WorldWideScience

Sample records for significant predictive power

  1. 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

  2. Predicting significant torso trauma.

    Science.gov (United States)

    Nirula, Ram; Talmor, Daniel; Brasel, Karen

    2005-07-01

    Identification of motor vehicle crash (MVC) characteristics associated with thoracoabdominal injury would advance the development of automatic crash notification systems (ACNS) by improving triage and response times. Our objective was to determine the relationships between MVC characteristics and thoracoabdominal trauma to develop a torso injury probability model. Drivers involved in crashes from 1993 to 2001 within the National Automotive Sampling System were reviewed. Relationships between torso injury and MVC characteristics were assessed using multivariate logistic regression. Receiver operating characteristic curves were used to compare the model to current ACNS models. There were a total of 56,466 drivers. Age, ejection, braking, avoidance, velocity, restraints, passenger-side impact, rollover, and vehicle weight and type were associated with injury (p < 0.05). The area under the receiver operating characteristic curve (83.9) was significantly greater than current ACNS models. We have developed a thoracoabdominal injury probability model that may improve patient triage when used with ACNS.

  3. 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.

  4. Swiss solar power statistics 2007 - Significant expansion

    International Nuclear Information System (INIS)

    Hostettler, T.

    2008-01-01

    This article presents and discusses the 2007 statistics for solar power in Switzerland. A significant number of new installations is noted as is the high production figures from newer installations. The basics behind the compilation of the Swiss solar power statistics are briefly reviewed and an overview for the period 1989 to 2007 is presented which includes figures on the number of photovoltaic plant in service and installed peak power. Typical production figures in kilowatt-hours (kWh) per installed kilowatt-peak power (kWp) are presented and discussed for installations of various sizes. Increased production after inverter replacement in older installations is noted. Finally, the general political situation in Switzerland as far as solar power is concerned are briefly discussed as are international developments.

  5. The Real World Significance of Performance Prediction

    Science.gov (United States)

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

    2012-01-01

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

  6. Economic Operation of Power Systems with Significant Wind Power Penetration

    DEFF Research Database (Denmark)

    Farashbashi-Astaneh, Seyed-Mostafa

    This dissertation addresses economic operation of power systems with high penetration of wind power. Several studies are presented to address the economic operation of power systems with high penetration of variable wind power. The main concern in such power systems is high variability...... and unpredictability. Unlike conventional power plants, the output power of a wind farm is not controllable. This brings additional complexity to operation and planning of wind dominant power systems. The key solution in face of wind power uncertainty is to enhance power system flexibility. The enhanced flexibility......, cooperative wind-storage operation is studied. Lithium-Ion battery units are chosen as storage units. A novel formulation is proposed to investigate optimal operation of a storage unit considering power system balancing conditions and wind power imbalances. An optimization framework is presented to increase...

  7. 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.

  8. Music: Its Expressive Power and Moral Significance

    Directory of Open Access Journals (Sweden)

    Sarah Whitfield

    2010-05-01

    Full Text Available The creation and practice of music is tightly wound with human emotion, character, and experience. Music arouses sentiment and cannot be underestimated as a powerful shaper of human virtue, character, and emotion. As vehicles of musical expression, musicians possess the ability to profoundly influence an audience for good or for evil. Thus, the nature of music and the manner in which musicians utilize it creates innumerable ramifications that cannot be ignored. The pervasiveness of this notion is largely attributed to the Greek theorists, who ascribed various emotions and moral implications to particular modes. The prominent Greek philosophers Plato and Aristotle affirmed that music contained an intrinsic element that was conducive to the promotion of moral or spiritual harmony and order in the soul. Plato and his contemporaries attributed specific character-forming qualities to each of the individual harmonia, or musical modes, believing that each could shape human character in a distinct way. These ideas inevitably persisted and continue to endure. Theorists throughout history have agreed that music profoundly influences human character and shapes morality.

  9. 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

  10. 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...

  11. 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....

  12. HVDC Transmission an Outlook and Significance for Pakistani Power Sector

    Science.gov (United States)

    Ahmad, Muhammad; Wang, Zhixin; Wang, Jinjian; Baloach, Mazhar H.; Longxin, Bao; Hua, Qing

    2018-04-01

    Recently a paradigm shift in the power sector is observed, i.e., countries across the globe have deviated their attention to distributed generation rather than conventional centralized bulk generation. Owing to the above narrative, distributed energy resources e.g., wind and PV have gained the adequate attention of governments and researchers courtesy to their eco-friendly nature. On the contrary, the increased infiltration of distributed generation to the power system has introduced many technical and economical glitches such as long-distance transmission, transmission lines efficiency, control capability and cost etc. To mitigate these complications, the utility of high voltage direct current (HVDC) transmission has emerged as a possible solution. In this context, this paper includes a brief discussion on the fundamentals HVDC and its significance in Pakistani power sector. Furthermore, the potential of distributed energy resources for Pakistan is also the subject matter of this paper, so that significance of HVDC transmission can effectively be deliberated.

  13. 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

  14. 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...

  15. 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...

  16. Simulators predict power plant operation

    Energy Technology Data Exchange (ETDEWEB)

    Peltier, R.

    2002-07-01

    Mix the complexity of a new construction or major retrofit project with today's 'do more with less', a pinch of 'personnel inexperience,' and a dash of 'unintended consequences', and you have got a recipe for insomnia. Advanced simulation tools, however, can help you wring out your design train your operators before the first wire is terminated and just may be get a good night's rest. The article describes several examples of uses of simulation tools. Esscor recently completed a simulation project for a major US utility exploring the potential for furnace/duct implosion that could result from adding higher volumetric flow induced-draft fans and selective catalytic reduction to a 650-MW coal-fired plant. CAF Electronics Inc. provided a full-scope simulator for Alstom's KA24-1 combined-cycle power plant in Paris, France. Computational fluid dynamics (CFD) tools are being used by the Gas Technology Institute to simulate the performance of the next generation of pulverized coal combustors. 5 figs.

  17. Early signs that predict later haemodynamically significant patent ductus arteriosus.

    Science.gov (United States)

    Engür, Defne; Deveci, Murat; Türkmen, Münevver K

    2016-03-01

    Our aim was to determine the optimal cut-off values, sensitivity, specificity, and diagnostic power of 12 echocardiographic parameters on the second day of life to predict subsequent ductal patency. We evaluated preterm infants, born at ⩽32 weeks of gestation, starting on their second day of life, and they were evaluated every other day until ductal closure or until there were clinical signs of re-opening. We measured transductal diameter; pulmonary arterial diastolic flow; retrograde aortic diastolic flow; pulsatility index of the left pulmonary artery and descending aorta; left atrium and ventricle/aortic root ratio; left ventricular output; left ventricular flow velocity time integral; mitral early/late diastolic flow; and superior caval vein diameter and flow as well as performed receiver operating curve analysis. Transductal diameter (>1.5 mm); pulmonary arterial diastolic flow (>25.6 cm/second); presence of retrograde aortic diastolic flow; ductal diameter by body weight (>1.07 mm/kg); left pulmonary arterial pulsatility index (⩽0.71); and left ventricle to aortic root ratio (>2.2) displayed high sensitivity and specificity (p0.9). Parameters with moderate sensitivity and specificity were as follows: left atrial to aortic root ratio; left ventricular output; left ventricular flow velocity time integral; and mitral early/late diastolic flow ratio (p0.05) had low diagnostic value. Left pulmonary arterial pulsatility index, left ventricle/aortic root ratio, and ductal diameter by body weight are useful adjuncts offering a broader outlook for predicting ductal patency.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. The significance of structural power in Strategic Environmental Assessment

    International Nuclear Information System (INIS)

    Hansen, Anne Merrild; Kørnøv, Lone; Cashmore, Matthew; Richardson, Tim

    2013-01-01

    This article presents a study of how power dynamics enables and constrains the influence of actors upon decision-making and Strategic Environmental Assessment (SEA). Based on structuration theory, a model for studying power dynamics in strategic decision-making processes is developed. The model is used to map and analyse key decision arenas in the decision process of aluminium production in Greenland. The analysis shows that communication lines are an important resource through which actors exercise power and influence decision-making on the location of the aluminium production. The SEA process involved not only reproduction of formal communication and decision competence but also production of alternative informal communication structures in which the SEA had capability to influence. It is concluded, that actors influence strategic decision making, and attention needs to be on not only the formal interactions between SEA process and strategic decision-making process but also on informal interaction and communication between actors as the informal structures, which can be crucial to the outcome of the decision-making process. This article is meant as a supplement to the understanding of power dynamics influence in IA processes and as a contribution to the IA research field with a method to analyse power dynamics in strategic decision-making processes. The article also brings reflections of strengths and weaknesses of using the structuration theory as an approach to power analysis. - Highlights: ► Informal interaction influenced process despite the presence of formalised rules. ► Interdependence of actors influenced SEA effectiveness. ► SEA practitioners successfully exercised power to influence decision-making. ► Power dynamics are properties of actors' interactions in decision-making. ► Power structures can be enabling and not solely limiting.

  3. 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.

  4. The significance of structural power in Strategic Environmental Assessment

    DEFF Research Database (Denmark)

    Hansen, Anne Merrild; Kørnøv, Lone; Cashmore, Matthew Asa

    2013-01-01

    , that actors influence both outcome and frames for strategic decision making and attention needs to be on not only the formal interactions between SEA process and strategic decision-making process but also on informal interaction and communication between actors. The informal structures shows crucial...... to the outcome of the decision-making process. The article is meant as a supplement to the understanding of power dynamics influence in IA processes emphasising the capacity of agents to mobilise and create change. Despite epistemological challenges of using ST theory as an approach to power analysis, this meta......This article presents a study of how power dynamics enables and constrains the influence of actors upon decision-making and Strategic Environmental Assessment (SEA). Based on Anthony Giddens structuration theory (ST), a model for studying power dynamics in strategic decision-making processes...

  5. The Relative Importance of Low Significance Level and High Power in Multiple Tests of Significance.

    Science.gov (United States)

    Westermann, Rainer; Hager, Willi

    1983-01-01

    Two psychological experiments--Anderson and Shanteau (1970), Berkowitz and LePage (1967)--are reanalyzed to present the problem of the relative importance of low Type 1 error probability and high power when answering a research question by testing several statistical hypotheses. (Author/PN)

  6. 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)

  7. 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.

  8. Predictive Maintenance (PdM) Centralization for Significant Energy Savings

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Dale

    2010-09-15

    Cost effective predictive maintenance (PdM) technologies and basic energy calculations can mine energy savings form processes or maintenance activities. Centralizing and packaging this information correctly empowers facility maintenance and reliability professionals to build financial justification and support for strategies and personnel to weather global economic downturns and competition. Attendees will learn how to: Systematically build a 'pilot project' for applying PdM and tracking systems; Break down a typical electrical bill to calculate energy savings; Use return on investment (ROI) calculations to identify the best and highest value options, strategies and tips for substantiating your energy reduction maintenance strategies.

  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. 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...

  11. 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...

  12. 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.

  13. Clinical Significance of Hemostatic Parameters in the Prediction for Type 2 Diabetes Mellitus and Diabetic Nephropathy

    Directory of Open Access Journals (Sweden)

    Lianlian Pan

    2018-01-01

    Full Text Available It would be important to predict type 2 diabetes mellitus (T2DM and diabetic nephropathy (DN. This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT, platelet (PLT, and D-dimer (D-D levels compared with controls (P<0.01. Fibrinogen (FIB, PLT, and D-D increased in DN patients compared with those without complications (P<0.001. Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P<0.01, resp., and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P<0.01, resp.. The area under ROC curve (AUC of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85% and specificity (76% for DN, and that of PLT was 0.564, with sensitivity (60% and specificity (89% based on the cutoff values of 3.15 g/L and 245 × 109/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.

  14. 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.

  15. Statistical significance of theoretical predictions: A new dimension in nuclear structure theories (I)

    International Nuclear Information System (INIS)

    DUDEK, J; SZPAK, B; FORNAL, B; PORQUET, M-G

    2011-01-01

    In this and the follow-up article we briefly discuss what we believe represents one of the most serious problems in contemporary nuclear structure: the question of statistical significance of parametrizations of nuclear microscopic Hamiltonians and the implied predictive power of the underlying theories. In the present Part I, we introduce the main lines of reasoning of the so-called Inverse Problem Theory, an important sub-field in the contemporary Applied Mathematics, here illustrated on the example of the Nuclear Mean-Field Approach.

  16. 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.

  17. 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

  18. The role and significance of nuclear power in China in response to climate change

    International Nuclear Information System (INIS)

    Hou Huiqun; Wu Jin; Bai Yunsheng

    2010-01-01

    This paper analyzes international climate negotiations and the domestic situation. In accordance with the development goal of 2020, the emission reduction of nuclear power is measured as compared with thermal power from the sector of power generation and nuclear power chain respectively. It emphasized that nuclear power can play a significant role in the emission reduction with corresponding policy recommendations. (authors)

  19. 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.

  20. 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 ...

  1. 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.

  2. 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

  3. The significance of collateral vessels, as seen on chest CT, in predicting SVC obstruction

    International Nuclear Information System (INIS)

    Yeouk, Young Soo; Kim, Sung Jin; Bae, Il Hun; Kim, Jae Youn; Hwang, Seung Min; Han, Gi Seok; Park, Kil Sun; Kim, Dae Young

    1998-01-01

    To evaluate the significance of collateral veins, as seen on chest CT, in the diagnosis of superior vena cava obstruction. We retrospectively the records of 81 patients in whom collateral veins were seen on chest CT. On spiral CT(n=49), contrast material was infused via power injector, and on conventional CT(n=32), 50 ml bolus infusion was followed by 50 ml drip infusion. Obstruction of the SVC was evaluated on chest CT; if, however, evaluation of the SVC of its major tributaries was difficult, as in five cases, the patient underwent SVC phlebography. Collateral vessels were assigned to one of ten categories. On conventional CT, the jugular venous arch in the only collateral vessel to predict SVC obstruction; on spiral CT, however, collateral vessels are not helpful in the diagnosis of SVC obstruction, but are a nonspecific finding. (author). 12 refs., 2 tab., 2 figs

  4. 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...

  5. 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...

  6. 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.

  7. 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...

  8. 75 FR 11575 - James A. Fitzpatrick Nuclear Power Plant Environmental Assessment and Finding of No Significant...

    Science.gov (United States)

    2010-03-11

    ... Power Plant Environmental Assessment and Finding of No Significant Impact The U.S. Nuclear Regulatory...), for the operation of the James A. FitzPatrick Nuclear Power Plant (JAFNPP) located in Oswego County... the Final Environmental Statement for the James A. FitzPatrick Nuclear Power Plant, Docket No. 50-333...

  9. 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.

  10. 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....

  11. 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.

  12. Power Load Prediction Based on Fractal Theory

    OpenAIRE

    Jian-Kai, Liang; Cattani, Carlo; Wan-Qing, Song

    2015-01-01

    The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and...

  13. 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...

  14. 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.

  15. 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)

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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...

  1. 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.

  2. Analysing the significance of silence in qualitative interviewing: questioning and shifting power relations

    DEFF Research Database (Denmark)

    Bengtsson, Tea Torbenfeldt; Fynbo, Lars

    2017-01-01

    In this article we analyse the significance of silence in qualitative interviews with 36 individuals interviewed about high-risk, illegal activities. We describe how silence expresses a dynamic power relationship between interviewer and interviewee. In the analysis, we focus on two different types...... significant data. We conclude that silence constitutes possibilities for interviewees and interviewers to handle the complex power at play in qualitative interviewing either by maintaining or by losing control of the situation....

  3. Concrete component aging and its significance relative to life extension of nuclear power plants

    International Nuclear Information System (INIS)

    Naus, D.J.

    1986-09-01

    The objectives of this study are to (1) expand upon the work that was initiated in the first two Electric Power Research Institute studies relative to longevity and life extension considerations of safety-related concrete components in light-water reactor (LWR) facilities and (2) provide background that will logically lead to subsequent development of a methodology for assessing and predicting the effects of aging on the performance of concrete-based materials and components. These objectives are consistent with Nuclear Plant Aging Research (NPAR) Program goals: (1) to identify and characterize aging and service wear effects that, if unchecked, could cause degradation of structures, components, and systems and, thereby, impair plant safety; (2) to identify methods of inspection, surveillance, and monitoring or of evaluating residual life of structures, components, and systems that will ensure timely detection of significant aging effects before loss of safety function; and (3) to evaluate the effectiveness of storage, maintenance, repair, and replacement practices in mitigating the rate and extent of degradation caused by aging and service wear

  4. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

    Science.gov (United States)

    Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

    2016-11-01

    Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

  5. 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.

  6. 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...

  7. 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...

  8. 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

  9. 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.

  10. Testing earthquake prediction algorithms: Statistically significant advance prediction of the largest earthquakes in the Circum-Pacific, 1992-1997

    Science.gov (United States)

    Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.

    1999-01-01

    Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier

  11. A new model using routinely available clinical parameters to predict significant liver fibrosis in chronic hepatitis B.

    Directory of Open Access Journals (Sweden)

    Wai-Kay Seto

    Full Text Available OBJECTIVE: We developed a predictive model for significant fibrosis in chronic hepatitis B (CHB based on routinely available clinical parameters. METHODS: 237 treatment-naïve CHB patients [58.4% hepatitis B e antigen (HBeAg-positive] who had undergone liver biopsy were randomly divided into two cohorts: training group (n = 108 and validation group (n = 129. Liver histology was assessed for fibrosis. All common demographics, viral serology, viral load and liver biochemistry were analyzed. RESULTS: Based on 12 available clinical parameters (age, sex, HBeAg status, HBV DNA, platelet, albumin, bilirubin, ALT, AST, ALP, GGT and AFP, a model to predict significant liver fibrosis (Ishak fibrosis score ≥3 was derived using the five best parameters (age, ALP, AST, AFP and platelet. Using the formula log(index+1 = 0.025+0.0031(age+0.1483 log(ALP+0.004 log(AST+0.0908 log(AFP+1-0.028 log(platelet, the PAPAS (Platelet/Age/Phosphatase/AFP/AST index predicts significant fibrosis with an area under the receiving operating characteristics (AUROC curve of 0.776 [0.797 for patients with ALT <2×upper limit of normal (ULN] The negative predictive value to exclude significant fibrosis was 88.4%. This predictive power is superior to other non-invasive models using common parameters, including the AST/platelet/GGT/AFP (APGA index, AST/platelet ratio index (APRI, and the FIB-4 index (AUROC of 0.757, 0.708 and 0.723 respectively. Using the PAPAS index, 67.5% of liver biopsies for patients being considered for treatment with ALT <2×ULN could be avoided. CONCLUSION: The PAPAS index can predict and exclude significant fibrosis, and may reduce the need for liver biopsy in CHB patients.

  12. 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.

  13. A critical discussion of null hypothesis significance testing and statistical power analysis within psychological research

    DEFF Research Database (Denmark)

    Jones, Allan; Sommerlund, Bo

    2007-01-01

    The uses of null hypothesis significance testing (NHST) and statistical power analysis within psychological research are critically discussed. The article looks at the problems of relying solely on NHST when dealing with small and large sample sizes. The use of power-analysis in estimating...... the potential error introduced by small and large samples is advocated. Power analysis is not recommended as a replacement to NHST but as an additional source of information about the phenomena under investigation. Moreover, the importance of conceptual analysis in relation to statistical analysis of hypothesis...

  14. 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.

  15. Analysis of events significant with regard to safety of Bohunice V-1 nuclear power plant

    International Nuclear Information System (INIS)

    Suchomel, J.; Maron, V.; Kmosena, J.

    1986-01-01

    An analysis was made of operating safety of the V-1 nuclear power plant in Jaslovske Bohunice for the years 1980 - 1983. Of the total number of 676 reported failures only three were events with special safety significance, namely a complete loss of power supply for own consumption from the power grid, a failure of pins on the collectors of steam generators, and a failure of the heads of heat technology inspection channels. The failures were categorized according to the systems used in the USSR and in the USA and compared with data on failures in nuclear power plants in the two countries. The conclusions show that the operation of the V-1 nuclear power plant achieves results which are fully comparable with those recorded in 9 WWER-440 power plants operating in various countries. The average coefficient of availability is 0.72 and ranks the power plant in the fourth place among the said 9 plants. A comparison of the individual power plant units showed that of the total number of 22, the first unit of the V-1 plant ranks fifth with a coefficient of 0.78 and the second unit with a coefficient of 0.69 ranks 15th. (Z.M.)

  16. Exploring the significance of human mobility patterns in social link prediction

    KAUST Repository

    Alharbi, Basma Mohammed

    2014-01-01

    Link prediction is a fundamental task in social networks. Recently, emphasis has been placed on forecasting new social ties using user mobility patterns, e.g., investigating physical and semantic co-locations for new proximity measure. This paper explores the effect of in-depth mobility patterns. Specifically, we study individuals\\' movement behavior, and quantify mobility on the basis of trip frequency, travel purpose and transportation mode. Our hybrid link prediction model is composed of two modules. The first module extracts mobility patterns, including travel purpose and mode, from raw trajectory data. The second module employs the extracted patterns for link prediction. We evaluate our method on two real data sets, GeoLife [15] and Reality Mining [5]. Experimental results show that our hybrid model significantly improves the accuracy of social link prediction, when comparing to primary topology-based solutions. Copyright 2014 ACM.

  17. A fast method for the unit scheduling problem with significant renewable power generation

    International Nuclear Information System (INIS)

    Osório, G.J.; Lujano-Rojas, J.M.; Matias, J.C.O.; Catalão, J.P.S.

    2015-01-01

    Highlights: • A model to the scheduling of power systems with significant renewable power generation is provided. • A new methodology that takes information from the analysis of each scenario separately is proposed. • Based on a probabilistic analysis, unit scheduling and corresponding economic dispatch are estimated. • A comparison with others methodologies is in favour of the proposed approach. - Abstract: Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays, this problem is solved using Monte Carlo Simulation (MCS) approach, which allows considering important statistical characteristics of wind and solar power production such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. However, MCS method requires the analysis of a representative amount of trials, which is an intensive calculation task that increases considerably with the number of scenarios considered. In this paper, a model to the scheduling of power systems with significant renewable power generation based on scenario generation/reduction method, which establishes a proportional relationship between the number of scenarios and the computational time required to analyse them, is proposed. The methodology takes information from the analysis of each scenario separately to determine the probabilistic behaviour of each generator at each hour in the scheduling problem. Then, considering a determined significance level, the units to be committed are selected and the load dispatch is determined. The proposed technique was illustrated through a case study and the comparison with stochastic programming approach was carried out, concluding that the proposed methodology can provide an acceptable solution in a reduced computational time

  18. A shift from significance test to hypothesis test through power analysis in medical research.

    Science.gov (United States)

    Singh, G

    2006-01-01

    Medical research literature until recently, exhibited substantial dominance of the Fisher's significance test approach of statistical inference concentrating more on probability of type I error over Neyman-Pearson's hypothesis test considering both probability of type I and II error. Fisher's approach dichotomises results into significant or not significant results with a P value. The Neyman-Pearson's approach talks of acceptance or rejection of null hypothesis. Based on the same theory these two approaches deal with same objective and conclude in their own way. The advancement in computing techniques and availability of statistical software have resulted in increasing application of power calculations in medical research and thereby reporting the result of significance tests in the light of power of the test also. Significance test approach, when it incorporates power analysis contains the essence of hypothesis test approach. It may be safely argued that rising application of power analysis in medical research may have initiated a shift from Fisher's significance test to Neyman-Pearson's hypothesis test procedure.

  19. A shift from significance test to hypothesis test through power analysis in medical research

    Directory of Open Access Journals (Sweden)

    Singh Girish

    2006-01-01

    Full Text Available Medical research literature until recently, exhibited substantial dominance of the Fisher′s significance test approach of statistical inference concentrating more on probability of type I error over Neyman-Pearson′s hypothesis test considering both probability of type I and II error. Fisher′s approach dichotomises results into significant or not significant results with a P value. The Neyman-Pearson′s approach talks of acceptance or rejection of null hypothesis. Based on the same theory these two approaches deal with same objective and conclude in their own way. The advancement in computing techniques and availability of statistical software have resulted in increasing application of power calculations in medical research and thereby reporting the result of significance tests in the light of power of the test also. Significance test approach, when it incorporates power analysis contains the essence of hypothesis test approach. It may be safely argued that rising application of power analysis in medical research may have initiated a shift from Fisher′s significance test to Neyman-Pearson′s hypothesis test procedure.

