pressure and temperature at the inlet and outlet of each primary component of a 15kW R22 water cooled scroll chiller. Entropy generation of each component was then calculated using the First and Second Laws of Thermodynamics. Good correlation was found between the measured and computed chiller power consumption. This irreversibility analysis can be also effectively used as a performance monitoring tool in vapor compression chillers as higher entropy generation is anticipated during faulty operations.

  3. Basal and dynamic relationships between implicit power motivation and estradiol in women.

    Science.gov (United States)

    Stanton, Steven J; Schultheiss, Oliver C

    2007-12-01

    This study investigated basal and reciprocal relationships between implicit power motivation (n Power), a preference for having impact and dominance over others, and both salivary estradiol and testosterone in women. 49 participants completed the Picture Story Exercise, a measure of n Power. During a laboratory contest, participants competed in pairs on a cognitive task and contest outcome (win vs. loss) was experimentally varied. Estradiol and testosterone levels were determined in saliva samples collected at baseline and several times post-contest, including 1 day post-contest. n Power was positively associated with basal estradiol concentrations. The positive correlation between n Power and basal estradiol was stronger in single women, women not taking oral contraceptives, or in women with low-CV estradiol samples than in the overall sample of women. Women's estradiol responses to a dominance contest were influenced by the interaction of n Power and contest outcome: estradiol increased in power-motivated winners but decreased in power-motivated losers. For power-motivated winners, elevated levels of estradiol were still present the day after the contest. Lastly, n Power and estradiol did not correlate with self-reported dominance and correlated negatively with self-reported aggression. Self-reported dominance and aggression did not predict estradiol changes as a function of contest outcome. Overall, n Power did not predict basal testosterone levels or testosterone changes as a function of dominance contest outcome.

  4. Power Prediction and Technoeconomic Analysis of a Solar PV Power Plant by MLP-ABC and COMFAR III, considering Cloudy Weather Conditions

    Directory of Open Access Journals (Sweden)

    M. Khademi

    2016-01-01

    Full Text Available The prediction of power generated by photovoltaic (PV panels in different climates is of great importance. The aim of this paper is to predict the output power of a 3.2 kW PV power plant using the MLP-ABC (multilayer perceptron-artificial bee colony algorithm. Experimental data (ambient temperature, solar radiation, and relative humidity was gathered at five-minute intervals from Tehran University’s PV Power Plant from September 22nd, 2012, to January 14th, 2013. Following data validation, 10665 data sets, equivalent to 35 days, were used in the analysis. The output power was predicted using the MLP-ABC algorithm with the mean absolute percentage error (MAPE, the mean bias error (MBE, and correlation coefficient (R2, of 3.7, 3.1, and 94.7%, respectively. The optimized configuration of the network consisted of two hidden layers. The first layer had four neurons and the second had two neurons. A detailed economic analysis is also presented for sunny and cloudy weather conditions using COMFAR III software. A detailed cost analysis indicated that the total investment’s payback period would be 3.83 years in sunny periods and 4.08 years in cloudy periods. The results showed that the solar PV power plant is feasible from an economic point of view in both cloudy and sunny weather conditions.

  5. Influence of strength on magnitude and mechanisms of adaptation to power training.

    Science.gov (United States)

    Cormie, Prue; McGuigan, Michael R; Newton, Robert U

    2010-08-01

    To determine whether the magnitude of performance improvements and the mechanisms driving adaptation to ballistic power training differ between strong and weak individuals. Twenty-four men were divided into three groups on the basis of their strength level: stronger (n = 8, one-repetition maximum-to-body mass ratio (1RM/BM) = 1.97 +/- 0.08), weaker (n = 8, 1RM/BM = 1.32 +/- 0.14), or control (n = 8, 1RM/BM = 1.37 +/- 0.13). The stronger and weaker groups trained three times per week for 10 wk. During these sessions, subjects performed maximal-effort jump squats with 0%-30% 1RM. The impact of training on athletic performance was assessed using a 2-d testing battery that involved evaluation of jump and sprint performance as well as measures of the force-velocity relationship, jumping mechanics, muscle architecture, and neural drive. Both experimental groups showed significant (P < or = 0.05) improvements in jump (stronger: peak power = 10.0 +/- 5.2 W.kg, jump height = 0.07 +/- 0.04 m; weaker: peak power = 9.1 +/- 2.3 W.kg, jump height = 0.06 +/- 0.04 m) and sprint performance after training (stronger: 40-m time = -2.2% +/- 2.0%; weaker: 40-m time = -3.6% +/- 2.3%). Effect size analyses revealed a tendency toward practically relevant differences existing between stronger and weaker individuals in the magnitude of improvements in jump performance (effect size: stronger: peak power = 1.55, jump height = 1.46; weaker: peak power = 1.03, jump height = 0.95) and especially after 5 wk of training (effect size: stronger: peak power = 1.60, jump height = 1.59; weaker: peak power = 0.95, jump height = 0.61). The mechanisms driving these improvements included significant (P < or = 0.05) changes in the force-velocity relationship, jump mechanics, and neural activation, with no changes to muscle architecture observed. The magnitude of improvements after ballistic power training was not significantly influenced by strength level. However, the training had a tendency toward

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  7. Steady-State Plant Model to Predict Hydroden Levels in Power Plant Components

    Energy Technology Data Exchange (ETDEWEB)

    Glatzmaier, Greg C.; Cable, Robert; Newmarker, Marc

    2017-06-27

    The National Renewable Energy Laboratory (NREL) and Acciona Energy North America developed a full-plant steady-state computational model that estimates levels of hydrogen in parabolic trough power plant components. The model estimated dissolved hydrogen concentrations in the circulating heat transfer fluid (HTF), and corresponding partial pressures within each component. Additionally for collector field receivers, the model estimated hydrogen pressure in the receiver annuli. The model was developed to estimate long-term equilibrium hydrogen levels in power plant components, and to predict the benefit of hydrogen mitigation strategies for commercial power plants. Specifically, the model predicted reductions in hydrogen levels within the circulating HTF that result from purging hydrogen from the power plant expansion tanks at a specified target rate. Our model predicted hydrogen partial pressures from 8.3 mbar to 9.6 mbar in the power plant components when no mitigation treatment was employed at the expansion tanks. Hydrogen pressures in the receiver annuli were 8.3 to 8.4 mbar. When hydrogen partial pressure was reduced to 0.001 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.001 mbar to 0.02 mbar. When hydrogen partial pressure was reduced to 0.3 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.25 mbar to 0.28 mbar. Our results show that controlling hydrogen partial pressure in the expansion tanks allows us to reduce and maintain hydrogen pressures in the receiver annuli to any practical level.

  8. Recent advances in prediction of emission of hazardous air pollutants from coal-fired power plants

    International Nuclear Information System (INIS)

    Senior, C.L.; Helble, J.J.; Sarofim, A.F.

    2000-01-01

    Coal-fired power plants are a primary source of mercury discharge into the atmosphere along with fine particulates containing arsenic, selenium, cadmium, and other hazardous air pollutants. Information regarding the speciation of these toxic metals is necessary to accurately predict their atmospheric transport and fate in the environment. New predictive tools have been developed to allow utilities to better estimate the emissions of toxic metals from coal-fired power plants. These prediction equations are based on fundamental physics and chemistry and can be applied to a wide variety of fuel types and combustion conditions. The models have significantly improved the ability to predict the emissions of air toxic metals in fine particulate and gas-phase mercury. In this study, the models were successfully tested using measured mercury speciation and mass balance information collected from coal-fired power plants

  9. Formation of visual memories controlled by gamma power phase-locked to alpha oscillations

    Science.gov (United States)

    Park, Hyojin; Lee, Dong Soo; Kang, Eunjoo; Kang, Hyejin; Hahm, Jarang; Kim, June Sic; Chung, Chun Kee; Jiang, Haiteng; Gross, Joachim; Jensen, Ole

    2016-06-01

    Neuronal oscillations provide a window for understanding the brain dynamics that organize the flow of information from sensory to memory areas. While it has been suggested that gamma power reflects feedforward processing and alpha oscillations feedback control, it remains unknown how these oscillations dynamically interact. Magnetoencephalography (MEG) data was acquired from healthy subjects who were cued to either remember or not remember presented pictures. Our analysis revealed that in anticipation of a picture to be remembered, alpha power decreased while the cross-frequency coupling between gamma power and alpha phase increased. A measure of directionality between alpha phase and gamma power predicted individual ability to encode memory: stronger control of alpha phase over gamma power was associated with better memory. These findings demonstrate that encoding of visual information is reflected by a state determined by the interaction between alpha and gamma activity.

  10. Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

    Science.gov (United States)

    Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

    2009-07-01

    Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

  11. Basic study on dynamic reactive-power control method with PV output prediction for solar inverter

    Directory of Open Access Journals (Sweden)

    Ryunosuke Miyoshi

    2016-01-01

    Full Text Available To effectively utilize a photovoltaic (PV system, reactive-power control methods for solar inverters have been considered. Among the various methods, the constant-voltage control outputs less reactive power compared with the other methods. We have developed a constant-voltage control to reduce the reactive-power output. However, the developed constant-voltage control still outputs unnecessary reactive power because the control parameter is constant in every waveform of the PV output. To reduce the reactive-power output, we propose a dynamic reactive-power control method with a PV output prediction. In the proposed method, the control parameter is varied according to the properties of the predicted PV waveform. In this study, we performed numerical simulations using a distribution system model, and we confirmed that the proposed method reduces the reactive-power output within the voltage constraint.

  12. The right of the stronger: The play Sisyphus and critias

    Directory of Open Access Journals (Sweden)

    Jordović Ivan

    2004-01-01

    Full Text Available The Focus of this study is the standpoint of the play Sisyphus and critias the leader of the thirty towards the right of the stronger. this is a question of constant interest in scientific circles, since its answer can serve as the indicator of the influence this famous theory has had. this interest has been encouraged by the fact that critias’ authorship of the play is questionable. however, the question of the author is not of primary importance for this article, because there are some arguments, among some well known ones, which were not considered and which Show that in this satire, regardless of the author and the purpose of this fragment, the right of the stronger is actually non-existant. the first argument to support this theory is that nomosphysis antithesis is nowhere explicitly mentioned although it is the crucial element of the right of the stronger. in addition there is no claim in the play that the exploitation of the strong by the week or by law accrued. the second argument is that despite the incapability of laws to prevent the secret injustice, they and their importance for the human society are depicted in a positive light. it should also be noted that, unlike callicles and glaucon, laws are created to stop the bad and not the good. the third argument is that the invention of religion is accepted as a positive achievement, which finally enables the overcoming of primeval times and lawlessness. the reflection of this argument is a positive characterization of the individual who invented the fear of gods. the fourth argument, which has not been taken into consideration so far is the way the supporters and opponents of lawlessness are described and marked as κακοί and έσξλοί in the satire only physically strong are considered as strong as opposed to callicles, where they are also spiritually superior. intelectually superior in Sisyphus is the inventor of the fear of gods who is also in favor of law and order. the fact

  13. Women's political participation leads to stronger local economies ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Edgard Rodriguez - IDRC. Women attend a self-help group meeting near Hyderabad, India. Keenara Khanderia. Under changes to India's constitution, Indian women are gaining a stronger political voice. Legal reforms are encouraging women to contribute to economic growth and investments in community growth.

  14. Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Pedersen, Benjamin Pjedsted; Larsen, Jan

    2009-01-01

    Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction...

  15. Multivariate power-law models for streamflow prediction in the Mekong Basin

    Directory of Open Access Journals (Sweden)

    Guillaume Lacombe

    2014-11-01

    New hydrological insights for the region: A combination of 3–6 explanatory variables – chosen among annual rainfall, drainage area, perimeter, elevation, slope, drainage density and latitude – is sufficient to predict a range of flow metrics with a prediction R-squared ranging from 84 to 95%. The inclusion of forest or paddy percentage coverage as an additional explanatory variable led to slight improvements in the predictive power of some of the low-flow models (lowest prediction R-squared = 89%. A physical interpretation of the model structure was possible for most of the resulting relationships. Compared to regional regression models developed in other parts of the world, this new set of equations performs reasonably well.

  16. Fathers see stronger family resemblances than non-fathers in unrelated children's faces.

    Science.gov (United States)

    Bressan, Paola; Dal Pos, Stefania

    2012-12-01

    Even after they have taken all reasonable measures to decrease the probability that their spouses cheat on them, men still face paternal uncertainty. Such uncertainty can lead to paternal disinvestment, which reduces the children's probability to survive and reproduce, and thus the reproductive success of the fathers themselves. A theoretical model shows that, other things being equal, men who feel confident that they have fathered their spouses' offspring tend to enjoy greater fitness (i.e., leave a larger number of surviving progeny) than men who do not. This implies that fathers should benefit from exaggerating paternal resemblance. We argue that the self-deceiving component of this bias could be concealed by generalizing this resemblance estimation boost to (1) family pairs other than father-child and (2) strangers. Here, we tested the prediction that fathers may see, in unrelated children's faces, stronger family resemblances than non-fathers. In Study 1, 70 men and 70 women estimated facial resemblances between children paired, at three different ages (as infants, children, and adolescents), either to themselves or to their parents. In Study 2, 70 men and 70 women guessed the true parents of the same children among a set of adults. Men who were fathers reported stronger similarities between faces than non-fathers, mothers, and non-mothers did, but were no better at identifying childrens' real parents. We suggest that, in fathers, processing of facial resemblances is biased in a manner that reflects their (adaptive) wishful thinking that fathers and children are related.

  17. Application of Model Predictive Control for Active Load Management in a Distributed Power System with High Wind Penetration

    DEFF Research Database (Denmark)

    Zong, Yi; Kullmann, Daniel; Thavlov, Anders

    2012-01-01

    management. It also presents in detail how to implement a thermal model predictive controller (MPC) for the heaters' power consumption prediction in the PowerFlexHouse. It demonstrates that this MPC strategy can realize load shifting, and using good predictions in MPC-based control, a better matching...

  18. Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm

    Directory of Open Access Journals (Sweden)

    Qunli Wu

    2015-12-01

    Full Text Available Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA–least squares support vector machine (LSSVM hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models.

  19. Model Predictive Voltage Control of Wind Power Plants

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei

    2018-01-01

    the efficacy of the proposed WFVC, two case scenarios were designed: the wind farm is under normal operating conditions and the internal wind power fluctuation is considered; and besides internal power fluctuation, the impact of the external grid on the wind farm is considered.......This chapter proposes an autonomous wind farm voltage controller (WFVC) based on model predictive control (MPC). It also introduces the analytical expressions for the voltage sensitivity to tap positions of a transformer. The chapter then describes the discrete models for the wind turbine...... generators (WTGs) and static var compensators (SVCs)/static var generators (SVGs). Next, it describes the implementation of the on‐load tap changing (OLTC) in the MPC. Furthermore, the chapter examines the cost function as well as the constraints of the MPC‐based WFVC for both control modes. In order to test...

  20. Attracted to power: challenge/threat and promotion/prevention focus differentially predict the attractiveness of group power

    Science.gov (United States)

    Scholl, Annika; Sassenrath, Claudia; Sassenberg, Kai

    2015-01-01

    Depending on their motivation, individuals prefer different group contexts for social interactions. The present research sought to provide more insight into this relationship. More specifically, we tested how challenge/threat and a promotion/prevention focus predict attraction to groups with high- or low-power. As such, we examined differential outcomes of threat and prevention focus as well as challenge and promotion focus that have often been regarded as closely related. According to regulatory focus, individuals should prefer groups that they expect to “feel right” for them to join: Low-power groups should be more attractive in a prevention (than a promotion) focus, as these groups suggest security-oriented strategies, which fit a prevention focus. High-power groups should be more attractive in a promotion (rather than a prevention) focus, as these groups are associated with promotion strategies fitting a promotion focus (Sassenberg et al., 2007). In contrast, under threat (vs. challenge), groups that allow individuals to restore their (perceived) lack of control should be preferred: Low-power groups should be less attractive under threat (than challenge) because they provide low resources which threatened individuals already perceive as insufficient and high-power groups might be more attractive under threat (than under challenge), because their high resources allow individuals to restore control. Two experiments (N = 140) supported these predictions. The attractiveness of a group often depends on the motivation to engage in what fits (i.e., prefer a group that feels right in the light of one’s regulatory focus). However, under threat the striving to restore control (i.e., prefer a group allowing them to change the status quo under threat vs. challenge) overrides the fit effect, which may in turn guide individuals’ behavior in social interactions. PMID:25904887

  1. Nonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant

    Directory of Open Access Journals (Sweden)

    Xiangjie Liu

    2014-01-01

    Full Text Available Reliable power and temperature control in pressurized water reactor (PWR nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC, by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation.

  2. Hierarchical model-based predictive control of a power plant portfolio

    DEFF Research Database (Denmark)

    Edlund, Kristian; Bendtsen, Jan Dimon; Jørgensen, John Bagterp

    2011-01-01

    One of the main difficulties in large-scale implementation of renewable energy in existing power systems is that the production from renewable sources is difficult to predict and control. For this reason, fast and efficient control of controllable power producing units – so-called “portfolio...... design for power system portfolio control, which aims specifically at meeting these demands.The design involves a two-layer hierarchical structure with clearly defined interfaces that facilitate an object-oriented implementation approach. The same hierarchical structure is reflected in the underlying...... optimisation problem, which is solved using Dantzig–Wolfe decomposition. This decomposition yields improved computational efficiency and better scalability compared to centralised methods.The proposed control scheme is compared to an existing, state-of-the-art portfolio control system (operated by DONG Energy...

  3. Power flow prediction in vibrating systems via model reduction

    Science.gov (United States)

    Li, Xianhui

    This dissertation focuses on power flow prediction in vibrating systems. Reduced order models (ROMs) are built based on rational Krylov model reduction which preserve power flow information in the original systems over a specified frequency band. Stiffness and mass matrices of the ROMs are obtained by projecting the original system matrices onto the subspaces spanned by forced responses. A matrix-free algorithm is designed to construct ROMs directly from the power quantities at selected interpolation frequencies. Strategies for parallel implementation of the algorithm via message passing interface are proposed. The quality of ROMs is iteratively refined according to the error estimate based on residual norms. Band capacity is proposed to provide a priori estimate of the sizes of good quality ROMs. Frequency averaging is recast as ensemble averaging and Cauchy distribution is used to simplify the computation. Besides model reduction for deterministic systems, details of constructing ROMs for parametric and nonparametric random systems are also presented. Case studies have been conducted on testbeds from Harwell-Boeing collections. Input and coupling power flow are computed for the original systems and the ROMs. Good agreement is observed in all cases.

  4. Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

    Science.gov (United States)

    Nateghi, Roshanak; Guikema, Seth D; Quiring, Steven M

    2011-12-01

    This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. © 2011 Society for Risk Analysis.

  5. State-space model predictive control method for core power control in pressurized water reactor nuclear power stations

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Guo Xu; Wu, Jie; Zeng, Bifan; Wu, Wangqiang; Ma, Xiao Qian [School of Electric Power, South China University of Technology, Guangzhou (China); Xu, Zhibin [Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou (China)

    2017-02-15

    A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

  6. Achievement motivation revisited : New longitudinal data to demonstrate its predictive power

    NARCIS (Netherlands)

    Hustinx, P.W.J.; Kuyper, H.; Van der Werf, M.P.C.; Dijkstra, Pieternel

    2009-01-01

    During recent decades, the classical one-dimensional concept of achievement motivation has become less popular among motivation researchers. This study aims to revive the concept by demonstrating its predictive power using longitudinal data from two cohort samples, each with 20,000 Dutch secondary

  7. Earlier adolescent substance use onset predicts stronger connectivity between reward and cognitive control brain networks.

    Science.gov (United States)

    Weissman, David G; Schriber, Roberta A; Fassbender, Catherine; Atherton, Olivia; Krafft, Cynthia; Robins, Richard W; Hastings, Paul D; Guyer, Amanda E

    2015-12-01

    Early adolescent onset of substance use is a robust predictor of future substance use disorders. We examined the relation between age of substance use initiation and resting state functional connectivity (RSFC) of the core reward processing (nucleus accumbens; NAcc) to cognitive control (prefrontal cortex; PFC) brain networks. Adolescents in a longitudinal study of Mexican-origin youth reported their substance use annually from ages 10 to 16 years. At age 16, 69 adolescents participated in a resting state functional magnetic resonance imaging scan. Seed-based correlational analyses were conducted using regions of interest in bilateral NAcc. The earlier that adolescents initiated substance use, the stronger the connectivity between bilateral NAcc and right dorsolateral PFC, right dorsomedial PFC, right pre-supplementary motor area, right inferior parietal lobule, and left medial temporal gyrus. The regions that demonstrated significant positive linear relationships between the number of adolescent years using substances and connectivity with NAcc are nodes in the right frontoparietal network, which is central to cognitive control. The coupling of reward and cognitive control networks may be a mechanism through which earlier onset of substance use is related to brain function over time, a trajectory that may be implicated in subsequent substance use disorders. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  8. Unstable power threatens the powerful and challenges the powerless: evidence from cardiovascular markers of motivation

    Science.gov (United States)

    Scheepers, Daan; Röell, Charlotte; Ellemers, Naomi

    2015-01-01

    Possessing social power has psychological and biological benefits. For example, during task interactions, people high in power are more likely to display a benign cardiovascular (CV) response pattern indicative of “challenge” whereas people low in power are more likely to display a maladaptive CV pattern indicative of “threat” (Scheepers et al., 2012). Challenge is marked by high cardiac output (CO) and low total peripheral resistance (TPR), while threat is marked by low CO and high TPR (Blascovich and Mendes, 2010). In the current work we addressed a possible moderator of the power-threat/challenge relationship, namely the stability of power. We examined the influence of the stability of power (roles could or could not change) on CV responses during a dyadic task where one person was the “chief designer” (high power) and one person was the “assistant” (low power). During the task, different CV-measures were taken [CO, TPR, heart rate, pre-ejection period). Whereas participants in the unstable low power condition showed a stronger tendency toward challenge, participants in the unstable high power condition showed a stronger tendency toward threat. Moreover, participants in the stable low power condition showed CV signs of task disengagement. Results are discussed in terms of the importance of contextual variables in shaping the relationship between power and benign/maladaptive physiological responses. PMID:26074860

  9. Application of neural networks to signal prediction in nuclear power plant

    International Nuclear Information System (INIS)

    Wan Joo Kim; Soon Heung Chang; Byung Ho Lee

    1993-01-01

    This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well

  10. Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems

    International Nuclear Information System (INIS)

    Su, Yan; Chan, Lai-Cheong; Shu, Lianjie; Tsui, Kwok-Leung

    2012-01-01

    Highlights: ► We develop online prediction models for solar photovoltaic system performance. ► The proposed prediction models are simple but with reasonable accuracy. ► The maximum monthly average minutely efficiency varies 10.81–12.63%. ► The average efficiency tends to be slightly higher in winter months. - Abstract: This paper develops new real time prediction models for output power and energy efficiency of solar photovoltaic (PV) systems. These models were validated using measured data of a grid-connected solar PV system in Macau. Both time frames based on yearly average and monthly average are considered. It is shown that the prediction model for the yearly/monthly average of the minutely output power fits the measured data very well with high value of R 2 . The online prediction model for system efficiency is based on the ratio of the predicted output power to the predicted solar irradiance. This ratio model is shown to be able to fit the intermediate phase (9 am to 4 pm) very well but not accurate for the growth and decay phases where the system efficiency is near zero. However, it can still serve as a useful purpose for practitioners as most PV systems work in the most efficient manner over this period. It is shown that the maximum monthly average minutely efficiency varies over a small range of 10.81% to 12.63% in different months with slightly higher efficiency in winter months.

  11. Method of critical power prediction based on film flow model coupled with subchannel analysis

    International Nuclear Information System (INIS)

    Tomiyama, Akio; Yokomizo, Osamu; Yoshimoto, Yuichiro; Sugawara, Satoshi.

    1988-01-01

    A new method was developed to predict critical powers for a wide variety of BWR fuel bundle designs. This method couples subchannel analysis with a liquid film flow model, instead of taking the conventional way which couples subchannel analysis with critical heat flux correlations. Flow and quality distributions in a bundle are estimated by the subchannel analysis. Using these distributions, film flow rates along fuel rods are then calculated with the film flow model. Dryout is assumed to occur where one of the film flows disappears. This method is expected to give much better adaptability to variations in geometry, heat flux, flow rate and quality distributions than the conventional methods. In order to verify the method, critical power data under BWR conditions were analyzed. Measured and calculated critical powers agreed to within ±7%. Furthermore critical power data for a tight-latticed bundle obtained by LeTourneau et al. were compared with critical powers calculated by the present method and two conventional methods, CISE correlation and subchannel analysis coupled with the CISE correlation. It was confirmed that the present method can predict critical powers more accurately than the conventional methods. (author)

  12. A study on the characteristics, predictions and policies of China’s eight main power grids

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Dong, Yao; Jiang, He

    2014-01-01

    Highlights: • Indian blackout is analyzed as a warning for China’s power system. • Issues and recommendations of China’s eight power grids are presented. • Five models are employed for scenario analysis on power generation and consumption. • The optimized combined model outperforms other models. • Methods towards balancing power generation and environmental impacts are proposed. - Abstract: Electricity is an indispensable energy source for modern social and economic development. However, large-scale blackouts can cause incalculable loss to society. In 2012, three major Indian power grids collapsed, resulting in the interruption of the electricity supply to over 600 million people. To avoid an event like that, China needs to forecast the power generation and consumption of eight power grids effectively. This paper first analyzes the characteristics of eight power grids and then proposes a combined model based on three improved grey models optimized by a differential evolution algorithm to predict electricity production and consumption of each power grid. The optimized combined forecasting model provides a better prediction than other models, and it is also the most workable and satisfactory model. Experiment results show electricity production and consumption would increase. In consideration of the real situation and existing problems, some suggestions are proposed. The government could decrease thermal power and exploit renewable energy power, like hydroelectric power, wind power and solar power, to ensure the safe and reliable operation of China’s major power grids and protect environment

  13. Predictive Power Estimation Algorithm (PPEA--a new algorithm to reduce overfitting for genomic biomarker discovery.

    Directory of Open Access Journals (Sweden)

    Jiangang Liu

    Full Text Available Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA, which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1 PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2 the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3 using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4 more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

  14. Prediction and attendance of Angra 2 nuclear power plant cycle extension

    International Nuclear Information System (INIS)

    Dias, Amory; Ferreira Junior, Decio Brandes M.; Morgado, Mario Monteiro; Santos, Barbara Oliveira dos; Oliveira, Monica Georgia Nunes

    2007-01-01

    The Report Project Nuclear and Thermohydraulic (RPNT) of the Nuclear Power Plant Angra 2 previews extension of the cycle, using a feedback of core reactor reactivity, through the reduction of the moderator average temperature and power. In this phase, the reactor power remains almost invariable. Furthermore, the extension of cycle can be stretch after the limit of the temperature reduction has been reached, through of reactor power fall until the determined date for the end cycle and the start outage for the next cycle. The proposal of this work is to show the Power Plant results during the phase of moderator temperature reduction and to compare with the predict values obtained from reactivity balance calculation methodology used for the Reactor Physics. In general, the results of this work can collaborate for the extension behavior evaluation of the cycles of the Nuclear Power Plant 2, being used the procedure of cooling reduction average temperature, as well as, it will also collaborate for methodology qualification applied for the Reactor Physics during the reactivity balance calculation. (author)

  15. Validation of Lifetime Prediction of IGBT Modules Based on Linear Damage Accumulation by Means of Superimposed Power Cycling Tests

    DEFF Research Database (Denmark)

    Choi, Ui-Min; Ma, Ke; Blaabjerg, Frede

    2018-01-01

    In this paper, the lifetime prediction of power device modules based on the linear damage accumulation is studied in conjunction with simple mission profiles of converters. Superimposed power cycling conditions, which are called simple mission profiles in this paper, are made based on a lifetime ...... prediction of IGBT modules under power converter applications.......In this paper, the lifetime prediction of power device modules based on the linear damage accumulation is studied in conjunction with simple mission profiles of converters. Superimposed power cycling conditions, which are called simple mission profiles in this paper, are made based on a lifetime...... model in respect to junction temperature swing duration. This model has been built based on 39 power cycling test results of 600-V 30-A three-phase-molded IGBT modules. Six tests are performed under three superimposed power cycling conditions using an advanced power cycling test setup. The experimental...

  16. Conservatives Anticipate and Experience Stronger Emotional Reactions to Negative Outcomes.

    Science.gov (United States)

    Joel, Samantha; Burton, Caitlin M; Plaks, Jason E

    2014-02-01

    The present work examined whether conservatives and liberals differ in their anticipation of their own emotional reactions to negative events. In two studies, participants imagined experiencing positive or negative outcomes in domains that do not directly concern politics. In Study 1, 190 American participants recruited online (64 male, Mage  = 32 years) anticipated their emotional responses to romantic relationship outcomes. In Study 2, 97 Canadian undergraduate students (26 male, Mage  = 21 years) reported on their anticipated and experienced emotional responses to academic outcomes. In both studies, more conservative participants predicted they would feel stronger negative emotions following negative outcomes than did more liberal participants. Furthermore, a longitudinal follow-up of Study 2 participants revealed that more conservative participants actually felt worse than more liberal participants after receiving a lower-than-desired exam grade. These effects remained even when controlling for the Big Five traits, prevention focus, and attachment style (Study 1), and optimism (Study 2). We discuss how the relationship between political orientation and anticipated affect likely contributes to differences between conservatives and liberals in styles of decision and policy choices. © 2013 Wiley Periodicals, Inc.

  17. Getting data for prediction of electricity generation from photovoltaic power plants

    International Nuclear Information System (INIS)

    Majer, V.; Hejtmankova, P.

    2012-01-01

    This paper deals with the short term prediction of generated electricity from photovoltaic power plants. This way of electricity generation is strongly dependent on the actual weather, mainly solar radiation and temperature. In this paper the simple method for getting solar radiation data is presented. (Authors)

  18. Prediction of small hydropower plant power production in Himreen Lake dam (HLD using artificial neural network

    Directory of Open Access Journals (Sweden)

    Ali Thaeer Hammid

    2018-03-01

    Full Text Available In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there's a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient (R between the variables of predicted and observed output that would be higher than 0.96. Keywords: Himreen Lake Dam, Small Hydropower plants, Artificial Neural Networks, Feed forward-back propagation model, Generation system's prediction

  19. Distributed Model Predictive Control for Active Power Control of Wind Farm

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  20. Powering Africa: Meeting the financing and reform challenges

    International Nuclear Information System (INIS)

    Eberhard, Anton; Shkaratan, Maria

    2012-01-01

    Sub-Saharan Africa faces chronic power problems, including insufficient generation capacity, low connectivity, poor reliability, and high costs, all of which constrain development. The investment requirements to meet Africa's power needs are noted and strategies to address the funding gap are set out. The time for an ideological debate on public versus private investment is over—both are needed. Africa's key challenges are the management of hybrid power markets, the reform of state-owned utilities, cost-reflective pricing, better targeting of subsidies, the nimbler rollout of electrification, and stronger regional integration. - Highlights: ► Africa has insufficient power generation, low connectivity, poor reliability, and high costs. ► Investment requirements to meet Africa's power needs are calculated. ► Key challenges are the management of hybrid power markets and the reform of state-owned utilities. ► Other challenges are cost-reflective pricing, extending access, and stronger regional integration.

  1. Achievement Motivation Revisited: New Longitudinal Data to Demonstrate Its Predictive Power

    Science.gov (United States)

    Hustinx, Paul W. J.; Kuyper, Hans; van der Werf, Margaretha P. C.; Dijkstra, Pieternel

    2009-01-01

    During recent decades, the classical one-dimensional concept of achievement motivation has become less popular among motivation researchers. This study aims to revive the concept by demonstrating its predictive power using longitudinal data from two cohort samples, each with 20,000 Dutch secondary school students. Two measures of achievement…

  2. Applying model predictive control to power system frequency control

    OpenAIRE

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

    2013-01-01

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

  3. Comparison of Comet Enflow and VA One Acoustic-to-Structure Power Flow Predictions

    Science.gov (United States)

    Grosveld, Ferdinand W.; Schiller, Noah H.; Cabell, Randolph H.

    2010-01-01

    Comet Enflow is a commercially available, high frequency vibroacoustic analysis software based on the Energy Finite Element Analysis (EFEA). In this method the same finite element mesh used for structural and acoustic analysis can be employed for the high frequency solutions. Comet Enflow is being validated for a floor-equipped composite cylinder by comparing the EFEA vibroacoustic response predictions with Statistical Energy Analysis (SEA) results from the commercial software program VA One from ESI Group. Early in this program a number of discrepancies became apparent in the Enflow predicted response for the power flow from an acoustic space to a structural subsystem. The power flow anomalies were studied for a simple cubic, a rectangular and a cylindrical structural model connected to an acoustic cavity. The current investigation focuses on three specific discrepancies between the Comet Enflow and the VA One predictions: the Enflow power transmission coefficient relative to the VA One coupling loss factor; the importance of the accuracy of the acoustic modal density formulation used within Enflow; and the recommended use of fast solvers in Comet Enflow. The frequency region of interest for this study covers the one-third octave bands with center frequencies from 16 Hz to 4000 Hz.

  4. LHC Season 2: A stronger machine

    CERN Multimedia

    Dominguez, Daniel

    2015-01-01

    1) New magnets / De nouveaux aimants 2) Stronger connections / Des jonctions électriques renforcées 3) Safer magnets / Des aimants plus sûrs 4) Higher energy beams / Des faisceaux d’énergie plus élevée 5) Narrower beams / Des faisceaux plus serrés 6) Smaller but closer proton packets / Des groupes de protons plus petits mais plus rapprochés 7) Higher voltage / Une tension plus haute 8) Superior cryogenics / Un système cryogénique amélioré 9) Radiation-resistant electronics / Une électronique qui résiste aux radiations 10) More secure vacuum / Un vide plus sûr

  5. An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Qiang Ni

    2017-10-01

    Full Text Available High quality photovoltaic (PV power prediction intervals (PIs are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

  6. Lower-Body Muscle Structure and Jump Performance of Stronger and Weaker Surfing Athletes.

    Science.gov (United States)

    Secomb, Josh L; Nimphius, Sophia; Farley, Oliver R; Lundgren, Lina; Tran, Tai T; Sheppard, Jeremy M

    2016-07-01

    To identify whether there are any significant differences in the lower-body muscle structure and countermovement-jump (CMJ) and squat-jump (SJ) performance between stronger and weaker surfing athletes. Twenty elite male surfers had their lower-body muscle structure assessed with ultrasonography and completed a series of lower-body strength and jump tests including isometric midthigh pull (IMTP), CMJ, and SJ. Athletes were separated into stronger (n = 10) and weaker (n = 10) groups based on IMTP performance. Large significant differences were identified between the groups for vastus lateralis (VL) thickness (P = .02, ES = 1.22) and lateral gastrocnemius (LG) pennation angle (P = .01, ES = 1.20), and a large nonsignificant difference was identified in LG thickness (P = .08, ES = 0.89). Furthermore, significant differences were present between the groups for peak force, relative peak force, and jump height in the CMJ and SJ (P Stronger surfing athletes in this study had greater VL and LG thickness and LG pennation angle. These muscle structures may explain their better performance in the CMJ and SJ. A unique finding in this study was that the stronger group appeared to better use their strength and muscle structure for braking as they had significantly higher eccentric peak velocity and vertical displacement during the CMJ. This enhanced eccentric phase may have resulted in a greater production and subsequent utilization of stored elastic strain energy that led to the significantly better CMJ performance in the stronger group.

  7. Self-Powered Wireless Sensor Network for Automated Corrosion Prediction of Steel Reinforcement

    Directory of Open Access Journals (Sweden)

    Dan Su

    2018-01-01

    Full Text Available Corrosion is one of the key issues that affect the service life and hinders wide application of steel reinforcement. Moreover, corrosion is a long-term process and not visible for embedded reinforcement. Thus, this research aims at developing a self-powered smart sensor system with integrated innovative prediction module for forecasting corrosion process of embedded steel reinforcement. Vibration-based energy harvester is used to harvest energy for continuous corrosion data collection. Spatial interpolation module was developed to interpolate corrosion data at unmonitored locations. Dynamic prediction module is used to predict the long-term corrosion based on collected data. Utilizing this new sensor network, the corrosion process can be automated predicted and appropriate mitigation actions will be recommended accordingly.

  8. Comparison of different risk perception measures in predicting seasonal influenza vaccination among healthy Chinese adults in Hong Kong: a prospective longitudinal study.

    Science.gov (United States)

    Liao, Qiuyan; Wong, Wing Sze; Fielding, Richard

    2013-01-01

    Risk perception is a reported predictor of vaccination uptake, but which measures of risk perception best predict influenza vaccination uptake remain unclear. During the main influenza seasons (between January and March) of 2009 (Wave 1) and 2010 (Wave 2),505 Chinese students and employees from a Hong Kong university completed an online survey. Multivariate logistic regression models were conducted to assess how well different risk perceptions measures in Wave 1 predicted vaccination uptake against seasonal influenza in Wave 2. The results of the multivariate logistic regression models showed that feeling at risk (β = 0.25, p = 0.021) was the better predictor compared with probability judgment while probability judgment (β = 0.25, p = 0.029 ) was better than beliefs about risk in predicting subsequent influenza vaccination uptake. Beliefs about risk and feeling at risk seemed to predict the same aspect of subsequent vaccination uptake because their associations with vaccination uptake became insignificant when paired into the logistic regression model. Similarly, to compare the four scales for assessing probability judgment in predicting vaccination uptake, the 7-point verbal scale remained a significant and stronger predictor for vaccination uptake when paired with other three scales; the 6-point verbal scale was a significant and stronger predictor when paired with the percentage scale or the 2-point verbal scale; and the percentage scale was a significant and stronger predictor only when paired with the 2-point verbal scale. Beliefs about risk and feeling at risk are not well differentiated by Hong Kong Chinese people. Feeling at risk, an affective-cognitive dimension of risk perception predicts subsequent vaccination uptake better than do probability judgments. Among the four scales for assessing risk probability judgment, the 7-point verbal scale offered the best predictive power for subsequent vaccination uptake.