  20. 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)

  1. 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...

  2. Corruption Significantly Increases the Capital Cost of Power Plants in Developing Contexts

    Directory of Open Access Journals (Sweden)

    Kumar Biswajit Debnath

    2018-03-01

    Full Text Available Emerging economies with rapidly growing population and energy demand, own some of the most expensive power plants in the world. We hypothesized that corruption has a relationship with the capital cost of power plants in developing countries such as Bangladesh. For this study, we analyzed the capital cost of 61 operational and planned power plants in Bangladesh. Initial comparison study revealed that the mean capital cost of a power plant in Bangladesh is twice than that of the global average. Then, the statistical analysis revealed a significant correlation between corruption and the cost of power plants, indicating that higher corruption leads to greater capital cost. The high up-front cost can be a significant burden on the economy, at present and in the future, as most are financed through international loans with extended repayment terms. There is, therefore, an urgent need for the review of the procurement and due diligence process of establishing power plants, and for the implementation of a more transparent system to mitigate adverse effects of corruption on megaprojects.

  3. 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.

  4. 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...

  5. 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.

  6. 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)

  7. 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

  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. 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)

  10. 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...

  11. 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.)

  12. 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...

  13. 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

  14. 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.

  15. 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.

  16. Artificial neural networks to predict presence of significant pathology in patients presenting to routine colorectal clinics.

    Science.gov (United States)

    Maslekar, S; Gardiner, A B; Monson, J R T; Duthie, G S

    2010-12-01

    Artificial neural networks (ANNs) are computer programs used to identify complex relations within data. Routine predictions of presence of colorectal pathology based on population statistics have little meaning for individual patient. This results in large number of unnecessary lower gastrointestinal endoscopies (LGEs - colonoscopies and flexible sigmoidoscopies). We aimed to develop a neural network algorithm that can accurately predict presence of significant pathology in patients attending routine outpatient clinics for gastrointestinal symptoms. Ethics approval was obtained and the study was monitored according to International Committee on Harmonisation - Good Clinical Practice (ICH-GCP) standards. Three-hundred patients undergoing LGE prospectively completed a specifically developed questionnaire, which included 40 variables based on clinical symptoms, signs, past- and family history. Complete data sets of 100 patients were used to train the ANN; the remaining data was used for internal validation. The primary output used was positive finding on LGE, including polyps, cancer, diverticular disease or colitis. For external validation, the ANN was applied to data from 50 patients in primary care and also compared with the predictions of four clinicians. Clear correlation between actual data value and ANN predictions were found (r = 0.931; P = 0.0001). The predictive accuracy of ANN was 95% in training group and 90% (95% CI 84-96) in the internal validation set and this was significantly higher than the clinical accuracy (75%). ANN also showed high accuracy in the external validation group (89%). Artificial neural networks offer the possibility of personal prediction of outcome for individual patients presenting in clinics with colorectal symptoms, making it possible to make more appropriate requests for lower gastrointestinal endoscopy. © 2010 The Authors. Colorectal Disease © 2010 The Association of Coloproctology of Great Britain and Ireland.

  17. 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.

  18. 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...

  19. Electric power and its significance as the energy for innovation and progress

    International Nuclear Information System (INIS)

    Klinger, H.; Boehmer, T.

    1999-01-01

    The significance of electric power as the essential form of energy to support innovation and progress well into the future is explained with respect to four major domains of application: 1. Innovative activities in microelectronics and semiconductor technology, for applications such as automation and computer technology, instrumentation and control technology, facility and systems management and control. 2. Energy efficiency programmes and schemes for increasing the penetration of energiy from renewable sources in the market. Example: Heat pump technology. 3. Electric power as an energy boosting innovation in industrial production processes. Examples are given from the transportation sector. (orig./CB) [de

  20. 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.

  1. 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

  2. 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.    

  3. 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.

  4. A Note on Comparing the Power of Test Statistics at Low Significance Levels.

    Science.gov (United States)

    Morris, Nathan; Elston, Robert

    2011-01-01

    It is an obvious fact that the power of a test statistic is dependent upon the significance (alpha) level at which the test is performed. It is perhaps a less obvious fact that the relative performance of two statistics in terms of power is also a function of the alpha level. Through numerous personal discussions, we have noted that even some competent statisticians have the mistaken intuition that relative power comparisons at traditional levels such as α = 0.05 will be roughly similar to relative power comparisons at very low levels, such as the level α = 5 × 10 -8 , which is commonly used in genome-wide association studies. In this brief note, we demonstrate that this notion is in fact quite wrong, especially with respect to comparing tests with differing degrees of freedom. In fact, at very low alpha levels the cost of additional degrees of freedom is often comparatively low. Thus we recommend that statisticians exercise caution when interpreting the results of power comparison studies which use alpha levels that will not be used in practice.

  5. Method of extracting significant trouble information of nuclear power plants using probabilistic analysis technique

    International Nuclear Information System (INIS)

    Shimada, Yoshio; Miyazaki, Takamasa

    2005-01-01

    In order to analyze and evaluate large amounts of trouble information of overseas nuclear power plants, it is necessary to select information that is significant in terms of both safety and reliability. In this research, a method of efficiently and simply classifying degrees of importance of components in terms of safety and reliability while paying attention to root-cause components appearing in the information was developed. Regarding safety, the reactor core damage frequency (CDF), which is used in the probabilistic analysis of a reactor, was used. Regarding reliability, the automatic plant trip probability (APTP), which is used in the probabilistic analysis of automatic reactor trips, was used. These two aspects were reflected in the development of criteria for classifying degrees of importance of components. By applying these criteria, a simple method of extracting significant trouble information of overseas nuclear power plants was developed. (author)

  6. Performance evaluation recommendations and manuals of nuclear power plants outdoor significant civil structures earthquake resistance

    International Nuclear Information System (INIS)

    2005-06-01

    Performance evaluation recommendations and manuals of nuclear power plants outdoor significant civil structures earthquake resistance have been updated in June 2005 by the Japan Society of Civil Engineers. Based on experimental and analytical considerations on the recommendations of May 2002, analytical seismic models of soils for underground structures, effects of vertical motions on time-history dynamic analysis and shear fracture of reinforced concretes by cyclic loadings have been evaluated and incorporated in new recommendations. (T. Tanaka)

  7. Performance evaluation recommendations of nuclear power plants outdoor significant civil structures earthquake resistance. Technical documentation

    International Nuclear Information System (INIS)

    2005-06-01

    The Japan Society of Civil Engineers has updated performance evaluation recommendations of nuclear power plants outdoor significant civil structures earthquake resistance in June 2005. Experimental and analytical considerations on the seismic effects evaluation criteria, such as analytical seismic models of soils for underground structures, effects of vertical motions on time-history dynamic analysis and shear fracture of reinforced concretes by cyclic loadings, were shown in this document and incorporated in new recommendations. (T. Tanaka)

  8. Performance evaluation recommendations of nuclear power plants outdoor significant civil structures earthquake resistance. Performance evaluation examples

    International Nuclear Information System (INIS)

    2005-06-01

    The Japan Society of Civil Engineers has updated performance evaluation recommendations of nuclear power plants outdoor significant civil structures earthquake resistance in June 2005. Based on experimental and analytical considerations, analytical seismic models of soils for underground structures, effects of vertical motions on time-history dynamic analysis and shear fracture of reinforced concretes by cyclic loadings have been incorporated in new recommendations. This document shows outdoor civil structures earthquake resistance and endurance performance evaluation examples based on revised recommendations. (T. Tanaka)

  9. 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...

  10. 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...

  11. 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)

  12. 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)

  13. 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)

  14. 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.

  15. Power, effects, confidence, and significance: an investigation of statistical practices in nursing research.

    Science.gov (United States)

    Gaskin, Cadeyrn J; Happell, Brenda

    2014-05-01

    improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. Aging of concrete components and its significance relative to life extension of nuclear power plants

    International Nuclear Information System (INIS)

    Naus, D.J.

    1987-01-01

    Nuclear power currently supplies about 16% of the US electricity requirements, with the percentage expected to rise to 20% by 1990. Despite the increasing role of nuclear power in energy production, cessation of orders for new nuclear plants in combination with expiration of operating licenses for several plants in the next 15 to 20 years results in a potential loss of electrical generating capacity of 50 to 60 gigawatts during the time period 2005 to 2020. A potential timely and cost-effective solution to the problem of meeting future energy demand is available through extension of the service life of existing nuclear plants. Any consideration of plant life extension, however, must consider the concrete components in these plants, since they play a vital safety role. Under the USNRC Nuclear Plant Aging Research (NPAR) Program, a study was conducted to review operating experience and to provide background that will lead to subsequent development of a methodology for assessing and predicting the effects of aging on the performance of concrete-based structures. The approach followed was in conformance with the NPAR strategy

  1. Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System.

    Science.gov (United States)

    Doyle, Andy; Katz, Graham; Summers, Kristen; Ackermann, Chris; Zavorin, Ilya; Lim, Zunsik; Muthiah, Sathappan; Butler, Patrick; Self, Nathan; Zhao, Liang; Lu, Chang-Tien; Khandpur, Rupinder Paul; Fayed, Youssef; Ramakrishnan, Naren

    2014-12-01

    Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years.

  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. 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.

  4. 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

  5. Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients: CoCoNet study.

    Science.gov (United States)

    Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

    2016-06-01

    There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings.

  6. Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients

    Science.gov (United States)

    Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

    2016-01-01

    Abstract There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings. PMID:27310982

  7. Bilirubin nomogram for prediction of significant hyperbilirubinemia in north Indian neonates.

    Science.gov (United States)

    Pathak, Umesh; Chawla, Deepak; Kaur, Saranjit; Jain, Suksham

    2013-04-01

    (i) To construct hour-specific serum total bilirubin (STB) nomogram in neonates born at =35 weeks of gestation; (ii)To evaluate efficacy of pre-discharge bilirubin measurement in predicting hyperbilirubinemia needing treatment. Diagnostic test performance in a prospective cohort study. Teaching hospital in Northern India. Healthy neonates with gestation =35 weeks or birth weight =2000 g. Serum total bilirubin was measured in all enrolled neonates at 24 ± 6, 72-96 and 96-144 h of postnatal age and when indicated clinically. Neonates were followed up during hospital stay and after discharge till completion of 7th postnatal day. Key outcome was significant hyperbilirubinemia (SHB) defined as need of phototherapy based on modified American Academy of Pediatrics (AAP) guidelines. In neonates born at 38 or more weeks of gestation middle line and in neonates born at 37 or less completed weeks of gestation, lower line of phototherapy thresholds were used to initiate phototherapy. For construction of nomogram, STB values were clubbed in six-hour epochs (age ± 3 hours) for postnatal age up to 48 h and twelve-hour epochs (age ± 6 hours) for age beyond 48 h. Predictive ability of the nomogram was assessed by calculating sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratio, by plotting receiver-operating characteristics (ROC) curve and calculating c-statistic. 997 neonates (birth weight: 2627 ± 536 g, gestation: 37.8 ± 1.5 weeks) were enrolled, of which 931 completed followup. Among enrolled neonates 344 (34.5%) were low birth weight. Rate of exclusive breastfeeding during hospital stay was more than 80%. Bilirubin nomogram was constructed using 40th, 75th and 95th percentile values of hour-specific bilirubin. Pre-discharge STB of =95th percentile was assigned to be in high-risk zone, between 75th and 94th centile in upper-intermediate risk zone, between 40th and 74th centile in lower-intermediate risk zone and below 40th

  8. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

    Directory of Open Access Journals (Sweden)

    Tomislav Hengl

    Full Text Available 80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na. We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring

  9. 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...

  10. 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.

  11. The prognostic significance of UCA1 for predicting clinical outcome in patients with digestive system malignancies.

    Science.gov (United States)

    Liu, Fang-Teng; Dong, Qing; Gao, Hui; Zhu, Zheng-Ming

    2017-06-20

    Urothelial Carcinoma Associated 1 (UCA1) was an originally identified lncRNA in bladder cancer. Previous studies have reported that UCA1 played a significant role in various types of cancer. This study aimed to clarify the prognostic value of UCA1 in digestive system cancers. The meta-analysis of 15 studies were included, comprising 1441 patients with digestive system cancers. The pooled results of 14 studies indicated that high expression of UCA1 was significantly associated with poorer OS in patients with digestive system cancers (HR: 1.89, 95 % CI: 1.52-2.26). In addition, UCA1 could be as an independent prognostic factor for predicting OS of patients (HR: 1.85, 95 % CI: 1.45-2.25). The pooled results of 3 studies indicated a significant association between UCA1 and DFS in patients with digestive system cancers (HR = 2.50; 95 % CI = 1.30-3.69). Statistical significance was also observed in subgroup meta-analysis. Furthermore, the clinicopathological values of UCA1 were discussed in esophageal cancer, colorectal cancer and pancreatic cancer. A comprehensive retrieval was performed to search studies evaluating the prognostic value of UCA1 in digestive system cancers. Many databases were involved, including PubMed, Web of Science, Embase and Chinese National Knowledge Infrastructure and Wanfang database. Quantitative meta-analysis was performed with standard statistical methods and the prognostic significance of UCA1 in digestive system cancers was qualified. Elevated level of UCA1 indicated the poor clinical outcome for patients with digestive system cancers. It may serve as a new biomarker related to prognosis in digestive system cancers.

  12. 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.

  13. 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...

  14. 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).

  15. 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.

  16. Significance of SYT8 For the Detection, Prediction, and Treatment of Peritoneal Metastasis From Gastric Cancer.

    Science.gov (United States)

    Kanda, Mitsuro; Shimizu, Dai; Tanaka, Haruyoshi; Tanaka, Chie; Kobayashi, Daisuke; Hayashi, Masamichi; Iwata, Naoki; Niwa, Yukiko; Yamada, Suguru; Fujii, Tsutomu; Sugimoto, Hiroyuki; Murotani, Kenta; Fujiwara, Michitaka; Kodera, Yasuhiro

    2018-03-01

    To develop novel diagnostic and therapeutic targets specific for peritoneal metastasis of gastric cancer (GC). Advanced GC frequently recurs because of undetected micrometastases even after curative resection. Peritoneal metastasis has been the most frequent recurrent pattern after gastrectomy and is incurable. We conducted a recurrence pattern-specific transcriptome analysis in an independent cohort of 16 patients with stage III GC who underwent curative gastrectomy and adjuvant S-1 for screening candidate molecules specific for peritoneal metastasis of GC. Next, another 340 patients were allocated to discovery and validation sets (1:2) to evaluate the diagnostic and predictive value of the candidate molecule. The results of quantitative reverse-transcription PCR and immunohistochemical analysis were correlated with clinical characteristics and survival. The effects of siRNA-mediated knockdown on phenotype and fluorouracil sensitivity of GC cells were evaluated in vitro, and the therapeutic effects of siRNAs were evaluated using a mouse xenograft model. Synaptotagmin VIII (SYT8) was identified as a candidate biomarker specific to peritoneal metastasis. In the discovery set, the optimal cut-off of SYT8 expression was established as 0.005. Expression levels of SYT8 mRNA in GC tissues were elevated in the validation set comprising patients with peritoneal recurrence or metastasis. SYT8 levels above the cut-off value were significantly and specifically associated with peritoneal metastasis, and served as an independent prognostic marker for peritoneal recurrence-free survival of patients with stage II/III GC. The survival difference between patients with SYT8 levels above and below the cut-off was associated with patients who received adjuvant chemotherapy. Inhibition of SYT8 expression by GC cells correlated with decreased invasion, migration, and fluorouracil resistance. Intraperitoneal administration of SYT8-siRNA inhibited the growth of peritoneal nodules and

  17. Predicting Intentions of a Familiar Significant Other Beyond the Mirror Neuron System

    Directory of Open Access Journals (Sweden)

    Stephanie Cacioppo

    2017-08-01

    Full Text Available Inferring intentions of others is one of the most intriguing issues in interpersonal interaction. Theories of embodied cognition and simulation suggest that this mechanism takes place through a direct and automatic matching process that occurs between an observed action and past actions. This process occurs via the reactivation of past self-related sensorimotor experiences within the inferior frontoparietal network (including the mirror neuron system, MNS. The working model is that the anticipatory representations of others' behaviors require internal predictive models of actions formed from pre-established, shared representations between the observer and the actor. This model suggests that observers should be better at predicting intentions performed by a familiar actor, rather than a stranger. However, little is known about the modulations of the intention brain network as a function of the familiarity between the observer and the actor. Here, we combined functional magnetic resonance imaging (fMRI with a behavioral intention inference task, in which participants were asked to predict intentions from three types of actors: A familiar actor (their significant other, themselves (another familiar actor, and a non-familiar actor (a stranger. Our results showed that the participants were better at inferring intentions performed by familiar actors than non-familiar actors and that this better performance was associated with greater activation within and beyond the inferior frontoparietal network i.e., in brain areas related to familiarity (e.g., precuneus. In addition, and in line with Hebbian principles of neural modulations, the more the participants reported being cognitively close to their partner, the less the brain areas associated with action self-other comparison (e.g., inferior parietal lobule, attention (e.g., superior parietal lobule, recollection (hippocampus, and pair bond (ventral tegmental area, VTA were recruited, suggesting that the

  18. 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

  19. 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.

  20. Spatially resolved flux measurements of NOx from London suggest significantly higher emissions than predicted by inventories.

    Science.gov (United States)

    Vaughan, Adam R; Lee, James D; Misztal, Pawel K; Metzger, Stefan; Shaw, Marvin D; Lewis, Alastair C; Purvis, Ruth M; Carslaw, David C; Goldstein, Allen H; Hewitt, C Nicholas; Davison, Brian; Beevers, Sean D; Karl, Thomas G

    2016-07-18

    To date, direct validation of city-wide emissions inventories for air pollutants has been difficult or impossible. However, recent technological innovations now allow direct measurement of pollutant fluxes from cities, for comparison with emissions inventories, which are themselves commonly used for prediction of current and future air quality and to help guide abatement strategies. Fluxes of NOx were measured using the eddy-covariance technique from an aircraft flying at low altitude over London. The highest fluxes were observed over central London, with lower fluxes measured in suburban areas. A footprint model was used to estimate the spatial area from which the measured emissions occurred. This allowed comparison of the flux measurements to the UK's National Atmospheric Emissions Inventory (NAEI) for NOx, with scaling factors used to account for the actual time of day, day of week and month of year of the measurement. The comparison suggests significant underestimation of NOx emissions in London by the NAEI, mainly due to its under-representation of real world road traffic emissions. A comparison was also carried out with an enhanced version of the inventory using real world driving emission factors and road measurement data taken from the London Atmospheric Emissions Inventory (LAEI). The measurement to inventory agreement was substantially improved using the enhanced version, showing the importance of fully accounting for road traffic, which is the dominant NOx emission source in London. In central London there was still an underestimation by the inventory of 30-40% compared with flux measurements, suggesting significant improvements are still required in the NOx emissions inventory.

  1. THE PREDICTIVE POWER OF RELIGIOUS ORIENTATION TYPES ON AMBIVALENT SEXISM

    Directory of Open Access Journals (Sweden)

    Fatih Ozdemir

    2016-07-01

    Full Text Available The purpose of the present study was to predict ambivalent sexism (including hostile sexism and benevolent sexism with religious orientation types as intrinsic religiosity, extrinsic religiosity and quest religiosity. In addition, the effect of demographic variables (including age, gender, education on sexist attitudes was tested. 583 (N_female= 318; N_male= 265 university students who study in different universities of Ankara/Turkey (M_age= 22.10; SD = 2.33 completed Ambivalent Sexism Inventory, and Religious Orientation Scale. Findings indicated significant gender differences on study variables and significant associations between ambivalent sexism and religious orientation types within university students sample in Turkey.

  2. Predicting Community College Outcomes: Does High School CTE Participation Have a Significant Effect?

    Science.gov (United States)

    Dietrich, Cecile; Lichtenberger, Eric; Kamalludeen, Rosemaliza

    2016-01-01

    This study explored the relative importance of participation in high school career and technical education (CTE) programs in predicting community college outcomes. A hierarchical generalized linear model (HGLM) was used to predict community college outcome attainment among a random sample of direct community college entrants. Results show that…

  3. The prognostic significance of HOTAIR for predicting clinical outcome in patients with digestive system tumors.

    Science.gov (United States)

    Ma, Gaoxiang; Wang, Qiaoyan; Lv, Chunye; Qiang, Fulin; Hua, Qiuhan; Chu, Haiyan; Du, Mulong; Tong, Na; Jiang, Yejuan; Wang, Meilin; Zhang, Zhengdong; Wang, Jian; Gong, Weida

    2015-12-01

    Although some studies have assessed the prognostic value of HOTAIR in patients with digestive system tumors, the relationship between the HOTAIR and outcome of digestive system tumors remains unknown. The PubMed was searched to identify the eligible studies. Here, we performed a meta-analysis with 11 studies, including a total of 903 cases. Pooled hazard ratios (HRs) and 95 % confidence interval (CI) of HOTAIR for cancer survival were calculated. We found that the pooled HR elevated HOTAIR expression in tumor tissues was 2.36 (95 % CI 1.88-2.97) compared with patients with low HOTAIR expression. Moreover, subgroup analysis revealed that HOTAIR overexpression was also markedly associated with short survival for esophageal squamous cell carcinoma (HR 2.19, 95 % CI 1.62-2.94) and gastric cancer (HR 1.66, 95 % CI 1.02-2.68). In addition, up-regulated HOTAIR was significantly related to survival of digestive system cancer among the studies with more follow-up time (follow time ≥ 5 years) (HR 2.51, 95 % CI 1.99-3.17). When stratified by HR resource and number of patients, the result indicated consistent results with the overall analysis. Subgroup analysis on ethnicities did not change the prognostic influence of elevated HOTAIR expression. Additionally, we conducted an independent validation cohort including 71 gastric cancer cases, in which patients with up-regulated HOTAIR expression had an unfavorable outcome with HR of 2.10 (95 % CI 1.10-4.03). The results suggest that aberrant HOTAIR expression may serve as a candidate positive marker to predict the prognosis of patients with carcinoma of digestive system.

  4. 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

  5. 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....

  6. 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.

  7. Fish impingement at estuarine power stations and its significance to commercial fishing

    International Nuclear Information System (INIS)

    Turnpenny, A.W.H.

    1989-01-01

    The abstraction of cooling water (CW) at power stations sited on tidal waters inevitably leads to mortalities of some fish which are drawn in with the CW supply and become impinged on the intake screens. These fish are predominantly 0- or 1-group juveniles which, owing to their small size, are unable to resist intake currents. Commercial fishermen often object to the fact that juvenile fish are killed in this way. Their concern stems from the fact that in order to protect stocks, commercial fishing is restricted to fish which are above a statutory minimum landing size, whereas the majority of fish killed by impingement are below this size. This Report considers the significance of impingement mortalities at four estuarine sites in Britain for six commercially important species. Life tables are used to establish expected survival trajectories for each species and to compute reproductive potential. Each fish killed on intake screens is then considered in terms of the fraction of the reproductive potential of a single adult at maturity, and is ascribed an 'adult equivalent' value. Total catches of mixed juveniles and adults are then presented as 'adult equivalent' values. (author)

  8. 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...

  9. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    The understanding and mitigation of downhole vibration has been a heavily researched subject in the oil industry as it results in more expensive drilling operations, as vibrations significantly diminish the amount of effective drilling energy available to the bit and generate forces that can push the bit or the Bottom Hole Assembly (BHA) off its concentric axis of rotation, producing high magnitude impacts with the borehole wall. In order to drill ahead, a sufficient amount of energy must be supplied by the rig to overcome the resistance of the drilling system, including the reactive torque of the system, drag forces, fluid pressure losses and energy dissipated by downhole vibrations, then providing the bit with the energy required to fail the rock. If the drill string enters resonant modes of vibration, not only does it decreases the amount of available energy to drill, but increases the potential for catastrophic downhole equipment and drilling bit failures. In this sense, the mitigation of downhole vibrations will result in faster, smoother, and cheaper drilling operations. A software tool using Finite Element Analysis (FEA) has been developed to provide better understanding of downhole vibration phenomena in drilling environments. The software tool calculates the response of the drilling system at various input conditions, based on the design of the wellbore along with the geometry of the Bottom Hole Assembly (BHA) and the drill string. It identifies where undesired levels of resonant vibration will be driven by certain combinations of specific drilling parameters, and also which combinations of drilling parameters will result in lower levels of vibration, so the least shocks, the highest penetration rate and the lowest cost per foot can be achieved. With the growing performance of personal computers, complex software systems modeling the drilling vibrations using FEA has been accessible to a wider audience of field users, further complimenting with real time

  10. Exploring the significance of human mobility patterns in social link prediction

    KAUST Repository

    Alharbi, Basma Mohammed; Zhang, Xiangliang

    2014-01-01

    Link prediction is a fundamental task in social networks. Recently, emphasis has been placed on forecasting new social ties using user mobility patterns, e.g., investigating physical and semantic co-locations for new proximity measure. This paper

  11. 78 FR 27470 - Culturally Significant Objects Imported for Exhibition Determinations: “Shaping Power: Luba...

    Science.gov (United States)

    2013-05-10

    ... Determinations: ``Shaping Power: Luba Masterworks From the Royal Museum for Central Africa'' SUMMARY: Notice is... objects to be included in the exhibition ``Shaping Power: Luba Masterworks from the Royal Museum for Central Africa,'' imported from abroad for temporary exhibition within the United States, are of cultural...

  12. Significance of oceanographic field investigations for siting coastal thermal and nuclear power plants

    Digital Repository Service at National Institute of Oceanography (India)

    Krishnakumar, V.; Swamy, G.N.; Sastry, J

    stream_size 7 stream_content_type text/plain stream_name Irrig_Power_J_49_129.pdf.txt stream_source_info Irrig_Power_J_49_129.pdf.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 ...

  13. Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

    Directory of Open Access Journals (Sweden)

    Sers Christine T

    2010-12-01

    Full Text Available Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

  14. Background does not significantly affect power-exponential fitting of gastric emptying curves

    International Nuclear Information System (INIS)

    Jonderko, K.

    1987-01-01

    Using a procedure enabling the assessment of background radiation, research was done to elucidate the course of changes in background activity during gastric emptying measurements. Attention was focused on the changes in the shape of power-exponential fitted gastric emptying curves after correction for background was performed. The observed pattern of background counts allowed to explain the shifts of the parameters characterizing power-exponential curves connected with background correction. It was concluded that background had a negligible effect on the power-exponential fitting of gastric emptying curves. (author)

  15. 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.

  16. 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.

  17. Significance of relative velocity in drag force or drag power estimation for a tethered float

    Digital Repository Service at National Institute of Oceanography (India)

    Vethamony, P.; Sastry, J.S.

    There is difference in opinion regarding the use of relative velocity instead of particle velocity alone in the estimation of drag force or power. In the present study, a tethered spherical float which undergoes oscillatory motion in regular waves...

  18. Significance of High Resolution GHRSST on prediction of Indian Summer Monsoon

    KAUST Repository

    Jangid, Buddhi Prakash

    2017-02-24

    In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4 K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.

  19. Significant increase of Echinococcus multilocularis prevalencein foxes, but no increased predicted risk for humans

    NARCIS (Netherlands)

    Maas, M.; Dam-Deisz, W.D.C.; Roon, van A.M.; Takumi, K.; Giessen, van der J.W.B.

    2014-01-01

    The emergence of the zoonotic tapeworm Echinococcus multilocularis, causative agent ofalveolar echinococcosis (AE), poses a public health risk. A previously designed risk mapmodel predicted a spread of E. multilocularis and increasing numbers of alveolar echinococ-cosis patients in the province of

  20. The significance of parenchymal changes of acute cellular rejection in predicting chronic liver graft rejection

    NARCIS (Netherlands)

    Gouw, ASH; van den Heuvel, MC; van den Berg, AP; Slooff, NJH; de Jong, KP; Poppema, S

    2002-01-01

    Background. Chronic rejection (CR) in liver allografts shows a rapid onset and progressive course, leading to graft failure within the first year after transplantation. Most cases are preceded by episodes of acute cellular rejection (AR), but histological features predictive for the transition

  1. Significance of High Resolution GHRSST on prediction of Indian Summer Monsoon

    KAUST Repository

    Jangid, Buddhi Prakash; Kumar, Prashant; Attada, Raju; Kumar, Raj

    2017-01-01

    In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4 K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.

  2. 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.

  3. 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

  4. 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.

  5. Replicability and 40-Year Predictive Power of Childhood ARC Types

    Science.gov (United States)

    Chapman, Benjamin P.; Goldberg, Lewis R.

    2011-01-01

    We examined three questions surrounding the Undercontrolled, Overcontrolled, and Resilient--or Asendorpf-Robins-Caspi (ARC)--personality types originally identified by Block (1971). In analyses of the teacher personality assessments of over 2,000 children in 1st through 6th grade in 1959-1967, and follow-up data on general and cardiovascular health outcomes in over 1,100 adults recontacted 40 years later, we found: (1) Bootstrapped internal replication clustering suggested that Big Five scores were best characterized by a tripartite cluster structure corresponding to the ARC types; (2) this cluster structure was fuzzy, rather than discrete, indicating that ARC constructs are best represented as gradients of similarity to three prototype Big Five profiles; and (3) ARC types and degrees of ARC prototypicality showed associations with multiple health outcomes 40 years later. ARC constructs were more parsimonious, but neither better nor more consistent predictors than the dimensional Big Five traits. Forty-year incident cases of heart disease could be correctly identified with 68% accuracy by personality information alone, a figure approaching the 12-year accuracy of a leading medical cardiovascular risk model. Findings support the theoretical validity of ARC constructs, their treatment as continua of prototypicality rather than discrete categories, and the need for further understanding the robust predictive power of childhood personality traits for mid-life health. PMID:21744975

  6. The presence, predictive utility, and clinical significance of body dysmorphic symptoms in women with eating disorders

    Science.gov (United States)

    2013-01-01

    Background Both eating disorders (EDs) and body dysmorphic disorder (BDD) are disorders of body image. This study aimed to assess the presence, predictive utility, and impact of clinical features commonly associated with BDD in women with EDs. Methods Participants recruited from two non-clinical cohorts of women, symptomatic and asymptomatic of EDs, completed a survey on ED (EDE-Q) and BDD (BDDE-SR) psychopathology, psychological distress (K-10), and quality of life (SF-12). Results A strong correlation was observed between the total BDDE-SR and the global EDE-Q scores (r = 0.79, p 0.05) measured appearance checking, reassurance-seeking, camouflaging, comparison-making, and social avoidance. In addition to these behaviors, inspection of sensitivity (Se) and specificity (Sp) revealed that BDDE-SR items measuring preoccupation and dissatisfaction with appearance were most predictive of ED cases (Se and Sp > 0.60). Higher total BDDE-SR scores were associated with greater distress on the K-10 and poorer quality of life on the SF-12 (all p < 0.01). Conclusions Clinical features central to the model of BDD are common in, predictive of, and associated with impairment in women with EDs. Practice implications are that these features be included in the assessment and treatment of EDs. PMID:24999401

  7. 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....

  8. Ageing of significant to safety structure elements of nuclear power plants

    International Nuclear Information System (INIS)

    Maksimovas, G.; Ramanauskiene, A.; Ziliukas, A.

    1999-01-01

    The paper analyzes the ageing problems of structure elements in nuclear power plants. The standard documents and principal parts of the ageing evaluation program are presented. The ageing evaluation model is being worked out and degradation mechanisms of different atomic reactor materials are being compared. (author)

  9. Evaluation of PWR's operating experience. Significant events which influenced French nuclear power program

    International Nuclear Information System (INIS)

    Dupuis, M.C.

    1986-10-01

    This report discusses developments or changes in safety policy (whether statutory or otherwise) and in plant design and operation, which, in many cases, correlate. When considering these events, it is important to bear in mind the standardization policy characterizing the French nuclear power program, and implying central decision-making, both for the safety authorities and the operating utility [fr

  10. Significance of satellite sign and spot sign in predicting hematoma expansion in spontaneous intracerebral hemorrhage.

    Science.gov (United States)

    Yu, Zhiyuan; Zheng, Jun; Ali, Hasan; Guo, Rui; Li, Mou; Wang, Xiaoze; Ma, Lu; Li, Hao; You, Chao

    2017-11-01

    Hematoma expansion is related to poor outcome in spontaneous intracerebral hemorrhage (ICH). Recently, a non-enhanced computed tomography (CT) based finding, termed the 'satellite sign', was reported to be a novel predictor for poor outcome in spontaneous ICH. However, it is still unclear whether the presence of the satellite sign is related to hematoma expansion. Initial computed tomography angiography (CTA) was conducted within 6h after ictus. Satellite sign on non-enhanced CT and spot sign on CTA were detected by two independent reviewers. The sensitivity and specificity of both satellite sign and spot sign were calculated. Receiver-operator analysis was conducted to evaluate their predictive accuracy for hematoma expansion. This study included 153 patients. Satellite sign was detected in 58 (37.91%) patients and spot sign was detected in 38 (24.84%) patients. Among 37 patients with hematoma expansion, 22 (59.46%) had satellite sign and 23 (62.16%) had spot sign. The sensitivity and specificity of satellite sign for prediction of hematoma expansion were 59.46% and 68.97%, respectively. The sensitivity and specificity of spot sign were 62.16% and 87.07%, respectively. The area under the curve (AUC) of satellite sign was 0.642 and the AUC of spot sign was 0.746. (P=0.157) CONCLUSION: Our results suggest that the satellite sign is an independent predictor for hematoma expansion in spontaneous ICH. Although spot sign has the higher predictive accuracy, satellite sign is still an acceptable predictor for hematoma expansion when CTA is unavailable. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. 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

  12. 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.

  13. 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.

  14. Efficiency improvement of nuclear power plant operation: the significant role of advanced nuclear fuel technologies

    International Nuclear Information System (INIS)

    Velde Van de, A.; Burtak, F.

    2001-01-01

    Due to the increased liberalisation of the power markets, nuclear power generation is being exposed to high cost reduction pressure. In this paper we highlight the role of advanced nuclear fuel technologies to reduce the fuel cycle costs and therefore increase the efficiency of nuclear power plant operation. The key factor is a more efficient utilisation of the fuel and present developments at Siemens are consequently directed at (i) further increase of batch average burnup, (ii) improvement of fuel reliability, (iii) enlargement of fuel operation margins and (iv) improvement of methods for fuel design and core analysis. As a result, the nuclear fuel cycle costs for a typical LWR have been reduced during the past decades by about US$ 35 million per year. The estimated impact of further burnup increases on the fuel cycle costs is expected to be an additional saving of US$10 - 15 million per year. Due to the fact that the fuel will operate closer to design limits, a careful approach is required when introducing advanced fuel features in reload quantities. Trust and co-operation between the fuel vendors and the utilities is a prerequisite for the common success. (authors)

  15. Use of data mining to predict significant factors and benefits of bilateral cochlear implantation.

    Science.gov (United States)

    Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Perez, Daniel; Falconb, Juan Carlos; Ramosb, Angel

    2015-11-01

    Data mining (DM) is a technique used to discover pattern and knowledge from a big amount of data. It uses artificial intelligence, automatic learning, statistics, databases, etc. In this study, DM was successfully used as a predictive tool to assess disyllabic speech test performance in bilateral implanted patients with a success rate above 90%. 60 bilateral sequentially implanted adult patients were included in the study. The DM algorithms developed found correlations between unilateral medical records and Audiological test results and bilateral performance by establishing relevant variables based on two DM techniques: the classifier and the estimation. The nearest neighbor algorithm was implemented in the first case, and the linear regression in the second. The results showed that patients with unilateral disyllabic test results below 70% benefited the most from a bilateral implantation. Finally, it was observed that its benefits decrease as the inter-implant time increases.

  16. Emotion dysregulation and social competence: stability, change and predictive power.

    Science.gov (United States)

    Berkovits, L D; Baker, B L

    2014-08-01

    Social difficulties are closely linked to emotion dysregulation among children with typical development (TD). Children with developmental delays (DD) are at risk for poor social outcomes, but the relationship between social and emotional development within this population is not well understood. The current study examines the extent to which emotion dysregulation is related to social problems across middle childhood among children with TD or DD. Children with TD (IQ ≥ 85, n = 113) and children with DD (IQ ≤ 75, n = 61) participated in a longitudinal study. Annual assessments were completed at ages 7, 8 and 9 years. At each assessment, mothers reported on children's emotion dysregulation, and both mothers and teachers reported on children's social difficulties. Children with DD had higher levels of emotion dysregulation and social problems at each age than those with TD. Emotion dysregulation and social problems were significantly positively correlated within both TD and DD groups using mother report of social problems, and within the TD group using teacher report of social problems. Among children with TD, emotion dysregulation consistently predicted change in social problems from one year to the next. However, among children with DD, emotion dysregulation offered no unique prediction value above and beyond current social problems. Results suggested that the influence of emotion regulation abilities on social development may be a less salient pathway for children with DD. These children may have more influences, beyond emotion regulation, on their social behaviour, highlighting the importance of directly targeting social skill deficits among children with DD in order to ameliorate their social difficulties. © 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  17. Significance of collateral vessels on the prediction of superior vena cava syndrome on CT

    International Nuclear Information System (INIS)

    Kim, Hyun Sook; Kim, Hyung Jin; Lee, Hyeng Gon; Ahn, In Oak; Chung, Sung Hoon

    1993-01-01

    Although visible collateral vessels on computed tomography (CT) has been considered as an important finding in superior vena cava (SVC) syndrome, there is no systematical analysis concerning correlation between the CT evidence of collateral vessels and clinical evidence of SVC syndrome. The purpose of this study is to evaluate how accurately we predict the clinical presence of SVC syndrome by the collateral vessels in patients with apparent SVC obstruction in CT. Forty seven patients having a CT evidence of obstruction or compression of SVC and/or its major tributaries were included in this study. Lung cancer was the most common underlying disease (n=40). The enhanced CT scans were obtained through either arm vein using a combined bolus and drip-infusion technique. Analyzing the CT scans, we particularly paid attention to the site and pattern of venous compromise, presence of collateral vessels, and if present, their location, without knowing whether symptoms and sign were present or nor, and then compared them with clinical data by a thorough review of charts, To verify the frequency of visible collateral vessels in normal subjects, we also evaluated the CT scans of 50 patients without mediastinal disease and clinical SVC syndrome as a control group. On CT, collateral vessels were found in 24 patients, among whom three patient had a single collateral and 21 patients had two or more collateral channels. There were two false positive cases, in which clinically overt SVC syndrome appeared 10 days and three months after CT examination respectively, and one false negative case. The presence of collateral vessels on CT, respectively, and one false negative case. The presence of collateral vessels on CT, regardless of the number and location of collateral vessels and pattern of venous obstruction, was a good clue for predicting the presence of clinical SVC syndrome with the sensitivity and the specificity of 95.7% and 91.7%, respectively. In control group, collateral

  18. Examination of risk significant configuration during low power and shutdown with ORION and PSA

    Energy Technology Data Exchange (ETDEWEB)

    Park, Chul Kyu; Oh, Seung Jong [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

    2015-10-15

    This paper suggests an approach to calculate the increased CDF corresponding to Orange and Red states in ORION program and analyzed the result of calculation. This approach is expected to be useful for checking the adequacy of the LPSD PSA. And also, the result of this calculation can provide the information about which SSCs for certain SF are more sensitive to risk in particular POS. Defense-in-depth is a safety philosophy in which multiple lines of defense and conservative design and evaluation methods are applied to ensure the safety of the public. Based on this philosophy EPRI developed Outage Risk Assessment and Management (ORAM) program as a qualitative assessment to better manage the risk during low power and shutdown event after the Vogtle loss of vital AC power and RHR event in 1990. Each risk level of RED, ORANGE color status caused by the degradation of each key safety function might be different depend on the importance of each key safety function. However we can't know how much different. If we know the quantitative information about the risk level represented by color, we can take and prepare concrete actions to reduce the risk level of the plant with rescheduling maintenance, strengthen surveillance for important safety function, and developing outage management strategy. The probabilistic safety analysis for low power and shutdown period can provide risk information with quantitative value related on the degradation of redundancy and diversity level for the safety functions during outage. In this study, we calculated the increased Core Damage frequency (CDF) of each RED and ORANGE states in ORION program caused by the degradation of each key safety function by modifying LPSD PSA model. The result of calculation and analysis could be effective to check adequacy and find improvement for these two methods.

  19. Jobs for partners - significant stabilization factor for personnel of East Slovakia nuclear power plant

    International Nuclear Information System (INIS)

    Uvirova, E.

    1987-01-01

    The problem is discussed of employment of the wives of operating personnel of the nuclear power plant to be built in East Slovakia. It is expected that almost a half of the number of the wives have completed secondary education with final examination. A list is presented of openings best suitable for women. It is estimated that jobs for 1,580 women will have to be provided of which number 253 will be specialized jobs in health care and education. An increased number of vacancies are expected to be available, especially in the Kosice and Presov towns. (J.B.). 4 tabs., 4 refs

  20. 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

  1. C.A.R. insurance and its significance in the construction of power plants

    International Nuclear Information System (INIS)

    Friedrich, J.

    1975-01-01

    The construction of a power lant entails a multitude of risks for the operator and for all parties participating in the construction. The progress of construction technology, reduced manufacturing times and new construction materials and methods result in greater risks. As a property insurance, C.A.R. insurance covers unforeseen destruction of or damage to the construction works from commencement of construction until handling over. It is thereby irrelevant whether such loss or damage falls to the burden of the principal or the contractors and craftsmen. In the event of loss or damage occurring, the search for those responsible is thus eliminated. Rectification of the loss or damage and continuation of the work is not delayed. (orig.) [de

  2. Meta-Analysis of Predictive Significance of the Black Hole Sign for Hematoma Expansion in Intracerebral Hemorrhage.

    Science.gov (United States)

    Zheng, Jun; Yu, Zhiyuan; Guo, Rui; Li, Hao; You, Chao; Ma, Lu

    2018-04-27

    Hematoma expansion is related to unfavorable prognosis in intracerebral hemorrhage (ICH). The black hole sign is a novel marker on non-contrast computed tomography for predicting hematoma expansion. However, its predictive values are different in previous studies. Thus, this meta-analysis was conducted to evaluate the predictive significance of the black hole sign for hematoma expansion in ICH. A systematic literature search was performed. Original researches on the association between the black hole sign and hematoma expansion in ICH were included. Sensitivity and specificity were pooled to assess the predictive accuracy. Summary receiver operating characteristics curve (SROC) was developed. Deeks' funnel plot asymmetry test was used to assess the publication bias. Five studies with a total of 1495 patients were included in this study. The pooled sensitivity and specificity of the black hole sign for predicting hematoma expansion were 0.30 and 0.91, respectively. The area under the curve was 0.78 in SROC curve. There was no significant publication bias. This meta-analysis shows that the black hole sign is a helpful imaging marker for predicting hematoma expansion in ICH. Although the black hole sign has a relatively low sensitivity, its specificity is relatively high. Copyright © 2018 Elsevier Inc. All rights reserved.

  3. Prevalence, significance and predictive value of antiphospholipid antibodies in Crohn’s disease

    Science.gov (United States)

    Sipeki, Nora; Davida, Laszlo; Palyu, Eszter; Altorjay, Istvan; Harsfalvi, Jolan; Antal Szalmas, Peter; Szabo, Zoltan; Veres, Gabor; Shums, Zakera; Norman, Gary L; Lakatos, Peter L; Papp, Maria

    2015-01-01

    AIM: To assess the prevalence and stability of different antiphospholipid antibodies (APLAs) and their association with disease phenotype and progression in inflammatory bowel diseases (IBD) patients. METHODS: About 458 consecutive patients [Crohn’s disease (CD): 271 and ulcerative colitis (UC): 187] were enrolled into a follow-up cohort study in a tertiary IBD referral center in Hungary. Detailed clinical phenotypes were determined at enrollment by reviewing the patients’ medical charts. Disease activity, medical treatment and data about evolvement of complications or surgical interventions were determined prospectively during the follow-up. Disease course (development f complicated disease phenotype and need for surgery), occurrence of thrombotic events, actual state of disease activity according to clinical, laboratory and endoscopic scores and accurate treatment regime were recorded during the follow-up, (median, 57.4 and 61.6 mo for CD and UC). Sera of IBD patients and 103 healthy controls (HC) were tested on individual anti-β2-Glycoprotein-I (anti-β2-GPI IgA/M/G), anti-cardiolipin (ACA IgA/M/G) and anti-phosphatidylserine/prothrombin (anti-PS/PT IgA/M/G) antibodies and also anti-Saccharomyces cerevisiae antibodies (ASCA IgA/G) by enzyme-linked immunosorbent assay (ELISA). In a subgroup of CD (n = 198) and UC patients (n = 103), obtaining consecutive samples over various arbitrary time-points during the disease course, we evaluated the intraindividual stability of the APLA status. Additionally, we provide an overview of studies, performed so far, in which significance of APLAs in IBD were assessed. RESULTS: Patients with CD had significantly higher prevalence of both ACA (23.4%) and anti-PS/PT (20.4%) antibodies than UC (4.8%, P < 0.0001 and 10.2%, P = 0.004) and HC (2.9%, P < 0.0001 and 15.5%, P = NS). No difference was found for the prevalence of anti-β2-GPI between different groups (7.2%-9.7%). In CD, no association was found between APLA and ASCA

  4. Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene.

    Directory of Open Access Journals (Sweden)

    Liam R Brunham

    2005-12-01

    Full Text Available The human genome contains an estimated 100,000 to 300,000 DNA variants that alter an amino acid in an encoded protein. However, our ability to predict which of these variants are functionally significant is limited. We used a bioinformatics approach to define the functional significance of genetic variation in the ABCA1 gene, a cholesterol transporter crucial for the metabolism of high density lipoprotein cholesterol. To predict the functional consequence of each coding single nucleotide polymorphism and mutation in this gene, we calculated a substitution position-specific evolutionary conservation score for each variant, which considers site-specific variation among evolutionarily related proteins. To test the bioinformatics predictions experimentally, we evaluated the biochemical consequence of these sequence variants by examining the ability of cell lines stably transfected with the ABCA1 alleles to elicit cholesterol efflux. Our bioinformatics approach correctly predicted the functional impact of greater than 94% of the naturally occurring variants we assessed. The bioinformatics predictions were significantly correlated with the degree of functional impairment of ABCA1 mutations (r2 = 0.62, p = 0.0008. These results have allowed us to define the impact of genetic variation on ABCA1 function and to suggest that the in silico evolutionary approach we used may be a useful tool in general for predicting the effects of DNA variation on gene function. In addition, our data suggest that considering patterns of positive selection, along with patterns of negative selection such as evolutionary conservation, may improve our ability to predict the functional effects of amino acid variation.

  5. 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%.

  6. Prediction of significant conduction disease through noninvasive assessment of cardiac calcification.

    Science.gov (United States)

    Mainigi, Sumeet K; Chebrolu, Lakshmi Hima Bindu; Romero-Corral, Abel; Mehta, Vinay; Machado, Rodolfo Rozindo; Konecny, Tomas; Pressman, Gregg S

    2012-10-01

    Cardiac calcification is associated with coronary artery disease, arrhythmias, conduction disease, and adverse cardiac events. Recently, we have described an echocardiographic-based global cardiac calcification scoring system. The objective of this study was to evaluate the severity of cardiac calcification in patients with permanent pacemakers as based on this scoring system. Patients with a pacemaker implanted within the 2-year study period with a previous echocardiogram were identified and underwent blinded global cardiac calcium scoring. These patients were compared to matched control patients without a pacemaker who also underwent calcium scoring. The study group consisted of 49 patients with pacemaker implantation who were compared to 100 matched control patients. The mean calcium score in the pacemaker group was 3.3 ± 2.9 versus 1.8 ± 2.0 (P = 0.006) in the control group. Univariate and multivariate analysis revealed glomerular filtration rate and calcium scoring to be significant predictors of the presence of a pacemaker. Echocardiographic-based calcium scoring correlates with the presence of severe conduction disease requiring a pacemaker. © 2012, Wiley Periodicals, Inc.

  7. More powerful significant testing for time course gene expression data using functional principal component analysis approaches.