  9. Comparison of Different Risk Perception Measures in Predicting Seasonal Influenza Vaccination among Healthy Chinese Adults in Hong Kong: A Prospective Longitudinal Study

    Science.gov (United States)

    Liao, Qiuyan; Wong, Wing Sze; Fielding, Richard

    2013-01-01

    Background Risk perception is a reported predictor of vaccination uptake, but which measures of risk perception best predict influenza vaccination uptake remain unclear. Methodology During the main influenza seasons (between January and March) of 2009 (Wave 1) and 2010 (Wave 2),505 Chinese students and employees from a Hong Kong university completed an online survey. Multivariate logistic regression models were conducted to assess how well different risk perceptions measures in Wave 1 predicted vaccination uptake against seasonal influenza in Wave 2. Principal Findings The results of the multivariate logistic regression models showed that feeling at risk (β = 0.25, p = 0.021) was the better predictor compared with probability judgment while probability judgment (β = 0.25, p = 0.029 ) was better than beliefs about risk in predicting subsequent influenza vaccination uptake. Beliefs about risk and feeling at risk seemed to predict the same aspect of subsequent vaccination uptake because their associations with vaccination uptake became insignificant when paired into the logistic regression model. Similarly, to compare the four scales for assessing probability judgment in predicting vaccination uptake, the 7-point verbal scale remained a significant and stronger predictor for vaccination uptake when paired with other three scales; the 6-point verbal scale was a significant and stronger predictor when paired with the percentage scale or the 2-point verbal scale; and the percentage scale was a significant and stronger predictor only when paired with the 2-point verbal scale. Conclusions/Significance Beliefs about risk and feeling at risk are not well differentiated by Hong Kong Chinese people. Feeling at risk, an affective-cognitive dimension of risk perception predicts subsequent vaccination uptake better than do probability judgments. Among the four scales for assessing risk probability judgment, the 7-point verbal scale offered the best predictive

  10. Sexual aggression when power is new: Effects of acute high power on chronically low-power individuals.

    Science.gov (United States)

    Williams, Melissa J; Gruenfeld, Deborah H; Guillory, Lucia E

    2017-02-01

    Previous theorists have characterized sexually aggressive behavior as an expression of power, yet evidence that power causes sexual aggression is mixed. We hypothesize that power can indeed create opportunities for sexual aggression-but that it is those who chronically experience low power who will choose to exploit such opportunities. Here, low-power men placed in a high-power role showed the most hostility in response to a denied opportunity with an attractive woman (Studies 1 and 2). Chronically low-power men and women given acute power were the most likely to say they would inappropriately pursue an unrequited workplace attraction (Studies 3 and 4). Finally, having power over an attractive woman increased harassment behavior among men with chronic low, but not high, power (Study 5). People who see themselves as chronically denied power appear to have a stronger desire to feel powerful and are more likely to use sexual aggression toward that end. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  11. Prediction of requirements on labor force in the fuel and power generation sector

    International Nuclear Information System (INIS)

    Kaveckova, R.

    1990-01-01

    One of the aspects of socio-economic assessment of development is quantification of the expected requirements on the number of personnel. Predictions are discussed for the period before the year 2005 for solid fuel mining and treatment, gas production and bitumen mining, power and heat generation and also for the production of electricity and heat by nuclear power plants. They are based on an analysis of past development and the present state, on presumed implementation of various concept variants, on the type structure of nuclear power plants, on the rules of the electric power supply system, and also on foreign materials. It is expected that in 2005, nuclear power will employ 15,654 personnel. (M.D.). 4 tabs., 16 refs

  12. Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks

    Directory of Open Access Journals (Sweden)

    Cunwu Han

    2014-01-01

    Full Text Available A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI techniques. Simulation results are given to illustrate the effectiveness of the proposed method.

  13. Prediction of speech intelligibility based on a correlation metric in the envelope power spectrum domain

    DEFF Research Database (Denmark)

    Relano-Iborra, Helia; May, Tobias; Zaar, Johannes

    A powerful tool to investigate speech perception is the use of speech intelligibility prediction models. Recently, a model was presented, termed correlation-based speechbased envelope power spectrum model (sEPSMcorr) [1], based on the auditory processing of the multi-resolution speech-based Envel...

  14. Predicting lower body power from vertical jump prediction equations for loaded jump squats at different intensities in men and women.

    Science.gov (United States)

    Wright, Glenn A; Pustina, Andrew A; Mikat, Richard P; Kernozek, Thomas W

    2012-03-01

    The purpose of this study was to determine the efficacy of estimating peak lower body power from a maximal jump squat using 3 different vertical jump prediction equations. Sixty physically active college students (30 men, 30 women) performed jump squats with a weighted bar's applied load of 20, 40, and 60% of body mass across the shoulders. Each jump squat was simultaneously monitored using a force plate and a contact mat. Peak power (PP) was calculated using vertical ground reaction force from the force plate data. Commonly used equations requiring body mass and vertical jump height to estimate PP were applied such that the system mass (mass of body + applied load) was substituted for body mass. Jump height was determined from flight time as measured with a contact mat during a maximal jump squat. Estimations of PP (PP(est)) for each load and for each prediction equation were compared with criterion PP values from a force plate (PP(FP)). The PP(est) values had high test-retest reliability and were strongly correlated to PP(FP) in both men and women at all relative loads. However, only the Harman equation accurately predicted PP(FP) at all relative loads. It can therefore be concluded that the Harman equation may be used to estimate PP of a loaded jump squat knowing the system mass and peak jump height when more precise (and expensive) measurement equipment is unavailable. Further, high reliability and correlation with criterion values suggest that serial assessment of power production across training periods could be used for relative assessment of change by either of the prediction equations used in this study.

  15. Model Predictive Control for Flexible Power Consumption of Large-Scale Refrigeration Systems

    DEFF Research Database (Denmark)

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

    2014-01-01

    A model predictive control (MPC) scheme is introduced to directly control the electrical power consumption of large-scale refrigeration systems. Deviation from the baseline of the consumption is corresponded to the storing and delivering of thermal energy. By virtue of such correspondence...

  16. Computational models for residual creep life prediction of power plant components

    International Nuclear Information System (INIS)

    Grewal, G.S.; Singh, A.K.; Ramamoortry, M.

    2006-01-01

    All high temperature - high pressure power plant components are prone to irreversible visco-plastic deformation by the phenomenon of creep. The steady state creep response as well as the total creep life of a material is related to the operational component temperature through, respectively, the exponential and inverse exponential relationships. Minor increases in the component temperature can thus have serious consequences as far as the creep life and dimensional stability of a plant component are concerned. In high temperature steam tubing in power plants, one mechanism by which a significant temperature rise can occur is by the growth of a thermally insulating oxide film on its steam side surface. In the present paper, an elegantly simple and computationally efficient technique is presented for predicting the residual creep life of steel components subjected to continual steam side oxide film growth. Similarly, fabrication of high temperature power plant components involves extensive use of welding as the fabrication process of choice. Naturally, issues related to the creep life of weldments have to be seriously addressed for safe and continual operation of the welded plant component. Unfortunately, a typical weldment in an engineering structure is a zone of complex microstructural gradation comprising of a number of distinct sub-zones with distinct meso-scale and micro-scale morphology of the phases and (even) chemistry and its creep life prediction presents considerable challenges. The present paper presents a stochastic algorithm, which can be' used for developing experimental creep-cavitation intensity versus residual life correlations for welded structures. Apart from estimates of the residual life in a mean field sense, the model can be used for predicting the reliability of the plant component in a rigorous probabilistic setting. (author)

  17. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

    Science.gov (United States)

    Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

    2017-01-01

    Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  18. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

    Directory of Open Access Journals (Sweden)

    Xiang-ming Gao

    2017-01-01

    Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  19. Some Comparisons of Measured and Predicted Primary Radiation Levels in the Aagesta Power Plant

    Energy Technology Data Exchange (ETDEWEB)

    Aalto, E; Sandlin, R; Krell, Aa

    1968-05-15

    Neutron fluxes and gamma exposure rates in the primary shields of the Aagesta nuclear plant have been measured and the results compared with values predicted during shield design, and with values obtained later by the NRN bulk shielding code. The input data for the problems are given. The radial predictions are conservative by a factor of not more than 2 close to the reactor and by an unknown, higher factor further out. The conservatism is explainable by the differences between the true local conditions and core power distributions and those assumed in the predictions. The axial flux levels based on streaming calculations are found to agree quite well with the estimated values. The conservatism here is not so large and it seems to be necessary to be very careful when handling streaming problems. The experience gained shows that a power plant is less suitable for studying the accuracy of the shield design codes as such, but the practical results from the combined application of massive shield codes and void streaming predictions to complicated problems give information about the true degree of conservatism present.

  20. Ni2P Makes Application of the PtRu Catalyst Much Stronger in Direct Methanol Fuel Cells.

    Science.gov (United States)

    Chang, Jinfa; Feng, Ligang; Liu, Changpeng; Xing, Wei

    2015-10-12

    PtRu is regarded as the best catalyst for direct methanol fuel cells, but the performance decay resulting from the loss of Ru seriously hinders commercial applications. Herein, we demonstrated that the presence of Ni2 P largely reduces Ru loss, which thus makes the application of PtRu much stronger in direct methanol fuel cells. Outstanding catalytic activity and stability were observed by cyclic voltammetry. Upon integrating the catalyst material into a practical direct methanol fuel cell, the highest maximum power density was achieved on the PtRu-Ni2P/C catalyst among the reference catalysts at different temperatures. A maximum power density of 69.9 mW cm(-2) at 30 °C was obtained on PtRu-Ni2P/C, which is even higher than the power density of the state-of-the-art commercial PtRu catalyst at 70 °C (63.1 mW cm(-2)). Moreover, decay in the performance resulting from Ru loss was greatly reduced owing to the presence of Ni2 P, which is indicative of very promising applications. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology

    Directory of Open Access Journals (Sweden)

    M. Hameedullah

    2010-01-01

    Full Text Available Power consumption in turning EN-31 steel (a material that is most extensively used in automotive industry with tungstencarbide tool under different cutting conditions was experimentally investigated. The experimental runs were planned accordingto 24+8 added centre point factorial design of experiments, replicated thrice. The data collected was statisticallyanalyzed using Analysis of Variance technique and first order and second order power consumption prediction models weredeveloped by using response surface methodology (RSM. It is concluded that second-order model is more accurate than thefirst-order model and fit well with the experimental data. The model can be used in the automotive industries for decidingthe cutting parameters for minimum power consumption and hence maximum productivity

  2. A neural network based computational model to predict the output power of different types of photovoltaic cells.

    Science.gov (United States)

    Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng

    2017-01-01

    In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

  3. A neural network based computational model to predict the output power of different types of photovoltaic cells.

    Directory of Open Access Journals (Sweden)

    WenBo Xiao

    Full Text Available In this article, we introduced an artificial neural network (ANN based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-, multi-crystalline (multi-, and amorphous (amor- crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

  4. Research Design and the Predictive Power of Measures of Self-Efficacy

    Science.gov (United States)

    Moriarty, Beverley

    2014-01-01

    The purpose of this enquiry was to examine how research design impacts on the predictive power of measures of self-efficacy. Three cautions for designing research into self-efficacy drawn from the seminal work of Albert Bandura (1986) and a further caution proposed by the current author together form the analytical framework for this enquiry. For…

  5. Prediction of critical flow rates through power-operated relief valves

    International Nuclear Information System (INIS)

    Abdollahian, D.; Singh, A.

    1983-01-01

    Existing single-phase and two-phase critical flow models are used to predict the flow rates through the power-operated relief valves tested in the EPRI Safety and Relief Valve test program. For liquid upstream conditions, Homogeneous Equilibrium Model, Moody, Henry-Fauske and Burnell two-phase critical flow models are used for comparison with data. Under steam upstream conditions, the flow rates are predicted either by the single-phase isentropic equations or the Homogeneous Equilibrium Model, depending on the thermodynamic condition of the fluid at the choking plane. The results of the comparisons are used to specify discharge coefficients for different valves under steam and liquid upstream conditions and evaluate the existing approximate critical flow relations for a wide range of subcooled water and steam conditions

  6. Conceptual Software Reliability Prediction Models for Nuclear Power Plant Safety Systems

    International Nuclear Information System (INIS)

    Johnson, G.; Lawrence, D.; Yu, H.

    2000-01-01

    The objective of this project is to develop a method to predict the potential reliability of software to be used in a digital system instrumentation and control system. The reliability prediction is to make use of existing measures of software reliability such as those described in IEEE Std 982 and 982.2. This prediction must be of sufficient accuracy to provide a value for uncertainty that could be used in a nuclear power plant probabilistic risk assessment (PRA). For the purposes of the project, reliability was defined to be the probability that the digital system will successfully perform its intended safety function (for the distribution of conditions under which it is expected to respond) upon demand with no unintended functions that might affect system safety. The ultimate objective is to use the identified measures to develop a method for predicting the potential quantitative reliability of a digital system. The reliability prediction models proposed in this report are conceptual in nature. That is, possible prediction techniques are proposed and trial models are built, but in order to become a useful tool for predicting reliability, the models must be tested, modified according to the results, and validated. Using methods outlined by this project, models could be constructed to develop reliability estimates for elements of software systems. This would require careful review and refinement of the models, development of model parameters from actual experience data or expert elicitation, and careful validation. By combining these reliability estimates (generated from the validated models for the constituent parts) in structural software models, the reliability of the software system could then be predicted. Modeling digital system reliability will also require that methods be developed for combining reliability estimates for hardware and software. System structural models must also be developed in order to predict system reliability based upon the reliability

  7. Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

    International Nuclear Information System (INIS)

    Zhang, Yachao; Liu, Kaipei; Qin, Liang; An, Xueli

    2016-01-01

    Highlights: • Variational mode decomposition is adopted to process original wind power series. • A novel combined model based on machine learning methods is established. • An improved differential evolution algorithm is proposed for weight adjustment. • Probabilistic interval prediction is performed by quantile regression averaging. - Abstract: Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self-adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid

  8. Epileptic seizure prediction based on a bivariate spectral power methodology.

    Science.gov (United States)

    Bandarabadi, Mojtaba; Teixeira, Cesar A; Direito, Bruno; Dourado, Antonio

    2012-01-01

    The spectral power of 5 frequently considered frequency bands (Alpha, Beta, Gamma, Theta and Delta) for 6 EEG channels is computed and then all the possible pairwise combinations among the 30 features set, are used to create a 435 dimensional feature space. Two new feature selection methods are introduced to choose the best candidate features among those and to reduce the dimensionality of this feature space. The selected features are then fed to Support Vector Machines (SVMs) that classify the cerebral state in preictal and non-preictal classes. The outputs of the SVM are regularized using a method that accounts for the classification dynamics of the preictal class, also known as "Firing Power" method. The results obtained using our feature selection approaches are compared with the ones obtained using minimum Redundancy Maximum Relevance (mRMR) feature selection method. The results in a group of 12 patients of the EPILEPSIAE database, containing 46 seizures and 787 hours multichannel recording for out-of-sample data, indicate the efficiency of the bivariate approach as well as the two new feature selection methods. The best results presented sensitivity of 76.09% (35 of 46 seizures predicted) and a false prediction rate of 0.15(-1).

  9. Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power

    Directory of Open Access Journals (Sweden)

    Mashud Rana

    2016-10-01

    Full Text Available Solar energy generated from PhotoVoltaic (PV systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties for the large-scale integration of PV power in the electricity grid and requires accurate forecasting of the electricity generated by PV systems. In this paper we consider 2D-interval forecasts, where the goal is to predict summary statistics for the distribution of the PV power values in a future time interval. 2D-interval forecasts have been recently introduced, and they are more suitable than point forecasts for applications where the predicted variable has a high variability. We propose a method called NNE2D that combines variable selection based on mutual information and an ensemble of neural networks, to compute 2D-interval forecasts, where the two interval boundaries are expressed in terms of percentiles. NNE2D was evaluated for univariate prediction of Australian solar PV power data for two years. The results show that it is a promising method, outperforming persistence baselines and other methods used for comparison in terms of accuracy and coverage probability.

  10. Analyzing power in pp scattering at low energies: the Paris potential predictions

    International Nuclear Information System (INIS)

    Cote, J.; Pires, P.; Tourreil, R. de; Lacombe, M.; Loiseau, B.; Vinh Mau, R.

    1979-12-01

    Predictions of the Paris potential for the analyzing power in pp scattering at low energies are compared with recent high precision measurements at 6.14MeV and earlier measurements at 10 and 16MeV. Phase shift values are also presented and discussed in view of previous analyses

  11. A Stronger Reason for the Right to Sign Languages

    Science.gov (United States)

    Trovato, Sara

    2013-01-01

    Is the right to sign language only the right to a minority language? Holding a capability (not a disability) approach, and building on the psycholinguistic literature on sign language acquisition, I make the point that this right is of a stronger nature, since only sign languages can guarantee that each deaf child will properly develop the…

  12. Benefits for wind energy in electricity markets from using short term wind power prediction tools: a simulation study

    International Nuclear Information System (INIS)

    Usaola, J.; Ravelo, O.; Gonzalez, G.; Soto, F.; Davila, M.C.; Diaz-Guerra, B.

    2004-01-01

    One of the characteristics of wind energy, from the grid point of view, is its non-dispatchability, i.e. generation cannot be ordered, hence integration in electrical networks may be difficult. Short-term wind power prediction-tools could make this integration easier, either by their use by the grid System Operator, or by promoting the participation of wind farms in the electricity markets and using prediction tools to make their bids in the market. In this paper, the importance of a short-term wind power-prediction tool for the participation of wind energy systems in electricity markets is studied. Simulations, according to the current Spanish market rules, have been performed to the production of different wind farms, with different degrees of accuracy in the prediction tool. It may be concluded that income from participation in electricity markets is increased using a short-term wind power prediction-tool of average accuracy. This both marginally increases income and also reduces the impact on system operation with the improved forecasts. (author)

  13. Validation of the FAST skating protocol to predict aerobic power in ice hockey players.

    Science.gov (United States)

    Petrella, Nicholas J; Montelpare, William J; Nystrom, Murray; Plyley, Michael; Faught, Brent E

    2007-08-01

    Few studies have reported a sport-specific protocol to measure the aerobic power of ice hockey players using a predictive process. The purpose of our study was to validate an ice hockey aerobic field test on players of varying ages, abilities, and levels. The Faught Aerobic Skating Test (FAST) uses an on-ice continuous skating protocol on a course measuring 160 feet (48.8 m) using a CD to pace the skater with a beep signal to cross the starting line at each end of the course. The FAST incorporates the principle of increasing workload at measured time intervals during a continuous skating exercise. Step-wise multiple regression modelling was used to determine the estimate of aerobic power. Participants completed a maximal aerobic power test using a modified Bruce incremental treadmill protocol, as well as the on-ice FAST. Normative data were collected on 406 ice hockey players (291 males, 115 females) ranging in age from 9 to 25 y. A regression to predict maximum aerobic power was developed using body mass (kg), height (m), age (y), and maximum completed lengths of the FAST as the significant predictors of skating aerobic power (adjusted R2 = 0.387, SEE = 7.25 mL.kg-1.min-1, p < 0.0001). These results support the application of the FAST in estimating aerobic power among male and female competitive ice hockey players between the ages of 9 and 25 years.

  14. Development of equipment reliability process using predictive technologies at Hamaoka Nuclear Power Station

    International Nuclear Information System (INIS)

    Taniguchi, Yuji; Sakuragi, Futoshi; Hamada, Seiichi

    2014-01-01

    Development of equipment reliability(ER) process, specifically for predictive maintenance (PdM) technologies integrated condition based maintenance (CBM) process, at Hamaoka Nuclear Power Station is introduced in this paper. Integration of predictive maintenance technologies such as vibration, oil analysis and thermo monitoring is more than important to establish strong maintenance strategies and to direct a specific technical development. In addition, a practical example of CBM is also presented to support the advantage of the idea. (author)

  15. Characterizing and predicting the robustness of power-law networks

    International Nuclear Information System (INIS)

    LaRocca, Sarah; Guikema, Seth D.

    2015-01-01

    Power-law networks such as the Internet, terrorist cells, species relationships, and cellular metabolic interactions are susceptible to node failures, yet maintaining network connectivity is essential for network functionality. Disconnection of the network leads to fragmentation and, in some cases, collapse of the underlying system. However, the influences of the topology of networks on their ability to withstand node failures are poorly understood. Based on a study of the response of 2000 randomly-generated power-law networks to node failures, we find that networks with higher nodal degree and clustering coefficient, lower betweenness centrality, and lower variability in path length and clustering coefficient maintain their cohesion better during such events. We also find that network robustness, i.e., the ability to withstand node failures, can be accurately predicted a priori for power-law networks across many fields. These results provide a basis for designing new, more robust networks, improving the robustness of existing networks such as the Internet and cellular metabolic pathways, and efficiently degrading networks such as terrorist cells. - Highlights: • Examine relationship between network topology and robustness to failures. • Relationship is statistically significant for scale-free networks. • Use statistical models to estimate robustness to failures for real-world networks

  16. Safety prediction technique for nuclear power plants

    International Nuclear Information System (INIS)

    Henry, C.D. III; Anderson, R.T.

    1985-01-01

    This paper presents a safety prediction technique (SPT) developed by Reliability Technology Associates (RTA) for nuclear power plants. It is based on a technique applied by RTA to assess the flight safety of US Air Force aircraft. The purpose of SPT is to provide a computerized technique for objective measurement of the effect on nuclear plant safety of component failure or procedural, software, or human error. A quantification is determined, called criticality, which is proportional to the probability that a given component or procedural-human action will cause the plant to operate in a hazardous mode. A hazardous mode is characterized by the fact that there has been a failure/error and the plant, its operating crew, and the public are exposed to danger. Whether the event results in an accident, an incident, or merely the exposure to danger is dependent on the skill and reaction of the operating crew as well as external influences. There are three major uses of SPT: (a) to predict unsafe situations so that corrective action can be taken before accidents occur, (b) to quantify the impact of equipment malfunction or procedural, software, or human error on safety and thereby establish priorities for proposed modifications, and (c) to provide a means of evaluating proposed changes for their impact on safety prior to implementation and to provide a method of tracking implemented changes

  17. Power Transformer Operating State Prediction Method Based on an LSTM Network

    Directory of Open Access Journals (Sweden)

    Hui Song

    2018-04-01

    Full Text Available The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.

  18. Short-term prediction of windfarm power output - from theory to practice

    International Nuclear Information System (INIS)

    Landberg, L.

    1998-01-01

    From the very complicated and evolved theories of boundary-layer meteorology encompassing the equations of turbulence and mean flow, a model has been derived to predict the power output from wind farms. For practical dispatching purposes the predictions must reach as far into the future as 36 hours. The model has been put into an operation frame-work where the predictions for a number of wind farms scattered all over Europe are available on-line on the World Wide Web. The system is very versatile and new wind farms can be included within a few days. The system is made up of predictions from the Danish Meteorological Institute HIRLAM model which are refined using the WASP model from Risoe National Laboratory. The paper will describe this operation set-up, give examples of the performance of the model of wind farms in the UK, Denmark, Greece and the US. An analysis of the error for a one-year period will also be presented. Finally, possible improvements will be discussed. These include Kalman filtering and other statistical methods. (Author)

  19. Significant change of predictions related to the future of nuclear power

    International Nuclear Information System (INIS)

    Dumitrache, Ion

    2002-01-01

    During the last two decades of the 20th century, nuclear power contribution increased slowly in the world. This trend was mainly determined by the commissioning of new nuclear power plants, NPP, in the non-developed countries, except for Japan and South Korea. Almost all the forecasts offered the image of the stagnant nuclear power business. Sweden, Germany, Holland and Belgium Governments made clear the intention to stop the production of electricity based on fission. Recently, despite the negative effects on nuclear power of the terrorism events of September 11, 2001, the predictions related to the nuclear power future become much more optimistic. USA, Japan, South Korea and Canada made clear that new NPPs will offer their significant electricity contribution several decades, even after years 2020-2030. Moreover, several old NPP from USA obtained the license for an additional 20 years period of operation. The analysis indicated that most of the existing NPP in USA may increase the level of the maximum global power defined by the initial design. In the European Union the situation is much more complicated. About 35% of the electricity is based now on fission. Several countries, like Sweden and Germany, maintain the position of phasing out the NPPs, as soon as the licensed life-time is over. Finland decided to build a new power plant. France is very favorable to nuclear power, but does not need more energy. In the UK several very old NPP will be shut down, and companies like BNFL and British Energy intend to build new NPP, based on Westinghouse or AECL-Canada advanced reactors. Switzerland and Spain are favorable to the future use of nuclear power. In the eastern part of Europe, almost all the countries intend to base their electricity production on coal, fission, hydro and gas, nuclear contribution being significant. The most impressive increases of nuclear power output are related to Asia; in China, from 2.2 Gwe in 1999, to 18.7 Gwe in 2020, reference case, or 10

  20. Artificial Neural Networks to Predict the Power Output of a PV Panel

    Directory of Open Access Journals (Sweden)

    Valerio Lo Brano

    2014-01-01

    Full Text Available The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs for the power energy output forecasting of photovoltaic (PV modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP, a recursive neural network (RNN, and a gamma memory (GM trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

  1. A mathematical look at a physical power prediction model

    DEFF Research Database (Denmark)

    Landberg, L.

    1998-01-01

    This article takes a mathematical look at a physical model used to predict the power produced from wind farms. The reason is to see whether simple mathematical expressions can replace the original equations and to give guidelines as to where simplifications can be made and where they cannot....... The article shows that there is a linear dependence between the geostrophic wind and the local wind at the surface, but also that great care must be taken in the selection of the simple mathematical models, since physical dependences play a very important role, e.g. through the dependence of the turning...

  2. Numerical Predictions of Wind Turbine Power and Aerodynamic Loads for the NREL Phase II and IV Combined Experiment Rotor

    Science.gov (United States)

    Duque, Earl P. N.; Johnson, Wayne; vanDam, C. P.; Chao, David D.; Cortes, Regina; Yee, Karen

    1999-01-01

    Accurate, reliable and robust numerical predictions of wind turbine rotor power remain a challenge to the wind energy industry. The literature reports various methods that compare predictions to experiments. The methods vary from Blade Element Momentum Theory (BEM), Vortex Lattice (VL), to variants of Reynolds-averaged Navier-Stokes (RaNS). The BEM and VL methods consistently show discrepancies in predicting rotor power at higher wind speeds mainly due to inadequacies with inboard stall and stall delay models. The RaNS methodologies show promise in predicting blade stall. However, inaccurate rotor vortex wake convection, boundary layer turbulence modeling and grid resolution has limited their accuracy. In addition, the inherently unsteady stalled flow conditions become computationally expensive for even the best endowed research labs. Although numerical power predictions have been compared to experiment. The availability of good wind turbine data sufficient for code validation experimental data that has been extracted from the IEA Annex XIV download site for the NREL Combined Experiment phase II and phase IV rotor. In addition, the comparisons will show data that has been further reduced into steady wind and zero yaw conditions suitable for comparisons to "steady wind" rotor power predictions. In summary, the paper will present and discuss the capabilities and limitations of the three numerical methods and make available a database of experimental data suitable to help other numerical methods practitioners validate their own work.

  3. Robust nonlinear model predictive control for nuclear power plants in load following operations with bounded xenon oscillations

    International Nuclear Information System (INIS)

    Eliasi, H.; Menhaj, M.B.; Davilu, H.

    2011-01-01

    Research highlights: → In this work, a robust nonlinear model predictive control algorithm is developed. → This algorithm is applied to control the power level for load following. → The state constraints are imposed on the predicted trajectory during optimization. → The xenon oscillations are the main constraint for the load following problem. → In this algorithm, xenon oscillations are bounded within acceptable limits. - Abstract: One of the important operations in nuclear power plants is load-following in which imbalance of axial power distribution induces xenon oscillations. These oscillations must be maintained within acceptable limits otherwise the nuclear power plant could become unstable. Therefore, bounded xenon oscillation considered to be a constraint for the load-following operation. In this paper, a robust nonlinear model predictive control for the load-following operation problem is proposed that ensures xenon oscillations are kept bounded within acceptable limits. The proposed controller uses constant axial offset (AO) strategy to maintain xenon oscillations to be bounded. The constant AO is a robust state constraint for load-following problem. The controller imposes restricted state constraints on the predicted trajectory during optimization which guarantees robust satisfaction of state constraints without restoring to a min-max optimization problem. Simulation results show that the proposed controller for the load-following operation is so effective so that the xenon oscillations kept bounded in the given region.

  4. Predicting the radioactive contamination of the surroundings near a nuclear power plant

    Energy Technology Data Exchange (ETDEWEB)

    Khristova, M; Paskalev, Z

    1975-01-01

    Predicting the radioactive contamination requires determining the concentration of radioactive material emitted from the stack of a nuclear power plant into the air and deposited on the earth's surface. The main factors determining the degree of contamination are the distance from the stack, the wind velocity and air turbulence. Formulas are presented for predicting the amount of radioactivity as a function of the initial concentration of activity, the distance from the stack and the meteorological condition. Formulas are given for the maximum deposition of radioactive aerosols at a distance R from the stack under wet and dry condtions. 2 refs. (SJR)

  5. An adaptive predictive controller and its applications in power stations

    Energy Technology Data Exchange (ETDEWEB)

    Yang Zhiyuan; Lu Huiming; Zhang Xinggao [North China Electric Power University, Beijing (China); Song Chunping [Tsinghua University, Beijing (China). Dept. of Thermal Energy Engineering

    1999-07-01

    Based on the objective function in the form of integration of generalized model error, a globally convergent model reference adaptive predictive control algorithm (MRAPC) containing inertia-time compensators is presented in this paper. MRAPC has been successfully applied to control important thermal process of more than 20 units in many Chinese power stations. In this paper three representative examples are described. Continual operation results for years demonstrate that MRAPC is a successful attempt for the practical applications of adaptive control techniques. (author)

  6. Bayesian calibration of power plant models for accurate performance prediction

    International Nuclear Information System (INIS)

    Boksteen, Sowande Z.; Buijtenen, Jos P. van; Pecnik, Rene; Vecht, Dick van der

    2014-01-01

    Highlights: • Bayesian calibration is applied to power plant performance prediction. • Measurements from a plant in operation are used for model calibration. • A gas turbine performance model and steam cycle model are calibrated. • An integrated plant model is derived. • Part load efficiency is accurately predicted as a function of ambient conditions. - Abstract: Gas turbine combined cycles are expected to play an increasingly important role in the balancing of supply and demand in future energy markets. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance. In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction. The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. For this purpose, the Bayesian calibration framework of Kennedy and O’Hagan is used, which is especially suitable for high-dimensional industrial problems. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load. The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions

  7. Stronger vection in junior high school children than in adults.

    Science.gov (United States)

    Shirai, Nobu; Imura, Tomoko; Tamura, Rio; Seno, Takeharu

    2014-01-01

    Previous studies have shown that even elementary school-aged children (7 and 11 years old) experience visually induced perception of illusory self-motion (vection) (Lepecq et al., 1995, Perception, 24, 435-449) and that children of a similar age (mean age = 9.2 years) experience more rapid and stronger vection than do adults (Shirai et al., 2012, Perception, 41, 1399-1402). These findings imply that although elementary school-aged children experience vection, this ability is subject to further development. To examine the subsequent development of vection, we compared junior high school students' (N = 11, mean age = 14.4 years) and adults' (N = 10, mean age = 22.2 years) experiences of vection. Junior high school students reported significantly stronger vection than did adults, suggesting that the perceptual experience of junior high school students differs from that of adults with regard to vection and that this ability undergoes gradual changes over a relatively long period of development.

  8. Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

    Directory of Open Access Journals (Sweden)

    Dirk Cannon

    2017-06-01

    Full Text Available State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the resolved uncertainty associated with estimating the future large-scale atmospheric state is larger than the unresolved uncertainty associated with estimating the system-wide wind power response to a given large-scale state.The bounds of skilful ensemble forecast range are quantified for three leading global forecast systems. The power system of Great Britain (GB is used as an example because independent verifying data is available from National Grid. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time is found to be 6–8 days. The lower bound is found to be 1.4–2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5–5.5 days. The upper bound on this forecast range can only be extended by improving the global forecast system (outside the control of most users or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days.

  9. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

    International Nuclear Information System (INIS)

    Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor

    2017-01-01

    Highlights: • Genetic programming based ensemble of neural networks is employed for short term wind power prediction. • Proposed predictor shows resilience against abrupt changes in weather. • Genetic programming evolves nonlinear mapping between meteorological measures and wind-power. • Proposed approach gives mathematical expressions of wind power to its independent variables. • Proposed model shows relatively accurate and steady wind-power prediction performance. - Abstract: The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the

  10. Computerized heat balance models to predict performance of operating nuclear power plants

    International Nuclear Information System (INIS)

    Breeding, C.L.; Carter, J.C.; Schaefer, R.C.

    1983-01-01

    The use of computerized heat balance models has greatly enhanced the decision making ability of TVA's Division of Nuclear Power. These models are utilized to predict the effects of various operating modes and to analyze changes in plant performance resulting from turbine cycle equipment modifications with greater speed and accuracy than was possible before. Computer models have been successfully used to optimize plant output by predicting the effects of abnormal condenser circulating water conditions. They were utilized to predict the degradation in performance resulting from installation of a baffle plate assembly to replace damaged low-pressure blading, thereby providing timely information allowing an optimal economic judgement as to when to replace the blading. Future use will be for routine performance test analysis. This paper presents the benefits of utility use of computerized heat balance models

  11. Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System

    Directory of Open Access Journals (Sweden)

    Long Wu

    2018-02-01

    Full Text Available Solid oxide fuel cell (SOFC is widely considered as an alternative solution among the family of the sustainable distributed generation. Its load flexibility enables it adjusting the power output to meet the requirements from power grid balance. Although promising, its control is challenging when faced with load changes, during which the output voltage is required to be maintained as constant and fuel utilization rate kept within a safe range. Moreover, it makes the control even more intractable because of the multivariable coupling and strong nonlinearity within the wide-range operating conditions. To this end, this paper developed a multiple model predictive control strategy for reliable SOFC operation. The resistance load is regarded as a measurable disturbance, which is an input to the model predictive control as feedforward compensation. The coupling is accommodated by the receding horizon optimization. The nonlinearity is mitigated by the multiple linear models, the weighted sum of which serves as the final control execution. The merits of the proposed control structure are demonstrated by the simulation results.

  12. Predicting speech intelligibility based on the signal-to-noise envelope power ratio after modulation-frequency selective processing

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2011-01-01

    A model for predicting the intelligibility of processed noisy speech is proposed. The speech-based envelope power spectrum model has a similar structure as the model of Ewert and Dau [(2000). J. Acoust. Soc. Am. 108, 1181-1196], developed to account for modulation detection and masking data. The ...... process provides a key measure of speech intelligibility. © 2011 Acoustical Society of America.......A model for predicting the intelligibility of processed noisy speech is proposed. The speech-based envelope power spectrum model has a similar structure as the model of Ewert and Dau [(2000). J. Acoust. Soc. Am. 108, 1181-1196], developed to account for modulation detection and masking data....... The model estimates the speech-to-noise envelope power ratio, SNR env, at the output of a modulation filterbank and relates this metric to speech intelligibility using the concept of an ideal observer. Predictions were compared to data on the intelligibility of speech presented in stationary speech...

  13. A Model Predictive Control-Based Power Converter System for Oscillating Water Column Wave Energy Converters

    Directory of Open Access Journals (Sweden)

    Gimara Rajapakse

    2017-10-01

    Full Text Available Despite the predictability and availability at large scale, wave energy conversion (WEC has still not become a mainstream renewable energy technology. One of the main reasons is the large variations in the extracted power which could lead to instabilities in the power grid. In addition, maintaining the speed of the turbine within optimal range under changing wave conditions is another control challenge, especially in oscillating water column (OWC type WEC systems. As a solution to the first issue, this paper proposes the direct connection of a battery bank into the dc-link of the back-to-back power converter system, thereby smoothening the power delivered to the grid. For the second issue, model predictive controllers (MPCs are developed for the rectifier and the inverter of the back-to-back converter system aiming to maintain the turbine speed within its optimum range. In addition, MPC controllers are designed to control the battery current as well, in both charging and discharging conditions. Operations of the proposed battery direct integration scheme and control solutions are verified through computer simulations. Simulation results show that the proposed integrated energy storage and control solutions are capable of delivering smooth power to the grid while maintaining the turbine speed within its optimum range under varying wave conditions.

  14. Restricted conformal invariance in QCD and its predictive power for virtual two-photon processes

    CERN Document Server

    Müller, D

    1998-01-01

    The conformal algebra provides powerful constraints, which guarantee that renormalized conformally covariant operators exist in the hypothetical conformal limit of the theory, where the $\\beta$-function vanishes. Thus, in this limit also the conformally covariant operator product expansion on the light cone holds true. This operator product expansion has predictive power for two-photon processes in the generalized Bjorken region. Only the Wilson coefficients and the anomalous dimensions that are known from deep inelastic scattering are required for the prediction of all other two-photon processes in terms of the process-dependent off-diagonal expectation values of conformal operators. It is checked that the next-to-leading order calculations for the flavour non-singlet meson transition form factors are consistent with the corrections to the corresponding Wilson coefficients in deep inelasitic scattering.

  15. Improved methods of online monitoring and prediction in condensate and feed water system of nuclear power plant

    International Nuclear Information System (INIS)

    Wang, Hang; Peng, Min-jun; Wu, Peng; Cheng, Shou-yu

    2016-01-01

    Highlights: • Different methods for online monitoring and diagnosis are summarized. • Numerical simulation modeling of condensate and feed water system in nuclear power plant are done by FORTRAN programming. • Integrated online monitoring and prediction methods have been developed and tested. • Online monitoring module, fault diagnosis module and trends prediction module can be verified with each other. - Abstract: Faults or accidents may occur in a nuclear power plant (NPP), but it is hard for operators to recognize the situation and take effective measures quickly. So, online monitoring, diagnosis and prediction (OMDP) is used to provide enough information to operators and improve the safety of NPPs. In this paper, distributed conservation equation (DCE) and artificial immunity system (AIS) are proposed for online monitoring and diagnosis. On this basis, quantitative simulation models and interactive database are combined to predict the trends and severity of faults. The effectiveness of OMDP in improving the monitoring and prediction of condensate and feed water system (CFWS) was verified through simulation tests.

  16. An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

    Directory of Open Access Journals (Sweden)

    Guo-Qiang Zeng

    2017-11-01

    Full Text Available As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

  17. Is It Really Self-Control? Examining the Predictive Power of the Delay of Gratification Task

    Science.gov (United States)

    Duckworth, Angela L.; Tsukayama, Eli; Kirby, Teri A.

    2013-01-01

    This investigation tests whether the predictive power of the delay of gratification task (colloquially known as the “marshmallow test”) derives from its assessment of self-control or of theoretically unrelated traits. Among 56 school-age children in Study 1, delay time was associated with concurrent teacher ratings of self-control and Big Five conscientiousness—but not with other personality traits, intelligence, or reward-related impulses. Likewise, among 966 preschool children in Study 2, delay time was consistently associated with concurrent parent and caregiver ratings of self-control but not with reward-related impulses. While delay time in Study 2 was also related to concurrently measured intelligence, predictive relations with academic, health, and social outcomes in adolescence were more consistently explained by ratings of effortful control. Collectively, these findings suggest that delay task performance may be influenced by extraneous traits, but its predictive power derives primarily from its assessment of self-control. PMID:23813422

  18. The assessment of different models to predict solar module temperature, output power and efficiency for Nis, Serbia

    International Nuclear Information System (INIS)

    Pantic, Lana S.; Pavlović, Tomislav M.; Milosavljević, Dragana D.; Radonjic, Ivana S.; Radovic, Miodrag K.; Sazhko, Galina

    2016-01-01

    Five different models for calculating solar module temperature, output power and efficiency for sunny days with different solar radiation intensities and ambient temperatures are assessed in this paper. Thereafter, modeled values are compared to the experimentally obtained values for the horizontal solar module in Nis, Serbia. The criterion for determining the best model was based on the statistical analysis and the agreement between the calculated and the experimental values. The calculated values of solar module temperature are in good agreement with the experimentally obtained ones, with some variations over and under the measured values. The best agreement between calculated and experimentally obtained values was for summer months with high solar radiation intensity. The nonlinear model for calculating the output power is much better than the linear model and at the same time predicts better the total electrical energy generated by the solar module during the day. The nonlinear model for calculating the solar module efficiency predicts the efficiency higher than the STC (Standard Test Conditions) value of solar module efficiency for all conditions, while the linear model predicts the solar module efficiency very well. This paper provides a simple and efficient guideline to estimate relevant parameters of a monocrystalline silicon solar module under the moderate-continental climate conditions. - Highlights: • Linear model for solar module temperature gives accurate predictions for August. • The nonlinear model better predicts the solar module power than the linear model. • For calculating solar module power for Nis we propose the nonlinear model. • For calculating solar model efficiency for Nis we propose adoption of linear model. • The adopted models can be used for calculations throughout the year.

  19. Predicting the long tail of book sales: Unearthing the power-law exponent

    Science.gov (United States)

    Fenner, Trevor; Levene, Mark; Loizou, George

    2010-06-01

    The concept of the long tail has recently been used to explain the phenomenon in e-commerce where the total volume of sales of the items in the tail is comparable to that of the most popular items. In the case of online book sales, the proportion of tail sales has been estimated using regression techniques on the assumption that the data obeys a power-law distribution. Here we propose a different technique for estimation based on a generative model of book sales that results in an asymptotic power-law distribution of sales, but which does not suffer from the problems related to power-law regression techniques. We show that the proportion of tail sales predicted is very sensitive to the estimated power-law exponent. In particular, if we assume that the power-law exponent of the cumulative distribution is closer to 1.1 rather than to 1.2 (estimates published in 2003, calculated using regression by two groups of researchers), then our computations suggest that the tail sales of Amazon.com, rather than being 40% as estimated by Brynjolfsson, Hu and Smith in 2003, are actually closer to 20%, the proportion estimated by its CEO.

  20. PID and predictive control of electrical drives and power converters using MATLAB/Simulink

    CERN Document Server

    Wang, Liuping; Yoo, Dae; Gan, Lu; Ng, Ki

    2015-01-01

    A timely introduction to current research on PID and predictive control by one of the leading authors on the subject PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice.  The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis.    The book contains secti

  1. A mathematical look at a physical power prediction model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Riso National Lab., Roskilde (Denmark)

    1997-12-31

    This paper takes a mathematical look at a physical model used to predict the power produced from wind farms. The reason is to see whether simple mathematical expressions can replace the original equations, and to give guidelines as to where the simplifications can be made and where they can not. This paper shows that there is a linear dependence between the geostrophic wind and the wind at the surface, but also that great care must be taken in the selection of the models since physical dependencies play a very important role, e.g. through the dependence of the turning of the wind on the wind speed.

  2. Mentoring Support and Power: A Three Year Predictive Field Study on Protege Networking and Career Success

    Science.gov (United States)

    Blickle, Gerhard; Witzki, Alexander H.; Schneider, Paula B.

    2009-01-01

    Career success of early employees was analyzed from a power perspective and a developmental network perspective. In a predictive field study with 112 employees mentoring support and mentors' power were assessed in the first wave, employees' networking was assessed after two years, and career success (i.e. income and hierarchical position) and…

  3. Testing the Predictive Power of Coulomb Stress on Aftershock Sequences

    Science.gov (United States)

    Woessner, J.; Lombardi, A.; Werner, M. J.; Marzocchi, W.

    2009-12-01

    Empirical and statistical models of clustered seismicity are usually strongly stochastic and perceived to be uninformative in their forecasts, since only marginal distributions are used, such as the Omori-Utsu and Gutenberg-Richter laws. In contrast, so-called physics-based aftershock models, based on seismic rate changes calculated from Coulomb stress changes and rate-and-state friction, make more specific predictions: anisotropic stress shadows and multiplicative rate changes. We test the predictive power of models based on Coulomb stress changes against statistical models, including the popular Short Term Earthquake Probabilities and Epidemic-Type Aftershock Sequences models: We score and compare retrospective forecasts on the aftershock sequences of the 1992 Landers, USA, the 1997 Colfiorito, Italy, and the 2008 Selfoss, Iceland, earthquakes. To quantify predictability, we use likelihood-based metrics that test the consistency of the forecasts with the data, including modified and existing tests used in prospective forecast experiments within the Collaboratory for the Study of Earthquake Predictability (CSEP). Our results indicate that a statistical model performs best. Moreover, two Coulomb model classes seem unable to compete: Models based on deterministic Coulomb stress changes calculated from a given fault-slip model, and those based on fixed receiver faults. One model of Coulomb stress changes does perform well and sometimes outperforms the statistical models, but its predictive information is diluted, because of uncertainties included in the fault-slip model. Our results suggest that models based on Coulomb stress changes need to incorporate stochastic features that represent model and data uncertainty.

  4. Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

    Science.gov (United States)

    Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

    2018-01-01

    Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

  5. The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

    Science.gov (United States)

    Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

    2017-08-01

    The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

  6. Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

    Science.gov (United States)

    Ibrahim, Wael Refaat Anis

    The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an

  7. Investigation of Predictive Power of Mathematics Anxiety on Mathematics Achievement in Terms of Gender and Class Variables

    Directory of Open Access Journals (Sweden)

    Mustafa İLHAN

    2013-12-01

    Full Text Available This research aims to explore predictive power of mathematics anxiety in terms of gender and class variables. For this purpose relational model was used in the study. Working group of the research consists of 348 secondary school second stage students, 175 of whom are girls and 175 are boys, having education in four elementary schools in central district of Diyarbakır province, during 2011-2012 Academic Year, first Semester. “Math Anxiety Scale for Primary School Students” to determine students’ mathematics anxiety was used. Averages of students’ mathematics notes in the first term of 2011- 2012 academic year are taken as the achievement scores of mathematics. The collected data has been analyzed by SPSS 17.0. The relationship between mathematics achievement and math anxiety was analyzed with pearson correlation. The predictor power of math anxiety for mathematics achievement was determined by the regression analysis. According the research findings %17 of the total variance of mathematics achievement can be explained by math anxiety. It has been determined that predictive power of mathematics anxiety on mathematics success is higher in girls than boys. Furthermore, it has been determined in the research that predictive power of mathematics anxiety on mathematics success increases, as students proceed towards the next grade.

  8. Modeling and validation of a mechanistic tool (MEFISTO) for the prediction of critical power in BWR fuel assemblies

    International Nuclear Information System (INIS)

    Adamsson, Carl; Le Corre, Jean-Marie

    2011-01-01

    Highlights: → The MEFISTO code efficiently and accurately predicts the dryout event in a BWR fuel bundle, using a mechanistic model. → A hybrid approach between a fast and robust sub-channel analysis and a three-field two-phase analysis is adopted. → MEFISTO modeling approach, calibration, CPU usage, sensitivity, trend analysis and performance evaluation are presented. → The calibration parameters and process were carefully selected to preserve the mechanistic nature of the code. → The code dryout prediction performance is near the level of fuel-specific empirical dryout correlations. - Abstract: Westinghouse is currently developing the MEFISTO code with the main goal to achieve fast, robust, practical and reliable prediction of steady-state dryout Critical Power in Boiling Water Reactor (BWR) fuel bundle based on a mechanistic approach. A computationally efficient simulation scheme was used to achieve this goal, where the code resolves all relevant field (drop, steam and multi-film) mass balance equations, within the annular flow region, at the sub-channel level while relying on a fast and robust two-phase (liquid/steam) sub-channel solution to provide the cross-flow information. The MEFISTO code can hence provide highly detailed solution of the multi-film flow in BWR fuel bundle while enhancing flexibility and reducing the computer time by an order of magnitude as compared to a standard three-field sub-channel analysis approach. Models for the numerical computation of the one-dimensional field flowrate distributions in an open channel (e.g. a sub-channel), including the numerical treatment of field cross-flows, part-length rods, spacers grids and post-dryout conditions are presented in this paper. The MEFISTO code is then applied to dryout prediction in BWR fuel bundle using VIPRE-W as a fast and robust two-phase sub-channel driver code. The dryout power is numerically predicted by iterating on the bundle power so that the minimum film flowrate in the

  9. Kicking Back Cognitive Ageing: Leg Power Predicts Cognitive Ageing after Ten Years in Older Female Twins.

    Science.gov (United States)

    Steves, Claire J; Mehta, Mitul M; Jackson, Stephen H D; Spector, Tim D

    2016-01-01

    Many observational studies have shown a protective effect of physical activity on cognitive ageing, but interventional studies have been less convincing. This may be due to short time scales of interventions, suboptimal interventional regimes or lack of lasting effect. Confounding through common genetic and developmental causes is also possible. We aimed to test whether muscle fitness (measured by leg power) could predict cognitive change in a healthy older population over a 10-year time interval, how this performed alongside other predictors of cognitive ageing, and whether this effect was confounded by factors shared by twins. In addition, we investigated whether differences in leg power were predictive of differences in brain structure and function after 12 years of follow-up in identical twin pairs. A total of 324 healthy female twins (average age at baseline 55, range 43-73) performed the Cambridge Neuropsychological Test Automated Battery (CANTAB) at two time points 10 years apart. Linear regression modelling was used to assess the relationships between baseline leg power, physical activity and subsequent cognitive change, adjusting comprehensively for baseline covariates (including heart disease, diabetes, blood pressure, fasting blood glucose, lipids, diet, body habitus, smoking and alcohol habits, reading IQ, socioeconomic status and birthweight). A discordant twin approach was used to adjust for factors shared by twins. A subset of monozygotic pairs then underwent magnetic resonance imaging. The relationship between muscle fitness and brain structure and function was assessed using linear regression modelling and paired t tests. A striking protective relationship was found between muscle fitness (leg power) and both 10-year cognitive change [fully adjusted model standardised β-coefficient (Stdβ) = 0.174, p = 0.002] and subsequent total grey matter (Stdβ = 0.362, p = 0.005). These effects were robust in discordant twin analyses, where within

  10. Prediction of power consumption and performance in ultrafiltration of simulated latex effluent using non-uniform pore sized membranes

    Energy Technology Data Exchange (ETDEWEB)

    Abdelrasoul, Amira; Doan, Huu; Lohi, Ali; Cheng, Chil-Hung [Ryerson University, 350 Victoria Street, Toronto (Canada)

    2016-03-15

    Tha aim of the present study was to develop a series of numerical models for an accurate prediction of the power consumption in ultrafiltration of simulated latex effluent. The developed power consumption model incorporated fouling attachment, as well as chemical and physical factors in membrane fouling, in order to ensure accurate prediction and scale-up. This model was applied to heterogeneous membranes with non-uniform pore sizes at a given operating conditions and membrane surface charges. Polysulfone flat membrane, with a membrane molecular weight cutoff (MWCO) of 60,000 dalton, at different surface charges was used under a constant flow rate and cross-flow mode. In addition, the developed models were examined using various membranes at a variety of surface charges so as to test the overall reliability and accuracy of these models. The power consumption predicted by the models corresponded to the calculated values from the experimental data for various hydrophilic and hydrophobic membranes with an error margin of 6.0% up to 19.1%.

  11. Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter

    DEFF Research Database (Denmark)

    Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro

    2009-01-01

    A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...

  12. Prediction of hydrogen concentration in nuclear power plant containment under severe accidents using cascaded fuzzy neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Geon Pil; Kim, Dong Yeong; Yoo, Kwae Hwan; Na, Man Gyun, E-mail: magyna@chosun.ac.kr

    2016-04-15

    Highlights: • We present a hydrogen-concentration prediction method in an NPP containment. • The cascaded fuzzy neural network (CFNN) is used in this prediction model. • The CFNN model is much better than the existing FNN model. • This prediction can help prevent severe accidents in NPP due to hydrogen explosion. - Abstract: Recently, severe accidents in nuclear power plants (NPPs) have attracted worldwide interest since the Fukushima accident. If the hydrogen concentration in an NPP containment is increased above 4% in atmospheric pressure, hydrogen combustion will likely occur. Therefore, the hydrogen concentration must be kept below 4%. This study presents the prediction of hydrogen concentration using cascaded fuzzy neural network (CFNN). The CFNN model repeatedly applies FNN modules that are serially connected. The CFNN model was developed using data on severe accidents in NPPs. The data were obtained by numerically simulating the accident scenarios using the MAAP4 code for optimized power reactor 1000 (OPR1000) because real severe accident data cannot be obtained from actual NPP accidents. The root-mean-square error level predicted by the CFNN model is below approximately 5%. It was confirmed that the CFNN model could accurately predict the hydrogen concentration in the containment. If NPP operators can predict the hydrogen concentration in the containment using the CFNN model, this prediction can assist them in preventing a hydrogen explosion.

  13. A review on the young history of the wind power short-term prediction

    DEFF Research Database (Denmark)

    Costa, A.; Crespo, A.; Navarro, J.

    2008-01-01

    This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art oil models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought...... on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly...

  14. Prediction and measurement of the electromagnetic environment of high-power medium-wave and short-wave broadcast antennas in far field.

    Science.gov (United States)

    Tang, Zhanghong; Wang, Qun; Ji, Zhijiang; Shi, Meiwu; Hou, Guoyan; Tan, Danjun; Wang, Pengqi; Qiu, Xianbo

    2014-12-01

    With the increasing city size, high-power electromagnetic radiation devices such as high-power medium-wave (MW) and short-wave (SW) antennas have been inevitably getting closer and closer to buildings, which resulted in the pollution of indoor electromagnetic radiation becoming worsened. To avoid such radiation exceeding the exposure limits by national standards, it is necessary to predict and survey the electromagnetic radiation by MW and SW antennas before constructing the buildings. In this paper, a modified prediction method for the far-field electromagnetic radiation is proposed and successfully applied to predict the electromagnetic environment of an area close to a group of typical high-power MW and SW wave antennas. Different from currently used simplified prediction method defined in the Radiation Protection Management Guidelines (H J/T 10. 3-1996), the new method in this article makes use of more information such as antennas' patterns to predict the electromagnetic environment. Therefore, it improves the prediction accuracy significantly by the new feature of resolution at different directions. At the end of this article, a comparison between the prediction data and the measured results is given to demonstrate the effectiveness of the proposed new method. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. Wind power application research on the fusion of the determination and ensemble prediction

    Science.gov (United States)

    Lan, Shi; Lina, Xu; Yuzhu, Hao

    2017-07-01

    The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0-24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

  16. Wind power plant

    Energy Technology Data Exchange (ETDEWEB)

    Kling, A

    1977-01-13

    The wind power plant described has at least one rotor which is coupled to an electricity generator. The systems are fixed to a suspended body so that it is possible to set up the wind power plant at greater height where one can expect stronger and more uniform winds. The anchoring on the ground or on a floating body is done by mooring cables which can simultaneously have the function of an electric cable. The whole system can be steered by fins. The rotor system itself consists of at least one pair of contrarotating, momentum balanced rotors.

  17. Simulations research of the global predictive control with self-adaptive in the gas turbine of the nuclear power plant

    International Nuclear Information System (INIS)

    Su Jie; Xia Guoqing; Zhang Wei

    2007-01-01

    For further improving the dynamic control capabilities of the gas turbine of the nuclear power plant, this paper puts forward to apply the algorithm of global predictive control with self-adaptive in the rotate speed control of the gas turbine, including control structure and the design of controller in the base of expounding the math model of the gas turbine of the nuclear power plant. the simulation results show that the respond of the change of the gas turbine speed under the control algorithm of global predictive control with self-adaptive is ten second faster than that under the PID control algorithm, and the output value of the gas turbine speed under the PID control algorithm is 1%-2% higher than that under the control slgorithm of global predictive control with self-adaptive. It shows that the algorithm of global predictive control with self-adaptive can better control the output of the speed of the gas turbine of the nuclear power plant and get the better control effect. (authors)

  18. New materials for boilers in USC power plants

    International Nuclear Information System (INIS)

    Hong, Sung Ho; Hong, Seok Joo

    2003-01-01

    The efficiency of boiler in fossil power plants is a strong function of steam temperature and pressure. Thus, the main technology of increasing boiler efficiency is the development of stronger high temperature materials, capable of operating under high stresses at ever increasing temperature. This paper will presents the new material relating to boiler of USC power plant

  19. Differentiated influences of risk perceptions on nuclear power acceptance according to acceptance targets: Evidence from Korea

    International Nuclear Information System (INIS)

    Roh, Seung Kook; Lee, Jin Won

    2017-01-01

    The determinants of the public's nuclear power acceptance have received considerable attention as decisive factors regarding nuclear power policy. However, the contingency of the relative importance of different determinants has been less explored. Building on the literature of psychological distance between the individual and the object, the present study demonstrates that the relative effects of different types of perceived risks regarding nuclear power generation differ across acceptance targets. Using a sample of Korea, our results show that, regarding national acceptance of nuclear power generation, perceived risk from nuclear power plants exerts a stronger negative effect than that from radioactive waste management; however, the latter exerts a stronger negative effect than the former on local acceptance of a nuclear power plant. This finding provides implications for efficient public communication strategy to raise nuclear power acceptance

  20. Competitive power development

    Energy Technology Data Exchange (ETDEWEB)

    Garrity, T.F.; Stoll, H.G. [GE Power Systems Engineering, Schenectady, NY (United States)

    1994-12-31

    Electric power is essential to economic growth and the improvement in the standard of living in modern societies. Maximizing the overall economic efficiency of electric power production can lead to even stronger economic growth. Overall electricity efficiency can be driven by utilization of the newest and most economically efficient technologies, utilization of the most efficient financial structuring, and efficient integration of coproduction of electricity and process energy. The challenge is to drive the power generation strategy toward maximum economic efficiency while improving the overall country environment emissions. This paper reviews the key power generation technologies available today and in the near future. Of key importance is the capital cost, efficiency, environmental impacts, and reliability of each technology and how these technologies can be integrated with efficient financial structurings to maximize the country power generation economic efficiency. Examples of several countries are used to show recent successes in maximizing economic efficiency.

  1. Aggression in Primary Schools: The Predictive Power of the School and Home Environment

    Science.gov (United States)

    Kozina, Ana

    2015-01-01

    In this study, we analyse the predictive power of home and school environment-related factors for determining pupils' aggression. The multiple regression analyses are performed for fourth- and eighth-grade pupils based on the Trends in Mathematics and Science Study (TIMSS) 2007 (N = 8394) and TIMSS 2011 (N = 9415) databases for Slovenia. At the…

  2. The impact of climate change on US power grids

    International Nuclear Information System (INIS)

    Lu, N.; Wong, P.C.; Leung, L.Y.; Scott, M.; Taylor, T.; Jiang, W.; Correia, J.

    2009-01-01

    This presentation discussed a new model and system designed to interactively predict the impact of climate change on power grids and its wider implications on national security. Sensitivity curves for daily energy consumption were analyzed. The model was used to investigate the localized and system-wide impact of digital end use technologies; demand changes due to different dynamic pricing models; and the effects of greenhouse gas (GHG) mitigation options. The study showed that annual peak loads and energy consumption will increase in the southwest regions of the United States. Peak load months will include spring and autumn months. The Pacific northwest will experience hotter days in the summer months, and the use of air conditioning systems is expected to increase significantly in the future. Air conditioning loads will require stronger voltage support and more reactive power consumption. Fault-induced delayed voltage recovery phenomena will pose a threat to system voltage stability. It was concluded that new technologies will help to mitigate the adverse impacts caused by climatic changes. tabs., figs

  3. Auditory prediction during speaking and listening.

    Science.gov (United States)

    Sato, Marc; Shiller, Douglas M

    2018-02-02

    In the present EEG study, the role of auditory prediction in speech was explored through the comparison of auditory cortical responses during active speaking and passive listening to the same acoustic speech signals. Two manipulations of sensory prediction accuracy were used during the speaking task: (1) a real-time change in vowel F1 feedback (reducing prediction accuracy relative to unaltered feedback) and (2) presenting a stable auditory target rather than a visual cue to speak (enhancing auditory prediction accuracy during baseline productions, and potentially enhancing the perturbing effect of altered feedback). While subjects compensated for the F1 manipulation, no difference between the auditory-cue and visual-cue conditions were found. Under visually-cued conditions, reduced N1/P2 amplitude was observed during speaking vs. listening, reflecting a motor-to-sensory prediction. In addition, a significant correlation was observed between the magnitude of behavioral compensatory F1 response and the magnitude of this speaking induced suppression (SIS) for P2 during the altered auditory feedback phase, where a stronger compensatory decrease in F1 was associated with a stronger the SIS effect. Finally, under the auditory-cued condition, an auditory repetition-suppression effect was observed in N1/P2 amplitude during the listening task but not active speaking, suggesting that auditory predictive processes during speaking and passive listening are functionally distinct. Copyright © 2018 Elsevier Inc. All rights reserved.

  4. Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes

    Directory of Open Access Journals (Sweden)

    Trine Krogh-Madsen

    2017-12-01

    Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.

  5. Short-term load and wind power forecasting using neural network-based prediction intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

  6. Using Unsupervised Machine Learning for Outlier Detection in Data to Improve Wind Power Production Prediction

    OpenAIRE

    Åkerberg, Ludvig

    2017-01-01

    The expansion of wind power for electrical energy production has increased in recent years and shows no signs of slowing down. This unpredictable source of energy has contributed to destabilization of the electrical grid causing the energy market prices to vary significantly on a daily basis. For energy producers and consumers to make good investments, methods have been developed to make predictions of wind power production. These methods are often based on machine learning were historical we...

  7. Discussion of “Prediction intervals for short-term wind farm generation forecasts” and “Combined nonparametric prediction intervals for wind power generation”

    DEFF Research Database (Denmark)

    Pinson, Pierre; Tastu, Julija

    2014-01-01

    A new score for the evaluation of interval forecasts, the so-called coverage width-based criterion (CWC), was proposed and utilized.. This score has been used for the tuning (in-sample) and genuine evaluation (out-ofsample) of prediction intervals for various applications, e.g., electric load [1......], electricity prices [2], general purpose prediction [3], and wind power generation [4], [5]. Indeed, two papers by the same authors appearing in the IEEE Transactions On Sustainable Energy employ that score and use it to conclude on the comparative quality of alternative approaches to interval forecasting...

  8. A comprehensive review of on-board State-of-Available-Power prediction techniques for lithium-ion batteries in electric vehicles

    Science.gov (United States)

    Farmann, Alexander; Sauer, Dirk Uwe

    2016-10-01

    This study provides an overview of available techniques for on-board State-of-Available-Power (SoAP) prediction of lithium-ion batteries (LIBs) in electric vehicles. Different approaches dealing with the on-board estimation of battery State-of-Charge (SoC) or State-of-Health (SoH) have been extensively discussed in various researches in the past. However, the topic of SoAP prediction has not been explored comprehensively yet. The prediction of the maximum power that can be applied to the battery by discharging or charging it during acceleration, regenerative braking and gradient climbing is definitely one of the most challenging tasks of battery management systems. In large lithium-ion battery packs because of many factors, such as temperature distribution, cell-to-cell deviations regarding the actual battery impedance or capacity either in initial or aged state, the use of efficient and reliable methods for battery state estimation is required. The available battery power is limited by the safe operating area (SOA), where SOA is defined by battery temperature, current, voltage and SoC. Accurate SoAP prediction allows the energy management system to regulate the power flow of the vehicle more precisely and optimize battery performance and improve its lifetime accordingly. To this end, scientific and technical literature sources are studied and available approaches are reviewed.

  9. Changes in attitude structure toward nuclear power in the nuclear power plant locations of Tohoku district

    International Nuclear Information System (INIS)

    Tsujikawa, Norifumi; Tsuchida, Shoji; Shiotani, Takamasa; Nakagawa, Yuri

    2012-01-01

    This survey was examined the changes in structure of attitude toward nuclear power and the influence of environmental value on the attitude structure before and after the accident at the Fukushima No. 1 nuclear power plant. With residents of Aomori, Miyagi, and Fukushima prefectures as participants, we conducted online surveys in November 2009 and October 2011. Comparing the results before and after the accident, we found that trust in the management of nuclear power plants had a stronger influence on the perceived risk and benefit regarding nuclear power after the accident than before the accident. The value of concern about environmental destruction resulted in reduced trust in the management. (author)

  10. Stronger misdirection in curved than in straight motion

    Directory of Open Access Journals (Sweden)

    Jorge eOtero-Millan

    2011-11-01

    Full Text Available Illusions developed by magicians are a rich and largely untapped source of insight into perception and cognition. Here we show that curved motion, as employed by the magician in a classic sleight of hand trick, generates stronger misdirection than rectilinear motion, and that this difference can be explained by the differential engagement of the smooth pursuit and the saccadic oculomotor systems. This research moreover exemplifies how the magician’s intuitive understanding of the spectator’s mindset can surpass that of the cognitive scientist in specific instances, and that observation-based behavioral insights developed by magicians are worthy of quantitative investigation in the neuroscience laboratory.

  11. Active Power Optimal Control of Wind Turbines with Doubly Fed Inductive Generators Based on Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Guo Jiuwang

    2015-01-01

    Full Text Available Because of the randomness and fluctuation of wind energy, as well as the impact of strongly nonlinear characteristic of variable speed constant frequency (VSCF wind power generation system with doubly fed induction generators (DFIG, traditional active power control strategies are difficult to achieve high precision control and the output power of wind turbines is more fluctuated. In order to improve the quality of output electric energy of doubly fed wind turbines, on the basis of analyzing the operating principles and dynamic characteristics of doubly fed wind turbines, this paper proposes a new active power optimal control method of doubly fed wind turbines based on predictive control theory. This method uses state space model of wind turbines, based on the prediction of the future state of wind turbines, moves horizon optimization, and meanwhile, gets the control signals of pitch angle and generator torque. Simulation results show that the proposed control strategies can guarantee the utilization efficiency for wind energy. Simultaneously, they can improve operation stability of wind turbines and the quality of electric energy.

  12. Low power predictable memory and processing architectures

    OpenAIRE

    Chen, Jiaoyan

    2013-01-01

    Great demand in power optimized devices shows promising economic potential and draws lots of attention in industry and research area. Due to the continuously shrinking CMOS process, not only dynamic power but also static power has emerged as a big concern in power reduction. Other than power optimization, average-case power estimation is quite significant for power budget allocation but also challenging in terms of time and effort. In this thesis, we will introduce a methodology to support mo...

  13. Power Moderates the Effects of Social Dominance Orientation on Punishment: An Experimental Analysis.

    Science.gov (United States)

    Heering, Maria Sophia; Leone, Luigi

    2018-01-01

    In this study, it was hypothesized that experimentally manipulated levels of power would moderate the association of dispositional social dominance orientation (SDO) with preferences for harsh punishment. In particular, we expected to detect a stronger effect for dispositional SDO in the low-power condition, relying on the notion that low power enhances sensitivity to threats to the status quo, and that high SDO individuals are particularly motivated to enforce hierarchy-enhancing measures as punishment. SDO scores were measured two months prior to the experiment, and then immediately after the experimental session. As expected, preexperimental SDO interacted with the power manipulation. We found stronger preexperimental SDO effects on punishment among low-power participants. We also anticipated and found that individuals high in SDO increased further their postexperimental SDO scores if assigned to a high-power condition. The discussion focuses on the importance of SDO effects among low-status groups and on how situational roles shape dispositional self-descriptions.

  14. Bell inequalities stronger than the Clauser-Horne-Shimony-Holt inequality for three-level isotropic states

    International Nuclear Information System (INIS)

    Ito, Tsuyoshi; Imai, Hiroshi; Avis, David

    2006-01-01

    We show that some two-party Bell inequalities with two-valued observables are stronger than the CHSH inequality for 3x3 isotropic states in the sense that they are violated by some isotropic states in the 3x3 system that do not violate the CHSH inequality. These Bell inequalities are obtained by applying triangular elimination to the list of known facet inequalities of the cut polytope on nine points. This gives a partial solution to an open problem posed by Collins and Gisin. The results of numerical optimization suggest that they are candidates for being stronger than the I 3322 Bell inequality for 3x3 isotropic states. On the other hand, we found no Bell inequalities stronger than the CHSH inequality for 2x2 isotropic states. In addition, we illustrate an inclusion relation among some Bell inequalities derived by triangular elimination

  15. One Year After Fukushima, Nuclear Safety Is Stronger

    International Nuclear Information System (INIS)

    2012-01-01

    Full text: Nuclear power is safer than it was a year ago as the nuclear industry, regulators and governments act on the lessons of Fukushima, but that safety must never be taken for granted, said Yukiya Amano, Director General of the International Atomic Energy Agency (IAEA). Speaking ahead of the first anniversary of the Fukushima Daiichi nuclear accident on 11 March, Amano said a culture of constant vigilance and improvement was vital to ensure that the benefits of nuclear power could be harnessed as safely as humanly possible. 'Nuclear safety is stronger than it was a year ago', he said. 'Fukushima Daiichi was a very serious accident, but we know what went wrong and we have a clear course of action to tackle those causes - not only in Japan, but anywhere in the world. 'Now we have to keep up the momentum. Complacency can kill'. On 11 March 2011 a huge earthquake and tsunami left more than 20 000 people dead or missing in eastern Japan. Amidst widespread destruction, the tsunami slammed into the Fukushima Daiichi Nuclear Power Station, disabling cooling systems and leading to fuel meltdowns in three of the six Units. The accident was a jolt to the nuclear industry, regulators and governments. It was triggered by a massive force of nature, but it was existing weaknesses of design regarding defence against natural hazards, regulatory oversight, accident management and emergency response that allowed it to unfold as it did. For example: The nuclear regulator was not sufficiently independent, allowing weak oversight of the operator, TEPCO, and regulatory requirements fell short of international best practice; Not enough attention was paid to guarding against possible extreme events at the Fukushima Daiichi site, leaving critical safety functions such as cooling systems vulnerable to the tsunami; Training to respond to serious accidents was inadequate, as were mitigation measures to prevent hydrogen explosions and protect the venting system; and Accident command lines

  16. The state-of-the-art in short-term prediction of wind power. A literature overview

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Brownsword, R.; Kariniotakis, G.

    2003-08-01

    Based on an appropriate questionnaire (WP1.1) and some other works already in progress, this report details the state-of-the-art in short term prediction of wind power, mostly summarising nearly all existing literature on the topic. (au)

  17. 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.

  18. Differentiated influences of risk perceptions on nuclear power acceptance according to acceptance targets: Evidence from Korea

    Directory of Open Access Journals (Sweden)

    Seungkook Roh

    2017-08-01

    Full Text Available The determinants of the public's nuclear power acceptance have received considerable attention as decisive factors regarding nuclear power policy. However, the contingency of the relative importance of different determinants has been less explored. Building on the literature of psychological distance between the individual and the object, the present study demonstrates that the relative effects of different types of perceived risks regarding nuclear power generation differ across acceptance targets. Using a sample of Korea, our results show that, regarding national acceptance of nuclear power generation, perceived risk from nuclear power plants exerts a stronger negative effect than that from radioactive waste management; however, the latter exerts a stronger negative effect than the former on local acceptance of a nuclear power plant. This finding provides implications for efficient public communication strategy to raise nuclear power acceptance.

  19. Differentiated influences of risk perceptions on nuclear power acceptance according to acceptance targets: Evidence from Korea

    Energy Technology Data Exchange (ETDEWEB)

    Roh, Seung Kook [Policy Research Center, Korea Atomic Energy Research Institute (KAERI), Daejeon (Korea, Republic of); Lee, Jin Won [School of Management, Xiamen University, Xiamen (China)

    2017-08-15

    The determinants of the public's nuclear power acceptance have received considerable attention as decisive factors regarding nuclear power policy. However, the contingency of the relative importance of different determinants has been less explored. Building on the literature of psychological distance between the individual and the object, the present study demonstrates that the relative effects of different types of perceived risks regarding nuclear power generation differ across acceptance targets. Using a sample of Korea, our results show that, regarding national acceptance of nuclear power generation, perceived risk from nuclear power plants exerts a stronger negative effect than that from radioactive waste management; however, the latter exerts a stronger negative effect than the former on local acceptance of a nuclear power plant. This finding provides implications for efficient public communication strategy to raise nuclear power acceptance.

  20. Using EarthScope magnetotelluric data to improve the resilience of the US power grid: rapid predictions of geomagnetically induced currents

    Science.gov (United States)

    Schultz, A.; Bonner, L. R., IV

    2016-12-01

    Existing methods to predict Geomagnetically Induced Currents (GICs) in power grids, such as the North American Electric Reliability Corporation standard adopted by the power industry, require explicit knowledge of the electrical resistivity structure of the crust and mantle to solve for ground level electric fields along transmission lines. The current standard is to apply regional 1-D resistivity models to this problem, which facilitates rapid solution of the governing equations. The systematic mapping of continental resistivity structure from projects such as EarthScope reveals several orders of magnitude of lateral variations in resistivity on local, regional and continental scales, resulting in electric field intensifications relative to existing 1-D solutions that can impact GICs to first order. The computational burden on the ground resistivity/GIC problem of coupled 3-D solutions inhibits the prediction of GICs in a timeframe useful to protecting power grids. In this work we reduce the problem to applying a set of filters, recognizing that the magnetotelluric impedance tensors implicitly contain all known information about the resistivity structure beneath a given site, and thus provides the required relationship between electric and magnetic fields at each site. We project real-time magnetic field data from distant magnetic observatories through a robustly calculated multivariate transfer function to locations where magnetotelluric impedance tensors had previously been obtained. This provides a real-time prediction of the magnetic field at each of those points. We then project the predicted magnetic fields through the impedance tensors to obtain predictions of electric fields induced at ground level. Thus, electric field predictions can be generated in real-time for an entire array from real-time observatory data, then interpolated onto points representing a power transmission line contained within the array to produce a combined electric field prediction

  1. ANN-based wavelet analysis for predicting electrical signal from photovoltaic power supply system

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, A. [Medea Univ., Medea (Algeria). Inst. of Science Engineering, Dept. of Electronics

    2007-07-01

    This study was conducted to predict different electrical signals from a photovoltaic power supply system (PVPS) using an artificial neural networks (ANN) with wavelet analysis. It involved the creation of a database of electrical signals (PV-generator current, voltage, battery current voltage, regulator current and voltage) obtained from an experimental PVPS system installed in the south of Algeria. The potential applications were for sizing and analyzing the performance of PVPS systems; control of maximum power point tracker (MPPT) in order to deliver the maximum energy from the PV-array; prediction of the optimal configuration (PV-array and battery sizing) of PVPS systems; expert configuration of PV-systems; faults diagnosis; supervision; and, control and monitoring. First, based on the wavelet analysis each electrical signal was mapped in several time frequency domains. The PV-system was then divided into 3-subsystems corresponding to ANN-PV generator model, ANN-battery model, and ANN-regulator model. An example of day-by-day prediction for each electrical signal was presented. The results of the proposed approach were in good agreement with experimental results. In addition, the accuracy of the proposed approach was more satisfactory when only ANN was used. It was concluded that this methodology offers the possibility of developing a new expert configuration of PVPS by implementing the soft computing ANN-wavelet program with a digital signal processing (DSP) circuit. 26 refs., 1 tab., 5 figs.