    Science.gov (United States)

    Wu, Shuang; Wu, Hulin

    2013-01-16

    One of the fundamental problems in time course gene expression data analysis is to identify genes associated with a biological process or a particular stimulus of interest, like a treatment or virus infection. Most of the existing methods for this problem are designed for data with longitudinal replicates. But in reality, many time course gene experiments have no replicates or only have a small number of independent replicates. We focus on the case without replicates and propose a new method for identifying differentially expressed genes by incorporating the functional principal component analysis (FPCA) into a hypothesis testing framework. The data-driven eigenfunctions allow a flexible and parsimonious representation of time course gene expression trajectories, leaving more degrees of freedom for the inference compared to that using a prespecified basis. Moreover, the information of all genes is borrowed for individual gene inferences. The proposed approach turns out to be more powerful in identifying time course differentially expressed genes compared to the existing methods. The improved performance is demonstrated through simulation studies and a real data application to the Saccharomyces cerevisiae cell cycle data.

  8. New pulmonary vein Doppler echocardiographic index predicts significant interatrial shunting in secundum atrial septal defect.

    Science.gov (United States)

    Lam, Yat-Yin; Fang, Fang; Yip, Gabriel Wai-Kwok; Li, Zhi-An; Yang, Ya; Yu, Cheuk-Man

    2012-09-20

    The relation between pulmonary venous flow (PVF) pattern and degree of left-to-right interatrial shunting (IAS) in patients with secundum atrial septal defect (ASD) is unknown. Fifty consecutive ASD patients (14 males, 36 ± 17 years) received transthoracic echocardiography (TTE) before and 1 day after transcatheter closure and their results were compared to 40 controls. The ratio of pulmonary-to-systemic flows (Qp/Qs) was assessed by TTE and invasive oximetry. Pre-closure PV systolic (PVs), diastolic (PVd) velocities and velocity-time integral (PV-VTI) increased, time from onset of ECG Q-wave to the peak PV diastolic wave (Q-PVd) shortened and atrial reversal (PVar) velocity significantly decreased as compared to normals. These findings normalized after closure. Patients with large IAS (defined as invasive Qp/Qs ≥ 2) had higher PVs, PVd and PV-VTI, shorter Q-PVd but lower PVar (all pIAS. Invasive Qp/Qs ratios correlated with PVs, PVd, PV-VTI, Q-PVd and TTE-derived Qp/Qs ratios, ASD sizes and RV end-diastolic dimensions (all pIAS after multivariate analysis. The corresponding sensitivity, specificity and AUC were 89%, 82% and 0.90 respectively for a PV-VTI of 30 cm (pIAS have distinguishable PVF features. Doppler evaluation of PV-VTI is a novel additional tool for assessing the magnitude of shunting in these patients non-invasively. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  9. PREDICTIVE SIGNIFICANCE OF ANTI-HLA AUTOANTIBODIES IN HEART TRANSPLANT RECIPIENTS

    Directory of Open Access Journals (Sweden)

    O. P. Shevchenko

    2013-01-01

    Full Text Available Aim. The aim of this study was to define the role of preformed anti-HLA antibodies (anti-HLA in antibody-mediated rejection (AMR and cardiac allograft vasculopathy (CAV after heart transplantation. Materials and Methods. 140 heart transplant recipients were followed after heart transplantation performed for 106 dilated and 34 – ischemic cardiomyopathy. Anti-HLA was determined before transplantation by ELISA. Results. Recipients were divided into 2 groups: anti-HLA positive (n = 45, 32,1% and anti-HLA negative (n = 95, 67,9%. The incidence of AMR in anti-HLA positive group was 12 (26,67% and 11 (11,58% in anti-HLA negative group. Risk of AMR was significantly higher in anti-HLA positive recipients (RR 2,3: 95% CI 1,02–4,81, р = 0,03. During first three years after transplantation CAV was diagnosed in 9 (20% of anti-HLA positive recipients and in 7 (6,8% of patients without anti-HLA. (RR 2,7: 95% CI 1,08–6,82, р = 0,03. Survival in freedom from CAV in anti-HLA negative recipients was much higher than in anti-HLA positive recipients (0,89 ± 0,07, 0,72 ± 0,06, resp. (p = 0,02.Conclusions. The presence of preformed anti-HLA antibodies in candidates for heart transplantation increase the risk of AMR and CAV post transplantation in 2,3 and 2,7 times, respectively. 

  10. 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

  11. Safety significance of inadvertent operation of motor-operated valves in nuclear power plants

    International Nuclear Information System (INIS)

    Ruger, C.J.; Higgins, J.C.; Carbonaro, J.F.; Hall, R.E.

    1994-01-01

    Concerns about the consequences of valve mispositioning were brought to the forefront following an event at Davis Besse in 1985. The concern related to the ability to reposition open-quotes position-changeableclose quotes motor-operated valves (MOVs) from the control room in the event of their inadvertent operation and was documented in U.S. Nuclear Regulatory Commission (USNRC) Bulletin 85-03 and Generic Letter (GL) 89-10. The mispositioned MOVs may not be able to be returned to their required position due to high differential pressure or high flow conditions across the valves. The inability to reposition such valves may have significantly safety consequences, as in the Davis Besse event. However, full consideration of such mispositioning in safety analyses and in MOV test programs can be labor intensive and expensive. Industry raised concerns that consideration of position-changeable valves under GL 89-10 would not decrease the probability of core damage to an extent that would justify licensee costs. As a response, Brookhaven National Laboratory has conducted separate scoping studies for both boiling water reactors (BWRs) and pressurized water reactors (PWRs) using probabilistic risk assessment (PRA) techniques to determine if such valve mispositioning by itself is significant to safety. The approach used internal events PRA models to survey the order of magnitude of the risk-significance of valve mispositioning by considering the failure of selected position-changeable MOVs. The change in core damage frequency was determined for each valve considered, and the results were presented as a risk increase ratio for each of four assumed MOV failure rates. The risk increase ratios resulting from this failure rate sensitivity study can be used as a basis for a determination of the risk-significance of the MOV mispositioning issues for BWRs and PWRs

  12. Faculty Decisions on Serials Subscriptions Differ Significantly from Decisions Predicted by a Bibliometric Tool.

    Directory of Open Access Journals (Sweden)

    Sue F. Phelps

    2016-03-01

    of the faculty choices. The p-value for this relationship was less than 0.0001, also indicating that the result was not by chance. A quadratic model plotted alongside the previous linear model follows a similar pattern. The p-value of the comparison is 0.0002, which indicates the quadratic model’s fit cannot be explained by random chance. Main Results – The authors point out three outstanding findings. First, the match rate between faculty valuations and bibliometric scores for serials is 65%. This exceeds the 50% rate that would indicate random association, but also indicates a statistically significant difference between faculty and bibliometric valuations. Secondly, the match rate with the bibliometric scores for titles that faculty chose to keep (73% was higher than those they chose to cancel (54%. Thirdly, the match rate increased with higher bibliometric scores. Conclusions – Though the authors identify only a modest degree of similarity between faculty and bibliometric valuations of serials, it is noted that there is more agreement in the higher valued serials than the lower valued serials. With that in mind, librarians might focus faculty review on the lower scoring titles in the future, taking into consideration that unique faculty interests may drive selection at that level and would need to be balanced with the mission of the library.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    in the estimated/predicted property values, how to assess the quality and reliability of the estimated/predicted property values? The paper will review a class of models for prediction of physical and thermodynamic properties of organic chemicals and their mixtures based on the combined group contribution – atom......Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...

  18. 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

  19. 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...

  20. TECHNICAL BASIS DOCUMENT FOR AT-POWER SIGNIFICANCE DETERMINATION PROCESS (SDP) NOTEBOOKS

    International Nuclear Information System (INIS)

    AZARM, M.A.; SMANTA, P.K.; MARTINEZ-GURIDI, G.; HIGGINS, J.

    2004-01-01

    To support the assessment of inspection findings as part of the risk-informed inspection in the United States Nuclear Regulatory Commission's (USNRC's) Reactor Oversight Process (ROP), risk inspection notebooks, also called significance determination process (SDP) notebooks, have been developed for each of the operating plants in the United States. These notebooks serve as a tool for assessing risk significance of inspection findings along with providing an engineering understanding of the significance. Plant-specific notebooks are developed to capture plant-specific features, characteristics, and analyses that influence the risk profile of the plant. At the same time, the notebooks follow a consistent set of assumptions and guidelines to assure consistent treatment of inspection findings across the plants. To achieve these objectives, notebooks are designed to provide specific information that are unique both in the manner in which the information is provided and in the way the screening risk assessment is carried out using the information provided. The unique features of the SDP notebooks, the approaches used to present the information for assessment of inspection findings, the assumptions used in consistent modeling across different plants with due credit to plant-specific features and analyses form the technical basis of the SDP notebooks. In this document, the unique features and the technical basis for the notebooks are presented. The types of information that are included and the reasoning/basis for including that information are discussed. The rules and basis for developing the worksheets that are used by the inspectors in the assessment of inspection findings are presented. The approach to modeling plants' responses to different initiating events and specific assumptions/considerations used for each of the reactor types are also discussed

  1. 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.

  2. 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

  3. A critical review of predictive models for the onset of significant void in forced-convection subcooled boiling

    International Nuclear Information System (INIS)

    Dorra, H.; Lee, S.C.; Bankoff, S.G.

    1993-06-01

    This predictive models for the onset of significant void (OSV) in forced-convection subcooled boiling are reviewed and compared with extensive data. Three analytical models and seven empirical correlations are considered in this review. These models and correlations are put onto a common basis and are compared, again on a common basis, with a variety of data. The evaluation of their range of validity and applicability under various operating conditions are discussed. The results show that the correlations of Saha-Zuber seems to be the best model to predict OSV in vertical subcooled boiling flow

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

    African Journals Online (AJOL)

    2011-09-30

    Sep 30, 2011 ... 3Department of Chemical Engineering, IIT Kanpur, India. Abstract. When predicting pressure gradients for the flow of sludges in pipes, the rheology of the fluid ..... implicit in the stability analysis of Ryan and Johnson (1959).

  5. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

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

  6. Significance of earthquake risk in nuclear power plant probabilistic risk assessments

    International Nuclear Information System (INIS)

    Sues, R.H.; Amico, P.J.; Campbell, R.D.

    1990-01-01

    During the last eight years, approximately 25 utility-sponsored probabilistic risk assessments (PRAs) have been conducted for US nuclear reactors. Of these, ten have been published, seven of which have included complete seismic risk assessment. The results of the seven published PRAs are reviewed here in order to ascertain the significance of the risk due to earthquake initiating events. While PRA methodology has been in a state of development over the past seven years, and the results are subject to interpretation (as discussed in the paper), from the review conducted it is clear that earthquake-induced initiating events are important risk contributors. It is concluded that earthquake initiating events should not be dismissed, a priori, in any nuclear plant risk assessment. (orig.)

  7. Most significant preliminary results of the probabilistic safety analysis on the Juragua nuclear power plant

    International Nuclear Information System (INIS)

    Perdomo, Manuel

    1995-01-01

    Since 1990 the Group for PSA Development and Applications (GDA/APS) is working on the Level-1 PSA for the Juragua-1 NPP, as a part of an IAEA Technical Assistance Project. The main objective of this study, which is still under way, is to assess, in a preliminary way, the Reactor design safety to find its potential 'weak points' at the construction stage, using a eneric data base. At the same time, the study allows the PSA team to familiarize with the plant design and analysis techniques for the future operational PSA of the plant. This paper presents the most significant preliminary results of the study, which reveal some advantages of the safety characteristics of the plant design in comparison with the homologous VVER-440 reactors and some areas, where including slight modifications would improve the plant safety, considering the level of detail at which the study is carried out. (author). 13 refs, 1 fig, 2 tabs

  8. 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

  9. 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.)

  10. 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.

  11. 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...

  12. 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)

  13. 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...

  14. 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.

  15. 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)

  16. 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

  17. 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.

  18. Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography

    Energy Technology Data Exchange (ETDEWEB)

    Hell, Michaela M., E-mail: michaela.hell@uk-erlangen.de [Department of Cardiology, University of Erlangen (Germany); Dey, Damini [Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Taper Building, Room A238, 8700 Beverly Boulevard, Los Angeles, CA 90048 (United States); Marwan, Mohamed; Achenbach, Stephan; Schmid, Jasmin; Schuhbaeck, Annika [Department of Cardiology, University of Erlangen (Germany)

    2015-08-15

    Highlights: • Overestimation of coronary lesions by coronary computed tomography angiography and subsequent unnecessary invasive coronary angiography and revascularization is a concern. • Differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve, were assessed. • At a threshold of ≥24%, contrast density difference predicted hemodynamically significant lesions with a specificity of 75%, sensitivity of 33%, PPV of 35% and NPV of 73%. • The determination of contrast density difference required less time than transluminal attenuation gradient measurement. - Abstract: Objectives: Coronary computed tomography angiography (CTA) allows the detection of obstructive coronary artery disease. However, its ability to predict the hemodynamic significance of stenoses is limited. We assessed differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve (FFR). Methods: Lesion characteristics of 59 consecutive patients (72 lesions) in whom invasive FFR was performed in at least one coronary artery with moderate to high-grade stenoses in coronary CTA were evaluated by two experienced readers. Coronary CTA data sets were acquired on a second-generation dual-source CT scanner using retrospectively ECG-gated spiral acquisition or prospectively ECG-triggered axial acquisition mode. Plaque volume and composition (non-calcified, calcified), remodeling index as well as contrast density difference (defined as the percentage decline in luminal CT attenuation/cross-sectional area over the lesion) were assessed using a semi-automatic software tool (Autoplaq). Additionally, the transluminal attenuation gradient (defined as the linear regression coefficient between intraluminal CT attenuation and length from the ostium) was determined

  19. Surface tensions of multi-component mixed inorganic/organic aqueous systems of atmospheric significance: measurements, model predictions and importance for cloud activation predictions

    Directory of Open Access Journals (Sweden)

    D. O. Topping

    2007-01-01

    Full Text Available In order to predict the physical properties of aerosol particles, it is necessary to adequately capture the behaviour of the ubiquitous complex organic components. One of the key properties which may affect this behaviour is the contribution of the organic components to the surface tension of aqueous particles in the moist atmosphere. Whilst the qualitative effect of organic compounds on solution surface tensions has been widely reported, our quantitative understanding on mixed organic and mixed inorganic/organic systems is limited. Furthermore, it is unclear whether models that exist in the literature can reproduce the surface tension variability for binary and higher order multi-component organic and mixed inorganic/organic systems of atmospheric significance. The current study aims to resolve both issues to some extent. Surface tensions of single and multiple solute aqueous solutions were measured and compared with predictions from a number of model treatments. On comparison with binary organic systems, two predictive models found in the literature provided a range of values resulting from sensitivity to calculations of pure component surface tensions. Results indicate that a fitted model can capture the variability of the measured data very well, producing the lowest average percentage deviation for all compounds studied. The performance of the other models varies with compound and choice of model parameters. The behaviour of ternary mixed inorganic/organic systems was unreliably captured by using a predictive scheme and this was dependent on the composition of the solutes present. For more atmospherically representative higher order systems, entirely predictive schemes performed poorly. It was found that use of the binary data in a relatively simple mixing rule, or modification of an existing thermodynamic model with parameters derived from binary data, was able to accurately capture the surface tension variation with concentration. Thus

  20. 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)

  1. Improving nuclear power plant reliability through predictive maintenance

    International Nuclear Information System (INIS)

    Geilhausen, R.; Kunze, U.

    1996-01-01

    Maintenance strategies can be assigned to one of three categories: failure maintenance, periodic maintenance or condition-oriented maintenance. The optimum maintenance scheme can be selected on the basis of a cost-benefit analysis but the safety of life and limb or the political climate for NPP can hardly expressed in numbers. The implementation of preventive maintenance needs two preconditions: high-performance instrumentation in the form of stationary and mobile monitoring systems for the determination of the condition of the nuclear power plant components and provision of a tool that can handle both the organization of the work and the evaluation of the results obtained. (authors)

  2. Clinical significance and predictive factors of early massive recurrence after radiofrequency ablation in patients with a single small hepatocellular carcinoma

    Directory of Open Access Journals (Sweden)

    Ju-Yeon Cho

    2016-12-01

    Full Text Available Background/Aims Radiofrequency ablation (RFA is one of the most frequently applied curative treatments in patients with a single small hepatocellular carcinoma (HCC. However, the clinical significance of and risk factors for early massive recurrence after RFA—a dreadful event limiting further curative treatment—have not been fully evaluated. Methods In total, 438 patients with a single HCC of size ≤3 cm who underwent percutaneous RFA as an initial treatment between 2006 and 2009 were included. Baseline patient characteristics, overall survival, predictive factors, and recurrence after RFA were evaluated. In addition, the incidence, impact on survival, and predictive factors of early massive recurrence, and initial recurrence beyond the Milan criteria within 2 years were also investigated. Results During the median follow-up of 68.4 months, recurrent HCC was confirmed in 302 (68.9% patients, with early massive recurrence in 27 patients (6.2%. The 1-, 3-, and 5-year overall survival rates were 95.4%, 84.7%, and 81.8%, respectively, in patients with no recurrence, 99.6%, 86.4%, and 70.1% in patients with recurrence within the Milan criteria or late recurrence, and 92.6%, 46.5%, and 0.05% in patients with early massive recurrence. Multivariable analysis identified older age, Child-Pugh score B or C, and early massive recurrence as predictive of poor overall survival. A tumor size of ≥2 cm and tumor location adjacent to the colon were independent risk factors predictive of early massive recurrence. Conclusions Early massive recurrence is independently predictive of poor overall survival after RFA in patients with a single small HCC. Tumors sized ≥2 cm and located adjacent to the colon appear to be independent risk factors for early massive recurrence.

  3. Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

    Science.gov (United States)

    Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad

    2017-04-01

    Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients. Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. 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

  5. Optical trapping of nanoparticles with significantly reduced laser powers by using counter-propagating beams (Presentation Recording)

    Science.gov (United States)

    Zhao, Chenglong; LeBrun, Thomas W.

    2015-08-01

    Gold nanoparticles (GNP) have wide applications ranging from nanoscale heating to cancer therapy and biological sensing. Optical trapping of GNPs as small as 18 nm has been successfully achieved with laser power as high as 855 mW, but such high powers can damage trapped particles (particularly biological systems) as well heat the fluid, thereby destabilizing the trap. In this article, we show that counter propagating beams (CPB) can successfully trap GNP with laser powers reduced by a factor of 50 compared to that with a single beam. The trapping position of a GNP inside a counter-propagating trap can be easily modulated by either changing the relative power or position of the two beams. Furthermore, we find that under our conditions while a single-beam most stably traps a single particle, the counter-propagating beam can more easily trap multiple particles. This (CPB) trap is compatible with the feedback control system we recently demonstrated to increase the trapping lifetimes of nanoparticles by more than an order of magnitude. Thus, we believe that the future development of advanced trapping techniques combining counter-propagating traps together with control systems should significantly extend the capabilities of optical manipulation of nanoparticles for prototyping and testing 3D nanodevices and bio-sensing.

  6. 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.

  7. 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...

  8. 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…

  9. An approach toward estimating the safety significance of normal and abnormal operating procedures in nuclear power plants

    International Nuclear Information System (INIS)

    Grant, T.F.; Harris, M.S.

    1989-01-01

    The Nuclear Regulatory Commission's TMI Action Plan calls for a long-term plan to upgrade operating procedures in nuclear power plants. The scope of Generic Issue Human Factors 4.4, which stems from this requirement, includes the recommendation of improvements in nuclear power plant normal and abnormal operating procedures (NOPs and AOPs) and the implementation of appropriate regulatory action. This paper will describe the objectives, methodologies, and results of a Battelle-conducted value impact assessment to determine the costs and benefits of having the NRC implement regulatory action that would specify requirements for the preparation of acceptable NOPs and AOPs by the Commission's nuclear power plant licensees. The results of this value impact assessment are expressed in terms of ten cost/benefit attributes that can be affected by the NRC regulatory action. Five of these attributes require the calculation of change in public risk that could be expected to result from the action which, in this case, required determining the safety significance of NOPs and AOPs. In order to estimate this safety significance, a multi-step methodology was created that relies on an existing Probabilistic Risk Assessment (PRA) to provide a quantitative framework for modeling the role of operating procedures. The purpose of this methodology is to determine what impact the improvement of NOPs and AOPs would have on public health and safety

  10. 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.

  11. Role of hyaluronic acid and laminin as serum markers for predicting significant fibrosis in patients with chronic hepatitis B

    Directory of Open Access Journals (Sweden)

    Feng Li

    Full Text Available OBJECTIVES: The aim of this study was to evaluate the diagnostic performance of serum HA and LN as serum markers for predicting significant fibrosis in CHB patients. METHODS: Serum HA and LN levels of 87 patients with chronic hepatitis B and 19 blood donors were assayed by RIA. Liver fibrosis stages were determined according to the Metavir scoring-system. The diagnostic performances of all indexes were evaluated by the receiver operating characteristic (ROC curves. RESULTS: Serum HA and LN concentrations increased significantly with the stage of hepatic fibrosis, which showed positive correlation with the stages of liver fibrosis (HA: r = 0.875, p < 0.001; LN: r = 0.610, p < 0.001. There were significant differences of serum HA and LN levels between F2-4 group in comparison with those in F0-F1 group (p < 0.001 and controls (p < 0.001, respectively. From ROC curves, 185.3 ng/mL as the optimal cut-off value of serum HA for diagnosis of significant fibrosis, giving its sensitivity, specificity, PPV, NPV, LR+, LR- and AC of 84.2%, 83.3%, 90.6%, 73.5%, 5.04, 0.19 and 83.9, respectively. While 132.7 ng/mL was the optimal cut-off value of serum LN, the sensitivity, specificity, PPV, NPV, LR+, LR- and AC were 71.9%, 80.0%, 87.2%, 60.0%, 3.59%, 0.35% and 74.7, respectively. Combinations of HA and LN by serial tests showed a perfect specificity and PPV of 100%, at the same time sensitivity declined to 63.2% and LR+ increased to 18.9, while parallel tests revealed a good sensitivity of 94.7%, NPV to 86.4%, and LR- declined to 0.08. CONCLUSIONS: Serum HA and LN concentrations showed positive correlation with the stages of liver fibrosis. Detection of serum HA and LN in predicting significant fibrosis showed good diagnostic performance, which would be further optimized by combination of the two indices. HA and LN would be clinically useful serum markers for predicting significant fibrosis in patients with chronic hepatitis B, when liver biopsy is

  12. 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.

  13. 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.

  14. THE PREDICTIVE POWER OF RELIGIOUS ORIENTATION TYPES ON AMBIVALENT SEXISM

    Directory of Open Access Journals (Sweden)

    Gul Ulubayram

    2016-07-01

    Full Text Available Objective: The purpose of this study is to examine stress and stress related factors in tuberculosis patients. In addition, to determine the impact of socio-demographic variables such as age, gender and educational level over stress symptoms hereby comprises a further objective of this study. Method: The study included totally 129 tuberculosis patients and 161 non-patients (normal group participants. Tuberculosis patients registered in Ankara Tuberculosis Dispensary No.4, and Atatürk Chest Diseases and Chest Surgery Hospital. There are 75 Pulmonary Tuberculosis (AC TB and 54 Extra- Pulmonary Tuberculosis (AD TB patients. As regards data collection tools; Demographic Information Form, Brief Symptom Inventory, Stress Symptoms Scale, Stress Vulnerability Scale, and Stress Coping Scale were used. Results: Within the context of diagnosis groups; it was found that; stress symptoms of tuberculosis patients are higher than the normal group, they use their ineffective coping ways more and their life satisfactions are lower. There exists no gender and diagnosis group main effect in terms of the psychological symptoms of stress, however “gender x diagnosis group” interaction effect draws attention herein. In tuberculosis patients, ineffective coping the stress and relation pleasure variables are confronted as joint variables which are predicting both the psychological and physical health. Another point which draws attention in regression analyzes is that; “education” variable takes place among the variables which predict the psychological symptoms of stress in tuberculosis patients. Conclusion: Under the light of these findings, tuberculosis patients, during their treatment processes, may be encouraged to attend various training programs prepared for stress management and effective dealing strategies with stress. By increasing the patients’ motivation towards the treatment, these programs may provide supplementary benefits to the treatment by

  15. Study of prognostic significance of antenatal ultrasonography and renin angiotensin system activation in predicting disease severity in posterior urethral valves

    Directory of Open Access Journals (Sweden)

    Divya Bhadoo

    2015-01-01

    Full Text Available Aims: Study on prognostic significance of antenatal ultrasonography and renin angiotensin system activation in predicting disease severity in posterior urethral valves. Materials and Methods: Antenatally diagnosed hydronephrosis patients were included. Postnatally, they were divided into two groups, posterior urethral valve (PUV and non-PUV. The studied parameters were: Gestational age at detection, surgical intervention, ultrasound findings, cord blood and follow up plasma renin activity (PRA values, vesico-ureteric reflux (VUR, renal scars, and glomerular filtration rate (GFR. Results: A total of 25 patients were included, 10 PUV and 15 non-PUV. All infants with PUV underwent primary valve incision. GFR was less than 60 ml/min/1.73 m 2 body surface area in 4 patients at last follow-up. Keyhole sign, oligoamnios, absent bladder cycling, and cortical cysts were not consistent findings on antenatal ultrasound in PUV. Cord blood PRA was significantly higher (P < 0.0001 in PUV compared to non-PUV patients. Gestational age at detection of hydronephrosis, cortical cysts, bladder wall thickness, and amniotic fluid index were not significantly correlated with GFR while PRA could differentiate between poor and better prognosis cases with PUV. Conclusions: Ultrasound was neither uniformly useful in diagnosing PUV antenatally, nor differentiating it from cases with non-PUV hydronephrosis. In congenital hydronephrosis, cord blood PRA was significantly higher in cases with PUV compared to non-PUV cases and fell significantly after valve ablation. Cord blood PRA could distinguish between poor and better prognosis cases with PUV.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. Combined use of serum MCP-1/IL-10 ratio and uterine artery Doppler index significantly improves the prediction of preeclampsia.