  2. Predicting speech intelligibility based on a correlation metric in the envelope power spectrum domain

    DEFF Research Database (Denmark)

    Relaño-Iborra, Helia; May, Tobias; Zaar, Johannes

    2016-01-01

    A speech intelligibility prediction model is proposed that combines the auditory processing front end of the multi-resolution speech-based envelope power spectrum model [mr-sEPSM; Jørgensen, Ewert, and Dau (2013). J. Acoust. Soc. Am. 134(1), 436–446] with a correlation back end inspired by the sh...

  3. Different Predictive Control Strategies for Active Load Management in Distributed Power Systems with High Penetration of Renewable Energy Sources

    DEFF Research Database (Denmark)

    Zong, Yi; Bindner, Henrik W.; Gehrke, Oliver

    2013-01-01

    In order to achieve a Danish energy supply based on 100% renewable energy from combinations of wind, biomass, wave and solar power in 2050 and to cover 50% of the Danish electricity consumption by wind power in 2020, it requires more renewable energy in buildings and industries (e.g. cold stores......, greenhouses, etc.), and to coordinate the management of large numbers of distributed energy resources with the smart grid solution. This paper presents different predictive control (Genetic Algorithm-based and Model Predictive Control-based) strategies that schedule controlled loads in the industrial...... and residential sectors, based on dynamic power price and weather forecast, considering users’ comfort settings to meet an optimization objective, such as maximum profit or minimum energy consumption. Some field tests were carried out on a facility for intelligent, active and distributed power systems, which...

  4. Predatory blue crabs induce stronger nonconsumptive effects in eastern oysters Crassostrea virginica than scavenging blue crabs

    Directory of Open Access Journals (Sweden)

    Avery E. Scherer

    2017-02-01

    Full Text Available By influencing critical prey traits such as foraging or habitat selection, predators can affect entire ecosystems, but the nature of cues that trigger prey reactions to predators are not well understood. Predators may scavenge to supplement their energetic needs and scavenging frequency may vary among individuals within a species due to preferences and prey availability. Yet prey reactions to consumers that are primarily scavengers versus those that are active foragers have not been investigated, even though variation in prey reactions to scavengers or predators might influence cascading nonconsumptive effects in food webs. Oysters Crassostrea virginica react to crab predators by growing stronger shells. We exposed oysters to exudates from crabs fed live oysters or fed aged oyster tissue to simulate scavenging, and to controls without crab cues. Oysters grew stronger shells when exposed to either crab exudate, but their shells were significantly stronger when crabs were fed live oysters. The stronger response to predators than scavengers could be due to inherent differences in diet cues representative of reduced risk in the presence of scavengers or to degradation of conspecific alarm cues in aged treatments, which may mask risk from potential predators subsisting by scavenging.

  5. Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles

    International Nuclear Information System (INIS)

    Pei, Lei; Zhu, Chunbo; Wang, Tiansi; Lu, Rengui; Chan, C.C.

    2014-01-01

    The goal of this study is to realize real-time predictions of the peak power/state of power (SOP) for lithium-ion batteries in electric vehicles (EVs). To allow the proposed method to be applicable to different temperature and aging conditions, a training-free battery parameter/state estimator is presented based on an equivalent circuit model using a dual extended Kalman filter (DEKF). In this estimator, the model parameters are no longer taken as functions of factors such as SOC (state of charge), temperature, and aging; instead, all parameters will be directly estimated under the present conditions, and the impact of the temperature and aging on the battery model will be included in the parameter identification results. Then, the peak power/SOP will be calculated using the estimated results under the given limits. As an improvement to the calculation method, a combined limit of current and voltage is proposed to obtain results that are more reasonable. Additionally, novel verification experiments are designed to provide the true values of the cells' peak power under various operating conditions. The proposed methods are implemented in experiments with LiFePO 4 /graphite cells. The validating results demonstrate that the proposed methods have good accuracy and high adaptability. - Highlights: • A real-time peak power/SOP prediction method for lithium-ion batteries is proposed. • A training-free method based on DEKF is presented for parameter identification. • The proposed method can be applied to different temperature and aging conditions. • The calculation of peak power under the current and voltage limits is improved. • Validation experiments are designed to verify the accuracy of prediction results

  6. Mid-term load forecasting of power systems by a new prediction method

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2008-01-01

    Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran's power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem

  7. Decentralized model predictive based load frequency control in an interconnected power system

    Energy Technology Data Exchange (ETDEWEB)

    Mohamed, T.H., E-mail: tarekhie@yahoo.co [High Institute of Energy, South Valley University (Egypt); Bevrani, H., E-mail: bevrani@ieee.or [Dept. of Electrical Engineering and Computer Science, University of Kurdistan (Iran, Islamic Republic of); Hassan, A.A., E-mail: aahsn@yahoo.co [Faculty of Engineering, Dept. of Electrical Engineering, Minia University, Minia (Egypt); Hiyama, T., E-mail: hiyama@cs.kumamoto-u.ac.j [Dept. of Electrical Engineering and Computer Science, Kumamoto University, Kumamoto (Japan)

    2011-02-15

    This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.

  8. Decentralized model predictive based load frequency control in an interconnected power system

    International Nuclear Information System (INIS)

    Mohamed, T.H.; Bevrani, H.; Hassan, A.A.; Hiyama, T.

    2011-01-01

    This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.

  9. Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

    Science.gov (United States)

    Cepowski, Tomasz

    2017-06-01

    The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

  10. Seismic response prediction for cabinets of nuclear power plants by using impact hammer test

    Energy Technology Data Exchange (ETDEWEB)

    Koo, Ki Young [Department of Civil and Structural Engineering, University of Sheffield, Sheffield (United Kingdom); Gook Cho, Sung [JACE KOREA, Gyeonggi-do (Korea, Republic of); Cui, Jintao [Department of Civil Engineering, Kunsan National University, Jeonbuk (Korea, Republic of); Kim, Dookie, E-mail: kim2kie@kunsan.ac.k [Department of Civil Engineering, Kunsan National University, Jeonbuk (Korea, Republic of)

    2010-10-15

    An effective method to predict the seismic response of electrical cabinets of nuclear power plants is developed. This method consists of three steps: (1) identification of the earthquake-equivalent force based on the idealized lumped-mass system of the cabinet, (2) identification of the state-space equation (SSE) model of the system using input-output measurements from impact hammer tests, and (3) seismic response prediction by calculating the output of the identified SSE model under the identified earthquake-equivalent force. A three-dimensional plate model of cabinet structures is presented for the numerical verification of the proposed method. Experimental validation of the proposed method is carried out on a three-story frame which represents the structure of a cabinet. The SSE model of the frame is accurately identified by impact hammer tests with high fitness values over 85% of the actual frame characteristics. Shaking table tests are performed using El Centro, Kobe, and Northridge earthquakes as input motions and the acceleration responses are measured. The responses of the model under the three earthquakes are predicted and then compared with the measured responses. The predicted and measured responses agree well with each other with fitness values of 65-75%. The proposed method is more advantageous over other methods that are based on finite element (FE) model updating since it is free from FE modeling errors. It will be especially effective for cabinet structures in nuclear power plants where conducting shaking table tests may not be feasible. Limitations of the proposed method are also discussed.

  11. When surging seas meet stronger rain: Nuclear techniques in flood management

    International Nuclear Information System (INIS)

    Quevenco, Rodolfo

    2015-01-01

    Unusually high rainfall in many parts of the world is a result of climate change, scientists say. Since warmer air can hold more water, the rationale goes, increased temperatures will increase the chances of stronger rainfall events. And when surging seas combine with stronger rain, the outcome is almost certain: floods. Floods are the most frequently occurring natural disasters, and south-east Asia is particularly vulnerable. Climate change and variability are expected to bring about increased typhoon activities, rising sea levels and off-season monsoon rains in southeast Asia and other regions. These can cause devastating floods in countries like Cambodia, Laos, Pakistan, the Philippines, Thailand and Viet Nam. For the residents of these countries who have survived the ravages of major floods, the road to recovery can be long and arduous. As the flood water recedes, they have to contend with new forms of flood: floods of concern and worries as to how to rebuild their houses, their lives and their cities. Governments, too, face huge challenges in rebuilding roads, public buildings, infrastructure and natural resources destroyed or polluted by the flood.

  12. A New Weighted Injury Severity Scoring System: Better Predictive Power for Pediatric Trauma Mortality.

    Science.gov (United States)

    Shi, Junxin; Shen, Jiabin; Caupp, Sarah; Wang, Angela; Nuss, Kathryn E; Kenney, Brian; Wheeler, Krista K; Lu, Bo; Xiang, Henry

    2018-05-02

    An accurate injury severity measurement is essential for the evaluation of pediatric trauma care and outcome research. The traditional Injury Severity Score (ISS) does not consider the differential risks of the Abbreviated Injury Scale (AIS) from different body regions nor is it pediatric specific. The objective of this study was to develop a weighted injury severity scoring (wISS) system for pediatric blunt trauma patients with better predictive power than ISS. Based on the association between mortality and AIS from each of the six ISS body regions, we generated different weights for the component AIS scores used in the calculation of ISS. The weights and wISS were generated using the National Trauma Data Bank (NTDB). The Nationwide Emergency Department Sample (NEDS) was used to validate our main results. Pediatric blunt trauma patients less than 16 years were included, and mortality was the outcome. Discrimination (areas under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, concordance) and calibration (Hosmer-Lemeshow statistic) were compared between the wISS and ISS. The areas under the receiver operating characteristic curves from the wISS and ISS are 0.88 vs. 0.86 in ISS=1-74 and 0.77 vs. 0.64 in ISS=25-74 (ppredictive value, negative predictive value, and concordance when they were compared at similar levels of sensitivity. The wISS had better calibration (smaller Hosmer-Lemeshow statistic) than the ISS (11.6 versus 19.7 for ISS=1-74 and 10.9 versus 12.6 for ISS= 25-74). The wISS showed even better discrimination with the NEDS. By weighting the AIS from different body regions, the wISS had significantly better predictive power for mortality than the ISS, especially in critically injured children.Level of Evidence and study typeLevel IV Prognostic/Epidemiological.

  13. Loneliness among University Students: Predictive Power of Sex Roles and Attachment Styles on Loneliness

    Science.gov (United States)

    Ilhan, Tahsin

    2012-01-01

    This study examined the predictive power of sex roles and attachment styles on loneliness. A total of 188 undergraduate students (114 female, and 74 male) from Gazi University completed the Bem Sex Role Inventory, UCLA Loneliness Scale, and Relationship Scales Questionnaire. Hierarchic Multiple Regression analysis and t-test were used to test…

  14. Combined Active and Reactive Power Control of Wind Farms based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Wang, Jianhui

    2017-01-01

    This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power control, the proposed control scheme considers the significant impact of active power on voltage variations due to the low X=R ratio...... of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive power, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate...... the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme....

  15. Model Predictive Control of Offshore Power Stations With Waste Heat Recovery

    DEFF Research Database (Denmark)

    Pierobon, Leonardo; Chan, Richard; Li, Xiangan

    2016-01-01

    The implementation of waste heat recovery units on oil and gas offshore platforms demands advances in both design methods and control systems. Model-based control algorithms can play an important role in the operation of offshore power stations. A novel regulator based on a linear model predictive...... control (MPC) coupled with a steady-state performance optimizer has been developed in the SIMULINK language and is documented in the paper. The test case is the regulation of a power system serving an oil and gas platform in the Norwegian Sea. One of the three gas turbines is combined with an organic...... Rankine cycle (ORC) turbogenerator to increase the energy conversion efficiency. Results show a potential reduction of frequency drop up to 40%for a step in the load set-point of 4 MW, compared to proportional–integral control systems. Fuel savings in the range of 2–3% are also expected by optimizing on...

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  17. Prediction signatures in the brain: Semantic pre-activation during language comprehension

    Directory of Open Access Journals (Sweden)

    Burkhard Maess

    2016-11-01

    Full Text Available There is broad agreement that context-based predictions facilitate lexical-semantic processing. A robust index of semantic prediction during language comprehension is an evoked response, known as the N400, whose amplitude is modulated as a function of semantic context. However, the underlying neural mechanisms that utilize relations of the prior context and the embedded word within it are largely unknown. We measured magnetoencephalography (MEG data while participants were listening to simple German sentences in which the verbs were either highly predictive for the occurrence of a particular noun (i.e., provided context or not. The identical set of nouns was presented in both conditions. Hence, differences for the evoked responses of the nouns can only be due to differences in the earlier context. We observed a reduction of the N400 response for highly predicted nouns. Interestingly, the opposite pattern was observed for the preceding verbs: Highly predictive (that is more informative verbs yielded stronger neural magnitude compared to less predictive verbs. A negative correlation between the N400 effect of the verb and that of the noun was found in a distributed brain network, indicating an integral relation between the predictive power of the verb and the processing of the subsequent noun. This network consisted of left hemispheric superior and middle temporal areas and a subcortical area; the parahippocampus. Enhanced activity for highly predictive relative to less predictive verbs, likely reflects establishing semantic features associated with the expected nouns, that is a pre-activation of the expected nouns.

  18. Predicting plankton net community production in the Atlantic Ocean

    Science.gov (United States)

    Serret, Pablo; Robinson, Carol; Fernández, Emilio; Teira, Eva; Tilstone, Gavin; Pérez, Valesca

    2009-07-01

    We present, test and implement two contrasting models to predict euphotic zone net community production (NCP), which are based on 14C primary production (PO 14CP) to NCP relationships over two latitudinal (ca. 30°S-45°N) transects traversing highly productive and oligotrophic provinces of the Atlantic Ocean (NADR, CNRY, BENG, NAST-E, ETRA and SATL, Longhurst et al., 1995 [An estimation of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research 17, 1245-1271]). The two models include similar ranges of PO 14CP and community structure, but differ in the relative influence of allochthonous organic matter in the oligotrophic provinces. Both models were used to predict NCP from PO 14CP measurements obtained during 11 local and three seasonal studies in the Atlantic, Pacific and Indian Oceans, and from satellite-derived estimates of PO 14CP. Comparison of these NCP predictions with concurrent in situ measurements and geochemical estimates of NCP showed that geographic and annual patterns of NCP can only be predicted when the relative trophic importance of local vs. distant processes is similar in both modeled and predicted ecosystems. The system-dependent ability of our models to predict NCP seasonality suggests that trophic-level dynamics are stronger than differences in hydrodynamic regime, taxonomic composition and phytoplankton growth. The regional differences in the predictive power of both models confirm the existence of biogeographic differences in the scale of trophic dynamics, which impede the use of a single generalized equation to estimate global marine plankton NCP. This paper shows the potential of a systematic empirical approach to predict plankton NCP from local and satellite-derived P estimates.

  19. Prediction of crack coalescence of steam generator tubes in nuclear power plants

    International Nuclear Information System (INIS)

    Abou-Hanna, Jeries; McGreevy, Timothy E.; Majumdar, Saurin

    2004-01-01

    Prediction of failure pressures of cracked steam generator tubes of nuclear power plants is an important ingredient in scheduling inspection and repair of tubes. Prediction is usually based on nondestructive evaluation (NDE) of cracks. NDE often reveals two neighboring cracks. If the cracks interact, the tube pressure under which the ligament between the two cracks fails could be much lower than the critical burst pressure of an individual equivalent crack. The ability to accurately predict the ligament failure pressure, called ''coalescence pressure,'' is important. The failure criterion was established by nonlinear finite element model (FEM) analyses of coalescence of two 100% through-wall collinear cracks. The ligament failure is precipitated by local instability of the ligament under plane strain conditions. As a result of this local instability, the ligament thickness in the radial direction decreases abruptly with pressure. Good correlation of FEM analysis results with experimental data obtained at Argonne National Laboratory's Energy Technology Division demonstrated that nonlinear FEM analyses are capable of predicting the coalescence pressure accurately for 100% through-wall cracks. This failure criterion and FEA work have been extended to axial cracks of varying ligament width, crack length, and cases where cracks are offset by axial or circumferential ligaments

  20. Collective Nostalgia Is Associated With Stronger Outgroup-Directed Anger and Participation in Ingroup-Favoring Collective Action

    Directory of Open Access Journals (Sweden)

    Wing-Yee Cheung

    2017-08-01

    Full Text Available Collective nostalgia refers to longing for the way society used to be. We tested whether collective nostalgia is associated with ingroup-favoring collective action and whether this association is mediated by outgroup-directed anger and outgroup-directed contempt. We conducted an online study of Hong Kong residents (N = 111 during a large-scale democratic social movement, the Umbrella Movement, that took place in Hong Kong in 2014 in response to proposed electoral reforms by the Chinese government in Mainland China. Reported collective nostalgia for Hong Kong’s past was high in our sample and collective nostalgia predicted stronger involvement in ingroup-favoring collective action, and it did so indirectly via higher intensity of outgroup-directed anger (but not through outgroup-directed contempt. We argue that collective nostalgia has implications for strengthening ingroup-serving collective action, and we highlight the importance of arousal of group-based emotions in this process.

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

    DEFF Research Database (Denmark)

    Hajizadeh, Amin; Shahirinia, Amir

    2017-01-01

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

  2. Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

    DEFF Research Database (Denmark)

    Preindl, Matthias; Schaltz, Erik

    2011-01-01

    The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive...... Direct Current Control (MPDCC) leads to an increase of torque control performance taking into account the discrete nature of inverters but temporary offsets and poor responses to load torque variations are still issues in speed control. A load torque estimator is proposed in this paper in order...

  3. Assess and Predict Automatic Generation Control Performances for Thermal Power Generation Units Based on Modeling Techniques

    Science.gov (United States)

    Zhao, Yan; Yang, Zijiang; Gao, Song; Liu, Jinbiao

    2018-02-01

    Automatic generation control(AGC) is a key technology to maintain real time power generation and load balance, and to ensure the quality of power supply. Power grids require each power generation unit to have a satisfactory AGC performance, being specified in two detailed rules. The two rules provide a set of indices to measure the AGC performance of power generation unit. However, the commonly-used method to calculate these indices is based on particular data samples from AGC responses and will lead to incorrect results in practice. This paper proposes a new method to estimate the AGC performance indices via system identification techniques. In addition, a nonlinear regression model between performance indices and load command is built in order to predict the AGC performance indices. The effectiveness of the proposed method is validated through industrial case studies.

  4. Prediction of Critical Power and W' in Hypoxia: Application to Work-Balance Modelling.

    Science.gov (United States)

    Townsend, Nathan E; Nichols, David S; Skiba, Philip F; Racinais, Sebastien; Périard, Julien D

    2017-01-01

    Purpose: Develop a prediction equation for critical power (CP) and work above CP (W') in hypoxia for use in the work-balance ([Formula: see text]) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W' at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W' at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W' were used to compute W' during HIIT using differential ([Formula: see text]) and integral ([Formula: see text]) forms of the [Formula: see text] model. Results: CP decreased at altitude ( P equations for CP and W' developed in this study are suitable for use with the [Formula: see text] model in acute hypoxia. This enables the application of [Formula: see text] modelling to training prescription and competition analysis at altitude.

  5. The Predictive Power of Evolutionary Biology and the Discovery of Eusociality in the Naked Mole-Rat.

    Science.gov (United States)

    Braude, Stanton

    1997-01-01

    Discusses how biologists use evolutionary theory and provides examples of how evolutionary biologists test hypotheses on specific modes of selection and evolution. Presents an example of the successful predictive power of one evolutionary hypothesis. Contains 38 references. (DDR)

  6. SIEX design predictions for the PNC fuel pins in the HEDL P-E01 power-to-melt test

    International Nuclear Information System (INIS)

    1979-01-01

    During the design phase of the HEDL P-E01 power-to-melt test, a series of design predictions were generated for the three PNC pins using the SIEX fuel pin modeling code. This document tabulates a series of selected PNC pin design predictions as requested by M. Shinohara during his visit to HEDL

  7. Gray model prediction of the sea wall profile survey in the first process of Qinshan Nuclear Power Plant

    International Nuclear Information System (INIS)

    Zang Deyan

    1998-01-01

    Based on gray system theory, the information about deformation observation of the first stage Qinshan nuclear power plant is analysed and predicted as well. The gray system theory is applied to engineering prediction and a large-scale building deformation observation. It is convenient to apply the model and it a has high degree of accuracy

  8. Monte Carlo simulation techniques for predicting annual power production

    International Nuclear Information System (INIS)

    Cross, J.P.; Bulandr, P.J.

    1991-01-01

    As the owner and operator of a number of small to mid-sized hydroelectric sites, STS HydroPower has been faced with the need to accurately predict anticipated hydroelectric revenues over a period of years. The typical approach to this problem has been to look at each site from a mathematical deterministic perspective and evaluate the annual production from historic streamflows. Average annual production is simply taken to be the area under the flow duration curve defined by the operating and design characteristics of the selected turbines. Minimum annual production is taken to be a historic dry year scenario and maximum production is viewed as power generated under the most ideal of conditions. Such an approach creates two problems. First, in viewing the characteristics of a single site, it does not take into account the probability of such an event occurring. Second, in viewing all sites in a single organization's portfolio together, it does not reflect the varying flow conditions at the different sites. This paper attempts to address the first of these two concerns, that being the creation of a simulation model utilizing the Monte Carlo method at a single site. The result of the analysis is a picture of the production at the site that is both a better representation of anticipated conditions and defined probabilistically

  9. Adjoint-based model predictive control of wind farms : Beyond the quasi steady-state power maximization

    NARCIS (Netherlands)

    Vali, M.; Petrović, Vlaho; Boersma, S.; van Wingerden, J.W.; Kuhn, Martin; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    2017-01-01

    In this paper, we extend our closed-loop optimal control framework for wind farms to minimize wake-induced power losses. We develop an adjoint-based model predictive controller which employs a medium-fidelity 2D dynamic wind farm model. The wind turbine axial induction factors are considered here

  10. Identification of the Predictive Power of Five Factor Personality Traits for Individual Instrument Performance Anxiety

    Science.gov (United States)

    Özdemir, Gökhan; Dalkiran, Esra

    2017-01-01

    This study, with the aim of identifying the predictive power of the five-factor personality traits of music teacher candidates on individual instrument performance anxiety, was designed according to the relational screening model. The study population was students attending the Music Education branch of Fine Arts Education Departments in…

  11. Lifetime prediction of high-power press-pack IGBTs in wind power applications

    DEFF Research Database (Denmark)

    Busca, Cristian

    The Wind Turbine (WT) industry is advancing at a rapid pace and the power rating of new WTs is continuously growing. The next generation large WTs are likely to be realized with full-scale power converters due to the advantages they offer in terms of grid code compliance, power density and decoup......The Wind Turbine (WT) industry is advancing at a rapid pace and the power rating of new WTs is continuously growing. The next generation large WTs are likely to be realized with full-scale power converters due to the advantages they offer in terms of grid code compliance, power density...... and decoupling of the generator and grid sides. Press-Pack (PP) Insulated Gate Bipolar Transistors (IGBTs) are promising semiconductor devices for the next generation large WTs due to the advantages they offer in terms of power capability, power density and thermal cycling capability. PP IGBTs require proper...

  12. Rainfall prediction using fuzzy inference system for preliminary micro-hydro power plant planning

    Science.gov (United States)

    Suprapty, B.; Malani, R.; Minardi, J.

    2018-04-01

    East Kalimantan is a very rich area with water sources, in the form of river streams that branch to the remote areas. The conditions of natural potency like this become alternative solution for area that has not been reached by the availability of electric energy from State Electricity Company. The river water in selected location (catchment area) which is channelled to the canal, pipeline or penstock can be used to drive the waterwheel or turbine. The amount of power obtained depends on the volume/water discharge and headwater (the effective height between the reservoir and the turbine). The water discharge is strongly influenced by the amount of rainfall. Rainfall is the amount of water falling on the flat surface for a certain period measured, in units of mm3, above the horizontal surface in the absence of evaporation, run-off and infiltration. In this study, the prediction of rainfall is done in the area of East Kalimantan which has 13 watersheds which, in principle, have the potential for the construction of Micro Hydro Power Plant. Rainfall time series data is modelled by using AR (Auto Regressive) Model based on FIS (Fuzzy Inference System). The FIS structure of the training results is then used to predict the next two years rainfall.

  13. Comparison of LOFT zero power physics testing measurement results with predicted values

    International Nuclear Information System (INIS)

    Rushton, B.L.; Howe, T.M.

    1978-01-01

    The results of zero power physics testing measurements in LOFT have been evaluated to assess the adequacy of the physics data used in the safety analyses performed for the LOFT FSAR and Technical Specifications. Comparisons of measured data with computed data were made for control rod worths, temperature coefficients, boron worths, and pressure coefficients. Measured boron concentrations at exact critical points were compared with predicted concentrations. Based on these comparisons, the reactivity parameter values used in the LOFT safety analyses were assessed for conservatism

  14. Reactive Power Impact on Lifetime Prediction of Two-level Wind Power Converter

    DEFF Research Database (Denmark)

    Zhou, Dao; Blaabjerg, Frede; Lau, M.

    2013-01-01

    The influence of reactive power injection on the dominating two-level wind power converter is investigated and compared in terms of power loss and thermal behavior. Then the lifetime of both the partial-scale and full-scale power converter is estimated based on the widely used Coffin-Manson model...

  15. Rehabilitation after stroke: predictive power of Barthel Index versus a cognitive and a motor index

    DEFF Research Database (Denmark)

    Engberg, A; Bentzen, L; Garde, B

    1995-01-01

    The aim of the present study was to investigate the predictive power of ratings of Barthel Index at Day 40 post stroke, compared with and/or combined with simultaneous ratings from a mobility scale (EG motor index) and a rather simple cognitive test scale (CT50). The parameter to be individually...

  16. The Prediction Power of Servant and Ethical Leadership Behaviours of Administrators on Teachers' Job Satisfaction

    Science.gov (United States)

    Güngör, Semra Kiranli

    2016-01-01

    The purpose of this study is to identify servant leadership and ethical leadership behaviors of administrators and the prediction power of these behaviors on teachers' job satisfaction according to the views of schoolteachers. This research, figured in accordance with the quantitative research processes. The target population of the research has…

  17. A Study of Performance in Low-Power Tokamak Reactor with Integrated Predictive Modeling Code

    International Nuclear Information System (INIS)

    Pianroj, Y.; Onjun, T.; Suwanna, S.; Picha, R.; Poolyarat, N.

    2009-07-01

    Full text: A fusion hybrid or a small fusion power output with low power tokamak reactor is presented as another useful application of nuclear fusion. Such tokamak can be used for fuel breeding, high-level waste transmutation, hydrogen production at high temperature, and testing of nuclear fusion technology components. In this work, an investigation of the plasma performance in a small fusion power output design is carried out using the BALDUR predictive integrated modeling code. The simulations of the plasma performance in this design are carried out using the empirical-based Mixed Bohm/gyro Bohm (B/gB) model, whereas the pedestal temperature model is based on magnetic and flow shear (δ α ρ ζ 2 ) stabilization pedestal width scaling. The preliminary results using this core transport model show that the central ion and electron temperatures are rather pessimistic. To improve the performance, the optimization approach are carried out by varying some parameters, such as plasma current and power auxiliary heating, which results in some improvement of plasma performance

  18. Prediction of power ramp defects - development of a physically based model and evaluation of existing criteria

    International Nuclear Information System (INIS)

    Notley, M.J.F.; Kohn, E.

    2001-01-01

    Power-ramp induced fuel failure is not a problem in the present CANDU reactors. The current empirical correlations that define probability of failure do not agree one-with-another and do not allow extrapolation outside the database. A new methodology, based on physical processes, is presented and compared to data. The methodology calculates the pre-ramp sheath stress and the incremental stress during the ramp, and whether or not there is a defect is predicted based on a failure threshold stress. The proposed model confirms the deductions made by daSilva from an empirical 'fit' to data from the 1988 PNGS power ramp failure incident. It is recommended that daSilvas' correlation be used as reference for OPG (Ontario Power Generation) power reactor fuel, and that extrapolation be performed using the new model. (author)

  19. Dependence of L-mode confinement on the electron cyclotron power deposition profile in the TCV tokamak

    Science.gov (United States)

    Kirneva, N. A.; Razumova, K. A.; Pochelon, A.; Behn, R.; Coda, S.; Curchod, L.; Duval, B. P.; Goodman, T. P.; Labit, B.; Karpushov, A. N.; Rancic, M.; Sauter, O.; Silva, M.; TCV Team

    2012-01-01

    Scenarios with different electron cyclotron heating power profile distributions and widths were compared for the first time in experiments on the Tokamak à Configuration Variable (TCV). The heating profile was changed from shot to shot over a wide range from localized on-axis, with normalized minor radius half-width at half maximum σ1/2 ~ 0.1, up to a widely distributed heating power profile with σ1/2 ~ 0.4 and finally to a profile peaked far off-axis. The global confinement, MHD activity, density, temperature and electron pressure profile evolution were compared. In particular, the energy confinement properties of discharges with localized on-axis heating and distributed on-axis heating were very similar, with degradation close to that predicted by the ITER L-mode scaling; in the case of off-axis heating, on the other hand, the confinement degradation was even stronger.

  20. The sigh of the oppressed: The palliative effects of ideology are stronger for people living in highly unequal neighbourhoods.

    Science.gov (United States)

    Sengupta, Nikhil K; Greaves, Lara M; Osborne, Danny; Sibley, Chris G

    2017-09-01

    Ideologies that legitimize status hierarchies are associated with increased well-being. However, which ideologies have 'palliative effects', why they have these effects, and whether these effects extend to low-status groups remain unresolved issues. This study aimed to address these issues by testing the effects of the ideology of Symbolic Prejudice on well-being among low- and high-status ethnic groups (4,519 Europeans and 1,091 Māori) nested within 1,437 regions in New Zealand. Results showed that Symbolic Prejudice predicted increased well-being for both groups, but that this relationship was stronger for those living in highly unequal neighbourhoods. This suggests that it is precisely those who have the strongest need to justify inequality that accrue the most psychological benefit from subscribing to legitimizing ideologies. © 2017 The British Psychological Society.

  1. Balancing computation and communication power in power constrained clusters

    Science.gov (United States)

    Piga, Leonardo; Paul, Indrani; Huang, Wei

    2018-05-29

    Systems, apparatuses, and methods for balancing computation and communication power in power constrained environments. A data processing cluster with a plurality of compute nodes may perform parallel processing of a workload in a power constrained environment. Nodes that finish tasks early may be power-gated based on one or more conditions. In some scenarios, a node may predict a wait duration and go into a reduced power consumption state if the wait duration is predicted to be greater than a threshold. The power saved by power-gating one or more nodes may be reassigned for use by other nodes. A cluster agent may be configured to reassign the unused power to the active nodes to expedite workload processing.

  2. Evaluation on the model of performance predictions for on-line monitoring system for combined-cycle power plant

    International Nuclear Information System (INIS)

    Kim, Si Moon

    2002-01-01

    This paper presents the simulation model developed to predict design and off-design performance of an actual combined cycle power plant(S-Station in Korea), which would be running combined with on-line performance monitoring system in an on-line real-time fashion. The first step in thermal performance analysis is to build an accurate performance model of the power plant, in order to achieve this goal, GateCycle program has been employed in developing the model. This developed models predict design and off-design performance with a precision of one percent over a wide range of operating conditions so that on-line real-time performance monitoring can accurately establish both current performance and expected performance and also help the operator identify problems before they would be noticed

  3. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  4. Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

    Institute of Scientific and Technical Information of China (English)

    Yi Zhang; Xiangjie Liu; Bin Qu

    2017-01-01

    Reliable load frequency control(LFC) is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control(DMPC) based on coordination scheme.The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints(GRCs), load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed–loop performance, and computational burden with the physical constraints.

  5. Quantitative power Doppler ultrasound measures of peripheral joint synovitis in poor prognosis early rheumatoid arthritis predict radiographic progression.

    Science.gov (United States)

    Sreerangaiah, Dee; Grayer, Michael; Fisher, Benjamin A; Ho, Meilien; Abraham, Sonya; Taylor, Peter C

    2016-01-01

    To assess the value of quantitative vascular imaging by power Doppler US (PDUS) as a tool that can be used to stratify patient risk of joint damage in early seropositive RA while still biologic naive but on synthetic DMARD treatment. Eighty-five patients with seropositive RA of power Doppler volume and 2D vascularity scores were the most useful US predictors of deterioration. These variables were modelled in two equations that estimate structural damage over 12 months. The equations had a sensitivity of 63.2% and specificity of 80.9% for predicting radiographic structural damage and a sensitivity of 54.2% and specificity of 96.7% for predicting structural damage on ultrasonography. In seropositive early RA, quantitative vascular imaging by PDUS has clinical utility in predicting which patients will derive benefit from early use of biologic therapy. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Geometrical prediction of maximum power point for photovoltaics

    International Nuclear Information System (INIS)

    Kumar, Gaurav; Panchal, Ashish K.

    2014-01-01

    Highlights: • Direct MPP finding by parallelogram constructed from geometry of I–V curve of cell. • Exact values of V and P at MPP obtained by Lagrangian interpolation exploration. • Extensive use of Lagrangian interpolation for implementation of proposed method. • Method programming on C platform with minimum computational burden. - Abstract: It is important to drive solar photovoltaic (PV) system to its utmost capacity using maximum power point (MPP) tracking algorithms. This paper presents a direct MPP prediction method for a PV system considering the geometry of the I–V characteristic of a solar cell and a module. In the first step, known as parallelogram exploration (PGE), the MPP is determined from a parallelogram constructed using the open circuit (OC) and the short circuit (SC) points of the I–V characteristic and Lagrangian interpolation. In the second step, accurate values of voltage and power at the MPP, defined as V mp and P mp respectively, are decided by the Lagrangian interpolation formula, known as the Lagrangian interpolation exploration (LIE). Specifically, this method works with a few (V, I) data points instead most of the MPP algorithms work with (P, V) data points. The performance of the method is examined by several PV technologies including silicon, copper indium gallium selenide (CIGS), copper zinc tin sulphide selenide (CZTSSe), organic, dye sensitized solar cell (DSSC) and organic tandem cells’ data previously reported in literatures. The effectiveness of the method is tested experimentally for a few silicon cells’ I–V characteristics considering variation in the light intensity and the temperature. At last, the method is also employed for a 10 W silicon module tested in the field. To testify the preciseness of the method, an absolute value of the derivative of power (P) with respect to voltage (V) defined as (dP/dV) is evaluated and plotted against V. The method estimates the MPP parameters with high accuracy for any

  7. Homage to Bateman: sex roles predict sex differences in sexual selection.

    Science.gov (United States)

    Fritzsche, Karoline; Arnqvis, Göran

    2013-07-01

    Classic sex role theory predicts that sexual selection should be stronger in males in taxa showing conventional sex roles and stronger in females in role reversed mating systems. To test this very central prediction and to assess the utility of different measures of sexual selection, we estimated sexual selection in both sexes in four seed beetle species with divergent sex roles using a novel experimental design. We found that sexual selection was sizeable in females and the strength of sexual selection was similar in females and males in role-reversed species. Sexual selection was overall significantly stronger in males than in females and residual selection formed a substantial component of net selection in both sexes. Furthermore, sexual selection in females was stronger in role-reversed species compared to species with conventional sex roles. Variance-based measures of sexual selection (the Bateman gradient and selection opportunities) were better predictors of sexual dimorphism in reproductive behavior and morphology across species compared to trait-based measures (selection differentials). Our results highlight the importance of using assays that incorporate components of fitness manifested after mating. We suggest that the Bateman gradient is generally the most informative measure of the strength of sexual selection in comparisons across sexes and/or species. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

  8. Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler

    Directory of Open Access Journals (Sweden)

    Zhenhao Tang

    2017-01-01

    Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.

  9. Outcome prediction in mild traumatic brain injury: age and clinical variables are stronger predictors than CT abnormalities.

    NARCIS (Netherlands)

    Jacobs, B.; Beems, T.; Stulemeijer, M.; Vugt, A.B. van; Vliet, A.M. van der; Borm, G.F.; Vos, P.E.

    2010-01-01

    Mild traumatic brain injury (mTBI) is a common heterogeneous neurological disorder with a wide range of possible clinical outcomes. Accurate prediction of outcome is desirable for optimal treatment. This study aimed both to identify the demographic, clinical, and computed tomographic (CT)

  10. Plant interactions alter the predictions of metabolic scaling theory.

    Directory of Open Access Journals (Sweden)

    Yue Lin

    Full Text Available Metabolic scaling theory (MST is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning. Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric, and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.

  11. Modeling and Predicting the EUR/USD Exchange Rate: The Role of Nonlinear Adjustments to Purchasing Power Parity

    OpenAIRE

    Jesús Crespo Cuaresma; Anna Orthofer

    2010-01-01

    Reliable medium-term forecasts are essential for forward-looking monetary policy decisionmaking. Traditionally, predictions of the exchange rate tend to be linked to the equilibrium concept implied by the purchasing power parity (PPP) theory. In particular, the traditional benchmark for exchange rate models is based on a linear adjustment of the exchange rate to the level implied by PPP. In the presence of aggregation effects, transaction costs or uncertainty, however, economic theory predict...

  12. BUILDING STRONGER STATE ENERGY PARTNERSHIPS WITH THE U.S. DEPARTMENT OF ENERGY

    Energy Technology Data Exchange (ETDEWEB)

    Kate Burke

    2002-11-01

    This technical progress report includes an update of the progress during the second year of cooperative agreement DE-FC26-00NT40802, Building Stronger State Energy Partnerships with the U.S. Department of Energy. The report also describes the barriers in conduct of the effort, and our assessment of future progress and activities.

  13. Gender, power, and intimate partner violence: a study on couples from rural Malawi.

    Science.gov (United States)

    Conroy, Amy A

    2014-03-01

    Gender-based power imbalances are perhaps the most compelling underlying explanation for intimate partner violence (IPV) among women in sub-Saharan Africa. However, an overemphasis on female victimization results in an incomplete understanding of men's experiences as victims and the broader dyadic context in which violence occurs. This study examines the role of three domains of relationship power (power resources, processes, and outcomes) on sexual and physical IPV victimization in a unique sample of 466 young couples from Malawi. Two power resources were studied, namely, income and education level. Power processes were captured with a measure of couple communication and collaboration called unity. Power outcomes included a measure of relationship dominance (male dominated or female-dominated/egalitarian). Multilevel logistic regression using the Actor Partner Interpersonal Model framework was used to test whether respondent and partner data were predictive of IPV. The findings show that unity and male dominance were salient power factors that influenced young people's risk for sexual IPV. Unity had a stronger protective effect on sexual IPV for women than for men. Involvement in a male-dominated relationship increased the risk of sexual IPV for women, but decreased the risk for men. The findings also showed that education level and unity were protective against physical IPV for both men and women. Contrary to what was expected, partner data did not play a role in the respondent's experience of IPV. The consistency of these findings with the literature, theory, and study limitations are discussed.

  14. Simulation of the impact of wind power on the transient fault behavior of the Nordic power system

    DEFF Research Database (Denmark)

    Jauch, Clemens; Sørensen, Poul Ejnar; Norheim, Ian

    2007-01-01

    influences the post-fault behavior of the Nordic power system. It is concluded that an increasing level of wind power penetration leads to stronger system oscillations in case of fixed speed wind turbines. It is found that fixed speed wind turbines that merely ride through transient faults have negative......In this paper the effect of wind power on the transient fault behavior of the Nordic power system is investigated. The Nordic power system is the interconnected power system of the countries Norway, Sweden, Finland and Denmark. For the purpose of these investigations the wind turbines installed...... and connected in eastern Denmark are taken as study case. The current and future wind power situation in eastern Denmark is modeled and short circuit faults in the system simulated. The simulations yield information on (i) how the faults impact on the wind turbines and (ii) how the response of the wind turbines...

  15. Do illness rating systems predict discharge location, length of stay, and cost after total hip arthroplasty?

    Directory of Open Access Journals (Sweden)

    Sarah E. Rudasill, BA

    2018-06-01

    Conclusions: These findings suggest that although ASA classifications predict discharge location and SOI scores predict length of stay and total costs, other factors beyond illness rating systems remain stronger predictors of discharge for THA patients.

  16. Automated lake-wide erosion predictions and economic damage calculations upstream of the Moses-Saunders power dam

    International Nuclear Information System (INIS)

    Zuzek, P.; Baird, W.F.; International Joint Commission, Ottawa, ON

    2008-01-01

    This presentation discussed an automated flood and erosion prediction system designed for the upstream sections of the Moses-Saunders power dam. The system included a wave prediction component along with 3-D maps, hourly run-ups, geographic information system (GIS) tools and a hazard analysis tool. Parcel, reach, township, and county databases were used to populate the system. The prediction system was used to develop detailed study sites of shore units in the study area. Shoreline classes included sand and cohesive buffs, low banks, coarse beaches, and cobble or boulder lags. Time series plots for Lake Ontario water and wave levels were presented. Great Lakes ice cover data were also included in the system as well as erosion predictions from 1961 to 1995. The system was also used to develop bluff recession equations and cumulative recession analyses for different regulation plans. Cumulative bluff recession and protection requirements were outlined. Screenshots of the flood and erosion prediction system interface were also included. tabs., figs

  17. HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

    Science.gov (United States)

    Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

    2015-04-01

    In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the downscaling and post-processing of ensemble extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological ensemble predictions, post-processing techniques need to be tested in order to improve the quality of the

  18. Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

    Directory of Open Access Journals (Sweden)

    Weibo Zhao

    2017-12-01

    Full Text Available Power generation industry is the key industry of carbon dioxide (CO2 emission in China. Assessing its future CO2 emissions is of great significance to the formulation and implementation of energy saving and emission reduction policies. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT, the influencing factors analysis model of CO2 emission of power generation industry is established. The ridge regression (RR method is used to estimate the historical data. In addition, a wavelet neural network (WNN prediction model based on Cuckoo Search algorithm optimized by Gauss (GCS is put forward to predict the factors in the STIRPAT model. Then, the predicted values are substituted into the regression model, and the CO2 emission estimation values of the power generation industry in China are obtained. It’s concluded that population, per capita Gross Domestic Product (GDP, standard coal consumption and thermal power specific gravity are the key factors affecting the CO2 emission from the power generation industry. Besides, the GCS-WNN prediction model has higher prediction accuracy, comparing with other models. Moreover, with the development of science and technology in the future, the CO2 emission growth in the power generation industry will gradually slow down according to the prediction results.

  19. Variability of ULF wave power at the magnetopause: a study at low latitude with Cluster data

    Science.gov (United States)

    Cornilleau-Wehrlin, N.; Grison, B.; Belmont, G.; Rezeau, L.; Chanteur, G.; Robert, P.; Canu, P.

    2012-04-01

    Strong ULF wave activity has been observed at magnetopause crossings since a long time. Those turbulent-like waves are possible contributors to particle penetration from the Solar Wind to the Magnetosphere through the magnetopause. Statistical studies have been performed to understand under which conditions the ULF wave power is the most intense and thus the waves can be the most efficient for particle transport from one region to the other. Clearly the solar wind pressure organizes the data, the stronger the pressure, the higher the ULF power (Attié et al 2008). Double STAR-Cluster comparison has shown that ULF wave power is stronger at low latitude than at high latitude (Cornilleau-Wehrlin et al, 2008). The different studies performed have not, up to now, shown a stronger power in the vicinity of local noon. Nevertheless under identical activity conditions, the variability of this power, even at a given location in latitude and local time is very high. The present work intends at understanding this variability by means of the multi spacecraft mission Cluster. The data used are from spring 2008, while Cluster was crossing the magnetopause at low latitude, in particularly quite Solar Wind conditions. The first region of interest of this study is the sub-solar point vicinity where the long wavelength surface wave effects are most unlikely.

  20. Increased fire frequency promotes stronger spatial genetic structure and natural selection at regional and local scales in Pinus halepensis Mill.

    Science.gov (United States)

    Budde, Katharina B; González-Martínez, Santiago C; Navascués, Miguel; Burgarella, Concetta; Mosca, Elena; Lorenzo, Zaida; Zabal-Aguirre, Mario; Vendramin, Giovanni G; Verdú, Miguel; Pausas, Juli G; Heuertz, Myriam

    2017-04-01

    The recurrence of wildfires is predicted to increase due to global climate change, resulting in severe impacts on biodiversity and ecosystem functioning. Recurrent fires can drive plant adaptation and reduce genetic diversity; however, the underlying population genetic processes have not been studied in detail. In this study, the neutral and adaptive evolutionary effects of contrasting fire regimes were examined in the keystone tree species Pinus halepensis Mill. (Aleppo pine), a fire-adapted conifer. The genetic diversity, demographic history and spatial genetic structure were assessed at local (within-population) and regional scales for populations exposed to different crown fire frequencies. Eight natural P. halepensis stands were sampled in the east of the Iberian Peninsula, five of them in a region exposed to frequent crown fires (HiFi) and three of them in an adjacent region with a low frequency of crown fires (LoFi). Samples were genotyped at nine neutral simple sequence repeats (SSRs) and at 251 single nucleotide polymorphisms (SNPs) from coding regions, some of them potentially important for fire adaptation. Fire regime had no effects on genetic diversity or demographic history. Three high-differentiation outlier SNPs were identified between HiFi and LoFi stands, suggesting fire-related selection at the regional scale. At the local scale, fine-scale spatial genetic structure (SGS) was overall weak as expected for a wind-pollinated and wind-dispersed tree species. HiFi stands displayed a stronger SGS than LoFi stands at SNPs, which probably reflected the simultaneous post-fire recruitment of co-dispersed related seeds. SNPs with exceptionally strong SGS, a proxy for microenvironmental selection, were only reliably identified under the HiFi regime. An increasing fire frequency as predicted due to global change can promote increased SGS with stronger family structures and alter natural selection in P. halepensis and in plants with similar life history traits

  1. How Many Model Evaluations Are Required To Predict The AEP Of A Wind Power Plant?

    DEFF Research Database (Denmark)

    Murcia Leon, Juan Pablo; Réthoré, Pierre-Elouan; Natarajan, Anand

    2015-01-01

    (AEP) predictions expensive. The objective of the present paper is to minimize the number of model evaluations required to capture the wind power plant's AEP using stationary wind farm flow models. Polynomial chaos techniques are proposed based on arbitrary Weibull distributed wind speed and Von Misses...... distributed wind direction. The correlation between wind direction and wind speed are captured by defining Weibull-parameters as functions of wind direction. In order to evaluate the accuracy of these methods the expectation and variance of the wind farm power distributions are compared against...... the traditional binning method with trapezoidal and Simpson's integration rules. The wind farm flow model used in this study is the semi-empirical wake model developed by Larsen [1]. Three test cases are studied: a single turbine, a simple and a real offshore wind power plant. A reduced number of model...

  2. Value Of Three Dimensional Power Doppler Ultrasound In Prediction Of Endometrial Carcinoma In Patients With Postmenopausal Bleeding

    International Nuclear Information System (INIS)

    Abou-Gabal, A.; Akl, Sh.A.; Hussain, Sh.H.; Allam, H.A.

    2013-01-01

    Objective: to determine whether endometrial volume or power Doppler indices as measured by 3D ultrasound imaging can discriminate between benign and malignant endometrium in women with postmenopausal bleeding and endometrial thickness > 5 mm. Study design: Eighty-four patients with postmenopausal bleeding and endometrial thickness > 5 mm underwent 3D power Doppler ultrasound examination of the corpus uteri. The endometrial volume was calculated, along with the vascularisation index (VI), flow index and vascularisation flow index (VFI) in the endometrium. The gold standard was the histological diagnosis of the endometrium. Results: There were 56 benign and 28 malignant endometrial. Endometrial thickness and volume were significantly larger in malignant than in benign endometrial, and flow indices in the endometrium were Significantly higher. The area under the ROC curve (AUC) of endometrial thickness was 0.83, that of endometrial volume 0.73, and that of the best power Doppler variable FI 0.93. The best logistic regression model for predicting malignancy contained the variables endometrial thickness and FI. Its AUC was 0.93. Conclusion: the diagnostic performance of endometrial volume measured by 3d imaging with regard to discriminating between benign and malignant endometrium was not superior to that of endometrial thickness measured by 2D ultrasound examination, but 3D power Doppler flow indices are good diagnostic tool in predicting endometrial carcinoma

  3. Application of an estimation model to predict future transients at US nuclear power plants

    International Nuclear Information System (INIS)

    Hallbert, B.P.; Blackman, H.S.

    1987-01-01

    A model developed by R.A. Fisher was applied to a set of Licensee Event Reports (LERs) summarizing transient initiating events at US commercial nuclear power plants. The empirical Bayes model was examined to study the feasibility of estimating the number of categories of transients which have not yet occurred at nuclear power plants. An examination of the model's predictive ability using an existing sample of data provided support for use of the model to estimate future transients. The estimate indicates that an approximate fifteen percent increase in the number of categories of transient initiating events may be expected during the period 1983--1993, assuming a stable process of transients. Limitations of the model and other possible applications are discussed. 10 refs., 1 fig., 3 tabs

  4. Specific predictive power of automatic spider-related affective associations for controllable and uncontrollable fear responses toward spiders

    NARCIS (Netherlands)

    Huijdlng, J; de Jong, PJ; Huijding, J.

    This study examined the predictive power of automatically activated spider-related affective associations for automatic and controllable fear responses. The Extrinsic Affective Simon Task (EAST; De Houwer, 2003) was used to indirectly assess automatic spider fear-related associations. The EAST and

  5. Wind power and the conditions at a liberalized power market

    International Nuclear Information System (INIS)

    Morthorst, P.E.

    2003-01-01

    Wind power is undergoing a rapid development nationally as well as globally and in a number of countries covers an increasing part of the power supply. At the same time an ongoing liberalization of power markets is taking place and to an increasing extent the owners of wind power plants will themselves have to be responsible for trading the power at the spot market and financially handling the balancing. In the western part of Denmark (Jutland/Funen area), wind-generated power from time to time covers almost 100% of total power consumption. Therefore some examples are chosen from this area to analyse in more detail how well large amounts of wind power in the short-term are handled at the power spot market. It turns out that there is a tendency that more wind power in the system in the short run leads to relatively lower spot prices, while less wind power implies relatively higher spot prices, although, with the exception of December 2002, in general no strong relationship is found. A stronger relationship is found at the regulating market, where there is a fairly clear tendency that the more wind power produced, the higher is the need for down-regulation, and, correspondingly, the less wind power produced, the higher is the need for up-regulation. In general for the Jutland/Funen area the average cost of down-regulation is calculated as 1 2 c euros/kWh regulated for 2002, while the cost of up-regulation amounts to 0 7 c euros/kWh regulated. (author)

  6. Explicit model predictive control applications in power systems: an AGC study for an isolated industrial system

    DEFF Research Database (Denmark)

    Jiang, Hao; Lin, Jin; Song, Yonghua

    2016-01-01

    Model predictive control (MPC), that can consider system constraints, is one of the most advanced control technology used nowadays. In power systems, MPC is applied in a way that an optimal control sequence is given every step by an online MPC controller. The main drawback is that the control law...

  7. Faster-higher-stronger -- greener

    International Nuclear Information System (INIS)

    Burgess, A.

    2000-01-01

    The Toronto Olympic Bid Committee is reported to have adopted a strong environmental orientation in its bid to bring the 2008 Olympic Games to Toronto. In a recent address, the President of the Committee outlined details of the bid's environmental component which emphasizes the role of sustainable development within the Olympics and the consequences of this orientation on the design, construction and operation of facilities. The Toronto Bid Committee has gained inspiration and momentum for its 'green bid' from the host city of the 2000 Olympic Games, Sidney, Australia, which has won widespread praise for its efforts to clean up Homebush Bay, a brownfield site long seen as a liability for the city. The Toronto Bid Committee is making itself accountable for: creating the healthiest possible conditions for the athletes, visitors and residents; designing for sustainability; protecting, restoring and enhancing human and natural habitats; conserving resources and minimizing the ecological impact of the Games; promoting innovative, technically proven Canadian environmental technology; and fostering environmental awareness and education. The Committee intends to make the environment a priority and not just an afterthought in the bidding process. It hopes to develop specific goals and where possible, quantifiable targets in non-polluting designs for all Olympic housing and sports facilities. Wherever possible renewable power such as wind, solar and fuel cells will be used, and cleaner fuels such as natural gas where green power is not a viable option

  8. Essentially stronger - 1999 EPCOR annual report

    International Nuclear Information System (INIS)

    1999-01-01

    The year 1999 has been a year of consolidation for EPCOR Utilities, uniting the the former brands of Edmonton Power, Aquaalta and Eltec under a new single brand, EPCOR, to provide Edmontonians with a safe, high quality and reliable essential service at competitive prices . The company is building for growth by augmenting its product line with natural gas and green power, accessing new capital, proceeding with new projects at various sites, creating EPCOR Power Development Corporation with an ambitious mandate to grow beyond the Utilities traditional service areas. In proof of that, EPCOR Water Services won a strategically important contract in Port Hardy, BC; EPCOR Technologies also has been involved in projects beyond Alberta. As a sign of confidence in the company, the City of Edmonton voted to retain ownership of the company in July. The Utility also managed to win national awards for both safety and environmental practices and is the first utility company to have all its generating plants meet ISO 14001 standards. During 2000 the company will tackle the evolution of industry restructuring , will explore more diverse financial structures to accommodate growth and the increase in demand for services to make sure that EPCOR will be a leading provider of electric power and natural gas services as the era of deregulated competitive electrical services in Alberta begins in 2001. This report provides details of the achievements of the company's business units in 1999, accompanied by a consolidated financial statement

  9. Feature Selection and ANN Solar Power Prediction

    OpenAIRE

    O’Leary, Daniel; Kubby, Joel

    2017-01-01

    A novel method of solar power forecasting for individuals and small businesses is developed in this paper based on machine learning, image processing, and acoustic classification techniques. Increases in the production of solar power at the consumer level require automated forecasting systems to minimize loss, cost, and environmental impact for homes and businesses that produce and consume power (prosumers). These new participants in the energy market, prosumers, require new artificial neural...

  10. Predictive Ability of the Medicine Ball Chest Throw and Vertical Jump Tests for Determining Muscular Strength and Power in Adolescents

    Science.gov (United States)

    Hackett, Daniel A.; Davies, Timothy B.; Ibel, Denis; Cobley, Stephen; Sanders, Ross

    2018-01-01

    This study examined the predictive ability of the medicine ball chest throw and vertical jump for muscular strength and power in adolescents. One hundred and ninety adolescents participated in this study. Participants performed trials of the medicine ball chest throw and vertical jump, with vertical jump peak power calculated via an estimation…

  11. Physics and nuclear power

    International Nuclear Information System (INIS)

    Buttery, N E

    2008-01-01

    Nuclear power owes its origin to physicists. Fission was demonstrated by physicists and chemists and the first nuclear reactor project was led by physicists. However as nuclear power was harnessed to produce electricity the role of the engineer became stronger. Modern nuclear power reactors bring together the skills of physicists, chemists, chemical engineers, electrical engineers, mechanical engineers and civil engineers. The paper illustrates this by considering the Sizewell B project and the role played by physicists in this. This covers not only the roles in design and analysis but in problem solving during the commissioning of first of a kind plant. Looking forward to the challenges to provide sustainable and environmentally acceptable energy sources for the future illustrates the need for a continuing synergy between physics and engineering. This will be discussed in the context of the challenges posed by Generation IV reactors

  12. 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....

  13. Bad is stronger than good

    NARCIS (Netherlands)

    Baumeister, R.F.; Bratslavsky, E.; Finkenauer, C.; Vohs, K.D.

    2001-01-01

    The greater power of bad events over good ones is found in everyday events, major life events (e.g., trauma), close relationship outcomes, social network patterns, interpersonal interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more impact than good ones, and bad

  14. Power Relative to Body Mass Best Predicts Change in Core Temperature During Exercise-Heat Stress.

    Science.gov (United States)

    Gibson, Oliver R; Willmott, Ashley G B; James, Carl A; Hayes, Mark; Maxwell, Neil S

    2017-02-01

    Gibson, OR, Willmott, AGB, James, CA, Hayes, M, and Maxwell, NS. Power relative to body mass best predicts change in core temperature during exercise-heat stress. J Strength Cond Res 31(2): 403-414, 2017-Controlling internal temperature is crucial when prescribing exercise-heat stress, particularly during interventions designed to induce thermoregulatory adaptations. This study aimed to determine the relationship between the rate of rectal temperature (Trec) increase, and various methods for prescribing exercise-heat stress, to identify the most efficient method of prescribing isothermic heat acclimation (HA) training. Thirty-five men cycled in hot conditions (40° C, 39% R.H.) for 29 ± 2 minutes. Subjects exercised at 60 ± 9% V[Combining Dot Above]O2peak, with methods for prescribing exercise retrospectively observed for each participant. Pearson product moment correlations were calculated for each prescriptive variable against the rate of change in Trec (° C·h), with stepwise multiple regressions performed on statistically significant variables (p ≤ 0.05). Linear regression identified the predicted intensity required to increase Trec by 1.0-2.0° C between 20- and 45-minute periods and the duration taken to increase Trec by 1.5° C in response to incremental intensities to guide prescription. Significant (p ≤ 0.05) relationships with the rate of change in Trec were observed for prescriptions based on relative power (W·kg; r = 0.764), power (%Powermax; r = 0.679), rating of perceived exertion (RPE) (r = 0.577), V[Combining Dot Above]O2 (%V[Combining Dot Above]O2peak; r = 0.562), heart rate (HR) (%HRmax; r = 0.534), and thermal sensation (r = 0.311). Stepwise multiple regressions observed relative power and RPE as variables to improve the model (r = 0.791), with no improvement after inclusion of any anthropometric variable. Prescription of exercise under heat stress using power (W·kg or %Powermax) has the strongest relationship with the rate of change in

  15. ELMO model predicts the price of electric power

    International Nuclear Information System (INIS)

    Antila, H.

    2001-01-01

    Electrowatt-Ekono has developed a new model, by which it is possible to make long-term prognoses on the development of electricity prices in the Nordic Countries. The ELMO model can be used as an analysis service of the electricity markets and estimation of the profitability of long-term power distribution contracts with different scenarios. It can also be applied for calculation of technical and economical fundamentals for new power plants, and for estimation of the effects of different taxation models on the emissions of power generation. The model describes the whole power generation system, the power and heat consumption and transmission. The Finnish power generation system is based on the Electrowatt-Ekono's boiler database by combining different data elements. Calculation is based on the assumption that the Nordic power generation system is used optimally, and that the production costs are minimised. In practise the effectively operated electricity markets ensure the optimal use of the production system. The market area to be described consists of Finland and Sweden. The spot prices have long been the same. Norway has been treated as a separate market area. The most potential power generation system, the power consumption and the power transmission system are presumed for the target year during a normal rainfall situation. The basic scenario is calculated on the basis of the preconditional data. The calculation is carried out on hourly basis, which enables the estimation of the price variation of electric power between different times during the day and seasons. The system optimises the power generation on the basis of electricity and heat consumption curves and fuel prices. The result is an hourly limit price for electric power. Estimates are presented as standard form reports. Prices are presented as average annuals, in the seasonal base, and in hourly or daily basis for different seasons

  16. Predictive Current Control of a 7-level AC-DC back-to-back Converter for Universal and Flexible Power Management System

    DEFF Research Database (Denmark)

    Bifaretti, Steffano; Zanchetta, Pericle; Iov, Florin

    2008-01-01

    The paper proposes a novel power conversion system for Universal and Flexible Power Management (UNIFLEX-PM) in Future Electricity Network. Its structure is based on a back-to-back three-phase AC-DC 7-level converter; each AC side is connected to a different PCC, representing the main grid and....../or various distributed generation systems. Effective and accurate power flow control is demonstrated through simulation in Matlab- Simulink environment on a model based on a two-port structure and using a Predictive Control technique. Control of different Power flow profiles has been successfully tested...

  17. Predictive maintenance: A new approach in maintenance of nuclear power plants

    International Nuclear Information System (INIS)

    Benvenuto, F.; Ferrari, L.

    2005-01-01

    The maintenance services for a Nuclear Power Plant are in general aimed at reaching the following goals: - Increase component availability and consequently decrease intervention frequency; - Reduce unexpected costs from unexpected repairs; - Progressively decrease the time of each intervention; - Improve the spare parts supply efficiency; - Improve spare parts and consumable warehouse managing; - Decrease maintenance costs. Most of the currently used maintenance activities refer to run-to-failure or preventive approaches: - Run-to-failure or Corrective Maintenance means that work is only carried out when a component or system is faulty and unable to perform its critical function. Non critical components such as filters or components with spare may be maintained in this way; - Preventive or Scheduled Maintenance involves a regular pre-set schedule programme of maintenance work. Programme outlined by the manufacturer of the component in question based on the design life of the component and based on past experience by operation. One step further than Preventive Maintenance is represented by Predictive Maintenance. Whereas Preventive Maintenance bases its schedules on past performance data, a predictive system acquires condition data from the machine to be maintained whilst the machine is in operation. The information obtained from this analysis indicates the condition in real time, provides a diagnosis of wear and shows any trend towards critical conditions. Predictive maintenance mainly consists of the following interventions: - Lubricant analysis; - Collection / analysis of functional parameters, such as motor absorption, flow rate, pressure, temperature, noise, vibration of rotating equipment, thermal efficiency, etc; - Periodical test of lifting systems; - Other operations to acquire sensitive equipment parameters. Predictive Maintenance can reduce the accidental intervention and extend the components life, and, in the end, is increasing the global availability

  18. Stronger interference from distractors in the right hemifield during visual search.

    Science.gov (United States)

    Carlei, Christophe; Kerzel, Dirk

    2018-03-01

    The orientation-bias hypothesis states that there is a bias to attend to the right visual hemifield (RVF) when there is spatial competition between stimuli in the left and right hemifield [Pollmann, S. (1996). A pop-out induced extinction-like phenomenon in neurologically intact subjects. Neuropsychologia, 34(5), 413-425. doi: 10.1016/0028-3932(95)00125-5 ]. In support of this hypothesis, stronger interference was reported for RVF distractors with contralateral targets. In contrast, previous studies using rapid serial visual presentation (RSVP) found stronger interference from distractors in the left visual hemifield (LVF). We used the additional singleton paradigm to test whether this discrepancy was due to the different distractor features that were employed (colour vs. orientation). Interference from the colour distractor with contralateral targets was larger in the RVF than in the LVF. However, the asymmetrical interference disappeared when observers had to search for an inconspicuous colour target instead of the inconspicuous shape target. We suggest that the LVF orienting-bias is limited to situations where search is driven by bottom-up saliency (singleton search) instead of top-down search goals (feature search). In contrast, analysis of the literature suggests the opposite for the LVF bias in RSVP tasks. Thus, the attentional asymmetry may depend on whether the task involves temporal or spatial competition, and whether search is based on bottom-up or top-down signals.

  19. The Integration Role of European Defense Procurement in Achieving a More Competitive and Stronger European Defense Equipment Market

    Science.gov (United States)

    2015-06-01

    and systems, even monopolistic ) essence of the supply side of the defense market . There are only a few suppliers that can meet today’s complex...DEFENSE PROCUREMENT IN ACHIEVING A MORE COMPETITIVE AND STRONGER EUROPEAN DEFENSE EQUIPMENT MARKET by Kiril O. Angelov June 2015 Thesis Advisor...COMPETITIVE AND STRONGER EUROPEAN DEFENSE EQUIPMENT MARKET 5. FUNDING NUMBERS 6. AUTHOR(S) Kiril O. Angelov 7. PERFORMING ORGANIZATION NAME(S) AND

  20. Validation of the Predicted Circumferential and Radial Mode Sound Power Levels in the Inlet and Exhaust Ducts of a Fan Ingesting Distorted Inflow

    Science.gov (United States)

    Koch, L. Danielle

    2012-01-01

    Fan inflow distortion tone noise has been studied computationally and experimentally. Data from two experiments in the NASA Glenn Advanced Noise Control Fan rig have been used to validate acoustic predictions. The inflow to the fan was distorted by cylindrical rods inserted radially into the inlet duct one rotor chord length upstream of the fan. The rods were arranged in both symmetric and asymmetric circumferential patterns. In-duct and farfield sound pressure level measurements were recorded. It was discovered that for positive circumferential modes, measured circumferential mode sound power levels in the exhaust duct were greater than those in the inlet duct and for negative circumferential modes, measured total circumferential mode sound power levels in the exhaust were less than those in the inlet. Predicted trends in overall sound power level were proven to be useful in identifying circumferentially asymmetric distortion patterns that reduce overall inlet distortion tone noise, as compared to symmetric arrangements of rods. Detailed comparisons between the measured and predicted radial mode sound power in the inlet and exhaust duct indicate limitations of the theory.

  1. A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm

    International Nuclear Information System (INIS)

    Aghajani, Afshin; Kazemzadeh, Rasool; Ebrahimi, Afshin

    2016-01-01

    Highlights: • Proposing a novel hybrid method for short-term prediction of wind farms with high accuracy. • Investigating the prediction accuracy for proposed method in comparison with other methods. • Investigating the effect of six types of parameters as input data on predictions. • Comparing results for 6 & 4 types of the input parameters – addition of pressure and air humidity. - Abstract: This paper proposes a novel hybrid approach to forecast electric power production in wind farms. Wavelet transform (WT) is employed to filter input data of wind power, while radial basis function (RBF) neural network is utilized for primary prediction. For better predictions the main forecasting engine is comprised of three multilayer perceptron (MLP) neural networks by different learning algorithms of Levenberg–Marquardt (LM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and Bayesian regularization (BR). Meta-heuristic technique Imperialist Competitive Algorithm (ICA) is used to optimize neural networks’ weightings in order to escape from local minima. In the forecast process, the real data of wind farms located in the southern part of Alberta, Canada, are used to train and test the proposed model. The data are a complete set of six meteorological and technical characteristics, including wind speed, wind power, wind direction, temperature, pressure, and air humidity. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. Results of optimizations indicate the superiority of the proposed method over the other mentioned techniques; and, forecasting error is remarkably reduced. For instance, the average normalized root mean square error (NRMSE) and average mean absolute percentage error (MAPE) are respectively 11% and 14% lower for the proposed method in 1-h-ahead forecasts over a 24-h period with six types of input than those for the best of the compared models.

  2. The predictive power of SIMION/SDS simulation software for modeling ion mobility spectrometry instruments

    Science.gov (United States)

    Lai, Hanh; McJunkin, Timothy R.; Miller, Carla J.; Scott, Jill R.; Almirall, José R.

    2008-09-01

    The combined use of SIMION 7.0 and the statistical diffusion simulation (SDS) user program in conjunction with SolidWorks® with COSMSOSFloWorks® fluid dynamics software to model a complete, commercial ion mobility spectrometer (IMS) was demonstrated for the first time and compared to experimental results for tests using compounds of immediate interest in the security industry (e.g., 2,4,6-trinitrotoluene, 2,7-dinitrofluorene, and cocaine). The effort of this research was to evaluate the predictive power of SIMION/SDS for application to IMS instruments. The simulation was evaluated against experimental results in three studies: (1) a drift:carrier gas flow rates study assesses the ability of SIMION/SDS to correctly predict the ion drift times; (2) a drift gas composition study evaluates the accuracy in predicting the resolution; (3) a gate width study compares the simulated peak shape and peak intensity with the experimental values. SIMION/SDS successfully predicted the correct drift time, intensity, and resolution trends for the operating parameters studied. Despite the need for estimations and assumptions in the construction of the simulated instrument, SIMION/SDS was able to predict the resolution between two ion species in air within 3% accuracy. The preliminary success of IMS simulations using SIMION/SDS software holds great promise for the design of future instruments with enhanced performance.

  3. Optimal Active Power Control of A Wind Farm Equipped with Energy Storage System based on Distributed Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

    2016-01-01

    This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm...... is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads...

  4. Model Predictive Control of Power Converters for Robust and Fast Operation of AC Microgrids

    DEFF Research Database (Denmark)

    Dragicevic, Tomislav

    2018-01-01

    the load power at the same time. Those functionalities are conventionally achieved by hierarchical linear control loops. However, they have limited transient response and high sensitivity to parameter variations. This paper aims to mitigate these problems by firstly introducing an improvement of the FCS......This paper proposes the application of a finite control set model predictive control (FCS-MPC) strategy in standalone ac microgrids (MGs). AC MGs are usually built from two or more voltage source converters (VSCs) which can regulate the voltage at the point of common coupling, while sharing......-MPC strategy for a single VSC based on tracking of derivative of the voltage reference trajectory. Using only a single step prediction horizon, the proposed strategy exhibits low computational expense but provides steady state performance comparable to PWM, while its transient response and robustness...

  5. The predictive power of the business and bank sentiment of firms : A high-dimensional Granger causality approach

    NARCIS (Netherlands)

    Wilms, I.; Gelper, S.E.C.; Croux, C.

    2016-01-01

    We study the predictive power of industry-specific economic sentiment indicators for future macro-economic developments. In addition to the sentiment of firms towards their own business situation, we study their sentiment with respect to the banking sector – their main credit providers. The use of

  6. Gender, Power, and Intimate Partner Violence: A Study on Couples From Rural Malawi

    Science.gov (United States)

    Conroy, Amy A.

    2013-01-01

    Gender-based power imbalances are perhaps the most compelling underlying explanation for intimate partner violence (IPV) among women in sub-Saharan Africa. However, an overemphasis on female victimization results in an incomplete understanding of men’s experiences as victims and the broader dyadic context in which violence occurs. This study examines the role of three domains of relationship power (power resources, processes, and outcomes) on sexual and physical IPV victimization in a unique sample of 466 young couples from Malawi. Two power resources were studied, namely, income and education level. Power processes were captured with a measure of couple communication and collaboration called unity. Power outcomes included a measure of relationship dominance (male dominated or female-dominated/egalitarian). Multilevel logistic regression using the Actor Partner Interpersonal Model framework was used to test whether respondent and partner data were predictive of IPV. The findings show that unity and male dominance were salient power factors that influenced young people’s risk for sexual IPV. Unity had a stronger protective effect on sexual IPV for women than for men. Involvement in a male-dominated relationship increased the risk of sexual IPV for women, but decreased the risk for men. The findings also showed that education level and unity were protective against physical IPV for both men and women. Contrary to what was expected, partner data did not play a role in the respondent’s experience of IPV. The consistency of these findings with the literature, theory, and study limitations are discussed. PMID:24227592

  7. Gas Marbles: Much Stronger than Liquid Marbles

    Science.gov (United States)

    Timounay, Yousra; Pitois, Olivier; Rouyer, Florence

    2017-06-01

    Enwrapping liquid droplets with hydrophobic particles allows the manufacture of so-called "liquid marbles" [Aussillous and Quéré Nature (London) 411, 924 (2001); , 10.1038/35082026Mahadevan Nature (London)411, 895 (2001), 10.1038/35082164]. The recent intensive research devoted to liquid marbles is justified by their very unusual physical and chemical properties and by their potential for various applications, from microreactors to water storage, including water pollution sensors [Bormashenko Curr. Opin. Colloid Interface Sci. 16, 266 (2011), 10.1016/j.cocis.2010.12.002]. Here we demonstrate that this concept can be successfully applied for encapsulating and protecting small gas pockets within an air environment. Similarly to their liquid counterparts, those new soft-matter objects, that we call "gas marbles," can sustain external forces. We show that gas marbles are surprisingly tenfold stronger than liquid marbles and, more importantly, they can sustain both positive and negative pressure differences. This magnified strength is shown to originate from the strong cohesive nature of the shell. Those interesting properties could be exploited for imprisoning valuable or polluted gases or for designing new aerated materials.

  8. Assessment of prediction skill in equatorial Pacific Ocean in high resolution model of CFS

    Science.gov (United States)

    Arora, Anika; Rao, Suryachandra A.; Pillai, Prasanth; Dhakate, Ashish; Salunke, Kiran; Srivastava, Ankur

    2018-01-01

    The effect of increasing atmospheric resolution on prediction skill of El Niño southern oscillation phenomenon in climate forecast system model is explored in this paper. Improvement in prediction skill for sea surface temperature (SST) and winds at all leads compared to low resolution model in the tropical Indo-Pacific basin is observed. High resolution model is able to capture extreme events reasonably well. As a result, the signal to noise ratio is improved in the high resolution model. However, spring predictability barrier (SPB) for summer months in Nino 3 and Nino 3.4 region is stronger in high resolution model, in spite of improvement in overall prediction skill and dynamics everywhere else. Anomaly correlation coefficient of SST in high resolution model with observations in Nino 3.4 region targeting boreal summer months when predicted at lead times of 3-8 months in advance decreased compared its lower resolution counterpart. It is noted that higher variance of winds predicted in spring season over central equatorial Pacific compared to observed variance of winds results in stronger than normal response on subsurface ocean, hence increases SPB for boreal summer months in high resolution model.

  9. High Fidelity, “Faster than Real-Time” Simulator for Predicting Power System Dynamic Behavior - Final Technical Report

    Energy Technology Data Exchange (ETDEWEB)

    Flueck, Alex [Illinois Inst. of Technology, Chicago, IL (United States)

    2017-07-14

    The “High Fidelity, Faster than Real­Time Simulator for Predicting Power System Dynamic Behavior” was designed and developed by Illinois Institute of Technology with critical contributions from Electrocon International, Argonne National Laboratory, Alstom Grid and McCoy Energy. Also essential to the project were our two utility partners: Commonwealth Edison and AltaLink. The project was a success due to several major breakthroughs in the area of large­scale power system dynamics simulation, including (1) a validated faster than real­ time simulation of both stable and unstable transient dynamics in a large­scale positive sequence transmission grid model, (2) a three­phase unbalanced simulation platform for modeling new grid devices, such as independently controlled single­phase static var compensators (SVCs), (3) the world’s first high fidelity three­phase unbalanced dynamics and protection simulator based on Electrocon’s CAPE program, and (4) a first­of­its­ kind implementation of a single­phase induction motor model with stall capability. The simulator results will aid power grid operators in their true time of need, when there is a significant risk of cascading outages. The simulator will accelerate performance and enhance accuracy of dynamics simulations, enabling operators to maintain reliability and steer clear of blackouts. In the long­term, the simulator will form the backbone of the newly conceived hybrid real­time protection and control architecture that will coordinate local controls, wide­area measurements, wide­area controls and advanced real­time prediction capabilities. The nation’s citizens will benefit in several ways, including (1) less down time from power outages due to the faster­than­real­time simulator’s predictive capability, (2) higher levels of reliability due to the detailed dynamics plus protection simulation capability, and (3) more resiliency due to the three­ phase unbalanced simulator’s ability to

  10. Composable and Predictable Power Management

    NARCIS (Netherlands)

    Nelson, A.T.