    Science.gov (United States)

    Cui, Shihong; Gao, Yanan; Zhang, Linlin; Wang, Yuan; Zhang, Lindong; Liu, Pingping; Liu, Ling; Chen, Juan

    2017-10-01

    Monocyte chemotactic protein-1 (MCP-1, or CCL2) is a member of the chemokine subfamily involved in recruitment of monocytes in inflammatory tissues. IL-10 is a key regulator for maintaining the balance of anti-inflammatory and pro-inflammatory milieu at the feto-maternal interface. Doppler examination has been routinely performed for the monitoring and management of preeclampsia patients. This study evaluates the efficiency of these factors alone, or in combination, for the predication of preeclampsia. The serum levels of MCP-1 and IL-10 in 78 preeclampsia patients and 143 age-matched normal controls were measured. The Doppler ultrasonography was performed and Artery Pulsatility Index (PI) and Resistance Index (RI) were calculated for the same subjects. It was found that while the second-trimester serum MCP-1, IL-10, MCP-1/IL-10 ratio, PI, and RI showed some power in predicting preeclampsia, the combination of MCP-1/IL-10 and PI and RI accomplishes the highest efficiency, achieving an AUC of 0.973 (95% CI, 0.000-1.000, Ppreeclampsia. Future studies using a larger sample can be conducted to construct an algorithm capable of quantitative assessment on the risk of preeclampsia. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. 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.

  9. Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

    Science.gov (United States)

    Dedes, I.; Dudek, J.

    2018-03-01

    We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

  10. 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

  11. 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.

  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. 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.

  15. 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.

  16. Towards an improved prediction of the free radical scavenging potency of flavonoids: the significance of double PCET mechanisms.

    Science.gov (United States)

    Amić, Ana; Marković, Zoran; Dimitrić Marković, Jasmina M; Stepanić, Višnja; Lučić, Bono; Amić, Dragan

    2014-01-01

    The 1H(+)/1e(-) and 2H(+)/2e(-) proton-coupled electron transfer (PCET) processes of free radical scavenging by flavonoids were theoretically studied for aqueous and lipid environments using the PM6 and PM7 methods. The results reported here indicate that the significant contribution of the second PCET mechanism, resulting in the formation of a quinone/quinone methide, effectively discriminates the active from inactive flavonoids. The predictive potency of descriptors related to the energetics of second PCET mechanisms (the second O-H bond dissociation enthalpy (BDE2) related to hydrogen atom transfer (HAT) mechanism, and the second electron transfer enthalpy (ETE2) related to sequential proton loss electron transfer (SPLET) mechanism) are superior to the currently used indices, which are related to the first 1H(+)/1e(-) processes, and could serve as primary descriptors in development of the QSAR (quantitative structure-activity relationships) of flavonoids. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

    DEFF Research Database (Denmark)

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

    2011-01-01

    Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... and from the last aerobic bioreactor upstream to the SST (Garrett/hydraulic method). For model structure uncertainty, two one-dimensional secondary settling tank (1-D SST) models are assessed, including a first-order model (the widely used Takács-model), in which the feasibility of using measured...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based...

  18. Predictive significance of standardized uptake value parameters of FDG-PET in patients with non-small cell lung carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Duan, X-Y.; Wang, W.; Li, M.; Li, Y.; Guo, Y-M. [PET-CT Center, The First Affiliated Hospital of Xi' an, Jiaotong University, Xi' an, Shaanxi (China)

    2015-02-03

    {sup 18}F-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is widely used to diagnose and stage non-small cell lung cancer (NSCLC). The aim of this retrospective study was to evaluate the predictive ability of different FDG standardized uptake values (SUVs) in 74 patients with newly diagnosed NSCLC. {sup 18}F-FDG PET/CT scans were performed and different SUV parameters (SUV{sub max}, SUV{sub avg}, SUV{sub T/L}, and SUV{sub T/A}) obtained, and their relationship with clinical characteristics were investigated. Meanwhile, correlation and multiple stepwise regression analyses were performed to determine the primary predictor of SUVs for NSCLC. Age, gender, and tumor size significantly affected SUV parameters. The mean SUVs of squamous cell carcinoma were higher than those of adenocarcinoma. Poorly differentiated tumors exhibited higher SUVs than well-differentiated ones. Further analyses based on the pathologic type revealed that the SUV{sub max}, SUV{sub avg}, and SUV{sub T/L} of poorly differentiated adenocarcinoma tumors were higher than those of moderately or well-differentiated tumors. Among these four SUV parameters, SUV{sub T/L} was the primary predictor for tumor differentiation. However, in adenocarcinoma, SUV{sub max} was the determining factor for tumor differentiation. Our results showed that these four SUV parameters had predictive significance related to NSCLC tumor differentiation; SUV{sub T/L} appeared to be most useful overall, but SUV{sub max} was the best index for adenocarcinoma tumor differentiation.

  19. 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.

  20. 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.

  1. 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.

  2. 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)

  3. Safety at basic nuclear facilities other than nuclear power plants. Lessons learned from significant events reported in 2011 and 2012

    International Nuclear Information System (INIS)

    2014-01-01

    The third report on the safety of basic nuclear installations in France other than power reactors presents an IRSN's analysis of significant events reported to the Nuclear Safety Authority in the years 2011 and 2012. It covers plants, laboratories, research reactors and facilities for the treatment, storage or disposal of waste. This report aims to contribute to a better understanding by stakeholders and more widely by the public of the safety and radiation protection issues associated with the operation of nuclear facilities, the progress made in terms of safety as well as the identified deficiencies. The main trend shows, once again, the significant role of organizational and human factors in the significant events that occurred in 2011 and 2012, of which the vast majority are without noteworthy consequences. Aging mechanisms are another major cause of equipment failure and require special attention. The report also provides IRSN's analysis of specific events that are particularly instructive for facility safety and a synthesis of assessments performed by IRSN on topics that are important for safety and radiation protection. IRSN also includes an overview of its analysis of measures proposed by licensees for increasing the safety of their facilities after the March 2011 accident at the Fukushima Daiichi nuclear power plant in Japan, which consist of providing a 'hardened safety core' to confront extreme situations (earthquake, flooding, etc.) that are unlikely but plausible and can bring about levels of hazards higher than those taken into account in the design of the facilities

  4. 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.

  5. The power within: The experimental manipulation of power interacts with trait BDD symptoms to predict interoceptive accuracy.

    Science.gov (United States)

    Kunstman, Jonathan W; Clerkin, Elise M; Palmer, Kateyln; Peters, M Taylar; Dodd, Dorian R; Smith, April R

    2016-03-01

    This study tested whether relatively low levels of interoceptive accuracy (IAcc) are associated with body dysmorphic disorder (BDD) symptoms. Additionally, given research indicating that power attunes individuals to their internal states, we sought to determine if state interoceptive accuracy could be improved through an experimental manipulation of power.. Undergraduate women (N = 101) completed a baseline measure of interoceptive accuracy and then were randomized to a power or control condition. Participants were primed with power or a neutral control topic and then completed a post-manipulation measure of state IAcc. Trait BDD symptoms were assessed with a self-report measure. Controlling for baseline IAcc, within the control condition, there was a significant inverse relationship between trait BDD symptoms and interoceptive accuracy. Continuing to control for baseline IAcc, within the power condition, there was not a significant relationship between trait BDD symptoms and IAcc, suggesting that power may have attenuated this relationship. At high levels of BDD symptomology, there was also a significant simple effect of experimental condition, such that participants in the power (vs. control) condition had better interoceptive accuracy. These results provide initial evidence that power may positively impact interoceptive accuracy among those with high levels of BDD symptoms.. This cross-sectional study utilized a demographically homogenous sample of women that reflected a broad range of symptoms; thus, although there were a number of participants reporting elevated BDD symptoms, these findings might not generalize to other populations or clinical samples. This study provides the first direct test of the relationship between trait BDD symptoms and IAcc, and provides preliminary evidence that among those with severe BDD symptoms, power may help connect individuals with their internal states. Future research testing the mechanisms linking BDD symptoms with IAcc, as

  6. 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.

  7. Significance of cardiac sympathetic nervous system abnormality for predicting vascular events in patients with idiopathic paroxysmal atrial fibrillation

    International Nuclear Information System (INIS)

    Akutsu, Yasushi; Kaneko, Kyouichi; Kodama, Yusuke; Li, Hui-Ling; Kawamura, Mitsuharu; Asano, Taku; Hamazaki, Yuji; Tanno, Kaoru; Kobayashi, Youichi; Suyama, Jumpei; Shinozuka, Akira; Gokan, Takehiko

    2010-01-01

    Neuronal system activity plays an important role for the prognosis of patients with atrial fibrillation (AF). Using 123 I metaiodobenzylguanidine ( 123 I-MIBG) scintigraphy, we investigated whether a cardiac sympathetic nervous system (SNS) abnormality would be associated with an increased risk of vascular events in patients with paroxysmal AF. 123 I-MIBG scintigraphy was performed in 69 consecutive patients (67 ± 13 years, 62% men) with paroxysmal AF who did not have structural heart disease. SNS integrity was assessed from the heart to mediastinum (H/M) ratio on delayed imaging. Serum concentration of C-reactive protein (CRP) was measured before 123 I-MIBG study. During a mean of 4.5 ± 3.6 years follow-up, 19 patients had myocardial infarction, stroke or heart failure (range: 0.2-11.5 years). SNS abnormality (H/M ratio <2.7) and high CRP (≥0.3 mg/dl) were associated with the vascular events (58.3% in 14 of 24 patients with SNS abnormality vs 11.1% in 5 of 45 patients without SNS abnormality, p < 0.0001, 52.4% in 11 of 21 patients with high CRP vs 16.7% in 8 of 48 patients without high CRP, p < 0.0001). After adjustment for potential confounding variables such as age, left atrial dimension and left ventricular function, SNS abnormality was an independent predictor of vascular events with a hazard ratio of 4.1 [95% confidence interval (CI): 1.3-12.6, p = 0.014]. Further, SNS abnormality had an incremental and additive prognostic power in combination with high CRP with an adjusted hazard ratio of 4.1 (95% CI: 1.5-10.9, p = 0.006). SNS abnormality is predictive of vascular events in patients with idiopathic paroxysmal AF. (orig.)

  8. Significance of cardiac sympathetic nervous system abnormality for predicting vascular events in patients with idiopathic paroxysmal atrial fibrillation

    Energy Technology Data Exchange (ETDEWEB)

    Akutsu, Yasushi; Kaneko, Kyouichi; Kodama, Yusuke; Li, Hui-Ling; Kawamura, Mitsuharu; Asano, Taku; Hamazaki, Yuji; Tanno, Kaoru; Kobayashi, Youichi [Showa University School of Medicine, Division of Cardiology, Department of Medicine, Tokyo (Japan); Suyama, Jumpei; Shinozuka, Akira; Gokan, Takehiko [Showa University School of Medicine, Department of Radiology, Tokyo (Japan)

    2010-04-15

    Neuronal system activity plays an important role for the prognosis of patients with atrial fibrillation (AF). Using {sup 123}I metaiodobenzylguanidine ({sup 123}I-MIBG) scintigraphy, we investigated whether a cardiac sympathetic nervous system (SNS) abnormality would be associated with an increased risk of vascular events in patients with paroxysmal AF. {sup 123}I-MIBG scintigraphy was performed in 69 consecutive patients (67 {+-} 13 years, 62% men) with paroxysmal AF who did not have structural heart disease. SNS integrity was assessed from the heart to mediastinum (H/M) ratio on delayed imaging. Serum concentration of C-reactive protein (CRP) was measured before {sup 123}I-MIBG study. During a mean of 4.5 {+-} 3.6 years follow-up, 19 patients had myocardial infarction, stroke or heart failure (range: 0.2-11.5 years). SNS abnormality (H/M ratio <2.7) and high CRP ({>=}0.3 mg/dl) were associated with the vascular events (58.3% in 14 of 24 patients with SNS abnormality vs 11.1% in 5 of 45 patients without SNS abnormality, p < 0.0001, 52.4% in 11 of 21 patients with high CRP vs 16.7% in 8 of 48 patients without high CRP, p < 0.0001). After adjustment for potential confounding variables such as age, left atrial dimension and left ventricular function, SNS abnormality was an independent predictor of vascular events with a hazard ratio of 4.1 [95% confidence interval (CI): 1.3-12.6, p = 0.014]. Further, SNS abnormality had an incremental and additive prognostic power in combination with high CRP with an adjusted hazard ratio of 4.1 (95% CI: 1.5-10.9, p = 0.006). SNS abnormality is predictive of vascular events in patients with idiopathic paroxysmal AF. (orig.)

  9. 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.

  10. 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...

  11. 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.

  12. 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...

  13. 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.

  14. Development and application of a methodology for the analysis of significant human related event trends in nuclear power plants

    International Nuclear Information System (INIS)

    Cho, H.Y.

    1981-01-01

    A methodology is developed to identify and flag significant trends related to the safety and availability of U.S. commercial nuclear power plants. The development is intended to aid in reducing likelihood of human errors. To assure that the methodology can be easily adapted to various types of classification schemes of operation data, a data bank classified by the Transient Analysis Classification and Evaluation (TRACE) scheme is selected for the methodology. The significance criteria for human-initiated events affecting the systems and for events caused by human deficiencies were developed. Clustering analysis was used to verify the learning trend in multidimensional histograms. A computer code is developed based on the K-Means algorithm and applied to find the learning period in which error rates are monotonously decreasing with plant age. The Freeman-Tukey (F-T) deviates are used to select generic problems identified by a large positive value (here approximately over 2.0) for the deviate. The identified generic problems are: decision errors which are highly associated with reactor startup operations in the learning period of PWR plants (PWRs), response errors which are highly associated with Secondary Non-Nuclear Systems (SNS) in PWRs, and significant errors affecting systems and which are caused by response action are highly associated with startup reactor mode in BWRS

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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)

  1. 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

  2. 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...

  3. Integrated genomic and immunophenotypic classification of pancreatic cancer reveals three distinct subtypes with prognostic/predictive significance.

    Science.gov (United States)

    Wartenberg, Martin; Cibin, Silvia; Zlobec, Inti; Vassella, Erik; Eppenberger-Castori, Serenella M M; Terracciano, Luigi; Eichmann, Micha; Worni, Mathias; Gloor, Beat; Perren, Aurel; Karamitopoulou, Eva

    2018-04-16

    Current clinical classification of pancreatic ductal adenocarcinoma (PDAC) is unable to predict prognosis or response to chemo- or immunotherapy and does not take into account the host reaction to PDAC-cells. Our aim is to classify PDAC according to host- and tumor-related factors into clinically/biologically relevant subtypes by integrating molecular and microenvironmental findings. A well-characterized PDAC-cohort (n=110) underwent next-generation sequencing with a hotspot cancer panel, while Next-generation Tissue-Microarrays were immunostained for CD3, CD4, CD8, CD20, PD-L1, p63, hyaluronan-mediated motility receptor (RHAMM) and DNA mismatch-repair proteins. Previous data on FOXP3 were integrated. Immune-cell counts and protein expression were correlated with tumor-derived driver mutations, clinicopathologic features (TNM 8. 2017), survival and epithelial-mesenchymal-transition (EMT)-like tumor budding.  Results: Three PDAC-subtypes were identified: the "immune-escape" (54%), poor in T- and B-cells and enriched in FOXP3+Tregs, with high-grade budding, frequent CDKN2A- , SMAD4- and PIK3CA-mutations and poor outcome; the "immune-rich" (35%), rich in T- and B-cells and poorer in FOXP3+Tregs, with infrequent budding, lower CDKN2A- and PIK3CA-mutation rate and better outcome and a subpopulation with tertiary lymphoid tissue (TLT), mutations in DNA damage response genes (STK11, ATM) and the best outcome; and the "immune-exhausted" (11%) with immunogenic microenvironment and two subpopulations: one with PD-L1-expression and high PIK3CA-mutation rate and a microsatellite-unstable subpopulation with high prevalence of JAK3-mutations. The combination of low budding, low stromal FOXP3-counts, presence of TLTs and absence of CDKN2A-mutations confers significant survival advantage in PDAC-patients. Immune host responses correlate with tumor characteristics leading to morphologically recognizable PDAC-subtypes with prognostic/predictive significance. Copyright ©2018

  4. Preoperative Metabolic Syndrome Is Predictive of Significant Gastric Cancer Mortality after Gastrectomy: The Fujian Prospective Investigation of Cancer (FIESTA Study

    Directory of Open Access Journals (Sweden)

    Dan Hu

    2017-02-01

    Full Text Available Metabolic syndrome (MetS has been shown to be associated with an increased risk of gastric cancer. However, the impact of MetS on gastric cancer mortality remains largely unknown. Here, we prospectively examined the prediction of preoperative MetS for gastric cancer mortality by analyzing a subset of data from the ongoing Fujian prospective investigation of cancer (FIESTA study. This study was conducted among 3012 patients with gastric cancer who received radical gastrectomy between 2000 and 2010. The latest follow-up was completed in 2015. Blood/tissue specimens, demographic and clinicopathologic characteristics were collected at baseline. During 15-year follow-up, 1331 of 3012 patients died of gastric cancer. The median survival time (MST of patients with MetS was 31.3 months, which was significantly shorter than that of MetS-free patients (157.1 months. The coexistence of MetS before surgery was associated with a 2.3-fold increased risk for gastric cancer mortality (P < 0.001. The multivariate-adjusted hazard ratios (HRs were increased with invasion depth T1/T2 (HR = 2.78, P < 0.001, regional lymph node metastasis N0 (HR = 2.65, P < 0.001, positive distant metastasis (HR = 2.53, P < 0.001, TNM stage I/II (HR = 3.00, P < 0.001, intestinal type (HR = 2.96, P < 0.001, negative tumor embolus (HR = 2.34, P < 0.001, and tumor size ≤4.5 cm (HR = 2.49, P < 0.001. Further survival tree analysis confirmed the top splitting role of TNM stage, followed by MetS or hyperglycemia with remarkable discrimination ability. In this large cohort study, preoperative MetS, especially hyperglycemia, was predictive of significant gastric cancer mortality in patients with radical gastrectomy, especially for early stage of gastric cancer.

  5. 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

  6. 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.

  7. 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.

  8. The role of predictive on-line monitoring systems in the power generation industry

    Energy Technology Data Exchange (ETDEWEB)

    Gittings, S.D.; Baldwin, J. [AEA Technology Energy plc (United Kingdom)

    1998-12-31

    It has been apparent in the power generation sector for some time that utilities are moving away from large scale, labour intensive, inspection and overhaul programmes. These are being replaced by targeted inspection and replacement programmes supported by engineering assessment. Such engineering assessments address the predominant long-term damage mechanisms and are aimed at predicting failure timescales based upon historic operating data. From these predictions extended inspection intervals can be justified and replacement schedules planned in advance, ensuring maximum plant availability and deferred capital expenditure. However, as these assessments are based upon an examination of past operating history they must be periodically re-visited and up-dated. The cost of such reassessments is usually close to that performed initially, although cost savings can arise from reductions in work-scope which have been previously justified. As computers have become more advanced (and significantly cheaper) some of the expertise used in these assessments has been transferred into software based products. However, these products are generally aimed at replacing specific parts of the desk-based analysis, necessitating a suite of products to be used in order to address all of the components and damage mechanisms, which must be assessed. As these products require specialized knowledge to be used effectively they are often employed by consultants and rarely by plant operators, except in the largest of organisations which can support an in-house team of `experts`. This has in essence led to an increase in the number of companies capable of offering assessment services, but has maintained, in the majority of cases, the plant operators reliance upon external consultants. These software based assessment methods rely upon historic operating data (in the same manner as their desk- based counterparts) and hence also need to be periodically up-dated. However, as previous assessments can

  9. The role of predictive on-line monitoring systems in the power generation industry

    Energy Technology Data Exchange (ETDEWEB)

    Gittings, S D; Baldwin, J [AEA Technology Energy plc (United Kingdom)

    1999-12-31

    It has been apparent in the power generation sector for some time that utilities are moving away from large scale, labour intensive, inspection and overhaul programmes. These are being replaced by targeted inspection and replacement programmes supported by engineering assessment. Such engineering assessments address the predominant long-term damage mechanisms and are aimed at predicting failure timescales based upon historic operating data. From these predictions extended inspection intervals can be justified and replacement schedules planned in advance, ensuring maximum plant availability and deferred capital expenditure. However, as these assessments are based upon an examination of past operating history they must be periodically re-visited and up-dated. The cost of such reassessments is usually close to that performed initially, although cost savings can arise from reductions in work-scope which have been previously justified. As computers have become more advanced (and significantly cheaper) some of the expertise used in these assessments has been transferred into software based products. However, these products are generally aimed at replacing specific parts of the desk-based analysis, necessitating a suite of products to be used in order to address all of the components and damage mechanisms, which must be assessed. As these products require specialized knowledge to be used effectively they are often employed by consultants and rarely by plant operators, except in the largest of organisations which can support an in-house team of `experts`. This has in essence led to an increase in the number of companies capable of offering assessment services, but has maintained, in the majority of cases, the plant operators reliance upon external consultants. These software based assessment methods rely upon historic operating data (in the same manner as their desk- based counterparts) and hence also need to be periodically up-dated. However, as previous assessments can

  10. 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

  11. 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.

  12. 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.

  13. 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...

  14. 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

  15. 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.

  16. Validation of DAB2IP methylation and its relative significance in predicting outcome in renal cell carcinoma

    Science.gov (United States)

    Zhao, Liang-Yun; Kapur, Payal; Wu, Kai-Jie; Wang, Bin; Yu, Yan-Hong; Liao, Bing; He, Da-Lin; Chen, Wei; Margulis, Vitaly; Hsieh, Jer-Tsong; Luo, Jun-Hang

    2016-01-01

    We have recently reported tumor suppressive role of DAB2IP in RCC development. In this study, We identified one CpG methylation biomarker (DAB2IP CpG1) located UTSS of DAB2IP that was associated with poor overall survival in a cohort of 318 ccRCC patients from the Cancer Genome Atlas (TCGA). We further validated the prognostic accuracy of DAB2IP CpG methylation by pyrosequencing quantitative methylation assay in 224 ccRCC patients from multiple Chinese centers (MCHC set), and 239 patients from University of Texas Southwestern Medical Center at Dallas (UTSW set) by using FFPE samples. DAB2IP CpG1 can predict the overall survival of patients in TCGA, MCHC, and UTSW sets independent of patient age, Fuhrman grade and TNM stage (all p<0.05). DAB2IP CpG1 successfully categorized patients into high-risk and low-risk groups with significant differences of clinical outcome in respective clinical subsets, regardless of age, sex, grade, stage, or race (HR: 1.63-7.83; all p<0.05). The detection of DAB2IP CpG1 methylation was minimally affected by ITH in ccRCC. DAB2IP mRNA expression was regulated by DNA methylation in vitro. DAB2IP CpG1 methylation is a practical and repeatable biomarker for ccRCC, which can provide prognostic value that complements the current staging system. PMID:27129174

  17. 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.

  18. Clinical significance of power spectral analysis of heart rate variability and {sup 123}I-metaiodobenzylguanidine (MIBG) myocardial imaging for assessing the severity of heart failure

    Energy Technology Data Exchange (ETDEWEB)

    Ishida, Yoshio; Fukuoka, Shuji; Shimotsu, Yoriko; Sasaki, Tatsuya; Kamakura, Shiro; Yasumura, Yoshio; Miyatake, Kunio; Shimomura, Katsuro [National Cardiovascular Center, Suita, Osaka (Japan); Tani, Akihiro

    1997-04-01

    The significance of power spectral analysis of heart rate variability and of MIBG myocardial imaging to see the sympathetic nervous function was evaluated in patients with congestive heart failure due to dilated cardiomyopathy. Subjects were 10 normal volunteers and 8 patients with severity NYHA II; 10 normals and 25 patients with NYHA II and III; and 17 patients treated with a beta-blocker (metoprolol 5-40 mg). ECG was recorded with a portable ECG recorder for measuring RR intervals for 24 hr, which were applied for power spectral analysis. Early and delayed imagings with 111 MBq of {sup 123}I-MIBG were performed at 15 min and 4 hr, respectively, after its intravenous administration for acquisition of anterior planar and SPECT images. Myocardial blood flow SPECT was also done with 111 MBq of {sup 201}Tl given intravenously, and difference of total defect scores between MIBG and Tl images was computed. MIBG myocardial sympathetic nerve imaging in those patients was found useful to assess the severity of heart failure, to predict the risk patients for beta-blocker treatment and to assess the risk in complicated ventricular tachycardia. (K.H.)

  19. 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…

  20. 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

  1. 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…

  2. 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...

  3. 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...

  4. 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)

  5. 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...

  6. 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...

  7. 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…

  8. 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

  9. 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...

  10. 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...

  11. 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…

  12. 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…

  13. 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…

  14. 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.

  15. 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

  16. 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

  17. 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)

  18. Triaging TIA/minor stroke patients using the ABCD2 score does not predict those with significant carotid disease.

    Science.gov (United States)

    Walker, J; Isherwood, J; Eveson, D; Naylor, A R

    2012-05-01

    'Rapid Access' TIA Clinics use the ABCD(2) score to triage patients as it is not possible to see everyone with a suspected TIA TIA/minor stroke or 'carotid territory' TIA/minor stroke. Between 1.10.2008 and 31.04.2011, 2452 patients were referred to the Leicester Rapid Access TIA Service. After Stroke Physician review, 1273 (52%) were thought to have suffered a minor stroke/TIA. Of these, both FD/ED referrer and Specialist Stroke Consultant ABCD(2) scores and carotid Duplex ultrasound studies were available for 843 (66%). The yield for identifying a ≥50% stenosis or carotid occlusion was 109/843 (12.9%) in patients with 'any territory' TIA/minor stroke and 101/740 (13.6%) in those with a clinical diagnosis of 'carotid territory' TIA/minor stroke. There was no association between ABCD(2) score and the likelihood of encountering significant carotid disease and analyses of the area under the receiver operating characteristic curve (AUC) for FD/ED referrer and stroke specialist ABCD(2) scores showed no prediction of carotid stenosis (FD/ED: AUC 0.50 (95%CI 0.44-0.55, p = 0.9), Specialist: AUC 0.51 (95%CI 0.45-0.57, p = 0.78). The ABCD(2) score was unable to identify TIA/minor stroke patients with a higher prevalence of clinically important ipsilateral carotid disease. Copyright © 2012 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

  19. Patient-specific metrics of invasiveness reveal significant prognostic benefit of resection in a predictable subset of gliomas.

    Directory of Open Access Journals (Sweden)

    Anne L Baldock

    Full Text Available Malignant gliomas are incurable, primary brain neoplasms noted for their potential to extensively invade brain parenchyma. Current methods of clinical imaging do not elucidate the full extent of brain invasion, making it difficult to predict which, if any, patients are likely to benefit from gross total resection. Our goal was to apply a mathematical modeling approach to estimate the overall tumor invasiveness on a patient-by-patient basis and determine whether gross total resection would improve survival in patients with relatively less invasive gliomas.In 243 patients presenting with contrast-enhancing gliomas, estimates of the relative invasiveness of each patient's tumor, in terms of the ratio of net proliferation rate of the glioma cells to their net dispersal rate, were derived by applying a patient-specific mathematical model to routine pretreatment MR imaging. The effect of varying degrees of extent of resection on overall survival was assessed for cohorts of patients grouped by tumor invasiveness.We demonstrate that patients with more diffuse tumors showed no survival benefit (P = 0.532 from gross total resection over subtotal/biopsy, while those with nodular (less diffuse tumors showed a significant benefit (P = 0.00142 with a striking median survival benefit of over eight months compared to sub-totally resected tumors in the same cohort (an 80% improvement in survival time for GTR only seen for nodular tumors.These results suggest that our patient-specific, model-based estimates of tumor invasiveness have clinical utility in surgical decision making. Quantification of relative invasiveness assessed from routinely obtained pre-operative imaging provides a practical predictor of the benefit of gross total resection.

  20. Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs.

    Science.gov (United States)

    Heim, Catherine; Cole, Elaine; West, Anita; Tai, Nigel; Brohi, Karim

    2016-09-01

    Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement. We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator. Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care. TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs. Copyright © 2016 Elsevier

  1. Lack of predictive power of trait fear and anxiety for conditioned pain modulation (CPM).

    Science.gov (United States)

    Horn-Hofmann, Claudia; Priebe, Janosch A; Schaller, Jörg; Görlitz, Rüdiger; Lautenbacher, Stefan

    2016-12-01

    In recent years the association of conditioned pain modulation (CPM) with trait fear and anxiety has become a hot topic in pain research due to the assumption that such variables may explain the low CPM efficiency in some individuals. However, empirical evidence concerning this association is still equivocal. Our study is the first to investigate the predictive power of fear and anxiety for CPM by using a well-established psycho-physiological measure of trait fear, i.e. startle potentiation, in addition to two self-report measures of pain-related trait anxiety. Forty healthy, pain-free participants (female: N = 20; age: M = 23.62 years) underwent two experimental blocks in counter-balanced order: (1) a startle paradigm with affective picture presentation and (2) a CPM procedure with hot water as conditioning stimulus (CS) and contact heat as test stimulus (TS). At the end of the experimental session, pain catastrophizing (PCS) and pain anxiety (PASS) were assessed. PCS score, PASS score and startle potentiation to threatening pictures were entered as predictors in a linear regression model with CPM magnitude as criterion. We were able to show an inhibitory CPM effect in our sample: pain ratings of the heat stimuli were significantly reduced during hot water immersion. However, CPM was neither predicted by self-report of pain-related anxiety nor by startle potentiation as psycho-physiological measure of trait fear. These results corroborate previous negative findings concerning the association between trait fear/anxiety and CPM efficiency and suggest that shifting the focus from trait to state measures might be promising.

  2. 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.

  3. 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.

  4. 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)

  5. 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.

  6. Experience and benefits from using the EPRI MOV Performance Prediction Methodology in nuclear power plants

    International Nuclear Information System (INIS)

    Walker, T.; Damerell, P.S.

    1999-01-01

    The EPRI MOV Performance Prediction Methodology (PPM) is an effective tool for evaluating design basis thrust and torque requirements for MOVs. Use of the PPM has become more widespread in US nuclear power plants as they close out their Generic Letter (GL) 89-10 programs and address MOV periodic verification per GL 96-05. The PPM has also been used at plants outside the US, many of which are implementing programs similar to US plants' GL 89-10 programs. The USNRC Safety Evaluation of the PPM and the USNRC's discussion of the PPM in GL 96-05 make the PPM an attractive alternative to differential pressure (DP) testing, which can be costly and time-consuming. Significant experience and benefits, which are summarized in this paper, have been gained using the PPM. Although use of PPM requires a commitment of resources, the benefits of a solidly justified approach and a reduced need for DP testing provide a substantial safety and economic benefit. (author)

  7. On the Use of Energy Storage Technologies for Regulation Services in Electric Power Systems with Significant Penetration of Wind Energy

    DEFF Research Database (Denmark)

    Yang, Bo; Makarov, Yuri; Desteese, John

    2008-01-01

    Energy produced by intermittent renewable resources is sharply increasing in the United States. At high penetration levels, volatility of wind power production could cause additional problems for the power system balancing functions such as regulation. This paper reports some partial results...... and frequently changing regulation signal. Several energy storage options have been analyzed based on thirteen selection criteria. The evaluation process resulted in the selection of flywheels, pumped hydro electric power (or conventional hydro electric power) plant and sodium sulfur or nickel cadmium batteries...... of a project work, recently conducted by the Pacific Northwest National Laboratory (PNNL) for Bonneville Power Administration (BPA). The project proposes to mitigate additional intermittency with the help of Wide Area Energy Management System (WAEMS) that would provide a two-way simultaneous regulation service...

  8. 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)

  9. 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.

  10. 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

  11. 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.

  12. 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

  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. 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.

  15. 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...

  16. 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...

  17. 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...

  18. 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.

  19. 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

  20. 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)

  1. 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.

  2. Fukushima- the aftermath. The japanese power consumption has significantly decreased to adapt to a fading nuclear activity. Enerdata- Energy Efficiency and Demand

    International Nuclear Information System (INIS)

    2012-01-01

    In this study, a series of analyses supported by graphs assess the power supply evolution in Japan since Fukushima, which has decreased by 11% of reactors in operation since December 2011. Measures implemented in summer 2011 to adapt demand to a lower supply and their significant impact on the power consumption are also analyzed. (authors)

  3. 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.

  4. 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)

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences.

    Science.gov (United States)

    Mi, Tian; Merlin, Jerlin Camilus; Deverasetty, Sandeep; Gryk, Michael R; Bill, Travis J; Brooks, Andrew W; Lee, Logan Y; Rathnayake, Viraj; Ross, Christian A; Sargeant, David P; Strong, Christy L; Watts, Paula; Rajasekaran, Sanguthevar; Schiller, Martin R

    2012-01-01

    Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

  10. 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...

  11. 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...

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. Predictive Power and Value Relevance of Comprehensive Income Statement: an analysis of Brazilian companies listed on the BM&FBovespa

    Directory of Open Access Journals (Sweden)

    Marcello Angotti

    2016-09-01

    Full Text Available The study aimed to analyze if the information about comprehensive income (CI and its individual components have predictive power to determine the Operating Cash Flow of the subsequent period (OCFt+1 at the Brazilian capital market companies. The research methodology has used financial data collected from Economatica® and CVM databases. The sample selection was performed considering the availability of variables: OCFt+1 and share prices. The period analyzed comprehended the years 2012-2014. It were used quantitative valuation techniques, establishing two moments of study: the first moment by testing the hypothesis that CI has greater predictive power than Net Income (NI for forecasting OCFt+1 (528 firms/years; and the second moment by testing the hypothesis that CI and its components have a value relevance at the Brazilian capital market (605 firms/years. The results suggest that the analysis of the consolidated CI individually would not be incremental for forecasting OCFt+1. However, an increase was observed in the predictive capacity for OCFt+1, with the inclusion of Other Comprehensive Income (OCI. Could not be verified, with aggregate disruption of the individual items of OCI, incremental capacity to determine the OCFt+1. It was observed that the Equity and NI have value relevance, but that is not confirmed to consolidated CI. Added to that, only the cash flow hedge was significant to explain the market value of the company, indicating there is informational benefit to stakeholders that employ the CI in their analysis.

  19. A prediction score for significant coronary artery disease in Chinese patients ≥50 years old referred for rheumatic valvular heart disease surgery.

    Science.gov (United States)

    Xu, Zhenjun; Pan, Jun; Chen, Tao; Zhou, Qing; Wang, Qiang; Cao, Hailong; Fan, Fudong; Luo, Xuan; Ge, Min; Wang, Dongjin

    2018-04-01

    Our goal was to establish a prediction score and protocol for the preoperative prediction of significant coronary artery disease (CAD) in patients with rheumatic valvular heart disease. Using multivariate logistic regression analysis, we validated the model based on 490 patients without a history of myocardial infarction and who underwent preoperative screening coronary angiography. Significant CAD was defined as ≥50% narrowing of the diameter of the lumen of the left main coronary artery or ≥70% narrowing of the diameter of the lumen of the left anterior descending coronary artery, left circumflex artery or right coronary artery. Significant CAD was present in 9.8% of patients. Age, smoking, diabetes mellitus, diastolic blood pressure, low-density lipoprotein cholesterol and ischaemia evident on an electrocardiogram were independently associated with significant CAD and were entered into the multivariate model. According to the logistic regression predictive risk score, preoperative coronary angiography is recommended in (i) postmenopausal women between 50 and 59 years of age with ≥9.1% logistic regression predictive risk score; (ii) postmenopausal women who are ≥60 years old with a logistic regression predictive risk score ≥6.6% and (iii) men ≥50 years old whose logistic regression predictive risk score was ≥2.8%. Based on this predictive model, 246 (50.2%) preoperative coronary angiograms could be safely avoided. The negative predictive value of the model was 98.8% (246 of 249). This model was accurate for the preoperative prediction of significant CAD in patients with rheumatic valvular heart disease. This model must be validated in larger cohorts and various populations.

  20. 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

  1. Spontaneous Alpha Power Lateralization Predicts Detection Performance in an Un-Cued Signal Detection Task.

    Directory of Open Access Journals (Sweden)

    Gonzalo Boncompte

    Full Text Available Focusing one's attention by external guiding stimuli towards a specific area of the visual field produces systematical neural signatures. One of the most robust is the change in topological distribution of oscillatory alpha band activity across parieto-occipital cortices. In particular, decreases in alpha activity over contralateral and/or increases over ipsilateral scalp sites, respect to the side of the visual field where attention was focused. This evidence comes mainly from experiments where an explicit cue informs subjects where to focus their attention, thus facilitating detection of an upcoming target stimulus. However, recent theoretical models of attention have highlighted a stochastic or non-deterministic component related to visuospatial attentional allocation. In an attempt to evidence this component, here we analyzed alpha activity in a signal detection paradigm in the lack of informative cues; in the absence of preceding information about the location (and time of appearance of target stimuli. We believe that the unpredictability of this situation could be beneficial for unveiling this component. Interestingly, although total alpha power did not differ between Seen and Unseen conditions, we found a significant lateralization of alpha activity over parieto-occipital electrodes, which predicted behavioral performance. This effect had a smaller magnitude compared to paradigms in which attention is externally guided (cued. However we believe that further characterization of this spontaneous component of attention is of great importance in the study of visuospatial attentional dynamics. These results support the presence of a spontaneous component of visuospatial attentional allocation and they advance pre-stimulus alpha-band lateralization as one of its neural signatures.

  2. 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.

  3. Addendum to the article: Misuse of null hypothesis significance testing: Would estimation of positive and negative predictive values improve certainty of chemical risk assessment?

    Science.gov (United States)

    Bundschuh, Mirco; Newman, Michael C; Zubrod, Jochen P; Seitz, Frank; Rosenfeldt, Ricki R; Schulz, Ralf

    2015-03-01

    We argued recently that the positive predictive value (PPV) and the negative predictive value (NPV) are valuable metrics to include during null hypothesis significance testing: They inform the researcher about the probability of statistically significant and non-significant test outcomes actually being true. Although commonly misunderstood, a reported p value estimates only the probability of obtaining the results or more extreme results if the null hypothesis of no effect was true. Calculations of the more informative PPV and NPV require a priori estimate of the probability (R). The present document discusses challenges of estimating R.

  4. 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.

  5. Significant correlation between spleen volume and thrombocytopenia in liver transplant patients: a concept for predicting persistent thrombocytopenia.

    Science.gov (United States)

    Ohira, Masahiro; Ishifuro, Minoru; Ide, Kentaro; Irei, Toshimitsu; Tashiro, Hirotaka; Itamoto, Toshiyuki; Ito, Katsuhide; Chayama, Kazuaki; Asahara, Toshimasa; Ohdan, Hideki

    2009-02-01

    Interferon (IFN) therapy with or without ribavirin treatment is well established as a standard antiviral treatment for hepatitis C virus (HCV)-infected patients. However, susceptibility to thrombocytopenia is a major obstacle for initiating or continuing this therapy, particularly in liver transplant (LTx) recipients with HCV. Studies have reported that splenectomy performed concurrently with LTx is a feasible strategy for conditioning patients for anti-HCV IFN therapy. However, the relationship between the severity of splenomegaly and alterations in the blood cytopenia in LTx recipients remains to be clarified. Here, we analyzed the relationship between spleen volume (SV) and thrombocytopenia in 45 patients who underwent LTx at Hiroshima University Hospital. The extent of pre-LTx splenomegaly [the SV to body surface area (BSA) ratio in an individual] was inversely correlated with both the post-LTx white blood cell count and platelet (PLT) count (P or= 400), persistent thrombocytopenia is predictable after LTx. (c) 2009 AASLD.

  6. 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.

  7. 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.

  8. 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.

  9. The predictive power of depression screening procedures for veterans with coronary artery disease

    Directory of Open Access Journals (Sweden)

    Shankman SA

    2012-04-01

    Full Text Available Stewart A Shankman1*, Jeffrey Nadelson2*, Sarah Kate McGowan1, Ali A Sovari2, Mladen I Vidovich21Department of Psychiatry and Psychology, University of Illinois, 2Department of Cardiology, Jesse Brown VA Medical Center, Chicago, IL, USA*These authors contributed equally to this workAbstract: Depression leads to a worse outcome for patients with coronary artery disease (CAD. Thus, accurately identifying depression in CAD patients is imperative. In many veterans affairs (VA hospitals, patients are screened for depression once a year using the patient health questionnaire (PHQ-9. Although the PHQ-9 is generally considered a specific and sensitive measure of depression, there is reason to believe that these screening procedures may miss a large number of cases of depression within CAD patients and cardiology patients more generally. The goal of this study was to provide data as to the predictive power of this depression screening procedure by (a comparing the prevalence rate of depression identified by the PHQ-9 to known prevalence rates and (b examining whether patients identified as “depressed” also had conditions that consistently co-occur with depression (eg, post-traumatic stress disorder [PTSD], other medical issues. Participants were 813 consecutive patients who received an angiogram in the cardiac catheterization laboratory at a large VA Medical Center. Prevalence of depression was 6.9% in the overall sample and less than 6% when the sample was restricted to CAD patients with significant stenosis. Depression was significantly associated with PTSD, smoking, and alcohol problems. However, depression was not associated with other medical problems such as diabetes, renal failure, peripheral vascular disease, or anemia. In conclusion, the low prevalence rate of depression and lack of associations with comorbid medical problems may suggest that the VA’s depression screening procedures have low sensitivity for identifying depression in CAD

  10. The significance and predictive value of free light chains in the urine of patients with chronic inflammatory rheumatic disease.

    Science.gov (United States)

    Bramlage, Carsten Paul; Froelich, Britta; Wallbach, Manuel; Minguet, Joan; Grupp, Clemens; Deutsch, Cornelia; Bramlage, Peter; Koziolek, Michael; Müller, Gerhard Anton

    2016-12-01

    In patients with rheumatic diseases, reliable markers for determining disease activity are scarce. One potential parameter is the level of immunoglobulin free light chains (FLCs), which is known to be elevated in the blood of patients with certain rheumatic diseases. Few studies have quantified FLCs in urine, a convenient source of test sample, in patients with different rheumatic diseases. We carried out a retrospective analysis of patients with rheumatic disease attending the University hospital of Goettingen, Germany. Subjects were included if they had urine levels of both κ and λ FLCs available and did not have myeloma. Data regarding systemic inflammation and kidney function were recorded, and FLC levels were correlated with inflammatory markers. Of the 382 patients with rheumatic disease, 40.1 % had chronic polyarthritis, 21.2 % connective tissue disease, 18.6 % spondyloarthritis and 15.7 % vasculitis. Elevated levels of κ FLCs were found for 84 % of patients and elevated λ for 52.7 %. For the patients with rheumatoid arthritis, FLCs correlated with C-reactive protein (κ, r = 0.368, p rheumatic disease, but not in κ/λ ratio. The correlation between FLCs and inflammatory markers in patients with rheumatoid arthritis demonstrates their potential for predicting disease activity.

  11. 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)

  12. 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...

  13. 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

  14. 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...

  15. 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.

  16. 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)

  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. Significant pre-accession factors predicting success or failure during a Marine Corps officer’s initial service obligation

    OpenAIRE

    Johnson, Jacob A.

    2015-01-01

    Approved for public release; distribution is unlimited Increasing diversity and equal opportunity in the military is a congressional and executive priority. At the same time, improving recruiting practices is a priority of the commandant of the Marine Corps. In an effort to provide information to the Marine Corps that may improve recruiting practice and enable retention of a higher quality and more diverse officer corps, probit econometric models are estimated to identify significant facto...

  19. 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...

  20. 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

  1. 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....

  2. 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

  3. 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

  4. 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...

  5. 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...

  6. 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

  7. On accuracy of the wave finite element predictions of wavenumbers and power flow: A benchmark problem

    Science.gov (United States)

    Søe-Knudsen, Alf; Sorokin, Sergey

    2011-06-01

    This rapid communication is concerned with justification of the 'rule of thumb', which is well known to the community of users of the finite element (FE) method in dynamics, for the accuracy assessment of the wave finite element (WFE) method. An explicit formula linking the size of a window in the dispersion diagram, where the WFE method is trustworthy, with the coarseness of a FE mesh employed is derived. It is obtained by the comparison of the exact Pochhammer-Chree solution for an elastic rod having the circular cross-section with its WFE approximations. It is shown that the WFE power flow predictions are also valid within this window.

  8. 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

  9. 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

  10. 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.

  11. 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)

  12. 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.

  13. 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.

  14. THE SIGNIFICANCE OF STRUCTURAL AND GEOLOGICAL RELATIONSHIP ASSESSMENT IN THE CONSTRUCTION OF THE OMBLA UNDERGROUND HYDROELECTRIC POWER PLANT

    Directory of Open Access Journals (Sweden)

    Renato Buljan

    1997-12-01

    Full Text Available The construction design of the underground hydroelectric plant Ombla required geological and structural investigations to he carried out. Due to past earthquakes in the area permanent tectonic movements were inferred. Therefore, in the wider and adjacent surroundings of the Ombla spring it was necessary to analyze the structural fabric and the geodynamic characteristics of the area. The most active zone encountered is the front part of a thrust fault belonging to the Dinaricum regional structural unit. The compressive regime is maintained as a response to the regional stress of an approximately S-N orientation. Different displacements of various parts of the Dinaricum unit are present. Along the rim of the structural blocks, the Hum-Om-bla fault zone extends, accompanied by left transcurrent faults, Through this zone the main groundwater drainage occurs supplying the Ombla spring. In the local Ombla spring area this zone is characterized by three sub-blocks and three major faults. The most important fault for the vital facilities of the Ombla hydroelectric power plant is the Pločice fault which divides the structural sub-blocks. Along this fault zone there are four mutually connected. The lowest two arc active groundwater draining systems supplying the Ombla spring. The data on local stress implies the following deformation of sub-blocks: sub-blocks 2c and 2f are displaced along normal faults from 20° to 30° to the left, downwards, while the sub-block 2 d is displaced along the Pločice thrust fault of 100° to 130° to the left, upwards. The structural data confirmed that the building of an underground dam with a height from 100 to 130 m was feasible. The connection between the caverns and the fault zone was determined. The unfavorable position of the active Pločice fault zone imposes the construction of vital Ombla power plant facilities underground.