    2014-01-01

    The functionality of embedded systems is ever growing. The computational power of embedded systems is growing to match this demand, with embedded multiprocessor systems becoming more common. The limitations of embedded systems are not always related to chip size but are commonly due to energy and/or

  11. Hybrid wind power balance control strategy using thermal power, hydro power and flow batteries

    OpenAIRE

    Gelažanskas, Linas; Baranauskas, Audrius; Gamage, Kelum A.A.; Ažubalis, Mindaugas

    2016-01-01

    The increased number of renewable power plants pose threat to power system balance. Their intermittent nature makes it very difficult to predict power output, thus either additional reserve power plants or new storage and control technologies are required. Traditional spinning reserve cannot fully compensate sudden changes in renewable energy power generation. Using new storage technologies such as flow batteries, it is feasible to balance the variations in power and voltage within very short...

  12. Aging predictions in nuclear power plants: Crosslinked polyolefin and EPR cable insulation materials

    International Nuclear Information System (INIS)

    Gillen, K.T.; Clough, R.L.

    1991-06-01

    In two earlier reports, we derived a time-temperature-dose rate superposition methodology, which, when applicable, can be used to predict cable degradation versus dose rate, temperature and exposure time. This methodology results in long-term predictive capabilities at the low dose rates appropriate to ambient nuclear power plant aging environments. The methodology was successfully applied to numerous important cable materials used in nuclear applications and the extrapolated predictions were verified by comparisons with long-term (7 to 12 year) results for similar or identical materials aged in nuclear environments. In this report, we test the methodology on three crosslinked polyolefin (CLPO) and two ethylene propylene rubber (EPR) cable insulation materials. The methodology applies to one of the CLPO materials and one of the EPR materials, allowing predictions to be made for these materials under low dose-rate, low temperature conditions. For the other materials, it is determined that, at low temperatures, a decrease in temperature at a constant radiation dose rate leads to an increase in the degradation rate for the mechanical properties. Since these results contradict the fundamental assumption underlying time-temperature-dose rate superposition, this methodology cannot be applied to such data. As indicated in the earlier reports, such anomalous results might be expected when attempting to model data taken across the crystalline melting region of semicrystalline materials. Nonetheless, the existing experimental evidence suggests that these CLPO and EPR materials have substantial aging endurance for typical reactor conditions. 28 refs., 26 figs., 3 tabs

  13. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

  14. Using Flow Characteristics in Three-Dimensional Power Doppler Ultrasound Imaging to Predict Complete Responses in Patients Undergoing Neoadjuvant Chemotherapy.

    Science.gov (United States)

    Shia, Wei-Chung; Huang, Yu-Len; Wu, Hwa-Koon; Chen, Dar-Ren

    2017-05-01

    Strategies are needed for the identification of a poor response to treatment and determination of appropriate chemotherapy strategies for patients in the early stages of neoadjuvant chemotherapy for breast cancer. We hypothesize that power Doppler ultrasound imaging can provide useful information on predicting response to neoadjuvant chemotherapy. The solid directional flow of vessels in breast tumors was used as a marker of pathologic complete responses (pCR) in patients undergoing neoadjuvant chemotherapy. Thirty-one breast cancer patients who received neoadjuvant chemotherapy and had tumors of 2 to 5 cm were recruited. Three-dimensional power Doppler ultrasound with high-definition flow imaging technology was used to acquire the indices of tumor blood flow/volume, and the chemotherapy response prediction was established, followed by support vector machine classification. The accuracy of pCR prediction before the first chemotherapy treatment was 83.87% (area under the ROC curve [AUC] = 0.6957). After the second chemotherapy treatment, the accuracy of was 87.9% (AUC = 0.756). Trend analysis showed that good and poor responders exhibited different trends in vascular flow during chemotherapy. This preliminary study demonstrates the feasibility of using the vascular flow in breast tumors to predict chemotherapeutic efficacy. © 2017 by the American Institute of Ultrasound in Medicine.

  15. A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Yanzhen Zhou

    2016-09-01

    Full Text Available Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.

  16. Crosstalk in concurrent repeated games impedes direct reciprocity and requires stronger levels of forgiveness.

    Science.gov (United States)

    Reiter, Johannes G; Hilbe, Christian; Rand, David G; Chatterjee, Krishnendu; Nowak, Martin A

    2018-02-07

    Direct reciprocity is a mechanism for cooperation among humans. Many of our daily interactions are repeated. We interact repeatedly with our family, friends, colleagues, members of the local and even global community. In the theory of repeated games, it is a tacit assumption that the various games that a person plays simultaneously have no effect on each other. Here we introduce a general framework that allows us to analyze "crosstalk" between a player's concurrent games. In the presence of crosstalk, the action a person experiences in one game can alter the person's decision in another. We find that crosstalk impedes the maintenance of cooperation and requires stronger levels of forgiveness. The magnitude of the effect depends on the population structure. In more densely connected social groups, crosstalk has a stronger effect. A harsh retaliator, such as Tit-for-Tat, is unable to counteract crosstalk. The crosstalk framework provides a unified interpretation of direct and upstream reciprocity in the context of repeated games.

  17. Report on the French power system. Version 1.0. Country profile

    Energy Technology Data Exchange (ETDEWEB)

    Kleiner, Mara Marthe (ed.)

    2015-02-15

    France is currently reshaping its energy and climate policy, with a commitment to reduce the share of nuclear energy in the power mix, together with ambitious goals for the development of renewables and energy efficiency. This French energy transition paradigm (transition energetique) is serving as a common ground for stronger energy cooperation with Germany. This report describes the status quo of the French power system and provides important numbers.

  18. Lean muscle volume of the thigh has a stronger relationship with muscle power than muscle strength in women with knee osteoarthritis.

    Science.gov (United States)

    Davison, Michael J; Maly, Monica R; Keir, Peter J; Hapuhennedige, Sandani M; Kron, Amie T; Adachi, Jonathan D; Beattie, Karen A

    2017-01-01

    Thigh lean muscle and intramuscular fat have been implicated in the impairment of physical function observed in people with knee osteoarthritis. We investigated the relationships of quadriceps and hamstrings intramuscular fat fraction and lean muscle volume with muscle power and strength, controlling for neuromuscular activation, and physical performance in women with knee OA. Women (n=20) 55years or older with symptomatic, radiographic knee osteoarthritis underwent a 3.0T magnetic resonance imaging scan of the thigh of their most symptomatic knee. Axial fat-separated images were analyzed using software to quantify intramuscular fat and lean muscle volumes of the quadriceps and hamstrings. To quantify strength and power of the knee extensors and flexors, participants performed maximum voluntary isometric contraction and isotonic knee extensions and flexions, respectively. Electromyography of the quadriceps and hamstrings was measured. Participants also completed five physical performance tests. Quadriceps and hamstrings lean muscle volumes were related to isotonic knee extensor (B=0.624; p=0.017) and flexor (B=1.518; p=0.032) power, but not knee extensor (B=0.001; p=0.615) or flexor (B=0.001; p=0.564) isometric strength. Intramuscular fat fractions were not related to isotonic knee extensor or flexor power, nor isometric strength. No relationships were found between intramuscular fat or lean muscle volume and physical performance. Muscle power may be more sensitive than strength to lean muscle mass in women with knee osteoarthritis. Thigh lean muscle mass, but neither intramuscular nor intermuscular fat, is related to knee extensor and flexor power in women with knee osteoarthritis. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Implicit motives predict affective responses to emotional expressions

    Directory of Open Access Journals (Sweden)

    Andreas G. Rösch

    2013-12-01

    Full Text Available We explored the influence of implicit motives and activity inhibition on subjectively experienced affect in response to the presentation of six different facial expressions of emotion (FEEs; anger, disgust, fear, happiness, sadness, and surprise and neutral faces from the NimStim set of facial expressions (Tottenham et al., 2009. Implicit motives and activity inhibition were assessed using a Picture Story Exercise (Schultheiss et al., 2009b. Ratings of subjectively experienced affect (arousal and valence were assessed using Self-Assessment Manikins (Bradley and Lang, 1994 in a sample of 84 participants. We found that people with either a strong implicit power or achievement motive experienced stronger arousal, while people with a strong affiliation motive experienced less aroused and felt more unpleasant across emotions. Additionally, we obtained significant power motive × activity inhibition interactions for arousal ratings in response to FEEs and neutral faces. Participants with a strong power motive and weak activity inhibition experienced stronger arousal after the presentation of neutral faces but no additional increase in arousal after the presentation of FEEs. Participants with a strong power motive and strong activity inhibition (inhibited power motive did not feel aroused by neutral faces. However, their arousal increased in response to all FEEs with the exception of happy faces, for which their subjective arousal decreased. These more differentiated reaction pattern of individuals with an inhibited power motive suggest that they engage in a more socially adaptive manner of responding to different FEEs. Our findings extend established links between implicit motives and affective processes found at the procedural level to declarative reactions to FEEs. Implications are discussed with respect to dual-process models of motivation and research in motive congruence.

  20. Fossil power plant automation

    International Nuclear Information System (INIS)

    Divakaruni, S.M.; Touchton, G.

    1991-01-01

    This paper elaborates on issues facing the utilities industry and seeks to address how new computer-based control and automation technologies resulting from recent microprocessor evolution, can improve fossil plant operations and maintenance. This in turn can assist utilities to emerge stronger from the challenges ahead. Many presentations at the first ISA/EPRI co-sponsored conference are targeted towards improving the use of computer and control systems in the fossil and nuclear power plants and we believe this to be the right forum to share our ideas

  1. Simulation of the impact of wind power on the transient fault behavior of the Nordic power system

    Energy Technology Data Exchange (ETDEWEB)

    Jauch, Clemens; Soerensen, Poul [Risoe National Laboratory, Wind Energy Department, P.O. Box 49, DK-4000 Roskilde (Denmark); Norheim, Ian [SINTEF Energy Research, The department of Energy Systems, Sem Saelands Vei 11, NO-7463 Trondheim (Norway); Rasmussen, Carsten [Elkraft System, 2750 Ballerup (Denmark)

    2007-02-15

    In this paper the effect of wind power on the transient fault behavior of the Nordic power system is investigated. The Nordic power system is the interconnected power system of the countries Norway, Sweden, Finland and Denmark. For the purpose of these investigations the wind turbines installed and connected in eastern Denmark are taken as study case. The current and future wind power situation in eastern Denmark is modeled and short circuit faults in the system simulated. The simulations yield information on (i) how the faults impact on the wind turbines and (ii) how the response of the wind turbines influences the post-fault behavior of the Nordic power system. It is concluded that an increasing level of wind power penetration leads to stronger system oscillations in case of fixed speed wind turbines. It is found that fixed speed wind turbines that merely ride through transient faults have negative impacts on the dynamic response of the system. These negative impacts can be mitigated though, if sophisticated wind turbine control is applied. (author)

  2. Parent-child value similarity in families with young children: The predictive power of prosocial educational goals.

    Science.gov (United States)

    Döring, Anna K; Makarova, Elena; Herzog, Walter; Bardi, Anat

    2017-11-01

    Value transmission from one generation to the next is a key issue in every society, but it is not clear which parents are the most successful in transmitting their values to their children. We propose parents' prosocial educational goals as key predictors of parent-child value similarity. Accordingly, we hypothesized that the more parents wanted their children to endorse values of self-transcendence (helping, supporting, and caring for others) and the less parents wanted their children to endorse the opposing values of self-enhancement (striving for power and achievement), the higher would be parent-child overall value similarity. Findings from two studies of families - Study 1: 261 Swiss families, children aged 7-9 years; Study 2: 157 German families, children aged 6-11 years - confirmed this hypothesis. The effect was even stronger after controlling for values that prevail in the Swiss and German society, respectively. We integrate evidence from this study of values in families with young children with existing findings from studies with adolescent and adult children, and we discuss potential pathways from parents' educational goals to parent-child value similarity. © 2017 The British Psychological Society.

  3. Predictive Power of Prospective Physical Education Teachers' Attitudes towards Educational Technologies for Their Technological Pedagogical Content Knowledge

    Science.gov (United States)

    Varol, Yaprak Kalemoglu

    2015-01-01

    The aim of the research is to determine the predictive power of prospective physical education teachers' attitudes towards educational technologies for their technological pedagogical content knowledge. In this study, a relational research model was used on a study group that consisted of 529 (M[subscript age]=21.49, SD=1.44) prospective physical…

  4. The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

    Directory of Open Access Journals (Sweden)

    Xiaomin Xu

    2015-01-01

    Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

  5. Transfer of infrared thermography predictive maintenance technologies to Soviet-designed nuclear power plants: experience at Chernobyl

    Science.gov (United States)

    Pugh, Ray; Huff, Roy

    1999-03-01

    The importance of infrared (IR) technology and analysis in today's world of predictive maintenance and reliability- centered maintenance cannot be understated. The use of infrared is especially important in facilities that are required to maintain a high degree of equipment reliability because of plant or public safety concerns. As with all maintenance tools, particularly those used in predictive maintenance approaches, training plays a key role in their effectiveness and the benefit gained from their use. This paper details an effort to transfer IR technology to Soviet- designed nuclear power plants in Russia, Ukraine, and Lithuania. Delivery of this technology and post-delivery training activities have been completed recently at the Chornobyl nuclear power plant in Ukraine. Many interesting challenges were encountered during this effort. Hardware procurement and delivery of IR technology to a sensitive country were complicated by United States regulations. Freight and shipping infrastructure and host-country customs policies complicated hardware transport. Training activities were complicated by special hardware, software and training material translation needs, limited communication opportunities, and site logistical concerns. These challenges and others encountered while supplying the Chornobyl plant with state-of-the-art IR technology are described in this paper.

  6. A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions.

    Science.gov (United States)

    Zheng, Yang; Zhou, Jianzhong; Xu, Yanhe; Zhang, Yuncheng; Qian, Zhongdong

    2017-05-01

    This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Two wind power prognosis criteria and regulating power costs

    DEFF Research Database (Denmark)

    Nielsen, Claus S.; Ravn, Hans F.; Schaumburg-Müller, Camilla

    2003-01-01

    . Basically, the choice is between focusing on predicting the energy content of the wind and focusing on the cost of buying regulating power to compensate for the prognosis errors. It will be shown that it can be expected that the two power curves thus estimated will differ, and that therefore also the hourly......The objective of the present work is to investigate the consequences of the choice of criterion in short-term wind power prognosis. This is done by investigating the consequences of choice of objective function in relation to the estimation of the power curve that is applied in the prognoses...... wind power production predicted will differ. In turn this will influence the operation and economics of the system. The consequences of this are illustrated by application to the integration of wind power in the Danish parts of the Nordpool area, using recent data. Using a regression analysis...

  8. A new solar power output prediction based on hybrid forecast engine and decomposition model.

    Science.gov (United States)

    Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

    2018-06-12

    Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  9. Strategy and your stronger hand.

    Science.gov (United States)

    Moore, Geoffrey A

    2005-12-01

    There are two kinds of businesses in the world, says the author. Knowing what they are--and which one your company is--will guide you to the right strategic moves. One kind includes businesses that compete on a complex-systems model. These companies have large enterprises as their primary customers. They seek to grow a customer base in the thousands, with no more than a handful of transactions per customer per year (indeed, in some years there may be none), and the average price per transaction ranges from six to seven figures. In this model, 1,000 enterprises each paying dollar 1 million per year would generate dollar 1 billion in annual revenue. The other kind of business competes on a volume-operations model. Here, vendors seek to acquire millions of customers, with tens or even hundreds of transactions per customer per year, at an average price of relatively few dollars per transaction. Under this model, it would take 10 million customers each spending dollar 8 per month to generate nearly dollar 1 billion in revenue. An examination of both models shows that they could not be further apart in their approach to every step along the classic value chain. The problem, though, is that companies in one camp often attempt to create new value by venturing into the other. In doing so, they fail to realize how their managerial habits have been shaped by the model they've grown up with. By analogy, they have a "handedness"--the equivalent of a person's right- or left-hand dominance--that makes them as adroit in one mode as they are awkward in the other. Unless you are in an industry whose structure forces you to attempt ambidexterity (in which case, special efforts are required to manage the inevitable dropped balls), you'll be far more successful making moves that favor your stronger hand.

  10. The predictive power of family history measures of alcohol and drug problems and internalizing disorders in a college population.

    Science.gov (United States)

    Kendler, Kenneth S; Edwards, Alexis; Myers, John; Cho, Seung Bin; Adkins, Amy; Dick, Danielle

    2015-07-01

    A family history (FH) of psychiatric and substance use problems is a potent risk factor for common internalizing and externalizing disorders. In a large web-based assessment of mental health in college students, we developed a brief set of screening questions for a FH of alcohol problems (AP), drug problems (DP) and depression-anxiety in four classes of relatives (father, mother, aunts/uncles/grandparents, and siblings) as reported by the student. Positive reports of a history of AP, DP, and depression-anxiety were substantially correlated within relatives. These FH measures predicted in the student, in an expected pattern, dimensions of personality and impulsivity, alcohol consumption and problems, smoking and nicotine dependence, use of illicit drugs, and symptoms of depression and anxiety. Using the mean score from the four classes of relatives was more predictive than using a familial/sporadic dichotomy. Interactions were seen between the FH of AP, DP, and depression-anxiety and peer deviance in predicting symptoms of alcohol and tobacco dependence. As the students aged, the FH of AP became a stronger predictor of alcohol problems. While we cannot directly assess the validity of these FH reports, the pattern of findings suggest that our brief screening items were able to assess, with some accuracy, the FH of substance misuse and internalizing psychiatric disorders in relatives. If correct, these measures can play an important role in the creation of developmental etiologic models for substance and internalizing psychiatric disorders which constitute one of the central goals of the overall project. © 2015 Wiley Periodicals, Inc.

  11. Analysis of Academic Self-Efficacy, Self-Esteem and Coping with Stress Skills Predictive Power on Academic Procrastination

    Science.gov (United States)

    Kandemir, Mehmet; Ilhan, Tahsin; Ozpolat, Ahmed Ragip; Palanci, Mehmet

    2014-01-01

    The goal of this research is to analyze the predictive power level of academic self-efficacy, self-esteem and coping with stress on academic procrastination behavior. Relational screening model is used in the research whose research group is made of 374 students in Kirikkale University, Education Faculty in Turkey. Students in the research group…

  12. Combined prediction model for supply risk in nuclear power equipment manufacturing industry based on support vector machine and decision tree

    International Nuclear Information System (INIS)

    Shi Chunsheng; Meng Dapeng

    2011-01-01

    The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)

  13. On the importance of paleoclimate modelling for improving predictions of future climate change

    Directory of Open Access Journals (Sweden)

    J. C. Hargreaves

    2009-12-01

    Full Text Available We use an ensemble of runs from the MIROC3.2 AGCM with slab-ocean to explore the extent to which mid-Holocene simulations are relevant to predictions of future climate change. The results are compared with similar analyses for the Last Glacial Maximum (LGM and pre-industrial control climate. We suggest that the paleoclimate epochs can provide some independent validation of the models that is also relevant for future predictions. Considering the paleoclimate epochs, we find that the stronger global forcing and hence larger climate change at the LGM makes this likely to be the more powerful one for estimating the large-scale changes that are anticipated due to anthropogenic forcing. The phenomena in the mid-Holocene simulations which are most strongly correlated with future changes (i.e., the mid to high northern latitude land temperature and monsoon precipitation do, however, coincide with areas where the LGM results are not correlated with future changes, and these are also areas where the paleodata indicate significant climate changes have occurred. Thus, these regions and phenomena for the mid-Holocene may be useful for model improvement and validation.

  14. Steps to nuclear power

    International Nuclear Information System (INIS)

    1975-01-01

    The recent increase in oil prices will undoubtedly cause the pace at which nuclear power is introduced in developing countries to quicken in the next decade, with many new countries beginning to plan nuclear power programmes. The guidebook is intended for senior government officials, policy makers, economic and power planners, educationalists and economists. It assumes that the reader has relatively little knowledge of nuclear power systems or of nuclear physics but does have a general technical or management background. Nuclear power is described functionally from the point of view of an alternative energy source in power system expansion. The guidebook is based on an idealized approach. Variations on it are naturally possible and will doubtless be necessary in view of the different organizational structures that already exist in different countries. In particular, some countries may prefer an approach with a stronger involvement of their Atomic Energy Commission or Authority, for which this guidebook has foreseen mainly a regulatory and licensing role. It is intended to update this booklet as more experience becomes available. Supplementary guidebooks will be prepared on certain major topics, such as contracting for fuel supply and fuel cycle requirements, which the present book does not go into very deeply

  15. How Many Model Evaluations Are Required To Predict The AEP Of A Wind Power Plant?

    International Nuclear Information System (INIS)

    Murcia, J P; Réthoré, P E; Natarajan, A; Sørensen, J D

    2015-01-01

    Wind farm flow models have advanced considerably with the use of large eddy simulations (LES) and Reynolds averaged Navier-Stokes (RANS) computations. The main limitation of these techniques is their high computational time requirements; which makes their use for wind farm annual energy production (AEP) predictions expensive. The objective of the present paper is to minimize the number of model evaluations required to capture the wind power plant's AEP using stationary wind farm flow models. Polynomial chaos techniques are proposed based on arbitrary Weibull distributed wind speed and Von Misses distributed wind direction. The correlation between wind direction and wind speed are captured by defining Weibull-parameters as functions of wind direction. In order to evaluate the accuracy of these methods the expectation and variance of the wind farm power distributions are compared against the traditional binning method with trapezoidal and Simpson's integration rules.The wind farm flow model used in this study is the semi-empirical wake model developed by Larsen [1]. Three test cases are studied: a single turbine, a simple and a real offshore wind power plant. A reduced number of model evaluations for a general wind power plant is proposed based on the convergence of the present method for each case. (paper)

  16. Nuclear power plant maintenance personnel reliability prediction (NPP/MPRP) effort at Oak Ridge National Laboratory

    International Nuclear Information System (INIS)

    Knee, H.E.; Haas, P.M.; Siegel, A.I.

    1981-01-01

    Human errors committed during maintenance activities are potentially a major contribution to the overall risk associated with the operation of a nuclear power plant (NPP). An NRC-sponsored program at Oak Ridge National Laboratory is attempting to develop a quantitative predictive technique to evaluate the contribution of maintenance errors to the overall NPP risk. The current work includes a survey of the requirements of potential users to ascertain the need for and content of the proposed quantitative model, plus an initial job/task analysis to determine the scope and applicability of various maintenance tasks. In addition, existing human reliability prediction models are being reviewed and assessed with respect to their applicability to NPP maintenance tasks. This paper discusses the status of the program and summarizes the results to date

  17. Predicting transmission of structure-borne sound power from machines by including terminal cross-coupling

    DEFF Research Database (Denmark)

    Ohlrich, Mogens

    2011-01-01

    of translational terminals in a global plane. This paired or bi-coupled power transmission represents the simplest case of cross-coupling. The procedure and quality of the predicted transmission using this improved technique is demonstrated experimentally for an electrical motor unit with an integrated radial fan......Structure-borne sound generated by audible vibration of machines in vehicles, equipment and house-hold appliances is often a major cause of noise. Such vibration of complex machines is mostly determined and quantified by measurements. It has been found that characterization of the vibratory source...

  18. Predicting local distributions of erosion-corrosion wear sites for the piping in the nuclear power plant using CFD models

    International Nuclear Information System (INIS)

    Ferng, Y.M.

    2008-01-01

    The erosion-corrosion (E/C) wear is an essential degradation mechanism for the piping in the nuclear power plant, which results in the oxide mass loss from the inside of piping, the wall thinning, and even the pipe break. The pipe break induced by the E/C wear may cause costly plant repairs and personal injures. The measurement of pipe wall thickness is a useful tool for the power plant to prevent this incident. In this paper, CFD models are proposed to predict the local distributions of E/C wear sites, which include both the two-phase hydrodynamic model and the E/C models. The impacts of centrifugal and gravitational forces on the liquid droplet behaviors within the piping can be reasonably captured by the two-phase model. Coupled with these calculated flow characteristics, the E/C models can predicted the wear site distributions that show satisfactory agreement with the plant measurements. Therefore, the models proposed herein can assist in the pipe wall monitoring program for the nuclear power plant by way of concentrating the measuring point on the possible sites of severe E/C wear for the piping and reducing the measurement labor works

  19. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

    Science.gov (United States)

    Son, S. W.; Lim, Y.; Kim, D.

    2017-12-01

    The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

  20. Study on the methodology for predicting and preventing errors to improve reliability of maintenance task in nuclear power plant

    International Nuclear Information System (INIS)

    Hanafusa, Hidemitsu; Iwaki, Toshio; Embrey, D.

    2000-01-01

    The objective of this study was to develop and effective methodology for predicting and preventing errors in nuclear power plant maintenance tasks. A method was established by which chief maintenance personnel can predict and reduce errors when reviewing the maintenance procedures and while referring to maintenance supporting systems and methods in other industries including aviation and chemical plant industries. The method involves the following seven steps: 1. Identification of maintenance tasks. 2. Specification of important tasks affecting safety. 3. Assessment of human errors occurring during important tasks. 4. Identification of Performance Degrading Factors. 5. Dividing important tasks into sub-tasks. 6. Extraction of errors using Predictive Human Error Analysis (PHEA). 7. Development of strategies for reducing errors and for recovering from errors. By way of a trial, this method was applied to the pump maintenance procedure in nuclear power plants. This method is believed to be capable of identifying the expected errors in important tasks and supporting the development of error reduction measures. By applying this method, the number of accidents resulting form human errors during maintenance can be reduced. Moreover, the maintenance support base using computers was developed. (author)

  1. The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems.

    Science.gov (United States)

    Reafee, Waleed; Salim, Naomie; Khan, Atif

    2016-01-01

    The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy.

  2. Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data

    Directory of Open Access Journals (Sweden)

    Alexander P. Kartun-Giles

    2018-04-01

    Full Text Available A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing and equilibrium (static sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling.

  3. A review on the young history of the wind power short-term prediction

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Alexandre; Navarro, Jorge [Wind Energy, Division of Renewable Energies, Department of Energy, CIEMAT, Av. Complutense, 22, Ed. 42, 28044 Madrid (Spain); Crespo, Antonio [Laboratorio de Mecanica de Fluidos, Departmento de Ingenieria Energetica y Fluidomecanica, ETSII, Universidad Politecnica de Madrid, C/Jose Gutierrez Abascal, 2-28006 Madrid (Spain); Lizcano, Gil [Oxford University Centre for the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY (United Kingdom); Madsen, Henrik [Informatics and Mathematical Modelling - IMM, Technical University of Denmark, Richard Petersens Plads, Building 321, Office 019, 2800 Kgs. Lyngby (Denmark); Feitosa, Everaldo [Brazilian Wind Energy Centre - CBEE, Centro de Tecnologia e Geociencias, UFPE-50.740-530 Recife, PE (Brazil)

    2008-08-15

    This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out. (author)

  4. Sexual harassment and emotional and behavioural symptoms in adolescence: stronger associations among boys than girls.

    Science.gov (United States)

    Kaltiala-Heino, Riittakerttu; Fröjd, Sari; Marttunen, Mauri

    2016-08-01

    To study the associations between subjection to sexual harassment and emotional (depression) and behavioural (delinquency) symptoms among 14-to-18-year-old adolescents, and gender differences within these associations. 90,953 boys and 91,746 girls aged 14-18 participated in the School Health Promotion Study (SHPS), a school-based survey designed to examine the health, health behaviours, and school experiences of teenagers. Experiences of sexual harassment were elicited with five questions addressing five separate forms of harassment. Depression was measured by the 13-item Beck Depression Inventory and delinquency with a modified version of the International Self-Report Delinquency Study (ISRD) instrument. Data were analysed using cross-tabulations with Chi-square statistics and logistic regression. All sexual harassment experiences studied were associated with both depression (adjusted odds ratios varied from 2.2 to 2.7 in girls and from 2.0 to 5.1 in boys) and delinquency (adjusted odds ratios 3.1-5.0 in girls and 1.7-6.9 in boys). Sexual name-calling had a stronger association with depression and with delinquency in girls (adjusted odds ratios, respectively, 2.4 and 4.2), than in boys (adjusted odds ratios, respectively, 2.0 and 1.7), but otherwise stronger associations with emotional and behavioural symptoms were seen in boys. Subjection to sexual harassment is associated with both emotional and behavioural symptoms in both girls and boys. The associations are mostly stronger for boys. Boys subjected to sexual harassment may feel particularly threatened regarding their masculinity, and there may be less support available for boys traumatised due to sexual harassment.

  5. Statistical prediction of biomethane potentials based on the composition of lignocellulosic biomass

    DEFF Research Database (Denmark)

    Thomsen, Sune Tjalfe; Spliid, Henrik; Østergård, Hanne

    2014-01-01

    Mixture models are introduced as a new and stronger methodology for statistical prediction of biomethane potentials (BPM) from lignocellulosic biomass compared to the linear regression models previously used. A large dataset from literature combined with our own data were analysed using canonical...

  6. Past as Prediction: Newcomb, Huxley, The Eclipse of Thales, and The Power of Science

    Science.gov (United States)

    Stanley, Matthew

    2009-12-01

    The ancient eclipse of Thales was an important, if peculiar, focus of scientific attention in the 19th century. Victorian-era astronomers first used it as data with which to calibrate their lunar theories, but its status became strangely malleable as the century progressed. The American astronomer Simon Newcomb re-examined the eclipse and rejected it as the basis for lunar theory. But strangely, it was the unprecedented accuracy of Newcomb's calculations that led the British biologist T.H. Huxley to declare the eclipse to be the quintessential example of the power of science. Huxley argued that astronomy's ability to create "retrospective prophecy” showed how scientific reasoning was superior to religion (and incidentally, helped support Darwin's theories). Both Newcomb and Huxley declared that prediction (of past and future) was what gave science its persuasive power. The eclipse of Thales's strange journey through Victorian astronomy reveals how these two influential scientists made the case for the social and cultural authority of science.

  7. Predictive power of task orientation, general self-efficacy and self-determined motivation on fun and boredom

    Directory of Open Access Journals (Sweden)

    Lorena Ruiz-González

    2015-12-01

    Full Text Available Abstract The aim of this study was to test the predictive power of dispositional orientations, general self-efficacy and self-determined motivation on fun and boredom in physical education classes, with a sample of 459 adolescents between 13 and 18 with a mean age of 15 years (SD = 0.88. The adolescents responded to four Likert scales: Perceptions of Success Questionnaire, General Self-Efficacy Scale, Sport Motivation Scale and Intrinsic Satisfaction Questionnaire in Sport. The results showed the structural regression model showed that task orientation and general self-efficacy positively predicted self-determined motivation and this in turn positively predicted more fun and less boredom in physical education classes. Consequently, the promotion of an educational task-oriented environment where learners perceive their progress and make them feel more competent, will allow them to overcome the intrinsically motivated tasks, and therefore they will have more fun. Pedagogical implications for less boredom and more fun in physical education classes are discussed.

  8. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

    Directory of Open Access Journals (Sweden)

    Hao Li

    2017-01-01

    Full Text Available Predicting the performance of solar water heater (SWH is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

  9. Model predictive control for wind power gradients

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Boyd, Stephen; Jørgensen, John Bagterp

    2015-01-01

    We consider the operation of a wind turbine and a connected local battery or other electrical storage device, taking into account varying wind speed, with the goal of maximizing the total energy generated while respecting limits on the time derivative (gradient) of power delivered to the grid. We...... ranges. The system dynamics are quite non-linear, and the constraints and objectives are not convex functions of the control inputs, so the resulting optimal control problem is difficult to solve globally. In this paper, we show that by a novel change of variables, which focuses on power flows, we can...... wind data and modern wind forecasting methods. The simulation results using real wind data demonstrate the ability to reject the disturbances from fast changes in wind speed, ensuring certain power gradients, with an insignificant loss in energy production....

  10. Discrete Model Predictive Control-Based Maximum Power Point Tracking for PV Systems: Overview and Evaluation

    DEFF Research Database (Denmark)

    Lashab, Abderezak; Sera, Dezso; Guerrero, Josep M.

    2018-01-01

    The main objective of this work is to provide an overview and evaluation of discrete model predictive controlbased maximum power point tracking (MPPT) for PV systems. A large number of MPC based MPPT methods have been recently introduced in the literature with very promising performance, however......, an in-depth investigation and comparison of these methods have not been carried out yet. Therefore, this paper has set out to provide an in-depth analysis and evaluation of MPC based MPPT methods applied to various common power converter topologies. The performance of MPC based MPPT is directly linked...... with the converter topology, and it is also affected by the accurate determination of the converter parameters, sensitivity to converter parameter variations is also investigated. The static and dynamic performance of the trackers are assessed according to the EN 50530 standard, using detailed simulation models...

  11. Nitrogen oxides emissions from thermal power plants in china: current status and future predictions.

    Science.gov (United States)

    Tian, Hezhong; Liu, Kaiyun; Hao, Jiming; Wang, Yan; Gao, Jiajia; Qiu, Peipei; Zhu, Chuanyong

    2013-10-01

    Increasing emissions of nitrogen oxides (NOx) over the Chinese mainland have been of great concern due to their adverse impacts on regional air quality and public health. To explore and obtain the temporal and spatial characteristics of NOx emissions from thermal power plants in China, a unit-based method is developed. The method assesses NOx emissions based on detailed information on unit capacity, boiler and burner patterns, feed fuel types, emission control technologies, and geographical locations. The national total NOx emissions in 2010 are estimated at 7801.6 kt, of which 5495.8 kt is released from coal-fired power plant units of considerable size between 300 and 1000 MW. The top provincial emitter is Shandong where plants are densely concentrated. The average NOx-intensity is estimated at 2.28 g/kWh, markedly higher than that of developed countries, mainly owing to the inadequate application of high-efficiency denitrification devices such as selective catalytic reduction (SCR). Future NOx emissions are predicted by applying scenario analysis, indicating that a reduction of about 40% by the year 2020 can be achieved compared with emissions in 2010. These results suggest that NOx emissions from Chinese thermal power plants could be substantially mitigated within 10 years if reasonable control measures were implemented effectively.

  12. Beyond clay: Towards an improved set of variables for predicting soil organic matter content

    Science.gov (United States)

    Rasmussen, Craig; Heckman, Katherine; Wieder, William R.; Keiluweit, Marco; Lawrence, Corey R.; Berhe, Asmeret Asefaw; Blankinship, Joseph C.; Crow, Susan E.; Druhan, Jennifer; Hicks Pries, Caitlin E.; Marin-Spiotta, Erika; Plante, Alain F.; Schadel, Christina; Schmiel, Joshua P.; Sierra, Carlos A.; Thompson, Aaron; Wagai, Rota

    2018-01-01

    Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron- and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to better reflect the role of soil physicochemical properties in SOM cycling.

  13. Multivariable predictive control considering time delay for load-frequency control in multi-area power systems

    Directory of Open Access Journals (Sweden)

    Daniar Sabah

    2016-12-01

    Full Text Available In this paper, a multivariable model based predictive control (MPC is proposed for the solution of load frequency control (LFC in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.

  14. The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

    DEFF Research Database (Denmark)

    Alessandrini, S.; Pinson, Pierre; Sperati, S.

    2011-01-01

    The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear...... by a recalibration procedure that allowed obtaining a more uniform distribution among the 51 intervals, making the ensemble spread large enough to include the observations. After that it was observed that the EPS power spread seemed to have enough correlation with the error calculated on the deterministic forecast...

  15. Prediction of Decommissioning Cost for Kijang Research Reactor Using Power Data of DACCORD

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Yun Jeong; Jin, Hyung Gon; Park, Hee Seong; Park, Seung Kook [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    There are 3 types of cost estimate that can be used, and each have a different level of accuracy: (i) Order of magnitude estimate: One without detailed engineering data, where an estimate is prepared using scale-up or -down factors and approximate ratios. It is likely that the overall scope of the project has not been well defined. The level of accuracy expected is -30% to +50%. The cost plans to predict referring to abroad examples as decommissioning cost estimation has still not developed and been commercial method for Kijang research reactor. In Kijang research reactor case, overall scope of business isn't yet decided. Then it is supposed to estimate cost with type (i). The IAEA project, entitled 'DACCORD' (Data Analysis and Collection for Costing of Research Reactor Decommissioning) performs decommissioning costing after collecting and analyzing the information related to research reactors around the world for several years. Also decommissioning costing method development tends to increase in the each country. This paper aims to estimate preliminary decommissioning cost based on total decommissioning cost per thermal power rate of research reactor presented in DACCORD project' data which is collected by member state. In this paper, preliminary decommissioning cost is estimated based on total decommissioning cost per thermal power rate of research reactor presented in DACCORD data which is collected by member state. Although there exists a general tendency for costs to increase with increasing thermal power, the limited data available show that decommissioning costs at any given power level can vary widely, with increased variability at higher power levels. Variations in decommissioning cost for the research reactors of the same or similar thermal power are caused by differences in reactor types and design, decommissioning project scopes, country- specific unit workforce costs, and other reactor or project factors. An important factor for the

  16. Prediction of accident sequence probabilities in a nuclear power plant due to earthquake events

    International Nuclear Information System (INIS)

    Hudson, J.M.; Collins, J.D.

    1980-01-01

    This paper presents a methodology to predict accident probabilities in nuclear power plants subject to earthquakes. The resulting computer program accesses response data to compute component failure probabilities using fragility functions. Using logical failure definitions for systems, and the calculated component failure probabilities, initiating event and safety system failure probabilities are synthesized. The incorporation of accident sequence expressions allows the calculation of terminal event probabilities. Accident sequences, with their occurrence probabilities, are finally coupled to a specific release category. A unique aspect of the methodology is an analytical procedure for calculating top event probabilities based on the correlated failure of primary events

  17. Predicting the outcomes of performance error indicators on accreditation status in the nuclear power industry

    International Nuclear Information System (INIS)

    Wilson, P.A.

    1986-01-01

    The null hypothesis for this study suggested that there was no significant difference in the types of performance error indicators between accredited and non-accredited programs on the following types of indicators: (1) number of significant event reports per unit, (2) number of forced outages per unit, (3) number of unplanned automatic scrams per unit, and (4) amount of equivalent availability per unit. A sample of 90 nuclear power plants was selected for this study. Data were summarized from two data bases maintained by the Institute of Nuclear Power Operations. Results of this study did not support the research hypothesis. There was no significant difference between the accredited and non-accredited programs on any of the four performance error indicators. The primary conclusions of this include the following: (1) The four selected performance error indicators cannot be used individually or collectively to predict accreditation status in the nuclear power industry. (2) Annual performance error indicator ratings cannot be used to determine the effects of performance-based training on plant performance. (3) The four selected performance error indicators cannot be used to measure the effect of operator job performance on plant effectiveness

  18. Discovery of a Novel Cationic Surfactant: Tributyltetradecyl-Phosphonium Chloride for Iron Ore Flotation: From Prediction to Experimental Verification

    Directory of Open Access Journals (Sweden)

    Pan Chen

    2017-12-01

    Full Text Available In this work, tributyltetradecyl-phosphonium chloride (TTPC, has been first introduced to be a novel and efficient cationic surfactant for cationic reverse flotation separation of quartz from magnetite. The first-principles density functional theory calculations, Zeta potential measurements and adsorption isotherm measurements consistently predict that TTPC may be a promising collector that is better than dodecyl triethyl ammonium chloride (DTAC, based on the facts that TTPC and DTAC both prefer to physically adsorb on the quartz surface owing to electrostatic force, but the active part (P+(C4H93 of TTPC takes much more positive charges than that (N+(CH33 of DTAC. The micro-flotation and Bench-scale flotation results further verify that TTPC presents a stronger collecting power and much better selectivity for iron ore reverse flotation in comparison to the conventional collector DTAC. Furthermore, the corresponding adsorption mechanism of TTPC on the quartz have also been investigated in detail. This work might show a good example to discover a potential candidate collector by analogy with a known excellent collector based on reasonable prediction.

  19. Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity.

    Science.gov (United States)

    Bashivan, Pouya; Bidelman, Gavin M; Yeasin, Mohammed

    2014-12-01

    We investigated the effect of memory load on encoding and maintenance of information in working memory. Electroencephalography (EEG) signals were recorded while participants performed a modified Sternberg visual memory task. Independent component analysis (ICA) was used to factorise the EEG signals into distinct temporal activations to perform spectrotemporal analysis and localisation of source activities. We found 'encoding' and 'maintenance' operations were correlated with negative and positive changes in α-band power, respectively. Transient activities were observed during encoding of information in the bilateral cuneus, precuneus, inferior parietal gyrus and fusiform gyrus, and a sustained activity in the inferior frontal gyrus. Strong correlations were also observed between changes in α-power and behavioral performance during both encoding and maintenance. Furthermore, it was also found that individuals with higher working memory capacity experienced stronger neural oscillatory responses during the encoding of visual objects into working memory. Our results suggest an interplay between two distinct neural pathways and different spatiotemporal operations during the encoding and maintenance of information which predict individual differences in working memory capacity observed at the behavioral level. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  20. Assessment of the prediction capability of the TRANSURANUS fuel performance code on the basis of power ramp tested LWR fuel rods

    International Nuclear Information System (INIS)

    Pastore, G.; Botazzoli, P.; Di Marcello, V.; Luzzi, L.

    2009-01-01

    The present work is aimed at assessing the prediction capability of the TRANSURANUS code for the performance analysis of LWR fuel rods under power ramp conditions. The analysis refers to all the power ramp tested fuel rods belonging to the Studsvik PWR Super-Ramp and BWR Inter-Ramp Irradiation Projects, and is focused on some integral quantities (i.e., burn-up, fission gas release, cladding creep-down and failure due to pellet cladding interaction) through a systematic comparison between the code predictions and the experimental data. To this end, a suitable setup of the code is established on the basis of previous works. Besides, with reference to literature indications, a sensitivity study is carried out, which considers the 'ITU model' for fission gas burst release and modifications in the treatment of the fuel solid swelling and the cladding stress corrosion cracking. The performed analyses allow to individuate some issues, which could be useful for the future development of the code. Keywords: Light Water Reactors, Fuel Rod Performance, Power Ramps, Fission Gas Burst Release, Fuel Swelling, Pellet Cladding Interaction, Stress Corrosion Cracking

  1. Daytime warming has stronger negative effects on soil nematodes than night-time warming

    OpenAIRE

    Yan, Xiumin; Wang, Kehong; Song, Lihong; Wang, Xuefeng; Wu, Donghui

    2017-01-01

    Warming of the climate system is unequivocal, that is, stronger warming during night-time than during daytime. Here we focus on how soil nematodes respond to the current asymmetric warming. A field infrared heating experiment was performed in the western of the Songnen Plain, Northeast China. Three warming modes, i.e. daytime warming, night-time warming and diurnal warming, were taken to perform the asymmetric warming condition. Our results showed that the daytime and diurnal warming treatmen...

  2. The nuclear power plant maintenance personnel reliability prediction (NPP/MPRP) effort at Oak Ridge National Laboratory

    International Nuclear Information System (INIS)

    Knee, H.E.; Haas, P.M.; Siegel, A.I.

    1982-01-01

    Human errors committed during maintenance activities are potentially a major contribution to the overall risk associated with the operation of a nuclear power plant (NPP). An NRC-sponsored program at Oak Ridge National Laboratory is attempting to develop a quantitative predictive technique to evaluate the contribution of maintenance errors to the overall NPP risk. The current work includes a survey of the requirements of potential users to ascertain the need for and content of the proposed quantitative model, plus an initial job/task analysis to determine the scope and applicability of various maintenance tasks. In addition, existing human reliability prediction models are being reviewed and assessed with respect to their applicability to NPP maintenance tasks. This paper discusses the status of the program and summarizes the results to date

  3. A Human Capital Framework for a Stronger Teacher Workforce. Advancing Teaching--Improving Learning. White Paper

    Science.gov (United States)

    Myung, Jeannie; Martinez, Krissia; Nordstrum, Lee

    2013-01-01

    Building a stronger teacher workforce requires the thoughtful orchestration of multiple processes working together in a human capital system. This white paper presents a framework that can be used to take stock of current efforts to enhance the teacher workforce in school districts or educational organizations, as well as their underlying theories…

  4. Maximal locality and predictive power in higher-dimensional, compactified field theories

    International Nuclear Information System (INIS)

    Kubo, Jisuke; Nunami, Masanori

    2004-01-01

    To realize maximal locality in a trivial field theory, we maximize the ultraviolet cutoff of the theory by fine tuning the infrared values of the parameters. This optimization procedure is applied to the scalar theory in D + 1 dimensional (D ≥ 4) with one extra dimension compactified on a circle of radius R. The optimized, infrared values of the parameters are then compared with the corresponding ones of the uncompactified theory in D dimensions, which is assumed to be the low-energy effective theory. We find that these values approximately agree with each other as long as R -1 > approx sM is satisfied, where s ≅ 10, 50, 50, 100 for D = 4,5,6,7, and M is a typical scale of the D-dimensional theory. This result supports the previously made claim that the maximization of the ultraviolet cutoff in a nonrenormalizable field theory can give the theory more predictive power. (author)

  5. Predictive power of inspection outcomes for future shipping accidents – an empirical appraisal with special attention for human factor aspects

    NARCIS (Netherlands)

    C. Heij (Christiaan); S. Knapp (Sabine)

    2018-01-01

    textabstractThis paper investigates whether deficiencies detected during port state control (PSC) inspections have predictive power for future accident risk, in addition to other vessel-specific risk factors like ship type, age, size, flag, and owner. The empirical analysis links accidents to past

  6. A stronger patch test elicitation reaction to the allergen hydroxycitronellal plus the irritant sodium lauryl sulfate

    DEFF Research Database (Denmark)

    Heydorn, S; Andersen, K E; Johansen, J D

    2003-01-01

    Household and cleaning products often contain both allergens and irritants. The aim of this double-blinded, randomized, paired study was to determine whether patch testing with an allergen (hydroxycitronellal) combined with an irritant [sodium lauryl sulfate (SLS)] cause a stronger patch test...

  7. Prediction of concrete compressive strength considering humidity and temperature in the construction of nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Seung Hee; Jang, Kyung Pil [Department of Civil and Environmental Engineering, Myongji University, Yongin (Korea, Republic of); Bang, Jin-Wook [Department of Civil Engineering, Chungnam National University, Daejeon (Korea, Republic of); Lee, Jang Hwa [Structural Engineering Research Division, Korea Institute of Construction Technology (Korea, Republic of); Kim, Yun Yong, E-mail: yunkim@cnu.ac.kr [Structural Engineering Research Division, Korea Institute of Construction Technology (Korea, Republic of)

    2014-08-15

    Highlights: • Compressive strength tests for three concrete mixes were performed. • The parameters of the humidity-adjusted maturity function were determined. • Strength can be predicted considering temperature and relative humidity. - Abstract: This study proposes a method for predicting compressive strength developments in the early ages of concretes used in the construction of nuclear power plants. Three representative mixes with strengths of 6000 psi (41.4 MPa), 4500 psi (31.0 MPa), and 4000 psi (27.6 MPa) were selected and tested under various curing conditions; the temperature ranged from 10 to 40 °C, and the relative humidity from 40 to 100%. In order to consider not only the effect of the temperature but also that of humidity, an existing model, i.e. the humidity-adjusted maturity function, was adopted and the parameters used in the function were determined from the test results. A series of tests were also performed in the curing condition of a variable temperature and constant humidity, and a comparison between the measured and predicted strengths were made for the verification.

  8. The trickle-down effect of predictability: Secondary task performance benefits from predictability in the primary task.

    Directory of Open Access Journals (Sweden)

    Magdalena Ewa Król

    Full Text Available Predictions optimize processing by reducing attentional resources allocation to expected or predictable sensory data. Our study demonstrates that these saved processing resources can be then used on concurrent stimuli, and in consequence improve their processing and encoding. We illustrate this "trickle-down" effect with a dual task, where the primary task varied in terms of predictability. The primary task involved detection of a pre-specified symbol that appeared at some point of a short video of a dot moving along a random, semi-predictable or predictable trajectory. The concurrent secondary task involved memorization of photographs representing either emotionally neutral or non-neutral (social or threatening content. Performance in the secondary task was measured by a memory test. We found that participants allocated more attention to unpredictable (random and semi-predictable stimuli than to predictable stimuli. Additionally, when the stimuli in the primary task were more predictable, participants performed better in the secondary task, as evidenced by higher sensitivity in the memory test. Finally, social or threatening stimuli were allocated more "looking time" and a larger number of saccades than neutral stimuli. This effect was stronger for the threatening stimuli than social stimuli. Thus, predictability of environmental input is used in optimizing the allocation of attentional resources, which trickles-down and benefits the processing of concurrent stimuli.

  9. The trickle-down effect of predictability: Secondary task performance benefits from predictability in the primary task.

    Science.gov (United States)

    Król, Magdalena Ewa; Król, Michał

    2017-01-01

    Predictions optimize processing by reducing attentional resources allocation to expected or predictable sensory data. Our study demonstrates that these saved processing resources can be then used on concurrent stimuli, and in consequence improve their processing and encoding. We illustrate this "trickle-down" effect with a dual task, where the primary task varied in terms of predictability. The primary task involved detection of a pre-specified symbol that appeared at some point of a short video of a dot moving along a random, semi-predictable or predictable trajectory. The concurrent secondary task involved memorization of photographs representing either emotionally neutral or non-neutral (social or threatening) content. Performance in the secondary task was measured by a memory test. We found that participants allocated more attention to unpredictable (random and semi-predictable) stimuli than to predictable stimuli. Additionally, when the stimuli in the primary task were more predictable, participants performed better in the secondary task, as evidenced by higher sensitivity in the memory test. Finally, social or threatening stimuli were allocated more "looking time" and a larger number of saccades than neutral stimuli. This effect was stronger for the threatening stimuli than social stimuli. Thus, predictability of environmental input is used in optimizing the allocation of attentional resources, which trickles-down and benefits the processing of concurrent stimuli.

  10. The social distance theory of power.

    Science.gov (United States)

    Magee, Joe C; Smith, Pamela K

    2013-05-01

    We propose that asymmetric dependence between individuals (i.e., power) produces asymmetric social distance, with high-power individuals feeling more distant than low-power individuals. From this insight, we articulate predictions about how power affects (a) social comparison, (b) susceptibility to influence, (c) mental state inference and responsiveness, and (d) emotions. We then explain how high-power individuals' greater experienced social distance leads them to engage in more abstract mental representation. This mediating process of construal level generates predictions about how power affects (a) goal selection and pursuit, (b) attention to desirability and feasibility concerns, (c) subjective certainty, (d) value-behavior correspondence, (e) self-control, and (f) person perception. We also reassess the approach/inhibition theory of power, noting limitations both in what it can predict and in the evidence directly supporting its proposed mechanisms. Finally, we discuss moderators and methodological recommendations for the study of power from a social distance perspective.

  11. Improved Wind Speed Prediction Using Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2018-05-01

    Full Text Available Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD, the Radial Basis Function Neural Network (RBF and the Least Square Support Vector Machine (SVM, an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM, the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.

  12. Building Stronger State Energy Partnerships with the U.S. Department of Energy

    Energy Technology Data Exchange (ETDEWEB)

    Marks, Kate

    2011-09-30

    This final technical report details the results of total work efforts and progress made from October 2007 – September 2011 under the National Association of State Energy Officials (NASEO) cooperative agreement DE-FC26-07NT43264, Building Stronger State Energy Partnerships with the U.S. Department of Energy. Major topical project areas in this final report include work efforts in the following areas: Energy Assurance and Critical Infrastructure, State and Regional Technical Assistance, Regional Initiative, Regional Coordination and Technical Assistance, and International Activities in China. All required deliverables have been provided to the National Energy Technology Laboratory and DOE program officials.

  13. Do External or Internal Technology Spillovers Have a Stronger Influence on Innovation Efficiency in China?

    Directory of Open Access Journals (Sweden)

    Xionghe Qin

    2017-09-01

    Full Text Available In this study, we bridge an important gap in the literature by comparing the extent to which external technology spillovers, as indicated by foreign direct investment (FDI, and internal technology spillovers, as indicated by university-institute-industry cooperation (UIC, influence innovation efficiency in China. We divide the innovation process into two sequential stages, namely the knowledge creation and technology commercialization stages, and employ a network data envelopment analysis approach to measure innovation efficiency at each stage. The spatial analysis of the distribution of knowledge creation efficiency and technology commercialization efficiency reveals the heterogeneity of innovation efficiency at the provincial level. Then, a panel data regression is used to analyze the effect of FDI and UIC on innovation efficiency at each stage, using data from 2009 to 2015 for 30 provinces in China. By comparing FDI with UIC, we find that FDI has a higher coefficient and stronger significance level at the knowledge creation stage, while only industry-institute linkages exhibit a stronger association with innovation efficiency at the technology commercialization stage.

  14. Research Developments on Power System Integration of Wind Power

    DEFF Research Database (Denmark)

    Chen, Zhe; Hansen, Jens Carsten; Wu, Qiuwei

    2011-01-01

    variability and prediction, wind power plant ancillary services, grid connection and operation, Smart grids and demand side management under market functionality. The topics of the first group of PhD program starting 2011 under the wind energy Sino-Danish Centre for Education & Research (SDC) are also......This paper presents an overview on the recent research activities and tendencies regarding grid integration of wind power in Denmark and some related European activities, including power electronics for enhancing wind power controllability, wind turbines and wind farms modeling, wind power...

  15. Felt power explains the link between position power and experienced emotions.

    Science.gov (United States)

    Bombari, Dario; Schmid Mast, Marianne; Bachmann, Manuel

    2017-02-01

    The approach/inhibition theory by Keltner, Gruenfeld, and Anderson (2003) predicts that powerful people should feel more positive and less negative emotions. To date, results of studies investigating this prediction are inconsistent. We fill this gap with four studies in which we investigated the role of different conceptualizations of power: felt power and position power. In Study 1, participants were made to feel more or less powerful and we tested how their felt power was related to different emotional states. In Studies 2, 3, and 4, participants were assigned to either a high or a low power role and engaged in an interaction with a virtual human, after which participants reported on how powerful they felt and the emotions they experienced during the interaction. We meta-analytically combined the results of the four studies and found that felt power was positively related to positive emotions (happiness and serenity) and negatively to negative emotions (fear, anger, and sadness), whereas position power did not show any significant overall relation with any of the emotional states. Importantly, felt power mediated the relationship between position power and emotion. In summary, we show that how powerful a person feels in a given social interaction is the driving force linking the person's position power to his or her emotional states. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  16. A GLOBAL ASSESSMENT OF SOLAR ENERGY RESOURCES: NASA's Prediction of Worldwide Energy Resources (POWER) Project

    Science.gov (United States)

    Zhang, T.; Stackhouse, P. W., Jr.; Chandler, W.; Hoell, J. M.; Westberg, D.; Whitlock, C. H.

    2010-12-01

    NASA's POWER project, or the Prediction of the Worldwide Energy Resources project, synthesizes and analyzes data on a global scale. The products of the project find valuable applications in the solar and wind energy sectors of the renewable energy industries. The primary source data for the POWER project are NASA's World Climate Research Project (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Release 3.0) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (V 4.0.3). Users of the POWER products access the data through NASA's Surface meteorology and Solar Energy (SSE, Version 6.0) website (http://power.larc.nasa.gov). Over 200 parameters are available to the users. The spatial resolution is 1 degree by 1 degree now and will be finer later. The data covers from July 1983 to December 2007, a time-span of 24.5 years, and are provided as 3-hourly, daily and monthly means. As of now, there have been over 18 million web hits and over 4 million data file downloads. The POWER products have been systematically validated against ground-based measurements, and in particular, data from the Baseline Surface Radiation Network (BSRN) archive, and also against the National Solar Radiation Data Base (NSRDB). Parameters such as minimum, maximum, daily mean temperature and dew points, relative humidity and surface pressure are validated against the National Climate Data Center (NCDC) data. SSE feeds data directly into Decision Support Systems including RETScreen International clean energy project analysis software that is written in 36 languages and has greater than 260,000 users worldwide.

  17. NOx, Soot, and Fuel Consumption Predictions under Transient Operating Cycle for Common Rail High Power Density Diesel Engines

    Directory of Open Access Journals (Sweden)

    N. H. Walke

    2016-01-01

    Full Text Available Diesel engine is presently facing the challenge of controlling NOx and soot emissions on transient cycles, to meet stricter emission norms and to control emissions during field operations. Development of a simulation tool for NOx and soot emissions prediction on transient operating cycles has become the most important objective, which can significantly reduce the experimentation time and cost required for tuning these emissions. Hence, in this work, a 0D comprehensive predictive model has been formulated with selection and coupling of appropriate combustion and emissions models to engine cycle models. Selected combustion and emissions models are further modified to improve their prediction accuracy in the full operating zone. Responses of the combustion and emissions models have been validated for load and “start of injection” changes. Model predicted transient fuel consumption, air handling system parameters, and NOx and soot emissions are in good agreement with measured data on a turbocharged high power density common rail engine for the “nonroad transient cycle” (NRTC. It can be concluded that 0D models can be used for prediction of transient emissions on modern engines. How the formulated approach can also be extended to transient emissions prediction for other applications and fuels is also discussed.

  18. Power effects on cognitive control: Turning conflict into action.

    Science.gov (United States)

    Schmid, Petra C; Kleiman, Tali; Amodio, David M

    2015-06-01

    Power is known to promote effective goal pursuit, especially when it requires one to overcome distractions or bias. We proposed that this effect involves the ability to engage and implement cognitive control. In Study 1, we demonstrated that power enhances behavioral performance on a response conflict task and that it does so by enhancing controlled processing rather than by reducing automatic processing. In Study 2, we used an event-related potential index of anterior cingulate activity to test whether power effects on control were due to enhanced conflict sensitivity or action implementation. Power did not significantly affect neural sensitivity to conflict; rather, high power was associated with a stronger link between conflict processing and intended action, relative to low power. These findings suggest a new perspective on how social factors can affect controlled processing and offer new evidence regarding the transition between conflict detection and the implementation of action control. (c) 2015 APA, all rights reserved).

  19. Seasonal optimal mix of wind and solar power in a future, highly renewable Europe

    Energy Technology Data Exchange (ETDEWEB)

    Heide, Dominik [Frankfurt Institute for Advanced Studies (FIAS) and Frankfurt International Graduate School for Science, Johann Wolfgang Goethe Universitaet, Ruth-Moufang-Strasse 1, D-60438 Frankfurt am Main (Germany); von Bremen, Lueder [ForWind - Center for Wind Energy Research, University of Oldenburg, Marie-Curie-Str. 1, D-26129 Oldenburg (Germany); Greiner, Martin [Corporate Research and Technology, Siemens AG, D-81730 Muenchen (Germany); Aarhus School of Engineering and Institute of Mathematical Sciences, Aarhus University, Ny Munkegade 118, 8000 Aarhus C (Denmark); Hoffmann, Clemens [Corporate Research and Technology, Siemens AG, D-81730 Muenchen (Germany); Speckmann, Markus; Bofinger, Stefan [Fraunhofer Institut fuer Windenergie und Energiesystemtechnik (IWES), Koenigstor 59, D-34119 Kassel (Germany)

    2010-11-15

    The renewable power generation aggregated across Europe exhibits strong seasonal behaviors. Wind power generation is much stronger in winter than in summer. The opposite is true for solar power generation. In a future Europe with a very high share of renewable power generation those two opposite behaviors are able to counterbalance each other to a certain extent to follow the seasonal load curve. The best point of counterbalancing represents the seasonal optimal mix between wind and solar power generation. It leads to a pronounced minimum in required stored energy. For a 100% renewable Europe the seasonal optimal mix becomes 55% wind and 45% solar power generation. For less than 100% renewable scenarios the fraction of wind power generation increases and that of solar power generation decreases. (author)

  20. Early Executive Function at Age Two Predicts Emergent Mathematics and Literacy at Age Five

    NARCIS (Netherlands)

    Mulder, Hanna; Verhagen, Josje; Van Der Ven, Sanne H. G.; Slot, Pauline L.; Leseman, Paul

    2017-01-01

    Previous work has shown that individual differences in executive function (EF) are predictive of academic skills in preschoolers, kindergartners, and older children. Across studies, EF is a stronger predictor of emergent mathematics than literacy. However, research on EF in children below age three

  1. Peptide-MHC class I stability is a stronger predictor of CTL immunogenicity than peptide affinity

    DEFF Research Database (Denmark)

    Harndahl, Mikkel Nors; Rasmussen, Michael; Nielsen, Morten

    2012-01-01

    Peptide-MHC class I stability is a stronger predictor of CTL immunogenicity than peptide affinity Mikkel Harndahla, Michael Rasmussena, Morten Nielsenb, Soren Buusa,∗ a Laboratory of Experimental Immunology, Faculty of Health Sciences, University of Copenhagen, Denmark b Center for Biological Seq...... al., 2007. J. Immunol. 178, 7890–7901. doi:10.1016/j.molimm.2012.02.025...

  2. Axial power deviation control strategy and computer simulation for Daya Bay Nuclear Power Station

    International Nuclear Information System (INIS)

    Liao Yehong; Zhou Xiaoling, Xiao Min

    2004-01-01

    Daya Bay Nuclear Power Station has very tight operation diagram especially at its right side. Therefore the successful control of axial power deviation for PWR is crucial to nuclear safety. After analyzing various core characters' effect on axial power distribution, several axial power deviation control strategies has been proposed to comply with different power varying operation scenario. Application and computer simulation of the strategies has shown that our prediction of axial power deviation evolution are comparable to the measurement values, and that our control strategies are effective. Engineering experience shows that the application of our methodology can predict accurately the transient of axial power deviation, and therefore has become a useful tool for reactor operation and safety control. This paper presents the axial power control characteristics, reactor operation strategy research, computer simulation, and comparison to measurement results in Daya Bay Nuclear Power Station. (author)

  3. Estimation of the uncertainty in wind power forecasting

    International Nuclear Information System (INIS)

    Pinson, P.

    2006-03-01

    WIND POWER experiences a tremendous development of its installed capacities in Europe. Though, the intermittence of wind generation causes difficulties in the management of power systems. Also, in the context of the deregulation of electricity markets, wind energy is penalized by its intermittent nature. It is recognized today that the forecasting of wind power for horizons up to 2/3-day ahead eases the integration of wind generation. Wind power forecasts are traditionally provided in the form of point predictions, which correspond to the most-likely power production for a given horizon. That sole information is not sufficient for developing optimal management or trading strategies. Therefore, we investigate on possible ways for estimating the uncertainty of wind power forecasts. The characteristics of the prediction uncertainty are described by a thorough study of the performance of some of the state-of-the-art approaches, and by underlining the influence of some variables e.g. level of predicted power on distributions of prediction errors. Then, a generic method for the estimation of prediction intervals is introduced. This statistical method is non-parametric and utilizes fuzzy logic concepts for integrating expertise on the prediction uncertainty characteristics. By estimating several prediction intervals at once, one obtains predictive distributions of wind power output. The proposed method is evaluated in terms of its reliability, sharpness and resolution. In parallel, we explore the potential use of ensemble predictions for skill forecasting. Wind power ensemble forecasts are obtained either by converting meteorological ensembles (from ECMWF and NCEP) to power or by applying a poor man's temporal approach. A proposal for the definition of prediction risk indices is given, reflecting the disagreement between ensemble members over a set of successive look-ahead times. Such prediction risk indices may comprise a more comprehensive signal on the expected level

  4. Enforcement costs: some humanitarian alternatives to stronger patent rights.

    Science.gov (United States)

    Trotter, Andrew

    2012-01-01

    Diseases that cause comparatively few problems in developed countries kill millions of people in the Third World each year. In many cases, people die because they cannot afford the medication needed to save their lives. In others, there are simply no drugs available because there are no wealthy western patients to justify pharmaceutical companies investing in a cure. This reveals a deep-seated problem within the patent system and the pharmaceutical industry that emphasises markets and profits at the expense of health and global welfare. Global efforts have seen substantial improvements in access to medicines in isolated areas, but with international agreements driving towards stronger patent protection and the expiry date for the TRIPS grace period fast approaching, it is time to consider alternatives which will allow the patent system to work for the humanitarian cause rather than against it. This paper considers two such problems in the patent system and pharmaceutical industry - prohibitive pricing and misdirected incentives - to offer a mode of regulation and enforcement that will support both a viable pharmaceutical industry and the human right to health and medication.

  5. Power of Companies in Supply Chains and Their Effect on Network Development

    OpenAIRE

    Tamás Brányi; László Józsa

    2015-01-01

    A general supply chain functions as a closed cluster and consists of at least three companies: supplier, producer and buyer. In an optimal case the companies within a supply chain are well integrated, partnership rests on trust which results in common strategic decisions. Business practices show that there is a stronger company within the chain that uses its power position to influence network development. The objective of the research is to measure how and what kind of power position is need...

  6. Do Implicit Attitudes Predict Actual Voting Behavior Particularly for Undecided Voters?

    Science.gov (United States)

    Friese, Malte; Smith, Colin Tucker; Plischke, Thomas; Bluemke, Matthias; Nosek, Brian A.

    2012-01-01

    The prediction of voting behavior of undecided voters poses a challenge to psychologists and pollsters. Recently, researchers argued that implicit attitudes would predict voting behavior particularly for undecided voters whereas explicit attitudes would predict voting behavior particularly for decided voters. We tested this assumption in two studies in two countries with distinct political systems in the context of real political elections. Results revealed that (a) explicit attitudes predicted voting behavior better than implicit attitudes for both decided and undecided voters, and (b) implicit attitudes predicted voting behavior better for decided than undecided voters. We propose that greater elaboration of attitudes produces stronger convergence between implicit and explicit attitudes resulting in better predictive validity of both, and less incremental validity of implicit over explicit attitudes for the prediction of voting behavior. However, greater incremental predictive validity of implicit over explicit attitudes may be associated with less elaboration. PMID:22952898

  7. Development of a computing program for prediction of wind power for midsize and wide grid areas. Final report; Entwicklung eines Rechenmodells zur Vorhersage der Windleistung fuer mittlere und grosse Versorgungsgebiete. Abschlussbericht

    Energy Technology Data Exchange (ETDEWEB)

    Reeder, L.; Rohrig, K.; Ernst, B.; Schorn, P.; Bettels, B.

    2002-06-30

    In co-operation with partners out of industry and research a machine program was developed predicting the output of wind power plants. Three attributes should be realised by this prediction tool: Short computing time, usable for various grid regions, high reliability. Therewith the transmission system operators get a tool for reducing the amount of control energy which is needed to ensure the balance between power generation and consumption in their networks. This prediction tool for up to two days was developed exemplary for the northern grid area of the transmission system operator 'E.ON Netz GmbH' (ENE). The wind power prediction is based on numerical weather forecast from the German weather service (Deutscher Wetterdienst). The weather forecast is given for 16 representative sites within the ENE-area. The meso-scale model KLIMM (Klima Model Mainz) was used to calculate the meteorological variables near to the wind farms, which are connected to the one transformer substation belonging to one representative place. Therefor KLIMM is fed with the weather forecast given for one limited location in the representative sites. The transformation of the meteorological variables to the output of wind power plants at the representative site is done by Neural Networks. These Neural Networks have been trained with corresponding measurements. Using an existing online-model the total wind power for the whole ENE-area will be calculated from the individual wind power of the representative sites. The Evaluation of the prediction- and measured data from 2001 shows comparing with reference-models, that the prediction-model evolved in the project lead to very good results. (orig.)

  8. Dynamics of warm power-law plateau inflation with a generalized inflaton decay rate: predictions and constraints after Planck 2015

    Energy Technology Data Exchange (ETDEWEB)

    Jawad, Abdul [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); Videla, Nelson [FCFM, Universidad de Chile, Departamento de Fisica, Santiago (Chile); Gulshan, Faiza [Lahore Leads University, Department of Mathematics, Lahore (Pakistan)

    2017-05-15

    In the present work, we study the consequences of considering a new family of single-field inflation models, called power-law plateau inflation, in the warm inflation framework. We consider the inflationary expansion is driven by a standard scalar field with a decay ratio Γ having a generic power-law dependence with the scalar field φ and the temperature of the thermal bath T given by Γ(φ,T) = C{sub φ}(T{sup a})/(φ{sup a-1}). Assuming that our model evolves according to the strong dissipative regime, we study the background and perturbative dynamics, obtaining the most relevant inflationary observable as the scalar power spectrum, the scalar spectral index and its running and the tensor-to-scalar ratio. The free parameters characterizing our model are constrained by considering the essential condition for warm inflation, the conditions for the model evolves according to the strong dissipative regime and the 2015 Planck results through the n{sub s}-r plane. For completeness, we study the predictions in the n{sub s}-dn{sub s}/d ln k plane. The model is consistent with a strong dissipative dynamics and predicts values for the tensor-to-scalar ratio and for the running of the scalar spectral index consistent with current bounds imposed by Planck and we conclude that the model is viable. (orig.)

  9. Dynamics of warm power-law plateau inflation with a generalized inflaton decay rate: predictions and constraints after Planck 2015

    International Nuclear Information System (INIS)

    Jawad, Abdul; Videla, Nelson; Gulshan, Faiza

    2017-01-01

    In the present work, we study the consequences of considering a new family of single-field inflation models, called power-law plateau inflation, in the warm inflation framework. We consider the inflationary expansion is driven by a standard scalar field with a decay ratio Γ having a generic power-law dependence with the scalar field φ and the temperature of the thermal bath T given by Γ(φ,T) = C_φ(T"a)/(φ"a"-"1). Assuming that our model evolves according to the strong dissipative regime, we study the background and perturbative dynamics, obtaining the most relevant inflationary observable as the scalar power spectrum, the scalar spectral index and its running and the tensor-to-scalar ratio. The free parameters characterizing our model are constrained by considering the essential condition for warm inflation, the conditions for the model evolves according to the strong dissipative regime and the 2015 Planck results through the n_s-r plane. For completeness, we study the predictions in the n_s-dn_s/d ln k plane. The model is consistent with a strong dissipative dynamics and predicts values for the tensor-to-scalar ratio and for the running of the scalar spectral index consistent with current bounds imposed by Planck and we conclude that the model is viable. (orig.)

  10. Immune indexes of larks from desert and temperate regions show weak associations with life history but stronger links to environmental variation in microbial abundance.

    Science.gov (United States)

    Horrocks, Nicholas P C; Hegemann, Arne; Matson, Kevin D; Hine, Kathryn; Jaquier, Sophie; Shobrak, Mohammed; Williams, Joseph B; Tinbergen, Joost M; Tieleman, B Irene

    2012-01-01

    Immune defense may vary as a result of trade-offs with other life-history traits or in parallel with variation in antigen levels in the environment. We studied lark species (Alaudidae) in the Arabian Desert and temperate Netherlands to test opposing predictions from these two hypotheses. Based on their slower pace of life, the trade-off hypothesis predicts relatively stronger immune defenses in desert larks compared with temperate larks. However, as predicted by the antigen exposure hypothesis, reduced microbial abundances in deserts should result in desert-living larks having relatively weaker immune defenses. We quantified host-independent and host-dependent microbial abundances of culturable microbes in ambient air and from the surfaces of birds. We measured components of immunity by quantifying concentrations of the acute-phase protein haptoglobin, natural antibody-mediated agglutination titers, complement-mediated lysis titers, and the microbicidal ability of whole blood. Desert-living larks were exposed to significantly lower concentrations of airborne microbes than temperate larks, and densities of some bird-associated microbes were also lower in desert species. Haptoglobin concentrations and lysis titers were also significantly lower in desert-living larks, but other immune indexes did not differ. Thus, contrary to the trade-off hypothesis, we found little evidence that a slow pace of life predicted increased immunological investment. In contrast, and in support of the antigen exposure hypothesis, associations between microbial exposure and some immune indexes were apparent. Measures of antigen exposure, including assessment of host-independent and host-dependent microbial assemblages, can provide novel insights into the mechanisms underlying immunological variation.

  11. Early Executive Function at Age Two Predicts Emergent Mathematics and Literacy at Age Five.

    Science.gov (United States)

    Mulder, Hanna; Verhagen, Josje; Van der Ven, Sanne H G; Slot, Pauline L; Leseman, Paul P M

    2017-01-01

    Previous work has shown that individual differences in executive function (EF) are predictive of academic skills in preschoolers, kindergartners, and older children. Across studies, EF is a stronger predictor of emergent mathematics than literacy. However, research on EF in children below age three is scarce, and it is currently unknown whether EF, as assessed in toddlerhood, predicts emergent academic skills a few years later. This longitudinal study investigates whether early EF, assessed at two years, predicts (emergent) academic skills, at five years. It examines, furthermore, whether early EF is a significantly stronger predictor of emergent mathematics than of emergent literacy, as has been found in previous work on older children. A sample of 552 children was assessed on various EF and EF-precursor tasks at two years. At age five, these children performed several emergent mathematics and literacy tasks. Structural Equation Modeling was used to investigate the relationships between early EF and academic skills, modeled as latent factors. Results showed that early EF at age two was a significant and relatively strong predictor of both emergent mathematics and literacy at age five, after controlling for receptive vocabulary, parental education, and home language. Predictive relations were significantly stronger for mathematics than literacy, but only when a verbal short-term memory measure was left out as an indicator to the latent early EF construct. These findings show that individual differences in emergent academic skills just prior to entry into the formal education system can be traced back to individual differences in early EF in toddlerhood. In addition, these results highlight the importance of task selection when assessing early EF as a predictor of later outcomes, and call for further studies to elucidate the mechanisms through which individual differences in early EF and precursors to EF come about.