  15. Evaluation of ischemic corticospinal tract damage by diffusion tensor MRI. Its significance to predict functional outcome of corona radiata infarct

    International Nuclear Information System (INIS)

    Tanaka, Hideki

    2010-01-01

    Motor impairment is one of the most frequent symptoms among stroke patients and often leads to poststroke dependency. Recent advances of diffusion tensor MR imaging made it possible to identify corticospinal tract (CST) three-dimensionally and evaluate structural damage, so precise evaluation of the ischemic CST damage became feasible.Motor impairment, lesion size and location upon diffusion weighted MR image and clinical outcome were assessed in 23 acute to subacute capsular and corona radiata infarct patients. According to the lesion size, patients were grouped into A, maximal diameter below 15 mm and B, that above 15 mm. Motor impairment was graded severe: limb movement synergy level, moderate: selective muscle activity possible and mild: isolated movements well co-ordinated, each corresponding to Brunnstrom stage 1-3, 4-5, and 6, respectively. Outcome at the time of discharge was assessed by modified Rankin Scale (mRS), discharge destination and length of hospital stay were also registered. Diffusion tensor MR imaging was conducted in 15 corona radiata infarct patients at 2.3+-2.2 days from the onset of the clinical symptoms. CST was 3-dimensionally identified with dTV. II. SR and Volume-one 1.72 and CST-FA ratio (ipsi-/contralesional CST-FA) and CST-Area% (CST lesion free area/whole CST area) were obtained at the level where ischemic damage was most prominent and correlation of these parameters to motor impairment and clinical outcome was studied. CST-FA ratio and CST-Area% were in good correlation to motor impairment at presentation. Patients with severe motor impairment had lower CST-FA ratio and CSF-Area% than those with moderate or mild. CST-FA ratio was 0.73+-0.22 in patients with poor clinical outcome (mRS 3-6) and 0.93+-0.09 with good clinical outcome (mRS 0-2) (p=0.038). Diffusion tensor MR imaging is useful in evaluating motor impairment and predicting functional outcome of corona radiata infarct patient in the acute to subacute stage. (author)

  16. Sebum and Hydration Levels in Specific Regions of Human Face Significantly Predict the Nature and Diversity of Facial Skin Microbiome.

    Science.gov (United States)

    Mukherjee, Souvik; Mitra, Rupak; Maitra, Arindam; Gupta, Satyaranjan; Kumaran, Srikala; Chakrabortty, Amit; Majumder, Partha P

    2016-10-27

    The skin microbiome varies across individuals. The causes of these variations are inadequately understood. We tested the hypothesis that inter-individual variation in facial skin microbiome can be significantly explained by variation in sebum and hydration levels in specific facial regions of humans. We measured sebum and hydration from forehead and cheek regions of healthy female volunteers (n = 30). Metagenomic DNA from skin swabs were sequenced for V3-V5 regions of 16S rRNA gene. Altogether, 34 phyla were identified; predominantly Actinobacteria (66.3%), Firmicutes (17.7%), Proteobacteria (13.1%) and Bacteroidetes (1.4%). About 1000 genera were identified; predominantly Propionibacterium (58.6%), Staphylococcus (8.6%), Streptococcus (4.0%), Corynebacterium (3.6%) and Paracoccus (3.3%). A subset (n = 24) of individuals were sampled two months later. Stepwise multiple regression analysis showed that cheek sebum level was the most significant predictor of microbiome composition and diversity followed by forehead hydration level; forehead sebum and cheek hydration levels were not. With increase in cheek sebum, the prevalence of Actinobacteria (p = 0.001)/Propionibacterium (p = 0.002) increased, whereas microbiome diversity decreased (Shannon Index, p = 0.032); this was opposite for other phyla/genera. These trends were reversed for forehead hydration levels. Therefore, the nature and diversity of facial skin microbiome is jointly determined by site-specific lipid and water levels in the stratum corneum.

  17. 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)

  18. 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

  19. 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.

  20. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis

    NARCIS (Netherlands)

    Eekhout, I.; Wiel, M.A. van de; Heymans, M.W.

    2017-01-01

    Background. Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels

  1. Future needs for dry or peak shaved dry/wet cooling and significance to nuclear power plants. Final report

    International Nuclear Information System (INIS)

    Clukey, H.V.; McNelly, M.J.; Mitchell, R.C.

    1976-02-01

    U.S. requirements for uncommitted nuclear installations in water scarce areas that might require dry cooling tower systems are minimal through the year 2000 (6 to 23 GWe). In these areas it appears that peak-shaved dry/wet cooling systems are more attractive than all-dry tower cooling unless water costs were to approach the high level of several cents per gallon. The differential cooling system evaluated cost of peak-shaved dry/wet cooling systems above wet towers is typically $20 to $30/kWe for steam turbines; whereas, dry towers can represent an incremental burden of as much as $80/kWe. Gas turbine (Brayton Cycle) systems show similar benefits from an evaporative heat sink to those for steam turbine cycles--lower cooling system evaluated costs for peak-shaved dry/wet cooling systems than for conventional wet towers. These cooling system cost differentials do not reflect total costs for Brayton Cycle gas turbine plants. Together these added costs and uncertainties may substantially exceed the dollar incentives available for development of the Brayton Cycle for power generation needs for water deficient sites

  2. 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.

  3. 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.

  4. 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

  5. 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…

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants

    International Nuclear Information System (INIS)

    Ellingwood, B.R.; Mori, Yasuhiro

    1993-01-01

    A probability-based methodology is being developed in support of the NRC Structural Aging Program to assist in evaluating the reliability of existing concrete structures in nuclear power plants under potential future operating loads and extreme evironmental and accidental events. The methodology includes models to predict structural deterioration due to environmental stressors, a database to support the use of these models, and methods for analyzing time-dependent reliability of concrete structural components subjected to stochastic loads. The methodology can be used to support a plant license extension application by providing evidence that safety-related concrete structures in their current (service) condition are able to withstand future extreme events with a level of reliability sufficient for public health and safety. (orig.)

  2. 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....

  3. 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

    A kinetic model for predicting the composition of chlorinated water discharged from power plant cooling systems has been developed. The model incorporates the most important chemical reactions that are known to occur when chlorine is added to natural fresh waters. The simultaneous differential equations, which describe the rates of these chemical reactions, are solved numerically to give the composition of the water as a function of time. A listing of the computer program is included, along with a description of the input variables. A worked-out example illustrates the application of the program to an actual cooling system. An appendix contains a compilation of the known equilibrium and kinetic data for many of the chemical reactions that might be encountered in chlorinating natural fresh waters

  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. The macrophage activation marker sCD163 combined with markers of the Enhanced Liver Fibrosis (ELF) score predicts clinically significant portal hypertension in patients with cirrhosis

    DEFF Research Database (Denmark)

    Sandahl, T D; McGrail, R; Møller, H J

    2016-01-01

    BACKGROUND: Noninvasive identification of significant portal hypertension in patients with cirrhosis is needed in hepatology practice. AIM: To investigate whether the combination of sCD163 as a hepatic inflammation marker and the fibrosis markers of the Enhanced Liver Fibrosis score (ELF) can...... predict portal hypertension in patients with cirrhosis. METHODS: We measured sCD163 and the ELF components (hyaluronic acid, tissue inhibitor of metalloproteinase-1 and procollagen-III aminopeptide) in two separate cohorts of cirrhosis patients that underwent hepatic vein catheterisation. To test...... the predictive accuracy we developed a CD163-fibrosis portal hypertension score in an estimation cohort (n = 80) and validated the score in an independent cohort (n = 80). A HVPG ≥10 mmHg was considered clinically significant. RESULTS: Both sCD163 and the ELF components increased in a stepwise manner...

  6. 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).

  7. 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.

  8. 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)

  9. 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)

  10. Prostate cancer volume adds significantly to prostate-specific antigen in the prediction of early biochemical failure after external beam radiation therapy

    International Nuclear Information System (INIS)

    D'Amico, Anthony V.; Propert, Kathleen J.

    1996-01-01

    Purpose: A new clinical pretreatment quantity that closely approximates the true prostate cancer volume is defined. Methods and Materials: The cancer-specific prostate-specific antigen (PSA), PSA density, prostate cancer volume (V Ca ), and the volume fraction of the gland involved with carcinoma (V Ca fx) were calculated for 227 prostate cancer patients managed definitively with external beam radiation therapy. 1. PSA density PSA/ultrasound prostate gland volume 2. Cancer-specific PSA = PSA - [PSA from benign epithelial tissue] 3. V Ca = Cancer-specific PSA/[PSA in serum per cm 3 of cancer] 4. V Ca fx = V Ca /ultrasound prostate gland volume A Cox multiple regression analysis was used to test whether any of these-clinical pretreatment parameters added significantly to PSA in predicting early postradiation PSA failure. Results: The prostate cancer volume (p = 0.039) and the volume fraction of the gland involved by carcinoma (p = 0.035) significantly added to the PSA in predicting postradiation PSA failure. Conversely, the PSA density and the cancer-specific PSA did not add significantly (p > 0.05) to PSA in predicting postradiation PSA failure. The 20-month actuarial PSA failure-free rates for patients with calculated tumor volumes of ≤0.5 cm 3 , 0.5-4.0 cm 3 , and >4.0 cm 3 were 92, 80, and 47%, respectively (p = 0.00004). Conclusion: The volume of prostate cancer (V Ca ) and the resulting volume fraction of cancer both added significantly to PSA in their ability to predict for early postradiation PSA failure. These new parameters may be used to select patients in prospective randomized trials that examine the efficacy of combining radiation and androgen ablative therapy in patients with clinically localized disease, who are at high risk for early postradiation PSA failure

  11. 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.

  12. Basic tuning of hydrogen powered car and artificial intelligent prediction of hydrogen engine characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania, 7001 (Australia); Karri, Vishy [Australian College of Kuwait, P.O. Box 1411, Safat 13015 (Kuwait)

    2010-09-15

    Many studies of renewable energy have shown hydrogen is one of the major green energy in the future. This has lead to the development of many automotive application of using hydrogen as a fuel especially in internal combustion engine. Nonetheless, there has been a slow growth and less knowledge details in building up the prototype and control methodology of the hydrogen internal combustion engine. In this paper, The Toyota Corolla 4 cylinder, 1.8l engine running on petrol was systematically modified in such a way that it could be operated on either gasoline or hydrogen at the choice of the driver. Within the scope of this project, several ancillary instruments such as a new inlet manifold, hydrogen fuel injection, storage system and leak detection safety system were implemented. Attention is directed towards special characteristics related to the basic tuning of hydrogen engine such as: air to fuel ratio operating conditions, ignition timing and injection timing in terms of different engine speed and throttle position. Based on the experimental data, a suite of neural network models were tested to accurately predict the effect of different engine operating conditions (speed and throttle position) on the hydrogen powered car engine characteristics. Predictions were found to be {+-}3% to the experimental values for all of case studies. This work provided better understanding of the effect of hydrogen engine characteristic parameters on different engine operating conditions. (author)

  13. A Predictive Power Control Strategy for DFIGs Based on a Wind Energy Converter System

    Directory of Open Access Journals (Sweden)

    Xiaoliang Yang

    2017-07-01

    Full Text Available A feasible control strategy is proposed to control a doubly fed induction generator based on the wind energy converter system (DFIG-WECS. The main aim is to enhance the steady state and dynamic performance under the condition of the parameter perturbations and external disturbances and to satisfy the stator power response of the system. Within the proposed control method, the control scheme for the rotor side converter (RSC is developed on the model predictive control. Firstly, the self-adaptive reference trajectory is established from the deduced discrete state-space equation of the generator. Then, the rotor voltage is calculated by minimizing the global performance index under the current prediction steps at the sampling instant. Through the control scheme for the grid side converter (GSC and wind turbine, we have re-applied the conventional control. The effectiveness of the proposed control strategy is verified via time domain simulation of a 150 kW-575 V DFIG-WECS using Matlab/Simulink. The simulation result shows that the control of the DFIG with the proposed control method can enhance the steady and dynamic response capability better than the conventional ones when the system faces errors due to the parameter perturbations, external disturbances and the rotor speed.

  14. 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.

  15. Char characterization and DTF assays as tools to predict burnout of coal blends in power plants

    Energy Technology Data Exchange (ETDEWEB)

    C. Ulloa; A.G. Borrego; S. Helle; A.L. Gordon; X. Garcia [Universidad de Concepcion, Concepcion (Chile). Departamento de Ingenieria Quimica

    2005-02-01

    The aim of this study is to predict efficiency deviations in the combustion of coal blends in power plants. Combustion of blends, as compared to its single coals, shows that for some blends the behavior is non-additive in nature. Samples of coal feed and fly ashes from combustion of blends at two power plants, plus chars of the parent coals generated in a drop-tube furnace (DTF) at temperatures and heating rates similar to those found in the industrial boilers were used. Intrinsic kinetic parameters, burning profiles and petrographic characteristics of these chars correlated well with the burnout in power plants and DTF experiments. The blend combustion in a DTF reproduces both positive and negative burnout deviations from the expected weighted average. These burnout deviations have been previously attributed to parallel or parallel-series pathways of competition for oxygen. No deviations were found for blends of low rank coals of similar characteristics yielding chars close in morphology, optical texture and reactivity. Negative deviations were found for blends of coals differing moderately in rank and were interpreted as associated with long periods of competition. In this case, fly-ashes were enriched in material derived from the least reactive char, but also unburnt material attributed to the most reactive char was identified. Improved burnout compared to the weighted average was observed for blends of coals very different in rank, and interpreted as the result of a short interaction period, followed by a period where the less reactive char burns under conditions that are more favorable to its combustion. In this case, only unburned material from the least reactive char was identified in the fly-ashes. 20 refs., 9 figs., 5 tabs.

  16. 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.

  17. Sub-seasonal prediction of significant wave heights over the Western Pacific and Indian Oceans, part II: The impact of ENSO and MJO

    Science.gov (United States)

    Shukla, Ravi P.; Kinter, James L.; Shin, Chul-Su

    2018-03-01

    This study evaluates the effect of El Niño and the Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) events on 14-day mean significant wave height (SWH) at 3 weeks lead time (Wk34) over the Western Pacific and Indian Oceans using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). The WAVEWATCH-3 (WW3) model is forced with daily 10m-winds predicted by a modified version of CFSv2 that is initialized with multiple ocean analyses in both January and May for 1979-2008. A significant anomaly correlation of predicted and observed SWH anomalies (SWHA) at Wk34 lead-time is found over portions of the domain, including the central western Pacific, South China Sea (SCS), Bay of Bengal (BOB) and southern Indian Ocean (IO) in January cases, and over BOB, equatorial western Pacific, the Maritime Continent and southern IO in May cases. The model successfully predicts almost all the important features of the observed composite SWHA during El Niño events in January, including negative SWHA in the central IO where westerly wind anomalies act on an easterly mean state, and positive SWHA over the southern Ocean (SO) where westerly wind anomalies act on a westerly mean state. The model successfully predicts the sign and magnitude of SWHA at Wk34 lead-time in May over the BOB and SCS in composites of combined phases-2-3 and phases-6-7 of MJO. The observed leading mode of SWHA in May and the third mode of SWHA in January are influenced by the combined effects of ENSO and MJO. Based on spatial and temporal correlations, the spatial patterns of SWHA in the model at Wk34 in both January and May are in good agreement with the observations over the equatorial western Pacific, equatorial and southern IO, and SO.

  18. 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.

  19. User intent prediction with a scaled conjugate gradient trained artificial neural network for lower limb amputees using a powered prosthesis.

    Science.gov (United States)

    Woodward, Richard B; Spanias, John A; Hargrove, Levi J

    2016-08-01

    Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming. By using subject independent datasets, whereby a unique subject is tested against a pooled dataset of other subjects, we believe subject training time can be reduced while still achieving an accurate classification. We present here an intent recognition system using an artificial neural network (ANN) with a scaled conjugate gradient learning algorithm to classify gait intention with user-dependent and independent datasets for six unilateral lower limb amputees. We compare these results against a linear discriminant analysis (LDA) classifier. The ANN was found to have significantly lower classification error (P<;0.05) than LDA with all user-dependent step-types, as well as transitional steps for user-independent datasets. Both types of classifiers are capable of making fast decisions; 1.29 and 2.83 ms for the LDA and ANN respectively. These results suggest that ANNs can provide suitable and accurate offline classification in prosthesis gait prediction.

  20. 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)

  1. The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Zhiye; Liu, Mengqi [Chinese PLA General Hospital, Department of Radiology, Beijing (China); Hainan Branch of Chinese PLA General Hospital, Department of Radiology, Sanya (China); Zhou, Peng [Chinese Academy of Sciences, Research Center for Brain-inspired Intelligence, Institute of Automation, Beijing (China); University of Chinese Academy of Sciences, Beijing (China); Lv, Bin [Academy of Telecommunication Research of MIIT, Beijing (China); Wang, Yan; Wang, Yulin; Lou, Xin; Ma, Lin [Chinese PLA General Hospital, Department of Radiology, Beijing (China); Gui, Qiuping [Chinese PLA General Hospital, Department of Pathology, Beijing (China); He, Huiguang [Chinese Academy of Sciences, Research Center for Brain-inspired Intelligence, Institute of Automation, Beijing (China); University of Chinese Academy of Sciences, Beijing (China); Chinese Academy of Sciences, Center for Excellence in Brain Science and Intelligence Technology, Beijing (China)

    2017-12-15

    To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading. Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform. There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2. HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice. (orig.)

  2. Validation of three noninvasive laboratory variables to predict significant fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia

    International Nuclear Information System (INIS)

    Ado, Ayman A.; Al-Swat, Khalid; Azzam, N.; Al-Faleh, Faleh; Ahmed, S.

    2007-01-01

    We tested the clinical utility of the platelet count, aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio, and the AST to platelet ratio index (APRI) score in predicting the presence or absence of advanced fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia. Liver biopsy procedures performed on chronic hepatitis C patients in our gastroenterology unit at King Khalid University Hospital were traced form records between 1998 to 2003. The hospital computer database was then accessed and detailed laboratory parameters obtained. By plotting receiver operating characteristic curves (ROC), three selected models (platelet count, AST/ALT ratio and the APRI score) were compared in terms of the best variable to predict significant fibrosis. Two hundred and forty-six patients with hepatitis C were included in this analysis. Overall, 26% of patients had advanced fibrosis. When comparing the three above mentioned prediction models, APRI score was the one associated with the highest area under the curve (AUC) = 0.812 (95%Cl, 0.756-0.868) on the ROC curves, compared to the platelet count and AST/ALT ratio, which yielded an AUC of 0.783 (0.711-0.855) and 0.716 (0.642-0.789), respectively. The APRI score seemed to be the best predictive variable for the presence or absence of advanced fibrosis in Saudi hepatitis C patients. (author)

  3. 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.

  4. The honeymoon effect in job performance - Temporal increases in the predictive power of achievement motivation

    Science.gov (United States)

    Helmreich, Robert L.; Sawin, Linda L.; Carsrud, Alan L.

    1986-01-01

    Correlations between a job performance criterion and personality measures reflecting achievement motivation and an interpersonal orientation were examined at three points in time after completion of job training for a sample of airline reservations agents. Although correlations between the personality predictors and performance were small and nonsignificant for the 3-month period after beginning the job, by the end of six and eight months a number of significant relationships had emerged. Implications for the utility of personality measures in selection and performance prediction are discussed.

  5. 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...

  6. 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.

  7. 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.

  8. 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...

  9. Clinical Significance of the Prognostic Nutritional Index for Predicting Short- and Long-Term Surgical Outcomes After Gastrectomy: A Retrospective Analysis of 7781 Gastric Cancer Patients.

    Science.gov (United States)

    Lee, Jee Youn; Kim, Hyoung-Il; Kim, You-Na; Hong, Jung Hwa; Alshomimi, Saeed; An, Ji Yeong; Cheong, Jae-Ho; Hyung, Woo Jin; Noh, Sung Hoon; Kim, Choong-Bai

    2016-05-01

    To evaluate the predictive and prognostic significance of the prognostic nutritional index (PNI) in a large cohort of gastric cancer patients who underwent gastrectomy.Assessing a patient's immune and nutritional status, PNI has been reported as a predictive marker for surgical outcomes in various types of cancer.We retrospectively reviewed data from a prospectively maintained database of 7781 gastric cancer patients who underwent gastrectomy from January 2001 to December 2010 at a single center. From this data, we analyzed clinicopathologic characteristics, PNI, and short- and long-term surgical outcomes for each patient. We used the PNI value for the 10th percentile (46.70) of the study cohort as a cut-off for dividing patients into low and high PNI groups.Regarding short-term outcomes, multivariate analysis showed a low PNI (odds ratio [OR] = 1.505, 95% CI = 1.212-1.869, P cancer recurrence.

  10. 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.

  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. 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

  13. 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...

  14. Towards more accurate wind and solar power prediction by improving NWP model physics

    Science.gov (United States)

    Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

    2014-05-01

    The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during

  15. Diagnostic value of thallium-201 myocardial perfusion IQ-SPECT without and with computed tomography-based attenuation correction to predict clinically significant and insignificant fractional flow reserve

    Science.gov (United States)

    Tanaka, Haruki; Takahashi, Teruyuki; Ohashi, Norihiko; Tanaka, Koichi; Okada, Takenori; Kihara, Yasuki

    2017-01-01

    Abstract The aim of this study was to clarify the predictive value of fractional flow reserve (FFR) determined by myocardial perfusion imaging (MPI) using thallium (Tl)-201 IQ-SPECT without and with computed tomography-based attenuation correction (CT-AC) for patients with stable coronary artery disease (CAD). We assessed 212 angiographically identified diseased vessels using adenosine-stress Tl-201 MPI-IQ-SPECT/CT in 84 consecutive, prospectively identified patients with stable CAD. We compared the FFR in 136 of the 212 diseased vessels using visual semiquantitative interpretations of corresponding territories on MPI-IQ-SPECT images without and with CT-AC. FFR inversely correlated most accurately with regional summed difference scores (rSDS) in images without and with CT-AC (r = −0.584 and r = −0.568, respectively, both P system can predict FFR at an optimal cut-off of <0.80, and we propose a novel application of CT-AC to MPI-IQ-SPECT for predicting clinically significant and insignificant FFR even in nonobese patients. PMID:29390486

  16. 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)

  17. Clinical and angiographic predictors of haemodynamically significant angiographic lesions: development and validation of a risk score to predict positive fractional flow reserve.

    Science.gov (United States)

    Sareen, Nishtha; Baber, Usman; Kezbor, Safwan; Sayseng, Sonny; Aquino, Melissa; Mehran, Roxana; Sweeny, Joseph; Barman, Nitin; Kini, Annapoorna; Sharma, Samin K

    2017-04-07

    Coronary revascularisation based upon physiological evaluation of lesions improves clinical outcomes. Angiographic or visual stenosis assessment alone is insufficient in predicting haemodynamic stenosis severity by fractional flow reserve (FFR) and therefore cannot be used to guide revascularisation, particularly in the lesion subset system formulated. Of 1,023 consecutive lesions (883 patients), 314 (31%) were haemodynamically significant. Characteristics associated with FFR ≤0.8 include male gender, higher SYNTAX score, lesions ≥20 mm, stenosis >50%, bifurcation, calcification, absence of tortuosity and smaller reference diameter. A user-friendly integer score was developed with the five variables demonstrating the strongest association. On prospective validation (in 279 distinct lesions), the increasing value of the score correlated well with increasing haemodynamic significance (C-statistic 0.85). We identified several clinical and angiographic characteristics and formulated a scoring system to guide the approach to intermediate lesions. This may translate into cost savings. Larger studies with prospective validation are required to confirm our results.

  18. Mid-Treatment Sleep Duration Predicts Clinically Significant Knee Osteoarthritis Pain reduction at 6 months: Effects From a Behavioral Sleep Medicine Clinical Trial.

    Science.gov (United States)

    Salwen, Jessica K; Smith, Michael T; Finan, Patrick H

    2017-02-01

    To determine the relative influence of sleep continuity (sleep efficiency, sleep onset latency, total sleep time [TST], and wake after sleep onset) on clinical pain outcomes within a trial of cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid knee osteoarthritis and insomnia. Secondary analyses were performed on data from 74 patients with comorbid insomnia and knee osteoarthritis who completed a randomized clinical trial of 8-session multicomponent CBT-I versus an active behavioral desensitization control condition (BD), including a 6-month follow-up assessment. Data used herein include daily diaries of sleep parameters, actigraphy data, and self-report questionnaires administered at specific time points. Patients who reported at least 30% improvement in self-reported pain from baseline to 6-month follow-up were considered responders (N = 31). Pain responders and nonresponders did not differ significantly at baseline across any sleep continuity measures. At mid-treatment, only TST predicted pain response via t tests and logistic regression, whereas other measures of sleep continuity were nonsignificant. Recursive partitioning analyses identified a minimum cut-point of 382 min of TST achieved at mid-treatment in order to best predict pain improvements 6-month posttreatment. Actigraphy results followed the same pattern as daily diary-based results. Clinically significant pain reductions in response to both CBT-I and BD were optimally predicted by achieving approximately 6.5 hr sleep duration by mid-treatment. Thus, tailoring interventions to increase TST early in treatment may be an effective strategy to promote long-term pain reductions. More comprehensive research on components of behavioral sleep medicine treatments that contribute to pain response is warranted. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  19. Linking Compositional and Functional Predictions to Decipher the Biogeochemical Significance in DFAA Turnover of Abundant Bacterioplankton Lineages in the North Sea

    Directory of Open Access Journals (Sweden)

    Bernd Wemheuer

    2017-11-01

    Full Text Available Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria. Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.