  12. Early Executive Function at Age Two Predicts Emergent Mathematics and Literacy at Age Five

    Directory of Open Access Journals (Sweden)

    Hanna Mulder

    2017-10-01

    Full Text Available Previous work has shown that individual differences in executive function (EF are predictive of academic skills in preschoolers, kindergartners, and older children. Across studies, EF is a stronger predictor of emergent mathematics than literacy. However, research on EF in children below age three is scarce, and it is currently unknown whether EF, as assessed in toddlerhood, predicts emergent academic skills a few years later. This longitudinal study investigates whether early EF, assessed at two years, predicts (emergent academic skills, at five years. It examines, furthermore, whether early EF is a significantly stronger predictor of emergent mathematics than of emergent literacy, as has been found in previous work on older children. A sample of 552 children was assessed on various EF and EF-precursor tasks at two years. At age five, these children performed several emergent mathematics and literacy tasks. Structural Equation Modeling was used to investigate the relationships between early EF and academic skills, modeled as latent factors. Results showed that early EF at age two was a significant and relatively strong predictor of both emergent mathematics and literacy at age five, after controlling for receptive vocabulary, parental education, and home language. Predictive relations were significantly stronger for mathematics than literacy, but only when a verbal short-term memory measure was left out as an indicator to the latent early EF construct. These findings show that individual differences in emergent academic skills just prior to entry into the formal education system can be traced back to individual differences in early EF in toddlerhood. In addition, these results highlight the importance of task selection when assessing early EF as a predictor of later outcomes, and call for further studies to elucidate the mechanisms through which individual differences in early EF and precursors to EF come about.

  13. Predicted and observed cooling tower plume rise and visible plume length at the John E. Amos power plant

    Energy Technology Data Exchange (ETDEWEB)

    Hanna, S R

    1976-01-01

    A one-dimensional numerical cloud growth model and several empirical models for plume rise and cloud growth are compared with twenty-seven sets of observations of cooling tower plumes from the 2900 MW John E. Amos power plant in West Virginia. The three natural draft cooling towers are 200 m apart. In a cross wind, the plumes begin to merge at a distance of about 500 m downwind. In calm conditions, with reduced entrainment, the plumes often do not merge until heights of 1000 m. The average plume rise, 750 m, is predicted well by the models, but day-to-day variations are simulated with a correlation coefficient of about 0.5. Model predictions of visible plume length agree, on the average, with observations for visible plumes of short to moderate length (less than about 1 km). The prediction of longer plumes is hampered by our lack of knowledge of plume spreading after the plumes level off. Cloud water concentrations predicted by the numerical model agree with those measured in natural cumulus clouds (about 0.1 to 1 g kg/sup -1/).

  14. Power fluctuation reduction methodology for the grid-connected renewable power systems

    Science.gov (United States)

    Aula, Fadhil T.; Lee, Samuel C.

    2013-04-01

    This paper presents a new methodology for eliminating the influence of the power fluctuations of the renewable power systems. The renewable energy, which is to be considered an uncertain and uncontrollable resource, can only provide irregular electrical power to the power grid. This irregularity creates fluctuations of the generated power from the renewable power systems. These fluctuations cause instability to the power system and influence the operation of conventional power plants. Overall, the power system is vulnerable to collapse if necessary actions are not taken to reduce the impact of these fluctuations. This methodology aims at reducing these fluctuations and makes the generated power capability for covering the power consumption. This requires a prediction tool for estimating the generated power in advance to provide the range and the time of occurrence of the fluctuations. Since most of the renewable energies are weather based, as a result a weather forecast technique will be used for predicting the generated power. The reduction of the fluctuation also requires stabilizing facilities to maintain the output power at a desired level. In this study, a wind farm and a photovoltaic array as renewable power systems and a pumped-storage and batteries as stabilizing facilities are used, since they are best suitable for compensating the fluctuations of these types of power suppliers. As an illustrative example, a model of wind and photovoltaic power systems with battery energy and pumped hydro storage facilities for power fluctuation reduction is included, and its power fluctuation reduction is verified through simulation.

  15. Prediction of an optimum biodiesel-diesel blended fuel for compression ignition engine using GT-power

    International Nuclear Information System (INIS)

    Shah, A.N.; Shah, F.H.; Shahid, E.M.; Gardezi, S.A.R.

    2014-01-01

    This paper describes the development of a turbocharged direct-injection compression ignition (CI) engine model using fluid-dynamic engine simulation codes through a simulating tool known as GT Power. The model was first fueled with diesel, and then with various blends of biodiesel and diesel by allotting suitable parameters to predict an optimum blended fuel. During the optimization, main focus was on the engine performance, combustion, and one of the major regulated gaseous pollutants known as oxides of nitrogen (NOx). The combustion parameters such as Premix Duration (DP), Main Duration (DM), Premix Fraction (FP), Main Exponent (EM) and ignition delay (ID) affect the start of injection (SOI) angle, and thus played significant role in the prediction of optimum blended fuel. The SOI angle ranging from 5.2 to 5.7 degree crank angle (DCA) measured before top dead center (TDC) revealed an optimum biodiesel-diesel blend known as B20 (20% biodiesel and 80% diesel by volume). B20 exhibited the minimum possible NOx emissions, better combustion and acceptable engine performance. Moreover, experiments were performed to validate the simulated results by fueling the engine with B20 fuel and operating it on AC electrical dynamometer. Both the experimental and simulated results were in good agreement revealing maximum deviations of only 3%, 3.4%, 4.2%, and 5.1% for NOx, maximum combustion pressure (MCP), engine brake power (BP), and brake specific fuel consumption (BSFC), respectively. Meanwhile, a positive correlation was found between MCP and NOx showing that both the parameters are higher at lower speeds, relative to higher engine speeds. (author)

  16. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars

    2005-01-01

    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  17. Validation of the kinetic model for predicting the composition of chlorinated water discharged from power plant cooling systems

    International Nuclear Information System (INIS)

    Lietzke, M.H.

    1977-01-01

    The purpose of this report is to present a validation of a previously described kinetic model which was developed to predict the composition of chlorinated fresh water discharged from power plant cooling systems. The model was programmed in two versions: as a stand-alone program and as a part of a unified transport model developed from consistent mathematical models to simulate the dispersion of heated water and radioisotopic and chemical effluents from power plant discharges. The results of testing the model using analytical data taken during operation of the once-through cooling system of the Quad Cities Nuclear Station are described. Calculations are also presented on the Three Mile Island Nuclear Station which uses cooling towers

  18. Economic evaluation of offshore wind power in the liberalized Dutch power market

    NARCIS (Netherlands)

    Chang, J.; Ummels, B.C.; Sark, van W.G.J.H.M.; Rooijen, den H.P.G.M.; Kling, W.L.

    2009-01-01

    The variability and limited predictability of wind power challenges the operation of power systems, where the generation and load are required in balance at all times. The transmission system operator (TSO) is the responsible party. In a liberalized energy sector, key technical elements of power

  19. A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data

    DEFF Research Database (Denmark)

    Alessandrini, S.; Sperati, S.; Pinson, Pierre

    2013-01-01

    together with a single forecast power value for each future time horizon. A comparison between two different ensemble forecasting models, ECMWF EPS (Ensemble Prediction System in use at the European Centre for Medium-Range Weather Forecasts) and COSMO-LEPS (Limited-area Ensemble Prediction System developed...... ahead forecast horizon. A statistical calibration of the ensemble wind speed members based on the use of past wind speed measurements is explained. The two models are compared using common verification indices and diagrams. The higher horizontal resolution model (COSMO-LEPS) shows slightly better...

  20. Time response prediction of Brazilian Nuclear Power Plant temperature sensors using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Roberto Carlos dos; Pereira, Iraci Martinez, E-mail: rcsantos@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    This work presents the results of the time constants values predicted from ANN using Angra I Brazilian nuclear power plant data. The signals obtained from LCSR loop current step response test sensors installed in the process presents noise end fluctuations that are inherent of operational conditions. Angra I nuclear power plant has 20 RTDs as part of the protection reactor system. The results were compared with those obtained from traditional way. Primary coolant RTDs (Resistance Temperature Detector) typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. An in-situ test method called LCSR - loop current step response test was developed to measure remotely the response time of RTDs. In the LCSR method, the response time of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat transfer model. For this reason, this calculation is not simple and requires specialized personnel. This work combines the two methodologies, Plunge test and LCSR test, using neural networks. With the use of neural networks it will not be necessary to use the LCSR transformation to determine sensor's time constant and this leads to more robust results. (author)

  1. Time response prediction of Brazilian Nuclear Power Plant temperature sensors using neural networks

    International Nuclear Information System (INIS)

    Santos, Roberto Carlos dos; Pereira, Iraci Martinez

    2011-01-01

    This work presents the results of the time constants values predicted from ANN using Angra I Brazilian nuclear power plant data. The signals obtained from LCSR loop current step response test sensors installed in the process presents noise end fluctuations that are inherent of operational conditions. Angra I nuclear power plant has 20 RTDs as part of the protection reactor system. The results were compared with those obtained from traditional way. Primary coolant RTDs (Resistance Temperature Detector) typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. An in-situ test method called LCSR - loop current step response test was developed to measure remotely the response time of RTDs. In the LCSR method, the response time of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat transfer model. For this reason, this calculation is not simple and requires specialized personnel. This work combines the two methodologies, Plunge test and LCSR test, using neural networks. With the use of neural networks it will not be necessary to use the LCSR transformation to determine sensor's time constant and this leads to more robust results. (author)

  2. Becoming Stronger at Broken Places: A Model for Group Work with Young Adult from Divorced Families.

    Science.gov (United States)

    Hage, Sally M.; Nosanow, Mia

    2000-01-01

    Describes a model for group work with young adults from divorced families using an 8-session psychoeducational group intervention. Goals include reducing isolation, establishing connectedness, and building a stronger sense of identify. By educating young adults on topics such as assertiveness, communication skills, and self-esteem, it will give…

  3. EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task.

    Science.gov (United States)

    Küssner, Mats B; de Groot, Annette M B; Hofman, Winni F; Hillen, Marij A

    2016-01-01

    As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is-partly due to a lack of theory-driven research-no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck's theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence). Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and exact replications

  4. EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task.

    Directory of Open Access Journals (Sweden)

    Mats B Küssner

    Full Text Available As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is-partly due to a lack of theory-driven research-no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck's theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence. Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and

  5. Evaluation of Haddam Neck (Connecticut Yankee) Nuclear Power Plant, environmental impact prediction, based on monitoring programs

    International Nuclear Information System (INIS)

    Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Mahaffey, J.A.; Waton, D.G.

    1976-12-01

    A study was undertaken by the U.S. Nuclear Regulatory Commission (NRC) to evaluate the nonradiological environmental data obtained from three nuclear power plants operating for a period of one year or longer. The document presented reports the second of three nuclear power plants to be evaluated in detail by Battelle, Pacific Northwest Laboratories. Haddam Neck (Connecticut Yankee) Nuclear Power Plant nonradiological monitoring data were assessed to determine their effectiveness in the measurement of environmental impacts. Efforts were made to determine if: (1) monitoring programs, as designed, can detect environmental impacts, (2) appropriate statistical analyses were performed and if they were sensitive enough to detect impacts, (3) predicted impacts could be verified by monitoring programs, and (4) monitoring programs satisfied the requirements of the Environmental Technical Specifications. Both preoperational and operational monitoring data were examined to test the usefulness of baseline information in evaluating impacts. This included an examination of the methods used to measure ecological, chemical, and physical parameters, and an assessment of sampling periodicity and sensitivity where appropriate data sets were available. From this type of analysis, deficiencies in both preoperational and operational monitoring programs may be identified and provide a basis for suggested improvement

  6. Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

    DEFF Research Database (Denmark)

    Preindl, Matthias; Schaltz, Erik

    2010-01-01

    In drive systems the most used control structure is the cascade control with an inner torque, i.e. current and an outer speed control loop. The fairly small converter switching frequency in high power applications, e.g. wind turbines lead to modest speed control performance. An improvement bring...... the use of a current controller which takes into account the discrete states of the inverter, e.g. DTC or a more modern approach: Model Predictive Direct Current Control (MPDCC). Moreover overshoots and oscillations in the speed are not desired in many applications, since they lead to mechanical stress...

  7. Reliability of Power Electronic Converter Systems

    DEFF Research Database (Denmark)

    -link capacitance in power electronic converter systems; wind turbine systems; smart control strategies for improved reliability of power electronics system; lifetime modelling; power module lifetime test and state monitoring; tools for performance and reliability analysis of power electronics systems; fault...... for advancing the reliability, availability, system robustness, and maintainability of PECS at different levels of complexity. Drawing on the experience of an international team of experts, this book explores the reliability of PECS covering topics including an introduction to reliability engineering in power...... electronic converter systems; anomaly detection and remaining-life prediction for power electronics; reliability of DC-link capacitors in power electronic converters; reliability of power electronics packaging; modeling for life-time prediction of power semiconductor modules; minimization of DC...

  8. Contrasting cue-density effects in causal and prediction judgments.

    Science.gov (United States)

    Vadillo, Miguel A; Musca, Serban C; Blanco, Fernando; Matute, Helena

    2011-02-01

    Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.

  9. Determination of the in-core power and the average core temperature of low power research reactors using gamma dose rate measurements

    International Nuclear Information System (INIS)

    Osei Poku, L.

    2012-01-01

    Most reactors incorporate out-of-core neutron detectors to monitor the reactor power. An accurate relationship between the powers indicated by these detectors and actual core thermal power is required. This relationship is established by calibrating the thermal power. The most common method used in calibrating the thermal power of low power reactors is neutron activation technique. To enhance the principle of multiplicity and diversity of measuring the thermal neutron flux and/or power and temperature difference and/or average core temperature of low power research reactors, an alternative and complimentary method has been developed, in addition to the current method. Thermal neutron flux/Power and temperature difference/average core temperature were correlated with measured gamma dose rate. The thermal neutron flux and power predicted using gamma dose rate measurement were in good agreement with the calibrated/indicated thermal neutron fluxes and powers. The predicted data was also good agreement with thermal neutron fluxes and powers obtained using the activation technique. At an indicated power of 30 kW, the gamma dose rate measured predicted thermal neutron flux of (1* 10 12 ± 0.00255 * 10 12 ) n/cm 2 s and (0.987* 10 12 ± 0.00243 * 10 12 ) which corresponded to powers of (30.06 ± 0.075) kW and (29.6 ± 0.073) for both normal level of the pool water and 40 cm below normal levels respectively. At an indicated power of 15 kW, the gamma dose rate measured predicted thermal neutron flux of (5.07* 10 11 ± 0.025* 10 11 ) n/cm 2 s and (5.12 * 10 11 ±0.024* 10 11 ) n/cm 2 s which corresponded to power of (15.21 ± 0.075) kW and (15.36 ± 0.073) kW for both normal levels of the pool water and 40 cm below normal levels respectively. The power predicted by this work also compared well with power obtained from a three-dimensional neutronic analysis for GHARR-1 core. The predicted power also compares well with calculated power using a correlation equation obtained from

  10. Prediction of the local power factor in BWR fuel cells by means of a multilayer neural network

    International Nuclear Information System (INIS)

    Montes, J.L.; Ortiz, J.J.; Perusquia C, R.; Francois, J.L.; Martin del Campo M, C.

    2007-01-01

    To the beginning of a new operation cycle in a BWR reactor the reactivity of this it increases by means of the introduction of fresh fuel, the one denominated reload fuel. The problem of the definition of the characteristics of this reload fuel represents a combinatory optimization problem that requires significantly a great quantity of CPU time for their determination. This situation has motivated to study the possibility to substitute the Helios code, the one which is used to generate the new cells of the reload fuel parameters, by an artificial neuronal network, with the purpose of predicting the parameters of the fuel reload cell of a BWR reactor. In this work the results of the one training of a multilayer neuronal net that can predict the local power factor (LPPF) in such fuel cells are presented. The prediction of the LPPF is carried out in those condition of beginning of the life of the cell (0.0 MWD/T, to 40% of holes in the one moderator, temperature of 793 K in the fuel and a moderator temperature of 560 K. The cells considered in the present study consist of an arrangement of 10x10 bars, of those which 92 contains U 235 , some of these bars also contain a concentration of Gd 2 O 3 and 8 of them contain only water. The axial location inside the one assembles of recharge of these cells it is exactly up of the cells that contain natural uranium in the base of the reactor core. The training of the neuronal net is carried out by means of a retro-propagation algorithm that uses a space of training formed starting from previous evaluations of cells by means of the Helios code. They are also presented the results of the application of the neuronal net found for the prediction of the LPPF of some cells used in the real operation of the Unit One of the Laguna Verde Nuclear Power station. (Author)

  11. Optimal dispatch strategy for the agile virtual power plant

    DEFF Research Database (Denmark)

    Petersen, Mette Højgaard; Bendtsen, Jan Dimon; Stoustrup, Jakob

    2012-01-01

    The introduction of large ratios of renewable energy into the existing power system is complicated by the inherent variability of production technologies, which harvest energy from wind, sun and waves. Fluctuations of renewable power production can be predicted to some extent, but the assumption...... of perfect prediction is unrealistic. This paper therefore introduces the Agile Virtual Power Plant. The Agile Virtual Power Plant assumes that the base load production planning based on best available knowledge is already given, so imbalances cannot be predicted. Consequently the Agile Virtual Power Plant...... attempts to preserve maneuverability (stay agile) rather than optimize performance according to predictions. In this paper the imbalance compensation problem for an Agile Virtual Power Plant is formulated. It is proved formally, that when local units are power and energy constrained integrators a dispatch...

  12. A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant

    International Nuclear Information System (INIS)

    Bunyamin, Muhammad Afif; Yap, Keem Siah; Aziz, Nur Liyana Afiqah Abdul; Tiong, Sheih Kiong; Wong, Shen Yuong; Kamal, Md Fauzan

    2013-01-01

    This paper presents a new approach of gas emission estimation in power generation plant using a hybrid Genetic Algorithm (GA) and Linear Regression (LR) (denoted as GA-LR). The LR is one of the approaches that model the relationship between an output dependant variable, y, with one or more explanatory variables or inputs which denoted as x. It is able to estimate unknown model parameters from inputs data. On the other hand, GA is used to search for the optimal solution until specific criteria is met causing termination. These results include providing good solutions as compared to one optimal solution for complex problems. Thus, GA is widely used as feature selection. By combining the LR and GA (GA-LR), this new technique is able to select the most important input features as well as giving more accurate prediction by minimizing the prediction errors. This new technique is able to produce more consistent of gas emission estimation, which may help in reducing population to the environment. In this paper, the study's interest is focused on nitrous oxides (NOx) prediction. The results of the experiment are encouraging.

  13. Wind farms production: Control and prediction

    Science.gov (United States)

    El-Fouly, Tarek Hussein Mostafa

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

  14. Aggressive Behaviours of 48- to 66-Month-Old Children: Predictive Power of Teacher-Student Relationship, Cartoon Preferences and Mother's Attitude

    Science.gov (United States)

    Soydan, Sema Büyüktaskapu; Alakoç pirpir, Devlet; Azak, Hayriye

    2017-01-01

    The main purpose of this study is to identify the predictive power of the following variables for physical and relational aggression level of children: cartoon preferences of children, parental attitudes and teacher-student relationship. Study group consisted of 300 preschool children their mothers and 18 preschool teachers. The results showed a…

  15. Testing the predictive power of cognitive atypicalities in autistic children: evidence from a 3-year follow-up study.

    Science.gov (United States)

    Pellicano, Elizabeth

    2013-08-01

    This follow-up study investigated the predictive power of early cognitive atypicalities. Specifically, it examined whether early individual differences in specific cognitive skills, including theory of mind, executive function, and central coherence, could uniquely account for variation in autistic children's behaviors-social communication, repetitive behaviors, and interests and insistence on sameness-at follow-up. Thirty-seven cognitively able children with an autism spectrum condition were assessed on tests tapping verbal and nonverbal ability, theory of mind (false-belief prediction), executive function (planning ability, cognitive flexibility, and inhibitory control), and central coherence (local processing) at intake and their behavioral functioning (social communication, repetitive behaviors and interests, insistence on sameness) 3 years later. Individual differences in early executive but not theory of mind skills predicted variation in children's social communication. Individual differences in children's early executive function also predicted the degree of repetitive behaviors and interests at follow-up. There were no predictive relationships between early central coherence and children's insistence on sameness. These findings challenge the notion that distinct cognitive atypicalities map on to specific behavioral features of autism. Instead, early variation in executive function plays a key role in helping to shape autistic children's emerging behaviors, including their social communication and repetitive behaviors and interests. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.

  16. Online Short-term Solar Power Forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2011-01-01

    This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours.......This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours....

  17. Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers Disease Progression via Convex Fused Sparse Group Lasso.

    Science.gov (United States)

    Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Lepore, Natasha

    2014-03-21

    Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method 1 with novel MR-based multivariate morphometric surface map of the hippocampus 2 to predict future cognitive scores of patients. Previous work by Zhou et al. 1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al. 2 s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

  18. Evaluating the predictive power of multivariate tensor-based morphometry in Alzheimer's disease progression via convex fused sparse group Lasso

    Science.gov (United States)

    Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Lepore, Natasha

    2014-03-01

    Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method1 with novel MR-based multivariate morphometric surface map of the hippocampus2 to predict future cognitive scores of patients. Previous work by Zhou et al.1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al.2s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

  19. Prediction of Francis Turbine Prototype Part Load Pressure and Output Power Fluctuations with Hydroelectric Model

    Science.gov (United States)

    Alligné, S.; Nicolet, C.; Béguin, A.; Landry, C.; Gomes, J.; Avellan, F.

    2017-04-01

    The prediction of pressure and output power fluctuations amplitudes on Francis turbine prototype is a challenge for hydro-equipment industry since it is subjected to guarantees to ensure smooth and reliable operation of the hydro units. The European FP7 research project Hyperbole aims to setup a methodology to transpose the pressure fluctuations induced by the cavitation vortex rope from the reduced scale model to the prototype generating units. A Francis turbine unit of 444MW with a specific speed value of ν = 0.29, is considered as case study. A SIMSEN model of the power station including electrical system, controllers, rotating train and hydraulic system with transposed draft tube excitation sources is setup. Based on this model, a frequency analysis of the hydroelectric system is performed for all technologies to analyse potential interactions between hydraulic excitation sources and electrical components. Three technologies have been compared: the classical fixed speed configuration with Synchronous Machine (SM) and the two variable speed technologies which are Doubly Fed Induction Machine (DFIM) and Full Size Frequency Converter (FSFC).

  20. Frequency Transient Suppression in Hybrid Electric Ship Power Systems: A Model Predictive Control Strategy for Converter Control with Energy Storage

    Directory of Open Access Journals (Sweden)

    Viknash Shagar

    2018-03-01

    Full Text Available This paper aims to understand how the common phenomenon of fluctuations in propulsion and service load demand contribute to frequency transients in hybrid electric ship power systems. These fluctuations arise mainly due to changes in sea conditions resulting in significant variations in the propulsion load demand of ships. This leads to poor power quality for the power system that can potentially cause hazardous conditions such as blackout on board the ship. Effects of these fluctuations are analysed using a hybrid electric ship power system model and a proposed Model Predictive Control (MPC strategy to prevent propagation of transients from the propellers into the shipboard power system. A battery energy storage system, which is directly connected to the DC-link of the frequency converter, is used as the smoothing element. Case studies that involve propulsion and service load changes have been carried out to investigate the efficacy of the proposed solution. Simulation results show that the proposed solution with energy storage and MPC is able to contain frequency transients in the shipboard power system within the permissible levels stipulated by the relevant power quality standards. These findings will help ship builders and operators to consider using battery energy storage systems controlled by advanced control techniques such as MPC to improve the power quality on board ships.

  1. Decadel climate prediction: challenges and opportunities

    International Nuclear Information System (INIS)

    Hurrell, J W

    2008-01-01

    The scientific understanding of climate change is now sufficiently clear to show that climate change from global warming is already upon us, and the rate of change as projected exceeds anything seen in nature in the past 10,000 years. Uncertainties remain, however, especially regarding how climate will change at regional and local scales where the signal of natural variability is large. Addressing many of these uncertainties will require a movement toward high resolution climate system predictions, with a blurring of the distinction between shorter-term predictions and longer-term climate projections. The key is the realization that climate system predictions, regardless of timescale, will require initialization of coupled general circulation models with best estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface. Formidable challenges exist: for instance, what is the best method of initialization given imperfect observations and systematic errors in models? What effect does initialization have on climate predictions? What predictions should be attempted, and how would they be verified? Despite such challenges, the unrealized predictability that resides in slowly evolving phenomena, such as ocean current systems, is of paramount importance for society to plan and adapt for the next few decades. Moreover, initialized climate predictions will require stronger collaboration with shared knowledge, infrastructure and technical capabilities among those in the weather and climate prediction communities. The potential benefits include improved understanding and predictions on all time scales

  2. Regression and tracing methodology based prediction of oncoming demand and losses in deregulated operation of power systems

    DEFF Research Database (Denmark)

    Nallagownden, P.; Mukerjee, R.N.; Masri, S.

    2010-01-01

    of the transmission services hiring contract, inputs such as extent of use of a transmission circuit for a transaction and the associated power loss in the said transmission circuit are also required. To provide the necessary lead time to frame transaction and transmission contracts for an oncoming operational...... coefficients are used advantageously to predict a generator's contribution to a retailer's demand and power loss for this transaction. This paper proposes a procedure that can be implemented real time, to quantify losses in each transmission circuit used by a specific transaction, based on proportionality......The deregulated electricity market can be thought of as a conglomeration of generation providers, transmission service operators (TSO) and retailers, where both generation and retailing may have open access to the transmission grid for trading electricity. For a transaction contract bid to take...

  3. Training for vigilance: using predictive power to evaluate feedback effectiveness.

    Science.gov (United States)

    Szalma, James L; Hancock, Peter A; Warm, Joel S; Dember, William N; Parsons, Kelley S

    2006-01-01

    We examined the effects of knowledge of results (KR) on vigilance accuracy and report the first use of positive and negative predictive power (PPP and NPP) to assess vigilance training effectiveness. Training individuals to detect infrequent signals among a plethora of nonsignals is critical to success in many failure-intolerant monitoring technologies. KR has been widely used for vigilance training, but the effect of the schedule of KR presentation on accuracy has been neglected. Previous research on training for vigilance has used signal detection metrics or hits and false alarms. In this study diagnosticity measures were applied to augment traditional analytic methods. We examined the effects of continuous KR and a partial-KR regimen versus a no-KR control on decision diagnosticity. Signal detection theory (SDT) analysis indicated that KR induced conservatism in responding but did not enhance sensitivity. However, KR in both forms equally enhanced PPP while selectively impairing NPP. There is a trade-off in the effectiveness of KR in reducing false alarms and misses. Together, SDT and PPP/NPP measures provide a more complete portrait of performance effects. PPP and NPP together provide another assessment technique for vigilance performance, and as additional diagnostic tools, these measures are potentially useful to the human factors community.

  4. Why is the radial flow in central pA collisions stronger than in AA?

    International Nuclear Information System (INIS)

    Kalaydzhyan, Tigran; Shuryak, Edward

    2014-01-01

    Both the transverse size and entropy density per area in central pA collisions is smaller than in central AA, and yet the radial flow is stronger. We propose an explanation to this puzzle. Using a weak attraction between strings through the σ-meson exchange, fitted to the lattice data, we find collective implosion of the “spaghetti” multi-string state. Collectivization of the sigma field of the strings is the QCD analog of the black hole formation occurring in holographic models

  5. Predicting speech intelligibility in adverse conditions: evaluation of the speech-based envelope power spectrum model

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2011-01-01

    conditions by comparing predictions to measured data from [Kjems et al. (2009). J. Acoust. Soc. Am. 126 (3), 1415-1426] where speech is mixed with four different interferers, including speech-shaped noise, bottle noise, car noise, and cafe noise. The model accounts well for the differences in intelligibility......The speech-based envelope power spectrum model (sEPSM) [Jørgensen and Dau (2011). J. Acoust. Soc. Am., 130 (3), 1475–1487] estimates the envelope signal-to-noise ratio (SNRenv) of distorted speech and accurately describes the speech recognition thresholds (SRT) for normal-hearing listeners...... observed for the different interferers. None of the standardized models successfully describe these data....

  6. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

    Science.gov (United States)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

    2012-03-15

    To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

  7. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

    2012-03-15

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  8. PCCE-A Predictive Code for Calorimetric Estimates in actively cooled components affected by pulsed power loads

    International Nuclear Information System (INIS)

    Agostinetti, P.; Palma, M. Dalla; Fantini, F.; Fellin, F.; Pasqualotto, R.

    2011-01-01

    The analytical interpretative models for calorimetric measurements currently available in the literature can consider close systems in steady-state and transient conditions, or open systems but only in steady-state conditions. The PCCE code (Predictive Code for Calorimetric Estimations), here presented, introduces some novelties. In fact, it can simulate with an analytical approach both the heated component and the cooling circuit, evaluating the heat fluxes due to conductive and convective processes both in steady-state and transient conditions. The main goal of this code is to model heating and cooling processes in actively cooled components of fusion experiments affected by high pulsed power loads, that are not easily analyzed with purely numerical approaches (like Finite Element Method or Computational Fluid Dynamics). A dedicated mathematical formulation, based on concentrated parameters, has been developed and is here described in detail. After a comparison and benchmark with the ANSYS commercial code, the PCCE code is applied to predict the calorimetric parameters in simple scenarios of the SPIDER experiment.

  9. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    International Nuclear Information System (INIS)

    Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t

    2012-01-01

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  10. On the Predictability of Hub Height Winds

    DEFF Research Database (Denmark)

    Draxl, Caroline

    Wind energy is a major source of power in over 70 countries across the world, and the worldwide share of wind energy in electricity consumption is growing. The introduction of signicant amounts of wind energy into power systems makes accurate wind forecasting a crucial element of modern electrical...... grids. These systems require forecasts with temporal scales of tens of minutes to a few days in advance at wind farm locations. Traditionally these forecasts predict the wind at turbine hub heights; this information is then converted by transmission system operators and energy companies into predictions...... of power output at wind farms. Since the power available in the wind is proportional to the wind speed cubed, even small wind forecast errors result in large power prediction errors. Accurate wind forecasts are worth billions of dollars annually; forecast improvements will result in reduced costs...

  11. Study of s-component of the solar radio emission and short-term quantitative prediction of powerful solar flares

    International Nuclear Information System (INIS)

    Guseynov, Sh; Gakhramanov, I.G.

    2012-01-01

    Full text : All living and non-living things on Earth is dependent on the processes occurring in the Sun. Therefore the study of the Sun with the aim to predict powerful solar flares is of great scientific and practical importance. It is known that the main drawback of modern forecasting of solar flares and the low reliability of forecasts is the lack of use of the physical concepts of the mechanism of flares

  12. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-05-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  13. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-04-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  14. Empirical Models for Power and Energy Requirements II : A Powered Implement Operation in Serdang Sandy Clay Loam, Malaysia

    Directory of Open Access Journals (Sweden)

    A. F. Kheiralla

    2017-12-01

    Full Text Available Power and energy requirements were measured with an instrumented tractor for rotary tilling in Serdang sandy clay loam soil.  The effects of travel speed and rotor speed upon the measured data were investigated.  Power model from orthogonal regression analysis was formulated based on linear and quadratic functions of travel speed and bite length.  Fuel consumption model from regression analysis was formulated based on linear tractor PTO power as well as linear equivalent tractor PTO power.  Fuel consumption rates predicted by ASAE D497.3 were found to be 25% to 28% overestimates of the values predicted by the model developed.  However, fuel consumption rates reported by OECD Tractor Test were found to be 1% to 9% lower than the fuel consumption rates predicted by the model developed.  A comparison of power and energy requirements for both powered and draught implements showed that the disk harrow was the most energy efficient implement in terms of fuel consumption and specific energy followed by the rotary tiller, disk plough and mouldboard.  Finally, average PTO power, fuel consumption, wheel slip, wheel power and specific energy for a powered implement are presented.

  15. Lightning prediction using radiosonde data

    Energy Technology Data Exchange (ETDEWEB)

    Weng, L.Y.; Bin Omar, J.; Siah, Y.K.; Bin Zainal Abidin, I.; Ahmad, S.K. [Univ. Tenaga, Darul Ehsan (Malaysia). College of Engineering

    2008-07-01

    Lightning is a natural phenomenon in tropical regions. Malaysia experiences very high cloud-to-ground lightning density, posing both health and economic concerns to individuals and industries. In the commercial sector, power lines, telecommunication towers and buildings are most frequently hit by lightning. In the event that a power line is hit and the protection system fails, industries which rely on that power line would cease operations temporarily, resulting in significant monetary loss. Current technology is unable to prevent lightning occurrences. However, the ability to predict lightning would significantly reduce damages from direct and indirect lightning strikes. For that reason, this study focused on developing a method to predict lightning with radiosonde data using only a simple back propagation neural network model written in C code. The study was performed at the Kuala Lumpur International Airport (KLIA). In this model, the parameters related to wind were disregarded. Preliminary results indicate that this method shows some positive results in predicting lighting. However, a larger dataset is needed in order to obtain more accurate predictions. It was concluded that future work should include wind parameters to fully capture all properties for lightning formation, subsequently its prediction. 8 refs., 5 figs.

  16. Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

    Science.gov (United States)

    van Veen, S H C M; van Kleef, R C; van de Ven, W P M M; van Vliet, R C J A

    2018-02-01

    This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts. Copyright © 2017 John Wiley & Sons, Ltd.

  17. 4th International Conference on Power Transmissions

    CERN Document Server

    2013-01-01

    This books contains the Proceedings of the 4th International Conference on Power Transmissions, that was held in Sinaia, Romania from June 20 -23, 2012. Power Transmissions is a very complex and multi-disciplinary scientific field of Mechanical Engineering that covers the different types of transmissions (mechanical, hydraulic, pneumatic) as well as all the machine elements involved, such as gears, bearings, shafts, couplings and a lot more. It concerns not only their basic theory but also their design, analysis, testing, application and maintenance. The requirements set to modern power transmissions are really tough to meet: They need to be more efficient, stronger, smaller, noiseless, easier to produce and to cost less. There is a strong demand to become easier in operation and maintenance, or even automatic and in maintenance-free. Last but not least, they should be easily recycled and respect the environment. Joint efforts of specialists from both academia and industry can significantly contribute to fulf...

  18. Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

    Science.gov (United States)

    Cipcigan, Flaviu S.; Sokhan, Vlad P.; Crain, Jason; Martyna, Glenn J.

    2016-12-01

    One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082-1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230-233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeller through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO_MD.

  19. Universal Inverse Power-Law Distribution for Fractal Fluctuations in Dynamical Systems: Applications for Predictability of Inter-Annual Variability of Indian and USA Region Rainfall

    Science.gov (United States)

    Selvam, A. M.

    2017-01-01

    Dynamical systems in nature exhibit self-similar fractal space-time fluctuations on all scales indicating long-range correlations and, therefore, the statistical normal distribution with implicit assumption of independence, fixed mean and standard deviation cannot be used for description and quantification of fractal data sets. The author has developed a general systems theory based on classical statistical physics for fractal fluctuations which predicts the following. (1) The fractal fluctuations signify an underlying eddy continuum, the larger eddies being the integrated mean of enclosed smaller-scale fluctuations. (2) The probability distribution of eddy amplitudes and the variance (square of eddy amplitude) spectrum of fractal fluctuations follow the universal Boltzmann inverse power law expressed as a function of the golden mean. (3) Fractal fluctuations are signatures of quantum-like chaos since the additive amplitudes of eddies when squared represent probability densities analogous to the sub-atomic dynamics of quantum systems such as the photon or electron. (4) The model predicted distribution is very close to statistical normal distribution for moderate events within two standard deviations from the mean but exhibits a fat long tail that are associated with hazardous extreme events. Continuous periodogram power spectral analyses of available GHCN annual total rainfall time series for the period 1900-2008 for Indian and USA stations show that the power spectra and the corresponding probability distributions follow model predicted universal inverse power law form signifying an eddy continuum structure underlying the observed inter-annual variability of rainfall. On a global scale, man-made greenhouse gas related atmospheric warming would result in intensification of natural climate variability, seen immediately in high frequency fluctuations such as QBO and ENSO and even shorter timescales. Model concepts and results of analyses are discussed with reference

  20. PROBING THE INFLATON: SMALL-SCALE POWER SPECTRUM CONSTRAINTS FROM MEASUREMENTS OF THE COSMIC MICROWAVE BACKGROUND ENERGY SPECTRUM

    International Nuclear Information System (INIS)

    Chluba, Jens; Erickcek, Adrienne L.; Ben-Dayan, Ido

    2012-01-01

    In the early universe, energy stored in small-scale density perturbations is quickly dissipated by Silk damping, a process that inevitably generates μ- and y-type spectral distortions of the cosmic microwave background (CMB). These spectral distortions depend on the shape and amplitude of the primordial power spectrum at wavenumbers k ∼ 4 Mpc –1 . Here, we study constraints on the primordial power spectrum derived from COBE/FIRAS and forecasted for PIXIE. We show that measurements of μ and y impose strong bounds on the integrated small-scale power, and we demonstrate how to compute these constraints using k-space window functions that account for the effects of thermalization and dissipation physics. We show that COBE/FIRAS places a robust upper limit on the amplitude of the small-scale power spectrum. This limit is about three orders of magnitude stronger than the one derived from primordial black holes in the same scale range. Furthermore, this limit could be improved by another three orders of magnitude with PIXIE, potentially opening up a new window to early universe physics. To illustrate the power of these constraints, we consider several generic models for the small-scale power spectrum predicted by different inflation scenarios, including running-mass inflation models and inflation scenarios with episodes of particle production. PIXIE could place very tight constraints on these scenarios, potentially even ruling out running-mass inflation models if no distortion is detected. We also show that inflation models with sub-Planckian field excursion that generate detectable tensor perturbations should simultaneously produce a large CMB spectral distortion, a link that could potentially be established with PIXIE.