  20. Linking Compositional and Functional Predictions to Decipher the Biogeochemical Significance in DFAA Turnover of Abundant Bacterioplankton Lineages in the North Sea.

    Science.gov (United States)

    Wemheuer, Bernd; Wemheuer, Franziska; Meier, Dimitri; Billerbeck, Sara; Giebel, Helge-Ansgar; Simon, Meinhard; Scherber, Christoph; Daniel, Rolf

    2017-11-05

    Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria . Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA) showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.

  1. Polymer-Polymer Förster Resonance Energy Transfer Significantly Boosts the Power Conversion Efficiency of Bulk-Heterojunction Solar Cells.

    Science.gov (United States)

    Gupta, Vinay; Bharti, Vishal; Kumar, Mahesh; Chand, Suresh; Heeger, Alan J

    2015-08-01

    Optically resonant donor polymers can exploit a wider range of the solar spectrum effectively without a complicated tandem design in an organic solar cell. Ultrafast Förster resonance energy transfer (FRET) in a polymer-polymer system that significantly improves the power conversion efficiency in bulk heterojunction polymer solar cells from 6.8% to 8.9% is demonstrated, thus paving the way to achieving 15% efficient solar cells. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. 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)

  3. 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

  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. 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. 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.

  7. 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…

  8. 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…

  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. Lack of predictive power of plasma lipids or lipoproteins for gestational diabetes mellitus in Japanese women.

    Science.gov (United States)

    Iimura, Yuko; Matsuura, Masaaki; Yao, Zemin; Ito, Satoru; Fujiwara, Mutsunori; Yoshitsugu, Michiyasu; Miyauchi, Akito; Hiyoshi, Toru

    2015-11-01

    To determine the diagnostic potential of plasma lipids and apolipoproteins in gestational diabetes mellitus (GDM), we carried out a retrospective cohort study of 1,161 Japanese women at 20-28 weeks of gestation who underwent a glucose challenge test (GCT). A total of 1,161 Japanese women at 20-28 weeks of gestation underwent a GCT. Participants with a positive test (GCT[+]) underwent a subsequent oral glucose tolerance test. Clinical and biochemical parameters were determined and quantification of apolipoproteins (Apo), including ApoB, ApoB48, ApoA-I and ApoC-III, was carried out. The prevalence of GCT(+; with a 130 mg/dL glucose cut-off) and GDM was 20% and 4%, respectively. There was a trend for increased triglycerides and ApoC-III in GDM(+) participants. However, the difference in plasma triglycerides, ApoC-III or ApoB48 did not reach statistical significance between GDM(+) and GDM(-) women. Values of 1-h glucose (P < 0.001) and fasting glucose (P = 0.002) were significant risk factors for GDM. Prediction of GDM using only the ApoC-III value is not easy, although triglycerides and ApoC-III were higher in the GDM(+) group. The present findings show no significant difference in plasma lipid levels between women diagnosed with GDM and those with normal glucose tolerance.

  11. 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)

  12. 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.

  13. Very short-term spatio-temporal wind power prediction using a censored Gaussian field

    DEFF Research Database (Denmark)

    Baxevani, Anastassia; Lenzi, Amanda

    2018-01-01

    Wind power is a renewable energy resource, that has relatively cheap installation costs and it is highly possible that will become the main energy resource in the near future. Wind power needs to be integrated efficiently into electricity grids, and to optimize the power dispatch, techniques...

  14. Prediction of power fluctuation classes for photovoltaic installations and potential benefits of dynamic reserve allocation

    NARCIS (Netherlands)

    Nijhuis, M.; Rawn, B.G.; Gibescu, M.

    2014-01-01

    During partly cloudy conditions, the power delivered by a photovoltaic array can easily fluctuate by three quarters of its rated power in 10 s. Fluctuations from photovoltaics of this size and on this time scale may necessitate adding an additional component to power system secondary and primary

  15. Comparison of the significance of the RENAL, PADUA, and C-index nephrometric scales for the prediction of the complexity of laparoscopic nephrectomy

    Directory of Open Access Journals (Sweden)

    Yu. G. Alyaev

    2018-01-01

    time, extent of intraoperative blood loss and possibility of development after complications (p = 0.049; 0.028; 0.046. None of indices were significant for multivariant analysis of prognosis the duration of laparoscopic partial nephrectomy. The indices  of the RENAL (p = 0.032 and C-index (p = 0.040 nephrometry score systems were significant for univariate analysis of prognosis the duration of the laparoscopic partial nephrectomy.Conclusion. The usage of RENAL, PADUA, C-index nephrometry score systems is useful for the prediction of warm ischaemic time, extent  of blood loss, duration of operative measure and possibility of rate of postoperative complications at laparoscopic partial nephrectomy. According to our data the index of RENAL nephrometry scoring system has the highest predictive value. Applications of 3D modelling for counting nephrometry indices in preoperative period makes the process of counting balls easier on all three nephrometry score systems.

  16. A proposal for a pharmacokinetic interaction significance classification system (PISCS) based on predicted drug exposure changes and its potential application to alert classifications in product labelling.

    Science.gov (United States)

    Hisaka, Akihiro; Kusama, Makiko; Ohno, Yoshiyuki; Sugiyama, Yuichi; Suzuki, Hiroshi

    2009-01-01

    Pharmacokinetic drug-drug interactions (DDIs) are one of the major causes of adverse events in pharmacotherapy, and systematic prediction of the clinical relevance of DDIs is an issue of significant clinical importance. In a previous study, total exposure changes of many substrate drugs of cytochrome P450 (CYP) 3A4 caused by coadministration of inhibitor drugs were successfully predicted by using in vivo information. In order to exploit these predictions in daily pharmacotherapy, the clinical significance of the pharmacokinetic changes needs to be carefully evaluated. The aim of the present study was to construct a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered with pharmacokinetic changes in a systematic manner. Furthermore, the classifications proposed by PISCS were compared in a detailed manner with current alert classifications in the product labelling or the summary of product characteristics used in Japan, the US and the UK. A matrix table was composed by stratifying two basic parameters of the prediction: the contribution ratio of CYP3A4 to the oral clearance of substrates (CR), and the inhibition ratio of inhibitors (IR). The total exposure increase was estimated for each cell in the table by associating CR and IR values, and the cells were categorized into nine zones according to the magnitude of the exposure increase. Then, correspondences between the DDI significance and the zones were determined for each drug group considering the observed exposure changes and the current classification in the product labelling. Substrate drugs of CYP3A4 selected from three therapeutic groups, i.e. HMG-CoA reductase inhibitors (statins), calcium-channel antagonists/blockers (CCBs) and benzodiazepines (BZPs), were analysed as representative examples. The product labelling descriptions of drugs in Japan, US and UK were obtained from the websites of each regulatory body. Among 220

  17. Predicting the environmental risks of radioactive discharges from Belgian nuclear power plants

    International Nuclear Information System (INIS)

    Vandenhove, H.; Sweeck, L.; Vives i Batlle, J.; Wannijn, J.; Van Hees, M.; Camps, J.; Olyslaegers, G.; Miliche, C.; Lance, B.

    2013-01-01

    An environmental risk assessment (ERA) was performed to evaluate the impact on non-human biota from liquid and atmospheric radioactive discharges by the Belgian Nuclear Power Plants (NPP) of Doel and Tihange. For both sites, characterisation of the source term and wildlife population around the NPPs was provided, whereupon the selection of reference organisms and the general approach taken for the environmental risk assessment was established. A deterministic risk assessment for aquatic and terrestrial ecosystems was performed using the ERICA assessment tool and applying the ERICA screening value of 10 μGy h −1 . The study was performed for the radioactive discharge limits and for the actual releases (maxima and averages over the period 1999–2008 or 2000–2009). It is concluded that the current discharge limits for the Belgian NPPs considered do not result in significant risks to the aquatic and terrestrial environment and that the actual discharges, which are a fraction of the release limits, are unlikely to harm the environment. -- Highlights: • Impact of radioactive discharges by the Belgian NPPs of Doel and Tihange on wildlife was evaluated. • Deterministic risk assessment for aquatic and terrestrial ecosystems performed with the ERICA tool. • NPP discharge limits do not result in significant risks to the aquatic and terrestrial environment. • Actual discharges, a fraction of the release limits, are unlikely to harm the environment

  18. Predicting compliance with command hallucinations: anger, impulsivity and appraisals of voices' power and intent.

    Science.gov (United States)

    Bucci, Sandra; Birchwood, Max; Twist, Laura; Tarrier, Nicholas; Emsley, Richard; Haddock, Gillian

    2013-06-01

    Command hallucinations are experienced by 33-74% of people who experience voices, with varying levels of compliance reported. Compliance with command hallucinations can result in acts of aggression, violence, suicide and self-harm; the typical response however is non-compliance or appeasement. Two factors associated with such dangerous behaviours are anger and impulsivity, however few studies have examined their relationship with compliance to command hallucinations. The current study aimed to examine the roles of anger and impulsivity on compliance with command hallucinations in people diagnosed with a psychotic disorder. The study was a cross-sectional design and included individuals who reported auditory hallucinations in the past month. Subjects completed a variety of self-report questionnaire measures. Thirty-two people experiencing command hallucinations, from both in-patient and community settings, were included. The tendency to appraise the voice as powerful, to be impulsive, to experience anger and to regulate anger were significantly associated with compliance with command hallucinations to do harm. Two factors emerged as significant independent predictors of compliance with command hallucinations; omnipotence and impulsivity. An interaction between omnipotence and compliance with commands, via a link with impulsivity, is considered and important clinical factors in the assessment of risk when working with clients experiencing command hallucinations are recommended. The data is highly suggestive and warrants further investigation with a larger sample. Copyright © 2013 Elsevier B.V. All rights reserved.

  19. Predicting weight status stability and change from fifth grade to eighth grade: the significant role of adolescents' social-emotional well-being.

    Science.gov (United States)

    Chang, Yiting; Gable, Sara

    2013-04-01

    The primary objective of this study was to predict weight status stability and change across the transition to adolescence using parent reports of child and household routines and teacher and child self-reports of social-emotional development. Data were from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K), a nationally representative sample of children who entered kindergarten during 1998-1999 and were followed through eighth grade. At fifth grade, parents reported on child and household routines and the study child and his/her primary classroom teacher reported on the child's social-emotional functioning. At fifth and eighth grade, children were directly weighed and measured at school. Nine mutually-exclusive weight trajectory groups were created to capture stability or change in weight status from fifth to eighth grade: (1) stable obese (ObeSta); (2) obese to overweight (ObePos1); (3) obese to healthy (ObePos2); (4) stable overweight (OverSta); (5) overweight to healthy (OverPos); (6) overweight to obese (OverNeg); (7) stable healthy (HelSta); (8) healthy to overweight (HelNeg1); and (9) healthy to obese (HelNeg2). Except for breakfast consumption at home, school-provided lunches, nighttime sleep duration, household and child routines did not predict stability or change in weight status. Instead, weight status trajectory across the transition to adolescence was significantly predicted by measures of social-emotional functioning at fifth grade. Assessing children's social-emotional well-being in addition to their lifestyle routines during the transition to adolescence is a noteworthy direction for adolescent obesity prevention and intervention. Copyright © 2013 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  20. Using reference trajectories to predicted uncertain systems: exemplified on a power plant

    DEFF Research Database (Denmark)

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

    2007-01-01

    This paper presents a method for prediction of uncertain closed loop systems, where the uncertainties are depending on operating points. Such model uncertainties are often present when complicated non-linear systems are predicted. The method uses precomputed mean and variances of the prediction e...

  1. Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency.

    Science.gov (United States)

    Mohamed, Omar; Wang, Jihong; Khalil, Ashraf; Limhabrash, Marwan

    2016-01-01

    This paper presents a novel strategy for implementing model predictive control (MPC) to a large gas turbine power plant as a part of our research progress in order to improve plant thermal efficiency and load-frequency control performance. A generalized state space model for a large gas turbine covering the whole steady operational range is designed according to subspace identification method with closed loop data as input to the identification algorithm. Then the model is used in developing a MPC and integrated into the plant existing control strategy. The strategy principle is based on feeding the reference signals of the pilot valve, natural gas valve, and the compressor pressure ratio controller with the optimized decisions given by the MPC instead of direct application of the control signals. If the set points for the compressor controller and turbine valves are sent in a timely manner, there will be more kinetic energy in the plant to release faster responses on the output and the overall system efficiency is improved. Simulation results have illustrated the feasibility of the proposed application that has achieved significant improvement in the frequency variations and load following capability which are also translated to be improvements in the overall combined cycle thermal efficiency of around 1.1 % compared to the existing one.

  2. Clinical significance of the neutrophil-lymphocyte ratio as an early predictive marker for adverse outcomes in patients with acute pancreatitis.

    Science.gov (United States)

    Jeon, Tae Joo; Park, Ji Young

    2017-06-07

    To investigated the prognostic value of the neutrophil-lymphocyte ratio (NLR) in patients with acute pancreatitis and determined an optimal cut-off value for the prediction of adverse outcomes in these patients. We retrospectively analyzed 490 patients with acute pancreatitis diagnosed between March 2007 and December 2012. NLRs were calculated at admission and 24, 48, and 72 h after admission. Patients were grouped according to acute pancreatitis severity and organ failure occurrence, and a comparative analysis was performed to compare the NLR between groups. Among the 490 patients, 70 had severe acute pancreatitis with 31 experiencing organ failure. The severe acute pancreatitis group had a significantly higher NLR than the mild acute pancreatitis group on all 4 d (median, 6.14, 6.71, 5.70, and 4.00 vs 4.74, 4.47, 3.20, and 3.30, respectively, P pancreatitis. Elevated baseline NLR correlates with severe acute pancreatitis and organ failure.

  3. Comparison of FLOWTRAN predictions of onset of significant voiding (OSV) to Savannah River Heat Transfer Laboratory subcooled boiling flow instability measurements, Part 1

    International Nuclear Information System (INIS)

    Laurinat, J.E.

    1988-10-01

    The onset of flow instability (OFI) was measured in the first of a scheduled series of subcooled boiling tests at the Savannah River Heat Transfer Laboratory (HTL). This report summarizes the benchmarking of predictions of the onset of significant voiding (OSV) using Version 16 of the FLOWTRANΩ reactor limits code against the HTL measurements. This study confirms that, for this series of HTL subcooled boiling tests, the Saha-Zuber OSV correlation was a conservative indicator of OFI for Peclet numbers between 30,000 and 80,000. The Saha-Zuber correlation was not a conservative indicator of OFI for Peclet numbers below 30,000. A conservative bound to the Saha-Zuber correlation (the Saha-Zuber constant Stanton number criterion -- 30%) was agreed to at a meeting of SRL, DOE, and the DOE EH and DP review panels. This bound was a conservative indicator of OFI for all measurements in this study

  4. Thermogravimetric analysis coupled with chemometrics as a powerful predictive tool for ß-thalassemia screening.

    Science.gov (United States)

    Risoluti, Roberta; Materazzi, Stefano; Sorrentino, Francesco; Maffei, Laura; Caprari, Patrizia

    2016-10-01

    β-Thalassemia is a hemoglobin genetic disorder characterized by the absence or reduced β-globin chain synthesis, one of the constituents of the adult hemoglobin tetramer. In this study the possibility of using thermogravimetric analysis (TGA) followed by chemometrics as a new approach for β-thalassemia detection is proposed. Blood samples from patients with β-thalassemia were analyzed by the TG7 thermobalance and the resulting curves were compared to those typical of healthy individuals. Principal Component Analysis (PCA) was used to evaluate the correlation between the hematological parameters and the thermogravimetric results. The thermogravimetric profiles of blood samples from β-thalassemia patients were clearly distinct from those of healthy individuals as result of the different quantities of water content and corpuscular fraction. The hematological overview showed significant decreases in the values of red blood cell indices and an increase in red cell distribution width value in thalassemia subjects when compared with those of healthy subjects. The implementation of a predictive model based on Partial Least Square Discriminant Analysis (PLS-DA) for β-thalassemia diagnosis, was performed and validated. This model permitted the discrimination of anemic patients and healthy individuals and was able to detect thalassemia in clinically heterogeneous patients as in the presence of δβ-thalassemia and β-thalassemia combined with Hb Lepore. TGA and Chemometrics are capable of predicting ß-thalassemia syndromes using only a few microliters of blood without any pretreatment and with an hour of analysis time. A fast, rapid and cost-effective diagnostic tool for the β-thalassemia screening is proposed. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. [Predictive factors of clinically significant drug-drug interactions among regimens based on protease inhibitors, non-nucleoside reverse transcriptase inhibitors and raltegravir].

    Science.gov (United States)

    Cervero, Miguel; Torres, Rafael; Jusdado, Juan José; Pastor, Susana; Agud, Jose Luis

    2016-04-15

    To determine the prevalence and types of clinically significant drug-drug interactions (CSDI) in the drug regimens of HIV-infected patients receiving antiretroviral treatment. retrospective review of database. Centre: Hospital Universitario Severo Ochoa, Infectious Unit. one hundred and forty-two participants followed by one of the authors were selected from January 1985 to December 2014. from their outpatient medical records we reviewed information from the last available visit of the participants, in relation to HIV infection, comorbidities, demographics and the drugs that they were receiving; both antiretroviral drugs and drugs not related to HIV infection. We defined CSDI from the information sheet and/or database on antiretroviral drug interactions of the University of Liverpool (http://www.hiv-druginteractions.org) and we developed a diagnostic tool to predict the possibility of CSDI. By multivariate logistic regression analysis and by estimating the diagnostic performance curve obtained, we identified a quick tool to predict the existence of drug interactions. Of 142 patients, 39 (29.11%) had some type of CSDI and in 11.2% 2 or more interactions were detected. In only one patient the combination of drugs was contraindicated (this patient was receiving darunavir/r and quetiapine). In multivariate analyses, predictors of CSDI were regimen type (PI or NNRTI) and the use of 3 or more non-antiretroviral drugs (AUC 0.886, 95% CI 0.828 to 0.944; P=.0001). The risk was 18.55 times in those receiving NNRTI and 27,95 times in those receiving IP compared to those taking raltegravir. Drug interactions, including those defined as clinically significant, are common in HIV-infected patients treated with antiretroviral drugs, and the risk is greater in IP-based regimens. Raltegravir-based prescribing, especially in patients who receive at least 3 non-HIV drugs could avoid interactions. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  6. 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.

  7. 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.

  8. 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)

  9. Independent predictive factors for significant liver histological changes in patients with HBeAg-positive high-viral-load chronic HBV infection and a normal alanine aminotransferase level

    Directory of Open Access Journals (Sweden)

    LI Qiang

    2016-07-01

    Full Text Available Objective To investigate the independent predictive factors for significant liver histological changes (SLHCs in patients with HBeAg-positive high-viral-load chronic hepatitis B virus (HBV infection and a normal alanine aminotransferase (ALT level. MethodsA retrospective analysis was performed on the clinical data of 116 previously untreated patients with HBeAg-positive high-viral-load (HBV DNA≥105 copies/ml chronic HBV infection and a normal ALT level (<50 U/L who were hospitalized in Shanghai Public Health Clinical Center Affiliated to Fudan University from June 2013 to August 2015. The definition of SLHCs was inflammation ≥G2 and/or fibrosis≥S2. The t-test or Mann-Whitney U rank sum test was used for comparison of continuous data between groups, and the chi-square test was used for comparison of categorical data between groups. Univariate and multivariate regression analyses were used to determine independent predictive factors for SLHCs. ResultsOf all the 116 patients, 47(40.5% had SLHCs. The multivariate analysis showed that age (OR=2.828, P<0.05, ALT (OR=1.011, P<0.05, and gamma-glutamyl transpeptidase (GGT (OR=1.089, P<0.05 were independent predictors for SLHCs in patients with HBeAg-positive high-viral-load chronic HBV infection and a normal ALT level. The patients aged ≤30 years had a significantly lower incidence rate of SLHCs than those aged>30 years (21.6% vs 49.4%, χ2=6.42, P=0.015, the patients with ALT ≤30 U/L had a significantly lower incidence rate of SLHCs than those with 30 U/L<ALT≤50 U/L (17.6% vs 50.0%, χ2=19.86, P<0.001, and the patients with GGT≤40 U/L had a significantly lower incidence rate of SLHCs than those with GGT>40 U/L (28.8% vs 66.7%, χ2=28.63, P<0.001. ConclusionIn patients with HBeAg-positive high-viral-load chronic HBV infection and a normal ALT level, those with an age of>30 years, ALT>30 U/L, and GGT>40 U/L tend to develop SLHCs and need liver biopsy.

  10. ELMO model predicts the price of electric power; ELMO-malli saehkoen hinnan ennustamiseksi

    Energy Technology Data Exchange (ETDEWEB)

    Antila, H. [Electrowatt-Ekono Oy, Helsinki (Finland)

    2001-07-01

    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.

  11. 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.

  12. 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

  13. Prevention of significant deterioration permit application for the Fueled Clad Fabrication System, the Radioisotope Power Systems Facility, and the Fuel Assembly Area

    Energy Technology Data Exchange (ETDEWEB)

    1989-08-01

    This New Source Review'' has been submitted by the US Department of Energy-Richland Operations Office (PO Box 550, Richland, Washington 99352), pursuant to WAC 173-403-050 and in compliance with the Department of Ecology Guide to Processing A Prevention Of Significant Deterioration (PSD) Permit'' for three new sources of radionuclide emissions at the Hanford Site in Washington State. The three new sources, the Fueled Clad Fabrication System (FCFS), the Radioisotope Power Systems Facility (RPSF), and the Fuel Assembly Area (FAA), will be located in one facility, the Fuels and Materials Examination Facility (FMEF) of the 400 Area. The FMEF was originally designed to provide for post-irradiation examination and fabrication of breeder reactor fuels. These FMEF missions were cancelled before the introduction of any fuel materials or any irradiated material. The current plans are to use the facility to fabricate power supplies for use in space applications and to produce Fast Flux Test Facility (FFTF) fuel and target assemblies. The FCFS and the RPSF will produce materials and assemblies for application in space. The FAA project will produce FFTF fuel and target assemblies. The FCFS and the RPSF will share the same building, stack, and, in certain cases, the same floor space. Given this relationship, these systems will be dealt with separately to the extent possible. The FAA is a comparatively independent operation though it will share the FMEF complex.

  14. Assessment of CPPF Reduction After Adapting Our Own Power Distribution Prediction Method

    International Nuclear Information System (INIS)

    Kim, Sung-min; Park, Joong-woo; Kho, Dae-hack; Seo, Hyung-beom; Han, Bong-gyun

    2006-01-01

    CANDU reactor which uses natural Uranium as a fuel requires daily on-power refueling due to its insufficient excess reactivity. So its power distribution changes unceasingly. Reactor physicists identify the change twice a week using RFSP (Reactor Fuelling Simulation Program) code, and check if it is operated within power limitation (380 channels and 4560 bundles). We also calculate ROP (Regional Overpower Protection trip system) detector calibration target value (DC, Detector Calibration) and determine refueling channels and their order. DC is composed of CPPF (Channel Power Peaking Factor), Plant ageing penalty and the factor considering PHT (Primary Heat Transport system) condition, etc. The lower CPPF means the lower DC, and the lower DC means the larger operating margin. If the reactor is not operated in full power state as Wolsong Unit no. 1, the additional operating margin can be converted to the additional operating power directly

  15. 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

  16. 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.

  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. POWER STABILITY MONITORING BASED ON VOLTAGE INSTABILITY PREDICTION APPROACH THROUGH WIDE AREA SYSTEM

    OpenAIRE

    H. H. Goh; Q. S. Chua; S. W. Lee; B. C. Kok; K. C. Goh; K. T.K. Teo

    2014-01-01

    Nowadays, power systems are being forced to operate closer to its security limit due to current economic growth and the difficulties to upgrade the existing grid infrastructure. With the sudden increment of power demand, voltage instability problem has become a main concern to the power system operator because voltage instability has led or crucially contributed to some major blackouts throughout the world. Hence, methods for early warning and early prevention are required to prevent the powe...

  19. 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.

  20. 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...